DOI: 10.5061/dryad.djh9w0w8h
title: Data from: Interspecific interactions among major carnivores in Panna Tiger Reserve: A multispecies occupancy approach

description: Aim: Large carnivores play a crucial role in trophic cascades, affecting
 the population dynamics of both co-predators and prey within an ecosystem.
 Understanding the significance of these carnivores in trophic interactions
 is essential for developing effective conservation and management
 strategies. We examined the effects of occupancy dynamics and patterns of
 species interactions and co-existence within the carnivore guild in the
 Panna Tiger Reserve in India. Methodology: We collected camera trap data
 (two seasons, 2019) in a presence-absence framework and applied
 multispecies occupancy models to assess the occupancy, co-occurrence, and
 interactions among species. We also examined activity overlap to
 understand the temporal segregation in the carnivore guild. Results: The
 mean marginal occupancy was highest for leopards in winter (Ψwinter
 0.92±0.02, Ψsummer 0.63±0.05) and hyenas in summer (Ψsummer 0.93±0.03,
 Ψwinter 0.78±0.03) and was lowest for tigers in both seasons (Ψwinter
 0.62±0.05, Ψsummer 0.15±0.05). Co-occurrence probability among carnivores
 was higher in winter than in summer, and conditional occupancy was
 consistently higher when other species were present. Different
 environmental factors influenced marginal occupancy and co-occurrence
 patterns across seasons. Strong temporal overlaps were recorded between
 tiger–leopard (0.87–0.91) and tiger–hyena (0.78–0.79). Conclusion: We
 detected a significant spatial segregation between tigers and leopards, as
 they prefer different habitat types in different seasons, along with high
 temporal overlap. Resource availability strongly governs the association
 of carnivores with their habitat selection. Hyenas demonstrated higher
 dependency on tigers than on leopards for resources. These findings
 indicate that co-existence with apex-predator species is feasible through
 strategic adaptation to fulfil resource requisition.
description: <strong>Data collection</strong>
 <strong><em>Field
 methods</em></strong> We conducted a camera
 trap survey from 2017 to 2021 during the summer and winter seasons.
 However, for this study, we used the two seasons of camera trap data for
 2019 (winter, 475 stations; summer, 338 stations). We deployed camera
 traps during January–February for winter sampling (average 32 days) and
 May–June for summer sampling (average 38 days). We deployed a pair of
 automated motion-triggered digital camera traps (Cuddeback C1;
 www.cuddeback.com) in a 2 km<sup>2</sup> grid cell size
 framework (Jhala et al., 2018). The average distance between two adjacent
 camera trap stations was 1.05 km, ensuring intensive sampling. Camera
 traps were installed on either side (facing each other) of animal trails,
 forest roads, and the riverbed in mid-slope regions at 30–40 cm above the
 ground to optimize the capture of large carnivores (Chen et al., 2009;
 Johnson et al., 2009; Evans et al., 2019). All camera traps were active
 for 24 hours during the sampling period and programmed to take one
 photograph per trigger with an interval of 5 second. We checked the camera
 trap data every 15 days during the observation period. This research was
 carried out under permit number Technical/4301/, dated on 09 June 2015 and
 issued by the Principal Chief Conservator of Forest (Wildlife Division),
 state of Madhya Pradesh, India.
 <strong>Analytical methods</strong>
 Retrieved images from camera trap data were identified up to the
 species level. Data were sorted into species-specific folders for all the
 sites by season. Following species-level data segregation, we prepared a
 detection/non-detection matrix for three large carnivores: tiger, leopard,
 and hyena. <strong><em>Predictor
 variables</em></strong> We obtained data at
 each camera-trap sampling location by extracting values from remotely
 sensed raster datasets with a buffer size of 100 meters. We used
 continuous variables such as tree canopy density (in %), normalized
 differentiated vegetation index (NDVI), distance to a village (km),
 distance to a water source (km), land surface temperature (LST, °C), and
 slope (degree, Table S1). We prepared these thematic layers and resampled
 at 100-m resolution using the ‘raster’ package (Hijmans, 2024) in R (R
 Core Team, 2023). We used NDVI and slope to represent vegetation cover and
 topography, respectively. Distance to a village and distance to a water
 body were used as surrogates for human disturbance and fundamental
 resources for carnivores, respectively. LST was used as a substitute to
 explore the micro-climatic conditions at the camera trap station. Prior to
 analyses, we standardized all covariates using
 <em>z</em>-transformation. To check the multicollinearity
 among the covariates, we applied the <em>cor</em> function in
 the ‘corrplot’ package (Wei and Simko, 2017) in R 4.3.2 (R Core Team,
 2023) to perform Pearson’s pairwise correlation test with a threshold
 value of |r| ≥ 0.70 (Figure S1, S2; Dornmann et al., 2013).
 <strong><em>Multispecies occupancy
 modeling</em></strong> We implemented a
 multispecies occupancy model (Rota et al., 2016) of three large carnivores
 of PTR using the ‘unmarked’ package (Fiske and Chandler, 2011) in R ver.
