Shifting Climates, Rising Tensions: Community Insights on Human-Wildlife Conflicts in Wayanad Wildlife Sanctuary, India

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This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6892314/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is reshaping natural landscapes, profoundly affecting ecosystems and human communities. A key outcome is the rise in human–wildlife conflict, especially in regions like Wayanad Wildlife Sanctuary in the Western Ghats of southern India, where people and wildlife closely coexist. This study examines local community perceptions of human–wildlife conflict under changing climatic conditions. Using a questionnaire-based survey, we assessed community views on climate change and its role in escalating human–wildlife conflict. Random Forest regression model used to assess complex relationship between community perceptions of climate change and the perceived severity of human-wildlife conflicts, while a time series analysis of regional temperature data was conducted to trace climatic trends. Results show a widespread acknowledgment of climate change, with most respondents linking increased human–wildlife conflict to factors like rising temperatures and shifting rainfall patterns. Temperature trends from time series analysis support these perceptions, revealing a significant warming trend. Variables such as education, ethnicity, and location significantly shaped community understanding of climate impacts. This study highlights the value of integrating community perspectives into strategies for managing human–wildlife conflict, stressing that local knowledge is essential for effective, climate-resilient conflict mitigation. Strengthening community engagement is crucial to address the growing challenges of human–wildlife coexistence in an era of rapid environmental change. Human–wildlife conflict Climate change Community perception Random Forest model Western Ghats India Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Climate change has become a defining environmental challenge of the 21st century, exerting widespread and cascading effects on ecosystems and biodiversity. Early projections warned of significant biodiversity loss due to climate-induced range shifts and habitat fragmentation (Thuiller et al., 2008 ; Bellard et al., 2012 ). Over time, these concerns have been reinforced by mounting empirical evidence that climate change alters species interactions, disrupts ecosystem functions, and intensifies ecological instability (Watson et al., 2018 ). One of the emerging consequences of such disruptions is the escalation of human-wildlife conflict (HWC), as animals increasingly move beyond traditional habitats in search of food, water, or refuge (IPCC, 2021 ). This trend is particularly pronounced in forest fringe and agrarian landscapes, where people and wildlife compete for shared resources. As species respond behaviourally and spatially to environmental stressors, human settlements often become unintended zones of wildlife activity, increasing the frequency and intensity of conflict (Abrahms, 2021 ; Abrahms et al., 2023 ). Understanding how local communities perceive these climate-induced ecological and socio-economic changes is essential for developing adaptive conservation and livelihood strategies. Such insights are particularly crucial in biodiversity-rich regions undergoing rapid ecological transitions due to climate variability. Human-wildlife conflict (HWC) represents a growing challenge in many regions where human settlements and wildlife habitats increasingly overlap. These interactions, often rooted in competition for shared resources, can have adverse outcomes for both people and animals (Nyhus, 2016 ). The manifestations of HWC are diverse, ranging from crop damage (Gemeda & Meles, 2018 ; Hill, 2018 ; Alemayehu & Tekalign, 2022 ) and livestock depredation (Sangay & Vernes, 2008 ; Bano et al., 2021 ), to human injury (Acharya et al., 2016 ; Acharya et al., 2017 ) and the transmission of zoonotic diseases (Obanda et al., 2008 ; Magouras et al., 2020 ). These experiences can understandably foster negative perceptions of wildlife, sometimes prompting retaliatory actions that further endanger both species and people. In this context, understanding the local communities’ perceptions and experiences becomes critical. Community-based knowledge not only sheds light on the lived realities of conflict but also serves as a vital input for developing effective and locally acceptable mitigation strategies (Kolinski & Milich, 2021 ; Nkansah-Dwamena, 2023; Kidane et al., 2024 ). While community perceptions of climate change have been the subject of considerable research over the past two decades (Lorenzoni & Pidgeon, 2006 ; Wolf & Moser, 2011 ; Buys et al., 2012 ), the ways in which climate change influences human-wildlife conflict (HWC) remain relatively underexplored. Although recent efforts have begun to bridge this gap (Senkondo et al., 2024 ), there is still a pressing need for deeper inquiry, particularly in regions where shifting climate patterns are exacerbating interactions between people and wildlife. In the Indian context, several studies have documented local perspectives on climate change (Halder et al., 2012 ; Moghariya & Smardon, 2014 ; Pandey et al., 2018 ) and human-wildlife conflict (Karanth et al., 2019 ; Datta et al., 2023 ). However, the intersection of these two critical issues like how climate-induced ecological changes are reshaping the nature, frequency, and impact of HWC remains poorly understood. Local communities residing near forested landscapes are often the first to perceive and respond to ecological disruptions, making their knowledge and lived experiences crucial for crafting effective mitigation strategies (Manfredo & Dayer, 2004 ; Dickman, 2010 ). Perceptions of human-wildlife conflict (HWC), however, are shaped by a complex interplay of social factors. Gender, for instance, has been shown to influence how individuals experience and interpret conflict with wildlife (Ogra, 2008 ; Ogra, 2012 ; Gore & Kahler, 2012 ). Ethnic identity also plays a key role, particularly in regions with culturally distinct relationships to nature and wildlife (Hartter et al., 2011 ; Bhatia et al., 2020 ). Education further influences both awareness and attitudes, with more educated individuals often demonstrating a greater propensity for coexistence and support for conservation (Baruch-Mordo et al., 2011 ; Foerster et al., 2022 ). Understanding these intersecting social dimensions is vital for designing inclusive, locally resonant conservation strategies. Human-wildlife interactions impose substantial socio-economic burdens, particularly on forest-dependent and agrarian communities who often bear the brunt of such conflicts (Newmark et al., 1994 ; Treves et al., 2006 ; Mhuriro-Mashapa et al., 2018 ; Horgan & Kudavidanage, 2020 ). In the Indian context, where an estimated 275 million people rely in full or in part on forest ecosystems for their livelihoods (World Bank, 2006 ), the ramifications of these conflicts are especially pronounced. The Western Ghats, a globally recognized biodiversity hotspot, has become a flashpoint for increasing human-wildlife conflict in recent decades. Here, a combination of habitat fragmentation, shifting land-use patterns, and climate variability has intensified interactions between people and wildlife (Jayson & Christopher, 2008 ; Rohini et al., 2016 ; Karanth & Ranganathan, 2018 ; Ramesh et al., 2022 ). These dynamics not only threaten conservation goals but also exacerbate socio-economic vulnerabilities among local populations. Wayanad: A Hotspot for Human-Wildlife Conflict in the Western Ghats Wayanad, situated in the biodiverse Western Ghats of Kerala, has emerged as one of the most severely affected regions by human-wildlife conflict (HWC) in southern India (Anoop & Ganesh, 2020 ; Bijosh et al., 2022 ). The district’s forested landscapes support a variety of wildlife, but as habitats shrink and overlap with human settlements, conflict has intensified. Marginalized and forest-dependent communities are at the frontline of these interactions, experiencing disproportionate socio-economic consequences (Münster & Münster, 2012 ). Agriculture, the economic backbone of the region, is especially susceptible—frequent crop raids and livestock losses due to wildlife incursions have severely undermined livelihood stability (Sumitha & Shaharban, 2022 ). This escalating situation underscores the urgent need for localized, community-sensitive conflict mitigation strategies. Local communities in Wayanad share longstanding cultural relationships with the forest, which have historically supported forms of coexistence with wildlife (Jolly et al., 2022 , 2024 ). However, these relationships are being tested by rapid socio-economic transformations. The expansion of tourism, infrastructure development, and urbanization are reshaping the region’s socio-ecological landscape, often intensifying the frequency and severity of human-wildlife conflict (Varghese & Natori, 2024 ). At the same time, land use and land cover changes, driven by shifting agricultural practices and development pressures, have contributed to ecological instability (John et al., 2020 ). Climate variability has further exacerbated these challenges. In particular, the devastating floods and landslides of 2018 and 2019 have served as stark reminders of Wayanad’s growing climate vulnerability, significantly raising local awareness of environmental change and its cascading effects (Aswathi, 2021 ). While prior research in Wayanad has primarily examined species-specific interactions (Esa & Sankar, 2001; Anoop et al., 2023 a, 2023 b; Vineetha et al., 2024 ) and the socio-cultural dimensions of human-wildlife conflict (Münster, 2016 ; Sengupta et al., 2020 ; Sumitha & Shaharban, 2022 ), the role of climate change as a shaping force in these dynamics remains underexplored. This study seeks to fill that gap by addressing two interrelated questions: (1) How are community perceptions of human-wildlife conflict evolving in the face of climate change, and to what extent are these perceptions influenced by social, ethnic, and geographic contexts? (2) What links can be drawn between the perceived severity of human-wildlife conflict incidents, local responses, and the broader public understanding of climate change and wildlife behavior in areas experiencing tangible climatic impacts? By examining these questions, the study aims to generate a nuanced understanding of how climate change intersects with human-wildlife conflict (HWC) in a high-risk landscape. In doing so, it seeks to inform the development of context-specific mitigation strategies that meaningfully incorporate the perceptions, experiences, and adaptive capacities of local communities. Study Area Wayanad Wildlife Sanctuary (11°37'35" N & 76°05'20" E), located in the southern Indian state of Kerala, lies at the heart of the Western Ghats, one of the world’s eight “hottest hotspots” of biodiversity, and forms an integral part of the Nilgiri Biosphere Reserve. Spanning approximately 344 km², the sanctuary functions as a critical ecological corridor, facilitating wildlife movement between major protected areas such as Nagarhole and Bandipur in Karnataka, and Mudumalai in Tamil Nadu. Its landscape is characterized by a complex mosaic of moist deciduous and semi-evergreen forests, riparian zones, plantations, and human-dominated agricultural mosaics. Among its unique features are marshy lowlands known locally as vayals , which provide crucial dry-season refugia for megafauna, including elephants ( Elephas maximus ) and tigers ( Panthera tigris ). These habitats are increasingly intersected by human settlements and farmlands, creating a dynamic and often contested socio-ecological interface where wildlife and human livelihoods intersect. The sanctuary is also home to several indigenous communities, notably the Paniya, Kuruma, and Kattunaikka tribes, whose lives are deeply intertwined with the forest. These communities contribute richly to the socio-ecological fabric of the region, with traditional ecological knowledge and culturally rooted conservation practices that inform both their resilience and vulnerability in the face of environmental change. As such, Wayanad provides a critical lens through which to examine the interlinkages between biodiversity conservation, human-wildlife conflict, and the lived realities of climate change. Data Collection Fieldwork for this study was carried out between September 2023 and August 2024 across three key forest ranges of the Wayanad Wildlife Sanctuary,Kurichiyad, Sultham Bathery, and Muthanga, as well as a 5 km buffer zone extending beyond the sanctuary boundaries (Fig. 1 ). The study area covered 200 km², which was divided into 2 km × 2 km grid cells to ensure systematic spatial coverage. The survey was conducted in adherence to the ethical guidelines established by the Human Ethics Committee of the University of Calicut and the Indian Council of Social Science Research (ICSSR). Prior informed consent was obtained from all participants after clearly explaining the purpose and scope of the study. Participants were assured that their identities would remain confidential, and no personally identifiable information would be disclosed or used in any stage of data analysis or publication. A total of 612 households were randomly selected for participation in a structured questionnaire survey. Respondents ranged in age from 21 to 75 years and represented a cross-section of socio-demographic groups, including tribal and non-tribal communities. The survey explored local perceptions of climate change impacts and human-wildlife conflict (HWC), employing a 5-point Likert scale to gauge the intensity of respondent attitudes and observations (Table 1 ). Table 1 Focal themes and questions asked during the survey SI No Focal theme Question Response scale 1 Impact of Climate Change on Wildlife Movement into human-dominated landscapes How strongly do you agree that climate change is causing wildlife to move into human-inhabited areas? 1 (Strongly disagree) to 5 (Strongly agree) 2 Changes in Wildlife Behavior Due to Climate To what extent do you believe that climate change is affecting the behavior of wildlife in your region?" 1 (Not at all) to 5 (Very much) 3 Food Availability for Wildlife How much do you think climate change is impacting the availability of natural food sources for wildlife? 1 (Not at all) to 5 (Very much) 4 Increased Human-Wildlife Conflicts How strongly do you believe that climate change is leading to an increase in human-wildlife conflicts in your community? 1 (Strongly disagree) to 5 (Strongly agree) 5 Changes in Ecosystem Services How significantly do you think climate change is altering the ecosystem services that wildlife relies on? 1 (Not significant) to 5 (Very significant) 6 Adaptation Strategies To what extent do you think local communities are adapting to the impacts of climate change on wildlife? 1 (Not at all) to 5 (Very much) 7 Impact on Livelihoods How much do you believe that climate change affects your livelihood due to changes in wildlife behavior or populations? 1 (Not at all) to 5 (Very much) 8 Wildlife Population Trends How concerned are you about the effects of climate change on the population trends of local wildlife species? 1 (Not concerned) to 5 (Very concerned) 9 Community Awareness of Climate Issues How aware do you feel the community is about the impacts of climate change on wildlife and HWC? 1 (Not aware) to 5 (Very aware) 10 Need for Climate Action How important do you think it is to take action on climate change to mitigate human-wildlife conflict? 