RISK ASSESSMENT FOR MARINE MAMMAL AND SEABIRD … · Michelle Cronin, Mick Mackey, Simon N. Ingram...
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RISK ASSESSMENT FOR MARINE MAMMAL AND SEABIRD POPULATIONS IN SOUTH- WESTERN IRISH WATERS (R.A.M.S.S.I.) Daphne Roycroft, Michelle Cronin, Mick Mackey, Simon N. Ingram Oliver O’Cadhla Coastal and Marine Resources Centre, University College Cork March 2007 Higher Education Authority An tÚdarás um Ard-Oideachas HEA
Transcript of RISK ASSESSMENT FOR MARINE MAMMAL AND SEABIRD … · Michelle Cronin, Mick Mackey, Simon N. Ingram...
RISK ASSESSMENT FOR MARINE MAMMAL AND SEABIRD POPULATIONS IN SOUTH-WESTERN IRISH WATERS (R.A.M.S.S.I.)
Daphne Roycroft,
Michelle Cronin,
Mick Mackey,
Simon N. Ingram
Oliver O’Cadhla
Coastal and Marine Resources Centre,
University College Cork
March 2007
Higher Education AuthorityAn tÚdarás um Ard-Oideachas
HEA
CONTENTS
i) Summary
ii) Acknowledgements
General Introduction Seabirds and marine mammals in southwest Ireland 2 Rationale for RAMSSI 6 Study sites 7 Inshore risks to seabirds and marine mammals 11
i. Surface pollution 11 ii. Ballast water 13
iii. Organochlorine pollution and antifoulants 14 iv. Disease 15 v. Acoustic pollution 15
vi. Disturbance from vessels 16 vii. Wind farming 17
viii. Mariculture 17 ix. Fisheries 19
Aims and Objective 22 References 23 Appendix 33 Chapter 1. Seabird distribution and habitat-use in Bantry Bay 1.1 Abstract 35 1.2 Introduction 35 1.3 Study site 37 1.4 Methods 37 1.4.1 Line transect techniques 37 1.4.2 Data preparation 40 1.4.3 Data analysis 45 1.5 Results 46 1.5.1 Modelling 46 1.5.2 Relative abundance 54 1.6 Discussion 59 1.7 References 66 1.8 Appendix 70
Chapter 2. Shore-based observations of seabirds in southwest Ireland 2.1 Abstract 72 2.2 Introduction 73 2.3 Methods 74 2.3.1 Shore-watch techniques 74 2.3.2 Analysis of relative abundance 75 2.3.3 Density calculation 77 2.3.4 Comparison of shore and boat-based densities 79 2.4 Results 80 2.4.1 Relative abundance 80 2.4.2 Density 87 2.5 Discussion 89 2.6 References 93 Chapter 3. Temporal variation in the use of haul-out sites by Harbour seals in Bantry Bay and the Kenmare River 3.1 Abstract 96 3.2 Introduction 97 3.3 Materials and Methods 99 3.3.1 Study site 99 3.3.2 Seal counts 102 3.3.3 Statistical modelling 102 3.4 Results 105 3.4.1 Seal counts 105 3.4.2 Model output and validation 106 3.5 Discussion 121 3.6 References 127
Chapter 4 Haul-out behaviour of Harbour Seals in the Kenmare River, Co. Kerry 4.1 Abstract 133 4.2 Introduction 134 4.3 Methods 136 4.3.1 Study site 136 4.3.2 Capturing and handling procedure and tag deployment 136 4.3.3 Tag operation 137 4.3.4 Information relay and interpretation 138 4.3.5 Statistical modelling 138
4.3.6 Bootstrap variance estimation 140 4.4 Results 141 4.4.1 Capture and tag deployment 141 4.4.2 Duration of transmission 141 4.4.3 Examination of haul-out data 142
4.4.4 Model outputs and validation 143 4.5 Discussion 163 4.5.1 Effects of time of day and tidal cycle on haul-out behaviour 163 4.5.2 Seasonal changes in haul-out behaviour 164 4.6 References 171 Chapter 5. Cetacean distribution and relative abundance in southwest Ireland 5.1 Introduction and methodology 180 5.2 Species accounts 180 5.2.1 Harbour porpoise 181 5.2.2 Common dolphin 182 5.2.3 Risso’s dolphin 183 5.2.4 Bottlenose dolphin 184 5.2.5 Minke whales 184 5.2.6 Fin whale 185 5.3 Conservation recommendations 189 5.4 References 190 Conclusions 192 Recommendations for future work 194 References 197
SUMMARY
The rugged coastline of southwest Ireland is home to the highest concentrations
of breeding seabirds in the country, as well as high numbers of resident and
migrating cetacean species, many of which are of conservation concern.
Furthermore, the sheltered inlets of Bantry Bay and the Kenmare River provide a
base for important populations of Harbour seals, an Annex II species under the
EU Habitat’s Directive. This inshore environment contains a variety of potential
risks to seabirds and marine mammals, most notably from surface & acoustic
pollution, fisheries and the rapidly expanding mariculture industry.
The year-round distribution and habitat-use of seabirds and marine mammals in
Ireland’s inshore marine environment is poorly studied. Such baseline data is
critical for the assessment of the effects of anthropogenic disturbance on marine
mammals and seabirds in the coastal environment. The main aim of this study
was to establish the spatio-temporal distribution and habitat-use of seabirds and
marine mammals in southwest Ireland.
A total of 21 seabird species were recorded in the survey area during the course
of the three-year study. Seabird communities around selected headlands in
southwest Ireland were dominated by Manx shearwaters (Puffinus puffinus) and
Northern gannets (Morus basanus) while the seabird community in Bantry Bay
was dominated by auks (mainly guillemots and razorbills, Uria aalge and Alca
torda). Diversity was high in Bantry Bay however the relative abundance of
many seabird species was higher at the outer headlands. Peak numbers of many
species occurred in autumn (August - October) and the tidal cycle did not
significantly influence the abundance of any of the species studied. Vulnerable
concentrations of seabirds in areas of high risk from oil-pollution were identified.
Investigations of seabird habitat-use using generalized linear and generalized
additive modelling in Bantry Bay revealed that seaward distance (the distance
from the most inshore point of the study site) was a significant positive
determinant of total seabird distribution in Bantry Bay. The density of many
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seabird species was also positively related to distance from the nearest coast,
while depth was a limiting factor in Phalacrocoracidae distribution. The possible
conservation applications of this data for seabirds in similar habitats are
discussed.
The year round changes in harbour seal abundance and haul-out site use in
southwest Ireland were investigated by carrying out year round counts of seals at
haul-out sites over a two and a half year period. There was a difference in the
seasonal patterns of seal abundance between haul-out sites. The effect of the time
of day on seal abundance at haul-out sites varied between the sites and was only
significant at sites that also showed a seasonal pattern in abundance. Fewer seals
were observed during strong winds and rain.
The haul-out behaviour and habitat use of individual seals was examined using
telemetry. The haul-out behaviour of tagged seals varied over the tagging period
with animals spending a higher proportion of time ashore post moult in October,
decreasing over the winter months to a minimum in February. A strong tidal
influence on haul-out behaviour was evident with tagged seals hauling-out more
frequently at low tide. There was overall large variation in the patterns in
behaviour over the tagging period (i) between individuals and (ii) between tidal
periods for each individual.
A minimum of six cetacean species were recorded over the course of the three
year study, with the most abundant and widespread species being the Harbour
porpoise (Phocoena phocoena) and Common dolphin (Delphinus delphis).
Minke whales (Balaenoptera acutorostrata) were also common across the study
site and at least one Fin whale (Balaenoptera physalus) was sighted from the
outer headlands. Peak numbers of many species occurred in Autumn and calves
or immature adults were present throughout the year.
The findings of the study are discussed in relation to those conducted in
different parts of the species geographical range and the significance of the
information put into context of conservation management and monitoring
requirements for the species.
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ACKNOWLEDGEMENTS
This research was made possible through funding from the Higher Education
Authority (HEA, Prtl-3) and was facilitated by Coastal and Marine Resources
Centre and the Department of Zoology, Ecology and Plant Science, University
College Cork.
This project was greatly enhanced by the expert supervision of Dr Tom Kelly
and Dr Emer Rogan, of the Department of Zoology, Ecology and Plant Science,
UCC.
Thanks to: Dr Derek Scott for his Dursey Island cable-car taxi service; Ann &
Brendan Finch for accommodation on Dursey Island; Ann & Jerome Harrington
for allowing access to Black Ball Head; Mr Stephan O'Sullivan (Irish Lights) for
allowing free access to Mizen Head signal-tower; Vicki O’Donnell for help with
GIS mapping; Lesley Lewis, Pete Jones, Mark Wilson, Alain Zuur and Lisa
Borges for advice on statistical analysis; Claire Pollock for help with correction
factors; Tom Hubbard for assistance on a number of boat surveys; Steve Newton
and the Birdwatch Ireland/Seabird 2000 crew for kindly providing breakdowns
of breeding seabird counts for southwest Ireland; Dave Millard and colleagues at
BIM, as well as Gavin Burnell, Julie Maguire and Claire Lehane for information
on mussel suspension culture. Thanks to Bernie McConnell, Alisa Hall (SMRU),
Declan O’Donnell, Clare Heardman (NPWS) and Allen Whittaker for help with
seal tagging.
All photographs in this report are the copyright of Mick Mackey and Michelle
Cronin.
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GENERAL INTRODUCTION
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SEABIRDS & MARINE MAMMALS IN SOUTHWEST IRELAND
The coastal and offshore waters of southwest Ireland are essential feeding
grounds for many seabirds and marine mammals, including non-breeders and
passage migrants, throughout the year. A total of 52 species of seabirds and 21
cetacean species have been recorded both on and off the Irish shelf (Berrow &
Rogan, 1997; Pollock et al., 1997; Mackey et al., 2004a; Mackey et al., 2004b).
The heavily indented, cliff-edged coastline of Ireland with its numerous
offshore islands and accessible rich feeding grounds, offers an ideal base for the
formation of seabird colonies. A total of 24 seabird species breed in the
Republic of Ireland (Mitchell et al., 2004). Six of these are listed in Annex 1 of
the E.U. Birds Directive (79/409/EEC) as species of conservation priority. A
further nine species are listed as Birds of Conservation Concern in Ireland
(BoCCI) either because their breeding populations are in moderate decline or of
international importance (Table 1). A further eight regularly occurring winter or
passage migrants are also listed as Annex 1 species, or as Birds of Conservation
Concern in Ireland (Table 2). Member states of the European Union are required
to protect important populations of Annex 1 species through the designation of
Special Protection Areas (SPA’s).
Breeding colonies in the southwest of Ireland hold the largest concentration of
breeding seabirds in the country (Figure 2 and Table 3). A total of 78% of the
Irish Manx shearwater, Puffinus puffinus population and 77% of the Irish
European storm-petrel, Hydrobates pelagicus population breed on the islands off
southwest Ireland. The Island of Inishtooskert in the Blasket Island group holds
the largest storm-petrel colony (27,297 pairs) in Britain and Ireland, and
probably the world (Mitchell et al., 2004). The two largest gannet, Morus
bassanus colonies in Ireland, holding 94% of the Irish breeding population, are
also located off the southwest coast of Ireland on Skellig Michael and the Bull
Rock (Appendix 1). Furthermore, over 45% of the Irish puffin, Fratercula arctica
population breeds in southwest Ireland, with Puffin Island alone holding a
quarter of the total (c. 5,000 individuals) (Mitchell et al., 2004).
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Table 1. List of breeding seabird species in the Republic of Ireland, their breeding numbers (Seabird 2000 survey), percentage of the total biogeographical population, relevant biogeographical area and highest conservation status. Modified from Mitchell et al., (2004). Species Total pairs
Rep. of Ireland
Max % of biogeog-raphical population
Biogeog- raphical area
Highest Conservation status
Northern Fulmar 32,918 1.4 Atlantic Manx Shearwater 32,545 17.9 World BoCCI (B,E) European Storm-Petrel 99,065 42.7 NE Atlantic Annex 1 Leach’s Storm-Petrel 310 0.0 N Atlantic Annex 1 Northern Gannet 35,4571 9.01 World BoCCI (B,E) Great Cormorant 4,548 10.0 N Atlantic BoCCI (B) European Shag 3,426 5.6 NE Atlantic Great Skua 1 0.0 World Mediterranean Gull 3 0.0 Europe Black-headed Gull 3,876 0.7 World BoCCI (B) Mew (Common) Gull 1,060 0.4 NW & C Europe BoCCI (E) Lesser Black-backed Gull 2,876 2.7 NE Atlantic Herring Gull 5,521 0.9 NW Europe Great Black-backed Gull 2,243 2.2 Europe Black-legged Kittiwake 36,100 2.0 N Atlantic Sandwich Tern 1,762 5.4 Europe Annex 1 Roseate Tern 734 38.8 Europe Annex 1 Common Tern 2,485 1.9 Europe Annex 1 Arctic Tern 2,735 0.7 Europe/N Atlan. Annex 1 Little Tern 206 1.2 Europe Annex 1 Common Guillemot 138,108* 5.7 N Atlantic BoCCI (E) Razorbill 25,980* 6.6 NW Europe BoCCI (B) Black Guillemot 3,367* 1.7 Atlantic BoCCI (E) Atlantic Puffin 19,641 0.4 Atlantic BoCCI (B,E) *number of individuals (not pairs), 1 2004 Census (Newton pers.comm.) Annex 1 = Rare or vulnerable species listed in the E.U Birds Directive (79/409/EEC) BoCCI = Birds of Conservation Concern in Ireland (Newton et al., 1999) BoCCI (B) = Amber List. Breeding species with moderate decline, rare/sporadic breeding and/or internationally important or localized. BoCCI (E) = Amber List. European Conservation Concern
Table 2. List of regularly occurring winter/passage seabirds in the Republic of Ireland, their maximum wintering numbers and highest conservation status. Species Maximum Rep. of Ireland
wintering individuals* Highest Conservation status
Red-throated Diver 136 Annex 1 Black-throated Diver 31 Annex 1 Great-northern Diver Great-crested Grebe Common Scoter Cory’s Shearwater Great Shearwater Sooty Shearwater
318 1,157 7,198
Annex 1 BoCCI (B,W) BoCCI (D,H) Annex 1 BoCCI (W) BoCCI (W)
*I-WeBS counts for winter 2000/01 (Colhoun, 2002) BoCCI (W) = Amber List. Wintering/passage species which are internationally important BoCCI (D) = Red List. Breeding population has declined by >50% in the last 25yrs. BoCCI (H) = Red List. Breeding population Historically declining (since 1900).
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igure 2 The location of the major seabird breeding colonies in southwest
Table 3 The important breeding populations, number of breeding seabird species
ecies
Designation
FIreland.
and conservation designation of the major breeding colonies in southwest Ireland (after Larner & Douglas, 2002; Mitchell et al., 2004), Site Important Species Number of
(>1000 pairs) Breeding Sp
Puffin Island Mx, SP, P 11 SPA, NHA
Skellig Rocks P WHSG, SP, Mx, 12 SPA, NHA,
Blasket Islands SP, Mx, F 14 SPA, SAC, NHA
Scarriff & Deenish SP, Mx 7 NHA
Bull & Cow Rocks G, SP 9 SPA, NHA
SP = European Storm Petrel, Mx = Manx Shearwater, F = Northern Fulmar, G = Northern Gannet, P = Atlantic Puffin. SPA = Special Protection Area, SAC = Special Area of Conservation, NHA = Natural Heritage Area and WHS = World Heritage Site.
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Ireland’s continental slope – which is located less than 75km from the
southwest coast - has been identified as an area of high importance for many
cetacean species (Pollock et al., 1997; Mackey et al., 2004b). Several studies
have also reported high densities of minke whales (Balaenoptera acutorostrata)
and harbour porpoises (Phocoena phocoena) in the inshore waters of southwest
Ireland in summer, indicating that this area provides an important habitat for
these species (Pollock et al., 1997; Hammond et al., 2002; Mackey et al., 2004b).
In addition there is evidence to suggest that Irelands Atlantic Margin forms part
of the migratory pathway of a number of large baleen whales, including
humpback, Megaptera novaeangliae, fin, Balaenoptera physalus, sei
Balaenoptera borealis and even blue whales, Balaenoptera musculus, as they
move from winter calving grounds in the south to summer feeding grounds at
high latitudes (Charif et al., 2001; Harwood & Wilson, 2001; Mackey et al.,
2004b and Irish Whale and Dolphin Group anecdotal sightings, www.IWDG.ie).
Two seal species, the harbour seal (Phoca vitulina) and grey seal
(Halichoerus grypus), breed on the Irish coastline, with high numbers occurring
on the southwest coast. Recent Harbour seal population estimates carried out
during the 2003 moult season yielded a minimum population estimate of 2,905
for the Republic of Ireland with concentrations of this species in the southwest,
west and northwest of the country (Cronin et al., 2004). In addition,
approximately one third of the Irish grey seal population breed on the Blasket
Islands, in southwest Ireland (Cronin & O'Cadhla, 2004) indicating the
importance of this area for pinnipeds.
All cetaceans present in European waters are protected under EU law and
listed in Annex IV of the EU Habitats Directive, as species of community interest
in need of strict protection. In addition the bottlenose dolphin (Tursiops
truncatus), harbour porpoise and the harbour and grey seal are listed in Annex II
as requiring the designation of Special Areas of Conservation (SAC’s). In 1991
the Irish government declared Irish waters a whale and dolphin sanctuary
including the State’s 200-mile exclusive fishery limit (Rogan & Berrow, 1995).
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RATIONALE FOR RAMSSI
Despite the obvious importance of the inshore waters of southwest Ireland both
for seabirds and marine mammals (many of which are of conservation concern),
very little baseline data exist on their distribution and seasonal abundance in the
area. The location and abundance of seabirds at breeding colonies in the region
is well known (Mitchell et al., 2004) however the at-sea distribution of breeding
populations as well as non-breeding migrants in inshore waters is poorly
understood – apart from a few localized land-based records (e.g. Hutchinson,
1981). The biggest risks to seabirds and marine mammals from anthropogenic
activities exist in inshore waters and so it is particularly important to assess the
distribution of sensitive species in this region.
In general, recent studies of seabird and cetacean distribution in Irish waters
have been carried out on relatively large scales with a focus on offshore areas
and have indicated that the inshore (neritic) zone holds a higher diversity of
seabird species with higher abundances than continental slope or oceanic waters
(Pollock, 1994; Pollock et al., 1997; Mackey et al., 2004a). To date, there has
been no attempt to determine relationships between the at-sea seabird and
cetacean distribution described in these studies and physical habitat variables.
Harbour and Grey seals spend a significant proportion of their lives in the coastal
zone, where they use terrestrial habitat to come ashore to haul-out to rest, breed
and moult and inshore waters to forage in and to navigate through to more
offshore areas. Despite recent efforts in addressing the shortfall in population
data on a national scale information on the year round patterns in abundance and
habitat use of harbour seals in Ireland was lacking. Such information is critical
for the effective management and conservation of the species as required under
national and international legislation.
Reproductive output is comparatively low in seabirds and marine mammals as
both groups are k-strategists often producing only one offspring in each breeding
attempt. This means that the rate at which their breeding populations can
increase is relatively slow and populations may take several years to recover
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from a discrete mass adult mortality event such as an oil spill. It is therefore
essential that areas of high seabird and marine mammal use - particularly in
inshore waters where risks from anthropogenic activities are high - be identified
in order to protect these sensitive species.
Global climate change is likely to influence seabird and marine mammal
distribution and abundance around Ireland in the coming decades - or even years.
Slight changes in oceanographic conditions could have large-scale and pervasive
effects on seabird distributions, feeding ecology, reproductive success and
populations (Montevecchi & Myers, 1997). Marine mammals are adapted to
specific temperature regimes and may be forced to leave otherwise suitable
habitat if temperatures fall outside the ranges to which they are adapted (Hoelzel,
2002). A recent study has suggested that negative effects of climate change on
sandeel availability may increasing the likelihood of starvation in harbour
porpoise populations in the North Sea at certain times of year (MacLeod et al.,
2007). Sandvik et al. (2005) documented correlations between the North
Atlantic Oscillation (NAO) index and adult survival in North Atlantic seabirds
(common guillemot, Brunnich’s guillemot, Uria lomvia, razorbill and puffin).
Their evidence suggests that meteorological parameters affect seabird mortality
indirectly, possibly through the food chain (see also Alheit & Hagen, 1997).
However the effects of the NAO and global climate change may take many years
to become apparent in long-lived marine top predators, e.g. northern fulmars,
Fulmarus glacialis (Thompson & Ollason, 2001). It is vital that current baseline
data on seabird and marine mammal abundance and distribution be gathered so
that future distributional shifts resulting from climate change can be identified.
STUDY SITES
The RAMSSI study area comprises the inshore waters of southwest Ireland
between Mizen Head (51o 27’N, 09 o 49’W) to the south and Lambs Head (51 o
45’N, 10 o08’W) on the northern shore of Kenmare River. Within this study area
the major inlets of Bantry Bay and the Kenmare River were the focus of harbour
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seal surveys, while Bantry Bay and its approaches (including Mizen Head)
formed the seabird and cetacean survey area (Figure 2).
The coastline of the southwest coast of Ireland is characterized by steep cliffs
and heavily indented rocky shores with numerous rocky islands. Several long,
narrow inlets – typically drowned river valleys (ria’s), separated by exposed
sandstone headlands, define this region. Due to their northeast-southwest
orientations these inlets are open to the prevailing south-westerly winds and have
a primarily wind-driven circulation (Raine et al., 1990; Irish Hydrodata, 1990).
The tidal regime is semi-diurnal with two high waters and two low waters each
day. Tidal currents are generally weak (<1 km.h-1) in the region however strong
currents occur around the headlands (e.g. 6.5km.h-1 off Dursey Island; 5.6km.h-1
off the Mizen Head) and in areas of constricted water flow such as south of
Whiddy Island (e.g. 2.8km.h-1) in Bantry Bay (Admiralty Charts, No. 2495, 2424
& 1838 respectively). Mean sea-surface temperatures range from 8oC in winter
to 16oC in summer (Lee & Ramster, 1981).
The hydrography of the area is complex. Around the southwest coast of
Ireland a frontal system known as the Irish Shelf Front comes very close to the
coastline and in summer, on occasion, has been found at the mouth of Bantry
Bay (Edwards et al., 1996; Raine & McMahon, 1998). When this inshore
movement of the front occurs, the weak clockwise circulation of coastal water
from the Celtic Sea is cut off and deep sub-thermocline Atlantic Shelf Water
encroaches towards the coast. This results in an upwelling of highly productive
water which promotes dense concentrations of phytoplankton. The overall
consequence is that the coastal seas off southwest Ireland are highly productive
(Raine et al., 1990; Edwards et al., 1996; Raine & Joyce, 1996) and provide rich
feeding grounds for top predators.
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Figure 2. The RAMSSI study sites showing locations of shore-based observation points, boat survey routes and Harbour seal survey areas in southwest Ireland. The locations of the main seabed depth contours (50 and 100m) are also shown (light blue).
Bantry Bay (51o 42′N, 9o 31′W) is the largest of the long marine inlets in
southwest Ireland. The entrance to the bay at Sheep’s Head is approximately
10km wide, steadily narrowing to 3-4km at its head. Its length from Sheep’s
Head to the inner-most point is 35km. The northern ‘arm’ of the bay extends a
further 20km past Sheep’s Head to Dursey Island and the Bull Rock and thus
provides some shelter from northerly winds. Depths are typically less than 30m
in the inner bay but deepen to 70m in outer regions with a predominantly muddy
substrate (Smith & McLaverty, 1997). The Bay is broad, gently sloping and
steep-sided, particularly in the outer region, with only a few sections of muddy
inter-tidal zone habitat. Freshwater input is relatively low, with surface salinities
at the head of the bay typically in excess of 33 ‰ (Raine et al., 1993).
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There are a number of sheltered harbours along the northern shore of the Bay,
one of which (Glengarriff Harbour) has been designated a Special Area of
Conservation (SAC) due in part to its large population of Harbour Seals. The
bay is a known spawning ground for autumn-spawning herring Clupea harengus
(see Smith & McLaverty, 1997).
Kenmare River (51o 43’N, 10o 05’W) is approximately 41km long and 8km
wide at its mouth, between Lamb’s Head on the north side of the bay and Cod’s
Head on the south (Figure 2). The inter-tidal areas of Kenmare River are
dominated by rocky shores that run directly into the sea as cliffs at Dursey
Island, Cod’s Head and Lamb’s Head. The inner parts of the smaller bays, for
example Ardroom and Kilmackillogue harbours and the upper part of Kenmare
River are sedimentary shores dominated by muddy sands (Smith & McLaverty,
1997). The bedrock is mainly old red sandstone which forms reefs along the
middle of the bay throughout its length. Kenmare River can be described as a
partially mixed estuary with stratification occurring during the summer months,
and occasionally in winter due to high freshwater run-off. However the halocline
can be dissipated within hours in strong winds and despite the freshwater
influence, surface salinities approximately half way between the head and mouth
of the river are in excess of 34‰ (McGovern et al., 2001). Depths range from
30m in the innermost parts to 75-80m in the central part of the outer bay
(Admiralty Chart No. 2495). The bay is a designated Special Area of
Conservation due partly to the presence of the Annex II Harbour seal species.
Bantry Bay is heavily utilized for the shipping and storage of oil and other
hazardous substances - activities which potentially pose a high risk to seabirds
and marine mammals at sea. In addition, the sheltered inner regions of Bantry
Bay and Kenmare River are heavily utilized by the rapidly expanding mussel
longline industry, as well as marine finfish farming – activities which may
deprive seabirds and marine mammals of foraging habitat. Boat-based eco-
tourism is intensive in Glengarriff Harbour, Bantry Bay and in Kenmare River
during the summer, posing a potential source of disturbance to seals as well as
feeding seabirds and cetaceans.
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INSHORE RISKS TO SEABIRDS AND MARINE MAMMALS
i) Surface pollution
Many authors have identified the risks that water-borne pollution incidents pose
to seabird populations, particularly those species that frequently associate with
the ocean’s surface, e.g. auks, divers, sea-ducks, skuas, Manx shearwater,
European shag and great cormorant (Cramp et al., 1974; Tasker et al., 1990;
Williams et al., 1994; Webb et al., 1995; Pollock et al., 2000; Mackey et al.,
2004a). Even slight exposure to oil can be fatal to seabirds due to fouling of
feathers and pathological effects of oil ingestion (Briggs et al., 1997).
Marine mammals rely on their blubber for insulation and so are less vulnerable to
fouling by oil than seabirds, however they are at risk from evaporating
hydrocarbon vapours which they may inhale when breathing at the surface
(Geraci & St. Aubin, 1990). Symptoms from acute exposure to volatile
hydrocarbons include irritation to the eyes and lungs, lethargy, poor coordination
and difficulty breathing. These symptoms may occasionally result in drowning.
Neonatal seal pups (particularly grey seals) are most at risk from oil spills as they
lack blubber and rely on their fur for insulation. Grey seal pups in particular are
restricted to the natal haul out site until they are weaned and so are incapable of
leaving a contaminated area (Ekker et al., 1992).
During the past 30 years several oil spills have affected the Irish coast, with
many of these on the southwest coast. One of the first significant kills was
associated with the 1979 BETELGEUSE spill at the Whiddy Island oil terminal.
This resulted in the oiling of approximately 1,000 seabirds in the vicinity of
Bantry Bay (Cross et al., 1979). Subsequently, a spill of unknown origin in the
southeast resulted in the beaching of 545 birds in the winter of 1982/83. In 1986,
the loss of between 1,000 and 1,500 seabirds, primarily auks as well as a small
number of grey seals, was attributed to the sinking of the KOWLOON BRIDGE
on the Stag rocks near Baltimore, Co. Cork (Hutchinson, 1989; Smiddy, 1992).
A further 1,500 seabirds, primarily guillemots, as well as two grey seals were
killed in an oil spill in Cork Harbour (south coast) in November 1997 (Smiddy,
1998).
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Incidents involving very large crude carriers such as the grounding of the SEA
EMPRESS (72,000t of crude oil) at the entrance of Milford Haven in South
Wales in 1996, The PRESTIGE (25,000t of heavy fuel oil) off the Spanish Coast
in 2002 and the total loss of the BREAR (85,000t of crude oil) in the Shetland
Islands in 1993 also resulted in the spillage of crude oil on adjacent coastlines in
the northeast Atlantic and caused high seabird mortality (Fletcher, 2003).
Williams et al. (1994) developed an oil vulnerability index (OVI), based on
four easily scored factors, to assess the relative risks to different seabird species
from surface pollution. Briefly, these included: a) the proportion of time spent
on the sea surface by that species; b) the size and bio-geographical population of
the species; c) the potential rate of recovery following a reduction in numbers for
each species and d) the reliance on the marine environment by each species.
The maximum OVI score is 30. Using this technique, Webb et al. (1995)
calculated that divers (OVI=29), grebes (23-26), great skuas (25), shags (24)
auks (21-29) and Manx shearwaters (23) are the most vulnerable groups of
marine birds occurring south and west of Britain. These values can be applied to
seabird populations in southwest Ireland, thus enhancing the quality of
conservation advice that can be given, either in consultations or in reactive
situations following a pollution incident.
Chemicals are also transported at sea in bulk by specialist tankers. These
generally carry thousands of tonnes of one or a small number of chemicals. If
these vessels sink, as did the ‘PERINITIS’ in 1989 and the ‘IEVOLI SUN’ in
2000 in the English Channel, these chemicals can be released into the sea. In the
first case, 6 tonnes of insecticide were lost in a container carried on deck, and in
the second, 1000 tonnes each of two solvents were released from the wreck of
the vessel on the seabed (Fletcher, 2003). The impact of these spills on seabirds
and marine mammals are unknown, but likely result in mortalities.
The largest single threat to seabirds and marine mammals in Bantry Bay is the
oil terminal located on Whiddy Island, which was the site of a major oil spill in
1979 (Cross et al., 1979). This terminal provides storage facilities (1 million
12
tonnes capacity) for the Irish strategic oil reserve and for output from offshore oil
fields in Irish coastal waters. Both crude oil and refined products, including
kerosene, gas oil and jet fuel are pumped ashore from tankers that moor onto the
Single Point Mooring (SPM) - an offshore jetty linked to the terminal via
pipeline along the sea bed. The SPM is capable of handling tankers of up to
320,000 tonnes. Cargo ships, naval vessels and large trawlers also use the
terminal for re-fuelling (http://bantrybaycharter.ucc.ie).
A total of 52 trading vessels entered Bantry Bay in 2004, with almost half of
these carrying liquid bulk, amounting to a total of 535,000 tonnes of oil
(Anonymous, 2005). Additional shipping traffic (with associated fuel oil) is
generated by a small number of privately owned quarrying operations within the
coastal zone of Bantry Bay. On the north side of the bay at Leahill point, one of
the largest quarries in Ireland has its own jetty, which accommodates ships of up
to 75,000 tonnes. Quartzitic chippings from the quarry are exported to the UK
and continent on a regular basis from this jetty in the inner bay
(http://bantrybaycharter.ucc.ie).
ii) Ballast water
The use of seawater as ballast in ships has resulted in harmful introductions of
alien species. Translocations of marine life have become much more common in
recent decades, as vessels have become larger and faster. This can result in the
alteration of food-webs and consequently the food source of top-predators such
as seabirds and marine mammals. The introduction of the American comb
jellyfish (Mnemiopsis leidyi) via ballast water into the Black Sea and Sea of
Azov in 1982 resulted in the near collapse of the anchovy fishery which had
become its main prey species (Fletcher, 2003). Species of phytoplankton such as
Chatonella verruculosa, originally from Japan but introduced to the Skagerrak
and northern Kattegat in 1998 can form toxic blooms and cause deaths of wild
and farmed fish (i.e. 350 tonnes of farmed salmon in Scandinavia) (Fletcher,
2003). These effects of ballast water also constitute a potential ecological
disturbance which could adversely affect seabird and marine mammal
populations.
