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Fisheries Research 186 (2017) 177–191

Contents lists available at ScienceDirect

Fisheries Research

journa l homepage: www.e lsev ier .com/ locate / f i shres

ull length article

rtisanal fisheries on Kenya’s coral reefs: Decadal trends revealanagement needs

elita A. Samoilys ∗, Kennedy Osuka, George W. Maina 1, David O. OburaORDIO East Africa, P.O.BOX 10135, Mombasa 80101, Kenya

r t i c l e i n f o

rticle history:eceived 14 September 2015eceived in revised form 13 July 2016ccepted 27 July 2016

eywords:mall-scale fisheriesoral reefsisheries management

a b s t r a c t

Kenya’s small scale coral reef fisheries are extensively studied yet a practical understanding of theresilience and status of the main target species remains largely elusive to the manager. We combineda range of fishery and fish population descriptors to analyse Kenya’s coral reef fish and fisheries overa 20 year period from the 1980s, to determine the sustainability of current fishing levels and providerecommendations for management. Fishers reported over 13 different artisanal fishing gears of whichthere are data for only the five widely used gears. Average catch rates declined 4-fold from the mid 1980s(13.7 ± 1.6 kg/fisher/trip) to the 1990s (3.2 ± 0.1 kg/fisher/trip) and then stabilized. Species richness incatches of these historically multi-species fisheries declined dramatically and by 2007 only 2–3 speciesappeared in the top bracket (65–75% by number) with Siganus sutor (African whitespotted rabbitfish) andLeptoscarus vaigiensis (marbled parrotfish) consistently being in this bracket in beach seine, gill net andbasket trap catches, contributing up to a maximum of 45% and 47% of the catch, respectively. Lethrinus bor-bonicus dominated handline catches (50%). Relatively stable catch rates are reported from the 1990s to themid 2000s, likely maintained by shifting proportions of species in the catches. Patterns in fish populationdensities over time show National Parks have helped increase densities of Lethrinidae and Haemulidaeand reduced the decline in densities of Scaridae and Acanthuridae, but that National Reserves have hadno positive effect. We suggest that the National Parks, which are No Take Zones (NTZs), and the fisheriesregulations inside and outside of Reserves are inadequate for maintaining or restoring reef fishery target

families under current levels of fishery exploitation. We propose that recruitment overfishing of severalspecies and insufficient areas under full protection, all exacerbated by climate change, are contributing todriving Kenya’s artisanal coral reef fisheries to a tipping point. We recommend species–specific manage-ment options, changes in and enforcement of gear regulations and many more effective NTZs are neededurgently if these fisheries are to continue to provide livelihoods and food security on the Kenyan coast.

© 2016 Elsevier B.V. All rights reserved.

. Introduction

Small-scale artisanal coastal fisheries can provide up to 99%f the protein source to coastal households, provide over 80% ofouseholds’ income and therefore play a key role in food security

n developing countries (Barnes-Mauthe et al., 2013; Foale et al.,013; McClanahan et al., 2013). Despite this, they are frequently

ndervalued by developing countries’ national policies (Hensonnd Winnie, 2004; Hardman et al., 2013; Aloo et al., 2014). Further,rtisanal fishers operate largely to earn cash but also for subsis-

∗ Corresponding author.E-mail addresses: melita.samoilys@gmail.com

M.A. Samoilys), kosuka@cordioea.net (K. Osuka), gwmaina@tnc.org (G.W. Maina),obura@cordioea.net (D.O. Obura).1 Present address: The Nature Conservancy, Africa Regional Office, Nairobi, Kenya

ttp://dx.doi.org/10.1016/j.fishres.2016.07.025165-7836/© 2016 Elsevier B.V. All rights reserved.

tence, both of which are poorly quantified (Obura 2001; Ochiewo,2004; Cinner et al., 2009a). Consequently their contribution tonational income and livelihoods is poorly acknowledged.

Small-scale artisanal coastal fisheries are characterised as beingmulti-gear, multi-species and landed at multiple landings sites,as seen in Kenya (Kaunda-Arara et al., 2003; McClanahan andMangi, 2004) and typical of many tropical fisheries around theworld (Munro and Williams, 1985; Wright and Richards, 1985;Dalzell, 1996). This makes them difficult to monitor and manage. Ifmonitoring is done it frequently only records total landings with-out fishing effort, making the data almost meaningless (Luckhurstand Trott, 2009). Further, problems of overfishing and the use ofdestructive fishing methods in these fisheries are now widespread

and generally linked to poverty, over-population and poor gover-nance (Allison et al., 2009; Gutiérrez et al., 2011).

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Coral reef fisheries are extensively studied small scale arti-anal fisheries with many cases of over-exploitation (Russ 1991,002; Newton et al., 2007), yet management reference points andn applied understanding of the resilience of coral reef fishes toxploitation remain largely elusive to the fisheries manager on theround. These fisheries contribute a substantial portion of Kenya’srtisanal catches (Government of Kenya, 2008, 2012) and alsoepresent one of the most studied in a developing country (Kaunda-rara et al., 2003; Mangi and Roberts, 2007; McClanahan et al.,010; Hicks and McClanahan 2012) providing a valuable source ofata and information. Yet our understanding of the status of thesesheries and defining their most suitable management optionsemains challenging.

For decades researchers have tried to understand the con-ribution of fin-fishes in terms of annual marine production orield from coral reefs (Marten and Polovina, 1982; Munro andilliams, 1985; Russ, 1991; Dalzell, 1996). It is evident that reef

ields >5 mt km−2 yr−1 are possible, with the highest productivity>20 mt km−2 yr−1) reported from reefs in Philippines and Ameri-an Samoa (Craig et al., 1993; Maypa et al., 2002).

In this study we examined Kenya’s coral reef fisheries over twoecades to provide practical management advice, to estimate theirnnual yield and to contribute empirical evidence to recent debatesn management options such as conventional gear and size limitsontrols (Jennings et al., 2001; Hicks and McClanahan, 2012) andpatial closures such as MPAs (Roberts and Polunin, 1991; Jennings,009). We sought to determine the sustainability of artisanal fish-

ng gears, their deployment and effort and also provide definitionsf the gears. Specifically, we tested the following hypotheses thatre frequently stated by fishery stakeholders in tropical fisheries: i)rtisanal fishery catch rates are in decline and stocks are overfished;i) populations of reef fish are declining.

We used a series of meta analyses to combine a range of fisherynd fish population descriptors and parameters, both dependentnd independent of the fishery, to assess the status of Kenya’s coraleef fish and their fisheries from the 1980s to the 2000s, based onublished (e.g. Samoilys, 1988; Watson et al., 1996; Kaunda-Ararat al., 2003; McClanahan and Mangi, 2004; Mangi and Roberts,007; McClanahan et al., 2007; McClanahan and Hicks 2011) andnpublished data (e.g. Carrara and Coppola, 1985; Coastal Oceansesearch and Development Indian Ocean (CORDIO) unpubl.). Multi-le data sources including estimates of fish population abundancehat are independent of, but in combination with, fisheries datare important when trying to understand these complex fisheriesConnell et al., 1998; Daw et al., 2011). We examined trends inatch rates and fish population abundance, species composition ofatches and yields and juvenile retention rates of different gears tonderstand gear impacts and the sustainability of current fishing

evels.