1 (Not important) to 5 (Very important) To quantify the extent of HWC in the study area, a Conflict Severity Index (CSI) was developed based on self-reported incidents over the preceding two years. Each type of conflict was assigned a weighted value derived through participatory consultations with local communities: crop raiding = 1, livestock depredation = 2, property damage = 3, and human casualty = 5. This participatory approach aligns with best practices in conflict research, where community-based input is critical to ensure contextual relevance and legitimacy (Madden, 2004 ; Ogra, 2008 ). The combination of structured survey methods, spatial sampling, and locally grounded severity assessments aimed to capture both the tangible and perceived impacts of environmental change on human-wildlife relations in this highly sensitive landscape. $$\:CSI={\sum\:}_{i=1}^{n}\frac{\left(Wi*Ni\right)}{N\:total}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ Where: Wi ​: Weight assigned to each conflict type (1 for crop raiding, 2 for livestock depredation, 3 for property damage, 5 for human casualty). Ni ​: Number of incidences of each conflict type affecting households in the last two years. N total ​: Total number of conflict incidences recorded in the study. Data Analysis A combination of statistical and machine learning approaches was employed to examine community perceptions of climate change and human-wildlife conflict (HWC), as well as the factors influencing conflict severity. Non-parametric tests were used to analyze group-wise differences in perception data, given the ordinal nature of Likert-scale responses. The Mann–Whitney U test assessed differences in perceptions across gender, ethnicity (tribal vs. non-tribal), and location (residing inside vs. outside the sanctuary). To examine the influence of education level, a Kruskal–Wallis test was applied. To identify key factors associated with on-the-ground conflict intensity, a Random Forest regression model was employed, with the Conflict Severity Index (CSI) as the dependent variable. Independent variables included socio-demographic characteristics and respondents’ perception scores related to climate change and HWC. Random Forest, a robust ensemble machine learning algorithm, is well-suited for social science applications due to its capacity to model non-linear relationships, handle multicollinearity, and work effectively with categorical and continuous predictors (Breiman, 2001 ; Lingjun et al., 2019 ). Hyperparameter tuning was conducted through 5-fold cross-validation to enhance model generalizability and performance. The final configuration of the Random Forest model was optimized to enhance predictive performance while maintaining interpretability. Bootstrap sampling was enabled to allow sampling with replacement, thereby increasing the model’s robustness and reducing variance. The maximum depth of the trees was left unconstrained, allowing each tree to grow fully and capture intricate patterns within the data. To ensure sufficient representation in terminal nodes, the minimum number of samples per leaf was set to 1. Additionally, the minimum number of samples required to split an internal node was fixed at 10, which helped prevent over-fragmentation and improved model generalization. A total of 200 decision trees were used as estimators, providing a balanced trade-off between accuracy and computational efficiency. To contextualize perception data within broader climatic trends, temperature data from 1901 to 2022 was extracted from the Climatic Research Unit Time-Series (CRU TS) dataset (Harris et al., 2020 ). Pearson correlation analysis (via the pandas library) was used to explore associations between climatic trends and CSI values. Additionally, time series projections up to the year 2050 were generated using the Prophe t library, which accommodates seasonality and change points in historical climatic data. All modeling and statistical tests were conducted using Python 3.12, in jupyter notebook with implementation via the scikit-learn library. Visualizations were created using the seaborn package to illustrate variable importance, perception distributions, and CSI scores. Results Conflict Severity Index (CSI) The Conflict Severity Index (CSI), developed through community consultations, quantified the intensity of human-wildlife conflict based on both frequency and the relative seriousness of different conflict types (e.g., crop raiding, livestock loss, property damage, human injury). CSI values across the study landscape ranged from 0.01 (indicative of low severity) to 0.94 (high severity). These values reflect varying levels of exposure and vulnerability to wildlife conflict among communities. Spatial analysis revealed a clear pattern: grids located within the Wayanad Wildlife Sanctuary consistently exhibited higher CSI scores compared to those in the surrounding buffer zones. This trend underscores the increased risk faced by forest-fringe and sanctuary-residing households, who are in closer proximity to wildlife habitats and corridors. A detailed spatial visualization of CSI values is presented in Fig. 2 , highlighting hotspots of severe conflict and offering insights into priority areas for targeted mitigation. The CSI approach, informed by earlier frameworks that emphasize locally relevant indicators (Madden, 2004 ; Ogra, 2008 ), proves useful in identifying both acute and chronic conflict zones at a localized scale. Impact of Climate Change on Wildlife Movement into Human-Dominated Landscapes Respondents were asked to rate their level of agreement with the statement: "Climate change is causing wildlife to move into human-inhabited areas," . Notably, no respondent indicated strong disagreement with the statement. A minority of respondents (2.7%) expressed some level of agreement, while 17.64% remained neutral. The majority of participants agreed with the statement, with 36.11% agreeing and 42.48% strongly agreeing. A Mann-Whitney U test was conducted to assess whether opinions differed based on demographic factors such as location, ethnicity, or gender. Results revealed no statistically significant differences in agreement based on location (inside the sanctuary: mean rank = 317.32; outside the sanctuary: mean rank = 299.07; p = 0.178), ethnicity (tribal: mean rank = 311.34; non-tribal: mean rank = 303.45; p = 0.564), or gender (male: mean rank = 301.77; female: mean rank = 314.97; p = 0.342). However, higher mean ranks were observed for respondents residing inside the sanctuary, individuals from tribal communities, and female participants, suggesting that these groups might perceive wildlife movement into settlement areas more strongly. Similarly, a Kruskal-Wallis H test indicated no significant influence of education level on respondents' opinions ( H = 5.28, p = 0.259). These findings suggest that perceptions of wildlife movement driven by climate change are broadly consistent across diverse demographic groups and education levels, highlighting a shared understanding of the issue Changes in Wildlife Behaviour Attributed to Climate Change Respondents were surveyed to assess their perceptions of the extent to which climate change affects wildlife behavior in their region. The distribution of responses showed that 0.32% of respondents believed climate change had no impact on wildlife behavior, while 4.9% perceived only a minimal effect. A substantial proportion of respondents indicated significant impacts, with 17.64% reporting "somewhat," 38.72% reporting "quite a bit," and 28.75% reporting "very much." A Mann-Whitney U test revealed significant differences based on gender, with female respondents (mean rank = 314.97) expressing slightly stronger agreement than male respondents (mean rank = 301.77; p = 0.033). The location also significantly influenced perceptions, with respondents residing inside the sanctuary reporting higher agreement (mean rank = 329.14) compared to those outside the sanctuary (mean rank = 290.96; p = 0.006). In contrast, ethnicity did not significantly affect responses, as tribal (mean rank = 303.12) and non-tribal respondents (mean rank = 308.61) exhibited similar perceptions ( p = 0.680). The effect of education on perceptions was analyzed using a Kruskal-Wallis H test, which indicated a significant influence ( H = 9.26, p = 0.050). Post hoc Mann-Whitney pairwise comparisons revealed that respondents with higher education levels (Levels 2 and 3) exhibited significantly stronger agreement compared to those with lower education levels (Level 1 vs. Level 3, p = 0.000; Level 1 vs. Level 2, p = 0.000). These findings highlight variations in perceptions based on demographic factors such as gender, location, and education level, while ethnicity did not emerge as a significant determinant. Impact of Climate Change on Wildlife Food Availability Respondents were asked to assess the impact of climate change on the availability of natural food sources for wildlife. A small proportion (0.32%) of respondents believed that climate change did not affect food availability, while 4.2% reported minimal impact. A larger portion, 29.41%, perceived a moderate effect, 31.20% agreed on a significant impact, and 34.80% felt that climate change had a very substantial influence on food availability. When examining demographic factors, statistical analyses revealed no significant differences in opinions based on gender, ethnicity, or location. Specifically, male respondents (mean rank = 311.27) and female respondents (mean rank = 297.92) did not differ significantly ( p = 0.345). Similarly, ethnicity (tribal: mean rank = 303.13; non-tribal: mean rank = 308.61; p = 0.694) and location (inside the sanctuary: mean rank = 315.06; outside the sanctuary: mean rank = 300.62; p = 0.296) showed no significant differences. However, male respondents, non-tribal individuals, and those living inside the sanctuary exhibited slightly higher mean ranks, indicating a marginally stronger perception of the issue compared to their counterparts. Education level also did not significantly influence perceptions ( H = 1.45, p = 0.834), suggesting that respondents across different educational backgrounds shared similar views on the impact of climate change on natural food sources for wildlife. These findings highlight a broad consensus among respondents, indicating a collective acknowledgment of the detrimental effects of climate change on wildlife food availability, regardless of demographic characteristics. Increased Human–Wildlife Conflicts Due to Climate Change Respondents were asked to assess the extent to which they believe climate change is contributing to an increase in human-wildlife conflicts in their community. The distribution of responses revealed that a small percentage (0.65%) strongly disagreed, 5.71% disagreed, 22.54% remained neutral, 37.90% agreed, and 33.16% strongly agreed. Notably, over 70% of respondents acknowledged a connection between climate change and the escalation of human-wildlife conflicts. Further statistical analysis explored whether perceptions varied across demographic groups. A Mann-Whitney U test revealed no significant differences based on location ( inside the sanctuary : mean rank = 307.48; outside the sanctuary : mean rank = 305.82; p = 0.904), ethnicity ( tribal : mean rank = 294.57; non-tribal : mean rank = 313.98; p = 0.163), or gender ( male : mean rank = 308.06; female : mean rank = 303.69; p = 0.757). Similarly, a Kruskal-Wallis H test indicated no significant differences across education levels ( H = 5.39, p = 0.249), suggesting that perceptions of increasing human-wildlife conflicts due to climate change are consistent across all educational backgrounds. These results underscore a shared recognition among respondents across various demographic groups that human-wildlife conflicts have intensified, with climate change being a significant contributing factor to this trend. Changes in Ecosystem Services Due to Climate Change The survey examined respondents' perceptions of the extent to which climate change is altering ecosystem services. The distribution of responses revealed that 2.29% perceived the changes as "Not significant," 11.94% as "Slightly significant," 35.02% as "Moderately significant," 35.18% as "Significant," and 15.54% as "Highly significant." These results suggest that a majority of respondents recognize a meaningful impact of climate change on ecosystem services. Further analysis indicated that this perception was consistent across demographic groups, with no statistically significant differences found based on gender ( male : mean rank = 307.76; female : mean rank = 302.81; p = 0.727), ethnicity ( tribal : mean rank = 304.04; non-tribal : mean rank = 307.21; p = 0.820), or geographic location ( inside sanctuary : mean rank = 307.74; outside sanctuary : mean rank = 304.94; p = 0.850). These findings highlight a shared understanding of the changes in ecosystem services across diverse demographic groups. However, the Kruskal-Wallis H test revealed a significant effect of education level on respondents' perceptions of ecosystem changes ( H = 9.32, p = 0.050). Post hoc pairwise comparisons indicated that individuals with higher levels of education demonstrated a more informed understanding of ecosystem alterations than those with lower education levels ( Level 1 vs. Level 3 , p = 0.000; Level 2 vs. Level 1 , p = 0.000). This emphasizes the critical role of education in enhancing awareness and understanding of environmental issues. Adaptation Strategies to Climate Change Impacts on Wildlife The assessment of community adaptation strategies to the impacts of climate change on wildlife revealed a predominantly moderate level of adaptation. Approximately 47% of respondents indicated that communities are "somewhat" adapting, while only 9.47% recognized significant adaptation efforts. Notably, no respondents reported a "very much" level of adaptation, suggesting limited progress in implementing comprehensive adaptation measures. Analysis of demographic factors, such as gender and ethnicity, revealed no statistically significant differences in perceptions of adaptation efforts. Male respondents (mean rank = 300.38) and female respondents (mean rank = 317.47) expressed similar views ( p = 0.217), as did tribal (mean rank = 304.26) and non-tribal respondents (mean rank = 307.90; p = 0.789). However, location emerged as a significant factor, with respondents residing inside the sanctuary (mean rank = 327.14) perceiving adaptation strategies more positively compared to those living outside (mean rank = 292.34; p = 0.010). This trend suggests that closer proximity to wildlife may promote greater awareness and engagement in mitigation strategies for human-wildlife conflict (HWC) resulting from climate change. Education level emerged as the most influential demographic variable, with the Kruskal-Wallis test revealing a strong statistical significance ( H = 34.43, p < 0.001). Post hoc pairwise analyses indicated significant differences in adaptation perceptions across all education levels, particularly between the lowest education group (Level 1) and higher levels. Specifically, comparisons between Level 3 vs. Level 1 ( p < 0.001) and Level 2 vs. Level 1 ( p < 0.001) showed the most substantial contrasts, reflecting a notable disparity in understanding and awareness of climate adaptation strategies. The consistent statistical significance involving Level 1 underscores the crucial role of education in shaping perceptions of climate adaptation. Impact of Wildlife Population Changes on Livelihoods The survey results on the perceived impact of wildlife population changes due to climate change on livelihood activities revealed that the majority of respondents (50.16%) regarded the impact as "somewhat" significant, while 26.63% perceived it as "quite a bit" significant. A smaller proportion of respondents felt the impact was either "not at all" (4.08%) or "very much" (3.43%). Demographic factors such as gender (male: mean rank = 311.61, female: mean rank = 297.31; p = 0.298) and ethnicity (tribal: mean rank = 304.66, non-tribal: mean rank = 307.65; p = 0.825) did not show statistically significant effects on shaping these perceptions. However, location emerged as a significant factor. Respondents residing inside the sanctuary expressed higher concern about the disruption of livelihood activities caused by wildlife (mean rank = 327.88) compared to those living outside the sanctuary (mean rank = 292.84; p = 0.002). This difference may be attributed to the closer and more frequent interactions with wildlife that individuals inside the sanctuary experience, which directly affect their daily livelihoods. Education level was another significant factor influencing perceptions of the impact of wildlife population changes on livelihoods. The Kruskal-Wallis test revealed a strong statistical significance ( H = 20.79, p = 0.000). Pairwise post hoc comparisons indicated significant differences between respondents with lower education levels (Level 1) and those with higher education levels. Notably, significant differences were found between Level 1 and Level 3 ( p < 0.000), Level 2 ( p = 0.034), Level 4 ( p = 0.004), and Level 5 ( p = 0.033), suggesting that individuals with higher education levels tend to have a more nuanced understanding of the impact of wildlife population changes on livelihoods. In contrast, lower education levels may limit the depth of perception or awareness regarding this issue. Wildlife Population Trends This study assessed respondents' concerns about the effects of climate change on the population trends of local wildlife species. The distribution of responses revealed that a significant proportion of participants were moderately concerned (46.73%), while 25.16% expressed concern and 10.29% reported high concern. A smaller fraction indicated being slightly concerned (14.86%) or not concerned at all (2.94%). Statistical analyses indicated no significant variation in concern levels across demographic categories. Gender was not a determining factor, with male respondents (mean rank = 309.32) and female respondents (mean rank = 301.42) showing similar levels of concern ( p = 