13
iii) Organochlorine pollution and antifoulants
Polychlorinated Biphenyls (PCB’s) can cause infertility in Harbour seals
(Rejinders, 1986) and cause hormonal effects which reduce the ability of seabirds
to reproduce successfully (e.g. Dirksen et al., 1995; Bustnes et al., 2003).
Relatively high levels of PCBs (mean 2.38mg/kg² extractable fat, similar to that
found in Cork city) were found in otter spraints from the Bantry Bay coastal zone
in the early 1990’s, indicating possible contamination from oil-refining activities
here (O'Sullivan et al., 1993). A number of studies have recorded the presence
of PCB’s in tissue samples taken from live bottlenose dolphins, Tursiops
truncatus and bycaught common dolphins, Delphinus delphis and Harbour
porpoises in Irish waters (Smyth et al., 2000; Berrow et al., 2002). However
levels of PCB in the environment have been declining steadily since the 1970’s
and levels of these compounds in Europe are below the levels required to cause
symptoms (Becker, 1991; Berrow et al., 2002; Mitchell et al., 2004).
Organochlorine pesticides such as DDT (in the form of DDE) caused eggshell
thinning of many birds i.e white-tailed sea eagles, Haliaeetus albicilla in the
Baltic Sea in the 1960’s (Helander et al., 2002) however the effects are now
abating as concentrations fall.
Tributyltin (TBT) was used widely as an antifoulant until 1993 and caused
sexual abnormalities (i.e. imposex) in gastropods such as dogwhelks, Nucella
lipillus (Gibbs et al., 1987). It is also known to bio-magnify in the tissues of top
predators such as seabirds, particularly sea-duck species which feed on molluscs
and causes immunosuppression in marine mammals (Guruge et al., 1996;
Kannan et al., 1998; Berge et al., 2004). TBT-based anti-foulants are still being
used on large ships (>25m length) but will be phased out between 2003-2008
(Fletcher, 2003). Bantry Bay is subject to moderate levels of shipping traffic and
so may be contaminated to some degree with TBT. The effects of TBT on
seabirds is little known, but some species (i.e. the cormorant, Phalacrocorax
carbo) appear to be able to metabolise butyltins and to shed up to one fourth of
their body burden during a complete moulting cycle (Guruge et al., 1996).
14
iv) Disease
An outbreak of Phocine Distemper Virus (PDV) in 2002 caused over 22,000 seal
mortalities (mostly Harbour seals) in western Europe and affected animals in
Northern Ireland and parts of the Republic (see Cronin et al., 2004). This was
the second outbreak of PDV in western Europe, with the first occurring in the
summer of 1988 causing 17,000 seal mortalities (Van der Toorn, 1990). The
disease is perceived as a natural phenomenon, however future outbreaks could
pose a threat to the population.
v) Acoustic pollution
The noise associated with marine exploration and resource extraction represents
a source of acoustic degradation in the marine environment. Boat sonar, airguns,
drilling operations and shipping traffic all produce sounds with combined low
and high frequency components (Goold, 1996), which may potentially affect
both the low frequency-sensitive baleen whales and high frequency-sensitive
toothed cetaceans (Harwood & Wilson, 2001). These effects are heightened in
shallow waters due to the presence of subsidiary pulses resulting from reflection
off the seafloor.
Damage to marine mammals from high-level sound may be direct (lethal, sub-
lethal or non-lethal) or indirect (changes in behavioural or distribution patterns).
Direct injuries include physical damage (blast trauma) to the ear, lungs and
gastro-intestinal tracts (often causing mortalities) while indirect effects include
avoidance reactions and reduction of calling rates by various baleen whale
species (Ketten, 1993; Richardson et al., 1995). Other possible indirect effects
include disease, reduced foraging opportunities, reduced mating success,
decompression sickness, live mass stranding events and the exclusion of
cetaceans from important habitats. Detailed information on the distribution of
cetaceans is vital in order to mitigate for these indirect acoustic effects.
The southwest coast of Ireland is a ‘priority area’ for seabed mapping through
the INtegrated mapping FOr the sustainable development of Ireland’s MArine
Resource (INFOMAR) program (phase 2 – the successor to the Irish National
Seabed Survey INSS, which covered offshore areas of Ireland’s territorial
15
seabed). Bantry Bay in particular has been identified as one of 26 priority bays
which are currently being mapped (2006 onwards). The outer Kenmare River
and Dursey Island area were surveyed in 2004 by the Geological Survey of
Ireland (GSI) and the Marine Institute (MI) (www.gsiseabed.ie).
In addition to these surveys there has been an increase in oil and gas exploration
in the waters around Ireland, particularly along the west coast. All of these
investigations require the use of active acoustic survey techniques, the impacts of
which are poorly understood for marine mammals.
vi) Disturbance from vessels.
Many toothed whales appear to be tolerant of vessel noise and are regularly
observed in areas of heavy traffic (e.g. bottlenose dolphins) (Rogan et al., 2000).
However this is not true of many larger baleen whales. Sperm whales have been
reported to react to vessels with powerful outboard engines at distances of up to 2
km. Humpback whales and right whales are also reported to avoid large vessels
in some areas, with vessel collisions forming a serious cause of right whale
(Eubalaena glacialis) mortality in the North Atlantic (Katona & Kraus, 1999).
Fin whales are reputed to ignore large vessels, but they respond to close
approaches by whale-watching vessels by spending less time at the surface and
by making shorter dives (Richardson et al., 1995).
Boat-based ecotourism, mainly focused on seals, is popular in Glengarriff
Harbour in Bantry Bay and parts of Kenmare River. During the peak summer
months several tour vessels operate in close proximity to seal haul-out sites in
these areas and may disturb seals or deter them from hauling out. Although a
certain degree of habituation is likely, it is possible that critical behaviours are
disrupted, possibly resulting in high stress levels, energy expenditure and
reduced fitness. It is also possible that other Annex II marine mammal species
such as the Harbour porpoise are excluded from these areas of high vessel
activity due to avoidance responses.
16
Vessel collisions are also a possible source of mortality for some seabirds,
particularly shearwaters and petrels which are attracted to lights at night (Wiese
et al., 2001; Le Corre et al., 2002)
vii) Wind farming
At least 13,000 turbines are currently planned for northeast Atlantic marine
waters, although only ten marine wind farms with a total of 163 turbines are
operating worldwide. In Irish waters one large marine wind farm is operational
on the Arklow Bank, off County Wicklow in the Irish Sea and another is
proposed for Clogher Head, off County Louth. The impacts of these wind farms
on seabirds are poorly studied but are likely to include: death by collision with
turbines, as well as other effects resulting from flight avoidance, habitat
modification, feeding displacement and disturbance (Anonymous, 2003a). At
present the RAMSSI study site is free from any wind farm developments.
viii) Mariculture
Global seafood demand is projected to increase by 70% by the year 2025.
Aquaculture will have to increase production by 700% to a total of 77million
metric tonnes annually to meet that projected demand (Hayden, 2000). In the
northeast Atlantic and Baltic Sea, total mariculture production has increased 2½
times over the past two decades to 1.2 million tonnes, divided almost equally
between fish and molluscs (Fletcher, 2003).
It is widely acknowledged that seals and seabirds, particularly cormorants,
shags, herons, Ardea cinerea and eiders, Somateria mollissima directly benefit
from the enhanced food supply provided by marine fin-fish farming as well as
from lost fish pellet food (Leukona, 2002; Davenport et al., 2003; Quick et al.,
2003; Nash et al., 2000; Boylan et al., 2003). Gulls readily scavenge on remains
of fish and invertebrates, steal food pellets and take the growing product from
accessible areas of farms (Davenport et al., 2003). However, marine fin-fish
farming can also have an indirect negative impact on seabird and marine
mammal feeding success through the loss of wild fish stocks. In 2001 an
17
estimated 11 million tonnes of fish were taken from the wild to provide fish-meal
for carnivorous farmed fish such as salmon. As aquaculture continues to boom,
it will exact a growing toll on species such as sardines, Sardina pilchardus and
herring, Clupea harengus (Powell, 2003) and will likely affect the seabirds that
prey upon them.
The impact of intertidal mollusc farming on shorebirds (as well as gulls) is
relatively well known (Hilgerloh, 1997; Hilgerloh et al., 1997; Ferns et al., 2000;
Hilgerloh et al., 2001). Negative impacts have been found for some shorebirds at
oyster farms due to loss of open estuarine foraging habitat (Hilgerloh et al.,
2001) and over-harvesting of natural seed stocks (Anonymous, 2000). However
some species benefit from the readily available prey provided by intertidal
mussel farms (Goss-Custard et al., 1993).
Mussel suspension aquaculture occupies large sections of inshore seabird and
seal foraging habitat without providing a direct source of food for these
primarily-piscivorous species. The influence of this type of mariculture on the
seabird and seal community in Bantry Bay has generally been found to be
positive or neutral (Roycroft et al., 2004; Roycroft et al., 2006) at its current
intensity. However, the mariculture industry is undergoing a rapid expansion in
Ireland (Hayden, 2000) and the widespread alteration of inshore foraging habitat
by this industry could potentially impact upon the foraging success of seabirds,
even if this is not apparent at small scales. According to Gill et al. (2001) species
with little suitable habitat elsewhere cannot show marked avoidance of
disturbance even if the fitness costs are high. For species that feed on mobile or
highly aggregated prey, the costs of moving to alternative sites may be great,
especially if they are territorial or experience high levels of competition. Such
species could then be forced to tolerate disturbance which may or may not affect
survival or fecundity and hence population size (Gill et al., 2001). Therefore it is
essential to consistently monitor the impacts of mussel suspension culture on
seabirds in order to adequately determine future trends.
Potential impacts of mariculture on cetaceans include death or injury through
entanglement in gear, displacement, alteration of the food chain and human
18
persecution. Unlike pinnipeds, cetaceans have not been reported to consume fish
or shellfish from farms, but have been known to get entangled in equipment,
resulting in the damage of gear, release of fish, and self injury (Kemper & Gibbs,
2001; Hall & Donovan, 2002). Displacement of cetaceans by aquaculture may
also occur because they frequently share the same coastal habitat. Studies of
bottlenose dolphin movements around oyster farms in Shark Bay, Western
Australia showed that dolphins were less likely to go into areas where farming
was occurring compared to an ecologically similar area nearby (Watson-Capps &
Mann, 2005). Similar avoidance behaviour was observed in dusky dolphins at
mussel farming areas of the Marlborough Sounds, New Zealand (Markowitz et
al., 2004). For this reason, baseline information on marine mammal distribution
is needed in order to assess future impacts of this rapidly expanding industry.
Bantry Bay is the largest mussel producing bay in Ireland, with four fish farms,
13 oyster farms and over 54 mussel farms mainly concentrated in the inner bay
(BIM, 2001). Aquaculture is increasing in importance in Kenmare River with
extensive areas devoted to mussel suspension culture, especially sheltered areas
such as Kilmakilloge Harbour, Ardgroom Harbour, Coongar Harbour and outer
Sneem Harbour. There is also a new fin-fish farm development on the northern
shore.
ix) Fisheries
The effects of fishing on birds may be direct or indirect. Most direct effects
involve mortalities from fishing gear e.g. bycatch of albatross and petrel species
in long-lines in the north Pacific and southern ocean (Brothers et al., 1999;
Furness, 2003). Longlining occurs along the shelf edges of Ireland, possibly
causing fulmar mortalities (Brothers et al., 1999) however seabird bycatch
impacts in these areas tend to be of a localised nature, diluting any possible
population effect (Tasker et al., 2000). Drift nets (banned since 2000 in the
Atlantic) and other gillnets have had a considerable impact on seabirds in the
northern Pacific and northwest Atlantic (King, 1984). Inshore fixed gillnets may
be a source of considerable mortality for pursuit-diving seabirds, especially if set
close to large breeding colonies. Bycatch of seabirds in salmon nets also occurs
in Ireland and has been associated with population declines at some auk colonies
19
during the 1970’s and 1980’s (Lloyd et al., 1991). Some seabird mortality is also
caused by lost nets and lines, however these impacts are generally low
(Montevecchi, 2002).
Gannets feeding on discarded blue whiting (Photo. M. Mackey).
Indirect effects of fisheries on seabirds mostly work through the alteration of
food supplies. Fishing activities have led to depletion of some fish species (e.g.
lesser sandeels, Ammodytes marinus) fed upon by seabirds but may also lead to
an increase in small fish prey available to seabirds by reducing numbers of large
predatory fish (Furness & Tasker, 2000; Tasker et al., 2000; Furness, 2003) or by
provision of offal and discards (Hillis, 1971; Hudson & Furness, 1988; Tasker et
al., 2000). Increased populations of some scavenging seabirds (e.g. large gulls
and fulmars) have been attributed to the availability of discards (Furness, 1999;
Garthe et al., 1999), with an estimated 5.9 million scavenging individuals
potentially supported by fishery waste in the North Sea (Garthe et al., 1996).
Thus, it is not surprising that the recent declines in discard rates (Alverson et al.,
1994; Kelleher, 2004). have resulted in an apparent decline in breeding success,
population size and body condition of some scavenging species (see Furness,
2003). This reduction in discard availability can also result in an increase in
predation pressure on smaller birds by large scavenging species such as great
skuas, Stercorarius skua, particularly in the North Sea (Furness, 2003; Votier et
20
al., 2004). Therefore, declines in fisheries discards may not only affect
scavenging seabird populations but also a wide range of smaller seabird species.
There has been widespread concern for many years on the impact of fisheries
on marine mammals (Northridge, 1984; Northridge, 1991). Incidental capture
(by-catch) of cetaceans in fishing gear causes high mortalities of some species,
particularly Harbour porpoises and dolphins in areas of high fishing activity such
as the Celtic Sea and continental shelf. Different species of cetacean are at risk
from different fisheries, depending on the fishing gear and techniques used.
Passive gear, especially gillnets generally kill more marine mammals than
actively fished gear. Marine debris, especially derelict fishing gear, is also
responsible for substantial incidental mortality of marine mammals (Laist et al.,
1999).
Harbour porpoises are particularly susceptible to entanglement in bottom set
gillnets with an estimated annual mortality of 2200 porpoises (95% C.I. 900–
3500) in the Celtic Sea gillnet fishery off southern Ireland (Tregenza et al.,
1997). This is 6.2% of the estimated number of porpoises in the Celtic Sea and
there is serious cause for concern about the ability of the population to sustain
this level of by-catch. Bottlenose dolphins can be assumed to be at risk from the
same fishing gear while common dolphins appear particularly susceptible to
being caught in pelagic (mid-water) trawl gear (DEFRA, 2004).
A study of pelagic trawl fisheries by-catch in the northeast Atlantic recorded
mortalities of white-sided dolphin, Lagenorhynchus acutus, common dolphin and
grey seal as well as a probable record of bottlenose dolphin. This amounted to a
mortality rate of 1 marine mammal every 17 tows (or 1 per 80.6 h of towing)
with all dolphin by-catches occurring at night (Morizur et al., 1999).
The UK Department of the Environment, Food and Rural Affairs has
recommended the use of pingers (acoustic deterrents) in selected fisheries
including vessels of the Celtic Sea gillnet fishery operating 6 nautical miles or
more from the coast and using bottom-set gill nets (DEFRA, 2003; DEFRA,
2004). However the effectiveness of these measures are poorly understood.
21
Seals are generally perceived to benefit from inshore fisheries with reports of
fish theft, particularly from salmon drift nets by seals commonplace. For this
reason seals are regarded as pests by many fishermen and localized culls are
occasionally carried out illegally to reduce damage to fish stocks from seals, e.g
c. 50 seals culled on Beginish Island in November 2004 (www.marinetimes.ie).
Bantry Bay is the site of the country’s second largest fishing harbour,
Castletownbere. There are over 100 fishing vessels based in Castletownbere, of
which over 50 are more than 40 foot in length. In 1998 the principle demersal
species landed in Castletownbere were whiting, Merlangius merlangus, haddock,
Melangrammus aeglefinus, monkfish, Squatina squatina, megrim,
Lepidorhombus whiffiagonis, hake, Merluccius spp. and cod Gadus spp. and the
pelagic catch was dominated by mackerel, Scomber scombrus
(http://bantrybaycharter.ucc.ie). Bantry and Castletownbere ports together
landed 5,927 tonnes live weight of sea fish in 2002 (Anonymous, 2003b). The
bulk of the fishing effort from this fleet takes place in offshore regions, outside
of Bantry Bay however.
1.5 AIMS AND OBJECTIVES
The aims and objectives of this study were to:
i) identify significant determinants of seabird distribution in Bantry Bay
using physical habitat characteristics (Chapter 1),
ii) establish the spatio-temporal distribution of seabirds in southwest
Ireland using shore-based observation points (Chapter 2),
iii) examine seasonal changes in harbour seal abundance and haul-out site
use in southwest Ireland (Chapter 3),
iv) assess the haul-out behaviour and habitat use of individual seals using
telemetry (Chapter 4),
v) investigate seasonal variations in cetacean abundance and distribution
in southwest Ireland (Chapter 5).
22
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APPENDIX
Appendix 1 Breeding seabird numbers at major seabird colonies in southwest Ireland. Seabird 2000 results (Mitchell et al., 2004 and Newton pers. comm.). Important
Species/pop
Blasket
Islands
Puffin
Island
Skellig
Rocks
Scarriff &
Deenish
Bull &
Cow
Northern Fulmar 1305 447 761 385 1683
E. Storm Petrel 51,6911 5,177 9,994 6,2001 3,5002
Manx Shearwater 19,534 6,329 738 2,311
Northern Gannet 29,683** 3,694**
LBBG 439 139 97
GBBG 123
B-L. Kittiwake
Guillemot *
Razorbill *
Atlantic Puffin
336
471
511
389
25
92
35
5125
694
1422
386
4000
3563
14303
1323
2584
Other (<100
pairs)
AT, BG,
SH, MG,
HG
SH, HG,
GBBG
SH, HG,
LBBG,
GBBG
SH, HG,
GBBG
SH, MG
MG = Mew Gull, SH = European Shag, LBBG= Lesser black-backed gull, HG =
Herring Gull, GBBG = Great black-backed gull, AT = Arctic Tern, BG = Black
Guillemot
* Number of individuals, **2004 census, (Newton pers. comm.) 1counts for the islands of Inishnabro and Inishtearaght were estimated from previous
surveys (not counted in seabird 2000) 2mid-points of estimates from previous surveys, (not counted in seabird 2000) 31993 counts (Newton pers. comm.) 41994 count from Bull and 1970 count from Cow (Newton pers. comm.)
33
CHAPTER 1.
SEABIRD DISTRIBUTION AND HABITAT USE IN
BANTRY BAY, SOUTHWEST IRELAND.
34
1.1 ABSTRACT
The inshore waters of southwest Ireland provide foraging grounds for the highest
concentrations of breeding seabirds in the country, augmented by annual
movements of passage migrants. However, the fine-scale habitat use of these
seabirds, many of which are of conservation concern, is poorly understood in this
region. Reliable predictors of seabird distribution in inshore areas would provide
a valuable tool for the designation of marine protection areas for their
populations and for their conservation in the event of a major pollution event.
Habitat use of seabirds at sea was investigated using generalized linear and
generalized additive modelling of densities in relation to a number of physical
habitat variables in Bantry Bay, southwest Ireland. Seaward distance (the
distance from the most inshore point of the bay) was the most important
determinant of seabird distribution in the bay with shearwater (Puffinus sp.), auk
(Alca torda & Uria aalge) and kittiwake (Rissa tridactyla) density all increasing
with increasing distance from the inner bay. Distance from the nearest coast was
also an important variable for many species, while shags (Phalacrocorax
aristotelis) and cormorants (Phalacrocorax carbo) were influenced only by
depth. There was no significant relationship between large gulls (Larus sp.) and
any of the physical habitat variables. Determinants of seabird distribution (all
species combined) differed between winter and summer, with the inner bay of
greater relative importance in winter than in summer.
The outer bay was identified as a ‘hot-spot’ of seabird distribution in summer,
with many offshore species utilizing the rich feeding grounds provided by the
adjacent Irish Shelf Front.
1.2 INTRODUCTION
Seabird distribution at sea is largely determined by prey distribution (Veit et al.,
1993; Wright & Begg, 1997; Skov et al., 2000; Yen et al., 2004; Ainley et al.,
2005). Many studies have documented the relationships between seabirds and
large scale oceanographic features such as fronts (e.g. Haney & McGillivary,
35
1985; Begg & Reid, 1997; Durazo et al., 1998; Hoefer, 2000; Spear et al., 2001),
offshore eddies (e.g. Ribic et al., 1997) and areas of upwelling (Skov & Durinck,
2000; Ainley et al., 2005). These areas of high prey density accordingly (and
predictably) attract high numbers of seabirds. However, at small scales (i.e. 1-
100km), these oceanographic processes are highly variable, both spatially and
temporally (Edwards et al., 1996), and are difficult and time-consuming to
measure. This means that oceanographic variables may be unsuitable for use in
studies of seabird distribution (particularly those repeated over time) in small
inshore environments such as Bantry Bay.
Static habitat features such as depth, seabed slope and distance from the coast
provide a useful and readily measured representation of inshore seabird habitat
for longer-term studies of seabird distribution. These variables, if proved reliable
determinants of seabird distribution, could also aid the identification of Marine
Protected Areas (MPA’s) for seabirds (see Hyrenbach et al., 2000).
According to Hunt & Schneider (1987) the abundance of individual seabird
species at small scales (1-100km) often reflects opportunities to forage at local
concentrations of prey. These opportunities are likely to differ between species
because of differing foraging strategies and may be influenced by the physical
habitat. Many species such as gannets, shearwaters, terns and gulls take fish or
crustaceans from high up in the water column, while others such as guillemots
and razorbills dive deep in pursuit of prey (Shealer, 2002). Some species such as
cormorants, shags and divers take a proportion of benthic prey (e.g. flat-fish,
Pleuronectes sp. and crabs, Carcinus sp.) and so are likely to be influenced by
sea depth more-so than surface-feeding species. Some marine bird species such
as gulls are also partially dependent on terrestrial sources of food and so are
likely to be limited in distribution by distance from the coast.
Aims and Objectives
The aims and objectives of this study were to:
vi) map the distribution and identify ‘hotspots’ of seabird density in
Bantry Bay.
36
vii) identify significant determinants of seabird distribution within the
study site using available habitat characteristics.
1.3 STUDY SITE
For a full description of Bantry Bay refer the general introduction. The survey
area extended from the inner bay, east of Whiddy Island to Sheep’s Head.
A minimum of nine seabird species breed in Bantry Bay (Appendix 1.1). Over
100 pairs of Arctic terns (Sterna paradisaea), an Annex 1 species, breed near
Whiddy Island and over 50 pairs each of cormorants (Phalacrocorax carbo),
shags (Phalacrocorax aristotelis) and black guillemots (Cepphus grylle) also
breed in the inner bay. Herring gulls (Larus argentatus) and great black-backed
gulls (Larus marinus) breed in small numbers in both inner and outer regions of
the Bay. Dursey Island, in the outer bay, holds relatively large numbers (>500
pairs) of northern fulmars (Fulmarus glacialis), black guillemots (79 individuals)
and small numbers (<10) of lesser black-backed gulls (Larus fuscus) and
razorbills (Alca torda). The Annex 1 species, common tern (Sterna hirundo)
also breeds in small numbers (<50 pairs) in the neighbouring Special Area of
Conservation (SAC) in Kenmare bay (Mitchell et al., 2004). The bay is also
likely to be utilized by foraging seabirds from adjacent colonies, particularly
gannets (Morus bassanus), from the Bull and Cow Rocks as well as Manx
shearwaters (Puffinus puffinus) and storm petrels (Hydrobates pelagicus) from
Scarriff and Deenish or the Skellig Rocks (see Chapter 1, Figure 2).
1. 4 METHODS
1.4.1 Line-transect techniques.
Regular, standardised boat-based surveys of Bantry Bay were conducted
throughout the study using a modified version of techniques outlined by Tasker
et al. (1984) and Webb & Durinck (1992) for surveying seabirds at sea from
ships. The survey platform used for this study was a 5.8m Rigid Inflatable Boat
(RIB). To adjust for the reduced eye-height of a RIB-based observer (< 2m)
37
compared to a ship based observer (> 6m) for whom the technique was
developed, the transect width was reduced from 300m to 200m.
All birds associated with the water (including flying birds which made contact
with the water) within 200m abeam of one side of the boat and forward to the
horizon, were recorded as ‘on transect’ (Figure 1). Birds within this 90o arc were
further categorised into one of four different divisions depending on their
perpendicular distance from the observer (Band A: 0-50m; Band B: 51-100m;
Band C: 101-200m; Band D: 200+m). These bands allowed correction factors to
be calculated to account for the drop-off in detectability of birds with increasing
distance form the observer (see section 1.4.2). Birds in band D were not
included in density calculations or data analysis as they were recorded as far as
the eye could see. Distances were approximated by eye following a training
exercise using the boats’ Global Positioning System (GPS) and marker buoys of
known position (this was repeated regularly throughout the study). Flying birds
were not recorded.
Figure 1. The survey platform showing the trackline, observer position and transect band widths. Only bird sightings within this 200m transect were included in the density analysis (birds in band D were excluded).
38
30000
35000
40000
45000
50000
55000
63000 68000 73000 78000 83000 88000 93000 98000
Eastings
Nor
thin
gsGlengarriff
Harbour
Whiddy
Island
Bere Is.
Sheeps Head
N
Figure 2. The transect route used in Bantry Bay. The waypoints used to navigate boat surveys are shown in red with boat transect lines in black.
Birds associated with fishing vessels were excluded from all analyses.
Surveys followed an 80km predefined survey route at approximately 20km.h-1.
The survey route followed transects between GPS waypoints and consisted of an
outward and return leg spaced a minimum of 1km apart (Figure 2). The track of
the boat during each survey was recorded automatically every minute using the
onboard GPS. Data relating to seabird sightings were recorded using a
Dictaphone.
Surveys were initiated in July 2001 and were attempted on a monthly basis
(or bi-monthly in Summer) until September 2002. Surveys recommenced in
June 2003 and were attempted at least twice a month until September 2003. Boat
surveys were conducted on days with Beaufort sea-states of three or less and
were not conducted on consecutive days in order to reduce autocorrelation of
sightings data.
39
1.4.2 Data preparation.
Habitat Mapping
To investigate relationships between bird density and a number of physical
habitat variables in Bantry Bay, the survey area was divided into a grid of 1km2
squares. The following five habitat variables were chosen as possible
determinants of seabird distribution in the study sites (Figure 3). Depths were
taken from UK Hydrographic Office Admiralty Charts (chart no.’s 1840 & 1838)
and were reduced to chart datum (the Lowest Astronomical Tide): i) Seabed
slope; the difference between maximum and minimum depth (m) in each 1km2
section of the survey area. ii) Minimum Depth; the minimum recorded depth (in
metres) of each grid square, iii) Maximum Depth; the maximum recorded depth
(in metres) of each grid square, iv) Seaward Distance; the distance in kilometres
of each grid square (midpoint) from the most inshore point of the study site (i.e.
the head of the bay), v) Distance to Nearest Coast; the distance in metres of each
grid square (midpoint) from the nearest coast (including islands).
Density calculation
The total number of birds of each species was then calculated for each grid
square. To correct for effort (Figure 3f), the number of birds recorded in each
grid square (or 1km section of transect) was divided by the number of times the
square was surveyed. All grid squares surveyed less than five times were deleted
from the database. Mean densities (birds km-2) were then calculated by dividing
the mean number of birds observed in each 1km section of transect by the
transect area (1km x 0.2km). The resulting densities were then multiplied by the
relevant correction factor for that species (see below) to obtain the corrected
mean density km-2. These densities were then mapped using ArcView (version
8.1).
40
c) Maximum Depth
f) Effort e) Nearest Coast
d) Seaward
b) Minimum Deptha) Slope
NFigure 3 (a-e). Bantry Bay habitat maps showing levels of the five explanatory variables in each 1km grid square surveyed. Figure 3 (f) shows the number of times each grid square was sampled. Squares visited less than five times were excluded.
41
The following formula was used to calculate the correction factors:
X = 4A___ nA+nB+nC
Where: nA= number of birds on the water in transect band A (the first 50m) nB= number of birds on the water in transect band B (50-100m) nC= number of birds on the water in transect band C (100-200m) (adapted from Pollock et al., 2000)
Related species were pooled into taxonomic family groups to increase sample
size and data from an identical study in the Shannon Estuary (Roycroft, 2005)
were included for calculation of correction factors (again to improve sample
size). Table 1 shows the correction factors calculated for this study compared
with those from two ship-based (as opposed to RIB-based) studies.
Table 1. Correction Factors calculated for a 200m wide strip transect in sea states of <= 3 on Beaufort Scale and with an observer eye-height of < 2m above sea level. Also shown are correction factors from two ship-based surveys; Stone et al. (1995) and Pollock et al. (2000) for a 200m wide strip transect.
Species group
This study Stone et. al. (1995)
Pollock et. al. (2000)
Shearwaters 2.9 1.1 1 Gannets 1 1 1 Cormorants & Shags 1.7 1.1 1 Gulls & Kittiwakes 2.3 1.2 1.1 Auks 2.4 1.2 1-*1.3
*in sea states >=3 Beaufort scale.
Preparation for modelling
Related species showing similar distributions (when mapped using ArcView 8.1)
were grouped together to improve sample size for the purpose of data analysis.
Densities were calculated for the following groups: 1) Phalacrocoracidae: great
cormorant, Phalacrocorax carbo and northern shag, Phalacrocorax aristotelis,
42
2) Large Laridae (lesser & great black-backed gull, Larus fuscus and Larus
marinus and herring gull, Larus argentatus), 3) Kittiwakes, Rissa tridactyla
only, 4) Alcidae: razorbill, Alca torda and common guillemot, Uria aalge, 5)
Shearwaters (manx & sooty shearwater, Puffinus puffinus and Puffinus griseus)
and 6) Total seabirds (all birds except Larus spp, recorded in the study). Gulls
were excluded as they do not depend directly or entirely on the marine
environment for food, but utilise a wide range of terrestrial and/or man-made
food sources. Thus, members of the Laridae family are likely to be relatively
independent of the habitat variables measured in this study. To investigate
seasonal variations in total seabird distribution, the data were divided into two
groups; Winter (October to March inclusive) and Summer/breeding (April to
September inclusive). Effort per grid square was also calculated separately for
each season for density calculation. Only the ‘total seabird (excluding gulls)’
dataset was analysed by season as effort was low and bird observations
insufficient in Winter for seasonal analysis of individual species groups. (Note
shearwaters were only present in Summer).
Maximum species richness per square was also calculated. This comprised the
maximum number of species observed per grid square in any one survey.
