Data-Driven Insights into Human–Gaur Conflicts: Spatiotemporal Trends and Risk Mapping Across Tamil Nadu, India

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Despite various mitigation efforts globally, the frequency of HWC continues to rise. Among the conflict-prone species, the Indian gaur (Bos gaurus) has increasingly been involved in such interactions across southern India. To support the development of long-term mitigation strategies for Human–Gaur Conflict (HGC), we conducted a comprehensive study using data collected from compensation records across 48 forest divisions in Tamil Nadu between 2016 and 2024. We analyzed spatial and temporal trends, predicted conflict risk zones using ensemble modeling, and identified the key drivers influencing HGC. Our findings reveal that conflict intensity was highest in the Nilgiri division, followed by Dharmapuri and Kodaikanal. Crop damage was the predominant conflict type, followed by human injuries, with incident peaks observed during December to March. Elevation emerged as the most influential predictor in the risk models, with a clear positive correlation showing that the conflict risk increased with rising elevation. The model also predicted that 18,335 km² of the state falls under conflict risk zones, accounting for approximately 14.1% of Tamil Nadu's total geographical area. This study provides critical insights into the spatial ecology of HGC and highlights the utility of predictive modeling in identifying high-risk zones. The outcomes can inform conservationists and forest managers in designing and implementing proactive mitigation measures, especially in areas predicted to have a high likelihood of future conflict. Human–wildlife conflict Human–Gaur Conflict Bos gaurus ensemble modeling conflict risk prediction elevation Tamil Nadu Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Human-wildlife conflict (HWC) is a growing challenge for biodiversity conservation and a significant challenge for the well-being and livelihoods of rural communities ( 1 ). HWC arises from competition for shared resources, leading to habitat loss, fragmentation, and degradation ( 2 , 3 ). It results in crop and livestock losses, human injuries, deaths, and property damage ( 4 ). Carnivores and primates are the most conflict-prone species globally ( 5 ). Laws and policies, particularly those involving land-use planning and wildlife management, can contribute to or mitigate HWC ( 6 ). Addressing HWC requires a multifaceted approach, considering social, economic, and ecological dimensions to balance human needs and wildlife protection ( 7 ). HWC involving megaherbivores, such as elephants and gaur, is a growing conservation concern in India. Conserving large herbivores presents significant challenges due to their extensive home range requirements, naturally low population densities, and frequent conflicts with humans, particularly through crop-raiding activities ( 8 ). India's major human-animal conflict issues include crop raiding, livestock depredation, and human death ( 9 ). The gaur ( Bos gaurus ) is a large bovine native to the Indian Subcontinent and Southeast Asia, and is listed as Vulnerable ( 10 ). During the past century, the gaur population has declined by more than 80% due to habitat loss to agriculture and poaching for horn and Meat ( 11 ). Gaur plays a crucial ecological role in dry deciduous forests by maintaining physical habitat structure and was once a key component of the food chain in tiger-occupied landscapes ( 12 ). It is distributed in South and Southeast Asia, from India to peninsular Malaysia, and it occurs in India, Nepal, Bhutan, Bangladesh, Myanmar, Thailand, China, Laos, Cambodia, Vietnam, and Malaysia ( 13 ). In India, gaur is distributed across the hill forests of Western Ghats, Central highlands and Northeast India ( 14 ). The gaur population has declined considerably due to habitat loss and hunting in India over the last few decades ( 15 , 16 ). However, gaur populations are increasing in human-dominant areas, especially plantations, leading to more frequent encounters with humans and growing chances of negative interactions. Tamil Nadu has a forest cover of 26,419 km 2 , accounting for 20.34% of its geographical area ( 17 ). The region supports two megaherbivores, Asian elephants ( Elephas maximus , Endangered) and Indian gaur ( Bos gaurus , Vulnerable), both of which are frequently observed outside protected areas, venturing into human-modified landscapes ( 4 , 18 ). The region has two mountains, the Western Ghats and the Eastern Ghats. They are highly disturbed, with habitat fragmentation, poaching, and other anthropogenic pressures often restricting these species to small, isolated protected areas ( 19 ). In some cases, gaur populations have colonised plantations near protected areas, leading to frequent crop-raiding incidents and occasional human fatalities ( 20 , 18 ). In the hill stations of this region, solitary male gaurs are frequently observed in urban areas, scavenging for food waste along streets and in garbage dumps (Fig. 1 ). Due to various contributing factors, the increase in human-gaur conflicts (HGC) in Tamil Nadu underscores the importance of understanding their spatiotemporal dynamics and identifying high-risk zones to implement proactive mitigation measures. In this study, we analysed the spatial and temporal trends in HGC across Tamil Nadu using 9 years of data to identify conflict patterns and major drivers. We also employed ensemble modelling techniques to predict high-risk areas for future conflict management. Study area Tamil Nadu, a southern Indian state, is geographically situated between latitudes 8°05'N and 13°35'N and longitudes 76°15'E and 80°20'E ( 21 ). It shares its borders with Kerala to the west, Karnataka to the northwest, Andhra Pradesh to the north, the Bay of Bengal along the eastern coastline, and the Indian Ocean to the south ( 4 ). The state has 38 districts and 48 forest divisions. Tamil Nadu comprises four major physiographic regions: the Coastal Plains, the Eastern Ghats, the Central Plateau, and the Western Ghats. The Western Ghats, the longest hill range in the state, is recognised as one of the 25 global biodiversity hotspots and one of India's three mega centres of endemism ( 22 ). Tamil Nadu's Protected Area network includes five National Parks, 29 Wildlife Sanctuaries, and two Conservation Reserves, covering 4.97% of the state's geographical area. Tamil Nadu experiences a humid tropical climate, with annual rainfall ranging from 900 mm to 1,200 mm and temperatures varying between 19°C and 37°C ( 17 ). The diverse climatic conditions in Tamil Nadu give rise to a range of forest types, including tropical wet evergreen, tropical semi-evergreen, subtropical hill, and montane wet temperate forests, all of which support a rich variety of flora and fauna ( 21 ). The detailed map of the study area showing the forest divisions, protected areas, and reserved forest, along with the HGC incidents overlaid, is provided as (Fig. 2 ). The fringes of the forests of the study area are mostly disturbed due to agricultural purposes and the landscape is fragmented due to large-scale plantations, etc, as depicted in (Fig. 3 ). Data collection We collected data between 2016 and 2024 on HGC incidents across the forest divisions of Tamil Nadu from both secondary sources and field visits. As part of the Tamil Nadu Forest Department's conflict mitigation plan, conflict records are collected and maintained in forest divisions. These data had information about HGC, such as date of occurrence, latitude and longitude, type of conflict (i.e., crop damage, human