Research

INTRODUCTION

Globally, biodiversity is declining at unprecedented rates, with extinction rates orders of magnitude higher than natural baselines (IPBES 2019, World Wide Fund for Nature 2024). Such losses are worrisome, because many species are lost forever, but also because biodiversity underpins healthy ecosystems and supports numerous ecosystem services such as climate stability, soil fertility, food production, and water resources that support human well-being. Currently, habitat destruction is the leading driver of biodiversity loss, particularly where agriculture expands into tropical and subtropical forests (IPBES 2019, Williams et al. 2021). Approximately 6.4–8.8 million ha of forests were cleared annually between 2011 and 2015 for agriculture across tropical regions (Pendrill et al. 2022), leading to often devastating biodiversity outcomes, particularly where habitat destruction interacts with overexploitation (i.e., hunting, harvesting, logging; Peres 2001, IPBES 2019, Romero-Muñoz et al. 2020). These pressures on biodiversity are expected to increase further in the future (Pereira et al. 2024), making it important to understand the drivers and patterns of past biodiversity losses.

Terrestrial megafauna (here, species > 20 kg) are disproportionately affected by habitat destruction, and their loss can be catastrophic for ecosystems (Malhi et al. 2016). Megafauna play a critical role in influencing many ecological processes, including nutrient cycling, seed dispersal, and controlling the spread of invasive plants (Macdonald et al. 2013, Doughty et al. 2016, Mungi et al. 2023). For instance, large herbivores store carbon in ecosystems by moving carbon from plants to more stable soil carbon storage (Kristensen et al. 2022). Large carnivores also help maintain ecological balance through top-down control of food webs (Ripple et al. 2014). Moreover, many megafauna species are deeply embedded in Indigenous communities’ values, cultures, and traditions, symbolizing more than just their ecological importance (Chardonnet et al. 2002). Finally, megafauna are also important targets for conservation because they often act as umbrella species, and protecting them can benefit many other organisms that share the same habitat (Andelman and Fagan 2000, Di Minin and Moilanen 2014).

Despite their significance, megafauna are threatened because of their large body sizes, specific habitat needs, and life-history traits (Macdonald et al. 2013). Many megafauna require large habitat areas to maintain viable populations, but habitat destruction increasingly pushes them into small, isolated patches (Leimgruber et al. 2003, Prugh et al. 2008). While protected areas have played a crucial role in conserving the megafauna populations within them (Barnes et al. 2016), many have failed to meet the ecological needs of these species because they are too small to sustain viable populations (Graham et al. 2009, Cantú-Salazar and Gaston 2010). Consequently, as megafauna seek resources and space beyond protected areas, they often spill over into adjacent human-modified landscapes. Over time, some species may adapt to these landscapes, for instance, by using food sources linked to humans. Sometimes, megafauna can even favor these landscapes over protected areas (Yirga et al. 2012, Athreya et al. 2013, Behera et al. 2024). At the same time, where megafauna and people share landscapes, human-wildlife conflicts can ensue, for instance, where large carnivores prey on livestock, large herbivores raid crops, or megafauna pose a risk to human life (Acha et al. 2018, Gebresenbet et al. 2018, McKay et al. 2018). Managing and mitigating such human-wildlife conflicts are major challenges for conserving megafauna (Nyhus 2016, Lindsey et al. 2017). Overall, these dynamics underscore the need for conservation strategies that extend beyond the boundaries of protected areas, addressing both the ecological requirements of megafauna and the socioeconomic realities of human communities that share landscapes.

If the successful conservation of megafauna populations depends on how well they are protected across larger, human-dominated landscapes, then it is essential to gain an understanding of the complex social-ecological factors that shape these landscapes, leading to differences in megafauna retention and loss. Although environmental factors offer insights into habitat suitability, they alone cannot capture the full spectrum of human influences. It is crucial to include social, demographic, and economic profiles of local communities, which capture human livelihoods and patterns of natural resource use, to advance our understanding of where megafauna persist across heterogeneous landscapes because pressures on megafauna are often dynamic and fluctuate over time. For instance, incorporating variables related to types of livelihood strategies (e.g., subsistence farming, hunting, forest-based activities) and the number of people engaged in them can offer valuable insights into patterns of megafauna persistence because these factors together influence the intensity and spatial distribution of human pressures through varying levels of land and resource use (Escamilla et al. 2000, Naughton-Treves et al. 2003, Kanapaux and Child 2011). Furthermore, such factors can also highlight those regions where law enforcement is weak or where alternative resources are scarce.