Maximum species richness per grid square was not corrected for effort (no. of
times the square was surveyed). Instead, grid squares with low effort were
omitted until there was no significant relationship between effort and maximum
species richness. Figure 4 shows that an asymptote between mean maximum
species richness and effort was reached at an effort of 13 surveys. However,
there was no significant relationship between effort and maximum species
richness when grid squares surveyed less than nine times were removed (GLM,
P>0.05). For this reason, only grid squares with an effort of nine or more were
included in the habitat analysis.
43
00.5
11.5
22.5
33.5
4
(n=3
9)(n
=19)
(n=6
)(n
=7)
(n=6
)
(n=4
)(n
=5)
(n=6
)(n
=5)
(n=3
)(n
=5)
(n=2
)
(n=3
)(n
=6)
(n=8
)(n
=12)
(n=1
2)
(n=1
7)(n
=13)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Effort
Mea
n M
axim
um S
p. R
ichn
ess
Figure 4. The relationship between effort (number of surveys per grid square) and mean maximum species richness in Bantry Bay. n = the number of grid squares within each effort category. Standard error bars are displayed. Only grid squares with an effort of nine or over were included in the species richness habitat analysis.
Similar studies of seabird distribution have shown that correlation between
counts from survey segments that are close in space and time can be a problem
(Elphick & Hunt, 1993; Begg & Reid, 1997; Clarke et al., 2003; Ainley et al.,
2005). In order to carry out accurate statistical analysis, survey segments should
be independent of each other. Since bird densities in each grid square of this
study area were averaged over a 19-month period, the problem of autocorrelation
between neighbouring squares has been minimised.
Linear relationships existed between many of the explanatory variables in
Bantry Bay (Pearson product-moment correlation, p<0.01). Any two variables
with an r-value of more than 0.8 were considered to be highly co-linear (Alan
Zuur pers. comm.) and were not included together in the same model. For this
reason only maximum depth or seaward distance was included in Bantry Bay
models (r=0.95).
44
Table 2. Pearson’s product-moment correlation coefficients (r) among the physical explanatory variables. Relationships shown are significantly correlated (P<0.01). NS= not significantly correlated. Explanatory Variable
Slope Minimum Depth
Maximum Depth
Seaward Distance
Minimum Depth -0.75 Maximum Depth NS 0.68 Seaward Distance NS 0.62 0.95 Nearest Coast -0.57 0.76 0.5 0.47
1.4.3 Data analysis
Data analysis was carried out using R (free software) and the Brodgar statistical
package version 2.4.3 (Highland Statistics Ltd.). Generalized linear modelling,
GLM (McCullagh & Nelder, 1989) with a Poisson error structure (variance =
mean) and log link function was used to relate seabird density to physical habitat
variables.
For species with an over-dispersed distribution (i.e. variance > mean) a Quasi-
Poisson error structure with log link function was used. That is - a dispersion
parameter was introduced, which allowed for more spread than the standard
Poisson mean-variance relationship. For each species group, the Minimal
Adequate Model was selected using backwards and forwards stepwise selection
and the Akaike Information Criterion, AIC statistic (Akaike, 1973).
Generalized additive modelling (Hastie & Tibshirani, 1990) was applied to
species that had poor model fits using GLM. This technique investigates non-
linear relationships between one or more explanatory variable and the response
variable. Poisson or Quasi-Poisson families with log link functions were applied
and stepwise model selection using the AIC statistic was carried out in the same
way as for the GLM. Cross-validation was used to select the optimal amount of
smoothing for each variable. A cubic spline smoothing function was used in this
study. The concept of parsimony - that the simplest explanation is best - is
inherent in such modelling efforts (Guisan et al., 2002) therefore GLMs were
chosen over GAMs when similar results were obtained.
45
1.5 RESULTS
A total of 21 boat surveys of Bantry bay were carried out during the course of the
study. Sightings from 19 of these surveys (i.e. 67 survey hours or 1273 kms of
transect line) were successfully linked with the GPS track data and included in
the data analysis. Of the 178 grid squares surveyed in Bantry Bay, 107 were
considered to have sufficient survey repetition (effort >4). Only sightings from
these squares were included in the data analysis (apart from the winter analysis
as only five surveys were carried out in this season, thus only cells with effort <
2 were excluded). The mean effort per grid square was 13.9 (±0.4 standard error,
n = 107 grid squares) and the mode was 18.
1.5.1 Modelling
Significant linear relationships between densities of three seabird groups
(Phalacrocoracidae, Alcidae and shearwaters) and a number of habitat variables
were found using generalized linear modelling (Table 3). There was no
significant relationship (linear or otherwise) between large gull density and any
of the habitat variables. This was also the case for maximum species richness.
Kittiwakes showed a linear response to seaward distance and curvilinear
responses to slope and nearest coast (generalized additive modelling). Total
seabird density was positively related to seaward distance and nearest coast.
Total seabird densities in Summer showed a positive response to seaward
distance and nearest coast, however in Winter total seabird densities showed a
negative response to maximum depth and a positive response to nearest coast
(both excluding gull species). The percentage deviance explained by the models
varied between 30.7% and 64% of the total deviance.
46
Table 3. Summary results of generalized linear and generalized additive models showing determinants of seabird density and species richness n Bantry Bay. Gull species were omitted from ‘total’ bird densities but not sp. richness.
Variable
Phal- acro.
Large Laridae
Kitti-wakes
Alcidae Shear-waters
Max. Sp. Richness
Total Birds
Total Summer
Total Winter
Slope NS S-2 -5 NS
Minimum Depth NS NSMaximum Depth - NS NS -2 Seaward Distance NS +1 +1 + +1 NS +1 1
Nearest Coast NS S-3 + + + +2 2 NS +2 2 1 Interaction Terms *3&4 Model GLM GAM GLM GLM GLM GLM GLMDeviance explained 57.5% 0% 55.8% 42% 64% 0% 50.1% 44% 30.7%Dispersion Parameter 0.6 3.98 9.4 10.35 9.63 13.12 7.35 Shown are statistically significant relationships. (+) = positive response of seabird density/biomass/richness to increase in a variable, (-) = negative response (GLM’s). (S+) = significant smoother term with overall positive response, (S-) = overall negative response (GAM’s). Superscripts (1-5) denote the order of importance of the variables in explaining the variation in bird density/richness (1 = most important). NS = model not significant. * indicates that 2 or more of the significant terms have an interaction effect on bird density.
Phalacrocoracidae
Table 4. Results of the generalized linear model for cormorants and shags in Bantry Bay. Poisson distribution with a Log-link function. Significant Terms Estimate Std.Error z-value P-value Max. Depth -0.10449 0.01229 -8.502 <0.0001 Maximum Depth was the only significant variable influencing cormorant and
shag distribution in Bantry Bay (Table 4). The relationship was negative,
indicating that mean cormorant and shag density decreased linearly with
increasing depth. Figure 5(a) shows that this species group occurred in relatively
high mean densities (4-8/km2) in the shallow, inner portion of the bay (south and
east of Whiddy Island) but were not present at all in the deeper regions near the
mouth of the bay.
Large Laridae
Although appearing in higher mean densities in the outer half of Bantry Bay
(11-33/ km2) than in the inner portion (max 6-10/ km2) large gulls were not
significantly influenced by seaward distance or any other habitat variable
included in the model. Figure 5(b) shows that large gulls were distributed
ubiquitously throughout the bay in relatively high mean densities.
Kittiwakes Table 5. Results of the generalized additive model for kittiwakes in Bantry Bay. Quasi-Poisson distribution with a Log-link function. Smoother Terms edf Chi-sq Model r2 P-value Slope Nearest Coast Parametric terms
3.6 4.7 Estimate
18.9 16.84 std. Err
0.612 t-ratio
<0.01 <0.01
Seaward Distance 0.1971 0.027 7.297 <0.0001
There was a highly significant positive linear relationship between mean
kittiwake density and seaward distance in Bantry Bay (Table 5). Slope and
nearest coast had significant negative curvilinear effects on mean kittiwake
distribution however. Figure 6 (a & b) shows the smoothing curves produced
from generalized additive modelling. Mean kittiwake density decreased rapidly
with increasing slope in areas where slope was high but remained constant in
areas of lower slope (<25m). Kittiwake density decreased with increasing
distance from the coast up to a distance of 3500 metres, but then levelled off.
48
f) Max. Species Richness e) Shearwaters
d) Alcidae c) Kittiwakes
b) Large Laridae a) Phalacrocoracidae
Figure 5 (a-f). Distribution of the five seabird groups for which analysis was carried out as well as for maximum species richness in Bantry Bay. Flying birds are not included. Average densities per transect for the entire study period are shown (includes all seasons).
N
49
g) Total seabirds (excluding gulls)
i) Winter Totals (excluding h) Summer Totals (excluding gulls)
Figure 5 (g-i). Distribution of total seabird density g) and total seabird density in Summer h) and Winter i) in Bantry Bay. Flying birds are not included. Average biomass or densities per transect are shown. Gulls are not included.
N
50
ba
Figure 6. Smoothing curves produced from generalized additive modelling of mean kittiwake density and slope (a) and mean kittiwake density and nearest coast (b). Dotted lines represent confidence intervals. Degrees of freedom are shown in parenthesis on the y-axis label. The vertical lines above the x-axis show positions of the measured data points.
Alcidae Table 6. Results of the generalized linear model for Guillemots and Razorbills in Bantry Bay. Quasi-Poisson distribution with a Log-link function. * interaction terms Significant Terms Estimate Std.Error t-value P-value Slope -3.356e+02 1.510e-02 -2.222 <0.05 Seaward Distance Nearest Coast
1.618e-01 1.919e-03
3.898e-02 5.396e-04
4.152 3.556
<0.0001 <0.0001
Slope*NearCoast SeaDist*NearCoast
4.101e-05 -6.616e-05
1.186e-05 2.040e-05
3.458 -3.244
<0.0001 <0.01
Mean Alcidae density in Bantry Bay was highly positively correlated with
seaward distance and nearest coast but was negatively correlated with slope
(Table 6). These relationships were complex however with two interaction terms
being significant in the model. Coplots revealed that bird densities decreased
slightly with increasing slope at close proximity to the coast (<1km), but
increased markedly with slope at higher distances from the coast (>1.5km).
From the habitat map (Figure 3a), it can be seen that all areas of high slope
(>30m) in Bantry Bay occurred within 1.5km of the coast. Since mean bird
density increased significantly with distance from the coast (Table 6), this may
have resulted in the appearance of a negative response to slope. Coplots revealed
51
that bird density increased with increasing distance from the coast at all seaward
distances, but this relationship was much less marked in the inner bay. Figure
5(d) shows the distribution of Guillemots and Razorbills in Bantry Bay. Highest
mean densities (10-60/ km2) occurred in the outer half of the bay in areas with
relatively high distance from the coast.
Shearwaters Table 7. Results of the generalized linear model for Shearwaters in Bantry Bay. Quasi-Poisson distribution with a Log-link function. Significant Terms Estimate Std.Error t-value P-value Seaward Distance 0.2741 0.094 2.915 <0.01 Nearest Coast 0.0005 0.0002 2.271 <0.05
Mean shearwater density showed a positive linear relationship with seaward
distance and nearest coast (Table 7). Figure 5(e) shows the distribution of
shearwaters in Bantry Bay. High mean densities (55-75/km2) occurred only in
the outer-most regions of Bantry Bay, with no sightings recorded in the inner
bay. Shearwaters typically did not occur, or occurred only in low densities (<15/
km2) at close proximity to the coast.
Maximum Species Richness
There was no significant relationship (linear or otherwise) between maximum
species richness and any of the explanatory variables. Figure 5(f) shows that
maximum species richness was randomly distributed throughout Bantry Bay with
a clustering of higher values along the northern side of the bay and near the bay
mouth.
52
a) b)
Figure 7. Coplots showing: a) relationships between Alcidae density and slope at increasing distance from the coast and (b) relationships between density and nearest coast at increasing seaward distance. Panels are ordered from lower left to upper right, i.e. the lower left-hand panel of a) shows density (y) versus slope (x) at distances of 0-1000m from the coast and the upper right-hand panel shows density versus slope at distances >2000m. See section 2.2.4. for description of coplots.
Total seabird density (excluding gulls)
Table 8(a-c). Results of the generalized linear models for total seabirds (excluding gulls) in Bantry Bay in all seasons (a) in summer only (b) and in winter only (c). Quasi-Poisson distribution with a Log-link function.
a) All seasons Significant Terms Estimate Std.Error t-value P-value Seaward Distance 8.237e-02 1.510e-02 5.456 <0.0001 Nearest Coast 3.560e-04 8.172e-05 4.356 <0.0001 b) Summer Significant Terms Estimate Std.Error t-value P-value Seaward Distance 9.853e-02 1.694e-02 5.818 <0.0001 Nearest Coast 2.479e-04 9.635e-05 2.573 <0.05 c) Winter Significant Terms Estimate Std.Error t-value P-value Max depth -0.066 0.019 -3.460 <0.001 Nearest Coast 0.001 1 0.0002 5.207 <0.0001
Total seabird density (excluding gulls) in Simmer and Winter
When all seabird species (excluding gulls) in Bantry Bay were pooled across the
summer season, there was a positive linear relationship between mean density
and seaward distance as well as between mean density and nearest coast (Table
8a). Mean seabird densities in winter (October to March) however showed a
negative response to maximum depth (Table 8b). Seabird densities also showed
a positive linear relationship to nearest coast in winter, just as in summer. Figure
5(h & i) shows the distribution of all seabird species recorded in Bantry Bay in
summer and winter. Highest mean seabird densities (highest 150-230/km2,
Figure 5h) occurred in the outer half of Bantry Bay in the summer months.
However in winter seabird density was distributed in a more random fashion
with a small clustering of higher densities in the inner bay (Figure 5i).
1.5.2 Relative Abundance
Modelling was not carried out on species with low sighting rates. For these
species mean density was calculated if sufficient sightings were present (i.e.
gannets and black guillemots) but no statistical analysis was carried out.
54
For all other species with low sighting rates, or for species that were only
recorded in flight, density was not calculated, and the grid square system was not
used. The relative abundance of these species was represented using dot-plots of
the original sighting data (Figure 8 a-g). These sightings were not corrected for
effort and represent the total number of birds recorded ‘on transect’ over the
entire study period.
Great Northern Divers
A total of eight great northern divers (Gavia immer) were recorded ‘on transect’
during the course of the study. Figure 8(a) shows the distribution of these
sightings, all of which were associated with the water. All sightings occurred
within the shallow inner portion of the bay and consisted largely of solitary
individuals. Sightings took place between the months of November and April
only.
Fulmars
All fulmars recorded ‘on transect’ during the study were observed in flight.
Figure 8(b) shows that these birds were distributed mainly in the outer regions of
Bantry Bay and along its northern side. All sightings were of single flying
individuals.
Northern Gannets
Figure 8(c) shows the mean density of gannets associated with the water in
Bantry Bay. This species was distributed ubiquitously throughout the bay in
relatively low mean densities (max 3/km2), with the highest mean density
occurring at the mouth of the bay. The overall mean density of gannets in Bantry
Bay was 0.16/km² (±0.04se).
Great Skuas
A total of seven great skuas (Stercorarius skua) were sighted during the study
period. Figure 8(d) shows the distribution of these sightings, all of which were
flying individuals. Sightings were restricted to the outer half of Bantry Bay with
the majority occurring at the mouth of the bay.
55
Small Laridae
Small gulls were distributed mainly within the inner half of Bantry Bay in small
groups of less than 10 birds on the water (Figure 8e). These sightings consisted
mainly of black-headed gulls (Larus ridibundis) however some birds were
unidentified (possibly mew gulls, Larus canus). A large raft (150 individuals) of
black-headed gulls was sighted on the southern side of the bay. Flying birds
were not included in the sighting map.
Terns
Tern sightings were concentrated in the inner portion of Bantry Bay near the
arctic tern breeding colony east of Whiddy Island (Figure 8f). All terns recorded
were in flight and so were difficult to identify to species level. Most sightings
were likely to be arctic terns however common terns were also present as they
breed in the neighbouring Kenmare Bay (Mitchell, 2004). All sightings of these
Annex 1 species occurred between the months of June and September.
Black Guillemots
Mean black guillemot density was highest in the inner portion of Bantry bay in
the shallow region south of Whiddy Island (Figure 8g). Mean densities were low
with a maximum of 3 per km2. The overall mean density of black guillemots in
Bantry Bay was 0.09/km² (±0.04se)
56
a) Great Northern Divers b) Fulmars (flying)
c) Northern Gannets d) Skuas (flying)
e) Small Laridae f) Terns (flying)
Figure 8 (a-f). Total numbers of great northern divers (g), fulmars (h), gannets (i), skuas (j), small gulls (l) and terns (m) recorded during all surveys. Dot-plots include flying birds.
N
57
g) Black Guillemots
NFigure 8 (g). Distribution of black guillemots (average density/km2) sighted during the survey period in Bantry Bay.
58
1.6 DISCUSSION
Seaward distance and distance from the nearest coast were the most important
determinants of seabird distribution in Bantry Bay, followed by maximum depth
and slope.
Of the five seabird groups analysed, three were positively related to seaward
distance (i.e. kittiwakes, shearwaters, razorbills and guillemots). Shearwaters,
razorbills and guillemots were also positively related to distance from the coast,
however kittiwakes showed a negative response to this variable.
Phalacrocoracidae
The mean density of cormorants and shags in Bantry Bay was negatively related
to maximum depth. Cormorants and shags were recorded in highest densities in
waters of less than 30m depth and were never recorded in areas deeper than 45m
depth. Pollock et al. (1997) and Mackey et al. (2004) found a complete absence
of cormorants and shags in deepwater habitats around Ireland. The rock
cormorant (Phalacrocorax magellanicus) was also found to be negatively related
to sea depth in the Beagle Channel, Argentina (Raya Rey & Schiavini, 2000).
According to Cramp & Simmons (1977) cormorants prefer sheltered seas and
avoid deep water even close to land. Both species are foot-propelled pursuit
divers, feeding predominantly on fish obtained on or near the seabed (Cramp &
Simmons, 1977; Wanless et al., 1991). It is likely therefore that these species
minimise energy expenditure by foraging for benthic prey in shallow water.
Small numbers of shags (82 pairs) and cormorants (52 pairs) also breed within
the inner bay (Appendix 1.1), and so may be restricted by foraging range during
the summer months. Mean cormorant & shag density in Bantry Bay was 0.38
±0.1/km², which was similar to densities found in Irish coastal waters by Pollock
et al. (1997). The inner bay held mean densities of 2-8/ km² however, indicating
that this high-risk area is locally important for foraging cormorants and shags.
59
Large Laridae
There was no significant relationship between large gull density and any of the
habitat variables. This group consisted of great black-backed gulls, herring gulls
and lesser black-backed gulls in equal proportions (although the majority of
sightings were not identified to species level). All of these species are
scavengers and food pirates, taking a wide variety of terrestrial and marine prey.
Great black-backed gulls are also voracious predators, taking chicks and eggs
during the breeding season (Cramp & Simmons, 1983). The distribution of
many gull species at sea can be influenced by the location of fishing vessels
(Garthe, 1997; Pollock et al., 1997) and therefore is largely independent of
physical habitat variables. Pollock et al. (1997) and Mackey et al. (2004)
recorded low to moderate densities (typically <2/km2) of these species in Irish
coastal waters, with lesser black-backed gulls exhibiting a more offshore
distribution than the others. The mean density of large gulls in Bantry Bay was
2.4 ±0.5/km², with the flocking behaviour of these species resulting in local areas
of high density (20-60/km2) in the outer half of the bay.
Kittiwakes
Kittiwakes are the most pelagic of gull species, dispersing into and even across
the North Atlantic during winter (Shealer, 2002). They are surface feeders,
taking mostly fish and planktonic invertebrates obtained offshore (Shealer,
2002). The relatively high densities of kittiwakes in the outer half of Bantry Bay
may have been feeding on concentrations of prey associated with the nearby Irish
Shelf front or from trawler discards just offshore. Pollock et al. (1997) also
found high densities of kittiwakes over the shelf break southwest of Ireland and
this species is known to associate with a tidal mixing front in the Irish Sea in
summer (Durazo et al., 1998). As well as being positively related to seaward
distance, kittiwake density was also negatively related to slope and distance from
the coast. This was due to the presence of high numbers of kittiwakes (possibly
non-breeders) which chose to preen and roost on the steep-sided cliffs of Sheep’s
Head during the summer (Roycroft pers. obs.). This headland may have been
favoured over others because of its proximity to the rich feeding grounds of the
Irish Shelf front. Typical kittiwake densities in the outer half of Bantry Bay were
over 5/km2. Previous studies in Irish shelf and inshore waters by Pollock et al.
60
(1997) and Mackey et al. (2004) have found slightly lower densities of
kittiwakes (typically <2/ km2, including flying birds) with some areas of the
continental slope containing densities of 100/ km2.
Alcidae
Razorbill and guillemot density was positively related to seaward distance and
distance from the coast. Razorbills and guillemots have a typically inshore
distribution, remaining within the boundaries of the Irish Shelf throughout the
year (Pollock et al., 1997; Mackey et al., 2004). They are pursuit divers, taking
mainly mid-water schooling fish such as sand eels (Ammodytes spp.), herring
(Clupea harengus) and sprat (Sprattus sprattus) (Gaston & Jones, 1998). The
foraging range of these species is typically less than 30km from the breeding
colony (Gaston & Jones, 1998). This may have contributed to the low numbers
recorded in the inner portion of Bantry Bay as it is over 40km from the nearest
breeding colony on the Bull and Cow Rocks (while the outer bay is within this
foraging range). Garthe (1997) also found that guillemots were absent at
distances of over 25km from the colony. The prey of this species may also have
been higher in the outer bay due to the proximity of the Irish Shelf Front.
Razorbills and guillemots have been associated with fronts in the Irish Sea and in
the California Current system (guillemots only) for example (Durazo et al., 1998;
Hoefer, 2000; Ainley et al., 2005).
Guillemots and razorbills were recorded in higher densities away from the
coastline. These species may be wary of foraging close to land due to the
presence of predators such as gulls or due to human disturbance. Many auk
species are relatively clumsy on land and take off from it with difficulty (Gaston
& Jones, 1998) therefore they carry out all maintenance activities at sea or at
breeding colonies in the summer. Typical guillemot and razorbill density in the
outer half of Bantry bay was between 10 and 60/km2. Previous studies in Irish
shelf and inshore waters by Pollock et al. (1997) and Mackey et al. (2004) have
found similar densities of razorbills and guillemots (combined) with highest
densities occurring in late summer and autumn. That guillemots outnumber
razorbills by 13:1 is not surprising as the ratio of breeding guillemots to
razorbills on the nearest breeding colony (Bull & Cow Rocks) was 10:1 (General
61
Introduction, Appendix 1). The adult post-breeding moult of guillemots and
razorbills is complete, with birds becoming flightless for c. 45-50 days in late
summer (Gaston & Jones 1998). For this reason these species are highly
vulnerable to surface pollutants at this time of year.
Shearwaters
Manx Shearwaters, like most Procellariiformes, are pelagic in distribution for
much of the year, only entering inshore Irish waters to breed (manx shearwaters)
or forage (sooty shearwaters) in spring and summer (Shealer, 2002; Mackey et
al., 2004). Breeding manx shearwaters are known to make foraging trips of
some days duration (Gaston & Jones, 1998) with Irish breeding birds often
ranging as far as the Porcupine Bank in search of prey (Mackey et al., 2004).
Both manx and sooty shearwaters are surface feeders, relying on small fish,
cephalopods and crustaceans, many of which are abundant along the Irish Shelf
Front (Raine, 1990). The high density of shearwaters at the mouth of Bantry bay
may reflect high concentrations of prey associated with the Irish Shelf Front
which occurs close to the bay mouth in summer (Edwards et al., 1996). Sooty
shearwater distribution in particular has been associated with fronts by a number
of authors (Hoefer, 2000; Ainley et al., 2005).
These species are also typically wary of land because of predators such as gulls
- only returning to their breeding colonies under the protection of darkness for
example (Cramp & Simmons, 1977). It is not surprising therefore that
shearwater distribution increased with increasing distance from the coast in this
study. Mean shearwater density in the outer bay was very high (typically 15-
75/km2) even when averaged across all seasons. Previous studies in Irish shelf
and inshore waters by Pollock et al. (1997) and Mackey et al. (2004) have found
much lower densities of manx shearwaters (typically <10/ km2, including flying
birds) with sooty shearwater density typically less than 1/km2. This indicates
that the outer region of Bantry bay is an important foraging area for these species
in summer and autumn.
62
Maximum Species Richness
A total of 19 species were recorded in Bantry bay during the course of the study.
This is comparable to Pollock's (1994) study, which recorded 18 species in
neritic waters around the southwest coast of Ireland in Autumn. Areas of high
species richness (max 6/km) were distributed randomly throughout the bay,
showing no significant relationship with the physical habitat variables.
Total seabird distribution (excluding gulls)
The distribution of total seabird density in Bantry Bay was positively correlated
with seaward distance and distance from the coast. Highest seabird density (30-
135/km²) occurred in the outer bay, indicating that prey abundance is high in this
region and can supply the energy demands of high densities of seabirds. The
high densities of guillemots, razorbills and shearwaters in this study contribute
largely to the distribution of these variables. All of these species are highly
vulnerable to surface pollutants, indicating that these areas of high seabird
density are also areas of high sensitivity and should be of high conservation
concern.
Seasonal variation in total seabird distribution
Seabird density in Bantry Bay was much lower in winter (overall 10.3±2.6/km²)
than in summer (overall 34.6±5/km²) and exhibited a largely different
distribution. In summer seabird distribution was positively related to seaward
distance and nearest coast, however in winter densities were negatively related to
maximum depth and positively related to nearest coast. This difference in
distribution may be largely due to the complete absence of shearwaters in winter
and the more offshore location of the Irish Shelf Front (up to 30km further west)
and its associated rich feeding grounds at this time of year (McMahon et al.,
1995). Densities of many species such as kittiwakes, guillemots and razorbills,
which enter coastal waters to breed in summer, are also typically much lower in
winter (Pollock et al., 1997; Mackey et al., 2004). Great-northern divers are
winter migrants to the bay and show a preference for shallow inshore areas. This
species, together with other pursuit divers such as cormorants, shags and black
guillemots remain in the shallow inner bay over winter and are limited by depth
because of their preference for benthic prey. All of these species, particularly
63
great northern divers (an Annex 1 species) and black guillemots (which are
flightless for 5 weeks during the post-breeding moult, Gaston & Jones, 1998) are
highly vulnerable to surface pollutants and so are at high risk from any spillages
from the Whiddy Island oil terminal.
Offshore species
Gannets, fulmars and great skuas occurred in relatively low numbers in Bantry
Bay and were restricted mainly to its outer regions. These species are typically
more offshore in distribution; the former being among the most abundant species
recorded by Pollock et al. (1997) and Mackey et al. (2004). Great shearwaters
(Puffinus gravis), European storm petrels and Atlantic puffins (Fratercula
arctica) were also found in relatively high densities in offshore waters by
Pollock et al. (1997) and Mackey et al. (2004) but were never recorded in Bantry
bay. However, there is a record of a wreck of great shearwaters in Bantry Bay
(at Whiddy Island) in the past (T.Kelly, pers. comm.) indicating that this species
may make occasional use of the bay. European storm-petrels are also regularly
seen, sometimes in very large numbers, on sea-watches off Cape Clear Island,
Mizen Head and Dursey Island (e.g. Sharrock, 1973; Hutchinson, 1989),
indicating that this species is likely to occur in outer regions of the study site but
may not have been recorded due to its inconspicuous appearance. Gannets,
fulmars and puffins all breed in high numbers within 20km of Bantry Bay (on the
Bull and Cow rocks and Dursey Island, chapter 1, Appendix 1.1) but did not
regularly forage within the bay. It is likely therefore that the prey of these
species is more abundant in offshore waters.
Conclusions
The outer region of Bantry Bay is clearly a ‘hot-spot’ of seabird distribution in
summer, with large concentrations of shearwaters and Alcidae occurring here.
All species within these taxonomic groups are listed as Birds of Conservation
Concern in Ireland (BoCCI, Amber List) and are highly vulnerable to surface
pollutants (OVI of 22-29), particularly auks which are flightless for 5 weeks
during this time. Total seabird density is also high in the outer bay indicating
that this area provides rich feeding grounds for a wide range of seabird species.
64
Thus an oil-spill event in the outer bay at this time of year would have major
consequences for a wide range of vulnerable seabird species.
In winter however, the inner bay appears to be of much greater importance to
seabirds than the outer bay. Two highly vulnerable (OVI, 29) Annex 1 Gaviidae
species are present in the bay at this time of year, as well as notable
concentrations of black guillemots (OVI, 29), cormorants and shags (OVI, 20 &
24). The incidence of oil-pollution events is higher at this time of year (see
Cross et al., 1979; Smiddy, 1992; Smiddy, 1998), and in this region of the bay
(due to the presence of the Whiddy Island oil terminal) indicating that these
species are at high risk from an oil-spill event. The inner bay is also of high
importance to the breeding population of Annex 1 Arctic terns in summer.
The results of the modelling indicate that seaward distance appears to be a
reliable predictor of overall seabird density (particularly shearwaters, kittiwakes
and Alcidae) in summer (April-September) in Bantry Bay, i.e. high seaward
distances indicate high seabird density. This does not apply to all species groups
however. Maximum depth can be used as an indicator of the limit of
Phalacrocoracidae distribution, with very few individuals occurring in waters
deeper than 45m. Maximum depth also appears to be a limiting factor in winter
(March-October) for a wide range of wintering species. Further research is
needed to assess the transferability of these results to other bays in this region.
65
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thermal boundaries in the tropical Pacific: the thermocline versus the
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Tasker, M.L., Hope Jones, P., Dixon, T. & Blake, B.F. 1984. Counting
seabirds at sea from ships: A review of the methods employed and a
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pelagic predators and their principal prey, Antarctic Krill, near South
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Wanless, S., Burger, A.E. & Harris, M.P. 1991. Diving depths of shags
Phalacrocorax aristotelis breeding on the Isle of May. Ibis 133, 37-42.
Webb, A. & Durinck, J. 1992. Counting Birds from ships., Manual for
Aeroplane and ship surveys of waterfowl and seabirds. IWRB special
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69
1.8 APPENDIX
Appendix 1.1. Breeding seabird numbers in Bantry Bay, including the outer bay from Bere Island to Dursey Island. Seabird 2000 results (Newton pers. comm.). Number of Pairs Inner
Bantry Sheep’s Head
Bere Is - Dursey
Total Bantry
Northern Fulmar 11 575 586 Great Cormorant 52 52 European Shag 82 12 94 Lesser black-backed gull 4 4 Herring gull 19 21 40 Great black-backed gull 8 1 9 Arctic Tern 104 104 Razorbill* 7 7 Black Guillemot* 71 65 79 215 * Number of individuals
70
CHAPTER 2
SHORE-BASED OBSERVATIONS OF SEABIRDS IN SOUTHWEST IRELAND.