injury, and human death), and crop name. While visiting the conflict location, we collected information about the frequency, terrain information, and any other ground factors that could influence HGC in the region. We recorded (n = 743) conflict events, of which (n = 630) had associated geo-coordinates available for conflict risk modelling (Fig. 2 ). Data analysis The data analysis was conducted using Microsoft Excel and the R programming language. Initially, the total number of conflicts was calculated for each forest division. Subsequently, the conflicts were analyzed every year, followed by an analysis of monthly conflicts. The mean number of conflicts across the nine-year study period (2016–2024) was calculated to understand temporal variations in conflict occurrence. This provided insights into the average intensity of conflicts for each month. To assess the variability in the conflict data across the years, we calculated the Standard Deviation (SD) for each month. Additionally, we computed the Standard Error (SE) to evaluate the precision of the mean estimate. The 95% Confidence Interval (CI) was then calculated to offer a range of plausible values for each month’s mean conflict value. These analyses were performed using the dplyr package in R, which facilitated data manipulation and transformation. The results of the studies were organized in tabular format and further examined to identify patterns in the seasonal distribution of conflicts. The calculated average number of conflicts and their associated SD, SE, and CI were used to conclude the seasonal variation in conflict occurrence across the study period. Graphs were prepared for visualisation of the data using Excel and R. Environmental variables We selected three key bioclimatic variables: Annual Mean Temperature (Bio1), Isothermality (Bio3), and Annual Precipitation (Bio12) from the WorldClim database ( 23 ). To understand the topographical influence on the conflict risk, we downloaded the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) at a 30 m resolution ( 24 ) and also derived the terrain ruggedness index (TRI) using the raster ( 25 ) and rgdal ( 26 ) packages in R. Land cover data, including distance to forest cover, croplands, and built-up areas, were obtained from the ESA World Cover 2022 dataset ( 27 ) at a 30 m resolution. These raster layers were processed in R using the httr ( 28 ) and raster packages, converted into polygon formats, and their Euclidean distances were computed using the rgeos package ( 29 ). The maximum NDVI from 2016 to 2024 was calculated by creating a time-series composite using Google Earth Engine’s Image Collection function, which aggregates data yearly. The max() function was then applied to extract the highest NDVI value from the collection, representing the peak vegetation for each pixel across the specified period, ensuring accurate temporal representation for conflict risk modelling. The water body data were retrieved from the Humanitarian OpenStreetMap database ( https://data.humdata.org/ ) , and their Euclidean distances were computed. To understand the impact of human disturbance on the conflict risk, we downloaded the Global Human Modification of Terrestrial Systems dataset (HMI v1, 2016), which quantifies cumulative human impact on terrestrial ecosystems at a 1-km resolution ( 30 ). To ensure consistency across datasets, all environmental variables were resampled to a standardized 1 km² resolution using the raster package. To assess potential multicollinearity among the predictor variables, we conducted a Pearson correlation analysis with a 0.75 threshold in R. Since no strong correlations were detected, all selected variables were retained for modelling (Fig. 4 ). Conflict risk modelling We used ensemble modelling to predict the conflict risk. This model has recently been used in many HWC risk modelling studies across the globe ( 4 , 21 , 31 , 32 , 33 ). We utilised the sdm package ( 34 ), which integrates multiple algorithmic approaches implemented in the R platform (version 4.3.3) (R Core Team, 2024). This package allows users to generate candidate models, assess their performance, and combine them using ensemble techniques. Using the same package, we created pseudo-absence records equal to the presence records (n = 630). As the data consisted of presence-only records, we generated an equal number of pseudo-absences using the sdm package. These pseudo-absences, likely representing true absences, enhanced the accuracy of the models predicting areas of high occurrence probability ( 35 ). We developed conflict risk model using nine multiple algorithms, including Random Forest (RF), Generalized Additive Models (GAM), Boosted Regression Trees (BRT), Maximum Entropy (MaxEnt), Support Vector Machines (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Models (GLM), and Flexible Discriminant Analysis (FDA). Of 718 recorded conflict occurrences, 70% were randomly assigned for training, while the remaining 30% were used for testing. This split ensures model stability while reducing the risk of overfitting. The testing data provides an independent evaluation of model performance on unseen records. This process was repeated five times to enhance reliability, and the mean values for sensitivity, specificity, True Skill Statistics (TSS), kappa, and the Area Under the Curve (AUC) were calculated to assess model accuracy. The final ensemble model was constructed using a weighted averaging approach, where TSS was used as the evaluation metric, with a threshold set at maximum sensitivity and specificity. The ensemble model outputs were visualised and mapped using QGIS 3.36.1. Conflict risk zones were categorized into four distinct classes: “Very Low Risk” (0–0.25), “Low Risk” (0.25–0.5), “High Risk” (0.5–0.75), and “Very High Risk” (0.75–1). To quantify the extent of conflict risk areas, we computed the total area under the “High Risk” and “Very High Risk” categories using R. Results Spatial patterns The division-wise distribution of HGC revealed that the Nilgiris recorded the highest number of incidents (n = 174, 24.4%), followed by Kodaikanal (n = 105, 14.7%), Dindigul (n = 85, 11.9%) and Dharmapuri (n = 85, 11.9%), indicating high-conflict areas. Tiruchirappalli (n = 46, 6.5%), Attur (n = 51, 7.1%), WL ATR Pollachi (n = 35, 4.9%), and Salem (n = 32, 4.5%) recorded moderate conflict levels. Divisions with the lowest number of conflicts were Tiruvannamalai (n = 22, 3.1%), Vellore (n = 22, 3.1%), Coimbatore (n = 13, 1.8%), and Madurai (n = 13, 1.8%) (Fig. 5 ). Year-wise Conflict Analysis The annual number of HGC increased from (n = 10) in 2016 to (n = 69) in 2017 and (n = 103) in 2018, then declined to (n = 73) in 2019 and (n = 60) in 2020. Conflicts rose again to (n = 77) in 2021 before dropping sharply to (n = 18) in 2022. A pronounced peak occurred in 2023 (n = 175), followed by a modest reduction in 2024 (n = 160) (Fig. 6 a). Month-wise Conflict Analysis During winter (December–February), incident levels remain elevated, with December (n = 73), January (n = 78), and February (n = 68) all showing substantial conflict counts. As temperatures rise into summer (March–May), conflicts peak in March (n = 99), then decline through April (n = 75) and drop sharply by May (n = 43). With the onset of the monsoon (June–September), clashes moderate: June records 52 incidents, July sees a slight rebound to 74, August falls to 50, and September reaches one of the year’s lowest counts (n = 46). Finally, in the post-monsoon/autumn period (October–November), conflicts dip to their nadir, with October at 47 and November at 40(Fig. 6 b). Seasonal Dynamics of HGC (Monthly Means) The