Similarly, human demographic variables such as population size, age, gender, poverty level, education, and household size, also provide important contextual insights into the dynamics of megafauna persistence. For instance, densely populated regions, in addition to having higher poverty levels, may exert greater extractive pressures on natural habitats, increase poaching events, or increase human-wildlife encounters, potentially reducing the tolerance of local communities toward megafauna (Angelsen et al. 2014, van de Water and Matteson 2018, Lunstrum and Givá 2020). Conversely, areas with declining or ageing populations or where out-migration is prevalent may experience land abandonment and subsequent habitat regeneration, creating opportunities for wildlife recovery (Bowen et al. 2007, Tsunoda and Enari 2020). Similarly, accounting for gender differences in attitudes, perceptions, and behaviors toward megafauna, as well as recognizing the disproportionate costs that women often bear from human-wildlife interactions, can reveal important social dimensions that influence support for conservation initiatives. Given the widespread conversion of natural habitats into human-managed landscapes (Ellis et al. 2021), the socioeconomic profiles of local communities are likely to be key determinants of resource availability and habitat conditions for megafauna.

Understanding these issues is particularly critical for the world’s tropical dry woodlands. Tropical dry woodlands constitute approximately 20% of the global land surface (Dinerstein et al. 2017) and are exceptionally rich in biodiversity (Periago et al. 2015, Benítez-López et al. 2019). They also support millions of people, providing food, fuelwood, and shelter. However, expanding commodity agriculture has placed intense pressure on these ecosystems, leading to the loss of > 70 million ha of woodland globally since 2000 (Buchadas et al. 2022). This and other human activities have turned many dry woodland regions into defaunation hotspots. India is rich in dry woodlands, which constitute > 45% of tiger (Panthera tigris tigris) habitats (Chundawat et al. 1999) and support notable Asian elephant (Elephas maximus) populations (Fernando and Leimgruber 2011). However, historical and ongoing land-use changes, including colonial-era logging for timber (Bhojvaid et al. 2016), agricultural expansion, and infrastructure development, have driven habitat loss (Reddy et al. 2015, Ramachandran et al. 2018) and degradation (Davidar et al. 2010, Thakur et al. 2022). The legacies of historical land-use changes can persist over long periods (Munteanu et al. 2015, Semper-Pascual et al. 2021). For instance, colonial practices led to the loss of 86% of suitable habitats for wild elephants in India, resulting in their patchy and constrained distributions seen today (de Silva et al. 2023). Similarly, gaur (Bos gaurus) distributions have been shaped by habitat destruction, poaching, and insurgency since the 1950s (Choudhury 2002). Although India’s extensive network of > 1000 terrestrial protected areas (Koulgi et al. 2019) has been crucial in protecting megafauna, these protected areas are constrained by insufficient size, poor connectivity, and limited coverage (Ghosh-Harihar et al. 2019). Therefore, adopting a landscape-level approach that integrates human and megafauna needs could help to meet conservation targets beyond protected areas (Jiang et al. 2017). However, to do so, one must understand the social-ecological factors that shape contemporary megafauna distributions. Specifically, we assessed two overarching research questions.

1. Which social-ecological conditions are associated with contemporary megafauna presence in India?
2. How important are present-day vs. historical landscape patterns, specifically extent of dry forest, in determining contemporary megafauna distributions?

STUDY AREA

We define tropical dry woodlands as the following biomes: tropical and subtropical dry broadleaf forests, and desert and xeric shrublands (Fig. 1), based on the revised classification by Dinerstein et al. (2017), originally established by Olson et al. (2001). This corresponds to Champion and Seth’s (1968) definition of Indian tropical dry forests, which includes: tropical dry evergreen forests, tropical dry deciduous forests, and tropical thorn forests. Geographically, these dry woodlands extend from Punjab in the north to Tamil Nadu in southern India, and from Bengal in the east to Gujarat in the west. Dry woodlands are characterized by seasonal precipitation patterns ranging from 900–1500 mm, with average temperatures between 18°C and 35°C, and long dry seasons (Champion and Seth 1968).

Tropical dry woodlands in India host diverse habitat types and vegetation formations from closed, deciduous, and evergreen forests to shrublands to more open savanna systems and grasslands (Champion and Seth 1968). These woodlands are rich in biodiversity. For instance, dry woodlands in Sariska Tiger Reserve harbor > 190 bird species (Shahabuddin et al. 2004), dry woodlands of the Deccan peninsula harbor > 130 mammals (Chandra and Gupta 2022), and 149 woody plant species have been documented along the Coromandel coast of peninsular India (Parthasarathy et al. 2008). Indian dry woodlands are critical habitats for a high density of large herbivores, which serve as primary prey for many megafaunal predators. For instance, in Ranthambhore National Park, prey densities of 96.65 animals/km², dominated by chital (Axis axis) and sambar (Rusa unicolor), sustain large carnivores such as tigers, demonstrating the capacity of dry woodlands to support diverse megafauna (Bagchi et al. 2003). Although a range of protected areas within the ecosystem provide crucial havens for megafauna, even the largest are insufficient to support viable populations on their own, making it essential for these species to move between protected areas and into surrounding human-dominated landscapes to ensure their long-term survival (Ghosh-Harihar et al. 2019, Milda et al. 2020).

