Trophic Cascades and Habitat Suitability in Udanti Sitanadi Tiger Reserve: Impacts of Prey Depletion and Climate Change on Predator-Prey Dynamics

© 2025 Academia Sinica, Taiwan Open Access Trophic Cascades and Habitat Suitability in Udanti Sitanadi Tiger Reserve: Impacts of Prey Depletion and Climate Change on Predator-Prey Dynamics Krishnendu Basak1,* , Chiranjib Chaudhuri2,* , M. Suraj1, and Moiz Ahmed1 1Department of Wildlife Conservation, Nova Nature Welfare Society, H. No. 36/337, Choti Masjid, Byron Bazar, Raipur, Chhattisgarh 492001, India. *Correspondence: E-mail: [email protected] (Basak). Tel: +91 9836971670 E-mail: [email protected] (Suraj); [email protected] (Ahmed) 2AeroLine International. 181 Littleton Road, Unit 245, Chelmsford, Massachusetts, United States 01824. *Correspondence: E-mail: [email protected] (Chaudhuri) Received 11 August 2024 / Accepted 27 January 2025 / Published 22 April 2025 Communicated by Teng-Chiu Lin This study investigates the trophic cascades and habitat suitability in Udanti Sitanadi Tiger Reserve (USTR), highlighting the roles of apex predators, subordinate predators, and prey species in maintaining ecosystem balance. Using the Trophic Species Distribution Model (Trophic SDM), we explored prey-predator interactions and habitat suitability, revealing that tigers respond to prey depletion by increasingly relying on cattle, while leopards adapt by preying on smaller species. Additionally, climate change projections for 2021–2040 and 2081–2100 under CMIP6 scenarios SSP245 and SSP585 indicate significant regional habitat shifts, necessitating adaptive management strategies. Kulhadighat is projected to face habitat contraction, while Sitanadi may experience habitat expansion. This study emphasizes the need for effective conservation efforts such as habitat restoration, prey augmentation and predator recovery are the most important steps needed to maintain the purpose of a Tiger reserve and conservation potential of Chhattisgarh-Odisha Tiger Conservation Unit (TCU). To achieve these dynamics, focusing on community participation, anti-poaching measures, and scientific recommendations are the most crucial components to focus on. This comprehensive analysis underscores the critical role of targeted conservation activities in prey-depleted landscapes to ensure the long-term survival of tigers and the overall health of forest ecosystems, enhancing biodiversity and mitigating human-wildlife conflicts in USTR. Key words: Climate impact, Prey-predator interaction, Species distribution modeling, Trophic interaction, Trophic SDM Citation: Basak K, Chaudhuri C, Suraj M, Ahmed M. 2025. Trophic cascades and habitat suitability in Udanti Sitanadi Tiger Reserve: impacts of prey depletion and climate change on predator-prey dynamics. Zool stud 64:07. doi:10.6620/ZS.2025.64-07. BACKGROUND A trophic cascade is described as a process by which a perturbation propagates either up or down a food web with alternating negative and positive effects at different successive levels (Terborgh et al. 2006). Large carnivores are categorized by their large body size and for being apex predators, placed high in the trophic ladder (Edwards 2014). As top predators, they can inhibit the explosion of herbivore and subordinate predator populations in ecosystems, an effect that cascades throughout ecological communities and promotes biodiversity (Wallach et al. 2015). The effect of disappearance of such apex predators proceeds downwards successively across the lower trophic levels, resulting in population increases of mid-sized predators i.e., mesopredator release (Crooks and Soulé 1999; Prugh et al. 2009) or a higher abundance of subordinate Zoological Studies 64: 7 (2025) doi:10.6620/ZS.2025.64-07 1 © 2025 Academia Sinica, Taiwan predators, which may affect herbivores and local vegetation in various ways. Various ecosystems globally are facing heavy extirpation of apex predator populations due to habitat loss and persecution from humans. This has impacts down the line on the lower trophic levels and leads to “mesopredator release” (Estes et al. 2011; Ripple et al. 2014). The mesopredator-release hypothesis predicts that, if present, apex or top predators dominate their subordinate trophic levels, and, if removed, the successive counterparts will be ‘released’ from dominance and may increase in their numbers (Prugh et al. 2009). Studies in North America, Europe, and Australia indicate that wolves Canis lupus, lynx Lynx pardinus, and dingoes Canis lupus dingo are the top predators that control subordinate predators like coyotes Canis latrans and foxes Vulpes vulpes, and further investigate their impact on lower successive levels (Elmhagen and Rushton 2007; Berger et al. 2008; Letnic et al. 2012; Sarmento et al. 2021). In Asia, tiger Panthera tigris is the most iconic predator species, although it suffered serious population decline from anthropogenic pressures such as: prey depletion by human hunting, elimination of tigers for conflict mitigation, hunting for trading of their body parts, and