month-wise mean number of HGC incidents over the years showed the highest activity during the pre-monsoon period, with March recording the peak (11.00 ± 9.60), followed by April (8.33 ± 6.63) and May (6.14 ± 2.85). During winter, January (9.75 ± 7.94) and February (9.71 ± 6.85) also exhibited elevated conflict levels. The southwest monsoon months (June–September) saw moderate to low activity: June (5.78 ± 3.80), July (9.25 ± 4.83), August (6.25 ± 4.46) and September (6.57 ± 4.20). Conflict incidence reached its nadir in the post-monsoon autumn, October (5.22 ± 5.02) and November (5.00 ± 2.27), before rising again at the onset of winter in December (8.11 ± 6.05). These results confirm a pronounced seasonal peak in the pre-monsoon period, with markedly lower conflict levels during the monsoon and post-monsoon months (Table 1 ). Table 1 Monthly mean of conflict events and confidence intervals. Month Mean SD SE CI_Lower CI_Upper January 9.75 7.94 2.81 4.25 15.25 February 9.71 6.85 2.59 4.64 14.79 March 11.00 9.60 3.20 4.72 17.28 April 8.33 6.63 2.21 4.00 12.67 May 6.14 2.85 1.08 4.03 8.26 June 5.78 3.80 1.27 3.29 8.26 July 9.25 4.83 1.71 5.90 12.60 August 6.25 4.46 1.58 3.16 9.34 September 6.57 4.20 1.59 3.46 9.68 October 5.22 5.02 1.67 1.94 8.50 November 5.00 2.27 0.80 3.43 6.57 December 8.11 6.05 2.02 4.16 12.06 Conflict type Crop damage was the most prevalent type of HGC incident, accounting for 45.98% of the cases. This was followed by human injury, which comprised 30.70% of the total incidents. Livestock depredation and human deaths represented 11.66% and 8.98%, respectively. Property damage was the least reported conflict type, contributing to only 2.68% of the overall cases (Fig. 7 ). Model performance The performance of the eight modeling algorithms varied based on AUC, COR, TSS, and deviance values (Table 2 ). Random Forest (RF) showed the highest performance with an AUC of 0.96, COR of 0.85, TSS of 0.8, and the lowest deviance (0.49). Table 2 Performance metrics of different models used for predicting HGC Risk. Method AUC COR TSS Deviance RF 0.96 0.85 0.8 0.49 SVM 0.9 0.69 0.7 0.77 MaxEnt 0.89 0.65 0.7 0.87 GAM 0.89 0.68 0.7 0.88 BRT 0.88 0.65 0.6 0.98 MARS 0.88 0.65 0.6 0.89 GLM 0.81 0.52 0.5 1.05 FDA 0.8 0.51 0.5 1.04 Conflict risk The predicted conflict risk map shows that very high and high conflict risk zones are mainly concentrated in the western and northwestern regions of Tamil Nadu, primarily aligned with the Western Ghats and adjoining landscapes (Fig. 8). In the Nilgiris District, very high-risk areas are observed around Gudalur and parts of Ooty, overlapping with important protected areas like the Mudumalai tiger reserve, Mukurthi National Park, and adjoining reserved forests. Further south, significant high-risk zones are detected around the Anaimalai Tiger Reserve, especially near Pollachi and Valparai. Moving toward the east, Salem, Dharmapuri, and Attur regions exhibit notably high and very high conflict risk patches. Cauvery North Wildlife Sanctuary in Dharmapuri and Krishnagiri districts, and surrounding reserve forests in Salem district contribute to conflict hotspots in this belt. In the southwestern parts of the state, the Palani Hills region (Dindigul District) shows concentrated high-risk areas associated with the Kodaikanal Wildlife Sanctuary. Scattered but distinct high conflict patches are also visible along the Theni district boundary adjoining the Srivilliputhur–Megamalai tiger reserve and Periyar tiger reserve corridors. The Nilgiris, Coimbatore, Erode, Salem, Dharmapuri, Dindigul, and Theni regions emerge as major zones of predicted high HGC. The conflict risk strongly overlaps with multiple protected areas, underlining the need for landscape-level conflict mitigation and corridor management in Tamil Nadu. The total area of the conflict risk zone is 18335 km 2 . Variable importance Among the environmental and anthropogenic predictors analyzed, Elevation emerged as the most influential variable in explaining conflict risk (37.8%), followed by distance to road (18.9%) and distance to forest cover (17.5%). The human modification index (11.9%) and distance to water bodies (8.7%) also contributed significantly to the model. Moderate influence was observed for Annual mean temperature (9.0%), whereas variables such as annual precipitation (2.8%), distance to cropland (1.0%), NDVI (0.7%), and terrain ruggedness (0.7%) had minor contributions. Built-up Area (0.1%) and Isothermality (0%) had negligible effects on the predictions. These findings indicate that topographic and landscape features, especially elevation and road proximity, are central in predicting conflict risk (Fig. 9 ). Variable response The response curves for the variables predicting conflict risk reveal distinct influence patterns on the likelihood of HGC (Fig. 10 ). Annual mean temperature positively correlates with conflict risk, suggesting that areas with greater annual temperature may experience more frequent conflicts. Annual precipitation has a slight adverse effect, indicating that higher precipitation may reduce conflict risk. Isothermality demonstrates a non-linear effect, where moderate temperature variations between day and night are associated with lower conflict risk. Distance to built-up areas is negatively related to conflict risk. In contrast, the Digital Elevation Model shows a positive association, suggesting that higher elevation areas are linked to higher conflict risk. Distance to forest negatively correlates with conflict risk, highlighting that proximity to forests has higher conflict risk. The Human Modification Index positively correlates with conflict risk, which increases with highly modified areas. Normalized Difference Vegetation Index shows a slight positive trend, suggesting that areas with denser vegetation may have a high risk of conflict. Similarly, distance to roads shows an adverse effect, indicating that areas farther from roads tend to have lower conflict risks. The Terrain Ruggedness Index indicates that conflict risks are higher in areas with low to moderate terrain ruggedness, but these risks decrease as terrain ruggedness increases. Distance to water reveals a positive association, suggesting that areas near water sources are more prone to conflict risk. Discussion Our study has revealed that high-altitude mountains such as the Nilgiris, Kodaikanal division, have the highest HGC in Tamil Nadu. Gaurs are now more likely to occur at higher elevations than in the past, probably because suitable lower habitat has been increasingly disturbed ( 36 ). The Nilgiris are interspersed with plantations, woods and Shola forests. These regions also face a common HWC issue, with crop damage being the primary conflict reported ( 37 ). Anthropogenic pressures, such as reduced grass biomass and habitat degradation, are key drivers of HWC in the Nilgiri Biosphere Reserve (NBR) ( 38 ). Another key issue in this area that impacts wildlife, often driving them into human habitations in search of food, is the spread of invasive alien species. NBR is one of India's most significant invasion impact hotspots by area, primarily invaded by Lantana camara , Prosopis juliflora , and Chromolaena odorata ( 39 ). Historically, the NBR’s native grasslands were undervalued by both colonial and post-independence administrations, leading to their widespread conversion into plantations of exotic species like Eucalyptus and wattle, which were favoured for their commercial utility, especially in the paper industry. As a popular tourist destination, the Nilgiri Hills face growing pressure on wildlife habitats due to expanding infrastructure and increased human