Tropical dry woodlands in India have been undergoing widespread land-use change, with > 22 million ha—65% of Indian dry woodlands—lost since the 19th century (Kalam et al. 2025). These ecosystems have faced pressures from colonial-era logging of teak (Tectona grandis) and sal (Shorea robusta) for shipbuilding and railway sleepers, as well as agricultural expansion during the Green Revolution (Gadgil 1990, Bhojvaid et al. 2016, Sannigrahi et al. 2021). Today, > 250 million rural people depend on these woodlands for fuelwood, fodder, and non-timber forest products (Schmerbeck 2011), contributing to habitat degradation and fragmentation. Industrialization from mining, cement production, and thermal power generation (Sagar and Singh 2004), along with fire, invasive species (Schmerbeck and Fiener 2015, Mungi et al. 2023), and human-wildlife conflicts (Babu et al. 2012, Dhanwatey et al. 2013), have further reshaped these landscapes. These cumulative pressures have left strong legacy effects, intensifying the vulnerability of these critical habitats and threatening megafauna populations.

METHODS

Data pre-processing

Megafauna species data

We selected six megafauna species for this study: Asian elephant, leopard (Panthera pardus), sloth bear (Melursus ursinus), dhole (Cuon lupus), tiger, and gaur. Tropical dry woodlands serve as essential habitats for these species (Chundawat et al. 1999, Fernando and Leimgruber 2011), making them an appropriate choice for evaluating the importance of historical and contemporary woodland extent. Many of these megafauna are widely recognized as umbrella species whose protection can benefit numerous other species and broader ecosystems (Venkataraman et al. 2002, Walston et al. 2016, Sabu et al. 2022). Together, all six species represent a range of trophic levels and functional roles, including prey control, seed dispersal, and insect control (Bargali et al. 2004, Campos-Arceiz and Blake 2011, Kamler et al. 2012). All six species are threatened and of conservation concern and are known to interact frequently with humans across modified landscapes (Anand and Radhakrishna 2017). Lastly, species such as the tiger and Asian elephant are central to India’s conservation policies, exemplified by government initiatives such as Project Tiger and Project Elephant.

Several initiatives from the Indian government, such as the periodic tiger, elephant, and leopard census initiatives, coupled with independent studies from scientists, have helped progress in monitoring megafauna in the country. However, these assessments, and specifically their data, are never made available for public use, leading to constraints for scientific and conservation assessments. We use International Union for the Conservation of Nature (IUCN) range maps for six species as our baseline data for species presence and absence, which are freely available online for public use. More specifically, we used only their current range distribution identified as “extant”, and cross-verified them with regional studies (Yoganand et al. 2006, Menon 2014, IUCN 2024). However, in the case of the sloth bear, we included “possibly extant” because it matched more closely the ranges outlined by the Indian scientific community as the current range of the species (Yoganand et al. 2006). The IUCN range data have been used widely for broad-scale analyses such as understanding range contractions (Pacifici et al. 2020b), species richness and endemism in tropical forests (Pillay et al. 2022), and human impacts on species distributions (Pekin and Pijanowski 2012). Although these maps have limitations (Jetz et al. 2008, Herkt et al. 2017), they can be useful for analyzing broad-scale distributional patterns, particularly for species whose ranges are well understood, such as megafauna species. Likewise, range maps are useful data sets for studies comparing distributions to aggregated or regional-level social-ecological drivers such as human modification and land-use changes, which operate consistently. Fine-scale data may add little value when these broader trends remain stable across large geographic areas.

To analyze the relationship between megafauna distribution and social-ecological variables, we used a 10 × 10 km grid. We chose this cell size because it resembles the mean home range size of the species we selected, with a minimum home range size of approximately 58 km² for sloth bears (Gubbi et al. 2023) to 266 km² for elephants (Desai 1991). We rasterized each species range to match the resolution of our grid and coded presence as 1 (i.e., cells with at least 30% with the IUCN range polygon) and absence as 0 (i.e., all other cells).

Predictor variables

We chose 13 variables describing the extent of present-day and historical woodland. These included both environmental and socioeconomic variables, which we collectively refer to as social-ecological variables (Table 1, Appendix 1). All data sets used are available online for public use. We resampled all data sets to our 10-km grid in a common equal-area projection (EPSG 102028). All data were pre-processed and analyzed in R (version 2024.04.1).

