Methods 03 |

- Shwetha Nair, Pranav Chanchani, Rekha Warrier

Background and Significance 01 |

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).

Objectives 02 |

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).

Results 04 |

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)

Conclusion 05 |

• 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.