. Methods

.1. Study sites

Kenya’s coral reefs occupy the shallow inshore zone, extendingffshore to <45 m depth, and at a distance of 0.5 km to ∼2 km fromhe shoreline, except where river systems enter the sea (Obura et al.,000). The coast is contained administratively within five Counties,ormerly six Districts (Fig. 1). A wide range of study areas have beeneported, from small localised studies to the whole coast (Table 1).ased on the geography of the coast the commonly reported study

ites were grouped into the following six locations (N to S): a)iunga-Lamu, b) Malindi-Watamu, c) Mombasa, d) Diani–Chale, e)azi and f) Shimoni (Fig. 1). The four districts (Lamu, Malindi, Mom-asa, Kwale) reported by Carrara and Coppola (1985) were assigned

earch 186 (2017) 177–191

to the first four of these locations. Sufficient data for long term CPUEtrend analysis were only available for two locations: Mombasa andDiani-Chale.

A spatial overlay of nationally gazetted protected areas exists(Fig. 1) comprising four Parks and five Reserves (Table 2). To exam-ine impacts of protective management we allocated data to oneof three management zones: Parks (NTZs), Reserves and all otherareas called “Fished” which are not protected and are fully opento fishing. It should be noted that the enforcement of Parks andReserves is variable, though Parks are considered well enforcedsince the mid 1990s (Table 2; McClanahan et al., 2007).

2.2. Data collation

Published papers and some unpublished reports on fishing, fish-eries and fishes on the Kenya coast were reviewed to extract keyvariables on fin-fish for assessing long term changes in Kenya’s arti-sanal fisheries (Table 1). Catch per unit effort (CPUE) was used asa measure of the state of each fishery, where fishery refers to anartisanal gear. We defined as artisanal those gears used by localfishers within territorial waters, limited to within 12 nautical mileof the shore (GoK, 1989). These gears span those made from naturalfibres that have been used traditionally for decades, to more mod-ern gears involving man-made materials (Glaesel 1997; Samoilyset al., 2011a).

Papers that presented fishery-independent measures of fishpopulation density from underwater visual census (UVC) surveys,were used to assess long term trends in population abundance ofthe species taken by artisanal gears, to provide a fishery indepen-dent measure of stock status.

A total of 23 papers and reports published between 1985 and2012, documenting fisheries from 1984 to 2007 were used toextract key CPUE and catch composition variables and indepen-dent UVC estimates of fish density (Table 1). Where values werenot specified in the paper but only presented graphically, valueswere obtained using Data Thief lll software (Tummers, 2006) to onedecimal point. A nine year (1998–2006) dataset (CORDIO unpubl.)on artisanal fisheries (only data for fin-fish) in one location, Diani-Chale, including UVC estimates of population abundance, was alsoadded and used to define certain parameters (see below). In mostcases variables were estimated from means reported in papers andtherefore their precision may have been inflated.

2.3. Standardisation of variables and statistical analyses

Inevitably, reviewing a wide range of papers spanning manyyears encountered different methods, units and presentation ofresults. Therefore standardisation of variables was necessary. Somepapers lumped all species together for UVC estimates of fish densi-ties or CPUE, or aggregated locations and were therefore excludedfrom the analyses. The following sections describe the parametersthat were standardised and their statistical analyses. We cleanedthe data, ignored uncertain estimates or aggregated data and wereconservative in our calculations to minimise errors.

2.3.1. Fishing gears and yearsBased on sufficient sample sizes across locations and years, data

on five gears were analysed: large basket traps (∼6 cm mesh size),gill net, handline, speargun and beach seine. Where possible datawere extracted by year collected. Where data were not presentedannually the median for the study period was taken to representthe year of the data.

2.3.2. CPUEThe catch rate unit was standardised to kg/fisher/day for each

of the five gears (one day is equivalent to one fishing trip – fishers

M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191 179

Fig. 1. Kenyan coast showing key locations, coastal administrative Counties and government gazetted marine protected areas. Insert shows the reef area estimated forDiani-Chale for yield estimation.

180 M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191

Table 1Variables on Kenya’s artisanal coastal fisheries extracted from published papers, reports and unpublished CORDIO data with corresponding study areas, years and data source.Study Area refers to the study’s sites; Location in parenthesis refers to the locations in the present study to which the cited reference can be assigned. If the two correspondonly one name is given.

Parameter/question Variables Study Area (Location) Study Period Reference

CPUE (seasonalvariation)

Gear CPUE(no.fish/fisher/day)

Malindi to Watamu(Malindi-Watamu)

June 2000–February 2001 1. Kaunda and Rose (2004)

Malindi (Malindi-Watamu) January–December 2007 2. Locham et al. (2010)

CPUE (locationvariation over time)

Gear CPUE(kg/fisher/day)

Diani-Chale 1998–2006 3. CORDIO unpublished dataKenyatta, Mtwapa & Marina(Mombasa)

January 1998– October 1998 4. McClanahan and Mangi (2000)

Diani (Diani-Chale) 2003–2004 5. Mangi (2006)Kenyatta, North coast (Mombasa)South coast (Diani-Chale)

1996–2005 6. McClanahan et al. (2008)

Kwale, Mombasa, Malindi andLamu Districts (Diani-Chale,Mombasa, Malindi-Watamu,Kiunga-Lamu)

1984 7. Carrara and Coppola (1985)

Site CPUE(kg/fisher/day)

Kenyatta (Mombasa), Diani-Chale September 1995–June 1999 8. McClanahan and Mangi (2001)Diani (Diani-Chale) 1995–2004 9. McClanahan et al. (2005)

Catch composition Gear catch compositionby species/finfish,invertebrates, sharksand rays

Mombasa to Diani-Chale April 1998–August 2000 10. McClanahan and Mangi (2004)Malindi-Watamu(Malindi-Watamu)

June 2000–February 2001 1a Kaunda-Arara and Rose (2004)

Malindi (Malindi-Watamu) January–December 2007 2a Locham et al. (2010)Diani and Gazi (Diani-Chale) 1998–2006 3. CORDIO unpublished dataMombasa June 2007–August 2007 11. McGuiness (2007)Diani (Diani-Chale), Malindi andWatamu (Malindi-Watamu) &Kisite (Shimoni)

1998–2006 12. McClanahan et al. (2010)

Gear juvenile retention Mtwapa to Chale (Mombasa toDiani-Chale)

May 2003- March 2004 13. Mangi and Roberts (2006)

North coast (Mombasa) June 2007-August 2007 11. McGuiness (2007)

Fish densities Family and speciesdensity

Diani (Diani-Chale) 2001–2002 3. CORDIO unpublished dataParks, Reserves and Fished sites(Malindi-Watamu, Mombasa,Diani-Chale, Shimoni)