For environmental variables, we included precipitation, terrain ruggedness, and two variables, one describing contemporary (i.e., 2020) tropical dry woodland cover and the other historical (i.e., 1880–2020) woodland change (i.e., percentage change) from our previous work (Kalam et al. 2025). We developed a contemporary dry woodland cover map for India by assessing the accuracy of existing satellite-based woodland data sets and combining them into a single ensemble map. This map underwent rigorous independent validation, demonstrating high reliability with an overall accuracy of 90%. To assess long-term changes, we integrated this contemporary map with a time series of historical woody cover reconstructions dating back to 1880. Using this full time series, we calculated the absolute percentage change in woodland cover for each pixel by subtracting the value for 1880 from the value for 2020. We also included five variables describing human–nature interactions in Indian dry woodlands. These included three variables from the 2011 census of India (Census of India 2011), available at the district level. We included natural resource-based livelihoods as a proxy for human pressure on landscapes, which affect megafauna presence through habitat change, human-wildlife conflict, and resource competition. Similarly, we included nature-based resources used in daily activities for cooking and housing material as a measure of direct shared resource extraction from shared habitats. For each of the three variables, we calculated the percentage of people involved in these activities at the Indian district level (Table 1). The corresponding district boundaries for the 2011 census were obtained from “community-created maps of India” (http://projects.datameet.org/indian_village_boundaries). We consolidated the names of any districts that bifurcated after 2011 under their original pre-bifurcation names, resulting in 303 districts for our study. In cases where more than one district overlapped with a cell in our grid, we calculated an area-weighted mean using the share of districts in the cell as weights. We used the Human Modification Index, which integrates remote sensing and ground-based data to measure different global anthropogenic stressors between 1990 and 2017 (Kennedy et al. 2019). It combines 13 different variables such as population density, built-up areas, agriculture, transportation infrastructure, and energy production into a cumulative score of landscape modification (Kennedy et al. 2019), acting as an aggregate proxy of human pressures. Finally, we calculated the share of protected areas per cell based on data on the Indian protected area network from Koulgi et al. (2019) and the United Nations Environment Programme World Conservation Monitoring Centre (2024). These data included 199 protected areas, including National Parks and Wildlife Sanctuaries within tropical dry woodland boundaries.

Finally, we included four socioeconomic variables from the 2011 census of India (Census of India 2011). We included the poverty headcount ratio from Mohanty et al. (2016), who derived this metric based on the same census data. Poorer communities are often more likely to live near or within megafauna’s habitats, which can result in increased interactions between them. Similarly, we used household size as a proxy to capture household consumption of natural resources. Finally, we used education levels and the proportion of female-headed households as proxies for capturing perceptions and attitudes toward wildlife, given that higher education levels may foster greater awareness and support for conservation (Røskaft et al. 2007, Makumbe et al. 2022), and female-headed households can reflect unique sociocultural dynamics that influence tolerance, conflict mitigation, and coexistence with wildlife (Agarwal 2009, Westermann et al. 2005, Almuna et al. 2022).

Modeling strategy

We used generalized linear mixed models (GLMMs) to assess the association between variables and the contemporary distribution of megafauna. GLMMs combine properties of generalized linear models and mixed models (i.e., where fixed and random variables are incorporated; Bolker 2015), can accommodate various response variable types, and are suitable for situations in which observations (i.e., cells in our case) are grouped (i.e., districts in our case). GLMMs provide flexible approaches for handling data that are not normally distributed and are structured hierarchically (Bolker et al. 2009), and they are also useful for addressing spatial autocorrelation (Dormann et al. 2007). Our models are comprehensive, comprising both fixed (grids) and random (districts) effects. We chose to model random effects at the Indian district level because districts effectively capture spatial clustering of environmental and socioeconomic conditions. This approach accounts for local differences such as policies or conservation efforts, enhancing model efficiency by reducing overfitting and improving the robustness of our conclusions regarding species presence across diverse districts. A total of 303 Indian districts, falling within dry woodland boundaries, were included in our models.

We ran six models, one per species, which involved four key steps. In step one, we assessed collinearity among all variables using a Pearson correlation matrix, defining high correlation as r ≥ 0.5 (Booth et al. 1994). We used this threshold in addition to expert knowledge to improve model stability and interpretability. Similarly, we also log-transformed non-collinear continuous variables to reduce skewness and stabilize variance, and scaled them to facilitate model convergence and enhance coefficient interpretability. Each variable was subsequently scaled (mean of 0 and standard deviation of 1) to facilitate comparability on the same scale. In step two, we implemented a stepwise variable selection process by adding variables consecutively to a base model with only our response variable (species presence or absence), the intercept, and a random effect for districts. For this procedure, we used the glmmTMB package in R, which allows for different distributions and link functions (in our case, a binomial distribution with a logit link function; Brooks et al. 2017) to run our GLMMs. We incorporated districts as a random intercept (using the term 1 | Dist_ID) to account for potential similarities within districts and potential differences between districts across India. This procedure allowed for the baseline probability of megafauna presence to vary by district while maintaining the effects of predictor variables as constant across districts. We evaluated models based on the Akaike Information Criterion (AIC). If the differences in AIC values between models were minimal, we calculated ΔAIC to identify the model with the strongest support. The best model was further tested for spatial autocorrelation in the residuals using Moran’s I. In step three, we again used the glmmTMB package to run our GLMMs. We included only statistically significant variables (P < 0.05) from each species’ best model identified in the previous step. In the final model, we included second-order polynomial terms for spatial coordinates to account for spatial autocorrelation. We tested for over- or underdispersion in the residuals using the DHARMa package in R (R Core Team 2024). More specifically, we used the following formula:

(1)

where logit(p_i) is the probability of a binary outcome (species presence or absence) for the ith observation; β₀ is the baseline log-odds of the outcome when all predictor variables are 0 intercept; β_j are coefficients for fixed-effect variables x_ij (e.g., socioeconomic and environmental variables), where j = 1, ..., 13 indexes the predictor variables; γ₁ and γ₂ account for spatial autocorrelation; and uDistID accounts for the random effect associated with district-level variability.