2.1 ABSTRACT
The community composition and relative abundance of seabirds at sea in the
high-risk inshore environment of southwest Ireland was studied from six shore-
based observation points over a three-year period. Shearwaters (mainly manx
shearwaters, Puffinus puffinus) and gannets (Morus basanus) dominated the
species assemblage at the outer headland sites, while auks (mainly guillemot,
Uria aalge and razorbill, Alca torda) dominated the species assemblage at the
more inshore sites of Bantry Bay. The diversity and species richness of seabirds
was high in Bantry Bay due to the presence of both neritic and pelagic species,
however the total relative abundance of seabirds at the outer headland sites was
over double that of the Bantry Bay sites.
Peak numbers of many species occurred in autumn (August - October)
indicating that an oil-spill event at this time of year would have a large impact on
seabirds in these sites. The tidal cycle did not significantly influence the
abundance of any of the species studied, however further studies incorporating
seabird behaviour are recommended.
The use of a theodolite at selected sites allowed the distance to all seabird
sightings (excluding flying birds) to be calculated and the results showed a
significant decline in detection rate of seabirds at distances above 2km from the
observation point. Densities calculated within this 2km radius did not differ
significantly from densities calculated at these sites using boat-based surveys. It
was concluded that estimates of seabird density using fixed-point survey
techniques are reliable up to a distance of 2km from the observation point, but
are likely to be underestimated at larger scales (>2km).
72
2.2 INTRODUCTION
The inshore waters of southwest Ireland are likely to be utilized by large
numbers of foraging seabirds from some of the largest breeding colonies in the
country (i.e. the Blasket Islands and the Skellig rocks), as well as a number of
seasonal and passage migrants, many of which are protected under national or
EU legislation (e.g. storm petrel, Hyrdobates pelagicus, great-northern diver,
Gavia immer). These inshore waters represent areas of high potential risk to
seabirds due to the presence of anthropogenic activities such as mariculture and
the shipping and storage of oil (e.g. Bantry Bay). The density, distribution and
fine scale habitat use of seabirds in Bantry Bay have been described in detail
(chapter 1), however the seasonal use of the Bay and its surrounding waters by
individual species groups has not been studied. The influence of the tidal cycle
on seabird abundance at these sites is also relatively unknown and may be a
significant factor for diving species.
An understanding of the seasonal and tidal variations in use of these areas by
vulnerable seabird species is vital for the effective management and impact
assessment of major pollution events in high-risk areas. Significant seasonal
and/or tidal variations in abundance identified in these areas, could also be
applied to similar regions utilized by these seabirds and so are of broad-ranging
use.
Land-based observations are a widely used, inexpensive and easily executed
method of surveying seabirds at sea. The accuracy of bird density estimation
from fixed-point surveys has been discussed in detail by a number of authors
(e.g. Bibby et al., 1992; Buckland et al., 2001), however field-based ground-
truthing of these density estimates at fine scales has not been attempted to date
for seabirds at sea. This study provides a measure of the accuracy of shore-based
density estimates of seabirds by a comparison with boat-based density estimates
in Bantry Bay.
73
Aims
The aims of this study were to:
I. examine the abundance, diversity and community composition of
seabirds at selected sites in southwest Ireland – identifying hot-spots of
abundance and concentrations of species vulnerable to oil spills;
II. Investigate seasonal and tidal variations in seabird relative abundance at
the six shore-watch sites; and
III. Determine the reliability of density estimation from shore-based
observations through a comparison with boat-based survey techniques.
2.3 METHODS
2.3.1 Shore-watch techniques
Six observation points overlooking Bantry Bay and its approaches were selected
in order to represent both inner and outer sections of the study site (see Figure 2,
General Introduction for site map). All observation points were located within
200m of the shoreline, had a relatively un-obstructed view of the study site, and
were at least 45m above sea level (all apart from the Inner Bantry site were over
80m above sea level). Observations were carried out systematically each month,
using a telescope (Swarovski, 20-60x zoom lens) and binoculars (Leica 10 x 42).
A theodolite (Sokkia DT500, 30x magnification) was also used at the Sheep’s
Head and Inner Bantry sites in order to accurately record positions of birds on the
water (for ground-truthing of boat-survey data). This surveying instrument
measures a horizontal and a vertical angle in relation to a set reference point (e.g.
lighthouse) on each bearing. Using a precisely known observer point, reference
point and known observer height these angles can subsequently be used to derive
positions of remote objects at sea (Würsig et al., 1991; Lutkebohle, 1995).
Ideally the variation in sea level height caused by tidal fluctuations should be
accounted for, however for this study all seabird positions were grouped into a
1km grid-system and so fine-scale positioning of seabirds was not essential.
Scans were carried out during sea states of 3-4 or less on the Beaufort Scale
from each vantage point, over all tidal states and between the hours of 0900 and
74
1930 (1600 in winter). Typically, one watch (consisting of one or more scans)
per month was carried out from each vantage point in the winter (October to
March) and at least two per month (weather permitting) in the summer (April to
September). Scan duration varied between 30 and 350 minutes depending on the
number of seabirds present. The area of sea within a 0-180ºobservation arc (or
less depending on the view available) was surveyed as far as the eye could see
during each scan. The near-shore area was surveyed first using binoculars,
followed by systematic sweeps (e.g. 0º -180º arcs) with the telescope at
increasing distance increments from the shore until the horizon was reached. A
horizontal and vertical angle was obtained for each seabird sighting using the
theodolite, along with the relevant sighting details such as species, group size and
behaviour. Theodolite positions can only be obtained for birds on the water, thus
flying birds were not included in this analysis. Birds associated with fishing
vessels were also excluded from all calculations. Shore-watches were carried out
between June 2001 and February 2004 at the Sheep’s Head and inner Bantry
sites and between June 2003 and September 2004 at the four outer sites.
2.3.2 Analysis of relative abundance
Relative abundance was calculated as the number of birds on the water (for each
species group) in each scan divided by the number of scans at that site. The
resulting value is the mean number of birds per scan at each site and this was
used for analysis of community similarity and diversity. Flying birds and birds
associated with fishing vessels were excluded from all analyses.
Community composition and similarity
Diversity at the four shore-watch sites was measured using Simpson’s Index of
Diversity (Reciprocal 1/D) and community similarity was investigated using the
Bray-Curtis Index of Similarity (Magurran, 1988).
Simpson’s Index of Diversity (D) is calculated as:
D = ∑ ni (ni –1) N (N –1)
Where: ni = total number of individuals of a particular species
75
N = total number of individuals of all species
Simpson’s Reciprocal Index (1/D) was presented, as it is more intuitive (i.e. the
higher the value the greater the diversity).
The Bray-Curtis index incorporates the abundance of species as well as species
richness at the different sites. This index reflects the similarity in individuals
between habitats and is calculated as:
Cn = 2jn . (An + Bn) Where: j = number of species in common jn = sum of the lesser values of species in common to both An = total individuals in habitat A Bn = total individuals in habitat B Similarity between communities in different sites was expressed as a dendrogram
using Mountfords classification scheme based on simple average linkage. The
‘BioDiversity Professional’ program (version 2) was used for the calculation of
diversity and similarity indices.
Seasonal variation
Seasonal variations in seabird abundance (excluding flying birds which did not
make contact with the water surface) were displayed graphically at the six shore-
watch sites. The mean number of birds per scan in each season was computed
for this. The seasons were allocated as follows: Spring = February, March and
April; Summer = May, June and July, Autumn = August, September and October
and Winter = November, December and January. The same species groups
defined in chapter 1 were used here.
Tidal variations
Three paired measures of tidal variation were categorized to investigate the
influence of the tidal cycle on the abundance of a number of seabird families;
a) Tide height; ‘high’ (3 hours before and 3 hours after high tide), versus
‘low’ (3 hours before and 3 hours after low tide),
b) Direction of flow; ‘ebbing’ (6 hours) versus ‘flooding’ (6 hours),
76
c) Rate of flow; ‘slack’ (3 hours before and 3 hours after the turn of the
tide) versus ‘flow’ (the remaining 6 hours of faster tidal flow).
Tidal variations in abundance were investigated at the inner Bantry and Sheep’s
Head shore-watch sites separately and data from the four outer headland sites
(Mizen, Black Ball, Three Castle Heads and Dursey Island) were pooled to
improve sample size. The four most numerically abundant species groups at
each site were chosen for tidal analysis. This comprised the Alcidae, (guillemot
Uria aalge, razorbill Alca torda and black guillemot Cepphus grylle), Laridae
(all gull species, not including kittiwake), Phalacrocoracidae (cormorant
Phalacrocorax carbo and shag Phalacrocorax aristotelis) and Manx shearwaters
(Puffinus puffinus) at the inner Bantry site and Manx shearwaters, gannets
(Morus basanus), kittiwakes (Rissa tridactyla) and Alcidae at the outer sites.
The mean number of birds per month in each of the six tidal states was calculated
for the three shore-watch site groups. These abundances were compared between
tidal states using paired t-tests (all data were normally distributed).
2.3.3 Density calculation
Using spherical trigonometry, theodolite readings recorded at the inner Bantry
and Sheep’s Head shore-watch sites were used to derive geographical positions
of seabirds on the water. All sightings of birds on the water were allocated to a
1km² grid square using the grid system devised for boat survey data in chapter 1.
This was achieved by linking the grid-reference fields of the sighting data and
the grid square data in a Microsoft Access database program. The number of
birds on the water in each grid square was divided by the effort (number of scans
at that site) and by the area surveyed (1 in this case) to produce a density of birds
per kilometre squared. In this instance, densities were calculated for two periods
of the year; Summer (April to September) and Winter (October to March) for
comparison with the boat survey data. Densities were only calculated for total
seabirds pooled (excluded gulls), and not for separate species groups as the aim
of this exercise was to compare shore and boat-based survey results. The
densities derived from boat-based surveys of separate species groups in Bantry
Bay were described previously in chapter 1. All densities were mapped using
ArcView (version 8.1).
77
Figure 1 shows that the detection rate of seabirds (all species pooled) was not
constant across all grid squares. Mean seabird density was negatively related to
distance from the observation point (GLM, P<0.001) when all sites and seasons
were pooled. However there was no significant relationship between density and
distance from the observation point when only grid squares with midpoints
located less than 2km from the observation point were included (GLM, Quasi-
Poisson family, log-link function, t-value 4.712, P= 0.371, n=32). For this
reason, densities calculated for grid squares over 2km from the observation point
were considered as unreliable, and were omitted from further analysis, as they
were likely to be underestimated.
The overall density of seabirds in the surveyed shore-watch area (up to a
specified distance from the observation point) can be estimated using the
DISTANCE program (Buckland et al., 2001). This takes into account the
decline in detection rate of seabirds with increasing distance from a fixed point.
The area of the visible shore-watch site was calculated by estimating the angle of
the viewing arc (i.e. 0-140o ) and dividing it by 360º as the program assumes a
circular viewing area. The overall density of seabirds recorded from the Sheep’s
Head shore-watch site in summer was estimated using this program1. Overall
density values produced for large areas such as the outer Bantry Bay site using
this technique should be used with caution however as they are based on the
assumption that birds were distributed evenly throughout the outer bay (i.e. that
the density of seabirds close to the coast was representative of densities further
offshore). For the purposes of this study, densities in a 1km grid-system were
considered to be more appropriate as these showed fine-scale variations in
density and were directly comparable with boat-survey results. Thus only one
shore-watch site was analysed using the DISTANCE program for comparison
purposes.
1 A Uniform Key Function with a simple polynomial series expansion was selected as the best model using the AIC statistic. Simple polynomial adjustments were of the order 2, 4 and 6.
78
0123456789
10
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1000
0
1100
0
1200
0
6 26 49 72 86 100 90 92 74 66 60 44 24
Distance from observation point (m)
Mea
n D
ensi
ty (B
irds/
km2)
Figure 1. Mean (±SE) seabird density (excluding gulls) at all shore-watch sites (all seasons) with increasing distance from the observation point. The number of grid squares (n) per distance category is also shown on the x-axis. Distances were measured from the mid-point of each 1km2 grid square.
2.3.4 Comparison of shore and boat-based densities
In order to compare seabird densities calculated from shore-based and boat-based
surveys, all grid squares surveyed using the two techniques were identified.
Only grid squares with midpoints located less than 2km from the observation
point were included in the analysis as seabird detection rates from shore-watch
sites were accurate here. Densities in winter and summer at the two Bantry Bay
sites were compared using a paired t-test following normalization using log10
(+1) transformation. The SPSS statistical program (Version 11) was used for this
data analysis.
79
2.4 RESULTS
A total of 44 shore-watches (37 with theodolite readings) were carried out from
the Sheep’s Head observation point and 51 (46 with theodolite) were carried out
from the inner Bantry Bay site. A further 22 surveys were carried out at Three
Castle Head, 14 from Mizen Head, 13 from Black Ball Head and 9 from Dursey
Island. These totals are exclusive of surveys that were carried out during
deteriorating weather or poor visibility – these were deleted from the dataset.
2.4.1 Relative abundance
Table 1 shows the relative abundance of the five most commonly occurring
seabird groups at the six shore-watch sites. Mean seabird numbers per scan were
highest at the Black Ball Head site, with a mean of over 776 birds per scan over
the entire study period, followed by Three Castle Head (671 birds per scan) and
Mizen Head (651 birds per scan). Shearwaters and gannets comprised the bulk
of all sightings at these three sites. The inner Bantry Bay site recorded the lowest
numbers of seabirds with a mean of 149 birds per scan with Alcidae being the
most abundant species. The Dursey Island and Sheep’s Head sites held similar
seabird numbers (Mean ~320 per scan), however the species assemblage differed
markedly. The highest number of Alcidae at any site occurred at Sheep’s Head,
while the lowest occurred at Dursey Island. The lowest number of shearwaters
was also recorded at Dursey Island – despite the high abundance of this species
at the nearby Black Ball Head. This may reflect the small sample size of surveys
at this site however.
Table 1. Relative abundance (Mean number of birds per scan ± Standard Error) of selected seabird groups at the six shore-watch sites. The number of scans per site (N) is also shown). Species
Inner Bantry N = 51
Sheep’s Head N = 44
Dursey Island N = 9
Black Ball Head N = 13
Mizen Head N = 14
Three Castle Head N = 22
Shearwaters 23.8± 8.3 110.2±29.2 2.2± 2.2 400.4±209.2 394.6±251.5 247.2±107.8 Gannets 9.5± 2.4 30.1± 7.3 194.4±64.9 192.1± 66.2 113.6± 43.6 269.8± 72.0 Laridae 51.5±21.6 37.6±21.6 3.2± 1.2 61.6± 30.6 32.3± 11.8 8.0± 2.7 Kittiwakes 0.6± 0.5 19.5± 8.3 79.2±59.6 61.9± 42.6 43.1± 18.0 46.9± 15.6 Alcidae 64.1±19.7 124.1±33.1 40.8±25.5 60.8± 20.6 67.4± 42.8 99.6± 44.7
80
Figure 2(a-f) shows the community composition of seabirds at the six shore-
watch sites (not including flying birds). There are clear differences in
community composition across the sites with Alcidae forming a much larger
proportion of the species assemblage at the inner Bantry and Sheep’s Head sites
(16-34%) than any of the outer sites (2-8%). Conversely, Gannets formed a large
percentage of the overall assemblage (17-60%) at the outer sites but only
accounted for 6 to 9% of the species at the inner Bantry and Sheep’s Head sites.
Laridae were also much more abundant at the inner sites (12-31%) than at the
outer headlands (1-8%). Shearwaters formed a large proportion of the species
assemblage at all sites apart from Dursey Island (Figure 2c) where they
accounted for only 1% of the total assemblage.
Species richness and diversity
Table 2 shows that species richness was highest at the Three Castle Head site (18
species) followed by the inner Bantry and Sheep’s Head sites (16 species) and
the Mizen Head site (15 species). The sites with the lowest species richness
(Dursey Island and Black Ball Head) were also the sites with the lowest survey
effort indicating that this may be an incomplete representation of the species
utilizing these sites. Diversity was highest at the Sheep’s Head site (Simpson’s
Index 1/D = 3.5), followed by Inner Bantry (1/D = 3.3), Three Castle Head (1/D =
2.9) and the Black Ball Head (1/D = 2.7). Bray-Curtis cluster analysis showed
that the Mizen and Black Ball Head communities were very similar to each other
(87% similarity) and also to the Three Castle Head community (74%). The
Sheep’s Head and Inner Bantry communities were similar (59% similarity) to
each other but distinct from the four outer headland sites (28% similarity). The
Dursey Island community was quite distinct from the other sites with only 54%
similarity to any other community but low survey effort here may have
contributed to this..(Figure 3).
81
Alcidae8%
Kittiwakes25%
Gannets60%
Shearwaters1%
Other5%
Laridae1%
Alcidae2%
Shearwaters55%
Other1%
Laridae8%
Kittiwakes8%
Gannets26%
Gannets44%
Shearwaters39%
Laridae1%
Other3%
Alcidae6%
Kittiwakes7%
Alcidae38%
Laridae12%Kittiwakes
6%
Gannets9%
Shearwaters34%
Other1%
Alcidae44%
Laridae31%
Phalacro.2%
Gannets6%
Shearwaters16%
Other1%
Alcidae8%
Gannets17%
Shearwaters59%
Other4% Laridae
5%Kittiwakes
7%
f) Three Castle Head e) Mizen Head
d) Black Ball Head c) Dursey Island
b) Inner Bantry a) Sheep’s Head
Figure 2 (a-f). Community composition (% of total bird numbers) at the Sheep’s Head (a), Inner Bantry (b), Dursey Island (c), Black Ball Head (d), Mizen Head (e) and Three Castle Head (f) shore-watch sites.
82
Table 2. Species presence/absence at the six shore-watch sites during the entire study period. The number of surveys (N) per site is also shown. SPECIES Sheeps
Head N = 44
Inner Bantry N = 51
Dursey Island N = 9
Black Ball Head N= 13
Mizen Head N = 14
Three Castle Head N = 22
Red-throated Diver (Gavia stellata) • Great Northern Diver (Gavia immer) • • Northern Fulmar (Fulmarus glacialis) • • • • • • Manx Shearwater (Puffinus puffinus) • • • • • • Sooty Shearwater (Puffinus griseus) • European Storm Petrel (Hydrobates pelagicus) • Northern Gannet (Morus basanus) • • • • • • Great Cormorant (Phalacrocorax carbo) • • • • • • European Shag (Phalacrocorax aristotelis) • • • • • • Great Skua (Stercorarius skua) • • Arctic Skua (Stercorariu parasiticus) • • • Black-headed Gull (Larus ridibundus) • • • • Mew Gull (Larus canus) • Herring Gull (Larus argentatus) • • • • • • Lesser Black-backed Gull (Larus fuscus) • • • • • Great Black-backed Gull (Larus marinus) • • • • • • Black-legged Kittiwake (Rissa tridactyla) • • • • • • Common Guillemot (Uria aalge) • • • • • • Razorbill (Alca torda) • • • • • • Black Guillemot (Cepphus grylle) • • • • • Atlantic Puffin (Fratercula arctica) • • • • • • = present.
HD
Figure 3. Dendrogram produced from the Bray-Curtis Index of community similarity. The scale shows percentage similarity (links drawn close to 100% are highly similar).
83
Seasonal variation in relative abundance
Seasonal variations in seabird abundance at the six shore-watch sites are shown
in Figure 4(a-l). Mean seabird numbers were highly variable at all sites, with the
flocking behaviour of many species contributing to the high standard errors
recorded. Some consistent seasonal trends were present in the four most
commonly occurring species however (Figure 4). The most obvious seasonal
variation in abundance occur in the shearwaters as both manx and sooty
shearwaters are summer migrants – being almost completely absent from all sites
in winter and spring (Figure 4a-c). Mean numbers of this species group peaked
in spring at the outer headlands (apart from Dursey Island where few were
recorded) and peaked in autumn at the inner Bantry and Sheep’s Head sites.
Highest mean numbers of gannets occurred in either summer or autumn at all
sites (Figure 4 d-e) apart from Sheep’s Head where a large feeding flock was
recorded in winter. Autumn appears to be the most important time for kittiwakes
and Alcidae at most sites, with clear peaks in abundance occurring at this time
(Figure 4g-l). Mean Alcidae numbers peaked in winter at the Mizen Head and
Three Castle Head sites however with numbers increasing markedly at both sites
at this time of year (Figure 4(l)). This indicates that these sites may be important
winter foraging areas for these species. Winter migrants such as great-northern
and red-throated divers were exclusively recorded in winter and spring.
Tidal variation in relative abundance
There was no significant difference between mean Phalacrocoracidae, Alcidae,
Laridae or shearwater abundance across any of the tidal categories at the inner
Bantry site (paired t-test, P>0.05). Similarly there was no significant difference
between mean shearwater, gannet, kittiwake or Alcidae abundance at the Sheep’s
Head or the pooled outer headland sites (paired t-test, P>0.05). Some trends
were apparent however with gannets occurring in higher mean numbers during
the flooding tidal category than during tidal ebbing at Sheep’s head and the outer
sites. This difference was not significant however.
84
0200400600800
100012001400160018002000
Spring Summer Autumn Winter
Mea
n no
. Man
x S
hear
wat
ers
per s
can
Dursey Is. Black Ball Hd
0
50
100
150
200
250
300
Spring Summer Autumn Winter
Mea
n no
. She
arw
ater
s pe
r sca
n
Inner Bantry Sheeps Head
0
20
40
60
80
100
120
140
Spring Summer Autumn Winter
Mea
n no
. Gan
nets
per
sca
n
Inner Bantry Sheeps Head
0
500
1000
1500
2000
2500
3000
Spring Summer Autumn Winter
Mea
n no
. Man
x S
hear
wat
ers
per s
can
Mizen Hd Three Castle Hd
050
100150200250300350400450500
Spring Summer Autumn Winter
Mea
n no
. Gan
nets
per
sca
n
Dursey Is. Black Ball Hd
0
100
200
300
400
500
600
Spring Summer Autumn Winter
Mea
n no
. Gan
nets
per
sca
n
Mizen Hd Three Castle Hdf)
e)
d)
c)
b)
a)
Figure 4 (a-f). Seasonal abundance (Mean ± SE) of Manx shearwaters (a-c) and Gannets (d-e) at the Sheep’s Head, Inner Bantry, Dursey Island, Black ball Head, Mizen Head and Three Castle Head shore-watch sites.
85
0
10
20
30
40
50
60
Spring Summer Autumn Winter
Mea
n no
. Kitt
iwak
es p
er s
can
Inner Bantry Sheeps Head
0
50
100
150
200
250
300
350
Spring Summer Autumn Winter
Mea
n no
. Alc
idae
per
sca
n
Inner Bantry Sheeps Head
0
50
100
150
200
250
300
Spring Summer Autumn Winter
Mea
n no
. Kitt
iwak
e pe
r sca
n
Dursey Is. Black Ball Hd
0
20
40
60
80
100
120
140
Spring Summer Autumn Winter
Mea
n no
. Kitt
iwak
es p
er s
can
Mizen Hd Three Castle Hd
0
20
40
60
80
100
120
140
160
Spring Summer Autumn Winter
Mea
n no
. Alc
idae
per
sca
n
Dursey Is. Black Ball Hd
0
100
200
300
400
500
600
700
800
Spring Summer Autumn Winter
Mea
n no
. Alc
idae
per
sca
n
Mizen Hd Three Castle Hdl) i)
k) h)
j) g)
Figure 4 (g-l). Seasonal abundance (Mean ± SE) of Kittiwakes (g-i) and Alcidae (j-l) at the Sheep’s Head, Inner Bantry, Dursey Island, Black ball Head, Mizen Head and Three Castle Head shore-watch sites.
86
2.4.2 Density
Table 3 shows the mean seabird densities (excluding gulls) calculated at the two
Bantry Bay shore-watch sites in winter and summer using shore and boat-based
survey techniques. The densities calculated from the Sheep’s Head shore watch
site in winter (7.8±2.8/km²) were significantly higher than those calculated for
the same grid squares from boat surveys (0.6±0.6/km²) in winter (paired t-test,
df=4, t=3.597, P<0.05). There was no significant difference between densities
calculated from the two techniques at the inner Bantry site however.
Table 3. Mean seabird density/km² (excluding gulls) in grid squares surveyed using both shore and boat-based survey techniques in summer and winter at the Bantry shore-watch sites. Only grid squares located less than 2km from the land-watch point and surveyed using both techniques were included. Site Survey Sheeps Head Inner Bantry Summer Shore 17.2±5.8 (5) 5.7±1.5 (2) Boat 23.1±9.8 (5) 4.0±1.0 (2) Winter Shore 7.8±2.8 (5) 6.2±0.4 (2) Boat 0.6±0.6 (5) 7.5±7.5 (2)
Using the DISTANCE program, total seabird density (excluding gulls) at the
Sheep’s Head shore-watch site (up to a distance of 8km) was calculated as
6.7/km² (95% CI 4.9-9.2) in summer. This compares to the mean density of
5.6±1/km² (n=77 grid squares) calculated from the same shore-watch data
without accounting for the decline in detection rate of seabirds with distance.
Both of these densities are lower than those calculated from the near-shore grid
squares (n=5) at this site from shore (17.2±5.8/km²) and boat (23.1±9.8km²) -
based surveys (Table 3).
The distribution of total seabird density (excluding gulls) at the two Bantry
sites in summer and winter up to a distance of 7km from the observation point
can be seen in Figure 5(a-d). Total seabird densities were higher at the Sheep’s
Head site than at the Inner Bantry site in both seasons, however the difference
was less pronounced in winter (see also Table 3). The decrease in detectability
of seabirds (see section 2.3.3) can be clearly seen from these maps as highest
densities occur close to the observation points at both sites.
87
Figure 5(a-d). The distribution of mean seabird density (excluding gulls) at the
d) Inner Bantry Winter c) Inner Bantry Summer
b) Sheep’s Head Winter a) Sheep’s Head Summer
outer Bantry (Sheep’s Head) shore-watch site in summer (a) and winter (b) and at the inner Bantry site in summer (c) and winter (d). Only grid-squares up to 7km from the shore-watch point are shown as detection rates were very low beyond this.
88
2.5 DISCUSSION
Species richness, diversity and relative abundance
The high diversity recorded at the Sheep’s head and inner Bantry sites reflects the
evenness of abundance of the species assemblages here. These communities were not
dominated by any one species, such as at some of the outer sites (e.g. 60% gannets at
Dursey Island). Species richness was highest at the Three Castle Head site where
European storm petrels (Hydrobates pelagicus) and two skua species (great & Arctic
skua) were recorded as well as a variety of more neritic species such as cormorants
and shags. Species richness was also high at the Bantry Bay sites due to the mix of
neritic and pelagic species utilizing these sites. The high diversity and species
richness at these sites indicates that this region is of high importance to a wide range
of seabirds.
Although most had a lower species richness and diversity than the Bantry Bay sites,
the outer headlands held a higher relative abundance of many species groups. The
three outer sites of Three Castle, Mizen and Black-Ball Head held over double the
relative abundance of seabirds than the Sheep’s Head site and four times that of the
inner Bantry site. This may indicate that foraging conditions are optimal here – or
may simply reflect the proximity of these sites to large breeding colonies at the Bull
and Cow rocks and Scarriff and Deenish Island. It is likely that the species richness
and abundance of seabirds at the Dursey Island site was underestimated due to low
survey effort here - indicating that long term monitoring is essential for the accurate
assessment of seabird site utilization.
Community composition and similarity
The differing seabird communities (<30% similarity) utilizing the Bantry Bay sites
compared to the outer headlands may reflect the inshore location of the Bantry sites.
The Bantry Bay sites, (particularly the inner site) clearly represent an attractive
foraging location for neritic species such as cormorants, shags, divers and gulls but
are also utilized by large proportions of Alcidae – setting them apart from the outer
sites which were dominated by shearwaters and gannets. The seabird communities at
the outer headland sites were remarkably similar (>70% similarity), apart from
Dursey Island site which (unlike the other sites) was not utilized by large numbers of
89
shearwaters. It is likely that the low survey effort here resulted in an under-
representation of the species assemblage however. The exposed and open nature of
the outer headlands as well as their proximity to breeding colonies and the rich
foraging grounds of the Irish Shelf Front (Raine et al., 1990; Edwards et al., 1996)
makes the outer sites attractive to pelagic species such as shearwaters and gannets.
Vulnerability to surface pollutants
The outer Bantry shore-watch site contained the highest proportion (72%) of species
vulnerable to surface pollution (i.e. high Oil Vulnerability Index, OVI >20, see
General Introduction), followed by the inner Bantry site (62%). The species
contributing to this high vulnerability comprised the Alcidae group and shearwaters,
all of which are Birds of Conservation Concern in Ireland (BoCCI, amber list) (Table
1, General Introduction). In terms of relative abundance however the outer headland
sites should be of conservation priority as these areas were utilized by very high
numbers of protected (BoCCI amber list) shearwaters and gannets and well as the
Annex 1 European storm petrel.
Seasonal variations
The relative abundance of all seabird species was highly variable from season to
season and from site to site during this study, reflecting the patchy distribution of
these species. Autumn appeared to be the most important season for many species,
with high numbers of auks, shearwaters, gannets and kittiwakes occurring at this time.
All of these species breed on the islands off southwest Ireland and leave the colonies
in large numbers in late summer to forage before dispersing in late autumn (Webb et
al., 1995). Auks undergo a total flight-feather moult at this time of year, becoming
flightless for approximately six weeks (Gaston & Jones, 1998) and therefore are
highly vulnerable to surface pollution events at this time of year.
The high variability in gannet and Alcidae abundance in winter at some sites (e.g.
Mizen, Three Castle and Sheep’s Heads) reflects the presence of transient feeding
flocks at this time of year. The rich feeding grounds associated with the Irish Shelf
Front are located further offshore (up to 30km further west) at this time of year
(McMahon et al., 1995). As a consequence, seabird prey in inshore areas at this time
of year is likely to be patchier and less reliable than in summer and autumn. For
90
example, herring Clupea harengus are known to spawn in the vicinity of Bantry Bay
in October and November (Smith & McLaverty, 1997; Boelens et al., 1999) and may
be exploited by large numbers of feeding seabirds during this short time. The
Gaviidae group (consisting of the great northern and red-throated divers) occurred
exclusively in winter and spring in the inner bay. The frequency of oil pollution
events in southwest Ireland is also highest in winter due to the high winds and the
exposed nature of the coastline (see Cross et al., 1979; Smiddy, 1992; Smiddy, 1998)
indicating that these Annex 1 species are at high risk from future pollution incidents.
The high variability in seabird abundance at sea makes predicting the impacts of a
major pollution event difficult, particularly at small scales such as within bays and
estuaries. The largest and most vulnerable (high OVI) concentrations of seabirds in
these study sites occur in autumn (e.g. moulting auks and feeding shearwaters)
however the species of highest conservation concern (i.e. Annex 1 Gaviidae species)
occur in winter when the likelihood of an oil spill event is highest. Clearly there are
vulnerable (and often unpredictable) concentrations of seabirds in this region
throughout the year and longer-term monitoring is needed before reliable predictions
of seasonal abundance can be provided to inform management decisions.
Tidal variations
The tidal cycle did not significantly influence the abundance of any of the species
studied (i.e. Phalacrocoracidae, Alcidae, Laridae, gannets, kittiwakes and shearwaters)
at any of the shore-watch sites. The trend towards higher mean abundances of
gannets during flooding tides may indicate that these piscivorous birds are following
fish brought into these inshore sites on the incoming tide. Further investigations
incorporating seabird behaviour (i.e. foraging birds only) may produce clearer
patterns. Nevertheless it is clear that seabird abundance did not vary significantly
with the tidal cycle at these sites, indicating that this variable cannot be used as a
reliable predictor of seabird abundance when responding to a disturbance event in
these areas.
