The ecological and conservation significance of camera-trap...

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The ecological and conservation significance of camera-trap bycatch data: a case study of two reclusive, forest-specialised bird species from the Indian Ocean Coastal Belt Forest 1 D.A. Ehlers Smith, Y.C. Ehlers Smith & C.T. Downs University of KwaZulu-Natal [email protected] Joint SANBI BIMP & FBIP Forum, Cape St. Francis 14-08-2018

Transcript of The ecological and conservation significance of camera-trap...

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The ecological and conservation significance of camera-trap bycatch data:

a case study of two reclusive, forest-specialised bird species from the

Indian Ocean Coastal Belt Forest

1

D.A. Ehlers Smith, Y.C. Ehlers Smith & C.T. Downs

University of KwaZulu-Natal

[email protected]

Joint SANBI BIMP & FBIP Forum, Cape St. Francis

14-08-2018

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Introduction Methods Results Discussion

Bycatch data: Species recorded that were not

the initial target of the survey

Eligibility dependent upon certain factors: • Camera-trap placement

• Original survey design & assumptions

- proposed research question

- applicability to bycatch species

• Bycatch sample size

- Replicability, robustness of analyses

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Introduction Methods Results Discussion

Case study: Two reclusive,

terrestrial forest birds

Survey design: Mammalian community

Assumptions: • “Closed” survey

• Generalised for a community

• Replicable & long-term

• Explanatory covariates

appropriate & complementary

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Introduction Methods Results Discussion

Population Increase

Increased Agricultural

Outputs

Increase in housing required

Conversion of natural habitats

Increased threat to

biodiversity

Increased conflict

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• Indigenous Forest transformation = significant

changes in biodiversity 1

• Anthropogenic fragmentation of forested landscapes

is not a random process 2

- Commercial transformation

- Agriculture/Forestry

- Urbanisation

• Fragmentation =

- changes in diversity and abundance of specialised

structures, resources and concomitant niches

- changes in species richness and abundance,

community assemblages and decline in specialist

species 3

- decrease in connectivity & gene flow

- increase in “edge” habitats & effects

- increase in alien & invasive species

1 Watson et al. 2014; 2 Baldi et al. 2006; 3 Slade et al. 2013

www.adelaide.edu.au

www.desdemonadespar.net

Introduction Methods Results Discussion

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Introduction Methods Results Discussion

Scientific Name: Aplopelia larvatus / Columba

larvatus (Temmink, 1809)

Common Names: Lemon Dove, Cinnamon Dove

Scientific Name: Geokichla guttata / Zoothera

guttata (Vigors, 1831)

Common Names: Spotted Ground-thrush,

Natal Thrush

Both species may be problematic to record using

“traditional” (point-count) survey techniques

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• The Spotted Ground Thrush is a globally

endangered forest specialist

• Distribution and status of both species

poorly understood in the critically

endangered Indian Ocean Coastal Belt

Forest (IOCBF), east coast of southern Africa

• Historic/paleo-climatic changes

• Biogeographic forces

• Anthropogenic impacts

• > 70% of original IOCBF has been lost

• = Fragmented forest remnants within a

mosaic of different land uses.

Introduction Methods Results Discussion

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Introduction Methods Results Discussion

Main Objectives: Spotted Ground Thrush

• Establish non-breeding season habitat-

patch occupancy in southern KZN

• Examine habitat requirements and

response to fragmentation effects and

surrounding land use

Main Objectives: Lemon Dove

• Establish site occupancy

• Examine habitat requirements and

tolerance to disturbance

• Establish seasonal differences

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Introduction Methods Results Discussion • June 2014 – May 2016

• Camera traps deployed over 21 survey days (24 hrs, 30 s trigger)

• Across 3 different habitat types: - Coastal Scarp Forest - Coastal LL Forest - Coastal Dense Bush

• 4 management regimes: - Nature Reserves - Residential Areas - Farms - Eco-Estates

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11 1Bibby et al. (2000); 2 GeoTerra Image (2014)

www.illustrationsource.com

20 m fixed radius

Micro-habitat

characteristics1

1 km buffer

Land-cover

classifications2

Introduction Methods Results Discussion

400 m x

400m

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Calculate patch

size & isolation

distances

between forest

patches

Mainland

Scarp

Mainland

Coastal

Introduction Methods Results Discussion

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Single Season Occupancy Model:

• Site occupancy Ψ – probability that a site is occupied

• Detection probability P – probability of detecting the species, given the species is present at a site