November 1987-March 1988 14. Samoilys (1988)

Parks and Reserves (Shimoni) 1996 15. McClanahan et al. (1999)Parks and Reserves (Shimoni) September–October 1992 16. Watson and Ormond (1994)Parks and Reserves (Shimoni) January 1994–March 1994 17. Watson et al. (1996)Parks and Fished sites fromVipingo-Diani (Malindi-Watamu,Mombasa, Diani-Chale, Shimoni)

1995–2001 18. McClanahan et al. (2002)

Parks and Fished sites(Malindi-Watamu, Mombasa,Diani-Chale, Shimoni)

1992 19. McClanahan (1994)

Parks and Reserves (Mombasa,Malindi-Watamu, Shimoni)

2004–2007 20. Munga et al. (2012) (KWS)

Yield CPUE Diani-Chale 1999–2006 3. CORDIO unpublished dataFish families yield Whole Coast (Malindi-Watamu,

Mombasa, Diani-Chale, Shimoni)1978–2001 21. Kaunda-Arara et al. (2003)

Census of fishers Diani-Chale 2003–2006 22. Tuda et al. (2008)Fishing days per year Diani-Chale 2009–2010 23. Maina et al. (2013)

a experimental fishing.

Table 2Marine protected areas in Kenya (Source: IUCN 2004) administered by the Kenya Wildlife Service (under the Wildlife Conservation and Management Act 1976; 2013). Dateestablished = gazettment. Park = fully protected, no fishing allowed; Reserve = traditional fishing gears permitted (all artisanal gears except illegal gears). Index of enforcementis qualitative (MS, DO. pers. obs.).

Site IUCN Category Size (km2) Date established Protection status Relative index of enforcement

Malindi II 6.3 1968 Park GoodWatamu II 10 1968 Park GoodMalindi-Watamu VI 245 1968 Reserve PoorKisite II 28 1978 Park GoodMpunguti VI 11 1978 Reserve PoorKiunga VI 250 1979 Reserve Poor

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Mombasa II 200 19Mombasa VI 10 19Diani-Chale VI 75 19

ery rarely do >1 trip in a day Obura, 2001; Maina et al., 2008). Ithould be noted that zero catches are rarely recorded and thereforell estimates of catch rate are likely to be higher than, for example,

Park GoodReserve PoorReserve None

experimental fishing estimates, though they do, however, providegood relative estimates. Catch rates were for all species aggregated;they were rarely reported by species or family.

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Seasonal data were only available for basket traps as no.ndividuals per species per trap per day (Table 1, papers 1,2).hese data were used to assess seasonal differences in catchates using the Mann Whitney U test (Zar, 1996) because dataere non-normal after transformation. There was no significant

ifference (U = 4789.0, p = 0.896, n = 195) between the northeastonsoon (Nov-Mar; x = 0.172 ± 0.051 SE), and the southeast mon-

oon (Apr–Oct, x = 0.208 ± 0.096 SE), We therefore pooled datacross the seasons for all subsequent analyses.

The non-parametric Kruskal-Wallis test was used to compareatch rates between years and gears and the Mann-Whitney forost-hoc comparisons. It was not possible to compare Locationifferences in CPUE, due to inadequate data for locations.

.3.2.1. Converting location based CPUE to gear based CPUE. Twoapers (McClanahan and Mangi, 2001; McClanahan et al., 2005)eported a lumped CPUE by landing site in Diani-Chale and Mom-asa. This was converted to CPUE per gear based on gear catchate proportions determined from Maina et al. (2008) for Diani and

cClanahan and Mangi (2000) for Mombasa, as follows:

ear CPUE = Location CPUE × proportion of gear CPUE at location

he conversion was adjusted at three landing sites in Diani-Chalehere beach seines were either never used, or were excluded from

997 or 1998 onwards (based on McClanahan and Mangi, 2001).

.3.2.2. Converting kg/boat/day to kg/fisher/day. To convertg/boat/day CPUE per gear from Carrara and Coppola (1985)

nto kg/fisher/day we divided the boat catch by mean number ofsher per gear (crew size) sourced from all data sets.

.3.3. Gear catch compositionDifferences in species composition of catches between gears

ere examined graphically over time. Mean% composition ofpecies by gear was calculated from five papers (reference no.0,11,12,13,14, Table 1) including experimental fishing studies.ercentages were used because most papers did not report abso-

ute numbers of species per gear. Since these gears catch a veryarge number of species (up to 163, McClanahan and Mangi, 2004),nalysis set an upper limit of those species that contributed cumu-atively 65–75% of the catch to remove the infrequent species. Aange was necessary because species abundance cannot fall pre-isely at 75%. The mean% catch per species per gear thus combinedpecies across studies. All papers on catch composition reportedercentages of numbers of fish not weights of fish.

Catch composition for basket traps considered large mesh trapsnly as small mesh traps are a relatively recent introduction withew data available. For basket traps, beach seines and gill nets data

ere also plotted by year to examine changes in species composi-ion over time.

.3.4. Trends in fish densitiesUnderwater Visual Census (UVC) estimates of fish density of

even families (Lethrinidae, Lutjanidae, Haemulidae, Serranidae,canthuridae, Scaridae, and Siganidae) were obtained from pub-

ished results (Table 1) and unpublished data (CORDIO data fromiani-Chale) and standardised to number/1000m2. Since data wereot available for every year, we aggregated data into four discreteeriods which also corresponded with the coral bleaching eventnd impacts (Graham et al., 2007):

i) 1980s – earliest data, prior to strong management interventionsby government;

ii) Pre-bleaching – (1992–1997) prior to the El Nino event of1997/8 which killed many of the corals in Kenya;

earch 186 (2017) 177–191 181

ii) Post-bleaching – (1999–2002) <5 years after the El Nino eventof 1997/8;

iv) Post bleaching – (2004–2007) >5 years after the El Nino eventof 1997/8.

Densities were compared between the four periods and threemanagement zones (Park, Reserve, Fished). Our dataset did notextend beyond 2007 because other recent UVC studies could notbe disaggregated by family or site, or surveyed different taxa fromthose of our study. Data by the four locations (Malindi-Watamu,Mombasa, Diani-Chale and Shimoni) were patchy, therefore loca-tion differences were tested first combining years and managementzones and aggregating the 7 families, using the non-parametricKruskal-Wallis test (Zar, 1996). Locations were not significantlydifferent (H = 4.064, p = 0.2546) and were therefore pooled for sub-sequent analyses. Density of each family was compared across acombined factor of year periods and management zones (9 fac-tors) using the Kruskal-Wallis test followed by Mann-Whitneypost-hoc test (Zar, 1996), and results graphed using box plots. Multi-dimensional scaling (MDS) using the Bray-Curtis similarity index(Clarke and Warwick, 2001) was performed on three families withsufficient data (Scaridae, Acanthuridae, Siganidae) to test for differ-ences in fish densities over time and between management zones.A combined factor of management zone and year period was usedand due to missing data, only 10 out of the 12 levels were possible.In each MDS analysis a permutation-based analysis of similarities(ANOSIM) was applied to identify differences in management zonesand year periods (Clarke and Warwick, 2001). The ANOSIM on thePark sites showed that the two “Post-Bleach” year periods (<5yrand >5 yr after 1997/8) were not statistically different (R = −0.081,p = 0.634) and therefore were combined into one “Post-Bleach”period for subsequent analyses.