The reliability of density estimates from shore-based observation points This study provided the first known comparison of fine-scale (1km² grid network)
seabird density estimates calculated using shore and boat-based survey techniques.
91
The results indicate that only the area of sea within 2km of the observation point can
be accurately surveyed using shore-based surveys with the aid of a telescope (max
60x magnification). At distances above this there is a steep decline in detectability of
seabirds with increasing distance resulting in an underestimate of seabird density in
these areas. Thus, it is difficult to make inferences about seabird distribution from
density maps created from shore-watch observations. These results are applicable to
all mixed seabird surveys carried out with the aid of a telescope from shore-based
observation points in sea states of 3 or less on the Beaufort scale. The area over
which seabirds can be accurately surveyed may be greater if small species such as
auks and shearwaters are excluded however.
Densities calculated using observations from the first 2km of each shore-watch area
did not significantly differ from those calculated from boat-based surveys at the same
sites at fine scales (1km grid). However, sample sizes were small and variances high,
indicating that these results are not conclusive. The exclusion of all sightings
exceeding this distance threshold of 2km means that only a small sample of the
seabirds present can be used for density calculations and species known to avoid land
when foraging (e.g. shearwaters, see chapter 1, section 1.5.1) may be completely
excluded. The DISTANCE program can be used to estimate seabird densities at
larger scales, however sightings close to the observation point have a high influence
on the density value and may therefore bias results. For this reason shore-watch
observations do not provide a useful measure of seabird density and are better suited
for studies of relative abundance.
92
2.6 REFERENCES
Bibby, C.J., Burgess, N.D. & Hill, D.A. 1992. Bird Census Techniques. Academic
Press Ltd., London.
Boelens, R.G.V., Maloney, D.M., Parsons, A.P. & Walsh, A.R. 1999. Irelands
Marine and Coastal Areas and Adjacent Seas. An Environmental Assessment.
Marine Institute, Dublin, Ireland. pp 388.
Buckland, S., Anderson, D.R., Burnham, K., Laake, J., Thomas, L.T. &
Borchers, D.L. 2001. Introduction to Distance Sampling: Estimating
Abundance of Biological Populations. Oxford University Press, New York. pp
432.
Cross, T., Southgate, T. & Myers, A.A. 1979. The initial pollution of shores in
Bantry Bay, Ireland, by oil from the Tanker Betelgeuse. Marine Pollution
Bulletin 10, 104-107.
Edwards, A., Jones, K., Graham, J.M., Griffiths, C.R., MacDougall, N.,
Patching, J., Richard, J.M. & Raine, R. 1996. Transient Coastal upwelling
and water circulation in Bantry Bay, a Ria on the Southwest Coast of Ireland.
Estuarine, Coastal and Shelf Science 42, 213-230.
Gaston, A.J. & Jones, I.L. 1998. The Auks, Bird Families of the World. Oxford
University Press, New York. pp 349.
Lutkebohle, T. 1995. Dolphin movements and behaviour in the Kessosck Channel
and how these are influenced by boat traffic. Report to Scottish Natural
Heritage. pp 37.
Magurran, A.E. 1988. Ecological Diversity and its Measurement. Cambridge
University Press. pp 179.
McMahon, T., Raine, R. & Boychuk, S. 1995. Some oceanographic features of
northeastern Atlantic waters west of Ireland. ICES Journal of Marine Science
52, 221-232.
Raine, R., O`Mahony, J., McMahon, T. & Roden, C. 1990. Hydrography and
Phytoplankton of waters off Southwest Ireland. Estuarine, Coastal and Shelf
Science 30, 579-592.
Smiddy, P. 1992. The effect of the Kowloon Bridge oil spill in east Cork. Irish
Birds 4, 559-570.
93
Smiddy, P. 1998. The effect of the Cork Harbour Oil Spill of November 1997 on
birds. Irish Naturalists Journal 26, 32-37.
Smith, J. & McLaverty, A. 1997. The South West coast of Ireland. An
Environmental Appraisal. BHP, Chevron, Marathon, Occidental, Statoil and
Total, Ireland. pp 64.
Webb, A., Stronach, A., Tasker, M.L. & Stone, C.J. 1995. Vulnerable
Concentrations of Seabirds south and West of Britain. Joint nature
Conservation Committee
Würsig, B., Cipriano, F. & Würsig, M. 1991. Dolphin movement patterns:
information from radio and theodolite tracking studies. In: K. Pryor & K.S.
Norris (eds), Dolphin Societies, Discoveries and Puzzles. University of
California Press., Berkeley. pp 79-111.
94
CHAPTER 3
Harbour seal adult with suckling pup (Photo. M. Cronin).
TEMPORAL VARIATION IN THE USE OF HAUL-OUT SITES BY HARBOUR SEALS IN BANTRY BAY AND
THE KENMARE RIVER
95
3.1 ABSTRACT
The seasonal change in the distribution and abundance of harbour seals at terrestrial
haul-out sites in southwest Ireland was investigated. Statistical models of the
relationship between seal abundance at haul-out sites and covariates, such as the time
of year and time of day and environmental conditions at haul-out sites, provided a
means of assessing the influence of the covariates on the seals’ haul out behaviour and
if this varied between sites. There was a difference in the seasonal patterns of seal
abundance between haul-out sites. The effect of the time of day on seal abundance at
haul-out sites varied between the sites and was only significant at sites that also
showed a seasonal pattern in abundance. Fewer seals were observed during strong
winds and rain. The differences in site use and covariate effects on haul-out behaviour
are discussed in relation to the seals annual cycles and the physical characteristics of
the sites.
96
3.2 INTRODUCTION
The harbour seal (Phoca vitulina L.) is the most widely-distributed pinniped species,
inhabiting cold-temperate and temperate waters in the northern hemisphere on both
sides of the north Atlantic and north Pacific oceans (Bigg, 1981). Harbour seals, are
semi-aquatic mammals (Pitcher & McAllister, 1981) that spend time ashore at
terrestrial sites on which they haul-out to rest, breed, moult, engage in social activity
and escape predation (Pitcher & McAllister, 1981; Da Silva & Terhune, 1988;
Thompson, 1989; Watts, 1992; Boily, 1995). Haul-out substrate varies across the
harbour seals’ geographical range and includes tidal sand and mud bars, sand and
gravel beaches, inter-tidal rocks and reefs and ice floes and glacial drift (Stewart,
1984).
Seasonal changes in the numbers of harbour seals ashore at terrestrial haul-out sites
have been described (Thompson & Rothery, 1987; Thompson, 1989; Thompson et al.,
1989; Thompson & Miller, 1990; Härkönen et al., 1999) explained largely by
seasonal changes in haul-out behaviour, however, individual changes in site-use is
also a contributory factor (Thompson, 1987, 1989) and seasonal variations in haul-out
site use have also been described (Brown & Mate, 1983; Jeffries, 1986; Thompson,
1989; Thompson et al., 1994; Harding, 2000; Härkönen & Harding, 2001; Rehberg &
Small, 2001; Reder et al., 2003). Harbour seals, previously considered to be site
specific with generally limited movements (Pitcher & McAllister 1981, Brown &
Mate, 1983), have been shown to use a range of haul-out sites, even within a
particular season (Thompson, 1989) and long distance movements between haul-out
sites have been observed (Lowry et al., 2001, Rehberg & Small, 2001; Sharples et al.,
2004).
In addition to temporal factors, other factors or ‘covariates’ which influence haul-
out behaviour and site use include time of day, tidal effects, disturbance and local
weather (Stewart, 1984; Yochem et al., 1987; Thompson et al., 1989, 1994; 1997;
Thompson & Harwood 1990; Thompson & Miller, 1990; Grellier et al., 1996; Reder
et al., 2003). Examining the effects of these covariates on haul-out behaviour can help
explain seasonal patterns in abundance and site use.
97
Haul-out sites on the Irish coast used by harbour seals during the 2003 annual moult
were identified during a national aerial survey to determine a minimum population
estimate for the species. The resulting information on the distribution and abundance
of harbour seals on the Irish coastline was limited to the moult period (Cronin et al.,
2004). Hitherto no information was available on the year round patterns in the
distribution and abundance of harbour seals at specific haul-out sites in Ireland. The
harbour seal is listed as an Annex II species under the EC Habitats Directive
(92/43/EEC) which requires member states to designate Special Areas of Conservation
(SACs) for the protection of listed species. Identifying the full range of sites used
throughout the annual cycle is essential for the identification of and subsequent
monitoring and management of SACs for harbour seals in response to the Directive.
Over one third of the national minimum population estimate of harbour seals use
terrestrial haul-out sites in southwest Ireland (Cronin et al., 2004). Most of the
harbour seal haul-out sites in this region are located within the Kenmare River and
Bantry Bay. The Kenmare River and inner Bantry Bay have been designated as SACs
with the harbour seal listed as one of the qualifying interests. The National Parks and
Wildlife Service of the Department of Environment, Heritage and Local Government
have monitored the abundance of harbour seals in both bays since 1985 (Heardman et
al., 2006), however survey effort was limited to the summer months and no
information is available on the year-round patterns in abundance and site use.
The objectives of the present study were (i) to describe seasonal changes in the
abundance and distribution of harbour seals at individual haul-out sites within Bantry
Bay and Kenmare River and (ii) to investigate the effects of factors such as month,
time of day and weather on seal haul-out behaviour and the resulting distribution and
abundance of seals at the haul-out sites.
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3.3 MATERIALS & METHODS
3.3.1 Study area
Bantry Bay, County Cork (51o 36’N, 9o 50’W), a drowned river valley, is the longest
marine inlet in southwest Ireland with a varied coastline ranging from exposed rocky
shores to sheltered sediment shores at Whiddy Island. A detailed description of the
physical characteristics of Bantry Bay is given in chapter 2. Haul-out sites used by
harbour seals within Bantry Bay are predominantly located on the northern side of the
bay, the exception being Gerrane rocks off Whiddy Island in the inner part of the bay
(figure 1). Haul-out substrate is exclusively rocky and haul-out sites are generally on
skerries or islands located adjacent to the mainland shore. The majority of these sites
are in the inner part of Bantry Bay, in the northeast corner, Glengarriff harbour. Seals
use a number of haul-out sites located further west, the most westerly of these are
located in Adrigole harbour approximately 20km from the head of the bay. Ten main
discrete haul-out sites have been identified, some comprising of smaller adjacent sites,
and are shown in figure 2.
The Kenmare River, County Kerry (51o 43’N, 10o 05’W), is a partially mixed
estuary and its inter-tidal areas are dominated by rocky shores. A detailed description
of the physical characteristics of the Kenmare River is given in chapter 2. Haul-out
substrate is exclusively rocky and haul-out sites are located on skerries and islands
situated off the mainland shore. The majority of haul-out sites are located within
sheltered bays on the Iveragh peninsula, the exception being Brennel Island and
Ormond’s Island off the southerly shore and the most westerly of these sites are in
Westcove harbour, approximately 30km from haul-out sites located towards the head
of the river. A total of eleven discrete haul-out sites have been identified and shown in
figure 3.
99
Figure 1. The study area in southwest Ireland.
100
Figure 2. Harbour seal haul-out sites in Bantry Bay, Co. Cork, Ireland
1. Carrigskye 2. Inner Glengarriff harbour3. Garinish Island 4. Big point rocks 5. Coolieragh harbour 6. Coulagh rocks 7. Garinish west 8. Orthans Island 9. Adrigole harbour 10. Whiddy Island
1. Templenoe 2. Carrignaronomore 3. Brennel Island 4. Ormonds Island 5. Coongar harbour 6. Brown Island 7. Outer sneem harbour8. Inner sneem harbour9. Potato Island 10. Illaunsillagh 11. Westcove harbour
Figure 3. Harbour seal haul-out sites in the Kenmare River, Co. Kerry, Ireland
101
3.3.2 Seal counts
Comprehensive scoping surveys of Bantry Bay and the Kenmare River were carried
out in April 2003 in a Tornado 5.8m Rigid Inflatable Boat (RIB) and harbour seal
haul-out sites were identified. Between April 2003 and November 2005 surveys of
both bays were carried out by RIB and numbers of seals at haul-out sites counted
using Leica 10 x 42 binoculars and recorded on a Sony Dictaphone. Counts of seals at
each haul-out site were carried out independently and simultaneously by two
observers and repeated if necessary until consensus was agreed. Counts were initially
obtained from a distance of approximately 200m from the haul-out site and at
progressively closer ranges whilst preventing disturbance to the seals.
New pups were recorded only when it was obvious they were pups of the year,
identifiable by size and dark pelage. All other seals counted were considered as ‘non-
pups’ or adults due to the difficulty in differentiating juveniles from adults from a
distance.
Surveys were carried out on at least a monthly basis year-round and weekly during
the summer and autumn, weather permitting. Surveys were scheduled to occur within
two hours on either side of low tide and during daylight hours. Surveys began at the
head or mouth of the bay on alternate survey dates so that haul-out sites would not
always be surveyed in the same order within the four hour tidal period. On each
survey the coastline between ‘established’ haul-out sites was also checked for the
presence of seals to ensure that all possible haul-out sites used over the study period
were identified.
3.3.3 Statistical modelling
The effects of environmental variables on the numbers of adult seals hauled out
during surveys were modeled using generalized additive mixed modeling (GAMM), a
combination of generalized additive modeling (GAM) and mixed effects modeling
(described in chapter 2). All statistical analyses were carried out using Brodgar v 2.5.1
(www.brodgar.com) software package and R v 2.3.1 statistical program.
The environmental variables investigated, as explanatory variables, are month, time
of day, wind speed, wind direction and ‘weather’ (sun, cloud and rain). The response
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variable is harbour seal abundance. The effects of time of day and month were
modeled as smooth functions and the other effects modeled as categorical factors. The
variable ‘week time’ was included to explore potential significant variation in the
patterns in seal abundance over the entire study period. It was calculated as {year +
(week number-1)/52}. There was no evidence of collinearity between any of the
explanatory variables so they were all included in the subsequent analyses. An
examination of validation plots resulting from GAMs showing patterns in the
residuals of the explanatory variables, suggesting violation of homogeneity, in
addition to high leverages suggested model misspecification and therefore GAMMs
were applied.
The optimal GAMM model for the seal abundance data was selected using a two-
step approach to search for the most optimal random and fixed components. Once the
random components were selected, the most optimal model in terms of fixed
components was explored (Fitzmaurice et al., 2004). The restricted maximum
likelihood estimation (REML) was used to compare models with the same fixed terms
and different random components (Pinheiro & Bates, 2000). The optimal model in
terms of random components allowed for heterogeneity of variances at least by season
and auto-correlation was added in the form of an auto-regressive model AR-1,
allowing for correlation between residuals of sequential weeks.
To determine the most optimal model in terms of fixed terms a backward selection
was carried out and those explanatory variables that were found not to be significant
in explaining seal abundance in (p values >0.05) were dropped in turn and the AIC
(Akaike Information Criteria) (Akaike, 1973) of resulting models compared following
each step. To compare models with different fixed effects but with the same random
components the maximum likelihood method was used instead of REML (Zuur et al.,
2006).
The explanatory variables ‘month’ and ‘time of day’ were fitted using a 2-
dimensional smoother allowing the effect of the time of the day to change per month.
The reasons for this approach were based on observations made over the study period
which suggested that the effect of time of day on seal abundance at haul-out sites may
change over the year. A unique smoother per site was used so we could explore if the
103
effects of month and time of day on seal haul-out behaviour varied between sites. A
smoother s(X) + s(Y) is nested within s(X, Y). If an s(X, Y) smoother is not
significant, it is split into an s(x) and s(Y) smoother and the model re-run, and each
smoother is dropped from the model if not significant. Furthermore, due to a ‘by’
command in the R code, the smoothers will not influence each other, enabling
modifications to various smoothers simultaneously. This was continued in a stepwise
fashion removing non-significant fixed terms and using F tests to confirm the
improvements to the models.
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3.4 RESULTS
3.4.1 Seal counts
There was seasonal variation in haul-out site use by harbour seals in Bantry Bay and
the Kenmare River over the study period. Seasons are denoted in this study as spring
(February-April), summer (May-July) autumn (August-October) and winter
(November-January). Patterns of seasonal abundance differed between haul-out sites.
Within Bantry Bay sites 1 to 5 were generally used throughout the year with numbers
increasing during the summer and autumn months. Highest numbers of seals were
observed at sites 2 and 3. Sites 6 and 7 had limited use and generally only during
summer months. Site 9 was used throughout the year but unlike the majority of sites
within the bay, showed no obvious increase in numbers during summer/autumn and
site 10 was only used during summer and autumn (Figure 4). Within the Kenmare
River the majority of haul-out sites were used year round and the obvious summer
increase in numbers, evident at most of the Bantry Bay sites was apparent only at sites
1, 2, 7 and 10. These sites also had relatively higher counts than other sites within the
bay. Site 9 was used only during summer months (Figure 5).
Pups were recorded at all sites within Bantry Bay apart from sites 7 and 8. The most
important sites for pupping within the bay, based on highest pup counts, were sites 2,
3 and 4. Within the Kenmare River pups were recorded at all sites apart from sites 8
and 11 and sites 2, 3 and 7 had the highest pup counts (Figures 4 & 5).
The seal count data, modelled as a function of the covariates time of year, time of
day and weather describes how significantly different the patterns of seal abundance
were between sites and the effect of the covariates on these patterns.
105
3.4.2 Model output and validation
The optimal model for the seal abundance data from Bantry Bay contained terms for
month, time of day, weather and site:
SAmwsy = fs1(Mm, TDmwsy) + fs2(Mm) + fs4 (Mm, TDmwsy)+fs8(Mm, TDmwsy) + fs10(Mm,
TDmwsy) + Ss + Wmwsy + εmwsy
εmwsy ~ N (0, σ 2s)
| ''( , ) w w
mwsy mw sycor ε ε φ −= |
|
Where SAmwsy is the abundance of seals in month m, week w, site s, year y. The
explanatory variables ‘month’ and ‘time of day’ were fitted using a 2-dimensional
smoother f(Mm, TDmwsy) allowing the effect of time of the day to change per month.
The effect of covariates on seal abundance at individual sites could be examined by
using a unique smoother per site (fs1 to fs10) and a ‘by’ command in the R code enabled
modifications to various smoothers simultaneously. The model accounts for
autocorrelation by allowing for correlation between residuals of sequential weeks | '
'( , ) w wmwsy mw sycor ε ε φ −=
εmwsy ~ N (0, σ 2s) allows for different variances in each site (s).
The effect of time of day on seal abundance varies across the months however this is
only significant at sites 1, 4, 8 and 10 (p< 0.001). At site 2 the effect of month on seal
abundance is significant (p<0.01) but not time of day. At the remaining sites neither
month nor time of day explain the patterns in seal abundance at the haul-out sites over
the study period. The phi (φ) value of 0.294 denotes the correlation between
sequential weeks (Table I). The significance of the term ‘site’ confirms the difference
in patterns of abundance between sites (p<0.001). Individual p values per site explain
which sites differ the most from site 1 and all but sites 5 and 9 show different patterns
in abundance (p<0.05). The difference in the influence of ‘site’ on harbour seal
abundance within Bantry Bay is shown in figure 6, the larger site labels represent the
larger contribution from factor ‘site’ (taken from the ANOVA output). The
importance of sites 2, 3 and 10 as haul-out sites for seals is apparent however there is
also high variation in abundance and relatively high residual variances at these sites.
106
The residual variance component per site (variance of the ‘noise’ or information
unexplained by the model) is shown in Table I and sites 2 and 3 show the largest
residual variances. Box-plots of the abundance of seals conditional on site confirm
that these sites have higher variation in abundance. The relative difference in residual
variances per site is shown in figure 7, with sizes of site labels proportional to the
variances per site. The larger residual variances may be explained by other processes
influencing abundance at these particular sites that were not included in the models
(such as disturbance).
Weather had an effect on the numbers of seals counted, significantly higher numbers
(p<0.01) were observed in sunny weather (category 3).
The 2-D smoothing functions for sites where there was a significant effect of month
and time of day on seal abundance, are shown in Figure 8. At site 1 the interactive
effect of time of day and month is evident. The time of day does not appear to be as
influential on abundance of seals at this site in spring and later in the year but in the
summer months there appears to be an obvious effect of time of day, with numbers
peaking later in the day and decreasing at night. The same summer peak is seen at site
4 but there is not as sharp a rise in abundance as evident in site 1. In addition, the peak
in abundance is evident earlier in the day than at site 1, with the probability of high
numbers of seals at this site remaining high later in the day. A different pattern is
apparent at site 8 with probability of highest abundance in the spring decreasing
steadily throughout the year to an autumn/winter low. Time of day appears to only
have an effect in the spring at this site, when afternoon peaks are apparent, decreasing
rapidly later in the day. The smoothing functions from site 10 show a clear seasonal
pattern similar to site 1 with highest abundance in the summer months falling rapidly
following this peak. The time of day has a significant effect during the summer
months also, as in site 1, with abundance increasing steadily throughout the day to an
evening peak and falling at night. Overall, significant seasonal patterns in abundance
are apparent for sites 1, 2, 4, 8 and 10 and in all but site 2 there is also an effect of
time of day on seal abundance. This effect changes across the year and both the effect
of time of day and its interaction with month varies between sites.
107
Exploration of the seal abundance data from the Kenmare River involving co-plots,
lattice plots and scatter-plots led to a slightly different approach than with the Bantry
Bay seal abundance data. The co-plots and lattice-plots suggested including the same
fixed terms as in the initial model of the Bantry Bay dataset. However, patterns in the
residuals suggested different sites have different residual spread and in addition there
was also apparent heterogeneity both between and within seasons. Using site as a
variance component as in the previous analysis, will not account for the heterogeneity.
Spatial correlation in the data was checked for by recoding site, based on regional
distances (figure 9), into four areas and the heterogeneity of residual variance
examined (figure 10). No difference in the spread of residuals based on spatial
proximity was apparent. Square-root transformation of the response variable i.e. the
count data can be used to stabilise the relationship between the mean and the variance
(Zuur et al., 2006).
The optimal model for the seal abundance data from Kenmare River contained
terms for month, time of day, wind speed, year and site:
√SAmwsy = fs1(Mm, TDmwsy) + fs2(Mm, TDmwsy) + fs4(Mm) + fs5(Mm, TDmwsy) + fs6(Mm) +
fs7(Mm, TDmwsy) + fs9(Mm, TDmwsy) + WindSpeedmwsy + Ss + Yy + εmwsy
εmwsy ~ N (0, σ 2s)
| ''( , ) w w
mwsy mw sycor ε ε φ −= |
Where √SAmwsy is the square root of the abundance of seals in month m, week w, site
s, year y. The explanatory variables ‘month’ and ‘time of day’ were fitted using a 2-
dimensional smoother f(Mm, TDmwsy) allowing the effect of time of the day to change
per month. Unique smoothers per site are denoted by fs1 to fs10. The model accounts for
autocorrelation by allowing for correlation between residuals of sequential weeks and
allows for different variances in each site (s).
Seal abundance varies significantly over the year at all sites apart from sites 3, 8, 10
and 11. As was evident in the analyses of the abundance data for the entire bay, the
time of day has an influence on seal abundance, however not at all sites. The time of
day significantly effects seal abundance only at sites 1, 2, 5, 7 and 9 and the effect
108
varies over the year at these sites (P<0.01) (Table II). The significance of the term
‘site’ confirms the difference in patterns of abundance between sites (p<0.001).
Individual p values per site explain which sites differ the most from site 1 and sites
2, 7 and 10 show different patterns in abundance (p<0.01). The difference in the
influence of ‘site’ on harbour seal abundance within the Kenmare River is shown in
figure 11, the larger site labels represent the larger contribution from factor ‘site’
(taken from the ANOVA output). The importance of sites 7 and 10 as haul-out sites
for seals within the Kenmare River is apparent. The residual variances at all sites are
relatively similar (figure 12).
Wind speed had an effect on the numbers of seals counted, significantly fewer seals
(p<0.05) were observed in strong winds (Beaufort 5 or above). A significant year
effect was evident with significantly more seals counted in 2005 (P<0.01).
The 2-D smoothing functions for sites where there was a significant effect of month
and time of day on seal abundance, are shown in figures 13 & 14. At site 1 the
interactive effect of time of day and month is evident. This effect changes
significantly over the year, with higher numbers of seals earlier in the day during the
summer months but later in the day in the autumn and winter, as seen in the ‘flip’ in
the smoother. Site 2 shows the same pattern that was apparent at some of the Bantry
Bay sites (sites 1 and 10), with time of day influencing seal numbers only during
summer with a steady increase from morning until evening. There was no significant
effect of the time of day on seal numbers at site 4 and a weak effect of month with
numbers increasing gradually with highest counts at the end of the year; confidence
intervals are large however particularly at the start and end of the year. Seal numbers
at site 5 were highest early in the day at the start of the year and the time of day had
progressively less of an effect throughout the year. At site 6 seal abundance was
highest during the summer months, confidence intervals are also large at the start and
end of the year and the time of day did not significantly influence abundance.
Abundance peaked early in the year also at site 7 but late in the day and the time of
day was not influential throughout the remainder of the year. A summer peak in
abundance occurred at site 9 and a diurnal time of day effect was apparent with early
morning and late afternoon peaks in abundance. At other times of the year the time of
109
day did not appear to effect seal abundance at this site. Overall, significant seasonal
patterns in abundance are apparent for sites 1, 2, 4, 5, 6, 7 and 9 and in all but sites 4
and 6 there is also an effect of time of day on seal abundance. This effect changes
across the year and both the effect of time of day and its interaction with month varies
between sites
110
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Figure 4. Maximum harbour seal counts at haul-out sites in Bantry Bay, Co. Cork April 2003 to October 2005 (*= no count)
111
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112
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1010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010
E
Figure 6. The difference in the influence of ‘site’ on harbour seal abundance withinBantry Bay, the larger site labels represent the larger contribution from factor ‘site’.
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Figure 7. The relative differences in variances of harbour seal abundance per site within Bantry Bay with sizes of site labels proportional to the variances per site.
113
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Figure 8. 2-D smoothing function describing the partial effect of month (1 to 12) and time of
day(08.00 to 18.00) on the abundance of harbour seals at haul-out sites in Bantry Bay.
114
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Figure 9. Spatial proximity of sites within the Kenmare River
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Figure 10. Residual values versus sites within the Kenmare River
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Figure 11. The difference in the influence of ‘site’ on harbour seal abundancewithin the Kenmare River, the larger site labels represent the larger contributionfrom factor ‘site’
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Figure 12. The relative differences in variances of harbour seal abundance per site within the Kenmare River with sizes of site labels proportional to the variances per site
116
Site Site
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Figure 13. 2-D smoothing function describing the partial effect of month (1 to 12) and time of day (08.00 to 18.00) on the abundance of harbour seals at haul-out sites in Kenmare River
117
Site 4 Site 6
2 4 6 8 10 12
-3-2
-10
12
3
Month
2 4 6 8 10 12
-3-2
-10
12
3
Month
s(M
onth
,2.2
4)
s(M
onth
,1.9
7)
Figure 14. Smoothing functions describing the partial effect of month on the abundance of harbourseals at haul-out sites within the Kenmare River.
118
Table I. Summary of optimum generalized additive mixed models of seal abundance withinBantry Bay. For explanatory variables fitted as smoothers the estimated degrees of freedom(edf) are shown and for parametric terms the degrees of freedom (df) shown. For allexplanatory variables, including 2D smoothing functions, the associated probability value (p)is given. The Phi value resulting from the auto-correlation structure and the residual variancecomponent by site are shown.
Site Month/ Time of day
Month Weather Site Phi Variances
Overall d.f. p value
2 p<0.01
9 p<0.001
0.294
Site 1 e.d.f. p value
10.73 p<0.001
p<0.001
1.000
Site 2 e.d.f. p value
1 p<0.01
p<0.001
2.437
Site 3 e.d.f. p value
p<0.001
3.723
Site 4 e.d.f. p value
13.81 p<0.001
p<0.001
1.236
Site 5 e.d.f. p value
p=0.797
1.161
Site 6 e.d.f. p value
p<0.05
1.097
Site 7 e.d.f. p value
p<0.001
0.717
Site 8 e.d.f. p value
13.67 p<0.001
p<0.05
0.751
Site 9 e.d.f. p value
p=0.219
1.102
Site 10 e.d.f. p value
11.44 p<0.001
p<0.05
1.575
119
S
OdpSdpSdpSdpSdpSdpSdpSdpSdpSdpSdpSdp
Table II. Summary of optimum generalized additive mixed models of seal abundance within theKenmare River. For explanatory variables fitted as smoothers the estimated degrees of freedom(edf) are shown and for parametric terms the degrees of freedom (df) shown. For all explanatoryvariables, including 2D smoothing functions, the associated probability value (p) is given. The Phivalue resulting from the auto-correlation structure and the residual variance
ite Month/ Time of day
Month Wind speed Year Site Phi Variances
verall .f. value
5 p<0.001
4.08 p<0.05
10 p<0.001
0.227
ite 1 .f. value
7.71 p<0.001
1.000
ite 2 .f. value
11.68 p<0.001
p<0.01
0.857
ite 3 .f. value
p=0.559
1.049
ite 4 .f. value
1.07 p<0.05
p=0.617
0.749
ite 5 .f. value
2 p<0.001
p=0.051
0.596
ite 6 .f. value
2.24 p<0.05
p=0.981
0.701
ite 7 .f. value
7.2 p<0.01
p<0.001
0.958
ite 8 .f. value
p=0.157
1.129
ite 9 .f. value
12.06 p<0.001
p=0.06
0.869
ite 10 .f. value
p<0.001
0.624
ite 11 .f. value
p=0.546
0.832
120
3.5 DISCUSSION
Harbour seals use a number of haul-out sites within Bantry Bay and the Kenmare
River throughout their annual cycle. There was a difference in the seasonal patterns of
seal abundance between these sites. Modeling the numbers of harbour seals at the
sites, as a function of variables or covariates such as the time of day, time of year and
environmental parameters provided information on what factors were driving or
influencing the patterns in abundance observed. Within Bantry Bay a significant
seasonal pattern in abundance was evident at five of the ten haul-out sites. At
Carrigskye (site 1), Whiddy Island (site 10) and Big Point rocks (site 4) a late summer
peak in abundance occurred, more markedly at the former two sites. A spring peak
occurred at Orthan’s Island (site 8) and within inner Glengariff harbour (site 2) a
steady rise in abundance was evident over the year. The other haul-out sites showed
some seasonal changes in attendance, although not significantly so, apart from inner
Adrigole harbour (site 9), which was used by relatively consistent numbers of seals
throughout the annual cycle. All sites were used, albeit to different extents,
throughout the year with the exception of Whiddy Island which was not used between
the months of November to March.
The observed patterns in the abundance of seals at haul-out sites can largely be
explained by events in the seals’ annual cycle. The late summer increase in abundance
at Carrigskye, Whiddy Island and Big Point rocks is likely a result of these sites being
used for the annual moult. Moulting seals were observed at these sites during three
consecutive annual moults during August and September. Haul-out sites within
Glengarrif harbour, including Garinish Island (site 3) were also used by seals as
moulting sites. The increase in abundance around the time of moult was not as
apparent in Glengarrif harbour but this is probably due to the fact that the sites within
the harbour are used throughout the year and the relative increase in abundance during
the moult is not as apparent as at other sites. Haul-out sites within Glengarrif harbour,
including the inner harbour, Garinish Island and the rocks at Big Point in the outer
harbour, were used as breeding sites during the study period, evident from the higher
numbers of pups observed at these sites.