In step four, we ran two validation analyses, both focusing on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve to determine our model accuracies. In the first analysis, we employed a random 10-fold, group-wise cross-validation to evaluate model performance (Appendixes 2 and 3, AUC-1). In the second analysis, we used a spatial block 10-fold cross-validation, dividing our study area into spatial blocks (Appendixes 2 and 3, AUC-2). Block sizes were selected based on spatial autocorrelation analysis using the blockCV package in R, which estimates the range of spatial autocorrelation to ensure spatial independence between training and testing folds (Valavi et al. 2019). The values of the AUC-ROC range from 0.5 to 1.0 and indicate how well the model can distinguish between individuals who experience the outcome of interest (i.e., megafauna presence) and those who do not (megafauna absence; Hosmer et al. 2013). The model demonstrated robustness, with mean AUC values providing an estimate of predictive accuracy.

To address our second research question, we followed a similar modelling strategy, with two minor changes. In step one, we did not log-transform the historical change in the dry woodland cover (Woodland_Change) variable because it included both positive and negative values and exhibited a skewed distribution. Instead, we scaled it to ensure comparability with other predictors. We also excluded step two, as the significant variables had already been identified in the analysis for research question 1.

RESULTS

Our collinearity analysis among potential predictor variables revealed four pairs of correlated variables:Firewood_Cooking and Education_Level, Female_Households and Household_Size, TRI and HMI, and TRI and Contemporary_Woodland (see Table 1 for variable descriptions). Hence, in our final models, we retained Firewood_Cooking because firewood dependence directly reflects human reliance on forest resources, providing a more tangible measure of anthropogenic pressures on habitats. Similarly, we retained Female_Households because it captures unique decision-making patterns that could influence attitudes toward megafauna. Finally, we removed TRI because it was correlated with two variables.

Using GLMMs to analyze the importance of our remaining set of predictor variables per species showed that the number of variables identified as important varied by species (Table 2). Overall, Protected_Areas, Contemporary_Woodland, Firewood_Cooking, Female_Households, Poverty_Level, NR_Roof, and Precipitation were featured across all the top models and species selected from step one. Conversely, HMI was featured in five species models, and NR_Livelihoods was featured in only three. All our models demonstrated a high goodness-of-fit and no significant indication of over- or underdispersion in residuals (Tables 2 and 3, Appendices 2 and 3).

Analyzing variable importance in our models (Fig. 2) showed that Protected_Areas and Contemporary_Woodland were important for all six species, positively influencing their presence. Precipitation was consistently positively associated with dholes, leopards, gaur, and sloth bears, whereas NR_Roof was consistently negatively associated with the same species. Perc_Firewood was also positively associated with dholes, leopards, and sloth bears. HMI was associated with the presence of tigers, dholes, sloth bears, and gaur, which had positive effects on dholes and gaur and negative effects on tigers and sloth bears. Finally, Poverty_Level was positively associated with two species, dhole and sloth bear.

Sloth bear presence was associated with seven variables in total. The environmental variable, Precipitation (estimate = 1.26, standard error [SE] = 0.09, P < 0.05), had the strongest effect, influencing it positively, followed by the socioeconomic variable Poverty_Level (estimate = 1.20, SE = 0.2, P < 0.05, Fig. 2). Similarly, five environmental variables had significant associations with its presence, but with varying effects. Contemporary_Woodland (estimate = 0.95, SE = 0.04, P < 0.05) had strong positive associations, whereas Perc_NRRoof (estimate = −0.66, SE = 0.23, P < 0.05) had strong negative effects. Similarly, Firewood_Cooking (estimate = 0.62, SE = 0.02, P < 0.05) had positive influences on its presence, whereas HMI (estimate = −0.36, SE = 0.05, P < 0.05) had negative effects. Protected_Areas had the weakest but significant association (estimate = 0.20, SE = 0.03, P < 0.05), influencing sloth bear presence positively.

Similarly, dhole presence was associated with seven variables. Dhole presence had the strongest and negative association, with Perc_NRRoof (estimate = −1.24, SE = 0.30, P < 0.05), an environmental variable, followed by the socioeconomic variable Poverty_Level, with a strong positive association (estimate = 1.20, SE = 0.30, P < 0.05, Fig. 2). The environmental variable Precipitation influenced its presence positively (estimate = 0.95, SE = 0.09, P < 0.05). Other environmental variables also had significant associations, influencing dhole presence positively: Firewood_Cooking (estimate = 0.60, SE = 0.26, P < 0.05), Contemporary_Woodland (estimate = 0.56, SE = 0.05, P < 0.05), and Protected_Areas (estimate = 0.43, SE = 0.04, P < 0.05). HMI (estimate = 0.29, SE = 0.05, P < 0.05) had the weakest but significant positive influence on dhole presence.