2.3.5. YieldThe average yield of fin-fish on Kenya’s coral reef associated

coastal habitats was calculated for 1999 and 2006 from CORDIO’scatch landings data collected in Diani-Chale (see Maina et al., 2008for details of sampling). Total yield was calculated by the followingequation (Russ, 1991):

Y = C × F × FDA × 1000

Where:Y = annual yield (mt yr−1 km−2)C = catch (kg fisher−1 day−1)F = fishing effort (mean total number active fishers)FD = fishing days per year (days yr−1)A = area of reef fishing grounds (km2)The total area of reef-associated habitat of the Diani-Chale area

(see insert in Fig. 1) and the whole Kenyan coast was calculatedusing a GADM base map of the coastline, with depth contoursfrom a digitised Admirality Chart (2002) and a layer of mangrovesobtained from ReefBase (http://www.reefbase.org). Missing datafrom the Admirality Chart was supplemented by Google Earthimages. We calculated the area from shore to the 20 m-isobath lineusing GIS. This covered a mix of coastal habitats including creeksand bays, coral reefs and seagrass beds and some sandy lagoons butdid not include the mangrove forests or the large sand-mud Ung-wana Bay. The total area for Diani-Chale was estimated as 56.4 km2

and for the Kenya coast as 1724 km2, excluding 54.3 km2 underNational Park (Fig. 1).

Information from a census of fishers conducted in Diani-Chale

from 2003 to 2006 (Tuda et al., 2008) was used to provide an esti-mate of the total number of fishers in Diani-Chale that were activedaily in 2006 and the mean number of fishing days per year. Backcalculations on 2006 data were done to estimate total number of

1 ies Research 186 (2017) 177–191

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R − statistic p

Year periodsGlobal effect 0.233 0.0041980s vs. Pre-bleaching 0.484 0.0011980s vs. Post-bleaching 0.237 0.001Pre-bleaching vs. Post-bleaching 0.156 0.059

Management zonesGlobal effect 0.211 0.001Park vs. Fished 0.204 0.004

82 M.A. Samoilys et al. / Fisher

shers for 1999. This should be treated as an approximation sincehe time period is short for assuming a linear long term increase.ishing days per year (FD) of 219 days was taken from a recent studyn the Diani-Chale location (Maina et al., 2013).

. Results

.1. Fishing gears used on the Kenyan coast

Thirteen different fishing gears are used for fin-fish by artisanalshers with the gill net distinguished into 8 sub-types by fish-rs (Supplementary Table S1 in the online version at DOI: http://x.doi.org/10.1016/j.fishres.2016.07.025). These gill net sub-types,

ncluding the illegal monofilament gill nets, were not distinguishedn any datasets, including government frame surveys, and thereforepecific impacts of each type could not be determined. Five gearsbasket trap, gill net, handline, speargun and beach seine) domi-ated in terms of published data and usage. These five gears weresed only by men. The State Department of Fisheries (SDF)’s frameurvey reported four gears with more than 1000 pieces: basketraps (3169), gill net (3956), handline (4132) and speargun (1007)GoK, 2008). The beach seine had the lowest number (139) whichould be attributed to its illegal nature, though the speargun, alsollegal, was well represented.

.2. Catch species composition by gear

The species composition varied between gears but two speciesominated catches: Siganus sutor (African whitespotted rabbitfish)nd Leptoscarus vaigiensis (seagrass parrotfish) appearing in the topix species by number for all gears except handlines, contributingp to a maximum of 44.8% (S. sutor) and 47.3% (L. vaigiensis) ofhe catches (Fig. 2). Thus, although gears generally differ in designnd deployment their primary catches were remarkably similar.he exception was the handlines where catches were dominatedy lethrinids, notably Lethrinus borbonicus (49.9%). The greatestiversity among the dominant species in the catches was seen

n handline and basket trap catches, and the least in speargunatches (Fig. 2). The number of dominant species in the catch hasiminished over years in three gears: basket traps, beach seinend gill net. Further, the relative contribution of target species hasncreased 3 fold from 1999 to 2007 in beach seines (L. vaigiensis)nd basket traps (S. sutor) (Fig. 3).

.3. Trends in CPUE

Catch rates for the whole coast, all gears combined, declined-fold from the mid 1980s (average of 13.7 ± 1.6SE kg/fisher/trip)o the 1990s (3.2 ± 0.1SE kg/fisher/trip). Records from the earlyears are however only represented by one study (Carrara andoppola, 1985): there are no data from 1985 to 1993. Catchates from 1994 to 2006, with a mean of 3.2 ± 0.1SE kg/fisher/trip,id not range widely (Fig. 4) but there were significant differ-nces between years (Kruskal Wallis test: H = 77.22, p < 0.001).airwise comparisons showed differences which could berouped into: i) two peaks, 2006 (4.9 ± 0.6SE kg/fisher/trip) and995–1997 (3.5 ± 0.1SE kg/fisher/trip); ii) a trough, 1998–20002.7 ± 0.1SE kg/fisher/trip); iii) and moderate level catch rates in001–2005 (3.3 ± 0.1SE kg/fisher/trip) excluding 2003 (3.0 ±0.1SEg/fisher/trip). This dataset was only available for two locations:ombasa (average 2.9 ± 0.1SE kg/fisher/trip) and Diani-Chale

3.2 ± 0.1SE kg/fisher/trip) (Fig. 4). Data from Gazi just south of

iani-Chale showed higher catch rates during the 2000s (aver-ge 4.3 ± 1.6SE (2005) to 5.3 ± 1.5SE (2004) kg/fisher/trip across all

gears). Differences between years and locations were affectedy the relative proportions and catch rates of the five gears

Park vs. Reserve 0.329 0.002Reserve vs. Fished −0.004 0.442

(Fig. 4) which differed significantly (Kruskal-Wallis test: H = 105.08,p < 0.001). These were data pooled from 1994 to 2006 for threelocations: Diani-Chale, Mombasa and Gazi. Speargun catch rates(X = 3.9 ± 0.1 SE) were significantly higher (p < 0.01) than all othergears and beach seine catch rates (X = 2.4 ± 0.1) were significantlylower (p < 0.01) than all other gears (Fig. 4), whereas catch rates ofgill nets, handlines and basket traps were not significantly differentfrom each other (p > 0.05; Mann-Whitney tests). Each gear followeda similar pattern of peaks and troughs over the years (Fig. 4).

