A poster for the Young Ecologists Talk and Interact Conference 2016 explaining my research and results on status of fishing cats in the central terai landscape of India

1. Methods03 |
- Shwetha Nair, Pranav Chanchani, Rekha Warrier
Background and Significance01 |
The fishing cat is an endangered, nocturnal, rare and elusive cat with an apparently broad but
discontinuous distribution in Asia (Mukherjee et al., 2010). The IUCN Cats Red List Workshop
2007 suspected a decline of at least 50% of fishing cat population over the past three generations,
and a similar decline is anticipated over the next 18 years in the absence of intensive habitat
protection of fishing cats (Mukherjee et al., 2010).
In a time when charismatic megafauna receive the greatest attention and conservation funds,
there is a severe bias in conservation efforts directed towards smaller carnivores. Despite an
emphasis in research and conservation of wild felids, there is a great paucity of information on
the ecology and abundance of this species.
With this study, we attempt to use technology and applied ecology to extend a single species
monitoring program to other relevant species in the ecosystem. This study estimates occurrence
and population parameters for the fishing cat in the Central Terai Landscape of India (CTL).
Objectives02 |
1. To estimate the occupancy of fishing cats in CTL using the site occupancy framework.
2. To determine if habitat use is influenced by site-specific characters like:
 distance to water source (covariate: Distwater)
 distance to grasslands (covariate: Distgrass)
 distance from forest edge (covariate: Distedge)
 human disturbance (covariate: threat)
 protection designation (covariate: site, binary coded 1-protected area, 0-multiple use forests)
3. To estimate the abundance and density of fishing cats in the CTL using spatially explicit capture
recapture (SECR) models.
• Camera trapping in a phased manner was conducted between Nov 2012-June 2013 in Pilibhit complex
(PBCOMP), Dudhwa National Park (DNP), Katerniaghat Wildlife Sanctuary (KGT) and Kishanpur Wildlife
Sanctuary (KP) and had a sampling effort of 15194 trap nights over 2,434 km2 area (Fig. 1, Karanth and
Nichols 2002). We used ancillary data from the tiger monitoring to study fishing cats.
• Individual fishing cats were identified from pelage patterns for closed population mark recapture
modelling (Fig. 2).
• Site and occasion specific capture-recapture history matrices were created for single season single
species occupancy analysis.
• Species occurrence was modelled using above mentioned covariates (MacKenzie et al., 2006).
• Site specific estimates of abundance and density was produced using spatially explicit capture-recapture
model (Borges and Efford 2008, Efford and Fewster 2013).
Results04 |
Study
Site
Trap Stations Trap Nights
Area
km2 Detections Individuals
PBCOMP 312 6670 1465.95 47 26
DNP 202 4861 684.77 127 54
KGT 111 3663 483.72 39 14
Table 1. Summary of site-wise camera trap deployment and capture details
for 2013 Pilibhit complex (PBCOMP), Dudhwa National Park (DNP),
Katerniaghat Wildlife Sanctuary (KGT)
Conclusion05 |
• Analysis revealed that a priori hypotheses of fishing cat use of a site (presence at a camera station) was correct and was influenced by
o proximity to water (with more proximate sites having higher probability of use)
o distance from edge (sites that were further from the forest edge have higher probability of use)
o more widely present in protected areas as compared to multiple use forests
• For a data deficient species, it appears that these may be the first estimates of abundance and density across the globe.
• Density and abundance is higher in DNP as compared to PBCOMP and KGT. This result is more likely to be due to a higher proportion of suitable wetland habitat as compared to PBCOMP and KGT.
• Analysis of this data helps establish the role ancillary data can play in studying lesser known mammals. This study has helped advance our knowledge on fishing cats and can be used as pilot study to help design
future studies on fishing cat on a finer scale.
Acknowledgement
Logistical Support: Uttar Pradesh Forest Department
Funding: US Fish and Wildlife Service
Support: WWF India
Dr. Barry Noon
Ashish Bista, Dr. Mudit Gupta, Dr. Dipankar Ghose
Ruchir Sharma, Rohit Ravi, Macson Almeida
WWF Field Team
Kavitha Nair, Joel Almeida
Borchers DL, Efford MG 2008. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics
64: 377 - 385.
Efford MG, Fewster RM 2013. Estimating population size by spatially explicit capture–recapture. Oikos 122:918-928
Karanth KU, Nichols JD (editors) 2002. Monitoring tigers and their prey: a manual for researchers, managers and
conservationists in tropical Asia. Centre for Wildlife Studies, India.
Mackenzie DL, Nichols JD, Royle JA, Pollock KE, Bailey LL, Hines JE 2006. Occupancy estimation and modeling. Elsevier
Publishing.
Mukherjee S, Sanderson J, Duckworth W, Melisch R, Khan J, Wilting A, Sunarto S, Howard JG 2010. Prionailurus
viverrinus. In: IUCN 2011. IUCN Red List of Threatened Species. Version 2011.2. <www.iucnredlist.org>. Downloaded
on 12 May 2012
References
Model Delta AICc AICc Weights Model Likelihood
Number of
Parameters
{p(Distgrass)psi(site+Distedge+Distwater)} 0 0.424 1 6
{p(Distgrass)psi(site+Distgrass+Distwater+Distedge)} 0.5437 0.32308 0.762 7
{p(Distgrass)psi(Distwater+Distgrass+Distedge+threat+site)} 2.5634 0.11769 0.2776 8
{p(Distgrass)psi(site+Distwater)} 3.894 0.06051 0.1427 5
Table 2. Top ranked models (cumulative weight of 93%) estimating fishing cat occupancy (Ψ) and detection probability (p) for
study areas PBCOMP, DNP, KGT computed in MARK. The occupancy estimate (Ψ) of fishing cat in CTL is 0.24 ± S.E. 0.027
with a detection probability (p) of 0.05 ± S.E 0.004 for the year 2013.
Figure 3. Fishing cat occupancy (psi) shows a negative
association with distance from water
(β Distwater = -0.58 ± SE. 0.20), a positive association with
distance from edge (β Distedge = 0.21 ± SE. 0.09), a
positive association with covariate site
(β site 1.64 ± SE. 0.321) and detection probability (p)
shows a negative association with distance from grasslands
(β Distgrass = -0.61 ± SE. 0.18).
Figure 4. Pixel-specific fishing cat occupancy probability map
for CTL generated from top site occupancy model. Occupancy
has been modeled as a function of relevant site specific
covariates. Each pixel represents an area of 0.336 km2.
Figure. 1: Map of the Central Terai Landscape showing study
sites with camera trap stations and fishing cat capture sites.
Figure. 2: Identification of fishing cats using
pelage patterns noted from photographic
captures using camera traps.
Presenter Contact
Details
Shwetha Nair
nairshwetha@gmail.com
g0 (SE) σ(SE)
PBCOMP 0.007(0.002) 2068.114(264.210)
DNP 0.024(0.004) 1192.836(74.502)
KGT 0.010(0.004) 2065.848(337.138)
KP 0.006(0.002) 2129.706(340.704)
Figure 5. Estimates of density and abundance of fishing cats (fishing cat/100 km2)
from ML-SECR models for PBCOMP, DNP, KGT and KP from the years 2013. Error
bars on the MLSECR models are 95% confidence intervals.
Table 3. Estimates of
g0= detection probability,
σ =spatial scale parameter,
SE (Standard error) for fishing
cats using SECR method with
maximum-likelihood using a half
normal detection function.