• Each sampling occasion is treated as a temporal repeat of the survey • Presence/absence matrix: naïve

occupancy > 0.2 • Patch occupancy (SGT) • Site occupancy (LD) • AIC model selection

- ΔAIC < 2 considered equivalent

14 1 MacKenzie et al (2006)

Introduction Methods Results Discussion

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SGT: Cryptic - 42/275 camera sites & 23/82 camera patches

LD: 76/250 camera sites in spring-summer & 70/250 camera sites in autumn-winter

Introduction Methods Results Discussion

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Spotted Ground Thrush Ψ

0.39 ± 0.09

P

0.11 ± 0.03

Introduction Methods Results Discussion

y = 0.0007x + 0.294

R² = 0.3188

0

0,2

0,4

0,6

0,8

1

0 500 1000 1500

Ψ

Patch size

y = -0.0001x + 0.5317

R² = 0.2779

0

0,2

0,4

0,6

0,8

1

0 2000 4000 6000

Ψ

Isolation distance Patch size Isolation distance

TOP MODEL: Ψ(PS+iso+BG+SD6-10+SWI)

Covariates that influenced occupancy positively

• Ψ: Patch size (ß 28.32 ± 11.91; ωi = 1)

• Ψ: % bare ground (ß 17.72 ± 9.91; ωi = 0.995)

• Ψ: Urban Forest mosaic (ß 0.92 ± 0.72; ωi = 0.374)

Covariates that influenced occupancy negatively

•Ψ: Isolation distance (ß -2.12 ± 1.04; ωi = 1)

•Ψ: Stem density of trees 6-10 m (ß -1.18 ± 0.80; ωi = 0.989)

•Ψ: SWI (ß -1.21 ± 0.85; ωi = 0.310)

•Ψ: Coastal Dense Bush (ß -1.14 ± 0.89; ωi = 0.128)

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Lemon Dove

Ψ 0.39 ± 0.08

P

0.26 ± 0.05

Introduction Methods Results Discussion

• TOP MODEL: Ψ HC+SD6+W0+SWI (Autumn-winter)

• Ψ: Herbaceous cover (ß 28.32 ± 11.91; ωi = 1)

• Ψ: Stem density 6-10 m trees (ß 0.40 ± 0.17; ωi = 0.9943)

• Ψ: % woody cover (ß -0.27 ± 0.17; ωi = 0.9577)

• Ψ: SWI (ß 0.16 ± 0.15; ωi = 0.2846)

Ψ 0.37 ± 0.08

P

0.25 ± 0.04

Autumn-winter

(Mar-Aug)

Spring-summer

(Sep-Feb)

Autumn-winter

(Mar-Aug)

• TOP MODEL: Ψ LL+SD16+BG+GC+PSR (Spring-summer)

• Ψ: % bare ground (ß 0.28 ± 0.21; ωi = 0.9957)

• Ψ: % grass cover(ß 0.59 ± 0.40; ωi = 0.9953)

• Ψ: Stem density 16-20 m trees (ß 0.40 ± 0.26; ωi = 0.9282)

• Ψ: % leaf litter (ß 0.54 ± 0.35; ωi = 0.7297)

• Ψ: PSR (ß 0.17 ± 0.11; ωi = 0.6937)

Spring-summer

(Sep-Feb)

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• Preference for tall trees with open

understory implies dependence on

Indigenous Forest (both)

• Preference for complex habitat structures

and high plant diversity, and leaf litter/

bare ground in breeding season (LD)

• Avoidance of small, isolated patches in

proximity to agriculture implies negatively

affected by fragmentation (SGT)

• The success of this study supports the use

of camera traps for surveying reclusive,

terrestrial forest birds

• The robust conclusions highlight the

application and importance of bycatch

data for conservation and ecology

www.adelaide.edu.au

Introduction Methods Results Discussion

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• Bycatch data may provide critical insights into

the behaviour or response of biodiversity to

ecological conditions or anthropogenic pressure

• By examining the context of bycatch data,

camera-trap surveys offer a POTENTIAL

cornucopia of opportunities

• If bycatch data do not represent your research

interests, time or budget COLLABORATE!!

Overall Research Implications

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• Land owners & conservancies

• Ezemvelo KZN Wildlife Scientific Services

• UKZN Gay Langmuir Fieldwork grant

• UKZN CAES grant

• Hans Hoheisen Charitable Trust

• Whitley Wildlife Conservation Trust

• National Research Foundation Doctoral Scholarship

• Claude Leon Post-Doctoral Research Fellowship

Acknowledgements