3.4. Trends in fish densities

Yearly averages of fish densities for the seven families revealedhigh variability in the UVC data and several gaps with the most com-plete dataset coming from Shimoni (Supplementary Graphs: Fig.S1 in the online version at DOI: http://dx.doi.org/10.1016/j.fishres.2016.07.025). Analyses were therefore constrained (see Meth-ods) but nevertheless detected significant differences in medianfish densities between year periods and management zones forfour of the seven families: Lethrinidae, Haemulidae, Acanthuridaeand Scaridae (Fig. 5). Patterns varied between these four fam-ilies: lethrinid densities were significantly lower in the 1980scompared to Park sites in the Pre-bleaching (1992–1997) and com-bined Post-bleaching (1999–2007) year periods (Fig. 5: p < 0.05),suggesting lethrinid abundance has improved over time in Parksbut not Reserves. The haemulid dataset is limited to 1980s andPost-bleaching periods but also showed a significant increase indensity in Park sites in the Post-bleaching period (Fig. 5). Theherbivorous acanthurids and scarids showed different patterns:acanthurid densities were highest in the 1980s, where densitiesdid not differ between Fished, Reserve and Park sites. Densitiesdropped significantly in the Pre-bleaching period and continued todrop in the Post-bleaching period (Fig. 5). In both later periods den-sities were significantly higher in Park sites compared with FishedSites. Changes in scarid densities over time were highly variable,with the highest densities seen in the Reserve sites in the 1980s.Densities did not differ between the 1980s and subsequent yearperiods except between Fished sites in the 1980s and Park sites inthe Pre-bleaching and Post - bleaching periods where densities hadincreased. These latter periods also differed significantly in Parkswith densities lower during the Post-bleaching period (Fig. 5).

Comparing management zone and year period simultaneouslythrough the MDS plot provides a more powerful analysis ofchanges in fish densities over time and in relation to manage-ment zones, though sufficient data were only available for theherbivorous scarids, siganids and acanthurids. There is a clearclustering of sites surveyed in the 1980s, particularly for the

Fished sites (Fig. 6). The ANOSIM revealed significant differencesbetween year periods (Table 3) with 1980s abundance signifi-cantly different from Pre-bleach and Post-bleach periods. Park sitesin the Pre-bleaching and Post-bleaching periods also clustered

M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191 183

Siganu ssutor

Lethrinus

mahsena

Leptoscarus

vaigi en sis

Lethrinuslentjan

L et hr inu smi ni atus

Lethrinu s

bor bo nicu s

0

10

20

30

40

50

60

70 M

ean

rela

tive

abun

danc

e(%

)Basket trap

Lethrinus

borbonicus

L ethri nus

mahsen a

Lethrinuslen tjan

Lutjanus

fulviflamma

Lethrinus

xanthochilus

Cheiliosi nermis

0

10

20

30

40

50

60

70

Handline

Siganussutor

Lethrinushar ak

Leptoscar us

vaigiensis

Acan thurus

triostegus

Lethrinu smahsena

Un specified

0

10

20

30

40

50

60

70

Mea

nre

lativ

eab

unda

nce

(%)

Gillnet

Leptoscarus

vaigiensis

Siganussutor

Ca lato mus

carol inus

Unspecified

0

10

20

30

40

50

60

70

Mea

nre

lativ

eab

unda

nce

%

Species

Speargun

Leptoscarus

v aigi en sis

Leiognathus

equula

Lethrinusmahsena

Siganussutor

Lethrinuslen tjan

Sardinel lasp.

Let hri nu sharak

Unspecifi ed

0

10

20

30

40

50

60

70

Species

Beach se ine

F e domb es, anb

twzPcbdsdrmi

3

iiotebflc

ig. 2. Mean catch composition by gear with years and locations pooled, showing thy number. Note some studies used synonyms identifying L. lentjan as L. mahsenoidars are SE. (Data Source: 5 studies, Table 1).

ogether, while Reserve sites in Pre- and Post-bleaching periodsere widely scattered in different quadrants (Fig. 6). Management

ones differed significantly and pairwise comparisons revealedarks differed significantly from Fished and Reserve (Table 3) indi-ating fish densities in Reserves are similar to Fished sites. Thei-plot overlay of the direction of change in the three families’ abun-ance shows highest acanthurid densities associated with sitesurveyed in the 1980s and the reverse in the scarids with highestensities in both later year periods in the Parks; these changes areeflected in significant Mann-Whitney tests (Fig. 5). The siganids

irrored the acanthurids but the change was weak and not signif-cant (Fig. 6).

.5. Coral reef fisheries yield

Catch per unit effort (CPUE) for Diani-Chale increased signif-cantly from 2.9 kg fisher−1 day−1 in 1999 to 5.4 kg fisher−1 day−1

n 2006 (y = 0.228x + 3.100, R2 = 0.56). Fishing effort (total numberf active fishers per day) in Diani-Chale ranged from 264 in 2003o 292 in 2006, representing a mean annual increment of 3.6%,quivalent to an increase of ∼7 fishers per year, though this should

e treated as an approximation since the increase was not uni-orm each year and the time period is short for assuming a linearong term increase. With this assumption, these values allow back-alculated figures for 1999 of 228 daily active fishers and fishing

inant species − those that contribute up to 65% but not exceeding 75% of the catchd L. mahsena as L. sanguineus, these are updated to the senior synonym here. Error

intensity of 4.0 fishers km−2 each day. Using the Yield equation (seeMethods), these estimates gave a mean yield for Diani – Chale of2.29 mt yr−1 km−2 in 1999 and 6.09 mt yr−1 km−2 in 2006. This rep-resents an annual increment of 354 kg per year (y = 0.354x + 2.555,R2 = 0.70).

4. Discussion

Using a series of meta-analyses as well as field observations onfishing gear utilization to assess Kenya’s artisanal coral reef fish-eries we found 13 different artisanal gears. However, gill nets areonly reported as one gear yet their sub-types span >40 cm in meshsize. Their different catches therefore remain unquantified whichis a concern since fishing offshore with gill nets for more abun-dant pelagic species is often recommended to address dwindlingdemersal stocks close to shore (Bell et al., 2009).

4.1. Trends in catch per unit of effort (CPUE)

CPUE is often assumed to be proportional to fish abundance andis one of the most commonly used indices of stock abundance in

fisheries science (Richards and Schnute, 1986). Average catch ratesin Kenya in 2006 were 23% of what they were in the mid 1980s.Artisanal coral reef based fisheries typically yield around 25% oftheir original or “virgin” catch rates (Jennings and Polunin, 1995,

184

M.A

. Sam

oilys et

al. /

Fisheries R

esearch 186

(2017) 177–191

Siganus sutor

Lethrinus mahsena

Lethrinus lentjan

Leptoscarusvaigiensis

0

10

20

30

40

50

60

70

80 Mean relative abundance (%)

1999

Siganus sutor

Lethrinus mahsena

Lethrinus miniatus

0

10

20

30

40

50

60

70

80 2000

Siganus sutor

Leptoscarusvaigiensis

Lethrinusm

m

ahsena

Lethrinusahsenoides

Lethrinusborbonicus

0

10

20

30

40

50

60

70

80

Mean relative abundance (%)

2002

Siganussutor

Lethrinusmahsena

0

10

20

30

40

50

60

70

80 2007

Siganus sutor

Lethrinus harak

Leptoscarusvaigiensis

Acanthurustriostegus

0

10

20

30

40

50

60

70

Mean relative abundance (%)

1999

Siganus sutor

Leptoscarusvaigiensis

Unspecified

0

10

20

30

40

50

60

70 2007

i) Basket trap

Leiognathus equula

Leptoscarusvaigiensis

Lethrinus mahsena

Siganus sutor

Lethrinus lentjan

Sardinella sp.