121
In general site use was lower in winter, probably resulting from seals spending a
higher proportion of their time at sea, suggested by the behaviour of tagged seals in
the study area during this period (chapter 4). Glengarrif harbour and Adrigole
harbour, in inner and outer Bantry Bay respectively were used by seals throughout the
year, possibly because of the shelter they afford to seals during adverse weather. All
other haul-out sites within Bantry Bay are located on islands or rocky skerries outside
of sheltered harbours and exposed to strong winds and swell in winter. Within
Adrigole harbour a spring increase in numbers at Orthan's Island may be explained by
the sites relative proximity to potential foraging grounds, it is the nearest site to the
mouth of Bantry Bay and the open sea. Studies on the activity patterns of tagged seals
in Scotland suggest that they spend less time in inshore waters in winter (Thompson et
al., 1989; Sharples, 2005) and using haul-out sites closer to foraging areas would be
energetically less demanding. However in the absence of information on foraging
behaviour of harbour seals in southwest Ireland, the potential association of haul-out
site use with proximity to foraging areas should be interpreted with caution.
Overall within Bantry Bay significantly higher numbers of seals were observed
throughout the study period within Glengarrif harbour, including the inner harbour
and Garinish Island than at other haul-out sites in the bay. These sites, important
pupping sites, were also used at other times during the annual cycle. The sites
physical characteristics provide optimal haul-out conditions for practically all stages
of the annual cycle; steeply shelving rocky skerries providing immediate access to
deep water located within a sheltered harbour in the innermost part of Bantry
protected from adverse weather conditions. The high variation in counts observed at
these sites and high variances in residuals seen when abundance was modelled as a
function of covariates, suggests other factors not measured may be influential in
determining patterns of abundance at this location. Disturbance is a probable one as
Glengarrif harbour has a busy tourism industry, in particular during the summer
months. Ferries servicing Garinish Island pass within 20m of the haul-out sites within
the harbour and along with pleasure craft, including yachts, kayaks and power-boats,
have been observed to disturb seals from haul-out sites on occasion. Anthropogenic
disturbance, or potential of, has been shown to influence the selection of haul-out site
by harbour seals (Montgomery, 2005). As disturbance can lead to seals avoiding or
abandoning haul-out sites (Pauli & Terhune, 1987; Da Silva & Terhune, 1988), the
122
importance of Glengarrif harbour for breeding seals along with the relatively high
levels of anthropogenic disturbance during the breeding period should be important
considerations in the management of this SAC.
Within the Kenmare River a significant seasonal pattern in abundance was evident
at seven of the eleven haul-out sites. At Carrignaronomore (site 2) and Potato Island
(site 9) a summer peak in abundance occurred. Both of these sites are used as moult
sites and the former is an important pupping site within the Kenmare River. Brennel
Island (site 3) and outer Sneem harbour (site 7) are also used as pupping sites, but as
both of these sites are used throughout the year, the significant increase in seal
numbers during summer is not as apparent. A winter peak in abundance was evident
at the most easterly haul-out site within the Kenmare River at Templenoe (site 1) and
numbers in Coongar harbour (site 5) and outer Sneem harbour peaked in the spring.
All haul-out sites within the Kenmare River are used by seals, albeit to different
extents, throughout the year with the exception of Potato Island which is used
exclusively between June and September. Pups have been recorded at this site but it
appears to be primarily associated with the moult. Most haul-out sites within the
Kenmare River, apart from those located in the inner bay, are situated in sheltered
harbours apart from Potato Island and the exposed nature of this site may explain the
limited use of it by seals during the winter months. Overall significantly higher
numbers of seals used outer Sneem harbour and Illaunsillagh compared to other sites
within the Kenmare River throughout the study. Both of these sites also provide
optimal haul-out conditions for all stages of the annual cycle and afford significant
shelter from the prevailing winds as well as the heavy swell that frequently occurs
during winter months in the outer Kenmare River region.
No seasonal effects were obvious at some haul-out sites within Bantry Bay and the
Kenmare River and other factors may be influential in determining patterns in
abundance at these sites. The seasonal shift in availability of food resources is another
factor potentially influencing site use. Harbour seals have been observed to switch
sites to move closer to feeding grounds on west coast US (Brown & Mate, 1983;
Jeffries, 1986; Montgomery, 2005) however this has not been observed in studies of
harbour seal site use in Orkney, Scotland (Thompson, 1989). It is likely that temporal
and spatial changes in food availability, varies widely geographically, as does the
123
subsequent influence of this on haul-out site selection. Ongoing research into the
foraging ecology and habitat use of harbour seals in the study area involving
telemetry, dietary studies and photo-identification of individuals will explore this.
Other general factors considered to play an important role in the year-round
selection of haul-out sites are substrate type, distance from human disturbance, shelter
from prevailing winds and immediate access to deep water (Bigg, 1981; Scheffer &
Slipp, 1944; Bjorge et al., 2002; Montgomery, 2005). Haul-out site selection by seals
across the year may be determined by the physical characteristics of a site fulfilling
particular physiological or behavioural requirements. The use of haul-out sites
exclusively for pupping, have been observed (Vaughan, 1971; Jeffries, 1986).
Selected pupping sites generally have immediate access to deep water and are away
from human disturbance and other con-specifics, as females drive other seals away
from their pups (Thompson 1987, 1989, Montgomery, 2005). Interestingly the main
sites used for pupping in Bantry Bay and the Kenmare River are those most exposed
to human disturbance, primarily from ferries, boat-based eco-tourism and leisure
craft. These haul-out sites, generally found near the head of the bays, are the most
sheltered sites relative to all haul-out sites within the two bays, affording seals
protection from large swell that frequently occurs in the bays. Additionally these sites
are located in deeper water than that found in the immediate vicinity of other haul-out
sites within the bays. Such advantages may outweigh the costs of potential
disturbance and the reaction of seals to passing boats varied largely between haul-out
sites, suggesting potential habituation to disturbance at some sites.
Similar patterns of seasonal variation in abundance differing between sites have
been shown in other studies, with some sites used predominantly during the breeding
season, peak numbers occurring at other sites during winter (Brown & Mate, 1983;
Payne & Schneider, 1984; Thompson, 1989) and other sites used throughout the
whole year (Riseborough et al., 1980; Thompson, 1989). Seasonal changes in haul-
out behaviour and subsequent abundance at haul-out sites in the area, together with
changes in individuals’ use of particular sites are likely to explain different trends in
abundance between sites (Thompson, 1987). The actual composition of haul-out
groups has been shown to vary seasonally, with the heterogeneity in behaviour
explained largely by the different requirements of individuals of different age and sex
124
throughout various phases of the annual cycle (Thompson et al., 1989; Härkönen et
al., 1999). Information on the sex and age composition of haul-out groups in the study
area would be useful for further explaining the patterns in haul-out behaviour at sites
within the area.
The effect of the time of day on seal abundance varied between the haul-out sites
and was only significant at sites that also showed a seasonal pattern in abundance.
The effect was observed to change over the year. At sites where abundance peaked in
the spring, at Orthan’s Island in Bantry Bay and Coongar harbour and outer Sneem
harbour in the Kenmare River afternoon peaks in abundance were evident at that time
of year. At sites where there was a summer or autumn peak in abundance, at
Carrigskye, Big Point rocks, and Whiddy Island in Bantry Bay and at
Carrignaronomore, and Potato Island in the Kenmare River, this occurred later in the
day and remained high in the evening. Generally those sites where there was a
summer or autumn peak in abundance were used for moulting and haul-out habitat
was available during all tidal states. Sites used during the moult typically have habitat
available above high tide allowing seals to spend more time ashore (Jeffries, 1986;
Thompson, 1987, 1989). Excessive heat loss can occur if animals remain in the water
during moult (Boily, 1995) and seals generally spend more time ashore during moult
(Stevik et al., 2002); this may explain the temporal change in diurnal haul-out patterns
observed during the moulting period.
Wilson (1978) suggested that in areas where habitat for hauling out is available
above the high water level, diurnal cycles may be more influential than tidal cycles on
haul-out behaviour. At all sites that had a seasonal pattern in abundance, the
significant effect of the time of day on haul-out behaviour was evident apart from
inner Glengarrif harbour in Bantry Bay and at Ormond’s Island and Brown Island in
the Kenmare River. This might be explained by the inter-tidal nature of the rocky
skerries used at these haul-out sites, and the tidal cycle may possibly be the main
factor influencing seals’ use of these sites. As all counts were carried out at low tide
the potential influence of the tidal cycle on haul-out site use could not be explored.
Counts of seals conducted throughout the full tidal cycle at a range of sites in the
study area would identify tidally influenced site use.
125
Wind speed influenced seal abundance at haul-out sites in the Kenmare River only,
where significantly fewer seals were observed in strong winds. When the effects of
wind speed on the combined seal counts from all sites within both bays was explored,
the strength of the wind appeared not to significantly affect seals’ haul-out behaviour
(chapter 2); combining data from both bays apparently masked the effect of wind
speed on seal haul out behaviour and therefore numbers at sites in the Kenmare River.
Haul-out sites within Bantry Bay are mostly located in the inner part of the bay and
are afforded more shelter from the prevailing west/south-westerly winds relative to
haul-out sites in the outer Kenmare River area. The lower numbers of seals observed
during rain could be related to increased difficulties associated with counting in rain
however seals were observed on a number of occasions entering water in heavy
downpours. Both of these covariates have been shown to effect harbour seal haul-out
behaviour in a number of other studies with higher numbers hauled out when winds
are not strong (Venables & Venables 1955; Bishop, 1968; Boveng et al., 2003) and
rain not heavy (Pauli & Terhune, 1987; Olesiuk et al., 1990; Grellier et al., 1996;
Boveng et al., 2003).
For the effective designation and monitoring of potential and existing SACs for
which the harbour seal is a qualifying interest, it is essential to identify the full range
of sites used during the seals annual cycle. Additionally, determining the year round
patterns in site use highlights sites of particular importance during different parts of
the annual cycle. Local declines in harbour seal abundance in Orkney, Scotland may
have resulted from local redistribution, suggesting site use may be flexible over long
time periods and highlighting the importance of identifying and protecting a broad
range of sites within an SAC (Thompson et al., 2001).
126
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out patterns and site fidelity of harbour seals (Phoca vitulina richardsi) on San
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131
CHAPTER 4
Plate 1 Harbour seal with phone tag glued to fur at base of skull
HAUL-OUT BEHAVIOUR OF HARBOUR SEALS IN THE KENMARE RIVER, CO. KERRY.
132
4.1 ABSTRACT
The haul-out behaviour of ten harbour seals in the Kenmare River in southwest
Ireland was investigated using a telemetry system based on Global Systems for
Mobile Communications (GSM) technology. Statistical modeling techniques were
used to examine the influence of covariates tidal level, tidal state, time of day, and
month on the haul-out behaviour of tagged seals. The haul-out behaviour of tagged
seals varied over the tagging period with animals spending a higher proportion of time
ashore post moult in October, decreasing over the winter months to a minimum in
February. A strong tidal influence on haul-out behaviour was evident throughout the
tagging period, tagged seals hauled out more frequently at low tide. There was
variation between tagged seals in the influence of the time of day on their haul-out
behaviour. A cyclic pattern with lunar periodicity was evident in the haul-out
behaviour of seals tagged in October and the pattern varied between tidal periods.
There was overall large variation in the patterns in behaviour over the tagging period
(i) between individuals and (ii) between tidal periods for each individual. The large
variation in the behaviour between individual seals suggest caution should be
exercised when making inferences on the haul-out behaviour of the ‘population’
based on the behaviour of tagged individuals.
133
4.2 INTRODUCTION
The harbour seal (Phoca vitulina L.) is the most widely-distributed pinniped,
inhabiting cold-temperate and temperate waters in the northern hemisphere on both
sides of the north Atlantic and north Pacific oceans (Bigg, 1981). Harbour seals spend
time ashore at terrestrial sites on which they ‘haul-out’ to rest, breed, moult, engage in
social activity and escape predation (Pitcher & McAllister, 1981; Boily, 1985; Da
Silva & Terhune, 1988; Thompson, 1989; Watts, 1992). The haul-out behaviour of
harbour seals has been studied using telemetry (Yochem et al., 1987; Thompson et
al., 1989; Thompson & Miller, 1990; Thompson et al., 1997; Rehberg & Small, 2001;
Reder et al., 2003; Sharples, 2005), time lapse photography (Stewart, 1984;
Thompson & Harwood, 1990) and modelling count data (Adkinson & Small, 2001;
Jemison & Pendleton 2001; Simpkins et al., 2003; Boveng et al., 2003; Small et al.,
2003; Montgomery, 2005). Their haul-out behaviour has been shown to be influenced
by environmental and climatic factors or ‘covariates’ including the time of year, time
of day, tidal effects, disturbance and local weather (Stewart, 1984; Yochem et al.,
1987; Thompson et al., 1989, 1994; 1997; Thompson & Harwood 1990; Thompson &
Miller, 1990; Grellier et al., 1996; Reder et al., 2003)
Estimates of population size are derived from counting the numbers of individuals
ashore at haul-out sites, however counts of seals at terrestrial sites can only be
considered as minimum population estimates as a fraction of the population will be at
sea and unavailable to count. Minimum population estimates, as opposed to
abundance estimates, although sufficient for investigating population trends, are
inadequate for conservation and management requirements such as the identification
of Special Areas of Conservation (SACs) for seals required under the EC Habitats
Directive (92/43/EEC) and in determining the predation pressures on fish stocks by a
seal population. Information on harbour seals haul-out behaviour has been used to
derive a correction factor to account for the missing element of the population during
counts and obtain a true abundance estimate (Yochem et al., 1987; Thompson &
Harwood, 1990; Thompson et al., 1997; Ries et al., 1998; Huber et al., 2001,
Sharples, 2005). Understanding the effects of covariates on seal haul-out behaviour
and therefore numbers at haul-out sites helps to enhance the design of surveys and
covariates can be factored into the statistical analyses to improve the accuracy of
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resulting population estimates (Frost et al., 1999; Adkinson et al., 2003; Boveng et
al., 2003; Small et al., 2003).
Recent efforts in Ireland have addressed the shortfall in information on harbour seal
abundance and distribution at haul-out sites on the Irish coastline, including a national
census to establish a minimum population estimate (Cronin et al., 2004) and local
studies on the year round changes in the terrestrial distribution and abundance of
harbour seals in southwest Ireland (Cronin, 2006, in prep). Information on the haul-
out behaviour of individual seals helps explain seasonal changes in numbers of seals
at haul-out sites and could potentially be used to derive a correction factor for haul-
out counts to estimate the size of the population.
A telemetry system based on Global Systems for Mobile Communications (GSM)
technology (McConnell et al., 2004) was used in this study to provide detailed
information on the haul-out behaviour of tagged individuals. The mobile phone tag is
programmed to send text messages to a base phone, incorporating data on haul-out
events, thereby providing information on the amount of time tagged seals spent at sea
and ashore. The large global investment in GSM networks have resulted in a
telemetry technology with low capital and running costs and even though phone tags
do not have the global coverage of Argos satellite system (Fedak et al., 2002), the
lower cost allows for larger sample sizes and more powerful inferences to estimate
population parameters (McConnell et al., 2004). To acquire basic fundamental data on
haul-out behaviour, the mobile phone tags provide a cost-effective approach,
overcoming constraints associated with radio and satellite telemetry, which are
respectively labour intensive and costly.
Hitherto no information was available on the haul-out behaviour of harbour seals in
Ireland. The aims of the study are therefore to (i) determine the activity patterns of
harbour seals in the study area and how these change over the annual cycle, (ii)
determine what factors affect the haul-out behaviour of tagged seals and (iii) explore
the potential of deriving a correction factor to apply to haul-out counts of seals to
estimate the total number of seals in the study area.
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4.3 METHODS
4.3.1 Study site
Capture of harbour seals and deployment of tags was attempted in Bantry Bay Co.
Cork in October 2004 and the Kenmare River Co. Kerry in southwest Ireland in
October 2004 and April 2005. Harbour seals haul-out on rocky islands and skerries
generally off the north shores of both bays. Seal capture for tag deployment was
attempted at haul-out sites in both bays selected on the basis of haul-out group size,
group size greater than 10 and the bathymetry in the vicinity of haul-out sites, water
depth less than 3m (figure 1).
Figure 1 Study area showing haul-out sites selected for seal capture and tag deployment
4.3.2 Capturing and handling procedure and tag deployment
Tagging was staggered over the interval between moults (between early October and
August) as the anticipated length of attachment was approximately four months
(Sharples, SMRU pers comm.). The technique employed for catching seals was
similar to that described in Jeffries (1993). Two custom made nets, 60m long x 3m
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deep, with a buoyant headline and lead weighted sink line were used. Deployment
was at speed from two rigid inflatable boats (RIBs) which approached the haul-out
site from opposite sides and aimed to deploy the net around and as close to the haul-
out as possible. Seals entered into the water as soon as deployment commenced and
the nets formed a barrier in which they were entangled or trapped. Hoop nets were
used to assist in getting the captured animals into the boats. These consist of a 1m
diameter hoop made of 20mm plastic hosing and a funnel net of 10mm mesh attached.
Captured seals were brought ashore and remained in the hoop nets throughout the
administration of the anaesthetic and prior to the tagging procedure.
Seals were weighed and anaesthetised using 0.05ml of Zoletil per 10kg delivered
intravenously. If intravenous administration of the anaesthetic was difficult (as with a
struggling animal) an intra-muscular dose of 0.1ml of Zoletil per 10kg was delivered.
Length and girth of the animal were measured and the sex recorded. The fur was dried
with paper towels and degreased using acetone and the tag was secured in place using
fast setting epoxy resin (Fedak et al., 1983) at the base of the skull. Seal handling and
tagging procedures were carried out under National Parks & Wildlife Service licence
no. C18/2005.
4.3.3 Tag operation
The tag was designed by the Sea Mammal Research Unit (SMRU), St Andrews
University, Scotland and is based on Global Systems for Mobile Communications
(GSM) mobile phone technology. Details of the hardware design can be found in
McConnell et al (2004). The controlling software is designed to minimize energy
consumption while still obtaining a reasonable rate of text messages. The wet/dry
sensor in the tag is interrogated every 2.3 seconds. A haul-out event starts when the
tag is continuously dry for 10 min and ends when it is continuously wet for 2 min.
The start and end times of the haul-out event as well as a unique incremental number
are appended to a 160 character long buffer. When the buffer is full a short message
service (SMS), also know as a text message, is created and stored in the SIM card.
Every four hours the tag ‘wakes’ from sleep mode, waits until it is dry and then
attempts to send a text message. To do this it registers with a GSM network for a
maximum of 95 seconds. If registration is unsuccessful only diagnostic data for that
attempt are appended to the buffer. If registration is successful the delay from dry to
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registration, the ID code of the radio cell with which it has registered are also
appended to the buffer (after McConnell et al., 2004).
4.3.4 Information relay and interpretation
Successful registration requires that the phone is within radio contact of a GSM radio
cell. The maximum theoretical range is 35km but it is often less than this as a result of
obstruction of line of sight or radio interference (McConnell et al., 2004). It is not a
prerequisite that the tagged animal remains in the GSM coastal corridor of coverage
rather that it returns at some stage to this area resulting in successful registration with
the network and subsequent relaying of stored information. Text messages were
relayed via the GSM network to a base phone located at the SMRU and details
(including haul-out event start/end times, haul-out number, message id, radio cell id,
time to register) described in an Access database.
4.3.5 Statistical modelling
Statistical modelling techniques were used to examine the influence of covariates tidal
level, tidal state, time of day, and month on the haul-out behaviour of tagged seals.
The probability of a tagged seal being hauled out was modelled as a function of
explanatory variables (or covariates) using generalized additive models (GAM) with
logistic link function. These use smoothing curves to model the relationship between
the response variable and explanatory variables and allow for non-linear relationship
in the data (Hastie & Tibshirani, 1990). A GAM with a binomial distribution and
logistic link was used to model the response variable (0 = seal in water, 1 = seal
hauled out) as a function of the covariates tidal state, tidal level, time of day and
month. Tidal data (tide level, time, speed, direction) for the tagging period at the study
site was obtained from tide prediction software Polpred V.2 and an hourly tidal ‘state’
value (-6 to +6) and tidal level (m) assigned to each hour on both the ebb and flood
tides. Colinearity was expected between some of the variables, in particular those
related to tide and a scatter-plot was used to confirm this.
Cross-validation was applied to determine the optimal degrees of freedom for each
smoother. Once the optimal degrees of freedom were determined Pearson and
Deviance residuals were plotted versus the original explanatory variables. Any
patterns in such a plot may indicate problems with the cross-validation. An
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assumption in the GAM model is that the errors (εij) are independent, but as the
observations are made sequentially over time the independence assumption is
violated. Adding a correlation structure on the errors (εij) is a means of dealing with
this, such as an auto-regressive error structure AR (1), allowing for auto-correlation
between the residuals of sequential hours (Zuur et al., 2006). The length of each data
series is given in Table 1. Due to the length of the time series of data that resulted
from tags 4, 5, 6, 7, 11 and 20 the auto-regressive error structure in the gamm function
in the mgcv library could not be added, the algorithm is halted due to error messages
related to lack of computer memory. In the presence of auto-correlation, p-values of
smoothers can be seriously inflated (Ostrom, 1990). Instead of incorporating a
temporal correlation structure within the gam model to get ‘better’ p-values, it is also
possible to use bootstrapping for this (Davison & Hinkley, 1997).
A
ho
10
lib
Table 1 Details of tag deployments and measurements of 10 harbour seals captured on the
Kenmare River during 2004/2005.
Tag/Seal
no.
Date of
tagging
Location Sex Weight/
kg
Length/
cm
Girth/
cm
Duration
tagging/
days
Duration
tagging/
hours
6 16/10/04 Illaunsillagh male 80 156 97 187 4486
5 17/10/04 Illaunslea male 67 127 97 99 2385
4 18/10/04 Illaunslea male 40 112 84 104 2496
3 27/04/05 Sneem male 95 159 125 11 263
10 27/04/05 Sneem male 47 138 83 39 956
9 27/04/05 Sneem male 44 127 87 43 1024
7 28/04/05 Sneem male 87 154 118 92 2213
20 28/04/05 Sneem female 42 120 85 52 1242
11 29/04/05 Sneem male 63 143 103 73 1741
2 29/04/05 Sneem male 103 161 114 29 696
search for the optimal model was carried out for seals 4, 5, 6, 7, 11 and 20. As the
ur smoothers require long time series the length of the data from seals 2, 3, 9 and
was too short for inclusion in the models and the gamm function in the mgcv
rary, with an auto-regressive error structure series was applied.
139
4.3.6 Bootstrap variance estimation
Confidence intervals for the haul-out status were obtained using a non-parametric
bootstrap approach (Davison & Hinkley, 1997). Bootstrapping involves creating new
data sets from the original sample, analysing the new samples in the same way as the
original and the distribution of the statistic of interest is estimated from its empirical
distribution among the bootstrap sample (Davison & Hinkley, 1997). The following
bootstrap approach was applied:
1. The optimal GAM model (in terms of degrees of freedom and explanatory
variables) is determined and fitted values and residuals obtained
2. The time series of the residuals is divided into blocks of M days
3. The residuals within each block are permutated
4. The permutated residuals are added to the fitted values in step 1 and new data
obtained by rounding to 0 or 1
5. A GAM is applied to the new data obtained in step 4 using the same degrees
of freedom for each smoother as in step 1
6. Steps 2 to 5 are repeated 1000 times
7. 95% quartile confidence intervals are obtained by sorting the 1000
bootstrapped values at each point and using observation 25 and 975
In step 2 the time series is divided into blocks the length of which is such that within
a block the auto-correlation is captured, but points beyond this length are not auto-
correlated. Based on the auto-correlation function blocks of length M=2 or M=3 days
were used depending on the tag. The motivation to use the same degrees of freedom
in step 5 is motivated by computing time. Allowing the GAM algorithm to determine
the optimal degrees of freedom in each bootstrap drastically increases computing
time.
The method described above was used for two purposes: (i) to obtain 95% point-
wise confidence bands for each smoother, and (ii) to assess the importance of each
covariate in the model. The models with, and without a particular covariate were
compared using the difference in deviance. This difference was calculated for the
original data, but also for each bootstrapped data set. The null-hypothesis is that the
covariate has no effect (hence it is removed from the model) so, the null-model is the
140
GAM without the covariate. The alternative hypothesis is that the covariate does have
an effect. In this approach, the residuals from the null-model are used for permutation.
The data are permutated and the deviance calculated and the process repeated 1000
times. The difference in deviances between the full and null models divided by 1001
represents the p value for the omitted explanatory variable. The entire bootstrapping
approach is carried out for each covariate in turn. A more detailed description can be
found in Algorithm 7.4 in Davison & Hinkley (1997).
4.4 Results
4.4.1 Capture and tag deployment
A total of ten animals were successfully captured at haul-out sites in the Kenmare
River and tagged in October 2004 and April 2005. Nine males and one female were
captured and weights ranged from 40kg to 103kg (Table 1). Four of the ten seals
tagged weighed less than 60kg and based on harbour seal growth curves (Härkönen &
Heide-Jorgensen, 1990; Lydersen & Kovacs, 2005) are considered as juveniles.
4.4.2 Duration of transmission
Details of tag transmission durations are given in Table 1. The tags transmitted for a
period of between 11 and 187 days with an average duration of transmission of 75.5
days, approximately two and a half months. The last transmission during the study
period was from seal 7 on August 2nd 2005.
4.4.3 Examination of haul-out data
The duration of every haul-out event was calculated in an Access database using the
start time and end times of each haul-out event. A sequential haul-out number system
allocated to haul-out events and sent in the SMS enables the identification of lost
haul-out records. This happens as a result of ‘dropped’ messages which occurred
infrequently as a result of the tag failing to send a text message. It is important to
identify these as failure to do so would imply that the animal has spent a longer period
at sea than is the case.
Haul out records for each seal are shown in figures 2a and 2b. The blue lines
indicate haul-out records, the red lines indicate the period between sequential haul-out
141
events and absence of a line indicates that there is no information available for that
period. These charts enable the visualisation of the entire haul-out records for each
seal over the tagging period. The amount of time the tagged animals spent ashore
varied. The average length of a haul-out event was 215 minutes and the longest and
shortest haul-out events recorded were 1660 and 10 minutes respectively (10 minutes
was the set time programmed in the tag to constitute a haul-out event). 98% of all
haul-out events were less than 12 hours. The longest recorded period a tagged
individual spent at sea between haul-out events was 12607 minutes which amounts to
almost nine days. This individual, seal 6 made seven separate trips to sea of durations
of more than four days over the tagging period. This behaviour of spending extended
periods at sea was also evident in seal 7 but less than 1% of all trips to sea made by
tagged individuals over the tagging period were of duration longer than 4 days. All
tagged seals, apart from seal 3, made trips of one day or more, with variation between
individuals in the frequency of such trips. However most periods spent at sea by all
tagged animals were for hours as opposed to days, between daily haul-out events,
with 91% of all trips to sea lasting less than one day. The mean proportion of time
tagged seals spent hauled out monthly is shown in figure 3 ranging from 25% to 11%
of total time hauled out during October and February respectively. The larger standard
error in the value for April, relative to other months, is a result of a small number of
haul-out records for that month.
4.4.4 Model outputs and validation
An initial analysis was carried out on the data from tag 4 to validate the modeling
approach as it was one of the longest time series of data. The haul-out response of seal
4 was modeled as a function of tidal level, tidal state and time of day. Cyclic patterns
in the residuals with positive correlations with a time lag of 24 hours suggested the
model was missing a fixed term(s). The possibility that the effect of tidal state and/or
level was changing during different tidal cycles was explored by differentiating
between the first and second rising and falling tides per 24 hour period.
A nominal variable Ts with four levels was created:
1 if observation s is during the rise of tide 1
Ts = 2 if observation s is during the fall of tide 1
3 if observation s is during the rise of tide 2
142
An interaction term between the various tidal periods and explanatory variables tidal
level and state was added. Interactions between nominal variables and smoothers can
be implemented using the ‘by’ command in the gam function in the mgcv R library.
For example, a model that describes haul-out status as a function of tidal state and
level, plus an interaction between these two smoothers and Ts is formulated as:
logit(ps) = α + f (TidalState) * Ts + f(Level) * Ts
Where Status is Binomial distributed: Statuss ~ B (ps,1).
The interaction term between tidal state or level and tidal cycle allows for a different
trend in the rise of the first tide, fall of the first tide, rise in the second tide, and fall in
the second tide.
The adequacy of the model is explored by examining the residuals and the
autocorrelation function values. The residuals represent the difference between the
data and the model; the Pearson residual is used in GLM and GAM and comparable to
the standardized residuals used for linear models (Faraway, 2006). The
autocorrelation function (ACF) looks for correlations between residuals separated by
various amounts of time. Specifically its ith value expresses the amount of correlation
between pairs of residuals separated by i-1 time points. If no correlation amongst
residuals exists then the autocorrelation function values should be random (Gore,
2000).
The model was only a starting point and suggested an immediate problem. It was
applied on the data of tag 4 and the ACF of the Pearson residuals showed a clear
residual pattern (figure 4). Each time lag represents an hour. The ACF suggested that
the status at time s depends on s-1, s-2, but also on s-12, s-24, etc. Hence, the model
was missing a component that deals with the daily rhythms.
One option was to extend the model with the following components:
143
Logit(ps) = 3 3
1 2
3
( ) * ( ) *TimeOfDay TimeOfDaysin(2 ) cos(2 )
23 23( ) *
s s
s s
f TidalState T f Level T
f Hour T Month
α
β π β π
+ + +
+ +
+
The time of the day was converted into a daily cyclic pattern using
cos(2*pi*(Timeofday)/23) and sin(2*pi*(Timeofday)/23). To simplify notation, the
sin and cosine term are labelled Z1 and Z2 respectively: The term f (Hour) is the long
term smoother allowing potential patterns in the haul-out behaviour of the tagged seal
over the duration of the tagging period to be identified. For the longer time series, a
term Month (nominal) was added to the model.
3 3 1 1
3
( ) ( ) * ( )* ( ) *
s s s
s s
Logit p f TidalState T f Level T Z Zf Hour T Month
2 1s sα β β= + + + + ++
As collinearity was confirmed in a scatter-plot of tidal level versus tidal state for
seal 4 (figure 13), these variables were not used in the same model. The following two
models were therefore applied to the seal 4 data and compared to identify the optimal
model:
Model 1: 3 1 1 2 1 3( ) ( ) * ( )*s s s s sLogit p f Level T Z Z f Hour T Monthsα β β= + + + + +
Model 2: 3 1 1 2 1 3( ) ( ) * ( ) *s s s s sLogit p f TidalState T Z Z f Hour T Monthsα β β= + + + + +
The Akaike Information Criteria (AIC) values for the two models were compared.