Five variables determined leopard presence. Two environmental variables had the strongest and positive associations with leopard presence, Firewood_Cooking (estimate = 0.64, SE = 0.19, P < 0.05) and Contemporary_Woodland (estimate = 0.61, SE = 0.04, P < 0.05, Fig. 2). Similarly, Precipitation also had a positive influence (estimate = 0.61, SE = 0.07, P < 0.05). Lastly, the two other environmental variables influencing leopard presence had mixed effects. Perc_NRRoof (estimate = −0.52, SE = 0.21, P < 0.05) was the only variable influencing the species’ presence negatively, whereas Protected_Areas had the weakest but significant positive effect (estimate = 0.41, SE = 0.03, P < 0.05).

Similarly, gaur presence was associated with five variables. Perc_NRRoof (estimate = −2.98, SE = 0.75, P < 0.05) had the strongest association with gaurs and was the only variable influencing its presence negatively (Fig. 2). Precipitation had a strong influence, affecting its presence positively (estimate = 0.98, SE = 0.16, P < 0.05). The other three environmental variables also had positive associations with gaur presence, with HMI (estimate = 0.78, SE = 0.10, P < 0.05) and Contemporary_Woodland (estimate = 0.66, SE = 0.09, P < 0.05) having stronger effects, whereas Protected_Areas had the weakest but significant effect (estimate = 0.34, SE = 0.07, P < 0.05).

In the case of tigers, only environmental variables were associated with their presence. Contemporary_Woodland (estimate = 1.72, SE = 0.09, P < 0.05) had the strongest association, influencing its presence positively (Fig. 2). Conversely, HMI (estimate = −1.10, SE = 0.08, P < 0.05) was the only variable having a strong but negative effect. Lastly, Protected_Areas had the weakest but significant effect (estimate = 0.34, SE = 0.07, P < 0.05), influencing tiger presence positively.

Finally, the presence of elephants was explained solely by environmental variables. Contemporary_Woodland (estimate = 0.86, SE = 0.10, P < 0.05) had the strongest effect on the presence of the species, influencing it positively. Likewise, Protected_Areas also strongly affected elephant presence, influencing it positively (estimate = 0.48, SE = 0.07, P < 0.05). We found that the effect of variables varied across districts, with gaur and elephant exhibiting the highest variability compared to other species (variance = 70.3 and 66.1, respectively). In contrast, the leopard and tiger exhibited the least variability (variance = 7.6 and 7.2, respectively).

Analyzing the importance of past landscape change in determining contemporary megafauna presence showed that present-day conditions and historical woodland changes significantly influenced species presence, but the relative importance varied (Table 3, Fig. 3, Appendixes 3 and 4). Overall, Contemporary_Woodland consistently had a strong positive association with species’ presence across models, whereas Woodland_Change had a significant and consistently negative impact. Sloth bear presence was more strongly linked to present-day conditions, with Woodland_Change having weaker and negative influences (estimate = −0.2, SE = 0.04, P < 0.05). Similarly, dhole and leopard presences were strongly linked with present-day conditions, with Woodland_Change having a weaker effect (dholes: estimate = −0.35, SE = 0.05, P < 0.05, leopard: estimate = −0.16, SE = 0.03, P < 0.05). Gaur was not strongly associated with either Contemporary_Woodland (estimate = 0.51, SE = 0.09, P < 0.05) or Woodland_Change (estimate = −0.4, SE = 0.08, P < 0.05) compared to other variables. Similarly, tiger presence was more strongly associated with present-day conditions than with Woodland_Change (estimate = −0.49, SE = 0.06, P < 0.05). Among the three variables associated with elephant presence, Woodland_Change had the strongest negative effect (estimate = −0.6, SE = 0.12, P < 0.05). Compared to all other species, Woodland_Change was most influential for elephants, whereas other present-day variables were more important for the other five species.

DISCUSSION

Tropical dry woodlands worldwide provide critical habitats for megafauna species yet are under high and often increasing pressure from land-use change. How to enable the co-existence of people and megafauna in increasingly human-dominated tropical dry woodlands is therefore an important question for conservation research and practice. Much can be learned from India, where megafauna have persisted in many regions despite high current land-use pressure, high current human population densities, and vast historical forest conversions. Using GLMMs, we examined the social-ecological determinants of the current distributions of six megafaunal species within Indian tropical dry woodlands and how these relate to historical woodland cover change. These analyses revealed three main insights.