activity. The findings of ( 40 ) underscore the urgency, as they note that promoting tourism and industrial development has significantly altered the natural systems of the Nilgiris, making it the state's most industrialised and commercialised hill region. Deforestation, urbanization, and land use changes in the district have driven climate variability, marked by rising temperatures and a notable shift from forest cover to agricultural and built-up areas ( 41 ). Similarly, the Kodaikanal division, part of the Palani Hills, has lost native grasslands and forests over the last 40 years ( 42 ). A study based on 45 years of data (1973–2017) revealed that the Western Ghats have lost approximately 38% of their shola grasslands and 3% of shola forests ( 43 ). The analysis highlights significant habitat loss in mountain ranges such as the Nilgiris, Anamalais, and Palani Hills. The widespread loss of native grasslands is primarily attributed to introducing and expanding exotic tree species such as Acacia, Pine, and Eucalyptus in these regions. This large-scale invasion has reduced critical habitat and disrupted landscape connectivity. Therefore, the high levels of conflict in the Nilgiris and Kodaikanal (Palani Hills) divisions can be attributed to habitat degradation caused by invasive species and the resulting movement of wildlife into human-modified landscapes. Following this, the Dindigul division, which serves as a connecting landscape, has also shown high HGC. The next highest conflict was observed in the Dharmapuri division, which faces significant environmental challenges. These include the loss of landforms, reduced vegetation, and rising temperatures, all driven by rapid land use and land cover changes. These changes are further exacerbated by climatic shifts, population growth, and increased human encroachment. The urbanization and granite quarrying in this region, driven by the region’s granite deposits, have led to significant environmental changes. A Landsat imagery study (2000–2017) found that granite quarries expanded by 2,562.78 ha, quarry lakes increased by 5.3 ha, and vegetation cover decreased by 1,521 ha. This highlights the impact of quarrying on land cover and the environment in the region ( 44 ). The largest area of the Dharmapuri forest land is occupied by dry deciduous forests ( 45 ). The dry season is long, and most trees remain leafless, which may attract wildlife to the crop cultivation areas. This district also cultivates mango, banana, tomato, Bhendi, Brinjal, Radish, Gourds, and Tapioca crops. Our data also shows that conflicts increase during the winter and early summer. The winter season in Tamil Nadu is a critical period for elephant crop raiding, as it coincides with crop harvesting, making agricultural fields attractive food sources for wildlife ( 4 ). As these months are typically dry seasons in Tamil Nadu, wildlife often migrates towards human settlements in search of water and food, resulting in increased HWC. Large-bodied mammals, requiring substantial food and possessing extensive home ranges, usually venture into human habitats during foraging or territorial movements, increasing the likelihood of HWC ( 46 ). The gaurs' daily movements are influenced by habitat availability, food resources, and topography ( 47 ). Distance to forest, road and digital elevation emerged as the most influential predictors of gaur conflict risk. Conflict risk was higher near forest edges, likely due to the proximity of preferred gaur habitats to human-modified landscapes. Elevation, conversely, influences both vegetation types and land-use patterns, with mid-elevation zones often comprising a mosaic of forest and agriculture, making them hotspots for HGC. The habitat suitability of gaur’s peaks in the mid-elevation regions, particularly around 1500 meters ( 48 ). Similarly, the response curve of our study also indicated that conflict risk was higher within these elevation ranges, particularly in low-rugged terrain areas. Gaurs prefer flat or gently sloping terrain, which provides easier access to grazing areas and water while minimising energy expenditure during foraging. The partial response curve suggests that gaur conflict incidents are increasingly concentrated near the forest areas. The reserve forests tend to dry up during the summer, reducing water and forage availability. In response, gaurs are often forced to move beyond the forest boundary for sustenance, resulting in frequent crop raiding incidents along the forest periphery ( 20 ). This also correlates with the other variables, such as distance to human settlements, roads, built-up areas and croplands, showing high conflict probabilities of HGC in its proximity. Roads have positively influenced the habitat suitability of gaur ( 49 ). Gaur prefers living in the core area but travels through the road seasonally ( 36 ). Also, highways often intersect protected areas in our study area, which may also be a reason for this relationship. The response curve for NDVI shows an initial rise in habitat suitability followed by stabilization, indicating a threshold beyond which vegetation density has minimal effect. The predicted conflict risks are also near agricultural or human-modified lands, which typically exhibit lower to moderate and more seasonal vegetation density than the dense and stable canopy of protected forest areas. Interestingly, conflict risk increased with greater distance from water bodies, contrasting with previous studies indicating a preference for proximity to water in habitat suitability assessments ( 50 ). Gaurs may prefer habitats farther from water sources due to better forage availability along forest edges and open grasslands, which offer high-quality grazing opportunities ( 51 ). Conflict risk was also higher in areas with higher temperatures. Temperature variables were most strongly associated with gaurs' habitat suitability ( 52 ). These regions also receive ample rainfall, supporting the observed pattern that conflict risk increases in areas with increasing precipitation. The precipitation seasonality has a potential impact on the habitat suitability of gaur ( 36 , 50 ). The data used in this study were sourced from the secondary database maintained by the forest department’s compensation records. However, some conflicts may not be reported or may not qualify for the compensation scheme, leading to potential underrepresentation. Therefore, the findings presented in this paper should be considered partial indicators of conflict intensity, particularly regarding spatial and temporal patterns. These limitations should be taken into account when interpreting the results. Our study compiles and analyses long-term data on HGC across Tamil Nadu, identifying spatial patterns, key predictors, and high-risk zones. The conflict risk model provides a valuable decision-support tool for prioritizing areas that require immediate and targeted mitigation interventions. High-risk zones, especially in the northern, northwestern, and western districts, overlap with regions of dense human populations and intensive agriculture, particularly cultivation of gaur-attracting crops such as paddy and carrot. Seasonal trends revealed by the data underscore the need for season-specific management strategies, including crop protection measures and community awareness during peak conflict periods. The results highlight the importance of developing long-term, evidence-based mitigation plans that integrate ecological research, stakeholder collaboration, and habitat restoration, particularly the recovery of degraded grasslands and the management of invasive species. These proactive efforts, informed by scientific data, can prevent the escalation of HGC into more widespread and severe HWC scenarios across the state. Declarations Funding statement The Tamil Nādu Forest Department funded the study Competing interests policy The authors declare no competing interests Clinical trial number: not applicable. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. Data availability The data used in this study are included within the manuscript. Additional data, if required, can be obtained by contacting the corresponding author. Author Contribution TTS conceptualised and designed the study. TTS and PR did the fieldwork and analysed the data. TTS constructed figures. TTS and PR wrote the manuscript. AU, RK, SS, SS, DVK, and SS supervised the work, mobilised the funds, procured permission, and provided field and logistical support. Acknowledgement Acknowledgements The authors acknowledge the Tamil Nadu Forest Department, the PCCF & Head of Forest Force and the PCCF & Chief Wildlife Warden for granting the necessary permissions and all the forest department officials for providing full cooperation and support during our field visits. Data Availability The data used in this study are included within the manuscript. Additional data, if required, can be obtained by contacting the corresponding author. References Karanth K K, Gopalaswamy AM, Prasad PK, Dasgupta S (2013) Patterns of human–wildlife conflicts and compensation: Insights from Western Ghats protected areas. Biological Conservation, 166, 175–185. https://doi.org/https://doi.org/10.1016/j.biocon.2013.06.027 Mekonen S (2020) Coexistence between human and wildlife: the nature, causes and mitigations of human wildlife conflict around Bale Mountains National Park, Southeast Ethiopia. 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Global Change Biology, 25(3), 811–826. https://doi.org/10.1111/gcb.14549 Almasieh K, Zamani N, Piri R (2023) An ensemble modeling approach to predict spatial risk patches of the Persian leopard–livestock conflicts in Lorestan Province, Iran. Environmental Science and Pollution Research, 30, 93002–93013. https://doi.org/10.1007/s11356-023-28963-2 Karami P, Tavakoli S (2022) Identification and analysis of areas prone to conflict with wild boar (Sus scrofa) in the vineyards of Malayer County, western Iran. Ecological Modelling, 471, 110039. https://doi.org/10.1016/j.ecolmodel.2022.110039 Li W, Liu P, Yang N, Pan H, Chen S, Zhang L (2023) Spatio-temporal trend and mitigation of human–elephant conflict in Xishuangbanna, China. Journal of Wildlife Management, 87. https://doi.org/10.1002/jwmg.22485 Naimi B, Araújo MB (2016) Sdm: A reproducible and extensible R platform for species distribution modelling. Ecography, 39, 368–375. https://doi.org/10.1111/ecog.01881 Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x Prayoon U, Suksavate W, Chaiyes A, et al. (2024) Home range and habitat utilization of gaur (Bos gaurus) in transition zone between protected forest and human-dominated landscape, Eastern Thailand. Global Ecology and Conservation, 50, e02811. https://doi.org/10.1016/j.gecco.2024.e02811 Samson A, Ramakrishnan B (2020) Preliminary study on crop economic loss, mitigating measures and people's perceptions on human–wildlife conflict especially in agricultural fields in the upper reaches of the Nilgiris, Tamil Nadu, Southern India. International Journal of Pure and Applied Zoology, 8(3), 19–23. http://www.ijpaz.com Baskaran N, Sathishkumar S, Vanitha V, Arjun M, Keerthi P, Bandhala NG (2024) Unveiling the hidden causes: Identifying the drivers of human–elephant conflict in Nilgiri Biosphere Reserve, Western Ghats, Southern India. Animals, 14(22). https://doi.org/10.3390/ani14223193 Mungi NA, Qureshi Q, & Jhala YV (2023) Distribution, drivers and restoration priorities of plant invasions in India. Journal of Applied Ecology 60(11): 2400–2412. https://doi.org/10.1111/1365-2664.14506 Lakshumanan C, Pradeep Kishore V, Viveganandan S, Krishnakumar P, Muthusankar G (2012) Landuse/land cover dynamics study in Nilgiris district part of Western Ghats, Tamil Nadu. International Journal of Geomatics and Geosciences, 2(3), 911–923. Srinivasan K, SebastianAnand, Bilyaminu H, Haritha S (2021) Assessment of dynamic land system in Nilgiri Biosphere Reserve using MODIS derived temporal data sets during 2001 to 2018. International Journal of Environment and Climate Change 11(6): 132–149. https://doi.org/10.9734/IJECC/2021/v11i630430. Arasumani M, Khan D, Das A, Lockwood I, Stewart R, Kiran RA, Muthukumar M, Bunyan M, Robin VV (2018) Not seeing the grass for the trees: Timber plantations and agriculture shrink tropical montane grassland by two-thirds over four decades in the Palani Hills, a Western Ghats sky island. PLOS ONE, 13(1), e0190003. https://doi.org/10.1371/journal.pone.0190003 Arasumani M, Khan D, Vishnudas CK, Muthukumar M, Bunyan M, & Robin VV (2019) Invasion compounds an ecosystem-wide loss to afforestation in the tropical grasslands of the Shola Sky Islands. Biological Conservation 230: 141–150. https://doi.org/10.1016/j.biocon.2018.12.019 Nithya P, Arulselvi G (2019) Pattern classification technique to assess land use/cover changes in granite quarry area of Dharmapuri and Krishnagiri districts of Tamil Nadu. International Journal of Innovative Technology and Exploring Engineering, 8(12), 2947–2956. Dhinesh KS, Radhakrishnan S, Balasubramanian A, Sivakumar K (2019) Comparative study of soil nutrient status in three forest types of Dharmapuri Forest Circle, Tamil Nadu, South India. International Journal of Current Microbiology and Applied Sciences, 8(2), 1613–1621. https://doi.org/10.20546/ijcmas.2019.802.189 Owen-Smith N (1999) The interaction of humans, megaherbivores, and habitats in the Late Pleistocene extinction event. In: MacPhee RDE (ed) Extinctions in Near Time: Causes, Contexts, and Consequences. Springer US, New York, pp. 57–69. https://doi.org/10.1007/978-1-4757-5202-1_3 Horne JS, Garton EO, Krone SM, Lewis JS (2007) Analyzing animal movements using Brownian bridges. Ecology, 88(9), 2354–2363. https://doi.org/10.1890/06-0957.1 Qureshi Q, Kolipakam V, Jhala YV, et al. (2025) Status of ungulates in tiger habitats of India. National Tiger Conservation Authority, New Delhi, and Wildlife Institute of India, Dehradun. ISBN: 978-93-49520-77-6 Poudel S, Pokhrel B, Neupane B, Miya MS, Kc N, Basyal CR, Neupane A, Dhami B (2024) Ecological and anthropogenic factors influencing the summer habitat use of Bos gaurus and its conservation threats in Chitwan National Park, Nepal. PeerJ, 12, e18035. https://doi.org/10.7717/peerj.18035 Dhakal A, Neupane D, Pun S, Ghimire S, Sah MK, Adhikari B, Gautam J (2025) Habitat mapping of Bos gaurus in Parsa National Park, Nepal: Ensemble modeling approach. Ecology and Evolution, 15(3), e71148. https://doi.org/10.1002/ece3.71148 Bhattarai BP, Katuwal HB, Regmi S, Aryal B, Tamang K, KC S, Nepali A, Bhandari S, Basnet A, Kandel P, Paneru C, Subedi B, Regmi N, Koirala S, Acharya H, Belant JL, Sharma HP (2025) Factors affecting the occupancy of gaur (Bos gaurus) during winter season in Parsa National Park, Nepal. Ecology and Evolution, 15(4), e71189. https://doi.org/10.1002/ece3.71189 Trisurat Y, Pattanavibool A, Gale GA, Reed DH (2010) Improving the viability of large-mammal populations by using habitat and landscape models to focus conservation planning. Wildlife Research, 37(5), 401–412. https://doi.org/10.1071/WR09110 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 25 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 08 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 06 May, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6600101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455808326,"identity":"a5a2c2cd-1e6f-4cb2-980d-5b7741fbdbd1","order_by":0,"name":"Thekke Thumbath Shameer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC+RkQOoGxgfkAA2MDEVoMbkC1MDewJRCpRQKqhb2Bx4BILdLNTzd8qLiXxzsj55vEzx02cgzsh49uwKdFfs4xs5szzhQXS87I3SbZeybNmIEnLe0GXmtuJJjd5m1LSNwI1CLB23Y4sUGCx4yAlvRvt//+S0jcfyPnmeRf4rTkmN1mbEhIbJyRwyZNlC0GN3LKbvYcA2rpeWZsLduWZsxGyC/yM9K33fhRA9TSnvzw5ts2Gzl+9sPH8DsMCbCA44iNWOUgwPyBFNWjYBSMglEwcgAAu0dUY2AsEAEAAAAASUVORK5CYII=","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":true,"prefix":"","firstName":"Thekke","middleName":"Thumbath","lastName":"Shameer","suffix":""},{"id":455808327,"identity":"f21a2e37-ae3b-4d2c-b10f-a41e17334e46","order_by":1,"name":"Priyambada Routray","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Priyambada","middleName":"","lastName":"Routray","suffix":""},{"id":455808328,"identity":"3f78c227-2998-4ad6-9123-f6743c5e1cda","order_by":2,"name":"A Udhayan","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Udhayan","suffix":""},{"id":455808329,"identity":"72de8c82-d44f-4e1f-a436-649331e6605e","order_by":3,"name":"Rangaswamy Kanchana","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Rangaswamy","middleName":"","lastName":"Kanchana","suffix":""},{"id":455808330,"identity":"46329bd2-4e38-4ee7-b7a7-7b8142172e58","order_by":4,"name":"Senbagapriya Sekar","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Senbagapriya","middleName":"","lastName":"Sekar","suffix":""},{"id":455808331,"identity":"37dc1305-02fa-4540-ac23-a6748aac375d","order_by":5,"name":"Sivaranjani Shankar","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Sivaranjani","middleName":"","lastName":"Shankar","suffix":""},{"id":455808332,"identity":"d949bdda-4427-4d34-994f-1f255f21c15e","order_by":6,"name":"Dhayanithi Vasanthakumari","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Dhayanithi","middleName":"","lastName":"Vasanthakumari","suffix":""},{"id":455808333,"identity":"440b3a46-e1ab-4f4f-ad62-5c802375d9e2","order_by":7,"name":"Selvakumar Subramaniyam","email":"","orcid":"","institution":"advanced institute for wildlife conservation","correspondingAuthor":false,"prefix":"","firstName":"Selvakumar","middleName":"","lastName":"Subramaniyam","suffix":""}],"badges":[],"createdAt":"2025-05-06 07:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6600101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6600101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82801045,"identity":"d6c67204-031d-4cdd-8a61-a33f6285be86","added_by":"auto","created_at":"2025-05-15 11:17:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":512344,"visible":true,"origin":"","legend":"\u003cp\u003eA photograph of a gaur roaming a street in the study area. Photo (T.T. Shameer).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/22ff7e35671e6f9d14d2a777.jpeg"},{"id":82802069,"identity":"2ae84087-aab1-4857-8a45-9f8e0a84fe8f","added_by":"auto","created_at":"2025-05-15 11:33:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388992,"visible":true,"origin":"","legend":"\u003cp\u003eConflict locations overlaid on protected areas, reserved forests, and forest divisions within the study area.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/6db5d6bc1a59194f00058501.jpeg"},{"id":82801057,"identity":"c3e31128-0437-445c-977c-11bfe65a6a99","added_by":"auto","created_at":"2025-05-15 11:17:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":548516,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographs of the study area showing the connectivity loss due to the plantations and the forest fringes used for cultivation. Phot (T T Shameer)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/ba321a2d60f3f4a470d08c1d.jpeg"},{"id":82801844,"identity":"8f657775-3908-4de0-87db-2abddefbb3f9","added_by":"auto","created_at":"2025-05-15 11:25:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167573,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrix of environmental and anthropogenic variables used in conflict risk modeling\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/bbdfe04239596c7b367a3f86.jpeg"},{"id":82801842,"identity":"e3f23813-cd66-42e6-8c2a-d47f1f3f198b","added_by":"auto","created_at":"2025-05-15 11:25:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121748,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of conflict incidents across forest divisions (2016-2024). Divisions with fewer than 10 incidents are grouped under others.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/2c55cba62daeda2187a8a6e4.jpeg"},{"id":82802071,"identity":"f999e417-0455-4bc2-91b1-18919bdcf4af","added_by":"auto","created_at":"2025-05-15 11:33:53","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal patterns of HGC.\u003cbr\u003e\n (a)\u003c/strong\u003e Year-wise variation in conflict incidents from 2016 to 2024. \u003cstrong\u003e(b)\u003c/strong\u003e Month-wise distribution of conflict incidents.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/e494a96a8111ebb24a9c9bc7.jpeg"},{"id":82801048,"identity":"1d05986f-eea0-44e2-8730-d67297e677c5","added_by":"auto","created_at":"2025-05-15 11:17:53","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":75598,"visible":true,"origin":"","legend":"\u003cp\u003eshows the distribution of conflict types.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/fac36aadc10f110724739342.jpeg"},{"id":82801050,"identity":"b35fe085-4988-4438-914a-0f8ec04b8870","added_by":"auto","created_at":"2025-05-15 11:17:53","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":223166,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted conflict risk zones across the forest divisions of Tamil Nadu\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/ac873dda27b0251ce5bbee67.jpeg"},{"id":82801847,"identity":"0f1910c0-2e8f-4041-abb1-155aec25d381","added_by":"auto","created_at":"2025-05-15 11:25:53","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":340220,"visible":true,"origin":"","legend":"\u003cp\u003epresents the relative importance of predictor variables in the conflict risk model. The error bars represent the variability in importance estimates and indicate confidence intervals around the measured importance values.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/22b9b02eb4688c9dd8eb5f85.jpeg"},{"id":82801051,"identity":"bf5770ce-d1b3-4dda-aa50-eb416bc8c388","added_by":"auto","created_at":"2025-05-15 11:17:53","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":126346,"visible":true,"origin":"","legend":"\u003cp\u003ePartial response curve of variables used in HGC risk modelling\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/5f9d7e5ef55422c2513cde99.jpeg"},{"id":82803065,"identity":"e9001468-2aac-4cd4-9f5e-3af65fba4ac9","added_by":"auto","created_at":"2025-05-15 11:49:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3355014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6600101/v1/1de78661-d631-4a44-bd58-3b3c8dfb511d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven Insights into Human–Gaur Conflicts: Spatiotemporal Trends and Risk Mapping Across Tamil Nadu, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman-wildlife conflict (HWC) is a growing challenge for biodiversity conservation and a significant challenge for the well-being and livelihoods of rural communities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). HWC arises from competition for shared resources, leading to habitat loss, fragmentation, and degradation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It results in crop and livestock losses, human injuries, deaths, and property damage (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Carnivores