The AIC enables a measure of the goodness of fit and also employs a penalty for the
number of parameters in the model (Zuur et al., 2006) and the model with the smallest
AIC chosen as the most optimal. Examination of residual plots confirmed model 1 as
the optimal model for tag 4:
3 1 1 2 1 3( ) ( ) * ( )*s s s s sLogit p f Level T Z Z f Hour T Monthsα β β= + + + + +
This model still contained a residual auto-correlation structure. The AR-1 structure in
the residuals is mainly from the tidal period 1 residuals. Hence, processes are in place
144
for this time frame that are not explained well within the current model. Bootstrapping
techniques were therefore required to assess the true significance of the smoothers.
The smoothers for hour on the haul-out status of seal 4 over the entire tagging
period in four tidal periods are shown in figure 5. The coloured lines represent the
long term smoothers for the rising and falling parts of tide 1 and tide 2, the black dots
denote the full moon and the following patterns are evident: (i) a tidal pattern, a high
probability of the seal being at sea during the rising first or second daily tide but never
during both, (ii) a long term pattern, higher probability of the seal being at sea more-
so on the first rising tide for approximately the first 1500 hours and more-so on the
second rising ride from then onwards and (iii) a pattern with lunar periodicity, the
highest probability of being at sea occurred just after the full moon each month and
this occurred on the first rising tide, additionally the highest probability of being
hauled out on the second rising tide occurred at this time.
The probability of haul-out at different tidal levels during the four tidal periods and
during the first 400 hours of the tagging duration is shown in figure 6. The highest
probability of being hauled-out occurs during low tidal level or height (i.e. around low
tide) of all tidal periods apart from the second rising tide, suggesting the seal spent
more time at sea during the second rising tide (and at all levels of this tide) during the
first 400 hours. The hourly pattern of haul-out probability for the different tidal
periods during this time is shown in figure 7. This pattern changes later in the tagging
period when highest probability of haul-out occurs during the second rising tide and
lowest probability during all levels of the rising tide (figure 8).
The optimal GAMs for tags 5, 6, 7, 11 and 20 were variations on the optimal model
for tag 4: 3 1 1 2 1 3( ) ( ) * ( )*s s s s sLogit p f Level T Z Z f Hour T Monthsα β β= + + + + +
The difference in the optimal model for each tag was associated with the nominal
variable Ts which represents the four daily tidal periods. For some tags a level and/or
month smoother combining some or all of the four tidal periods was optimal.
The smoothers for hour per tidal period from GAMs of the haul-out data from the
tags on seals 5 and 6 are shown in figures 9 and 10. The coloured lines represent the
long term smoothers for the rising and falling parts of tide 1 and tide 2, the black dots
145
denote the full moon. The patterns in the haul-out behaviour of seal 5 are similar to
those of seal 4. The tidal pattern, a high probability of the seal being at sea during the
rising first or second daily tide but never during both; the long term pattern, higher
probability of the seal being at sea more-so on the first rising tide for approximately
the first 1500 hours and more-so on the second rising ride from then onwards; and a
lunar pattern, the highest probability of being at sea occurred just after the full moon
each month and this occurred on the first rising tide with the exception being in the
third month when the highest probability of being at sea occurred during the falling
part of tide 1 following a full moon. Although a cyclic pattern is also seen in the haul-
out behaviour of seal 6 it differs to that from seals 4 and 5. The alternating probability
of haul-out between the first rising and falling tides seen in the data from the tags on
the latter two seals is not obvious in the data from the tag on seal 6. A possible lunar
influence on haul-out behaviour is suggested by highest probabilities of being either at
sea or hauled out occurring after a full moon during all tidal periods and not just
during the first rising tide as with seals 4 and 5.
The cyclic patterns evident in the haul-out behaviour of seals 4, 5 and 6 throughout
the tagging period were not obvious in those seals tagged in a different period, seals 7,
11 and 20. Seal 7 showed a higher probability of being at sea after the first full moon
of its tagging period during all tidal periods however there was no cyclic pattern
evident and after approximately the first 1000 hours of the tagging period the
probability of this seal being at sea gradually increased (figure 11). Seal 11 showed a
similar pattern to seal 7 with a possible lunar influence during the first 1000 hours of
tagging followed by a gradual increase in the probability of being at sea; additionally
the tidal patterns seen in the behaviour of seals 4 and 5, a high probability of the seal
being at sea during the rising first or second daily tide but never during both is also
evident in the early stage of this seals tagging period (figure 12). Seal 20 showed a
gradual increase in haul-out probability over its tagging period (figure 13).
The patterns in hour smoothers for the different tidal periods for tags attached in the
latter part of the tagging period i.e. during the spring and early summer do not show
the cyclic patterns evident in the tags attached at the beginning of the tagging period
in the autumn. Apart from the similarity in the long term pattern in haul-out behaviour
between seals 4 and 5 there appears to be otherwise large variation in these long term
146
patterns in behaviour (i) between individuals and (ii) between tidal periods for each
individual and (iii) over the tagging period.
Bootstrapping provided a means of dealing with the problems of autocorrelation and
figures 14-19 show the bootstrapped hour and level smoothers with confidence bands
for individual seals. The smoothing functions describing the partial effect of level on
haul-out behaviour suggest all tagged seals had a higher probability of hauling out at
low tide and this decreased with rising tide. The confidence bands resulting from
bootstrapping the ‘hour’ data from the tag on seal 6 were slightly larger. Data from
the other tags suggested differences in the long-term patterns in the haul-out
behaviour of tagged seals between the autumn/winter and the spring/summer. The tag
on seal 6 remained attached for a longer period than other tags and included data on
haul-out patterns that potentially differed across the seasons. When only the first 2400
hrs of data from tag 6 were bootstrapped, ensuring that the residuals from the data
from this period were not fitted to data from later in the tagging period, the confidence
bands around the hour smoothers improved considerably. The confidence bands
around the smoothing functions describing the effect of both level and hour on the
haul-out behaviour of seal with tag 7 are wider (figure 17) suggesting additional
factors other than those included in the model are influencing the haul-out behaviour
of this animal.
The p values for the explanatory variables included in the GAMs of data from seals
4, 5, 6, 7, 11 and 20 are given in table II. The p values for explanatory variables level
and time of day resulting from GAMMs applied to the data from the tags with shorter
time series of data, on seals 2, 3, 9 and 10, are shown in table III. There was a
significant effect of tidal level on the haul-out behaviour of all tagged seals during all
tidal cycles throughout the tagging period (p<0.001 to p<0.01). The ‘hour’ smoother
depicts the long term pattern in haul-out behaviour over the tagging period. A
significant change in haul-out patterns over the tagging period was evident in seals 4,
6, 11 and 20 (p<0.001 to p<0.05). In the latter two this was evident over all tidal
periods. Seals 4 and 6 showed significant change in their haul-out pattern over the
tagging period during tidal periods other than the second falling and rising tides
respectively. Seals 5 and 7 did not significantly change their haul-out behaviour over
the tagging period. The long-term patterns in the haul-out behaviour of seals 2, 3, 9
147
and 10 could not be explored as the tagging periods were too short. A significant
change in the haul-out behaviour of tagged seals between months was apparent in seal
6 only (P<0.05). The time of day had a significant effect on the haul-out behaviour of
all seals (P<0.001 to P<0.05) apart from seals 4 and 7. The phi (φ) values in table III
denote the correlation between sequential hours. The AR(1) structure in the GAMM
dealt with the auto-correlation problem as no patterns were obvious in the auto-
correlation function of the Pearsons residuals.
148
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i7Tag
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i5Tag i4Tag
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Figure 2a. Haul-out records from tagged seals numbers 2-7 over the tagging period. The blue lineindicates haul-out periods, the red line indicates periods when the seals were known to be nothauled out. White space indicates periods from which there is no information.
149
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01-Apr
01-May
01-Jun
01-Jul
01-Aug
00:00 04:00 08:00 12:00 16:00 20:00 00:00
01-Apr
-May
-Jun
-Jul
-Aug
00:00 04:00 08:00 12:00 16:00 20:00 00:00
01-Apr
01-May
01-Jun
01-Jul
01-Aug
i20Tag 20 i11Tag 11
i10Tag 10 i9Tag 9
Figure 2b. Haul-out records from tagged seals numbers 9,10,11,20 over the tagging period.The blue line indicates haul-out periods, the red line indicates periods when the seals wereknown to be not hauled out. White space indicates periods from which there is no information.
150
N=2
D=3
4
N=5
D=1
16
N=7
D=1
84
N=8
D=3
5
N=1
D=3
1
N=1
D=2
8
N=3
D=8
7
N=3
D=9
3
N=3
D=9
0
N=3
D=4
5
0
0.05
0.1
0.15
0.2
0.25
0.3
October November December January February March April May June July
month
Mea
n pr
opor
tion
of ti
me
spen
t hau
led
out
Figure 3. Mean proportion of time tagged seals spent ashore ((±1 s.e) (N= number of tagged seals D= number of days of haul-out data)
0 10 20 30 40 50 60 70
0.0
0.2
0.4
0.6
0.8
1.0
Lag
ACF
Auto-correlation
Figure 4. Auto-correlation function of the Pearsons residuals resulting from GAM on haul-out data from tag 4
151
Smoother Tag 4 5 6 7 11 20
Level TP 1 edf
p value
1.005
p<0.001
1.776
p<0.001
1.887 (TP1-4) 2.596 (TP1-4)
p<0.001 p<0.001
1.001 (TP1-4)
p<0.001
Level TP 2 edf
p value
1.005 (TP2 & 3)
p<0.001
1.906
p<0.001
1.007
p<0.001
Level TP 3 edf
p value
1.987
p<0.01
1.925
p<0.001
Level TP 4 edf
p value
4.405
p<0.001
3.024
p<0.001
1.008
p<0.01
Hour TP 1 edf
p value
9.000
p<0.05
8.625
p=0.122
8.939
p<0.001
7.193
p=0.890
5.787 (TP1-4)
p<0.05
8.681 (TP1 & 2)
p<0.001
Hour TP 2 edf
p value
7.638
p=0.075
1.000
p=0.313
8.822
p<0.05
8.205
p=0.525
Hour TP 3 edf
p value
9.000
p<0.05
8.481
p=0.244
8.866
p=0.158
6.787
p=0.811
7.200
p<0.001
Hour TP 4 edf
p value
1.001
p=0.259
1.508
p=0.237
7.229
p<0.05
0.991
p=0.633
1.909
p<0.01`
Month d.f
p value
4.000
p=0.355
6.000
p<0.05
4.000
p=0.890
3.000
p=0.063
Cosine Time of day d.f
p value
1.000
p=0.237
1.000
p<0.001
1.000
p<0.001
1.843
p=0.207
1.000
p<0.001
1.000
p<0.001
Sine Time of day d.f
p value
1.000
p=0.369
1.000
p=0.976
1.000
p=0.086
1.000
p=0.177
1.000
p=0.081
1.000
p<0.001
probability value (p) is given; explanatory variables level and hour were fitted as separate smoothers for four tidal periods (TP1-TP4) unless otherwise stated. The estimated degrees
Table II. Summary of optimum generalized additive models of haul-out status of tagged seals within the Kenmare River. For all explanatory variables, the associated bootstrapped
of freedom (edf) are shown for variables fitted as smoothers and for parametric terms the degrees of freedom (df) shown
152
Table III. Summary of optimum generalized additive mixed models of haul-out status of tagged seals within the Kenmare River. Probability values of all explanatory variables, estimated degrees of freedoms (edf) for smoothers and degrees of freedom (df) for nominal variables and Phi (φ) values are given. Explanatory Variable Tag 2 3 9 10
Level edf
p value 1.000
p<0.001
1.000
p<0.01
1.000
p<0.001
2.689
p<0.001 Cosine Time of day df
p value 1.000
p<0.05
1.000
p<0.001
1.000
p<0.01
1.000
p<0.001
Sine Time of day df
p value 1.000
p<0.05
1.000
p=0.233
1.000
p=0.617
1.000
p<0.05
Phi (φ) value 0.7519 0.5809 0.6438 0.7488
153
Hour effect
s al
ue te
d
to fi
utio
n
ntrib
Co
Probabilities first 400 hours
0 500 1000 1500 2000 2500
-3-2
-10
12
3
t
vLT 1HT 1
LT 2
HT 2
Con
tribu
tion
to th
e fit
ted
valu
es
TimeTime/hours
Figure 5. Smoothing functions describing the partial effect of ‘Hour’ on the haul-outstatus of seal with tag 4 over the entire tagging period in four tidal periods. The blackline, labelled LT1, is the long term smoother for the rising part of tide 1, the green lineLT2 the rising part of tide 2, the red line HT1 the falling part of tide 1 and blue lineHT2 the falling part of tide 2. The black dots denote full moon.
0.5 1.0 1.5 2.0 2.5 3.0
0.0
0.2
0.4
0.6
0.8
1.0
LL
OO
Prob
abili
ty o
f hau
l-out
Tide level/m
Figure 6. Probability of seal with tag 4 being hauled out during the first 400 hoursof the tagging period for different tidal levels (0=not hauled out, 1= hauled-out;colours represent tidal period: black = rise in tide 1, blue=fall in tide 1, green = risein tide 2 red=fall in tide 2)
154
Probability
0 100 200 300 400
0.0
0.2
0.4
0.6
0.8
1.0
HH
OO
Figure 7. Probability of seal with tag 4 being hauled out during the first 400 hours of the tagging period; colours present tidal period (0=not hauled out, 1= hauled-out; black = rise in tide 1, blue=fall in tide 1, green = rise in tide 2, red=fall in tide 2)
Prob
abili
ty o
f hau
l-out
Time/hou
0.5 1.0 1.5 2.0 2.5 3.0 3.5
0.0
0.2
0.4
0.6
0.8
1.0
Figure 8. Probability of seal with tag 4 being hauled out during the hours of1200 and 1400 of the tagging period for different tidal levels (0=not hauledout, 1= hauled-out; black = rise in tide 1, blue=fall in tide 1, green = rise intide 2, red=fall in tide 2)
OO
Prob
abili
ty o
f hau
l-out
LLTide level/m
Probabilities between 1200 and 1400 hours
155
Hour effect
Con
tribu
tion
to fi
tted
valu
esC
ontri
butio
n to
the
fitte
d va
lues
Figure 9. Smoothing functions describing the partial effect of ‘Hour’ on the haul-out status ofseal with tag 5 over the entire tagging period in four tidal periods. The black line, labelled LT1,is the long term smoother for the rising part of tide 1, the green line LT2 the rising part of tide 2,the red line HT1 the falling part of tide 1 and blue line HT2 the falling part of tide 2. The blackdots denote full moon.
Con
tribu
tion
to fi
tted
valu
esC
ontri
butio
n to
the
fitte
d va
lues
Figure 10. Smoothing functions describing the partial effect of ‘Hour’ on the haul-out statusof seal with tag 6 over the entire tagging period in four tidal periods. The black line, labelledLT1, is the long term smoother for the rising part of tide 1, the green line LT2 the rising partof tide 2, the red line HT1 the falling part of tide 1 and blue line HT2 the falling part of tide 2.The black dots denote full moon.
0 500 1000 1500 2000 2500
-3-2
-10
12
3
Time
LT 1
HT 1
LT 2
HT 2
0 1000 2000 3000 4000
-3-2
-10
12
3
Hour effect
Time
LT 1
HT 1
LT 2
HT 2
Time/hours
Time/hours
156
5 0 0 0 5 5 0 0 6 0 0 0 6 5 0 0 7 0 0 0
-3-2
-10
12
3
T im e
L T 1H T 1L T 2
H T 2
Figure 11. Smoothing functions describing the partial effect of ‘Hour’ on the haul-outstatus of seal with tag 7 over the entire tagging period in four tidal periods. The blackline, labelled LT1, is the long term smoother for the rising part of tide 1, the green lineLT2 the rising part of tide 2, the red line HT1 the falling part of tide 1 and blue line HT2the falling part of tide 2. The black dots denote full moon.
Time/Ho
Con
tribu
tion
to fi
tted
valu
esC
ontri
butio
n to
the
fitte
d va
lues
H o u r e f f e c t
5000 5500 6000 6500
-3-2
-10
12
3
Time
LT 1HT 1
LT 2
HT 2
Figure 12. Smoothing functions describing the partial effect of ‘Hour’ on the haul-outstatus of seal with tag 11 over the entire tagging period in four tidal periods. The blackline, labelled LT1, is the long term smoother for the rising part of tide 1, the green lineLT2 the rising part of tide 2, the red line HT1 the falling part of tide 1 and blue line HT2the falling part of tide 2. The black dots denote full moon.
Time/Ho
Hour effect
Con
tribu
tion
to th
e fit
ted
valu
es
Con
tribu
tion
to fi
tted
valu
es
157
Con
tribu
tion
to th
e fit
ted
valu
es
-2-1
01
4500 5000 5500 6000
-32
3
LT & HT 1HT 1
LT 2
Time/Hours
Figure 13. Smoothing functions describing the partial effect of ‘Hour’ on the haul-out status of seal with tag 20 over the entire tagging period in four tidal periods. Thered line, labelled LT and HT 1, is the long term smoother for the rising and fallingparts of tide 1, the green line the rising part of tide 2, and blue line the falling part oftide 2. The black dots denote full moon.
158
Figure 15. Smoothing functions with 95% confidence intervals obtained by bootstrappingdescribing the partial effect of ‘Hour’ and ‘Level ‘on the haul-out status of seal with tag 5 over theentire tagging period in the tidal periods 1-4. 159
0.5 1.0 1.5 2.0 2.5 3.0 3.5
Level 1
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-4-2
02
4
Level 2
0.5 1.0 1.5 2.0 2.5 3.0
-4-2
02
4
Level 3
0.5 1.0 1.5 2.0
-4-2
02
4
Level 4
0 500 1000 1500 2000
Hour 1
0 500 1000 1500 2000
-4-2
02
4
Hour 2
0 500 1000 1500 2000
-4-2
02
4
Hour 3
0 500 1000 15
-4-2
02
4
Hour 4
Tidal level/m 0.5 1.0 1.5 2.0 2.5 3.0 3.5
4
Level 1
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-4-2
02
4
Level 2 & 3
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-4-2
02
4
Level 4
0 500 1000 1500 2000 2500
Hour 1
0 500 1000 1500 2000 2500
-4-2
02
4
Hour 2
0 500 1000 1500 2000 2500
-4-2
02
4
Hour 3
0 500 1000 1500 2000 2500
-4-2
02
4
Hour
Hour TP4 Hour TP3 Hour TP2 Hour TP1
Level TP4 Level TP2 & 3Level TP1
4Hours
42
02
4 -
4
-
2
0
2
2
02
4-4
-2
0
2
Con
tribu
tion
to th
e fit
ted
Figure 14. Smoothing functions with 95% confidence intervals obtained by bootstrappingdescribing the partial effect of ‘Hour’ and ‘Level ‘on the haul-out status of seal with tag 4 over theentire tagging period in the tidal periods 1-4.
Level TP1 Level TP2 Level TP3 Level TP4
42
02
4 -
4
-2
0
2
4
Con
tribu
tion
to th
e fit
ted
valu
es
2.5 3.0 3.5Tidal level/m
Hour TP1 Hour TP2 Hour TP3 Hour TP4
42
02
4
-4
-2
0
2
00 2000Hours
160
0 500 1000 1500 2000
-4-2
02
46
8
Hour 1
0 500 1000 1500 2000
-4-2
02
46
8
Hour 2
0 500 1000 1500 2000
-4-2
02
46
8
Hour 3
0 500 1000 1500 2000
-2
Hour 4
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-2-1
01
2
Level 2
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-2-1
01
2
Level 3
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-2-1
01
2
Level 4
Level TP2 Level TP3
Level TP4
Tidal level/m
Hour TP1 Hour TP2
Con
tribu
tion
to fi
tted
valu
es
24
68
Hour TP3 Hour TP4
0
4-
Hours
Figure 16. Smoothing functions with 95% confidence intervals obtained by bootstrappingdescribing the partial effect of ‘Hour’ and ‘Level ‘on the haul-out status of seal with tag 6 over thefirst 2400 hours of the tagging period in the tidal periods denoted 1-4.
0.5 1.0 1.5 2.0 2.5 3.0 3.5
-4-2
02
4
Level
5000 5500 6000 6500 7000
Hour 1
5000 5500 6000 6500 7000
-4-2
02
4
Hour 2
5000 5500 6000 6500 7000
Hour 3
5000 5500 6000 6500 7000
-4-2
02
4
Hour 4
Tidal
Hours
Hour TP4 Hour TP3
Hour TP2 Hour TP1
Level TP1-
42
02
4 -
4
-2
0
2
4
Con
tribu
tion
to fi
tted
valu
es
42
02
4
-4
-2
0
2
Figure 17. Smoothing functions with 95% confidence intervals obtained by bootstrappingdescribing the partial effect of ‘Hour’ and ‘Level ‘on the haul-out status of seal with tag 7over the entire tagging period in the tidal periods 1-4.
161
0.5 1.0 1.5 2.0 2.5 3.0
Level
5000 5500 6000 6500
Level TP1-
Tidal
-4
--2
0
2
4
Con
tribu
tion
to fi
tted
valu
es
-4
--2
0
2
4
Con
tribu
tion
to fi
tted
valu
es
Fdt
Figure 18. Smoothing functions with 95% confidence intervals obtained by bootstrappingdescribing the partial effect of ‘Hour’ and ‘Level ‘on the haul-out status of seal with tag 11over the entire tagging period in the tidal periods 1-4.
Hour 1
0.5 1.0 1.5 2.0
-4-2
02
4
Level
4800 5000 5200 5400
-4-2
02
4
Hour 3
Hour TP3
Level TP1-4
igure 19. Smoothing functiescribing the partial effect of he entire tagging period in the
Hour TP1-4
2.5
5600
Tidal
ons w‘Hourtidal p
Hours
3.0 4600 4800 5000 5200 5400 5600 5800 6000
-4-2
02
4
Hour 1 & 2
5800 6000 4800 5000 5200 5400 5600 5800 6000
-4-2
02
4
Hour 4 Hours
Hours
Hour TP4
ith 95% confidence intervals obtained by bootstrapping’ and ‘Level ‘on the haul-out status of seal with tag 20 overeriods 1-4.
Hour TP1&2
162
4.5 DISCUSSION
4.5.1 Effects of time of day and tidal cycle on haul-out behaviour
The effect of the tidal cycle and time of day on the haul-out behaviour of harbour
seals has been well studied across their geographical range using telemetry
(Thompson & Miller, 1990; Thompson et al., 1989; Thompson et al., 1997;
Yochem et al., 1987; Rehberg & Small, 2001; Reder et al., 2003; Sharples,
2005), time lapse photography (Stewart 1984; Thompson & Harwood, 1990,)
and modelling count data (Adkinson & Small, 2001; Jemison & Pendleton 2001;
Boveng et al., 2003; Simpkins et al., 2003; Small et al., 2003; Montgomery,
2005). Prior to this study no information was available on the effects of the time
of day and the tidal cycle on the haul-out behaviour of harbour seals in Ireland.
A significant tidal influence on the haul-out behaviour of tagged seals was
evident throughout the study period, tagged seals hauled out more frequently at
low tide. Haul-out sites in the study area are generally tidally influenced rocky
skerries, with haul-out habitat submerged at high tide. Tagged seals spent less
time ashore on a rising tide than a falling tide, possibly responding to local
increases in food availability on the incoming tide. The time of day had a
significant influence on the haul-out behaviour of all tagged seals other than seals
4 and 7, with variation between individuals’ daily haul-out patterns. A distinct
diurnal pattern in haul-out behaviour was evident from six of the tagged seals,
spending more time ashore during early to mid afternoon. Seal 5 appeared to
have a preference for hauling out at night while seal 4 displayed a bimodal
pattern, hauling out at night as well as mid-afternoon. There were no diurnal
patterns evident in the haul-out behaviour of seals 2 and 7. Overall, tagged seals
spent more time ashore during the day than at night, possibly returning to the
water to feed at night when foraging may be more profitable (Croxall et al.,
1985). Similar patterns have been observed in other studies of harbour seal haul-
out behaviour (Bouvla & McLaren, 1979; Thompson et al., 1989; Thompson &
Miller, 1990; Watts, 1993).
163
The two seals that showed no diurnal patterns in haul-out behaviour in the
present study were the only adult males tagged prior to and during the breeding
season. It is possible that these were breeding males spending more time in the
water prior to and during the mating period, increasing their chances of
intercepting females. As these animals were tagged in late April it is not possible
to say if this was a mating related shift in haul-out behaviour. A staggered
approach to tagging across the annual cycle resulted in a small sample size. This
together with a male sample bias and the fact the tagged animals were not aged,
makes it difficult to identify possible sex and age related differences in haul-out
behaviour. Considering the evidence for heterogeneity in harbour seal haul-out
behaviour among different population segments (Thompson & Rothery, 1987;
Härkönen et al., 1999; Daniel et al., 2003; Reder et al., 2003), it is unlikely that
limited numbers of study animals of unknown ages can be used to describe the
population (Härkönen et al., 1999). Considering the difficulty in catching seals in
the study area it is unfeasible to obtain a representative sample by age and sex of
the population; increasing the sample size of tagged animals in the study area
over time will however provide a less biased sample.
Haul-out bouts have been shown to be correlated with the tidal cycle in an
estuarine environment in Scotland (Thompson & Miller 1990; Thompson et al.,
1994; Thompson et al., 1997). In contrast, haul-out bouts of more than 24 hours
have been recorded in other areas where haul-out sites are available throughout
the tidal cycle (Yochem et al., 1987). Wilson (1978) suggested that in areas
where habitat for hauling out is available above the high water level, diurnal
cycles may be more influential than tidal cycles on haul-out behaviour. Haul-out
sites in the study area are generally tidally influenced rocky skerries, however
some habitat is available even at high tide, evident from one haul-out record of
over 27 hours.
4.5.2 Seasonal changes in haul-out behaviour
Most studies on harbour seal haul-out behaviour have focused on breeding and
moult periods mainly for the purpose of improving the accuracy of population
census (Stewart & Yochem, 1983; Yochem et al., 1987; Thompson & Harwood,
1990; Thompson & Miller, 1990; Thompson et al., 1997; Ries et al., 1998;
164
Huber et al., 2001; Simpkins et al, 2003; Reder et al., 2003) and apart from some
exceptions (Thompson et al., 1989; Rehberg & Small, 2003; Sharples, 2005),
there is limited information available on the seasonal change in the haul-out
behaviour of harbour seals.
The haul-out behaviour of tagged seals in this study varied over the tagging
period with animals spending a higher proportion of time ashore post moult in
October, decreasing over the winter months to a minimum in February,
increasing until April and remaining relatively constant through the proceeding
months until July. Changes in the haul-out behaviour of the tagged seals are
reflected in the seasonal change in abundance of seals at haul-out sites in the
study area (Cronin, 2006 in prep). The behaviour of individuals tagged over the
winter months differed from the behaviour of those tagged during spring and
early summer. Unfortunately because the tags transmitted for only two and a half
months on average, such information was obtained from two separate samples of
tagged seals, the first tagged in October and the second in April and the potential
seasonal change in the haul-out behaviour of individuals could not be fully
explored. A significant change in the haul-out behaviour of tagged seals between
months was apparent in seal 6 only; the tag on this animal transmitted from mid
October to late April providing the longest data series from any tag and suggests
a possible seasonal related change in behaviour.
Studies of the seasonal variation in body condition of harbour seals have shown
that they are at their fattest during winter (Drescher, 1979; Pitcher, 1986). We
have no information on the offshore behaviour or movement patterns of the
tagged animals in the present study and as a result cannot be certain that the
longer periods spent at sea in winter represent successful foraging strategy.
Winter activity patterns of radio tagged seals around Orkney in Scotland
suggested that they spend less time in inshore waters at this time of year
(Thompson et al., 1989); data from harbour seals tagged with satellite relay data
loggers in St. Andrews Bay suggests that the proportion of time tagged seals
spent near the haul-out increased steadily from winter through to summer and the
probability of being hauled out is much lower in winter months (Sharples, 2005).
Harbour seal pups tagged with satellite linked time depth recorders in Alaska
165
rapidly increased the proportion of time spent at sea, from deployment in August,
remaining constant through until February to April when a slight decrease was
seen (Rehberg & Small, 2003).
There were cyclic patterns apparent in the haul-out behaviour of the seals
tagged over the autumn/winter period. The two tagged juvenile males, seals 4
and 5, in particular had very similar patterns between October and January.
These seals showed a higher probability of being at sea during the rising first or
second daily tide but never during both and a higher probability of being at sea
more-so on the first rising daily tide for approximately the first 60 days and
more-so on the second rising daily tide from then onwards. There appeared also
to be a lunar influence on the seals activity and for both seals the highest
probability of being at sea occurred just after the full moon each month and this
occurred on the first rising tide only. A possible lunar influence on the haul-out
behaviour of the third seal tagged during this period, seal 6, an adult male, was
suggested by highest probabilities of being either at sea or hauled out evident
after a full moon during all tidal periods and not just during the first rising tide as
was the case with the younger seals.
Lunar cycles have been reported in many marine animals. Patterns of
movement, feeding and reproduction in inter-tidal organisms are closely
associated with the tidal regime and therefore the lunar cycle (Christy, 1986;
Berry, 1986). Many tropical fish species have lunar reproductive cycles and most
hypotheses concern the dispersal of planktonic eggs or larvae (Robertson et al.,
1990). Lunar patterns in zooplankton density observed in southeast Africa was
shown to be induced by predation by sardines that crop zooplankton more
efficiently on nights when the full or nearly full moon rises after sunset.
Zooplankton approaching the surface during darkness, become vulnerable in the
light of the rising moon and a sudden decrease in density occurs (Gliwicz, 1986).
Diel vertical migration by zooplankton is a common feature in marine and
freshwater environments and predators at higher trophic levels may modify their
behaviour to optimize the exploitation of vertically migrating prey (Hays, 2003).
Nocturnal feeding has been reported in harbour seals (Boulva & McLaren, 1979;
Thompson et al., 1989; Thompson & Miller, 1990) and it has been suggested that
166
seals feed nocturnally in response to changes in the vertical distribution or
schooling behaviour of their prey (Croxall et al., 1985; Thompson et al., 1989).
The possible lunar cycle in the behaviour of the seals tagged post-moult
observed in the present study may be linked to a lunar periodicity in the food
chain; fish abundance in surface layers may increase responding to enhanced
foraging opportunities of zooplankton near the surface as a result of the light
from the moon. Local increases in food availability on the incoming tide may
explain predilection shown by the tagged juvenile seals to go to sea on the first
rising tide following a full moon.
Tidal cycles are influenced by the gravitational forces of the moon and the sun
and ‘spring tides’, particularly strong tides, occur during the full moon and the
new moon each month. At intertidal haul-out sites more habitat will be available
for seals to haul out on during spring tides and a lunar cycle in haul-out
behaviour related to haul-out habitat availability may be expected. However the
periodicity of the cycle evident in the haul-out behaviour of the tagged seals
early in the tagging period was on a monthly basis and if it was habitat
availability driven a bi-monthly pattern of increased haul-out probability on
spring tides would be expected. The difference in the haul-out patterns of seals
between tidal periods identified, such as the preference shown by the two
juvenile seals for going to sea on the first rising tide following a full moon,
further suggests that the observed cycles in the haul-out behaviour of these seals
are possibly driven by enhanced foraging opportunities as opposed to habitat
availability. The latter may however be a factor in the cyclic patterns in
behaviour observed in the adult male seal; a potential lunar influenced cycle of
increased probability of being at sea was apparent, as seen in the juveniles,
however not every month and during some months there was instead an
increased probability of hauling out around the time of the full moon. This seal
spent several extended periods at sea during the tagging period followed by long
haul-out events, possibly taking advantage of increased availability of haul-out
habitat exposed during spring tides. Moreover, the extended trips to sea were
possibly further offshore and covariates other than those that were included in the
model (the covariates that influence haul-out behaviour such as the tidal cycle
167
and the time of day) are likely to be influential when the seal is not in the vicinity
of a haul-out site for extended periods of time.