habitat loss or degradation. In spite of conservation efforts over 50 years, wild tigers now occupy < 7% of their historic range. Reproducing tiger populations survive in < 1% of the ~1.6 million km2 potential habitat (Karanth et al. 2020). In India, the tiger is identified as a large predator that occupies the apex position in the food web/ trophic structure of most of the terrestrial ecosystems. They play a crucial role by exerting regulatory pressure on subordinate predators and herbivore populations, thereby regulating and maintaining the balance of forest ecosystems. Removal or local extinction of such predators may alter the stability of the ecosystem and bring considerable adverse changes. Securing tigers thus safeguards micro-niches in the forest ecosystem, which conserves life forms at the smallest levels, ensuring water and climate security as well (Jhala et al. 2020). Moreover, as a highly threatened and flagship species, the tiger absorbs a continuous flow of funding, which in turn serves a wide range of conservation benefits in India. As a top predator, tiger is successfully surviving in the country as compared to other tiger-ranging countries in Asia. In the last few decades, studies and monitoring programs aimed at large carnivore ecology in India has revealed that the tiger population is largely stable and increasing in the country. The project Tiger, started in 1972 with nine tiger reserves (~18,278 km2), has now extended to 53 tiger reserves (~75,796 km2) and successfully engaged about 2.23% of the geographical area of India that supports conservation of representative ecosystems and biodiversity therein (Qureshi et al. 2023). This rising population of tigers is sharing their space and resources with other copredators. Among these, leopards Panthera pardus are most frequently found to co-occur with tigers in various landscapes across India. Due to various ecological and administrative factors, the abundance of tigers is not evenly distributed in India; rather, many of the tiger reserves have insignificant number of tigers or no tigers at all, allowing the subordinate predators to utilize all the trophic levels in a habitat. This study was conducted in the Udanti Sitanadi Tiger Reserve (USTR), in central India. This forest is famous for its tiger population, a relict population of Asiatic wild buffalo Bubalus arnee and its rich avian and reptile diversity. From the time of its establishment in 2009, it is facing political unrest that has restricted all the ecological monitoring activities and paid a toll in terms of wildlife management (Stripes 2011; Putul 2021; Noronha 2022). Previous studies revealed that despite the designation of a tiger reserve, USTR had only one or two tigers from 2016–2022 (Jhala et al. 2020; Qureshi et al. 2023; Basak et al. 2023) in the entire tiger reserve encompassing an area of 1842.54 km2. Few studies have previously investigated the status of tigers, its co-predators and their prey species in USTR along with the possible threats to the tiger reserve (Basak et al. 2020 2023). In these studies, the authors explored the role of wildlife conservation in this landscape which was unnoticed for a long period of time since it was considered as an area of serious political insurgencies. They highlighted that generally, removal of top predators results in releases/increase of subordinate predators into the habitat, but in case of USTR despite the low population size of tigers, the leopard population did not increase as expected. Rather, it was found to be plummeting along with their sparse prey population in the presence of high human activity across the landscape (Basak et al. 2023). Forest dwelling communities living inside and outside the reserve are using USTR as their hunting ground and continue their traditional practice in the areas of the reserve (Basak et al. 2020). Their age-old uncontrolled hunting traditions pose serious threats to the wild ungulate populations, consequently affecting the food resources of carnivore populations in the study area. Moreover, USTR was under prolonged political violence and social unrest that acted as potential hindrance for curbing illegal activities in USTR within the existing legal frameworks. In this critical situation of wildlife conservation in USTR, interventions through species recovery plans are needed urgently. Recovery projects need selection of suitable sites that match the biotic and abiotic needs of the focal page 2 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan species under current and future climatic conditions. Therefore, in this study, we have aimed at unfolding the prey-predator interactions and habitat suitability for tiger and leopard by using the trophic SDM (webSDM, https://github.com/giopogg/webSDM) model (Trainor et al. 2014; Poggiato et al. 2022; Cosentino et al. 2023) in USTR. The Trophic SDM is a statistical distribution model that models the distribution of species by involving known trophic interactions among the species network present in an area. It provides a