Lethrinus harak

0

10

20

30

40

50

60

70

Mean relative abundance (%)

Species

1999

Leptoscarusvaigiensis

Siganus sutor

Unspecified

0

10

20

30

40

50

60

70

Species

2007

iii) Beach

seine

ii) Gill net

Fig. 3.

Declin

e in

nu

mber

of sp

ecies in

catches

over tim

e from

basket trap

s, gill

nets

and

beach sein

e (n

= 2-5

stud

ies, see

Table 1).

M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191 185

Mombasa

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

CPUE

(Kg/

fishe

r/day

)

0

1

2

3

4

5

6Basket trap Beach seineGillne t Han dline Speargun

Diani - Chale

Years

Years

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

CPUE

(kg/

fishe

r/day

)

1

2

3

4

5

6

7

Basket trapBeach seineGillnetHandlineSpeargun

Ken yan coas t19

92

1994

1996

1998

2000

2002

2004

2006

2008

CPU

E(K

g/fis

her/d

ay)

1

2

3

4

5

6

7

8

9Basket trapsBeach seineGillnet HandlineSpeargun

CPUE

(kg/

fishe

r/day

)

Years

F s, loca( Table

1csoAsfFr1e

ptTwdsfliac(1sv

4

cialHt

ig 4. Average CPUE values (kg/fisher/day) by year for the five most common gearData Source: Kenyan coast − 6 studies, Diani − 5 studies, Mombasa −3 studies, see

996). However, we know the 1980s CPUE cannot represent virginatch rates since artisanal fishing has occurred on Kenya’s coast foreveral hundred years (Glaesel, 1997). This suggests the mean valuef ∼3 kg per fishing trip from 1994 to 2006 across all gears is low.lthough the 1980s value of 14 kg/fisher/day is based on only onetudy (Carrara and Coppola, 1985), similar catch rates are knownrom coral reef fisheries elsewhere in the 1980s and early 1990s.or example, upper CPUE values from a range of fishing gears oneef and lagoon fisheries reviewed by Dalzell (1996) ranged from9 kg to 32 kg per set in Fiji, to 43 kg per trip in Kiribati, while Craigt al. (1993) report up to 21 kg per day in American Samoa in 1991.

Aggregated catch rates in the 1990s and 2000s showed twoeaks: in 1995–1997 and 2006 (3.5 & 4.9 kg/fisher/trip, respec-ively); and a minimum value of 2.7 kg/fisher/trip in 1998–2000.hese fluctuations might relate to the 1997/8 coral bleaching event,ith recovery suggested by catch rates averaging 3.3 kg/fisher/trip

uring 2001–2005. This interpretation is supported by the con-istent differences in gear CPUE which matched these sameuctuations. Our results found speargun catch rates were signif-

cantly higher and beach seine catch rates significantly lower thanll other gears. This contrasts with an artisanal fishery in Madagas-ar where beach seine catch rates are reported to be the highestDavies et al., 2009). Of concern is that the catch rates over the2 year period from 1994 may have been artificially maintained byhifting proportions of species in the catches, with S. sutor and L.aigiensis increasingly contributing to the catch.

.2. Species level changes and management implications

Reef fisheries are well known for being highly multi-species inharacter (Polunin and Roberts, 1996): typical catches in Kenyanclude over 160 species (McClanahan and Mangi, 2004). This char-

cteristic can lead to inferences of resilience at the fish communityevel since fishing pressure is spread across a wide range of species.ere we define resilience as the ability of the reef fish community

o resist, or absorb and recover its structure and functions. How-

tions pooled. Inserts show two locations separated. Error bars are standard errors. 1).

ever, by 2007 only 2–3 species appeared in the top bracket for anygear (using the top 65–75% number of fish in the catch) with S. sutorconsistently being in this bracket in beachseine, gill net and baskettrap catches, and L. vaigiensis in gill net and beach seine catches andLethrinus borbonicus in handline catches. Similarly, on the southernKenyan coast only 15 species contributed 90% of the catch with S.sutor, L. vaigiensis and Lethrinus lentjan being the three most com-monly caught species (Hicks and McClanahan, 2012), and studiesin Madagascar have similar findings (Davies et al., 2009). This dra-matic shift in species composition illustrates that Kenya’s artisanalfisheries can no longer be characterised as highly multi-species andthat some species have gone commercially extinct.

Our understanding of the resilience of S. sutor and L. vaigiensisto this increase in fishing pressure remains poor. Recent mortalityestimates for these two species in lagoonal fisheries on the southcoast reveal both are overexploited (Hicks and McClanahan, 2012).Other factors related to their biology and their market niche mayalso render S. sutor vulnerable to overfishing. They form spawningaggregations which are targeted by fishers making them poten-tially vulnerable to local extirpation, though this species with shortlife span and rapid growth is particularly resilient to fishing pres-sure (Robinson et al., 2011; Samoilys et al., 2013). In addition, S.sutor is a key target species in women’s (mama karanga) smallscale fish trade (Karuga and Abila, 2007; Samoilys and Tuda, 2009)which has been suggested as a driver for the persistent use ofillegal beach seines (McClanahan and Mangi, 2001; Harrison andSamoilys, 2009; McClanahan et al., 2008), though no such linkwas detected between fisher targets and women’s small scale fishtrade in Philippines (Pomeroy et al., 2016). Concerns over the sus-tainability of current fishing pressure on S. sutor and L. vaigiensisled to a Policy Brief to government in 2011 recommending fullstock assessments and focussed management plans for these two

species (Samoilys et al., 2011b), and full enforcement of the illegalbeach seine. A similar recommendation to reduce beach seine usecomes from Madagascar’s artisanal fisheries (Davies et al., 2009).

186 M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191

Fig. 5. Median densities (with 25%, 75% quartiles) pooled for the whole coast of four fish families that differed significantly across year periods and management zones.Medians are calculated from 4 to 7 studies per family (see Table 1). Plots with the identical lowercase letters are not significantly different based on Mann-Whitney post-hoct lation

Oc

vCccsyo(gtmal

est. * = data not collected. Open dots indicate outliers not included in median calcu

ur results belie the notion that the multi-species nature of Kenya’soral reef fisheries makes them more resilient to fishing pressure.