0.571). Similarly, ethnicity did not significantly influence perceptions, as tribal respondents (mean rank = 301.84) and non-tribal respondents (mean rank = 309.42) reported comparable levels of concern ( p = 0.581). Geographic location, whether within the sanctuary (mean rank = 314.12) or outside it (mean rank = 301.26), also did not significantly affect concern levels ( p = 0.345). Additionally, the Kruskal-Wallis test revealed that educational attainment had no significant impact on perceptions of climate change's effects on wildlife populations ( H = 6.24, p = 0.182). Community Awareness of Climate Issues Community awareness of climate change was assessed, revealing generally low to moderate levels of awareness among respondents. A majority of participants reported limited awareness, with 15.03% indicating no awareness, 32.51% slightly aware, 37.74% somewhat aware, 14.54% aware, and only 0.16% very aware. Statistical analyses showed no significant influence of gender ( male : mean rank = 309.14; female : mean rank = 301.74; p = 0.602) or ethnicity ( tribal : mean rank = 311.15; non-tribal : mean rank = 303.57; p = 0.587) on awareness levels. However, geographic location had a significant effect, with respondents residing within the sanctuary displaying lower awareness of climate change (mean rank = 291.19) compared to those living outside the sanctuary (mean rank = 317.00), a difference that was statistically significant ( p = 0.000). The Kruskal-Wallis test further revealed a significant impact of education on climate awareness ( H = 10.59, p = 0.031). Post hoc analyses indicated that individuals with higher education levels were significantly more aware of climate change than those with lower levels of education (Level 1 vs. Level 3: p = 0.003; Level 2 vs. Level 1: p = 0.010; Level 1 vs. Level 4: p = 0.018). Need for Climate Action Respondents were surveyed regarding the perceived importance of taking action to address climate change and its associated human-wildlife conflicts (HWC). Notably, none of the participants considered such actions to be unimportant or only slightly important. Instead, 13.88% of respondents rated the issue as moderately important, 43.46% as important, and 42.62% as highly important, indicating that over 85% emphasized the critical need for climate change action. Demographic analysis revealed no statistically significant differences in perceptions based on gender ( male : mean rank = 312.66; female : mean rank = 296.33; p = 0.245), ethnicity ( tribal : mean rank = 299.99; non-tribal : mean rank = 311.02; p = 0.382), or geographic location ( inside sanctuary : mean rank = 291.19; outside sanctuary : mean rank = 317.00; p = 0.052). However, the Kruskal-Wallis test identified education as a significant factor influencing perceptions of climate action ( H = 10.59, p = 0.031). Post hoc analyses further indicated that individuals with higher levels of education were significantly more aware of the need for action compared to those with lower education levels (e.g., Level 1 vs. Level 3: p = 0.003; Level 2 vs. Level 1: p = 0.010). These results underscore the widespread recognition of the urgency to address climate change across the community, with education playing a pivotal role in shaping awareness and attitudes. Insights from the community responses The analysis revealed several nuanced patterns underlying the broader findings. Education consistently emerged as a key determinant of climate perception, with individuals possessing higher education levels exhibiting greater awareness of climate impacts and a stronger inclination toward climate action. This underscores the transformative role of education in shaping environmental understanding and fostering adaptive behavior. While residents living within sanctuary zones reported more acute experiences of ecological and livelihood impacts, likely due to their direct proximity to wildlife and shifting ecosystems, they demonstrated comparatively lower conceptual awareness of climate change. This highlights a critical gap in climate communication, where experiential knowledge is not always matched by formal understanding. Gender differences were also evident, with women more likely to perceive ecological disturbances. This may be attributed to their direct and frequent engagement with natural resources, such as water, fuel, and food sources, which are often the first to be affected by environmental stressors. Despite widespread acknowledgment of climate-induced ecological changes and livelihood challenges, the uptake of adaptation measures remained limited, pointing to a persistent knowledge–action gap. Interestingly, ethnicity did not significantly influence climate perceptions, suggesting that shared vulnerabilities in high-risk landscapes may transcend cultural differences. This finding reinforces the importance of designing inclusive, community-based adaptation strategies that recognize common risks while ensuring local participation. Random Forest Model for Community Perception of Climate Change The Random Forest regression model offered valuable insights into the complex relationship between community perceptions of climate change and the perceived severity of human-wildlife conflicts. Despite the inherent variability and subjectivity associated with social science data, the model demonstrated acceptable predictive performance. It achieved a Mean Squared Error (MSE) of 0.0410, a Root Mean Squared Error (RMSE) of 0.2025, and a Mean Absolute Error (MAE) of 0.1514, indicating its ability to minimize prediction errors effectively. The model yielded an R-squared (R²) value of 0.4717 and an Explained Variance Score of 0.4829, reflecting moderate predictive power. It is important to note that in social science research, such moderate R² values are not uncommon, given the multidimensional and context-dependent nature of human attitudes and behaviors (Wooldridge, 2016 ). A feature importance analysis revealed the key predictors influencing the perceived severity of human-wildlife conflict in the context of climate change. The most influential variable was Changes in Ecosystem Services due to Climate Change, with a Gini importance score of 0.161, highlighting its substantial impact on model predictions. This was followed by: Increase in Human-Wildlife Conflict (0.137), Change in Wildlife Behavior (0.129), Movement of Wildlife into Human Habitats (0.128), Food Availability for Wildlife (0.114). These findings, visually summarized in Fig. 3 , emphasize the dominant role that ecosystem disruption and shifting wildlife dynamics play in shaping community perceptions of conflict severity. The model thus underscores the significance of ecological awareness in driving concern and urgency around climate-induced human-wildlife interactions. Assessing Evidence of Climate Change in Wayanad Wildlife Sanctuary To evaluate long-term climate trends in the Wayanad Wildlife Sanctuary, temperature data from the Climatic Research Unit Time-Series (CRU TS) dataset (Harris et al., 2020 ) was analyzed. This dataset offers a robust historical record of land surface temperatures from 1901 to 2022 (Fig. 4 a). Pearson correlation analysis revealed statistically significant warming trends during three key seasons: Monsoon: r = 0.693, p < 0.0001 (Fig. 4 b); Post-monsoon: r = 0.762, p < 0.0001 (Fig. 4 c); Summer: r = 0.698, p < 0.0001 (Fig. 4 d). These strong correlations indicate consistent and substantial warming patterns over the past century. To forecast future trends, the Facebook Prophet model was employed to project seasonal temperature changes from 2023 to 2050. The model results indicated a continued rise in temperatures across all seasons (Fig. 4 ), affirming the persistence of long-term warming. Model performance was evaluated using standard metrics: Monsoon season: MAE = 0.199, RMSE = 0.249, R² = 0.648; Post-monsoon: MAE = 0.180, RMSE = 0.224, R² = 0.771; Summer: MAE = 0.259, RMSE = 0.321, R² = 0.626. The relatively high R² values, particularly during the post-monsoon period, confirm the model’s reliability in capturing temperature dynamics. These findings offer compelling evidence of significant warming within the Wayanad region. The observed and projected increases in temperature are likely to contribute to habitat degradation, disruption of wildlife behavior, and an escalation in human-wildlife conflicts—pressing concerns for biodiversity conservation and local livelihoods in and around the sanctuary. Discussion Community Perceptions of Climate Change Impact on Human-Wildlife Conflict Climate change has emerged as a critical driver influencing the movement of wildlife into human-dominated landscapes, disrupting traditional behavioral patterns and intensifying resource competition (Guillaumet et al., 2017 ; Kiriya, 2018). In this study, 78% of respondents identified climate change as a major cause of increased wildlife encroachment, citing rising temperatures and declining availability of natural resources as principal factors (Abrahams, 2021; Bond et al., 2023 ). Remarkably, perceptions of this impact showed minimal variation across gender, ethnicity, location, and education levels. This widespread agreement stands in contrast to prior research that emphasized the role of demographic variables in shaping environmental perceptions (Carpenter, 2022 ; Overland et al., 2023 ). Such consensus may reflect the universal visibility of climate-driven changes in high-risk landscapes, where lived experiences often transcend demographic divides. Respondents widely recognized the profound effects of climate change on wildlife behavior, reporting noticeable changes in migration, foraging, and reproductive patterns (Shaolin et al., 2002 ; Varis, 2024 ). Women and residents living near wildlife habitats exhibited greater awareness of these shifts, consistent with literature suggesting that direct exposure enhances ecological sensitivity (Ma & Jiang, 2005 ) and that women often express stronger environmental concern (Jaina & Prajapati, 2023). Education also played a significant role; individuals with higher education levels were more adept at linking behavioral changes in wildlife to climate impacts, aligning with previous findings that education enhances environmental literacy (Poortinga et al., 2004 ). Disruptions to ecological food chains were another widely recognized impact of climate change. Sixty-five percent of respondents, particularly those residing near forests, reported declining food resources for wildlife, an issue reflecting both environmental degradation and increased community awareness (Heggs & Green, 2020 ; Cleland et al., 2006 ). This growing awareness presents a valuable opportunity to mobilize support for conservation efforts, echoing research that underscores the importance of local ecological knowledge in fostering stewardship (Parmesan, 2006 ). As wildlife increasingly encroaches into human settlements in search of food, human-wildlife conflicts have escalated, particularly in forest-bordering communities (Maldonado, 2014 ). Although the overall recognition of these conflicts was widespread, minor differences in concern were observed between tribal and non-tribal respondents, suggesting that socio-cultural and economic contexts influence conflict perception (Yoezer & Dema, 2023 ). Respondents also demonstrated a high level of awareness regarding the broader ecosystem disruptions caused by climate change, including impacts on natural processes and biodiversity. Education once again emerged as a key determinant of awareness, with individuals possessing higher education levels showing a deeper understanding of ecosystem degradation (Brown & Green, 2019 ; Smith & Johnson, 2020 ). Furthermore, those living closer to wildlife habitats reported heightened urgency and awareness regarding the need for adaptation strategies. The findings highlight the pivotal role of community involvement, where collective action and traditional knowledge serve as foundational components for effective adaptation and mitigation (Mastrorillo et al., 2016 ; Kollmuss & Agyeman, 2002 ). Gender inclusivity was also identified as essential to building climate-resilient communities. The meaningful participation of women and other underrepresented groups is vital for developing equitable and comprehensive responses to climate challenges, reinforcing the call for inclusive policies and planning frameworks (Onoh et al., 2022 ). A significant portion of respondents, especially those residing in ecologically sensitive zones, acknowledged climate change's detrimental effects on local livelihoods. This localized understanding reflects the importance of place-based experiences in shaping perceptions. Education continued to play a transformative role, strengthening individuals' capacity for resilience, adaptation, and informed decision-making (Füssler & Peters, 2020 ; Reid & Sahl, 2009 ). The collective recognition of climate-related threats to biodiversity underscores the need for sustained public engagement and coordinated responses at the community level. These insights align with broader literature emphasizing the crucial role of education, gender equity, and local engagement in enhancing social and ecological resilience to climate impacts (Adger, 2003 ; Pelling, 2011 ). Integrating Human Dimensions in Conservation Conservation in the context of climate change necessitates the integration of human dimensions alongside ecological considerations. This study reaffirms that effective conservation outcomes are best achieved when local communities are actively engaged and their socio-economic realities are acknowledged (Bennett et al., 2017 ). This participatory approach departs from conventional models that often exclude communities from their ecosystems (Rai et al., 2021 ). Findings indicate that local populations not only recognize the urgency of climate and environmental issues but also prioritize them, suggesting strong potential for collaborative conservation efforts. Holistic approaches that address the underlying socio-economic drivers of conflict are essential for sustainable solutions (Rust et al., 2016 ). Conservation education and outreach were identified as key tools in promoting long-term engagement. By improving access to environmental information, these efforts foster public concern, awareness, and proactive behavior (Waylen et al., 2010 ; Jacobson et al., 2015 ). Importantly, shared concerns about climate change and human-wildlife conflict transcended lines of ethnicity, gender, education, and geography. This universality strengthens the case for inclusive conservation policies that integrate community perspectives into decision-making. Global examples continue to demonstrate that locally grounded conservation initiatives achieve higher success rates and foster greater resilience (Waylen et al., 2010 ). Climate Change and the Intensification of Human-Wildlife Conflict in Wayanad The findings of this study reaffirm the growing body of evidence that positions climate change as a critical driver of human-wildlife conflict (HWC). Over the past 122 years, the region has experienced a steady and significant warming trend, one that is projected to continue. Long-term climatic assessments underscore this rise, particularly across the Western Ghats (Jha et al., 2020 ). As a major orographic feature, the Western Ghats regulate monsoon patterns and regional hydrology, functioning as a climate gatekeeper for peninsular India (Gunnell, 1997 ). However, this regulatory role is increasingly undermined by habitat fragmentation, land-use conversion, and deforestation (Jha et al., 2000 ; Kale et al., 2016 ), contributing to an ecological shift with cascading socio-environmental consequences. Observations from Wayanad Wildlife Sanctuary echo these broader patterns. Rising temperatures and altered rainfall regimes have diminished the productivity of forest ecosystems. One major implication is the reduction in natural forage and water availability, pushing wildlife, especially large herbivores, toward human-dominated landscapes in search of resources. This study’s Random Forest analysis highlights the synergistic role of ecological and climatic variables in explaining conflict patterns, reinforcing that HWC cannot be examined in isolation from climate-driven habitat degradation. As forest habitats become increasingly resource-scarce, wildlife exhibit notable behavioural shifts. Several respondents, particularly those residing within sanctuary boundaries, reported frequent foraging by species such as the spotted deer ( Axis axis ) in paddy fields. The attractiveness of cultivated crops, perceived to be more nutritious and accessible, encourages such incursions. These findings support earlier observations that wild animals, when faced with resource scarcity, often turn to residential gardens or ornamental plants that mimic their natural diets (Sarkar & Bhadra, 2022 ). While such behavior reflects the adaptability and resilience of wildlife, it also aligns