useful insight into the concept of trophic cascades in ecological science, which is practical in conservation and management demands. Careful use of the outcomes of the TrophicSDM analysis can be highly useful for wildlife managers and decision makers to predict and prioritize areas for species recovery in a prey-depleted landscape like USTR with the assistance of local communities. The objective of our study is three-fold, (1) Understanding the Trophic Cascade of USTR: This objective aims to investigate the cascading effects within the Udanti Sitanadi Tiger Reserve (USTR). By examining the interactions between different trophic levels, we will gain insights into how the presence or absence of apex predator, such as tigers, influences the population of subordinate predator leopard and prey species, ultimately affecting the entire ecosystem, (2) Establishing a Trophic Species Distribution Model (SDM): The goal here is to develop a Trophic SDM that incorporates known trophic interactions among species in USTR. This model will help us understand the intricate connections between tigers, leopards, and their prey. By integrating biotic and abiotic factors, the model aims to provide a detailed analysis of species distributions and habitat suitability, offering valuable insights for conservation efforts and (3) Formulating Comprehensive Recommendations for Apex Predator Habitat Recovery: Based on the findings from the trophic SDM and field observations, this objective focuses on developing actionable recommendations for the recovery and management of apex predator habitats in USTR. These recommendations will be grounded in mathematical modeling and empirical data, aiming to enhance habitat suitability, mitigate human-wildlife conflicts, and support the long-term survival of tigers and other co-predators within the reserve. Study Area USTR is spread over 1842.54 km2 of Gariyaband and Dhamtari districts of Chhattisgarh, central India (Fig. 1). It constitutes of Udanti and Sitanadi Wildlife Sanctuaries as cores and Taurenga, Indagaon, and Kulhadighat Ranges as buffers. The topography of the area includes hill ranges with intercepted strips of plains and lies in the basin of the Mahandi River. The forest types are chiefly dry tropical peninsular sal forest and southern tropical dry deciduous mixed forest (Champion and Seth 1968). Sal Shorea robusta is dominant, mixed with Terminalia sp., Anogeissus sp., Pterocarpus sp., and bamboo species. Sitandi includes dry teak forest, dry peninsular sal forest, and north dry mixed deciduous forest, as per Champion and Seth, 1968. Natural teak forests are mostly found in patches on the alluvial soil along the streams and rivers, while teak plantations have been established in other areas (Kanoje 2008). Schleichera oleosa, Terminalia arjuna, Terminalia tomentosa, Mangifera indica, Syzigium cumini, Fig. 1. Map of Central Indian landscape complex (CILC) showing location of present study area Udanti-Sitanadi Tiger Reserve (USTR). (Map Data: Google Satellite Basecamp). N page 3 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan Eugenia heyneana, Ficus racemosa, Ficus lacor, Ficus bengalensis, and Stereospermum chelonoides, all of which are characteristic of riverine or riparian areas. The tiger Panthera tigris is the apex predator in USTR, and other co-predators are the leopard Panthera pardus, dhole Cuon alpinus, Indian grey wolf Canis lupus, striped hyena Hyaena hyaena, and sloth bear Melursus ursinus. Various wild ungulate species are available as prey bases in the tiger reserve, ranging from small-sized Indian mouse deer Moschiola indica, fourhorned antelope Tetraceros qudricornis, and barking deer Muntiacus vaginalis, to mid-sized spotted deer Axis axis and wild pig Sus scrofa, to large-sized gaur Bos gaurus, sambar Rusa unicolor, and nilgai Boselaphus tragocamelus. Smaller carnivores include the jungle cat Felis chaus, rusty-spotted cat Prionailurus rubiginosus, and golden jackal Canis aureus. Apart from these, this landscape has a small population of Asiatic wild buffalo Bubalus arnee and forest areas with recently invaded transient herds of elephants Elephas maximus. Moreover, it houses 246 species of terrestrial and wetland birds (Bharos et al. 2018). Hence, this landscape supports high diversity and is worthy of conservation efforts. The aresa’ hilly topography is intercepted by plain strips which together play an important role in connectivity of the ChhattisgarhOdisha Tiger Conservation Unit (TCU). In the east, the tiger reserve is contiguous with the proposed Sonabeda Tiger Reserve in Odisha, forming the Udanti-SitanadiSonabeda Landscape which spreads over 3000 km2. In the west, the tiger reserve is connected to Indravati Tiger Reserve in the Bastar region, and in the north, it is connected to Dhamtari and Gariyaband Forest Divisions and further to Barnawapara Wildlife Sanctuary in Mahasamund District. Thus, this TCU has the potential to be of significant importance in the future of wildlife conservation (Qureshi et al. 2023; Basak et al. 2023). MATERIALS