Lethrinids are a valuable food fish group and range across a wideariety of habitats, not just coral reefs (Carpenter and Allen, 1989;onnell et al., 1998; Fitzpatrick et al., 2012). It is therefore of con-ern that they were absent in the 65–75% bracket of most recentatches (2007) of beach seines and gill nets. Analysis of UVC datahowed that lethrinid abundance has increased in Parks in recentears suggesting NTZs on coral reefs can provide a positive impactn these taxa. Since beach seine catches comprise 68% juvenilesMangi and Roberts, 2006), proper enforcement of the ban on thisear will protect immature fish, but this requires fishers’ coopera-ion in policing. In addition, gear regulations such as hook size and

esh size could be trialed to effect a minimum size limit (Hicksnd McClanahan, 2012) in Fished areas and Reserves to help restoreethrinid populations.

s.

Combining the results of gear specific CPUE rates, changes inspecies composition of catches and juvenile retention rates (Mangiand Roberts, 2006; McGuiness 2007) leads us to recommendthe following management options for these fisheries: a) prop-erly enforce the ban on beach seines particularly in the NationalReserves; b) trial a hook size limit to reduce juvenile retention oflethrinids in handline fishing; c) lift the ban on spearguns becausethey showed the lowest retention rate of juveniles, have no by-catch and have low input costs for young fishers starting theirfishing livelihood; d) review and test minimum and maximummesh size in gill nets to protect juveniles and reduce capture ofEndangered or Vulnerable mammals, sharks and turtles; and e) con-duct biological research on Siganus sutor and Leptoscarus vaigiensis

to complete parameters required for a full stock assessment. Theserecommendations should be combined within a Protected Areasmanagement framework, which ensures, for example, that the

M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191 187

F nd Sigm

l(

4

tnHt(dlsgtaiirtbarurmGePcn2padltr

ig. 6. PCA bi-plot of mean abundance of three families (Acanthuridae, Scaridae aanagement zones. (Data Source: 8 studies, Table 1).

argest individuals are protected thereby maximising fecundityBohnsack, 1996).

.3. Trends in fish densities

Long term trends in fish population abundance were difficulto discern partly due to highly variable and patchy UVC data,evertheless, significant differences in abundance of Lethrinidae,aemulidae, Scaridae and Acanthuridae were detected between

he 1980s and the Pre-bleaching (1992–1997) and Post-bleaching1999–2007) periods. Acanthurids and scarids were more abun-ant in the 1980s compared with both later periods, whereas

ethrinid and haemulid densities were lowest in the 1980s. Den-ities of all four families in Parks (equivalent to NTZs), wereenerally higher than in Reserves and Fished sites, and the latterwo zones did not differ, suggesting Park management is having

positive effect on fish densities whereas Reserve managements not. Patterns in scarid densities suggest Parks may have mit-gated a decline in scarid densities over years, though this waseduced by a negative impact from the 1998 bleaching. It is likelyhat Parks originally had higher coral cover and therefore woulde more impacted by bleaching. The patterns suggest that Parksre associated with either increasing fish population densities oreducing overall declines in population densities. Increased pop-lation densities (lethrinids and haemulids) after the 1980s mayelate to government improvements in protection and manage-

ent of reef habitats and new fisheries regulations (Samoilys, 1988;overnment of Kenya 1991, 2001; Watson et al., 1996; McClanahant al., 2007). Declines in densities of lethrinids and scarids inarks Post-bleaching is likely to reflect impacts from the 1998oral bleaching event (Graham et al., 2011). However, we foundo evidence for a lag effect of around six years (Graham et al.,007): fish densities did not differ between <5 years and >5 yearsost bleaching. No clear explanation was found for the decline incanthurids over the 20 year period. The lack of increase in fish

ensities in Fished and Reserve sites after the 1980s and much

arger declines in densities in these zones compared to Parks byhe Post-bleaching period, suggest that the fisheries managementegulations of Reserves and Fished sites are inadequate.

anidae) overlaid on MDS plot of the same data from three year periods and three

With declining trends in fish densities combined with declinesin number of species in the fishery catches, the sustainability ofcurrent fishing practises on Kenya’s coral reefs appears question-able. Parks are probably insufficient as a fisheries management toolsince Kenya protects only 3% of its shallow coastal reef associatedwaters, whereas international targets are set at 10% (Conventionon Biological Diversity Aichi Target 11) and as high as 25–30%(Fernandes et al., 2005). However, a positive development is thatcoastal communities in Kenya are increasingly establishing LocallyManaged Marine Areas (LMMAs), which include NTZs, in responseto catch declines and to exert ownership of their resources (Rocliffeet al., 2014; Kawaka et al., 2015). We recommend this is encouragedand that stronger management measures taken in the governmentReserves to improve their effectiveness.

4.4. Yield and value of Kenya’s reef fisheries

Yields (mt km−2 yr−1) from coral reef fisheries vary enormously(Table 4) partly due to a range of variables that are difficult tomeasure accurately (Russ, 1991; Arias-Gonzalez et al., 1994) andthe problematic assumptions in comparing multi-species fisheryyields (Koslow et al., 1994; Russ and Alcala, 1998; Jennings et al.,2001) all issues met in the current study. Variation in reference areasize is seen in previous estimates from Kenya which range from620 km2 to 1080 km2 (Sheppard and Wells, 1988; McClanahan andObura, 1995; Kaunda-Arara et al., 2003; Muthiga et al., 2008). Ourlarger area estimate (which gave a smaller fishery yield) includesadditional shallow-water habitats such as sandy lagoon, mangrovefringes and sea grass beds, as these are habitats in which arti-sanal fishers range to catch reef-associated fin-fishes (Bellwoodand Wainright, 2002). Moreover, when species compositions ofcatches change over time, as seen here, fishery productivity willalso change. Nevertheless, yields provide a perspective on thesecomplex fisheries that can help to understand the state of the fish-ery (Russ, 1991) and provide a guide for government. The annual

yield of 6 mt yr−1 km−2, estimated from Diani-Chale in 2006, is rel-atively high compared with typical global yields but is within theranges reported previously in Kenya, with two higher exceptions(Table 4). The highest yields, >15–20 mt yr−1 km−2, are seen from

188 M.A. Samoilys et al. / Fisheries Research 186 (2017) 177–191

Table 4Fisheries yield in metric tonnes per unit area per year (mt.km−2 year−1) of demersal fin-fish obtained in different coral reef areas in the Western Indian Ocean, the PacificIslands, Asia and Caribbean. Most recent yields presented first. n.a. = year not specified, publication date provides an indication.