animal behavior more closely with human livelihoods, intensifying conflict risks. These behavioural shifts are mirrored in global HWC patterns. Increasingly, climate change is cited as a catalyst that exacerbates both the frequency and intensity of conflict incidents (Abrahams et al., 2023). Changes in precipitation and temperature regimes are forcing wildlife into previously unaffected regions (Abrahams, 2021; Gaire & Acharya, 2023 ). In Wayanad, local perceptions substantiate this trend, with reports of rising crop raiding, livestock depredation, and human casualties, especially in fringe areas of the sanctuary. Beyond direct encounters, climate change also influences forest structure and species composition. For example, local knowledge, particularly from the indigenous Kattunaika community, indicates that a rare mass flowering of bamboo a decade ago, followed by widespread dieback, led to dramatic vegetation changes. The resulting open forest floors were rapidly colonized by grasses, attracting herbivores and subsequently predators such as tigers. These ecological transitions brought large mammals into closer proximity with human settlements. Scientific literature confirms that bamboo phenology is sensitive to warming trends, with increased temperatures accelerating flowering cycles (Zheng et al., 2020 ). These localized accounts are corroborated by satellite-based assessments, which show a decline in bamboo cover across riparian patches between 2004 and 2018 (John et al., 2020 ). Another key concern is the spread of invasive plant species. Senna spectabilis and Lantana camara , both widespread in the sanctuary, are known to suppress native plant regeneration and reduce forage quality for herbivores (Prasad, 2010 ; Sundaram & Hiremath, 2012 ). S. spectabilis , introduced in the 1980s, has now colonized nearly a quarter (23%) of the sanctuary’s area (Vinayan et al., 2020 ). Its poor palatability has driven herbivores to target agricultural crops, exacerbating tensions with local farmers. From an institutional perspective, the Forest Department’s records indicate 4,126 cases of crop damage, 384 livestock depredations, and 50 human attacks (including nine fatalities) between 2015 and 2020 from Wayanad. These figures align with community experiences and suggest a worrying upward trend in conflict intensity, particularly in ranges adjacent to degraded or highly fragmented forest areas. Among large herbivores, elephants emerge as a focal species in the landscape of HWC. In Wayanad, the widespread cultivation of fruit-bearing trees such as jackfruit ( Artocarpus heterophyllus ) and mango ( Mangifera indica ) in homesteads has inadvertently attracted elephants, resulting in frequent crop depredation and infrastructure damage. Even during periods when forest food is relatively abundant, elephants continue to forage in village plantations, highlighting their preference for these high-energy crops. This is consistent with findings by Anoop et al. ( 2023 ), who document repeated elephant foraging events in fruit tree-dominated homesteads. Alternative cropping strategies offer a promising, community-centered approach to mitigate elephant incursions. In Botswana, combining chilli ( Capsicum spp. ) with legumes has proven effective both in deterring elephants and boosting farmer income (Matsika et al., 2020 ). Similar success has been observed in Sri Lanka through the use of buffer crops such as orange ( Citrus sinensis ), which are less attractive to elephants and economically beneficial (Dharmarathne et al., 2020 ). Chilli, in particular, has long been recognized for its strong deterrent properties and is widely used across Africa (Graham & Ochieng, 2008 ). These interventions have also shown promise in Wayanad. Chelliah et al. ( 2010 ) reported the effectiveness of chilli-based deterrents in reducing elephant raids on crops. However, their long-term success hinges on participatory implementation. Engaging communities in identifying regionally suitable, economically viable, and ecologically sound crop alternatives is essential. Such bottom-up strategies not only reduce conflict but also enhance livelihood resilience and foster coexistence. Conclusion This study underscores the growing consensus among local communities that climate change is a key catalyst in the intensification of human-wildlife conflicts (HWC) in and around the Wayanad Wildlife Sanctuary. Perceived impacts include the decline of ecosystem services, diminished availability of natural forage within forest habitats, and shifts in wildlife behaviour, particularly increased foraging in human-modified landscapes. These dynamics are further exacerbated by the spread of invasive alien species, which reduce the palatability and accessibility of native vegetation, forcing herbivores to target cultivated crops. Importantly, these concerns are shared across diverse social groups, cutting across lines of gender, ethnicity, education, and geography. Such widespread perceptions reinforce the need for inclusive and community-driven strategies that integrate both ecological and socio-cultural dimensions of conservation. As climatic conditions continue to shift, marked by rising temperatures and erratic rainfall, there is an urgent need for proactive, climate-adaptive interventions. Mitigating the compounded effects of climate change and HWC will require holistic, multi-scalar approaches that address habitat restoration, adaptive land-use planning, sustainable agriculture, and participatory governance. Long-term coexistence between people and wildlife in the region is possible only if we recognize and address the connected environmental and human factors involved. Declarations Clinical trial number not applicable Human Ethics and Consent to Participate Declarations Written and verbal informed consent was obtained from all participants involved in the study. To ensure their privacy and anonymity, the names of respondents participating in interviews and surveys have been kept confidential. Ethics Approval Ethical approval for this research was granted by the Human Ethics Committee of the University of Calicut, Kerala, India. (U.O. No. 13449/2023/Admn dated 26.08.2023). Funding This work was carried out with financial assistance from the Directorate of Environment and Climate Change, Government of Kerala, India (DoECC/303/2023/E1 dated 10.11.2023). Author Contribution NMK. conducted the fieldwork, collected and analyzed the data, and prepared the manuscript draft. HCC. critically reviewed the manuscript and contributed substantially to its revision. Acknowledgement The authors are grateful to the Directorate of Environment and Climate Change, Government of Kerala, for providing financial support for this research. We also thank the Kerala Forest and Wildlife Department and the Directorate of Scheduled Tribes Development Department, Government of Kerala for granting permission to conduct the study. 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Main Report, Vol. 1, 107 p. https://documents.worldbank.org/curated/en/252971468294315118/India-Unlocking-Opportunities-for-Forest-Dependent-People-in-India Yoezer, K., & Dema, R. (2023). Assessment of the socio-economic impact of human-wildlife conflict on agriculture: A case of smallholder and subsistence farmers in eastern Bhutan. Journal of Economics Management and Trade , 29 (11), 85–89. Zheng, X., Lin, S., Fu, H., Wan, Y., & Ding, Y. (2020). The bamboo flowering cycle sheds light on flowering diversity. Frontiers in Plant Science , 11 , 381. https://doi.org/10.3389/fpls.2020.00381 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6892314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474662060,"identity":"9d0f39c4-e08a-4ff7-b701-e7d64a13e23f","order_by":0,"name":"Nandakumar M.K.","email":"","orcid":"","institution":"University of Calicut","correspondingAuthor":false,"prefix":"","firstName":"Nandakumar","middleName":"","lastName":"M.K.","suffix":""},{"id":474662061,"identity":"8910ed1f-db72-423f-b3c5-de68c5e345df","order_by":1,"name":"Harilal C.C.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACHiA2sGDgY28++ADE5SNSiwQDG8+xZAMQl404LQxALRI5ZhIgJkEt/O1nD36uKJCQZ2NIMKv8mmMnw8bA/PDRDTxaJM7kJUueMZAwbGM4kHZbdlsy0GFsxsY5eLQYSPAYSDYYSDC2MTYcuy25jRmohYdNmoAW459ALfZtzIxtxZLb6onSYgayJbGNjZmN8eO2w4S1SJzJMbMEaklu42FjlmbcdhxIEfALf/sZ45sNf2xs++Xff/z4c1u1PT9788PH+LSgAGZwHDETqxwEGH+QonoUjIJRMApGDAAAOS88Ll6SsAgAAAAASUVORK5CYII=","orcid":"","institution":"University of Calicut","correspondingAuthor":true,"prefix":"","firstName":"Harilal","middleName":"","lastName":"C.C.","suffix":""}],"badges":[],"createdAt":"2025-06-14 06:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6892314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6892314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85253440,"identity":"382da5dc-93fc-4ffd-a2a2-cb2b5697e328","added_by":"auto","created_at":"2025-06-24 01:57:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317759,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing study area and survey grids\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6892314/v1/86c1ad3599682f648b3aed68.jpeg"},{"id":85253447,"identity":"40ba55b3-53ad-4457-8d16-f9d1bed5dcdc","added_by":"auto","created_at":"2025-06-24 01:57:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":708426,"visible":true,"origin":"","legend":"\u003cp\u003eGrid-wise conflict severity index calculated from survey results. Red colour indicates high conflict severity.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6892314/v1/8de4b34752cd18c59dc68684.jpeg"},{"id":85253439,"identity":"3a5bce71-ba21-439d-8afd-ce33378c4295","added_by":"auto","created_at":"2025-06-24 01:57:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47712,"visible":true,"origin":"","legend":"\u003cp\u003eFeature importance plot from the Random Forest regression model. Y‑axis codes are expanded as follows: \u003cstrong\u003eCC_EcosystemServices\u003c/strong\u003e- Changes in Ecosystem Services; \u003cstrong\u003eCC_HWCIncrease\u003c/strong\u003e- Increased Human-Wildlife Conflicts; \u003cstrong\u003eCC_WildlifeBehavior\u003c/strong\u003e- Changes in Wildlife Behavior Due to Climate; \u003cstrong\u003eCC_HumanShift\u003c/strong\u003e- Impact of Climate Change on Wildlife Movement into human-dominated landscapes; \u003cstrong\u003eCC_NaturalFoodImpact\u003c/strong\u003e- Food carAvailability for Wildlife; \u003cstrong\u003eCC_AommunityAdaptation\u003c/strong\u003e- Adaptation Strategies; \u003cstrong\u003eCC_LivelihoodImpact\u003c/strong\u003e- Impact on Livelihoods; \u003cstrong\u003eCC_PopulationTrendsConcern\u003c/strong\u003e-Wildlife Population Trends; \u003cstrong\u003eCC_CommunityAwareness\u003c/strong\u003e- Community Awareness of Climate Issues; \u003cstrong\u003eCC_ActionImportance\u003c/strong\u003e- Need for Climate Action\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6892314/v1/5f7e7bf200aba53299841bcb.png"},{"id":85253443,"identity":"688a3fbf-475b-471d-a656-26e026d8387d","added_by":"auto","created_at":"2025-06-24 01:57:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269427,"visible":true,"origin":"","legend":"\u003cp\u003eplots showing trends in temperature in the study area. (a) Annual temperature trend from 1901-2022, (b) Monsoon temperature forecast, (c) Post Monsoon temperature forecast,(d) Summer temperature forecast\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6892314/v1/80b5d99494fc1dcdc23b44f7.png"},{"id":100367938,"identity":"84163807-f92b-45cb-9f3f-d616a9214429","added_by":"auto","created_at":"2026-01-16 07:57:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2657365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6892314/v1/c45f0170-8517-4cbe-92c6-2540361ebb57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shifting Climates, Rising Tensions: Community Insights on Human-Wildlife Conflicts in Wayanad Wildlife Sanctuary, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change has become a defining environmental challenge of the 21st century, exerting widespread and cascading effects on ecosystems and biodiversity. Early projections warned of significant biodiversity loss due to climate-induced range shifts and habitat fragmentation (Thuiller et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bellard et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Over time, these concerns have been reinforced by mounting empirical evidence that climate change alters species interactions, disrupts ecosystem functions, and intensifies ecological instability (Watson et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the emerging consequences of such disruptions is the escalation of human-wildlife conflict (HWC), as animals increasingly move beyond traditional habitats in search of food, water, or refuge (IPCC, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This trend is particularly pronounced in forest fringe and agrarian landscapes, where people and wildlife compete for shared resources. As species respond behaviourally and spatially to environmental stressors, human settlements often become unintended zones of wildlife activity, increasing the frequency and intensity of conflict (Abrahms, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abrahms et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding how local communities perceive these climate-induced ecological and socio-economic changes is essential for developing adaptive conservation and livelihood strategies. Such insights are particularly crucial in biodiversity-rich regions undergoing rapid ecological transitions due to climate variability.\u003c/p\u003e \u003cp\u003eHuman-wildlife conflict (HWC) represents a growing challenge in many regions where human settlements and wildlife habitats increasingly overlap. These interactions, often rooted in competition for shared resources, can have adverse outcomes for both people and animals (Nyhus, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The manifestations of HWC are diverse, ranging from crop damage (Gemeda \u0026amp; Meles, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hill, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alemayehu \u0026amp; Tekalign, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and livestock depredation (Sangay \u0026amp; Vernes, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to human injury (Acharya et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Acharya et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the transmission of zoonotic diseases (Obanda et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Magouras et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These experiences can understandably foster negative perceptions of wildlife, sometimes prompting retaliatory actions that further endanger both species and people. In this context, understanding the local communities\u0026rsquo; perceptions and experiences becomes critical. Community-based knowledge not only sheds light on the lived realities of conflict but also serves as a vital input for developing effective and locally acceptable mitigation strategies (Kolinski \u0026amp; Milich, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nkansah-Dwamena, 2023; Kidane et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile community perceptions of climate change have been the subject of considerable research over the past two decades (Lorenzoni \u0026amp; Pidgeon, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wolf \u0026amp; Moser, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Buys et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the ways in which climate change influences human-wildlife conflict (HWC) remain relatively underexplored. Although recent efforts have begun to bridge this gap (Senkondo et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), there is still a pressing need for deeper inquiry, particularly in regions where shifting climate patterns are exacerbating interactions between people and wildlife. In the Indian context, several studies have documented local perspectives on climate change (Halder et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Moghariya \u0026amp; Smardon, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and human-wildlife conflict (Karanth et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Datta et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the intersection of these two critical issues like how climate-induced ecological changes are reshaping the nature, frequency, and impact of HWC remains poorly understood.