First, we found contemporary dry woodland extent and protected area coverage to be strongly positively associated with megafauna distributions. This points to the importance of contiguous, well-protected swaths of natural habitat in tropical dry woodland landscapes for enabling the persistence of megafauna in them, a finding that is broadly in line with previously reported patterns from India (Karanth et al. 2009, Jathanna et al. 2015, Tripathy et al. 2021) and from around the world (Pacifici et al. 2020b, Torres-Romero et al. 2020). Protected areas are a cornerstone for conserving megafauna in India, but human pressures, including habitat degradation and poaching, frequently undermine their effectiveness (Ghosh-Harihar et al. 2019). Importantly, although many protected areas exist in India’s tropical dry woodlands, few are large enough to host viable populations of megafauna by themselves, and many species crucially depend on unprotected dry woodland habitats in their surroundings (Ghosal et al. 2013, Chundawat et al. 2016, Calabrese et al. 2017). This finding highlights the potential importance of protected areas as refuges, or “safe havens”, for megafauna in otherwise human-dominated landscapes, as well as the need to protect and manage megafauna populations that move between protected and unprotected habitats. Unfortunately, much of the megafauna research so far has focused on the protected parts of their ranges in India and elsewhere. The importance of contiguous, larger woodland patches that we found points to the potential benefit of restoring woodland patches and corridors in human-modified landscapes.

Second, whereas tropical dry woodland cover is critical for many species, particularly tigers and elephants, the relationship is more complex for leopards, gaurs, dholes, and sloth bears, with human pressures being the most important factors determining their distributions. This finding likely reflects the adaptability and generalist nature of these latter species. For instance, leopards can survive in plantations, agroforests, and even urban and peri-urban areas, despite high human densities, if prey and cover are sufficient (Athreya et al. 2014, 2015). Similarly, dholes can navigate human-modified landscapes by adjusting their time and space use to human activities (Pattekar et al. 2024), which is true for many meso-carnivores elsewhere as well, such as red fox (Vulpes vulpes) in Portugal (Alexandre et al. 2020). Importantly, the ability of megafauna to adapt to human pressures depends on refuge habitat such as protected areas, larger forest patches, or rugged terrain. For example, Eurasian lynx (Lynx lynx) increasingly relies on such refuge habitats in areas with higher levels of human landscape modification (Oeser et al. 2023). Similarly, lions (Panthera leo) increased their usage of Community Conservation Areas with increasing human pressure in the wider landscape (Schuette et al. 2013). In line with these studies, our findings suggest that megafauna are more likely to persist in human-modified landscapes when refuge habitats are available, underscoring their ability to persist across a broad range of social-ecological conditions.

Beyond the availability of refuge habitat and ecological adaptability, religious and cultural traditions also play a critical role in determining whether megafauna can persist in human-dominated landscapes where formal protection for these species is limited. Across India, several megafauna species, including elephants, tigers, and blackbucks, are deeply embedded in religious beliefs, traditional cosmologies, and folklore, which also influence norms around natural resource use (Oommen 2021, Kala 2022). For instance, the Bishnoi community has long upheld conservation-oriented practices rooted in spiritual beliefs (Reichert 2015), and elephants, revered as manifestations of the god Ganesha, are tolerated despite the damage they cause (Thekaekara et al. 2021). The inclusion of cultural variables, including those that capture traditional relations with megafauna and religious beliefs and practices, would be beneficial to explain megafauna persistence and loss.

However, while such examples highlight the potential of cultural values to support coexistence, these relationships are not universal. Broad-scale spatial variables are unlikely to capture these factors with the necessary nuance required to be informative in models such as ours. India is an inherently diverse country, at regional scales as well as local scales (Census of India 2011). Even within individual villages or communities, there can be a variety of religions, ethnicities, and cultural practices, including dietary traditions and hunting practices that shape human-wildlife interactions (Negi 2005, Oommen 2021). For example, Madhusudan and Karanth (2002) found that even communities whose religious beliefs included nature-revering deities or sacred associations with wildlife were involved in hunting megafauna. This finding suggests that socioeconomic pressures, regional practices, and individual choices can override religious teachings, making it difficult to generalize conservation attitudes from identity alone. Moreover, religious identity does not necessarily translate into practice. In Ladakh, for instance, Bhatia et al. (2017) found that religious identity alone did not predict attitudes toward megafauna; rather, gender, education, and legal awareness played stronger roles. Only when supplemented by more in-depth variables capturing the degree of religious practices (e.g., how often a person prayed or how often they attended and followed religious sermons), did a weak positive association emerge.

Although we employed a wide range of socioeconomic variables (e.g., related to land use) in our analysis, we purposely refrained from including broad-scale indicators (e.g., ethnicity or religion) because these data would inevitably miss the crucial fine-scale nuances needed for a robust understanding of the role of cultural beliefs and traditions. Although such fine-scale data are currently unavailable for India, we stress that these factors likely play important roles in determining megafauna persistence in the face of land-use change and forest loss, as shown in local case studies (Banerjee and Sharma 2022, Singh et al. 2024). We thus acknowledge that our models, while offering valuable insights, do not fully account for the profound effects of these diverse, local, sociocultural determinants of megafauna persistence in India. This caveat should be kept in mind when interpreting our results. Integrating fine-scale sociocultural variables in future modelling efforts is a major opportunity to develop an even more comprehensive understanding of conservation challenges and opportunities in India.