AND METHODS Collection of Species-Occurrence Data The National Tiger Conservation Authority (NTCA) and the Wildlife Institute of India (WII) developed a protocol for nationwide estimation and monitoring of tiger and prey populations, as well as their habitats, outlined in A Protocol on Phase-IV Monitoring (Technical Document No. 1/2011). Under this framework, it is essential to monitor changes in tiger populations through intensive surveillance of source populations within tiger reserves and protected areas across tiger landscape complexes (Phase-IV). This approach includes maintaining a centralized photo database of tigers at the NTCA (2011), derived from camera traps deployed throughout the reserves. Occurrence data were collected by repeatedly conducting camera trapping surveys in USTR from 2016 to 2017 and in 2018. We conducted camera trap‐based surveys under Phase IV tiger monitoring framework, in 2016–17 to obtain captures of large predators in USTR mainly focused on tiger bearing areas and areas where the chances were higher of having photo-captures of co-predators. The standard protocol involves a camera trap density of one camera pair within a 4–5 km2 grid. However, the placement of cameras is flexible and depends on the intensity of animal movements within the grid. While the center of the grid is generally considered optimal, cameras can be positioned anywhere within the grid where focal animal activity is observed to be high. The ranges were divided into 4 km2 grids and those grids were used for deploying cameras. Overall, 136 camera trap stations were installed in this session, in three different blocks across North Udanti, South Udanti, and Kulhadighat ranges. The next camera trapping session was carried out during All India Tiger Monitoring (AITM) program in 2018 when 2 km2 grid size was used for camera trapping. During this session, we covered Arsikanhar, Risgaon, Sitanadi and Kulhadighat ranges by installing 182 cameras. We deployed the cameras as per the results obtained from carnivore sign surveys before camera trapping. The total sampling duration was 90 days (about 3 months) each for 2016–17 and 2018 camera trapping sessions, while cameras were operational for 30 days in each block. Two camera traps were deployed in each location around forest trails based on indirect evidence of wild carnivore utilization, where possibility of photo‐ capturing them was higher. We deployed each camera at least 4–5 m from the center of each trail to capture full frame pictures of predators. All cameras were placed at knee height (1.5 feet) from the ground level to obtain identifiable animal‐flank photographs. Photo-captures of various herbivore and carnivore species obtained from these two camera trapping sessions were arranged to organize the data based on their presence and absence in the study area. We assigned 1 and 0 values for species presence and absence respectively for each camera point within the sampling time frames. Collection of Animal-Trap Data Hunting by using animal traps is now an alarming issue in places where biodiversity and hunting communities co-occur. Wild animals are often scared, suffocated and killed brutally while entrapped in such animal traps. The list of species that suffer traumatic page 4 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan killing in traps may start from a small rodent to an animal as large as an elephants. Controlling or reducing such criminal activities need very tedious and hard efforts from the concerned conservation authorities. Chhattisgarh is no exception in this case. USTR homes hunting communities who use these forests as their traditional hunting ground. With the advancement of the surrounding world gradually these communities have also adapted urban materials to manufacture traps to catch animals instead using traditional bows, arrows and natural materials. This landscape is a mosaic of forests and human dominated areas. Villages in this landscape have mostly tribal populations belonging to Kamars, Baigas, Gonds, Bhunjiyas and miscellaneous tribes who continued their traditional hunting for bush meat consumptions, illegal livelihoods, recreation and sports as well. Under such conditions, Anti-Snare Walks were conceptualized and conducted in 2021 to create awareness, reduce the effect of snare traps and wildlife poaching in the area and to initiate a practice of curbing down wildlife related crimes by involving the communities and the concerned Government authority. In addition to these, as a part of this program, frontline forest staffs were also trained to detect and destroy various animal traps that are used by the local communities to kill various mammalian species that ranges from small rodents to large herbivores like sambar. Overall, 97 beats were walked to uninstall snares and other traps. On average overall search effort was 6.21 km/walk, for USTR. GIS-Data Pre-processing As per the methodology, camera traps could be positioned anywhere within the designated grids based on animal movement intensity. So