\u003c/p\u003e \u003cp\u003eLocal communities residing near forested landscapes are often the first to perceive and respond to ecological disruptions, making their knowledge and lived experiences crucial for crafting effective mitigation strategies (Manfredo \u0026amp; Dayer, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dickman, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Perceptions of human-wildlife conflict (HWC), however, are shaped by a complex interplay of social factors. Gender, for instance, has been shown to influence how individuals experience and interpret conflict with wildlife (Ogra, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ogra, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gore \u0026amp; Kahler, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Ethnic identity also plays a key role, particularly in regions with culturally distinct relationships to nature and wildlife (Hartter et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bhatia et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Education further influences both awareness and attitudes, with more educated individuals often demonstrating a greater propensity for coexistence and support for conservation (Baruch-Mordo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Foerster et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding these intersecting social dimensions is vital for designing inclusive, locally resonant conservation strategies.\u003c/p\u003e \u003cp\u003eHuman-wildlife interactions impose substantial socio-economic burdens, particularly on forest-dependent and agrarian communities who often bear the brunt of such conflicts (Newmark et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Treves et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mhuriro-Mashapa et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Horgan \u0026amp; Kudavidanage, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the Indian context, where an estimated 275\u0026nbsp;million people rely in full or in part on forest ecosystems for their livelihoods (World Bank, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the ramifications of these conflicts are especially pronounced. The Western Ghats, a globally recognized biodiversity hotspot, has become a flashpoint for increasing human-wildlife conflict in recent decades. Here, a combination of habitat fragmentation, shifting land-use patterns, and climate variability has intensified interactions between people and wildlife (Jayson \u0026amp; Christopher, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rohini et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Karanth \u0026amp; Ranganathan, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ramesh et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These dynamics not only threaten conservation goals but also exacerbate socio-economic vulnerabilities among local populations.\u003c/p\u003e\n\u003ch3\u003eWayanad: A Hotspot for Human-Wildlife Conflict in the Western Ghats\u003c/h3\u003e\n\u003cp\u003eWayanad, situated in the biodiverse Western Ghats of Kerala, has emerged as one of the most severely affected regions by human-wildlife conflict (HWC) in southern India (Anoop \u0026amp; Ganesh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bijosh et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The district\u0026rsquo;s forested landscapes support a variety of wildlife, but as habitats shrink and overlap with human settlements, conflict has intensified. Marginalized and forest-dependent communities are at the frontline of these interactions, experiencing disproportionate socio-economic consequences (M\u0026uuml;nster \u0026amp; M\u0026uuml;nster, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Agriculture, the economic backbone of the region, is especially susceptible\u0026mdash;frequent crop raids and livestock losses due to wildlife incursions have severely undermined livelihood stability (Sumitha \u0026amp; Shaharban, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This escalating situation underscores the urgent need for localized, community-sensitive conflict mitigation strategies.\u003c/p\u003e \u003cp\u003eLocal communities in Wayanad share longstanding cultural relationships with the forest, which have historically supported forms of coexistence with wildlife (Jolly et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, these relationships are being tested by rapid socio-economic transformations. The expansion of tourism, infrastructure development, and urbanization are reshaping the region\u0026rsquo;s socio-ecological landscape, often intensifying the frequency and severity of human-wildlife conflict (Varghese \u0026amp; Natori, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the same time, land use and land cover changes, driven by shifting agricultural practices and development pressures, have contributed to ecological instability (John et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate variability has further exacerbated these challenges. In particular, the devastating floods and landslides of 2018 and 2019 have served as stark reminders of Wayanad\u0026rsquo;s growing climate vulnerability, significantly raising local awareness of environmental change and its cascading effects (Aswathi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile prior research in Wayanad has primarily examined species-specific interactions (Esa \u0026amp; Sankar, 2001; Anoop et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb; Vineetha et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the socio-cultural dimensions of human-wildlife conflict (M\u0026uuml;nster, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sengupta et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sumitha \u0026amp; Shaharban, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the role of climate change as a shaping force in these dynamics remains underexplored. This study seeks to fill that gap by addressing two interrelated questions: (1) How are community perceptions of human-wildlife conflict evolving in the face of climate change, and to what extent are these perceptions influenced by social, ethnic, and geographic contexts? (2) What links can be drawn between the perceived severity of human-wildlife conflict incidents, local responses, and the broader public understanding of climate change and wildlife behavior in areas experiencing tangible climatic impacts?\u003c/p\u003e \u003cp\u003eBy examining these questions, the study aims to generate a nuanced understanding of how climate change intersects with human-wildlife conflict (HWC) in a high-risk landscape. In doing so, it seeks to inform the development of context-specific mitigation strategies that meaningfully incorporate the perceptions, experiences, and adaptive capacities of local communities.\u003c/p\u003e "},{"header":"Study Area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eWayanad Wildlife Sanctuary (11\u0026deg;37'35\" N \u0026amp; 76\u0026deg;05'20\" E), located in the southern Indian state of Kerala, lies at the heart of the Western Ghats, one of the world\u0026rsquo;s eight \u0026ldquo;hottest hotspots\u0026rdquo; of biodiversity, and forms an integral part of the Nilgiri Biosphere Reserve. Spanning approximately 344 km\u0026sup2;, the sanctuary functions as a critical ecological corridor, facilitating wildlife movement between major protected areas such as Nagarhole and Bandipur in Karnataka, and Mudumalai in Tamil Nadu. Its landscape is characterized by a complex mosaic of moist deciduous and semi-evergreen forests, riparian zones, plantations, and human-dominated agricultural mosaics.\u003c/p\u003e \u003cp\u003eAmong its unique features are marshy lowlands known locally as \u003cem\u003evayals\u003c/em\u003e, which provide crucial dry-season refugia for megafauna, including elephants (\u003cem\u003eElephas maximus\u003c/em\u003e) and tigers (\u003cem\u003ePanthera tigris\u003c/em\u003e). These habitats are increasingly intersected by human settlements and farmlands, creating a dynamic and often contested socio-ecological interface where wildlife and human livelihoods intersect.\u003c/p\u003e \u003cp\u003eThe sanctuary is also home to several indigenous communities, notably the Paniya, Kuruma, and Kattunaikka tribes, whose lives are deeply intertwined with the forest. These communities contribute richly to the socio-ecological fabric of the region, with traditional ecological knowledge and culturally rooted conservation practices that inform both their resilience and vulnerability in the face of environmental change. As such, Wayanad provides a critical lens through which to examine the interlinkages between biodiversity conservation, human-wildlife conflict, and the lived realities of climate change.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eFieldwork for this study was carried out between September 2023 and August 2024 across three key forest ranges of the Wayanad Wildlife Sanctuary,Kurichiyad, Sultham Bathery, and Muthanga, as well as a 5 km buffer zone extending beyond the sanctuary boundaries (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study area covered 200 km\u0026sup2;, which was divided into 2 km \u0026times; 2 km grid cells to ensure systematic spatial coverage.\u003c/p\u003e \u003cp\u003e The survey was conducted in adherence to the ethical guidelines established by the Human Ethics Committee of the University of Calicut and the Indian Council of Social Science Research (ICSSR). Prior informed consent was obtained from all participants after clearly explaining the purpose and scope of the study. Participants were assured that their identities would remain confidential, and no personally identifiable information would be disclosed or used in any stage of data analysis or publication. A total of 612 households were randomly selected for participation in a structured questionnaire survey. Respondents ranged in age from 21 to 75 years and represented a cross-section of socio-demographic groups, including tribal and non-tribal communities. The survey explored local perceptions of climate change impacts and human-wildlife conflict (HWC), employing a 5-point Likert scale to gauge the intensity of respondent attitudes and observations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFocal themes and questions asked during the survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocal theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResponse scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact of Climate Change on Wildlife Movement into human-dominated landscapes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow strongly do you agree that climate change is causing wildlife to move into human-inhabited areas?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Strongly disagree) to 5 (Strongly agree)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChanges in Wildlife Behavior Due to Climate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo what extent do you believe that climate change is affecting the behavior of wildlife in your region?\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not at all) to 5 (Very much)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood Availability for Wildlife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow much do you think climate change is impacting the availability of natural food sources for wildlife?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not at all) to 5 (Very much)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased Human-Wildlife Conflicts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow strongly do you believe that climate change is leading to an increase in human-wildlife conflicts in your community?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Strongly disagree) to 5 (Strongly agree)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChanges in Ecosystem Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow significantly do you think climate change is altering the ecosystem services that wildlife relies on?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not significant) to 5 (Very significant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaptation Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo what extent do you think local communities are adapting to the impacts of climate change on wildlife?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not at all) to 5 (Very much)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact on Livelihoods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow much do you believe that climate change affects your livelihood due to changes in wildlife behavior or populations?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not at all) to 5 (Very much)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWildlife Population Trends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow concerned are you about the effects of climate change on the population trends of local wildlife species?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not concerned) to 5 (Very concerned)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity Awareness of Climate Issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow aware do you feel the community is about the impacts of climate change on wildlife and HWC?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not aware) to 5 (Very aware)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeed for Climate Action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow important do you think it is to take action on climate change to mitigate human-wildlife conflict?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Not important) to 5 (Very important)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo quantify the extent of HWC in the study area, a Conflict Severity Index (CSI) was developed based on self-reported incidents over the preceding two years. Each type of conflict was assigned a weighted value derived through participatory consultations with local communities: crop raiding\u0026thinsp;=\u0026thinsp;1, livestock depredation\u0026thinsp;=\u0026thinsp;2, property damage\u0026thinsp;=\u0026thinsp;3, and human casualty\u0026thinsp;=\u0026thinsp;5. This participatory approach aligns with best practices in conflict research, where community-based input is critical to ensure contextual relevance and legitimacy (Madden, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ogra, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe combination of structured survey methods, spatial sampling, and locally grounded severity assessments aimed to capture both the tangible and perceived impacts of environmental change on human-wildlife relations in this highly sensitive landscape.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CSI={\\sum\\:}_{i=1}^{n}\\frac{\\left(Wi*Ni\\right)}{N\\:total}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cem\u003eWi\u003c/em\u003e​: Weight assigned to each conflict type (1 for crop raiding, 2 for livestock depredation, 3 for property damage, 5 for human casualty).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNi\u003c/em\u003e​: Number of incidences of each conflict type affecting households in the last two years.\u003c/p\u003e \u003cp\u003e \u003cem\u003eN total\u003c/em\u003e​: Total number of conflict incidences recorded in the study.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eA combination of statistical and machine learning approaches was employed to examine community perceptions of climate change and human-wildlife conflict (HWC), as well as the factors influencing conflict severity. Non-parametric tests were used to analyze group-wise differences in perception data, given the ordinal nature of Likert-scale responses. The Mann\u0026ndash;Whitney U test assessed differences in perceptions across gender, ethnicity (tribal vs. non-tribal), and location (residing inside vs. outside the sanctuary). To examine the influence of education level, a Kruskal\u0026ndash;Wallis test was applied.\u003c/p\u003e \u003cp\u003eTo identify key factors associated with on-the-ground conflict intensity, a Random Forest regression model was employed, with the Conflict Severity Index (CSI) as the dependent variable. Independent variables included socio-demographic characteristics and respondents\u0026rsquo; perception scores related to climate change and HWC. Random Forest, a robust ensemble machine learning algorithm, is well-suited for social science applications due to its capacity to model non-linear relationships, handle multicollinearity, and work effectively with categorical and continuous predictors (Breiman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lingjun et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hyperparameter tuning was conducted through 5-fold cross-validation to enhance model generalizability and performance.\u003c/p\u003e \u003cp\u003eThe final configuration of the Random Forest model was optimized to enhance predictive performance while maintaining interpretability. Bootstrap sampling was enabled to allow sampling with replacement, thereby increasing the model\u0026rsquo;s robustness and reducing variance. The maximum depth of the trees was left unconstrained, allowing each tree to grow fully and capture intricate patterns within the data. To ensure sufficient representation in terminal nodes, the minimum number of samples per leaf was set to 1. Additionally, the minimum number of samples required to split an internal node was fixed at 10, which helped prevent over-fragmentation and improved model generalization. A total of 200 decision trees were used as estimators, providing a balanced trade-off between accuracy and computational efficiency.