Location mt km−2 year−1 Year Habitat Source

Western Indian OceanKenyaDiani-Chale 6.1 2006 reef This studyDiani-Chale 2.3 1999 reef This studyMombasa and Diani-Chale 3.0–16.0 1996–2005 reef McClanahan et al. (2008)Whole coast 2.1 1978–2001 reef Kaunda-Arara et al. (2003)Mombasa and Diani-Chale 6.6 1995 McClanahan and Mangi

(2001)Mombasa and Diani-Chale 4.5 1999 reef and lagoonKilifi (Malindi-Watamu) 8.8 1982–1984 reef Nzioka (1990)Northern coast (Lamu &Malindi-Watamu)

4.9 1977 reef FAO (1979)

Southern coast (Mombasa andDiani-Chale)

5.6 1977 reef FAO (1979)

Tanzania− whole coast 4.8 1977 reef FAO (1979)Seychelles − Mahe (East) 1.4 1977 reef FAO (1979)Seychelles − Mahe (West) 3.1 reef FAO (1979)Madagascar− Tulear Region,southwest

12.0 1991 barrier, lagoon and fringingreefs & coral banks

Laroche and Ramananarivo(1995)

Mauritius 3.5 1977 coralline shelf FAO (1979)Mauritius 4.7 reef Wheeler and Ommanney

(1953)Pacific IslandsAmerican Samoa − OuterIslands

2.3 2002–2003 Craig et al. (2008)

American Samoa − TutuilaIsland

21.2 1979 reef and reef flat Wass (1982)

FijiOno-i-Lau 2.9 2002 reefs Kuster et al. (2005)Ono-i-Lau 3.7 1982 reefs Kuster et al. (2005)Astrolabe reef 0.9 1992–1993 fringing reef Jennings (1998)Yanuca, Dravuni, Moala,Totoya, Nauluvatu

10.2 1992–1993 fringing reef Jennings and Polunin (1995)

Kiribati − Tarawa 4.4–7.2 n.a coral atoll (reefs and lagoon) Marriott (1984); Mees et al.(1988)

Micronesia − Kapingamarangi 0.7 n.a coral atoll Stevenson and Marshall (1974)Micronesia − Ifaluk Atoll 5.1 1962–1963 reef and lagoon Stevenson and Marshall (1974)Micronesia − Lamotrek andRaroi Atolls

0.45/0.09 n.a reef Stevenson and Marshall (1974)

AsiaPhilipinesApo Island 15.0–20.0 1997–2001 reef Maypa et al. (2002)San Salvador Island 7.0 14.0 1988/89 1989/90 reef Christie and White (1994)Sumilon Island 10.1 1976–1986 reef Alcala (1981); Alcala and Russ

(1990)CaribbeanBermuda 0.4 n.a reef Bardach and Menzel (1957)Bahamas 2.5 n.a reef Gulland (1971)

rpAdcitrricgfp

o(t1

Jamaica 2.0 n.a

Papua New Guinea − TigakIslands

0.4 n.a

eef areas with >50% live coral cover and where herbivores andlanktivores dominate catches such as in Philippines (Russ andlcala, 1989; Maypa et al., 2002). Kenyan artisanal catches are nowominated by the herbivorous siganids and scarids and averageoral cover is around 20% (Ateweberhan et al., 2011), placing Kenyan the mid-range of productivity of reef fisheries. We propose thathe increase in yield at Diani-Chale from 1999 to 2006 does noteflect a fishery below MSY (Jennings et al., 2001) but is likely to beelated to increasing modernisation of gear (Samoilys et al., 2011a),ncreasing use of nets, including the “ring net” and shifts in speciesomposition from carnivores and omnivores to herbivores. We sug-est, therefore, that a yield of 6 mt yr−1 km−2 is a realistic maximumor Kenya’s reef fisheries, but caution that current managementractices are unlikely to maintain this.

Small scale and artisanal fisheries account for more than 40%

f the global marine fisheries catch taken for human consumptionJennings et al., 2001). Kenya’s artisanal marine fisheries are con-ributing valuable fish landings and support the livelihoods of some3,000 fishers (GoK, 2012) on the coast. Extrapolating the yield val-

reef Munro (1969)Reef Wright and Richards (1985)

ues from Diani-Chale to the whole Kenyan coast gives a total yieldof 7754 mt of reef-associated fin-fish in 2006 valued at USD 9.69million (at KSh 100 per kg, USD 1 = KSh. 80), captured by 8682 fish-ers, slightly more than the annual production (6000–7000 mt) ofall marine fisheries reported as an aggregate value by government(GoK, 2005, 2008). Our estimate is crude as it assumes that the pro-ductivity of coastal habitats supporting these fisheries are broadlysimilar along the coast and that fisher density is also similar, nei-ther of which can be validated. In addition, it is an extrapolationfrom a small intensively fished reef area considered to be overex-ploited (McClanahan and Obura, 1995; Obura, 2001). Nevertheless,these calculations serve to illustrate the likely high value of Kenya’scoastal artisanal fisheries and hence the investment benefits inmanaging these fisheries for food security.

5. Conclusions

The long term impacts of fishing on Kenya’s coral reef fish popu-lations is easily hidden by aggregated catch data. Complacency over

ies Res

tyagccctiawaaKKodmtmcc

A

imit

A

fRCmmBMwMgKom

R

A

A

A

A

A

A

M.A. Samoilys et al. / Fisher

he state of these fisheries may be triggered by increasing fisheryields within the range expected for relatively healthy coral reefsnd the knowledge that Kenya’s Marine Parks are effective. We sug-est an alternative picture where shifts in species composition ofatches are contributing to very high pressure on just a few species,ausing recruitment overfishing (Pauly, 1988). The high juvenileapture of S. sutor, Lethrinus mahsena and L. lentjan by all the gears ofhe artisanal fishery (>75%, Mangi and Roberts, 2006), the increas-ng numbers of fishers entering the fishery each year (GoK, 2012),nd inadequate protection from Parks (too small) and Reserves (notell enforced) for some key fishery taxa, are all putting populations

t risk from over-exploitation. Since coral reef associated fishesre also highly susceptible to climate change (Graham et al., 2011;roeker et al., 2013) we suggest the artisanal coral reef fisheries ofenya may be approaching a tipping point. To safeguard the futuref Kenya’s coral reef fish populations, as well as the fishers depen-ent on them, we recommend a combination of species–specificanagement options, changes in gear regulations, and more NTZs

hrough better enforced Reserves and community LMMAs. Suchechanisms should improve the resilience of fish populations and

oral reefs to the combined and interacting effects of fishing andlimate (Perry et al., 2010).

uthor contributions

MS – initial concept, methods, data analysis oversight andnterpretation, results interpretation and lead author on writing

anuscript; KO – sourcing data, data analysis and interpretion,nputs to writing; GM – some data sourcing and analysis and inputso writing; DO – data collection and data interpretation.

cknowledgements

This work was funded through the United States Agencyor International Development (USAID) and implemented underesearch Permit Number NCST/RRI/12/1/BS/205 under Nationalouncil for Science and Technology, Kenya. USAID had no involve-ent in the research. We thank the following fishers and BMUembers who gave us valuable information on fishing gears:

uruku Hamis Hamsa, Hussein Ali Mwabori, Hemedi Mohammediwafujo, Ali Shekue, Hamisi Kirauni, Salim Sadik, Ahmed Sheb-ana and Emmanuel Yaa, and the following for field assistance:baraka Hemedi, Ali Mwabori and Pablo Samoilys. We are also

rateful to all those who collected data in the studies cited, notablyWS. We are grateful to Dr Boaz Kaunda-Arara for his critical reviewf an early draft, and to the helpful suggestions from three anony-ous reviewers.

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