Our third main finding was a legacy effect of historical changes in dry woodland cover, as past changes were negatively related to contemporary megafauna distributions for all species. The long-lasting legacies of habitat destruction are widely documented (Hoag and Svenning 2017, Ellis 2021, Davoli et al. 2024), particularly for long-lived and larger bodied species (Jung et al. 2019, Semper-Pascual et al. 2021). Our results suggest that megafauna today are more likely to occur in areas that had experienced greater historical woodland loss between 1880 and 2020. This could mean that more woodland loss generally occurs in areas with higher woodland cover, where it would be more likely that these species hold out (especially if remaining woodlands were protected, see Figure 2). This pattern was particularly evident for elephants, which showed the strongest association with historical woodland change. This pattern may reflect delayed responses to past habitat loss, whereby elephant populations persist in suboptimal remnants within historically deforested landscapes (de Silva et al. 2023). Although weaker for the other species, the consistently negative impact of historical woodland change on all species suggests that the legacy of past deforestation continues to determine megafauna distributions today. While research on the social-ecological factors supporting megafauna in human-modified landscapes is emerging, these studies rarely consider potential legacy effects, and more focused efforts are needed to understand how contemporary and historical factors facilitate or inhibit coexistence in today’s human-dominated landscapes (Rastogi et al. 2012, Kshettry et al. 2020, Habib et al. 2021, Naha et al. 2021).

Our models performed well and provided interesting insights into the factors linked with present-day megafauna distributions, although some limitations must be mentioned. First, we used IUCN range maps that are relatively coarse and can overestimate species distributions (Rondinini et al. 2006, Herkt et al. 2017). We accounted for this effect by selecting megafauna species for which range maps were both spatially detailed and conformed with other, more regional megafauna distributions in India. Moreover, we used a coarse resolution for our analyses to minimize commission errors, similar to other studies that have used these range maps (Di Marco et al. 2017). Still, we cannot fully rule out bias due to uncertain range maps. Second, because of the lack of up-to-date socioeconomic data, we relied on the 2011 census (Mir et al. 2015, Karanth 2016). Rural socioeconomic conditions such as poverty levels, natural resource dependency, and firewood use tend to change slowly, suggesting that these data can still effectively explain megafauna distributions. For instance, although government initiatives have promoted cleaner fuels such as liquefied petroleum gas in recent years, recent reports indicate ongoing reliance on firewood (Khanwilkar et al. 2023, Times of India 2023). Moreover, the census timeframe aligns well with the IUCN species evaluations we used (2015 to 2023). Third, as stressed above, we would have liked to include sociocultural variables pertaining to beliefs, traditions, and values, but such variables were not available at a sufficiently fine scale. Similarly, some of our variables, such as natural resources used in cooking, were only indirect proxies of the pressures affecting megafauna. Including more variables as well as more direct, causally related variables could improve our models. Fourth, our reconstructions of woodland change capture well changes in woodland cover, but not structural attributes (e.g., vegetation height, density, etc.), which likely also influence megafauna presence. Finally, we used a temporally and spatially detailed reconstruction of woodland change extending back to 1880. Other variables such as changes in human settlements, infrastructure, or agricultural areas may have enriched our historical analyses.

Our findings translate into clear implications for conservation planning and policy. First, there is an urgent need to protect the remaining patches of tropical dry woodlands. This action would entail further strengthening legal protections for existing protected areas and preventing protected area downgrading, downsizing, and degazettement events. Second, restoring tropical dry woodlands in degraded landscapes is imperative for enhancing connectivity between existing habitat patches. This is important because the loss of habitat connectivity impairs species’ abilities to move across long distances, undermining their adaptive capacity in the face of increasing human pressures (Krosby et al. 2010). Strategic restoration in landscapes that have experienced historical habitat degradation can help to connect and maintain contiguous woodland cover. Furthermore, such restoration efforts are more likely to be successful if they align with local communities’ priorities, traditional knowledge, and local social-ecologies (Singh et al. 2020, Haq et al. 2023). Third, there is an urgent need to strengthen community-based conservation measures across human-modified landscapes where megafauna persist. Although several community-based conservation programs already exist that foster local stewardship to monitor and address human-wildlife conflicts (Karanth and Vanamamalai 2020), protect and manage forests (Rai 2023), and promote traditional forest conservation practices (Applied Environmental Research Foundation 2023), these programs remain scattered across the country, lacking cohesive integration. Incorporating social-ecological nuances such as species-specific refuge habitat availability or tolerances toward human pressures, as an example of ecological indicators (Alexandre et al. 2020, Oeser et al. 2023), or including women in more decision-making can strengthen community initiatives (Oeser et al. 2023). Finally, more funding must be allocated toward fine-scale explorations of the social-ecological conditions fostering coexistence. Such efforts should acknowledge the intricate interplay between ecological processes, political realities, and human pressures. More broadly, megafauna are among the most challenging species to protect, and many tropical dry woodland regions currently see their distributions shrink and populations dwindle. For India, we show that megafauna can persist under a wide range of social-ecological conditions and that, despite legacy effects, current social-ecological conditions are the strongest determinants of their distributions. These findings provide hope for maintaining and restoring megafauna populations, provided that enough refuge habitat remains in the form of protected, larger forest patches.

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