\u003c/p\u003e \u003cp\u003eTo contextualize perception data within broader climatic trends, temperature data from 1901 to 2022 was extracted from the Climatic Research Unit Time-Series (CRU TS) dataset (Harris et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pearson correlation analysis (via the \u003cem\u003epandas\u003c/em\u003e library) was used to explore associations between climatic trends and CSI values. Additionally, time series projections up to the year 2050 were generated using the \u003cem\u003eProphe\u003c/em\u003et library, which accommodates seasonality and change points in historical climatic data. All modeling and statistical tests were conducted using Python 3.12, in jupyter notebook with implementation via the \u003cem\u003escikit-learn\u003c/em\u003e library. Visualizations were created using the \u003cem\u003eseaborn\u003c/em\u003e package to illustrate variable importance, perception distributions, and CSI scores.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConflict Severity Index (CSI)\u003c/h2\u003e \u003cp\u003eThe Conflict Severity Index (CSI), developed through community consultations, quantified the intensity of human-wildlife conflict based on both frequency and the relative seriousness of different conflict types (e.g., crop raiding, livestock loss, property damage, human injury). CSI values across the study landscape ranged from 0.01 (indicative of low severity) to 0.94 (high severity). These values reflect varying levels of exposure and vulnerability to wildlife conflict among communities.\u003c/p\u003e \u003cp\u003eSpatial analysis revealed a clear pattern: grids located within the Wayanad Wildlife Sanctuary consistently exhibited higher CSI scores compared to those in the surrounding buffer zones. This trend underscores the increased risk faced by forest-fringe and sanctuary-residing households, who are in closer proximity to wildlife habitats and corridors. A detailed spatial visualization of CSI values is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, highlighting hotspots of severe conflict and offering insights into priority areas for targeted mitigation. The CSI approach, informed by earlier frameworks that emphasize locally relevant indicators (Madden, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ogra, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), proves useful in identifying both acute and chronic conflict zones at a localized scale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Climate Change on Wildlife Movement into Human-Dominated Landscapes\u003c/h2\u003e \u003cp\u003eRespondents were asked to rate their level of agreement with the statement: \u003cem\u003e\"Climate change is causing wildlife to move into human-inhabited areas,\"\u003c/em\u003e. Notably, no respondent indicated strong disagreement with the statement. A minority of respondents (2.7%) expressed some level of agreement, while 17.64% remained neutral. The majority of participants agreed with the statement, with 36.11% agreeing and 42.48% strongly agreeing. A Mann-Whitney U test was conducted to assess whether opinions differed based on demographic factors such as location, ethnicity, or gender. Results revealed no statistically significant differences in agreement based on location (inside the sanctuary: mean rank\u0026thinsp;=\u0026thinsp;317.32; outside the sanctuary: mean rank\u0026thinsp;=\u0026thinsp;299.07; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.178), ethnicity (tribal: mean rank\u0026thinsp;=\u0026thinsp;311.34; non-tribal: mean rank\u0026thinsp;=\u0026thinsp;303.45; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.564), or gender (male: mean rank\u0026thinsp;=\u0026thinsp;301.77; female: mean rank\u0026thinsp;=\u0026thinsp;314.97; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342). However, higher mean ranks were observed for respondents residing inside the sanctuary, individuals from tribal communities, and female participants, suggesting that these groups might perceive wildlife movement into settlement areas more strongly. Similarly, a Kruskal-Wallis H test indicated no significant influence of education level on respondents' opinions (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.259). These findings suggest that perceptions of wildlife movement driven by climate change are broadly consistent across diverse demographic groups and education levels, highlighting a shared understanding of the issue\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChanges in Wildlife Behaviour Attributed to Climate Change\u003c/h3\u003e\n\u003cp\u003eRespondents were surveyed to assess their perceptions of the extent to which climate change affects wildlife behavior in their region. The distribution of responses showed that 0.32% of respondents believed climate change had no impact on wildlife behavior, while 4.9% perceived only a minimal effect. A substantial proportion of respondents indicated significant impacts, with 17.64% reporting \"somewhat,\" 38.72% reporting \"quite a bit,\" and 28.75% reporting \"very much.\" A Mann-Whitney U test revealed significant differences based on gender, with female respondents (mean rank\u0026thinsp;=\u0026thinsp;314.97) expressing slightly stronger agreement than male respondents (mean rank\u0026thinsp;=\u0026thinsp;301.77; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). The location also significantly influenced perceptions, with respondents residing inside the sanctuary reporting higher agreement (mean rank\u0026thinsp;=\u0026thinsp;329.14) compared to those outside the sanctuary (mean rank\u0026thinsp;=\u0026thinsp;290.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). In contrast, ethnicity did not significantly affect responses, as tribal (mean rank\u0026thinsp;=\u0026thinsp;303.12) and non-tribal respondents (mean rank\u0026thinsp;=\u0026thinsp;308.61) exhibited similar perceptions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.680). The effect of education on perceptions was analyzed using a Kruskal-Wallis H test, which indicated a significant influence (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050). Post hoc Mann-Whitney pairwise comparisons revealed that respondents with higher education levels (Levels 2 and 3) exhibited significantly stronger agreement compared to those with lower education levels (Level 1 vs. Level 3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000; Level 1 vs. Level 2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). These findings highlight variations in perceptions based on demographic factors such as gender, location, and education level, while ethnicity did not emerge as a significant determinant.\u003c/p\u003e\n\u003ch3\u003eImpact of Climate Change on Wildlife Food Availability\u003c/h3\u003e\n\u003cp\u003eRespondents were asked to assess the impact of climate change on the availability of natural food sources for wildlife. A small proportion (0.32%) of respondents believed that climate change did not affect food availability, while 4.2% reported minimal impact. A larger portion, 29.41%, perceived a moderate effect, 31.20% agreed on a significant impact, and 34.80% felt that climate change had a very substantial influence on food availability. When examining demographic factors, statistical analyses revealed no significant differences in opinions based on gender, ethnicity, or location. Specifically, male respondents (mean rank\u0026thinsp;=\u0026thinsp;311.27) and female respondents (mean rank\u0026thinsp;=\u0026thinsp;297.92) did not differ significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.345). Similarly, ethnicity (tribal: mean rank\u0026thinsp;=\u0026thinsp;303.13; non-tribal: mean rank\u0026thinsp;=\u0026thinsp;308.61; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.694) and location (inside the sanctuary: mean rank\u0026thinsp;=\u0026thinsp;315.06; outside the sanctuary: mean rank\u0026thinsp;=\u0026thinsp;300.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.296) showed no significant differences. However, male respondents, non-tribal individuals, and those living inside the sanctuary exhibited slightly higher mean ranks, indicating a marginally stronger perception of the issue compared to their counterparts. Education level also did not significantly influence perceptions (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.834), suggesting that respondents across different educational backgrounds shared similar views on the impact of climate change on natural food sources for wildlife. These findings highlight a broad consensus among respondents, indicating a collective acknowledgment of the detrimental effects of climate change on wildlife food availability, regardless of demographic characteristics.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIncreased Human\u0026ndash;Wildlife Conflicts Due to Climate Change\u003c/h2\u003e \u003cp\u003eRespondents were asked to assess the extent to which they believe climate change is contributing to an increase in human-wildlife conflicts in their community. The distribution of responses revealed that a small percentage (0.65%) strongly disagreed, 5.71% disagreed, 22.54% remained neutral, 37.90% agreed, and 33.16% strongly agreed. Notably, over 70% of respondents acknowledged a connection between climate change and the escalation of human-wildlife conflicts. Further statistical analysis explored whether perceptions varied across demographic groups. A Mann-Whitney U test revealed no significant differences based on location (\u003cem\u003einside the sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;307.48; \u003cem\u003eoutside the sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;305.82; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.904), ethnicity (\u003cem\u003etribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;294.57; \u003cem\u003enon-tribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;313.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.163), or gender (\u003cem\u003emale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;308.06; \u003cem\u003efemale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;303.69; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.757). Similarly, a Kruskal-Wallis H test indicated no significant differences across education levels (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.249), suggesting that perceptions of increasing human-wildlife conflicts due to climate change are consistent across all educational backgrounds. These results underscore a shared recognition among respondents across various demographic groups that human-wildlife conflicts have intensified, with climate change being a significant contributing factor to this trend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChanges in Ecosystem Services Due to Climate Change\u003c/h2\u003e \u003cp\u003eThe survey examined respondents' perceptions of the extent to which climate change is altering ecosystem services. The distribution of responses revealed that 2.29% perceived the changes as \"Not significant,\" 11.94% as \"Slightly significant,\" 35.02% as \"Moderately significant,\" 35.18% as \"Significant,\" and 15.54% as \"Highly significant.\" These results suggest that a majority of respondents recognize a meaningful impact of climate change on ecosystem services. Further analysis indicated that this perception was consistent across demographic groups, with no statistically significant differences found based on gender (\u003cem\u003emale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;307.76; \u003cem\u003efemale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;302.81; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.727), ethnicity (\u003cem\u003etribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;304.04; \u003cem\u003enon-tribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;307.21; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.820), or geographic location (\u003cem\u003einside sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;307.74; \u003cem\u003eoutside sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;304.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.850). These findings highlight a shared understanding of the changes in ecosystem services across diverse demographic groups. However, the Kruskal-Wallis H test revealed a significant effect of education level on respondents' perceptions of ecosystem changes (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050). Post hoc pairwise comparisons indicated that individuals with higher levels of education demonstrated a more informed understanding of ecosystem alterations than those with lower education levels (\u003cem\u003eLevel 1 vs. Level 3\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000; \u003cem\u003eLevel 2 vs. Level 1\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). This emphasizes the critical role of education in enhancing awareness and understanding of environmental issues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdaptation Strategies to Climate Change Impacts on Wildlife\u003c/h2\u003e \u003cp\u003eThe assessment of community adaptation strategies to the impacts of climate change on wildlife revealed a predominantly moderate level of adaptation. Approximately 47% of respondents indicated that communities are \"somewhat\" adapting, while only 9.47% recognized significant adaptation efforts. Notably, no respondents reported a \"very much\" level of adaptation, suggesting limited progress in implementing comprehensive adaptation measures. Analysis of demographic factors, such as gender and ethnicity, revealed no statistically significant differences in perceptions of adaptation efforts. Male respondents (mean rank\u0026thinsp;=\u0026thinsp;300.38) and female respondents (mean rank\u0026thinsp;=\u0026thinsp;317.47) expressed similar views (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.217), as did tribal (mean rank\u0026thinsp;=\u0026thinsp;304.26) and non-tribal respondents (mean rank\u0026thinsp;=\u0026thinsp;307.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.789). However, location emerged as a significant factor, with respondents residing inside the sanctuary (mean rank\u0026thinsp;=\u0026thinsp;327.14) perceiving adaptation strategies more positively compared to those living outside (mean rank\u0026thinsp;=\u0026thinsp;292.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). This trend suggests that closer proximity to wildlife may promote greater awareness and engagement in mitigation strategies for human-wildlife conflict (HWC) resulting from climate change. Education level emerged as the most influential demographic variable, with the Kruskal-Wallis test revealing a strong statistical significance (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post hoc pairwise analyses indicated significant differences in adaptation perceptions across all education levels, particularly between the lowest education group (Level 1) and higher levels. Specifically, comparisons between Level 3 vs. Level 1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Level 2 vs. Level 1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed the most substantial contrasts, reflecting a notable disparity in understanding and awareness of climate adaptation strategies. The consistent statistical significance involving Level 1 underscores the crucial role of education in shaping perceptions of climate adaptation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Wildlife Population Changes on Livelihoods\u003c/h2\u003e \u003cp\u003eThe survey results on the perceived impact of wildlife population changes due to climate change on livelihood activities revealed that the majority of respondents (50.16%) regarded the impact as \"somewhat\" significant, while 26.63% perceived it as \"quite a bit\" significant. A smaller proportion of respondents felt the impact was either \"not at all\" (4.08%) or \"very much\" (3.43%). Demographic factors such as gender (male: mean rank\u0026thinsp;=\u0026thinsp;311.61, female: mean rank\u0026thinsp;=\u0026thinsp;297.31; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.298) and ethnicity (tribal: mean rank\u0026thinsp;=\u0026thinsp;304.66, non-tribal: mean rank\u0026thinsp;=\u0026thinsp;307.65; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.825) did not show statistically significant effects on shaping these perceptions. However, location emerged as a significant factor. Respondents residing inside the sanctuary expressed higher concern about the disruption of livelihood activities caused by wildlife (mean rank\u0026thinsp;=\u0026thinsp;327.88) compared to those living outside the sanctuary (mean rank\u0026thinsp;=\u0026thinsp;292.