it was determined that the minimum distance between camera traps was 200 meters. Therefore, in our GIS analysis, we utilized a 100-meter grid, which is half of the estimated minimum distance, to ensure finer spatial resolution and more detailed extraction of zonal statistics. To improve model performance, we applied a quantile transform to the input variables with a target normal distribution. In this manuscript, we did not conduct formal collinearity checks because we were unsure of how they would affect the Trophic SDM, especially given our use of quantile-transformed variables. Nonetheless, we acknowledge that collinearity among predictor variables can potentially influence model performance, and we plan to explore appropriate variable-filtering approaches in future studies. These variables encompass a wide range of environmental and geographical attributes, including distances, aspect categories, land cover, and bioclimatic variables (Table S1). Further details are in the supplementary material section 1. Trophic Model The Trophic Species Distribution Model (TrophicSDM) (Poggiato et al. 2022) utilized in this study provides a sophisticated framework for understanding predator-prey interactions within the Udanti-Sitanadi Tiger Reserve (USTR). By integrating trophic relationships alongside abiotic factors, such as climate, terrain, land cover, and distance from anthropogenic layers, this model offers a detailed analysis of species distributions and habitat suitability. TrophicSDM sheds light on the ecological consequences of varying apex predator populations, such as tigers, and their influence on subordinate predators, such as leopards, and their prey species. This approach is especially pertinent in ecosystems like USTR, where apex predator numbers are critically low, allowing for the examination of ecological changes. In USTR, TrophicSDM was used to identify interactions between tigers, leopards, and their prey, which in turn was used to propose effective conservation practices. The hypothesized trophic connectivity diagram (Fig. 2) shows the interactions between prey and their predator species in USTR, specifically with tigers and leopards as the primary predators. Tigers are connected to a range of prey species, including sambar, nilgai, Indian gaur, wild pig, spotted deer, and cattle; this supports that the tigers are majorly inclined towards the large to middle sized prey species (Karanth and Sunquist 1995; Hayward et al. 2012; Basak et al. 2018 2020). Tiger’s preference for larger prey provides Fig. 2. The Trophic connectivity expected in Udanti-Sitanadi Tiger Reserve (USTR), Chhattisgarh, Central India. page 5 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan insights into their requirement of substantial biomass, which aligns with their status as apex predators requiring significant energy intake. Tigers’ inclusion of livestock in their diet can be attributed to the availability of higher biomass cattle, especially when wild prey is scarce. On the other hand, leopards are known for their adaptability to changing environment, often by widening their food choices as per the availability of prey species that includes varied prey sizes as well (Eisenberg and Lockhart 1972; Santiapillai et al. 1982; Johnsingh 1983; Rabinowitz 1989; Seidensticker et al. 1990; Bailey 1993; Karanth and Sunquist 1995; Daniel 1996; Edgaonkar and Chellam 1998; Sankar and Johnsingh 2002; Qureshi and Edgaonkar 2006; Edgaonkar 2008; Mondal et al. 2011; Sidhu et al. 2017). This flexibility in turn allows them to survive in diverse environments, including areas close to human settlements where livestock might be more accessible. In this landscape too, leopards are known to showcase their adaptability and opportunistic feeding behaviour (Basak et al. 2020). TrophicSDM analysis supports this too as we see that leopard’s prey includes both smaller animals, like Indian hare, northern plains langur, four horned antelope, mid-sized prey, such as wild pig to large sized preys like spotted deer and cattle. Figure 2 highlights the differences in biomass and size preferences between tigers and leopards. Tigers prefer larger prey that meets their high energy demands, while leopards exhibit a broader dietary range, adapting to prey availability and habitat conditions. The model has derived trophic relations between the predator and prey species of USTR and enhanced our understanding of how the bottom-up effect can impact the predators in such landscape where availability of both large and middle-sized prey species is shaping the large predator interactions and population dynamics. Trophic connections were derived at different confidence levels: 90% (a), 80% (b), and 70% (c). It is predicted that at 90% confidence, the connections will be conservative and show only significant relations. Whereas 80% and 70% confidence level will make the limit flexible and hence, can exhibit wider ranges of interactions that will be suitable for tigers in USTR, if prey sample increases. Model Setup for Trophic Interactions To set