The haul-out behaviour of seals tagged in the latter part of the tagging period
i.e. during the spring and early summer do not show the cyclic patterns in
behaviour shown by seals tagged in the autumn. What may be the end of a cyclic
pattern was evident in the behaviour of seals 7 and 11 at the beginning of the
second tagging period late April/early May, after which their probability of being
at sea increased. Both of these seals were adult males and the patterns observed
may be due to breeding season associated changes in their haul-out behaviour.
Studies of haul-out patterns of radio-tagged harbour seals in Svalbard, Norway
suggest that males adjust their haul-out behaviour to follow female distribution
and movement patterns during the breeding period (Reder et al., 2003).
Copulation takes place in the water (Van Parijs et al., 1997) and older adult
males have been shown to spend more time at sea during summer (Härkönen et
al., 1999); evidence from acoustic surveys suggests that breeding males restrict
their range to areas where they are most likely to intercept females, such as
females foraging grounds, near haul-out sites and in between both areas (Van
Parijs et al., 1997, 1999, 2000). The only female tagged in the present study, seal
20, did not show a cyclic pattern in haul-out activity, rather a gradual increase in
her probability of hauling out from late April until mid June when the tag
stopped transmitting. This seal was a juvenile and probably immature. Sexual
maturity of female and male harbour seals is reached between the ages of three to
six years and four to six respectively (Bigg, 1969; Härkönen & Heide-Jorgensen,
1990; Lydersen & Kovacs, 2005). Immature seals have been shown to haul-out
more frequently during the summer compared with older seals (Thompson &
Rothery, 1987; Härkönen et al., 1999). Haul-out behaviour has been found to
vary with age, sex and locality (Thompson, 1989, Thompson et al., 1989,
Härkönen et al., 1999, Frost et al., 2001; Reder et al., 2003). The small sample
size of tagged seals precludes the possibility of determining if the cyclic patterns
observed in the present study is a common phenomenon in harbour seal
behaviour, at least in the study area, and from exploring any potential age and/or
sex related differences in such a pattern.
168
Exploring the possibility of seasonal change in the cyclic patterns in the haul-
out activity of the tagged seals was only possible with data from six seals as the
tagging periods of four of the seals were too short. This could not be explored
fully unfortunately as the tagging of seals was ‘staggered’ over the entire study
period. Considering the data from the six seals, three of which were tagged in
October and April respectively, it appears that there might be a lunar influenced
pattern in foraging and haul-out behaviour between October and May. It is
suggested that in the months following the annual moult harbour seals haul-out
behaviour may be predominantly influenced by their foraging activity, following
which haul-out patterns are perhaps driven more by breeding related behaviour,
and that this foraging activity could be influenced by lunar cycles.
Prey-predator interactions may have been responsible for selecting for intrinsic
monthly rhythms in behaviour and physiology of animals with long life spans
(Gliwicz, 1986). If prey species change their behaviour in response to the lunar
cycle then seals foraging behaviour and haul-out behaviour may change
accordingly. Seasonal changes in the availability of prey species may affect this
pattern. Moreover as the diet of the harbour seal can vary considerably between
areas (Brown & Mate, 1983; Härkönen, 1987) it is suggested that the influence
of the lunar cycle on prey behaviour and the subsequent effects on seal behaviour
may vary both temporally and spatially. Heretofore there is no information on the
diet of the harbour seal in the study area. Such information together with data on
prey availability and temporal changes in this and on the foraging behaviour of
harbour seals in the study area would contribute to the interpretation of the
observed possible lunar patterns in behaviour.
A possible lunar influence on the hauling-out behaviour by the Pacific harbor
seal (Phoca vitulina richardsi) at a haul-out site in British Columbia was
reported by Watts (1993) who observed a significant reduction in the proportion
of seals hauled out during a full moon and suggests seals spend more time
foraging on bright moonlight nights. This hypothesis was based exclusively on
changes in attendance of seals at haul-out sites. The haul-out behaviour of
pinnipeds can now be more closely examined with the advances in telemetry and
statistical modeling techniques. Simpkins et al., (2003) used such techniques to
169
try and identify patterns in and influences on harbour seal haul-out behaviour in
Alaska. They suggest a 28 day periodicity evident in the date function may
represent haul-out peaks of different demographic classes however they also
acknowledged that the periodicity of the date function coincided with the lunar
tidal cycle. The haul-out data modeled was data from the period of mid August to
mid September only, so a monthly cyclic pattern, if present, was not identified.
Using telemetry and statistical modeling techniques, the present study is the first
to establish possible lunar patterns in the haul-out behaviour of individual seals
and to identify differences in behavioural patterns between individuals and
between tidal periods. Differentiating between tidal periods in the data
exploration accounts for the fact that a particular tidal level will differ between
periods e.g. a 1.5m tidal level on rising and falling first and second daily tides
present different environmental and ecological parameters such as current speed
and direction, daylight, food availability. The data suggest different haul-out
patterns between the four tidal periods. Not differentiating between tidal periods
when examining the effect of tidal level and other covariates such as time of day
and month on the seals haul-out behaviour would conceal these patterns.
Overall apart from the similarity in the cyclic pattern in haul-out behaviour
between seals 4 and 5 there appears to be otherwise large variation in the cyclic
patterns in behaviour (i) between individuals, (ii) between tidal periods for each
individual and (iii) over the tagging period. This has implications for using
information on the behaviour of tagged individuals to derive correction factors
for count data. Identifying an optimal model of the haul-out behaviour of a small
sample of tagged seals as a function of covariates, using mixed modeling
techniques and treating tag as a random factor are only applicable if all seals
behave with random variations around the main pattern. The data resulting from
this study shows large variation in behaviour between individuals. If the reason
for the variation in haul-out behaviour between individuals was established (e.g.
age, sex, location of haul-out site, season and possible interactions of such
variables) this could be accounted for in a random effects model and haul-out
probabilities under ‘ideal’ conditions or during surveys could be estimated,
providing a means for correcting count data. Increasing the sample size of tagged
170
seals, with a more balanced age and sex ratio and including as many covariates
as possible in the analysis would help to achieve this.
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178
CHAPTER 5
CETACEAN DISTRIBUTION AND RELATIVE ABUNDANCE IN SOUTHWEST IRELAND
179
5.1 INTRODUCTION AND METHODOLOGY
All cetaceans present in European waters are protected under EU law and listed
in Annex IV of the EU Habitats Directive, as species of community interest in
need of strict protection. In addition bottlenose dolphins (Tursiops truncatus),
harbour porpoises (Phocoena phocoena) and two seal species (harbour and grey
seal, Phoca vitulina & Halichoerus grypus) are listed in Annex II as requiring the
designation of Special Areas of Conservation (SAC’s). In 1991 the Irish
government declared Irish waters a whale and dolphin sanctuary including the
State’s 200-mile exclusive fishery limit (Rogan & Berrow, 1995).
A number of cetacean species were observed during the course of this study.
Results from both shore and boat-based surveys are presented here. Boat-based
line-transect techniques are detailed in chapter 1 and shore-watch methodologies
are described in chapter 2. Incidental sightings from boat-based surveys of seals
in Kenmare River were also included. Groups were defined as: an aggregation of
animals whose members were within 100m of one another, engaged in similar
activities and, if moving, heading in the same direction (Wells et al., 1987). All
observations were carried out in sea states of 4 or less on the Beaufort Scale.
5.2 SPECIES ACCOUNTS
A total of six cetacean species were recorded over the course of the study,
including four odontocete species and at least two mysticete species (Table 1).
Species richness was highest at the Mizen Head shore-watch site where six
species, including a large unidentified whale (probably fin Balaenoptera
physalus or sei Balaenoptera borealis whale) were recorded. Species richness
was also high at Dursey Island where 5 cetacean species were observed. Species
richness was lowest at the inner Bantry Bay site and incidental records from the
Kenmare river show a minimum of 2 cetacean species utilize this site. Harbour
porpoises were recorded at all sites and common dolphins (Delphinus delphis)
180
and minke whales (Balaenoptera acutorostrata) were recorded at all sites apart
from the Kenmare River.
Table 1. Presence/absence of cetacean species recorded in the RAMSSI study area from shore and boat-based surveys, including incidental sightings from seal surveys. ● = present SPECIES Sheeps
Head Inner Bantry
Dursey Island
Black Ball Head
Mizen Head
Three Castle Head
Ken- mare River
ODONTOCETES Harbour Porpoise (Phocoena phocoena) ● ● ● ● ● ● ● Common Dolphin (Delphinus delphis) ● ● ● ● ● ● Bottlenose Dolphin (Tursiops truncatus) ● ● ● Risso’s Dolphin (Grampus griseus) ● ● ● ● MYSTICETES Minke Whale (Balaenoptera acutorostrata) ● ● ● ● ● ● Fin Whale (Balaenoptera physalus) ● Large Whale (Balaenoptera sp.) ●
5.2.1 Harbour porpoise (Phocoena phocoena)
The harbour porpoise is the smallest of all the cetacean species, and probably the
most abundant in Irish waters (Rogan & Berrow, 1996). This species was
recorded at all sites in relatively high numbers e.g. 181 were recorded from the
Three Castle Head shore-watch site between July 2003 and September 2004, an
average of 8 per survey. An average of 19 porpoises per survey were recorded at
Dursey Island where only 9 surveys were carried out. Calves or juveniles were
sighted at all sites, particularly in summer in Bantry Bay but also in January and
February at the outer headlands. These are likely to have been born the previous
summer as this species is thought to have a summer breeding season in Irish
waters (Rogan and Berrow, 1996). The mean group size of porpoises was
2.1±0.1 with a maximum group size of 25 occurring at Sheep’s Head.
Boat-based surveys of Bantry Bay revealed that harbour porpoises were widely
distributed throughout the bay, occurring as far inshore as Whiddy Island (Figure
1). This is confirmed from plots of shore-based observations which showed
widespread use of the inner Bantry survey area (Figure 2b). Figure 3 (a-c) shows
the seasonal abundance of harbour porpoises at all shore-watch sites. This
species was present all year round at all sites apart from winter at the Bantry Bay
sites. Peak numbers occurred in autumn at most sites however numbers peaked
181
in summer at Dursey Island. Mean numbers remained relatively constant all year
round at Black Ball Head indicating the importance of this region to Harbour
porpoises throughout the year. Clearly Bantry Bay and its approaches represent
an important habitat for this Annex II species, and as such should be considered
for designation as an SAC. Previous studies in Irish waters have also identified
southwest Ireland as an area of high porpoise abundance in summer (Northridge
et al., 1995; Pollock et al., 1997; Mackey et al., 2004).
Figure 1. The average
number per kilometre of
harbour porpoises seen from
boat survey transects in
Bantry Bay during the study. N
5.2.2 Common Dolphin (Delphinus delphis)
Common dolphins were recorded at all shore-watch sites in relatively high
numbers. A total of 621 individuals were recorded during the course of the study
with the highest total number (313) recorded at the Three Castle Head site.
Figure 2 (c-d) shows the position of common dolphin sightings recorded from the
Bantry Bay shore-watch sites and reveals that group sizes of 15-30 individuals
were typical here. The mean group size of common dolphins over the entire
study area was 11.3±1.9 with a maximum group size of 80 occurring at Black
Ball Head in July. Figure 3 (d-f) shows that mean common dolphin numbers
peaked in autumn at most sites. A summer peak in abundance was recorded at
Dursey Island and Black Ball Head however. Common dolphins were not
recorded at any of the sites in spring and appeared largely absent in winter apart
from a group of 15 which was sighted from the Sheep’s Head site in January
(Figure 3d). Many of the groups sighted appeared to be actively foraging, with
frequent milling and surface-rush activities observed. These foraging groups
were often accompanied by feeding seabirds, most commonly gannets (Morus
bassanus) and manx shearwaters (Puffinus puffinus). On one occasion common
dolphins were seen feeding in close proximity to a minke whale. At least six
182
calves were observed in the survey area all between the months of August and
October with 2 of these occurring at the inner Bantry site. This indicates that the
entire survey area represents an important habitat for this species both as a
foraging and nursery ground. Common dolphins were the most abundant
cetacean species encountered in Irish shelf and offshore waters by (Pollock et al.,
1997; Mackey et al., 2004).
Common Dolphin (Photo. M. Mackey).
5.2.3 Risso’s Dolphin (Grampus griseus)
A total of 11 Risso’s dolphins were recorded from shore-watch observations in
the survey area, with a further 3 observed during boat-based surveys of Bantry
Bay. All sightings were of 1-3 individuals with no large groups observed.
Risso’s dolphins were exclusively recorded in the months of September and
October and were never recorded in the inner portion of Bantry Bay or at Black
Ball Head. This species is thought to prey exclusively on squid and is typically
offshore in distribution for much of the year, generally only coming inshore
between August and February (Leatherwood & Reeves, 1983).
Risso’s dolphins were recorded in relatively high numbers in waters off
southwest Ireland by Pollock et al. (1997) and Hammond et al. (2002), indicating
that this region may be an important local concentration of the species.
183
5.2.4 Bottlenose Dolphin (Tursiops truncatus)
The low sighting rate of bottlenose dolphins in this study is surprising
considering the abundance of this species in inshore waters on the west coast
(e.g. the Shannon Estuary and Tralee Bay) in summer (Ingram & Rogan, 2002;
Ingram et al., 2005). Studies in Ireland’s Atlantic Margin have also indicated
that the waters around the Porcupine Bank and Porcupine Seabight off southwest
Ireland may be important for this species (Mackey et al., 2004). Small numbers
(2-3) of this species were recorded from both the Mizen Head and Three Castle
Head shore-watch sites, with highest numbers (8-10) recorded on three occasions
during boat-based seal surveys of the Kenmare River. It is likely that bottlenose
dolphins were foraging for Atlantic salmon (Salmo salar) associated with the
Kenmare River as they are know to do so in the Shannon Estuary further north
on the west coast (Ingram & Rogan, 2002). All bottlenose dolphin sightings took
place in summer.
5.2.5 Minke whales (Balaenoptera acutorostrata)
Minke whales were recorded in relatively high numbers at all shore-watch sites
as well as from boat-based surveys in Bantry Bay. A total of 85 minke whales
were recorded during the course of the study, with 27 of these recorded at the
Three Castle Head site. A total of 19 minke whales were sighted from the outer
Bantry shore-watch site and one was sighted from the inner site (Figure 2 e-f).
Most sightings were of single individuals however several pairs were also
recorded. Boat-based surveys of Bantry Bay confirmed that minke whale
distribution was almost entirely restricted to outer regions of Bantry Bay. A total
of six minke whales were recorded from boat-based surveys, with all sightings
occurring near the mouth of the bay (Figure 4). Minke whale numbers peaked in
autumn at all sites and this species was never recorded in spring (Figure 3g-i).
Minke whales were largely absent in winter, however two individuals were
sighted at Three Castle Head in November. One calf and 20 immature minke
whales were observed in the study area over the course of the survey indicating
that this is an important habitat for this species. Pollock et al. (1997) found
relatively large numbers of minke whales in inshore waters around southwest
Ireland. It is likely that this species is attracted by the rich feeding grounds
184
associated with the Irish Shelf Front near the mouth of Bantry Bay in summer
(Raine et al., 1990).
A number of minke whale/seabird feeding associations were observed during
the course of this study. The species most commonly recorded in association
with minke whales were manx shearwaters, which alone accounted for 66% of
all associations. The remaining associations consisted of single-species feeding
flocks of gannets or kittiwakes (Rissa tridactyla) and mixed feeding flocks of
gannets, manx shearwaters, Alcidae (auks) and Larus (gull) species. Minke
whales were also commonly associated with harbour porpoises.
Figure 4. Numbers and
positions of minke whale
sightings in Bantry Bay
from boat-based
observations.
5.2.6 Fin Whale
(Balaenoptera physalus) One fin whale was observed from the Black Ball Head shore-watch site during
the course of this study. This whale was observed feeding and breaching in close
proximity to two minke whales. A further two large unidentified whales were
sighted from the Mizen Head shore-watch site and it is likely that these were
either fin or sei whales. All sightings took place in October. Incidental sightings
from shore-based observation points on the south coast of Ireland (e.g. Galley
Head and the Old Head of Kinsale) have shown that this species regularly occurs
along the south coast from October to December (www.IWDG.ie). There is
evidence to suggest that Irelands Atlantic Margin forms part of the migratory
pathway of a number of large baleen whales, including fin, sei, humpback
(Megaptera novaeangliae) and even blue whales (Balaenoptera musculus) as
they move from winter calving grounds in the south to summer feeding grounds
at high latitudes(Charif et al., 2001; Harwood & Wilson, 2001; Mackey et al.,
2004).
185
c) Sheep’s Head Common Dolphins d) Inner Bantry Common Dolphins
b) Inner Bantry Porpoise a) Sheep’s Head Porpoise
f) Inner Bantry Minke Whales e) Sheep’s Head Minke Whales
Figure 2 (a-f). Location and number of harbour porpoise’s (a-b), common dolphin’s(c-d) and minke whales (e-f) at the Sheep’s Head and Inner Bantry shore-watch sites during the entire survey period. Positions calculated using a theodolite. N
186
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1012141618
Spring Summer Autumn WinterMea
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Mizen Head Three castle Head
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Spring Summer Autumn Winter
Mea
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Spring Summer Autumn WinterMea
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Inner bantry Sheeps Head
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Spring Summer Autumn WinterMea
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Dursey Island Black Ball Head
0
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Spring Summer Autumn WinterMea
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Spring Summer Autumn WinterMea
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c)
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Figure 3 (a-f). Seasonal abundance (Mean ± SE) of harbour porpoises (a-c) and common dolphins (d-f) at the Sheep’s Head, Inner Bantry, Dursey Island, Black ball Head, Mizen Head and Three Castle Head shore-watch sites.
187
00.10.20.30.40.50.60.70.80.9
Spring Summer Autumn Winter
Mea
n no
. min
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hale
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Inner bantry Sheeps Head
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Spring Summer Autumn Winter
Mea
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. Min
ke W
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Dursey Island Black Ball Head
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Spring Summer Autumn Winter
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h)
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Figure 3 (g-i). Seasonal abundance (Mean ± SE) of minke whales at the Sheep’s Head & Inner Bantry (g), Dursey Island & Black ball Head (h), Mizen Head & Three Castle Head (i) shore-watch sites.
188
5.3 CONSERVATION RECOMMENDATIONS
Two of the six cetacean species recorded in this study are listed under Annex II
of the EU Habitats Directive as species of Community Interest, whose
conservation requires the designation of Special Areas of Conservation (SAC).
Glengarriff Harbour in Bantry Bay and The Kenmare River have already been
designated as SAC’s in order to protect the resident harbour seal populations
occurring there. The remainder of Bantry Bay and it’s approaches out to Dursey
Island and Mizen Head have no such designation however and should be
considered for SAC status because of it’s widespread use by the annex II harbour
porpoise throughout the year. This designation would also protect the large
numbers of feeding minke whales and common dolphins (including their young)
that frequent the region in summer and autumn.
189
5.4 REFERENCES
Charif, R., Clapham, P.J. & Clarke, C. 2001. Acoustic detections of singing
humpback whales in the deep waters off the British Isles. Marine
Mammal Science 17, 751-769.
Hammond, P.S., Heimlich, S., Benke, H., Berggren, P., Borchers, D.L.,
Buckland, S.T., Collet, A., Heide-Jorgensen, M.P., Hiby, A.R.,
Leopold, M.F. & Oien, N. 2002. Distribution and abundance of the
Harbour porpoise and other small cetaceans in the North Sea and adjacent
waters. Journal of Applied Ecology 29, 361 - 376.
Harwood, J. & Wilson, B. 2001. The implications of developments on the
Atlantic Frontier for marine mammals. Continental Shelf Research 21,
1073–1093.
Ingram, S. & Rogan, E. 2002. Identifying critical areas and habitat preferences
of bottlenose dolphins Tursiops truncatus. Marine Ecology Progress
Series 244, 247-255.
Ingram, S.N., Englund, A., O'Donovan, M., Walshe, L. & Rogan, E. 2005. A
survey of marine wildlife in Tralee Bay and adjacent waters. Report to
Tuatha Chiarraí Teo. University College Cork, Cork. pp 20.
Leatherwood, S. & Reeves, R.R. 1983. The Sierra Club handbook of whales
and dolphins. Sierra Club Books, San Francisco. pp 302.
Mackey, M., O'Cadhla, O., Kelly, T.C., Aguiler de Soto, N. & Connolly, N.
2004. Cetaceans and Seabirds of Ireland's Atlantic Margin. Volume 2 -
Cetacean distribution and abundance. Report on research carried out
under the Irish Infrastructure Programme (PIP): Rockall Studies Group
(RSG) projects 98/6 and 00/13, Porcupine Studies Group project P00/15
and Offshore Support Group (OSG) project 99/38, Cork. pp 89.
Northridge, S.P., Tasker, M.L., Webb, A. & Williams, J.M. 1995.
Distribution and relative abundance of harbour porpoises (Phocoena
phocoena L.), white-beaked dolphins (Lagenorhynchus albirostris Gray),
and minke whales (Balaenoptera acutorostrata Lacapede) around the
British Isles. ICES Journal of Marine Science 52, 55-66.
190
Pollock, C., Reid, J., Webb, A. & Tasker, M. 1997. The distribution of
seabirds and cetaceans in the waters around Ireland. JNCC Report, No.
267, Peterborough. pp 167.
Raine, R., O`Mahony, J., McMahon, T. & Roden, C. 1990. Hydrography and
Phytoplankton of waters off Southwest Ireland. Estuarine, Coastal and
Shelf Science 30, 579-592.
Rogan, E. & Berrow, S. 1995. The management of Irish waters as a whale and
dolphin sanctuary. In: A.S. Blix, L. Walloe & O. Ulltang (eds),
Developments in Marine Biology, 4. Whales, seals, fish and man.
Elsevier, Amsterdam. pp 671-683.
Rogan, E. & Berrow, S. 1996. A review of harbour porpoises, Phocoena
phocoena, in Irish waters. Report of the International Whaling
Commission 46, 595-605.
Wells, R.S., Scott, M.D. & Irvine, A.B. 1987. The social structure of free-
ranging bottlenose dolphins. In: H.H. Genoways (ed). Current
mammology Vol. 1. Plenum Press, New York. pp 247-305.
191
CONCLUSIONS
192
The information on seabird and marine mammal distribution and relative
abundance recorded in this study provides a valuable baseline for future
monitoring programs. The impacts of future disturbances on the communities in
these sites can now be reliably assessed through a comparison of ‘before’ (i.e.
this study) and ‘after’ (post-disturbance) datasets using standardized boat-survey
techniques. The use of shore-based monitoring of disturbed sites is also
recommended in order to establish seasonal trends and to accurately survey
species such as storm petrels, Alcidae and harbour porpoises which are known to
avoid survey vessels (see Tasker et al., 1984). This baseline data can also aid the
identification of future climate-induced shifts in species distribution.
The physical habitat variable ‘seaward distance’ provides a crude indication of
high overall seabird abundance for use in reactive situations following pollution
events in similar un-surveyed embayments. However further study of seabird
habitat-use in alternative sites is needed to establish the reliability of physical
habitat variables in determining seabird distribution for use in, for example, oil-
pollution management plans.
The high density and species richness of seabirds in the entire RAMSSI study
area, coupled with the presence of a number of Annex 1 species and species with
a high Oil Vulnerability Index (OVI>20) indicates that this site should be
considered for designation as a Marine Protected Area (MPA) for seabirds.
According to Hyrenbach et al. (2000), MPA’s designed to protect specific
foraging grounds could mitigate certain forms of habitat degradation (e.g. oil
spills). However larger-scale designations (encompassing foraging ranges and
migration routes) are needed to adequately protect far-ranging species such as
Manx shearwaters (Hyrenbach et al., 2000).
The widespread and year-round use of the study area by large numbers of the
Annex II Harbour porpoise indicates that this region, particularly Bantry Bay,
should be considered for designation as a Special Area of Conservation (SAC).
This designation would also protect the large numbers of feeding minke whales
and common dolphins (including their young) that frequent the region in summer
and autumn.
193
This study provided information, hitherto unavailable, on the seasonal changes
in the distribution and abundance of harbour seals within two SACs in southwest
Ireland and identified sites important for breeding and moulting and sites that are
used year round. The importance of Glengarriff harbour for breeding seals along
with the relatively high levels of anthropogenic disturbance during the breeding
period should be important considerations in the management of this SAC.
The GSM telemetry system proved an effective means of obtaining information
on the haul-out activity of harbour seals in the study area that was respectively
less labour intensive and cheaper than VHF and satellite telemetry. It also
provided a means of testing the efficiency of using GSM technology to relay data
ashore in southwest Ireland and served as a feasibility study for future work in
the area. This study identified large variation in the behaviour of individual seals
and suggests that caution should be exercised when making inferences on the
haul-out behaviour of the ‘population’ based on the behaviour of tagged
individuals.
RECOMMENDATIONS FOR FUTURE WORK
Further studies of seabird habitat-use in both estuarine and marine embayments
(e.g. Cork Harbour and Dingle Bay) should be carried out in order to determine
the reliability of physical habitat variables in determining seabird distribution.
Comparable techniques (i.e. RIB-based surveys using modified JNCC survey
methodology) should be employed and the use of a 1km² grid network is
recommended for data analysis. Ideally, information on oceanographic
parameters such as salinity, productivity and current flow should also be
recorded. Satellite data on sea surface temperatures (SST) and chlorophyll a
concentrations could also be used to identify seasonal variations in the position
of the Irish Shelf Front and other areas of high productivity (see Jaquemet et al.,
2005).
194
Investigations of seabird diet, e.g. from regurgitated pellets (Montevecchi et
al., 1988; Barrett et al., 1990; Zijlstra & Van Erden, 1995; Carss, 1997) stable
isotope analysis (Bearhop et al., 1999) or mist-netting (Corkhill, 1973) at seabird
colonies adjacent to the study sites could provide a useful indication of important
prey species for seabirds in these areas and reveal seasonal shifts in diet. Direct
relationships between seabird distribution and prey abundance could also be
determined using a towed bioacoustics instrument during boat surveys (Ainley et
al., 2005).
Similar investigations into the year-round patterns in Harbour seal abundance
and site use should be carried out in other areas along the Irish coastline, that
include a selection of harbour seal haul-out sites and represent the full range of
haul-out substrate type, both within and outside of protected areas. Statistical
modelling of the counts as a function of covariates will provide a means of
determining which processes are influencing the seals’ haul-out behaviour at
these sites and if this varies spatially and temporally.
A covariate modelling approach such as that used in this study could be
included in future national harbour seal surveys in Ireland to help improve the
accuracy of population estimates over a wide geographical area by ‘controlling’
for or standardising environmental conditions and accounting for covariate
associated variation in counts.
The potential seasonal change in Harbour seal behaviour suggested in the
present study could be explored further by obtaining information on the haul-out
behaviour of individuals throughout the entire annual cycle. Flipper mounted
telemetry devices (Huber et al., 2001; Simpkins et al., 2003) or implantable sub-
cutaneous VHF tags (Horning & Hill, 2005; Lander et al., 2005) could be a
possible solution to moult associated tag loss but the information from these tags
would be limited to haul-out patterns. The approach should be dependent on the
study objectives: if the main objective is to devise a correction factor for count
data, information on haul-out behaviour during the moult is critical and flipper
mounted tags would be appropriate; if the objective is to study movements and
behaviour of the seals offshore in order to identify home range and offshore
195
foraging areas, then pelage mounted tags are necessary as the antennae need to
break the water surface for successful data capture and relay.
The potential lunar effect on the haul-out behaviour of seals observed in this
study warrants further investigation. The approach described in the present study
could be used on existing and future data on the haul-out behaviour of individual
harbour seals across their geographical range to determine if the patterns in
behaviour observed in the present study are common phenomena and to identify
any potential demographical or geographical variation.
196
REFERENCES
Ainley, D.G., Spear, L.B., Tynan, C.T., Barth, J.A., Pierce, S.D., Ford, R.G.
& Cowles, T.J. 2005. Physical and biological variables affecting seabird
distributions during the upwelling season of the northern California
Current. Deep-Sea Research Part II 1-2, 123-143.
Barrett, R.T., N., R., Loen, J. & Montevecchi, W.A. 1990. Diets of Shags
Phalacrocorax aristotelis and cormorants P.carbo in Norway and
possible implications for gadoid stock recruitment. Marine Ecology
Progress Series 66, 205-218.
Bearhop, S., Thompson, D.R., Waldron, S., Russell, I.C., Alexander, G. &
Furness, R.W. 1999. Stable Isotopes indicate the extent of freshwater
feeding by cormorants Phalacrocorax carbo shot at inland fisheries in
England. Journal of Applied Ecology 36, 75-84.
Carss, D.N. 1997. Techniques for assessing Cormorant diet and food intake:
towards a consensus view. Suppl. Ric. Biol. Selvaggina xxvi, 197-230.
Corkhill, P. 1973. Food and feeding ecology of puffins. Bird Study 20, 207-220.
Horning, M. & Hill, R. D. (2005). Designing an archival satellite transmitter for
life-long deployments on oceanic vertebrates: the life history transmitter.
IEEE Journal of Oceanic Engineering, 30, 807-817.
Hyrenbach, K.D., Forney, K.A. & Dayton, P.K. 2000. Marine protection areas
and ocean basin management. Aquatic Conservation: Marine and
Freshwater Ecosystems 10, 437-458.
Huber, H. R., Jeffries, S. J., Brown, R. F., Delong, R. L., & Vanblaricom, G.
(2001). Correcting aerial survey counts of harbor seals (Phoca vitulina
richardsi) in Washington and Oregon. Marine Mammal Science, 17, 276-
293.
Jaquemet, S., Le Corre, M., Marsac, F., Potier, M. & Weimerskirch, H.
2005. Foraging habitats of the seabird community of Europa Island
(Mozambique Channel). Marine Biology 147, 573-582.
Lander, M. E., Haulena, M., Gullan, F. M. D. & Harvey, J. T. (2005).
Implantation of subcutaneous radio transmitters in the harbour seal
(Phoca vitulina). Marine Mammal Science, 21, 154-161.
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198
Montevecchi, W.A., Birt, V.L. & Cairns, D.K. 1988. Dietary Changes of
Seabirds Associated with Local Fisheries Failures. Biological
Oceanography 5, 153-161.
Simpkins, M. A., Withrow, D. E., Cesarone, J. C. & Boveng, P. L. (2003).
Stability in the proportion of harbor seals hauled out under locally ideal
conditions. Marine Mammal Science, 19, 792-805.
Tasker, M.L., Hope Jones, P., Dixon, T. & Blake, B.F. 1984. Counting
seabirds at sea from ships: A review of the methods employed and a
suggestion for a standardised approach. The Auk 101.
Zijlstra, M. & Van Erden, M.R. 1995. Pellet production and the use of Otoliths
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