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). This difference may be attributed to the closer and more frequent interactions with wildlife that individuals inside the sanctuary experience, which directly affect their daily livelihoods. Education level was another significant factor influencing perceptions of the impact of wildlife population changes on livelihoods. The Kruskal-Wallis test revealed a strong statistical significance (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). Pairwise post hoc comparisons indicated significant differences between respondents with lower education levels (Level 1) and those with higher education levels. Notably, significant differences were found between Level 1 and Level 3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.000), Level 2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), Level 4 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and Level 5 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), suggesting that individuals with higher education levels tend to have a more nuanced understanding of the impact of wildlife population changes on livelihoods. In contrast, lower education levels may limit the depth of perception or awareness regarding this issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWildlife Population Trends\u003c/h2\u003e \u003cp\u003eThis study assessed respondents' concerns about the effects of climate change on the population trends of local wildlife species. The distribution of responses revealed that a significant proportion of participants were moderately concerned (46.73%), while 25.16% expressed concern and 10.29% reported high concern. A smaller fraction indicated being slightly concerned (14.86%) or not concerned at all (2.94%). Statistical analyses indicated no significant variation in concern levels across demographic categories. Gender was not a determining factor, with male respondents (mean rank\u0026thinsp;=\u0026thinsp;309.32) and female respondents (mean rank\u0026thinsp;=\u0026thinsp;301.42) showing similar levels of concern (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.571). Similarly, ethnicity did not significantly influence perceptions, as tribal respondents (mean rank\u0026thinsp;=\u0026thinsp;301.84) and non-tribal respondents (mean rank\u0026thinsp;=\u0026thinsp;309.42) reported comparable levels of concern (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.581). Geographic location, whether within the sanctuary (mean rank\u0026thinsp;=\u0026thinsp;314.12) or outside it (mean rank\u0026thinsp;=\u0026thinsp;301.26), also did not significantly affect concern levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.345). Additionally, the Kruskal-Wallis test revealed that educational attainment had no significant impact on perceptions of climate change's effects on wildlife populations (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.182).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Awareness of Climate Issues\u003c/h2\u003e \u003cp\u003eCommunity awareness of climate change was assessed, revealing generally low to moderate levels of awareness among respondents. A majority of participants reported limited awareness, with 15.03% indicating no awareness, 32.51% slightly aware, 37.74% somewhat aware, 14.54% aware, and only 0.16% very aware. Statistical analyses showed no significant influence of gender (\u003cem\u003emale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;309.14; \u003cem\u003efemale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;301.74; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.602) or ethnicity (\u003cem\u003etribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;311.15; \u003cem\u003enon-tribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;303.57; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.587) on awareness levels. However, geographic location had a significant effect, with respondents residing within the sanctuary displaying lower awareness of climate change (mean rank\u0026thinsp;=\u0026thinsp;291.19) compared to those living outside the sanctuary (mean rank\u0026thinsp;=\u0026thinsp;317.00), a difference that was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). The Kruskal-Wallis test further revealed a significant impact of education on climate awareness (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). Post hoc analyses indicated that individuals with higher education levels were significantly more aware of climate change than those with lower levels of education (Level 1 vs. Level 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Level 2 vs. Level 1: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; Level 1 vs. Level 4: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNeed for Climate Action\u003c/h2\u003e \u003cp\u003eRespondents were surveyed regarding the perceived importance of taking action to address climate change and its associated human-wildlife conflicts (HWC). Notably, none of the participants considered such actions to be unimportant or only slightly important. Instead, 13.88% of respondents rated the issue as moderately important, 43.46% as important, and 42.62% as highly important, indicating that over 85% emphasized the critical need for climate change action. Demographic analysis revealed no statistically significant differences in perceptions based on gender (\u003cem\u003emale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;312.66; \u003cem\u003efemale\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;296.33; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.245), ethnicity (\u003cem\u003etribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;299.99; \u003cem\u003enon-tribal\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;311.02; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.382), or geographic location (\u003cem\u003einside sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;291.19; \u003cem\u003eoutside sanctuary\u003c/em\u003e: mean rank\u0026thinsp;=\u0026thinsp;317.00; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052). However, the Kruskal-Wallis test identified education as a significant factor influencing perceptions of climate action (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). Post hoc analyses further indicated that individuals with higher levels of education were significantly more aware of the need for action compared to those with lower education levels (e.g., Level 1 vs. Level 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Level 2 vs. Level 1: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). These results underscore the widespread recognition of the urgency to address climate change across the community, with education playing a pivotal role in shaping awareness and attitudes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInsights from the community responses\u003c/h2\u003e \u003cp\u003eThe analysis revealed several nuanced patterns underlying the broader findings. Education consistently emerged as a key determinant of climate perception, with individuals possessing higher education levels exhibiting greater awareness of climate impacts and a stronger inclination toward climate action. This underscores the transformative role of education in shaping environmental understanding and fostering adaptive behavior.\u003c/p\u003e \u003cp\u003eWhile residents living within sanctuary zones reported more acute experiences of ecological and livelihood impacts, likely due to their direct proximity to wildlife and shifting ecosystems, they demonstrated comparatively lower conceptual awareness of climate change. This highlights a critical gap in climate communication, where experiential knowledge is not always matched by formal understanding. Gender differences were also evident, with women more likely to perceive ecological disturbances. This may be attributed to their direct and frequent engagement with natural resources, such as water, fuel, and food sources, which are often the first to be affected by environmental stressors.\u003c/p\u003e \u003cp\u003eDespite widespread acknowledgment of climate-induced ecological changes and livelihood challenges, the uptake of adaptation measures remained limited, pointing to a persistent knowledge\u0026ndash;action gap. Interestingly, ethnicity did not significantly influence climate perceptions, suggesting that shared vulnerabilities in high-risk landscapes may transcend cultural differences. This finding reinforces the importance of designing inclusive, community-based adaptation strategies that recognize common risks while ensuring local participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRandom Forest Model for Community Perception of Climate Change\u003c/h2\u003e \u003cp\u003eThe Random Forest regression model offered valuable insights into the complex relationship between community perceptions of climate change and the perceived severity of human-wildlife conflicts. Despite the inherent variability and subjectivity associated with social science data, the model demonstrated acceptable predictive performance. It achieved a Mean Squared Error (MSE) of 0.0410, a Root Mean Squared Error (RMSE) of 0.2025, and a Mean Absolute Error (MAE) of 0.1514, indicating its ability to minimize prediction errors effectively.\u003c/p\u003e \u003cp\u003eThe model yielded an R-squared (R\u0026sup2;) value of 0.4717 and an Explained Variance Score of 0.4829, reflecting moderate predictive power. It is important to note that in social science research, such moderate R\u0026sup2; values are not uncommon, given the multidimensional and context-dependent nature of human attitudes and behaviors (Wooldridge, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA feature importance analysis revealed the key predictors influencing the perceived severity of human-wildlife conflict in the context of climate change. The most influential variable was Changes in Ecosystem Services due to Climate Change, with a Gini importance score of 0.161, highlighting its substantial impact on model predictions. This was followed by: Increase in Human-Wildlife Conflict (0.137), Change in Wildlife Behavior (0.129), Movement of Wildlife into Human Habitats (0.128), Food Availability for Wildlife (0.114).\u003c/p\u003e \u003cp\u003eThese findings, visually summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, emphasize the dominant role that ecosystem disruption and shifting wildlife dynamics play in shaping community perceptions of conflict severity. The model thus underscores the significance of ecological awareness in driving concern and urgency around climate-induced human-wildlife interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAssessing Evidence of Climate Change in Wayanad Wildlife Sanctuary\u003c/h2\u003e \u003cp\u003eTo evaluate long-term climate trends in the Wayanad Wildlife Sanctuary, temperature data from the Climatic Research Unit Time-Series (CRU TS) dataset (Harris et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was analyzed. This dataset offers a robust historical record of land surface temperatures from 1901 to 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Pearson correlation analysis revealed statistically significant warming trends during three key seasons: Monsoon: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.693, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb); Post-monsoon: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.762, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec); Summer: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.698, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). These strong correlations indicate consistent and substantial warming patterns over the past century.\u003c/p\u003e \u003cp\u003eTo forecast future trends, the Facebook Prophet model was employed to project seasonal temperature changes from 2023 to 2050. The model results indicated a continued rise in temperatures across all seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), affirming the persistence of long-term warming. Model performance was evaluated using standard metrics: Monsoon season: MAE\u0026thinsp;=\u0026thinsp;0.199, RMSE\u0026thinsp;=\u0026thinsp;0.249, R\u0026sup2; = 0.648; Post-monsoon: MAE\u0026thinsp;=\u0026thinsp;0.180, RMSE\u0026thinsp;=\u0026thinsp;0.224, R\u0026sup2; = 0.771; Summer: MAE\u0026thinsp;=\u0026thinsp;0.259, RMSE\u0026thinsp;=\u0026thinsp;0.321, R\u0026sup2; = 0.626. The relatively high R\u0026sup2; values, particularly during the post-monsoon period, confirm the model\u0026rsquo;s reliability in capturing temperature dynamics.\u003c/p\u003e \u003cp\u003eThese findings offer compelling evidence of significant warming within the Wayanad region. The observed and projected increases in temperature are likely to contribute to habitat degradation, disruption of wildlife behavior, and an escalation in human-wildlife conflicts\u0026mdash;pressing concerns for biodiversity conservation and local livelihoods in and around the sanctuary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Perceptions of Climate Change Impact on Human-Wildlife Conflict\u003c/h2\u003e \u003cp\u003eClimate change has emerged as a critical driver influencing the movement of wildlife into human-dominated landscapes, disrupting traditional behavioral patterns and intensifying resource competition (Guillaumet et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kiriya, 2018). In this study, 78% of respondents identified climate change as a major cause of increased wildlife encroachment, citing rising temperatures and declining availability of natural resources as principal factors (Abrahams, 2021; Bond et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Remarkably, perceptions of this impact showed minimal variation across gender, ethnicity, location, and education levels. This widespread agreement stands in contrast to prior research that emphasized the role of demographic variables in shaping environmental perceptions (Carpenter, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Overland et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such consensus may reflect the universal visibility of climate-driven changes in high-risk landscapes, where lived experiences often transcend demographic divides.\u003c/p\u003e \u003cp\u003eRespondents widely recognized the profound effects of climate change on wildlife behavior, reporting noticeable changes in migration, foraging, and reproductive patterns (Shaolin et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Varis, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Women and residents living near wildlife habitats exhibited greater awareness of these shifts, consistent with literature suggesting that direct exposure enhances ecological sensitivity (Ma \u0026amp; Jiang, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and that women often express stronger environmental concern (Jaina \u0026amp; Prajapati, 2023). Education also played a significant role; individuals with higher education levels were more adept at linking behavioral changes in wildlife to climate impacts, aligning with previous findings that education enhances environmental literacy (Poortinga et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisruptions to ecological food chains were another widely recognized impact of climate change. Sixty-five percent of respondents, particularly those residing near forests, reported declining food resources for wildlife, an issue reflecting both environmental degradation and increased community awareness (Heggs \u0026amp; Green, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cleland et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This growing awareness presents a valuable opportunity to mobilize support for conservation efforts, echoing research that underscores the importance of local ecological knowledge in fostering stewardship (Parmesan, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). As wildlife increasingly encroaches into human settlements in search of food, human-wildlife conflicts have escalated, particularly in forest-bordering communities (Maldonado, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although the overall recognition of these conflicts was widespread, minor differences in concern were observed between tribal and non-tribal respondents, suggesting that socio-cultural and economic contexts influence conflict perception (Yoezer \u0026amp; Dema, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRespondents also demonstrated a high level of awareness regarding the broader ecosystem disruptions caused by climate change, including impacts on natural processes and biodiversity. Education once again emerged as a key determinant of awareness, with individuals possessing higher education levels showing a deeper understanding of ecosystem degradation (Brown \u0026amp; Green, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith \u0026amp; Johnson, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, those living closer to wildlife habitats reported heightened urgency and awareness regarding the need for adaptation strategies. The findings highlight the pivotal role of community involvement, where collective action and traditional knowledge serve as foundational components for effective adaptation and mitigation (Mastrorillo et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender inclusivity was also identified as essential to building climate-resilient communities. The meaningful participation of women and other underrepresented groups is vital for developing equitable and comprehensive responses to climate challenges, reinforcing the call for inclusive policies and planning frameworks (Onoh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant portion of respondents, especially those residing in ecologically sensitive zones, acknowledged climate change's detrimental effects on local livelihoods. This localized understanding reflects the importance of place-based experiences in shaping perceptions. Education continued to play a transformative role, strengthening individuals' capacity for resilience, adaptation, and informed decision-making (F\u0026uuml;ssler \u0026amp; Peters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reid \u0026amp; Sahl, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The collective recognition of climate-related threats to biodiversity underscores the need for sustained public engagement and coordinated responses at the community level.