up our trophic model, we utilized a Bayesian framework implemented in the stan_glm function. Our model was designed with a binomial output using a logit-link function to accurately capture the probabilistic nature of abiotic and biotic interactions. We ran two independent Markov Chain Monte Carlo (MCMC) chains, each with a total of 2000 samples. To ensure the convergence and stability of our estimates, we specified a burn-in period of 1000 samples for each chain, discarding these initial samples to mitigate the influence of the starting values. Justification for using Trophic Model The justification for using the trophic model over the non-trophic model for tigers and leopards is compelling based on the provided statistics. For tigers, the trophic model exhibits a higher AUC (0.84 vs. 0.79) and TSS (0.66 vs. 0.51) based on the mean from a 5-fold cross-validation experiment, indicating superior predictive performance. Although the non-trophic model shows slightly higher AUC (0.69 vs. 0.66) and TSS (0.33 vs. 0.27) for leopards, the overall fit statistics favor the trophic model, which has a lower AIC (4442.54 vs. 4449.92) and a higher log-likelihood (-1817.27 vs. -1828.96), indicating a better fit to the data. The lower AUC for leopards could be due to their behavior as habitat generalists, thriving in diverse environments and making their presence more challenging to predict. In summary, the trophic model demonstrates better predictive power and fitness, particularly for tigers. By capturing crucial ecological interactions, it offers a more accurate and comprehensive representation of their natural habitats and behaviors, making it a valuable tool for conservation efforts. Trophic interaction In this experiment, we aimed to compare the observed habitat (Fig. 3a) with a hypothetical habitat of tigers (Fig. 4) by manipulating prey availability. Specifically, we excluded cattle from the tiger’s diet and made sambar and spotted deer available throughout the study area. The observed habitat represents the actual conditions and resources a species utilizes in the presence of biotic interactions, such as competition and predation, while the hypothetical habitat encompasses the potential range of conditions and resources a species could theoretically use without such interactions. Tigers have increasingly relied upon cattle due to the low wild ungulate population and the high biomass of available livestock. By ensuring the widespread availability of their preferred natural prey, such as sambar and spotted deer, and removing the availability of livestock, we observed changes in the tigers’ habitat use and distribution (Fig. 4). This adjustment was made to understand the tiger’s natural dietary preferences and habitat requirements better, free from the constraints of current prey availability. page 6 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan Fig. 3. Habitat of (a) tiger, and (b) leopard in USTR, estimated using the optimal threshold from Youden method. Fig. 4. Hypothetical tiger habitat assuming absence of cattle and maximum abundance sambar and spotted deer. (RC*Range contraction, RE*Range expansion, NC*No Change, NO*No habitat). page 7 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan Habitat Experiments We estimated the optimal threshold for each of the 200 MCMC inference samples using the Youden method, which identifies the threshold that maximizes the sum of sensitivity and specificity, thereby balancing true positive and true negative rates. Then, using a 90% confidence level of prediction, we set the 10th percentile of the sample optimum thresholds as a fixed threshold for experiments. Subsequently, we classified any region with a habitat suitability value higher than this threshold as Habitat, and values lower than this threshold as Not Suitable. For the subsequent habitat change experiment, we compared the realized habitat with the postexperiment simulated habitat. Grids that are not habitat in both scenarios are classified as NO (no habitat). Habitats that remain unchanged are classified as NC (no change). Areas where habitat expands are classified as RE (range expansion), and areas where habitat contracts are classified as RC (range contraction). Climate Change Experiment Setup For our climate change impact assessment on habitat suitability in the Udanti Sitanadi Tiger Reserve, we utilized habitat projections for the periods 2021–2040 and 2081–2100 under scenarios from the Coupled Model Inter-comparison Project Phase 6 (CMIP6), specifically SSP245 (low-emission scenario) and SSP585 (high-emission scenario). We employed four climate models: CMCC-ESM2, GISS-E2, HadGEM3-GC3, and UKESM1. The methodology involved swapping the bioclimatic variables from the abiotic layers with their equivalent bias-corrected bioclimatic variables for each model-scenario-time slice combination. RESULTS This section presents an in-depth analysis of habitat suitability for tigers and leopards in USTR, exploring the key predictors and ecological factors influencing their distribution. Subsequent