 3.5.1 (R Core Team, 2023). For multispecies occupancy models, a priori
 assumptions are not required to determine the dominant or subordination of
 one species over another (Rota et al., 2016). We investigated how
 environmental and anthropogenic variables affect the marginal occupancy
 (without accounting for interactions with other species), co-occurrence
 (overlap in marginal occupancy between species), and conditional occupancy
 (effects of one species’ presence on another species’ occupancy and
 detection) of tiger, leopard, and hyena in PTR. We compiled the data from
 two seasons (winter and summer of 2019) into 5-day sampling occasions. We
 selected the best marginal occupancy model of each season based on the
 lowest AIC value, using the R package ‘MuMIn’ (Bartoń, 2020), to explain
 the best covariates for each individual species. Based on the top
 candidate model, we constructed pairwise co-occurrence models between
 tiger–leopard, tiger–hyena, and leopard–hyena. Furthermore, we ran the
 multispecies occupancy model for the three carnivore species. Similarly,
 we used the effort as the detection probability. The best models from all
 three different multispecies occupancy categories were treated with a
 penalized likelihood function to obtain a more precise occupancy
 probability (Clipp et al., 2011).
 <strong><em>Temporal
 activity</em></strong> To determine the
 diel activity patterns of carnivores, we considered the independent
 capture events at 30-min intervals (O’Brien et al., 2003) obtained from
 the camera trap data. We used the non-parametric kernel-density functions
 (Ridout and Linkie, 2009) of the ‘overlap’ package (Meredith and Ridout,
 2014) in R 3.5.1 (R Core Team, 2023). This methodology yields a
 coefficient of overlap within the range of 0 to 1, with complete temporal
 separation between two species at 0 and absolute overlap at 1. A threshold
 of Δ &gt; 0.8 was considered indicative of strong overlap, while a
 range of 0.5 &lt; Δ &lt; 0.8 represented moderate overlap, in
 accordance with the criteria established by Lynam et al. (2013). Since our
 sample sizes exceeded 75, we employed the Δ4 estimator (Dhat4). To enhance
 robustness, we computed 10,000 bootstraps for each species and generated
 95% confidence intervals (CIs) for temporal overlap estimates (Meredith
 and Ridout, 2014).
 <strong>Reference:</strong> 
 Bartoń K (2024). MuMIn: Multi-Model Inference. R package version
 1.48.4, https://CRAN.R-project.org/package=MuMIn.
 Chen, M. T., Tewes, M. E., Pei, K. J., & Grassman Jr, L.
 I. (2009). Activity patterns and habitat use of sympatric small carnivores
 in southern Taiwan. Clipp, H. L., Evans, A. L.,
 Kessinger, B. E., Kellner, K., & Rota, C. T. (2021). A penalized
 likelihood for multispecies occupancy models improves predictions of
 species
 interactions. <em>Ecology</em>, <em>102</em>(12),
 e03520. Dornmann, C. F., Elith, J., Bacher, S.,
 Buchmann, C., Carl, G., Carré, G., et al. (2013). Collinearity: a review
 of methods to deal with it and a simulation study evaluating their
 performance. Ecography 36, 027–046. Evans, B. E.,
 Mosby, C. E., & Mortelliti, A. (2019). Assessing arrays of
 multiple trail cameras to detect North American mammals. <em>PloS
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 Fiske, I., & Chandler, R. (2011). Unmarked: an R package
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 abundance. <em>Journal of statistical
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 Hijmans R (2024). raster: Geographic Data Analysis and Modeling.
 R package version 3.6-27, https://rspatial.org/raster.
 Jhala, Y. V., Qureshi, Q., & Nayak, A. K. (Eds.).
 (2018). Status of Tigers Co-predators & Prey in India. National
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 Johnson, A., Vongkhamheng, C., & Saithongdam, T. (2009).
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 & Wibisono, H. T. (2003). Crouching tigers, hidden prey: Sumatran
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description: # Interspecific interactions among major carnivores in Panna Tiger
 Reserve: A multispecies occupancy approach
 [https://doi.org/10.5061/dryad.djh9w0w8h](https://doi.org/10.5061/dryad.djh9w0w8h) ## Description of the data and file structure **Data and File Structure Description:** ***1. species_matrix.csv:*** This file contains the capture history matrix for three carnivore species: tiger, leopard, and hyena. * *TrapCode:* Represents the camera trap station. - *Tiger_1 - Tiger_8:* Columns indicating the capture history of tigers across eight sessions. A value of '0' denotes that the species was not detected at the station, while '1' indicates detection. 'NA' denotes the camera trap was non-functional/ active. * *Leopard_1 - Leopard_8 / Hyena_1 - Hyena_8:* Columns representing the capture history of leopards and hyenas, following the same format as tigers. ***2. effort.csv:*** This file provides data on the effort (trap nights) associated with each camera trap station. * *TrapCode*: Represents the camera trap station. - *Session_1 - Session_8:* Contains the effort values for each sampling occasion, representing the number of trap nights per session. ***3. covariates.csv:*** This file includes environmental and anthropogenic covariates for each camera trap station. *TrapCode:* Represents the camera trap station. *canopy*: Percentage of canopy cover at each camera trap station. *lst:* Land Surface Temperature (°C) at each station, indicating the micro-climate conditions. *dist_v:* Distance to the nearest village (in meters), used as a proxy for human disturbance. *dist_w*: Distance to the nearest water source (in meters), representing a key fundamental resource. *ndvi*: Normalized Difference Vegetation Index (NDVI), reflecting the vegetation cover at each camera trap station. *slope:* Slope of the terrain (in degrees), representing the topographic gradient. ## Code/software The code (R Studio) can be obtained from Rota et al., 2016. [https://cran.r-project.org/web/packages/unmarked/vignettes/occuMulti.html](https://cran.r-project.org/web/packages/unmarked/vignettes/occuMulti.html) Rota, C. T., Ferreira, M. A., Kays, R. W., Forrester, T. D., Kalies, E. L., McShea, W. J., ... & Millspaugh, J. J. (2016). A multispecies occupancy model for two or more interacting species. *Methods in Ecology and Evolution*, *7 *(10), 1164-1173.