\u003c/p\u003e \u003cp\u003eThese insights align with broader literature emphasizing the crucial role of education, gender equity, and local engagement in enhancing social and ecological resilience to climate impacts (Adger, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Pelling, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eIntegrating Human Dimensions in Conservation\u003c/h2\u003e \u003cp\u003eConservation in the context of climate change necessitates the integration of human dimensions alongside ecological considerations. This study reaffirms that effective conservation outcomes are best achieved when local communities are actively engaged and their socio-economic realities are acknowledged (Bennett et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This participatory approach departs from conventional models that often exclude communities from their ecosystems (Rai et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Findings indicate that local populations not only recognize the urgency of climate and environmental issues but also prioritize them, suggesting strong potential for collaborative conservation efforts.\u003c/p\u003e \u003cp\u003eHolistic approaches that address the underlying socio-economic drivers of conflict are essential for sustainable solutions (Rust et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conservation education and outreach were identified as key tools in promoting long-term engagement. By improving access to environmental information, these efforts foster public concern, awareness, and proactive behavior (Waylen et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jacobson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, shared concerns about climate change and human-wildlife conflict transcended lines of ethnicity, gender, education, and geography. This universality strengthens the case for inclusive conservation policies that integrate community perspectives into decision-making. Global examples continue to demonstrate that locally grounded conservation initiatives achieve higher success rates and foster greater resilience (Waylen et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eClimate Change and the Intensification of Human-Wildlife Conflict in Wayanad\u003c/h2\u003e \u003cp\u003eThe findings of this study reaffirm the growing body of evidence that positions climate change as a critical driver of human-wildlife conflict (HWC). Over the past 122 years, the region has experienced a steady and significant warming trend, one that is projected to continue. Long-term climatic assessments underscore this rise, particularly across the Western Ghats (Jha et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a major orographic feature, the Western Ghats regulate monsoon patterns and regional hydrology, functioning as a climate gatekeeper for peninsular India (Gunnell, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). However, this regulatory role is increasingly undermined by habitat fragmentation, land-use conversion, and deforestation (Jha et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kale et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), contributing to an ecological shift with cascading socio-environmental consequences.\u003c/p\u003e \u003cp\u003eObservations from Wayanad Wildlife Sanctuary echo these broader patterns. Rising temperatures and altered rainfall regimes have diminished the productivity of forest ecosystems. One major implication is the reduction in natural forage and water availability, pushing wildlife, especially large herbivores, toward human-dominated landscapes in search of resources. This study\u0026rsquo;s Random Forest analysis highlights the synergistic role of ecological and climatic variables in explaining conflict patterns, reinforcing that HWC cannot be examined in isolation from climate-driven habitat degradation.\u003c/p\u003e \u003cp\u003eAs forest habitats become increasingly resource-scarce, wildlife exhibit notable behavioural shifts. Several respondents, particularly those residing within sanctuary boundaries, reported frequent foraging by species such as the spotted deer (\u003cem\u003eAxis axis\u003c/em\u003e) in paddy fields. The attractiveness of cultivated crops, perceived to be more nutritious and accessible, encourages such incursions. These findings support earlier observations that wild animals, when faced with resource scarcity, often turn to residential gardens or ornamental plants that mimic their natural diets (Sarkar \u0026amp; Bhadra, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While such behavior reflects the adaptability and resilience of wildlife, it also aligns animal behavior more closely with human livelihoods, intensifying conflict risks.\u003c/p\u003e \u003cp\u003eThese behavioural shifts are mirrored in global HWC patterns. Increasingly, climate change is cited as a catalyst that exacerbates both the frequency and intensity of conflict incidents (Abrahams et al., 2023). Changes in precipitation and temperature regimes are forcing wildlife into previously unaffected regions (Abrahams, 2021; Gaire \u0026amp; Acharya, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Wayanad, local perceptions substantiate this trend, with reports of rising crop raiding, livestock depredation, and human casualties, especially in fringe areas of the sanctuary.\u003c/p\u003e \u003cp\u003eBeyond direct encounters, climate change also influences forest structure and species composition. For example, local knowledge, particularly from the indigenous Kattunaika community, indicates that a rare mass flowering of bamboo a decade ago, followed by widespread dieback, led to dramatic vegetation changes. The resulting open forest floors were rapidly colonized by grasses, attracting herbivores and subsequently predators such as tigers. These ecological transitions brought large mammals into closer proximity with human settlements. Scientific literature confirms that bamboo phenology is sensitive to warming trends, with increased temperatures accelerating flowering cycles (Zheng et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These localized accounts are corroborated by satellite-based assessments, which show a decline in bamboo cover across riparian patches between 2004 and 2018 (John et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother key concern is the spread of invasive plant species. \u003cem\u003eSenna spectabilis\u003c/em\u003e and \u003cem\u003eLantana camara\u003c/em\u003e, both widespread in the sanctuary, are known to suppress native plant regeneration and reduce forage quality for herbivores (Prasad, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sundaram \u0026amp; Hiremath, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eS. spectabilis\u003c/em\u003e, introduced in the 1980s, has now colonized nearly a quarter (23%) of the sanctuary\u0026rsquo;s area (Vinayan et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its poor palatability has driven herbivores to target agricultural crops, exacerbating tensions with local farmers.\u003c/p\u003e \u003cp\u003eFrom an institutional perspective, the Forest Department\u0026rsquo;s records indicate 4,126 cases of crop damage, 384 livestock depredations, and 50 human attacks (including nine fatalities) between 2015 and 2020 from Wayanad. These figures align with community experiences and suggest a worrying upward trend in conflict intensity, particularly in ranges adjacent to degraded or highly fragmented forest areas.\u003c/p\u003e \u003cp\u003eAmong large herbivores, elephants emerge as a focal species in the landscape of HWC. In Wayanad, the widespread cultivation of fruit-bearing trees such as jackfruit (\u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e) and mango (\u003cem\u003eMangifera indica\u003c/em\u003e) in homesteads has inadvertently attracted elephants, resulting in frequent crop depredation and infrastructure damage. Even during periods when forest food is relatively abundant, elephants continue to forage in village plantations, highlighting their preference for these high-energy crops. This is consistent with findings by Anoop et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who document repeated elephant foraging events in fruit tree-dominated homesteads.\u003c/p\u003e \u003cp\u003eAlternative cropping strategies offer a promising, community-centered approach to mitigate elephant incursions. In Botswana, combining chilli (\u003cem\u003eCapsicum spp.\u003c/em\u003e) with legumes has proven effective both in deterring elephants and boosting farmer income (Matsika et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar success has been observed in Sri Lanka through the use of buffer crops such as orange (\u003cem\u003eCitrus sinensis\u003c/em\u003e), which are less attractive to elephants and economically beneficial (Dharmarathne et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Chilli, in particular, has long been recognized for its strong deterrent properties and is widely used across Africa (Graham \u0026amp; Ochieng, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese interventions have also shown promise in Wayanad. Chelliah et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported the effectiveness of chilli-based deterrents in reducing elephant raids on crops. However, their long-term success hinges on participatory implementation. Engaging communities in identifying regionally suitable, economically viable, and ecologically sound crop alternatives is essential. Such bottom-up strategies not only reduce conflict but also enhance livelihood resilience and foster coexistence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the growing consensus among local communities that climate change is a key catalyst in the intensification of human-wildlife conflicts (HWC) in and around the Wayanad Wildlife Sanctuary. Perceived impacts include the decline of ecosystem services, diminished availability of natural forage within forest habitats, and shifts in wildlife behaviour, particularly increased foraging in human-modified landscapes. These dynamics are further exacerbated by the spread of invasive alien species, which reduce the palatability and accessibility of native vegetation, forcing herbivores to target cultivated crops.\u003c/p\u003e \u003cp\u003eImportantly, these concerns are shared across diverse social groups, cutting across lines of gender, ethnicity, education, and geography. Such widespread perceptions reinforce the need for inclusive and community-driven strategies that integrate both ecological and socio-cultural dimensions of conservation. As climatic conditions continue to shift, marked by rising temperatures and erratic rainfall, there is an urgent need for proactive, climate-adaptive interventions.\u003c/p\u003e \u003cp\u003eMitigating the compounded effects of climate change and HWC will require holistic, multi-scalar approaches that address habitat restoration, adaptive land-use planning, sustainable agriculture, and participatory governance. Long-term coexistence between people and wildlife in the region is possible only if we recognize and address the connected environmental and human factors involved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eHuman Ethics and Consent to Participate Declarations\u003c/h2\u003e\n\u003cp\u003eWritten and verbal informed consent was obtained from all participants involved in the study. To ensure their privacy and anonymity, the names of respondents participating in interviews and surveys have been kept confidential.\u003c/p\u003e\n\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eEthical approval for this research was granted by the Human Ethics Committee of the University of Calicut, Kerala, India. (U.O. No. 13449/2023/Admn dated 26.08.2023).\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was carried out with financial assistance from the Directorate of Environment and Climate Change, Government of Kerala, India (DoECC/303/2023/E1 dated 10.11.2023).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eNMK. conducted the fieldwork, collected and analyzed the data, and prepared the manuscript draft. HCC. critically reviewed the manuscript and contributed substantially to its revision.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors are grateful to the Directorate of Environment and Climate Change, Government of Kerala, for providing financial support for this research. 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The bamboo flowering cycle sheds light on flowering diversity. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2020.00381\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2020.00381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human–wildlife conflict, Climate change, Community perception, Random Forest model, Western Ghats, India","lastPublishedDoi":"10.21203/rs.3.rs-6892314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6892314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is reshaping natural landscapes, profoundly affecting ecosystems and human communities. A key outcome is the rise in human\u0026ndash;wildlife conflict, especially in regions like Wayanad Wildlife Sanctuary in the Western Ghats of southern India, where people and wildlife closely coexist. This study examines local community perceptions of human\u0026ndash;wildlife conflict under changing climatic conditions. Using a questionnaire-based survey, we assessed community views on climate change and its role in escalating human\u0026ndash;wildlife conflict. Random Forest regression model used to assess complex relationship between community perceptions of climate change and the perceived severity of human-wildlife conflicts, while a time series analysis of regional temperature data was conducted to trace climatic trends. Results show a widespread acknowledgment of climate change, with most respondents linking increased human\u0026ndash;wildlife conflict to factors like rising temperatures and shifting rainfall patterns. Temperature trends from time series analysis support these perceptions, revealing a significant warming trend. Variables such as education, ethnicity, and location significantly shaped community understanding of climate impacts. This study highlights the value of integrating community perspectives into strategies for managing human\u0026ndash;wildlife conflict, stressing that local knowledge is essential for effective, climate-resilient conflict mitigation. Strengthening community engagement is crucial to address the growing challenges of human\u0026ndash;wildlife coexistence in an era of rapid environmental change.\u003c/p\u003e","manuscriptTitle":"Shifting Climates, Rising Tensions: Community Insights on Human-Wildlife Conflicts in Wayanad Wildlife Sanctuary, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 01:56:57","doi":"10.21203/rs.3.rs-6892314/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"373f4308-7359-4d4d-8cf8-fe751977f0dd","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T12:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 01:56:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6892314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6892314","identity":"rs-6892314","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}