subsections delve into trophic interactions between predators and prey, examine the impacts of habitat manipulation experiments on tiger distribution, and evaluate projected habitat shifts under climate change scenarios for both mid-century (2021–2040) and late-century (2081–2100) timeframes across various climate models and emission pathways. Predictor Significance Analysis of Habitat Suitability in USTR The habitat suitability analysis for various species in the Udanti Sitanadi Tiger Reserve provides insights into the critical abiotic and biotic factors influencing their distribution (Table 1). This discussion aims to elucidate these factors to develop effective conservation and management strategies within the reserve. Tiger Habitat Suitability In our trophic species distribution model (SDM) for tiger habitat, several variables emerged as significant predictors, each contributing differently to the model. The intercept, with a mean value of -9.45, represents the baseline log-odds of tiger presence when all predictor variables are set to zero. This negative value suggests that, in the absence of other factors, the likelihood of tiger presence is inherently low, highlighting the importance of the additional variables in predicting suitable tiger habitats. We have observed tigers mostly in Kulhadighat and Phase IV Block 2, with detections at 22 sites, and a total count of 45 observations. In our analysis, 255.88 km2 is found to be suitable for tiger habitat (Fig. 3a). One of the most significant variables is the minimum distance to the nearest snare (Min_Snare_ Distance), which has a mean coefficient of -1.48. This indicates that tigers are more likely to be found near snares, suggesting higher habitat suitability in these areas, likely due to higher prey abundance. However, this proximity increases the risk of animals being caught in traps, underscoring the urgent need for anti-poaching measures. Addressing this issue is crucial for protecting both prey and predator species within the reserve. Aspect category 2 (ASPECT_cat_2), representing East-facing slopes, has a positive mean coefficient of 0.36. This suggests that tigers are more likely to be found in habitats with these slope characteristics. Additionally, steep slopes categorized under SLOPE_ cat_4 (very steep slopes, > 30 degrees) also positively influence tiger presence, with a mean coefficient of 0.80. These findings indicate that tigers may prefer certain topographical features, possibly due to the cover and hunting advantages they provide. Among the bioclimatic variables, wc2.1_30s_ bio_12 (Annual Precipitation) and wc2.1_30s_bio_13 (Precipitation of Wettest Month) show contrasting effects. Annual precipitation has a negative mean coefficient of -5.49, suggesting that higher annual precipitation levels are associated with lower tiger presence. In contrast, precipitation during the wettest month has a positive mean coefficient of 5.46, page 8 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan indicating that areas with high precipitation in the wettest month may be more favourable for tigers. This contrast highlights the complex relationship between precipitation patterns and tiger habitat suitability. Lastly, the presence of cattle is a significant positive predictor, with a mean coefficient of 2.60. This suggests that even though tigers prefer to hunt wild ungulates with large biomass, when there is critically low abundance of such large-sized ungulates in USTR, they exploit available cattle population to meet their energy needs in the reserve. This practice of preying on cattle indicates a potential for human-tiger conflict in this area. Leopard Habitat Suitability In our trophic species distribution model (SDM) for leopard habitat, several variables emerged as significant predictors, each contributing differently to the model. The intercept, with a mean value of -2.87, represents the baseline log-odds of leopard presence when all predictor variables are set to zero. This negative value suggests that, in the absence of other factors, the likelihood of leopard presence is inherently low, underscoring the importance of the additional variables in predicting suitable leopard habitats. Unlike tiger we have observed Leopard presence in almost all the subdivisions, with detections at 159 sites, and a total count of 388 observations. In our analysis, 430.17 km2 is found to be suitable for Leopard habitat (Fig. 3b). Aspect categories play a notable role in determining leopard habitat preferences. ASPECT_cat_1 (North-facing slopes) has a positive mean coefficient of 0.15, indicating that leopards are more likely to be found on these slopes. Conversely, ASPECT_cat_4 (West-facing slopes) has a negative mean coefficient Table 1. Contribution of Environmental variables and selected prey species in suitable habitats of tiger and leopard in USTR. Min_Snare_Distance (Minimum distance to snare within a 0.1 km grid), ASPECT_cat_1 (North facing slopes), ASPECT_cat_2 (East facing slope), ASPECT cat 4 (West facing slope), wc2.1_30s_bio_5 (Max Temperature of Warmest Month), wc2.1_30s_bio_8 (Mean Temperature of Wettest Quarter), wc2.1_30s_bio_11 (Mean Temperature of Coldest Quarter), wc2.1_30s_bio_12 (Annual precipitation), wc2.1_30s_bio_13 (Precipitation for wettest month), ESRI_LULC_cat_11 (Forests), ESRI_LULC_cat_2 (rangelands/croplands), Slope cat 3 (steep slopes, 15–30 degree), Slope cat 4 (very steep slopes, > 30 degree), Cattle, Wild Pig, Northern Plains Langur and Indian Hare Tiger Variable mean 5% 95% Intercept -9.452 -14.666 -4.595 Min_Snare_Distance -1.480 -2.483 -0.614 ASPECT_cat_2 0.365 0.027 0.758 SLOPE_cat_4 0.799 0.267 1.355 wc2.1_30s_bio_12 -5.491 -10.461 -0.563 wc2.1_30s_bio_13 5.464 0.498 11.499 Cattle 2.601 1.097 4.219 Leopard Variable mean 5% 95% Intercept -2.868 -4.858 -0.849 ASPECT_cat_1 0.151 0.057 0.250 ASPECT_cat_4 -0.124 -0.222 -0.034 ESRI_LULC_cat_11 6.134 2.650 9.404 ESRI_LULC_cat_2 6.226 2.694 9.564 SLOPE_cat_3 0.163 0.018 0.320 wc2.1_30s_bio_11 6.362 1.248 11.791 wc2.1_30s_bio_12 1.284 0.165 2.372 wc2.1_30s_bio_5 2.278 0.204 4.633 wc2.1_30s_bio_8 -3.890 -7.738 -0.243 Wild_Pig 0.538 0.010 1.087 Northern_Plains_Langur 1.046 0.550 1.575 Indian_Hare 0.796 0.269 1.351 page 9 of 18Zoological Studies 64: 7 (2025) © 2025 Academia Sinica, Taiwan Regular engagement with local communities is essential, with targeted awareness programs designed for children, students, youth, and adults. These initiatives foster rapport between management and villagers, which is critical for reducing activities such as trapping and poaching. Building strong and efficient networks with communities residing within the reserve can further strengthen these efforts. Additionally, implementing regular anti-snare walks and snare removal programs will significantly curb poaching activities. Such proactive measures ensure a safer habitat for wildlife. Strengthening legal actions against poaching is equally vital to reinforce the rule of law and demonstrate the effectiveness of legal institutions in combating wildlife crimes. To address human-wildlife conflicts, specialized teams should be deployed to report incidents, follow up on cases, and assist villagers in filing for compensation. Providing timely support in securing compensation from the concerned authorities can build trust and reduce conflict between the communities and the reserve authority. These supportive measures can positively impact local communities, encouraging cooperation and enabling authorities to better manage conflicts in the future. Acknowledgments: We express earnest gratitude to Shri Kaushalendra Singh, former PCCF (Wildlife) and Shri Sudhir Agarwal, PCCF (Wildlife), Chhattisgarh. We also convey our gratitude to Mr. K. Murugan, former CCF (Wildlife) for his initiative and continuous support during the project implementation period. We would like to convey our deepest gratitude to Shri O.P. Yadav, Member Secretary Chhattisgarh for his trust in Nova Nature Welfare Society and provided with every possible support. We are always grateful to Chhattisgarh State Forest Department to keep faith on us and providing necessary permission and essential financial support to conduct the study. We would also like to sincerely mention that part of this research was supported by The Habitat Trust, Conservation Hero Grant 2020. We are thankful to Shri B.V. Reddy, Divisional Forest Officer, Shri Nair Vishnu Narendran, Divisional Forest Officer and Shri Ayush Jain, Divisional Forest Officer for their constant support during the field. We also want to thank Shri Sunil Sharma, SubDivisional Forest Officer for his directions in the field, without such guidance it might be impossible to collect data from the difficult terrain of Udanti Sitanadi Tiger Reserve. We truly appreciate Mr. Amit Kumar for his tedious effort regarding GIS data procurement and preprocessing. A heartfelt thanks to Dr. Upasana Ganguly for her invaluable support in enhancing the fluency and readability of the manuscript.We would like to convey our sincere thanks to biologist Mr. Chiranjivi Sinha for his rigorous contribution in the field during the tiger monitoring program. We also thank Mr. Ajaz Ahmed, Mr. Nitesh Sahu, Mr. Om Prakash Nagesh and the entire team from Nova Nature Welfare Society for their contribution to field works and all frontline forest staff from USTR for their support during the study. Authors’ contributions: Krishnendu Basak: substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; drafting the article or revising it critically for important intellectual content; and final approval of the version to be published. Chiranjib Chaudhuri: substantial contributions to conception and design, data analysis and interpretation of data. Drafting the article or revising it critically for important intellectual content; and final approval of the version to be published. M Suraj: Acquisition and interpretation of data. Drafting the article and final approval of the version to be published. Moiz Ahmed: Acquisition and interpretation of data. 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