and primates are the most conflict-prone species globally (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Laws and policies, particularly those involving land-use planning and wildlife management, can contribute to or mitigate HWC (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Addressing HWC requires a multifaceted approach, considering social, economic, and ecological dimensions to balance human needs and wildlife protection (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHWC involving megaherbivores, such as elephants and gaur, is a growing conservation concern in India. Conserving large herbivores presents significant challenges due to their extensive home range requirements, naturally low population densities, and frequent conflicts with humans, particularly through crop-raiding activities (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). India's major human-animal conflict issues include crop raiding, livestock depredation, and human death (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The gaur (\u003cem\u003eBos gaurus\u003c/em\u003e) is a large bovine native to the Indian Subcontinent and Southeast Asia, and is listed as Vulnerable (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). During the past century, the gaur population has declined by more than 80% due to habitat loss to agriculture and poaching for horn and Meat (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Gaur plays a crucial ecological role in dry deciduous forests by maintaining physical habitat structure and was once a key component of the food chain in tiger-occupied landscapes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). It is distributed in South and Southeast Asia, from India to peninsular Malaysia, and it occurs in India, Nepal, Bhutan, Bangladesh, Myanmar, Thailand, China, Laos, Cambodia, Vietnam, and Malaysia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In India, gaur is distributed across the hill forests of Western Ghats, Central highlands and Northeast India (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The gaur population has declined considerably due to habitat loss and hunting in India over the last few decades (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, gaur populations are increasing in human-dominant areas, especially plantations, leading to more frequent encounters with humans and growing chances of negative interactions.\u003c/p\u003e \u003cp\u003eTamil Nadu has a forest cover of 26,419 km\u003csup\u003e2\u003c/sup\u003e, accounting for 20.34% of its geographical area (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The region supports two megaherbivores, Asian elephants (\u003cem\u003eElephas maximus\u003c/em\u003e, Endangered) and Indian gaur (\u003cem\u003eBos gaurus\u003c/em\u003e, Vulnerable), both of which are frequently observed outside protected areas, venturing into human-modified landscapes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The region has two mountains, the Western Ghats and the Eastern Ghats. They are highly disturbed, with habitat fragmentation, poaching, and other anthropogenic pressures often restricting these species to small, isolated protected areas (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In some cases, gaur populations have colonised plantations near protected areas, leading to frequent crop-raiding incidents and occasional human fatalities (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In the hill stations of this region, solitary male gaurs are frequently observed in urban areas, scavenging for food waste along streets and in garbage dumps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to various contributing factors, the increase in human-gaur conflicts (HGC) in Tamil Nadu underscores the importance of understanding their spatiotemporal dynamics and identifying high-risk zones to implement proactive mitigation measures.\u003c/p\u003e \u003cp\u003eIn this study, we analysed the spatial and temporal trends in HGC across Tamil Nadu using 9 years of data to identify conflict patterns and major drivers. We also employed ensemble modelling techniques to predict high-risk areas for future conflict management.\u003c/p\u003e "},{"header":"Study area","content":"\u003cp\u003eTamil Nadu, a southern Indian state, is geographically situated between latitudes 8°05'N and 13°35'N and longitudes 76°15'E and 80°20'E (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). It shares its borders with Kerala to the west, Karnataka to the northwest, Andhra Pradesh to the north, the Bay of Bengal along the eastern coastline, and the Indian Ocean to the south (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The state has 38 districts and 48 forest divisions. Tamil Nadu comprises four major physiographic regions: the Coastal Plains, the Eastern Ghats, the Central Plateau, and the Western Ghats. The Western Ghats, the longest hill range in the state, is recognised as one of the 25 global biodiversity hotspots and one of India's three mega centres of endemism (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Tamil Nadu's Protected Area network includes five National Parks, 29 Wildlife Sanctuaries, and two Conservation Reserves, covering 4.97% of the state's geographical area. Tamil Nadu experiences a humid tropical climate, with annual rainfall ranging from 900 mm to 1,200 mm and temperatures varying between 19°C and 37°C (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The diverse climatic conditions in Tamil Nadu give rise to a range of forest types, including tropical wet evergreen, tropical semi-evergreen, subtropical hill, and montane wet temperate forests, all of which support a rich variety of flora and fauna (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The detailed map of the study area showing the forest divisions, protected areas, and reserved forest, along with the HGC incidents overlaid, is provided as (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The fringes of the forests of the study area are mostly disturbed due to agricultural purposes and the landscape is fragmented due to large-scale plantations, etc, as depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eWe collected data between 2016 and 2024 on HGC incidents across the forest divisions of Tamil Nadu from both secondary sources and field visits. As part of the Tamil Nadu Forest Department's conflict mitigation plan, conflict records are collected and maintained in forest divisions. These data had information about HGC, such as date of occurrence, latitude and longitude, type of conflict (i.e., crop damage, human injury, and human death), and crop name. While visiting the conflict location, we collected information about the frequency, terrain information, and any other ground factors that could influence HGC in the region. We recorded (n = 743) conflict events, of which (n = 630) had associated geo-coordinates available for conflict risk modelling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe data analysis was conducted using Microsoft Excel and the R programming language. Initially, the total number of conflicts was calculated for each forest division. Subsequently, the conflicts were analyzed every year, followed by an analysis of monthly conflicts. The mean number of conflicts across the nine-year study period (2016–2024) was calculated to understand temporal variations in conflict occurrence. This provided insights into the average intensity of conflicts for each month. To assess the variability in the conflict data across the years, we calculated the Standard Deviation (SD) for each month. Additionally, we computed the Standard Error (SE) to evaluate the precision of the mean estimate. The 95% Confidence Interval (CI) was then calculated to offer a range of plausible values for each month’s mean conflict value. These analyses were performed using the dplyr package in R, which facilitated data manipulation and transformation. The results of the studies were organized in tabular format and further examined to identify patterns in the seasonal distribution of conflicts. The calculated average number of conflicts and their associated SD, SE, and CI were used to conclude the seasonal variation in conflict occurrence across the study period. Graphs were prepared for visualisation of the data using Excel and R.\u003c/p\u003e\u003cp\u003eEnvironmental variables\u003c/p\u003e\u003cp\u003eWe selected three key bioclimatic variables: Annual Mean Temperature (Bio1), Isothermality (Bio3), and Annual Precipitation (Bio12) from the WorldClim database (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). To understand the topographical influence on the conflict risk, we downloaded the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) at a 30 m resolution (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and also derived the terrain ruggedness index (TRI) using the raster (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and rgdal (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) packages in R. Land cover data, including distance to forest cover, croplands, and built-up areas, were obtained from the ESA World Cover 2022 dataset (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) at a 30 m resolution. These raster layers were processed in R using the httr (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and raster packages, converted into polygon formats, and their Euclidean distances were computed using the rgeos package (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The maximum NDVI from 2016 to 2024 was calculated by creating a time-series composite using Google Earth Engine’s Image Collection function, which aggregates data yearly. The max() function was then applied to extract the highest NDVI value from the collection, representing the peak vegetation for each pixel across the specified period, ensuring accurate temporal representation for conflict risk modelling. The water body data were retrieved from the Humanitarian OpenStreetMap database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.humdata.org/\u003c/span\u003e\u003cspan address=\"https://data.humdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and their Euclidean distances were computed. To understand the impact of human disturbance on the conflict risk, we downloaded the Global Human Modification of Terrestrial Systems dataset (HMI v1, 2016), which quantifies cumulative human impact on terrestrial ecosystems at a 1-km resolution (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). To ensure consistency across datasets, all environmental variables were resampled to a standardized 1 km² resolution using the raster package. To assess potential multicollinearity among the predictor variables, we conducted a Pearson correlation analysis with a 0.75 threshold in R. Since no strong correlations were detected, all selected variables were retained for modelling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eConflict risk modelling\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe used ensemble modelling to predict the conflict risk. This model has recently been used in many HWC risk modelling studies across the globe (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). We utilised the sdm package (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which integrates multiple algorithmic approaches implemented in the R platform (version 4.3.3) (R Core Team, 2024). This package allows users to generate candidate models, assess their performance, and combine them using ensemble techniques. Using the same package, we created pseudo-absence records equal to the presence records (n = 630). As the data consisted of presence-only records, we generated an equal number of pseudo-absences using the sdm package. These pseudo-absences, likely representing true absences, enhanced the accuracy of the models predicting areas of high occurrence probability (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe developed conflict risk model using nine multiple algorithms, including Random Forest (RF), Generalized Additive Models (GAM), Boosted Regression Trees (BRT), Maximum Entropy (MaxEnt), Support Vector Machines (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Models (GLM), and Flexible Discriminant Analysis (FDA). Of 718 recorded conflict occurrences, 70% were randomly assigned for training, while the remaining 30% were used for testing. This split ensures model stability while reducing the risk of overfitting. The testing data provides an independent evaluation of model performance on unseen records. This process was repeated five times to enhance reliability, and the mean values for sensitivity, specificity, True Skill Statistics (TSS), kappa, and the Area Under the Curve (AUC) were calculated to assess model accuracy. The final ensemble model was constructed using a weighted averaging approach, where TSS was used as the evaluation metric, with a threshold set at maximum sensitivity and specificity.\u003c/p\u003e\u003cp\u003eThe ensemble model outputs were visualised and mapped using QGIS 3.36.1. Conflict risk zones were categorized into four distinct classes: “Very Low Risk” (0–0.25), “Low Risk” (0.25–0.5), “High Risk” (0.5–0.75), and “Very High Risk” (0.75–1). To quantify the extent of conflict risk areas, we computed the total area under the “High Risk” and “Very High Risk” categories using R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSpatial patterns\u003c/p\u003e \u003cp\u003eThe division-wise distribution of HGC revealed that the Nilgiris recorded the highest number of incidents (n\u0026thinsp;=\u0026thinsp;174, 24.4%), followed by Kodaikanal (n\u0026thinsp;=\u0026thinsp;105, 14.7%), Dindigul (n\u0026thinsp;=\u0026thinsp;85, 11.9%) and Dharmapuri (n\u0026thinsp;=\u0026thinsp;85, 11.9%), indicating high-conflict areas. Tiruchirappalli (n\u0026thinsp;=\u0026thinsp;46, 6.5%), Attur (n\u0026thinsp;=\u0026thinsp;51, 7.1%), WL ATR Pollachi (n\u0026thinsp;=\u0026thinsp;35, 4.9%), and Salem (n\u0026thinsp;=\u0026thinsp;32, 4.5%) recorded moderate conflict levels. Divisions with the lowest number of conflicts were Tiruvannamalai (n\u0026thinsp;=\u0026thinsp;22, 3.1%), Vellore (n\u0026thinsp;=\u0026thinsp;22, 3.1%), Coimbatore (n\u0026thinsp;=\u0026thinsp;13, 1.8%), and Madurai (n\u0026thinsp;=\u0026thinsp;13, 1.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eYear-wise Conflict Analysis\u003c/p\u003e \u003cp\u003eThe annual number of HGC increased from (n\u0026thinsp;=\u0026thinsp;10) in 2016 to (n\u0026thinsp;=\u0026thinsp;69) in 2017 and (n\u0026thinsp;=\u0026thinsp;103) in 2018, then declined to (n\u0026thinsp;=\u0026thinsp;73) in 2019 and (n\u0026thinsp;=\u0026thinsp;60) in 2020. Conflicts rose again to (n\u0026thinsp;=\u0026thinsp;77) in 2021 before dropping sharply to (n\u0026thinsp;=\u0026thinsp;18) in 2022. A pronounced peak occurred in 2023 (n\u0026thinsp;=\u0026thinsp;175), followed by a modest reduction in 2024 (n\u0026thinsp;=\u0026thinsp;160) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eMonth-wise Conflict Analysis\u003c/p\u003e \u003cp\u003eDuring winter (December\u0026ndash;February), incident levels remain elevated, with December (n\u0026thinsp;=\u0026thinsp;73), January (n\u0026thinsp;=\u0026thinsp;78), and February (n\u0026thinsp;=\u0026thinsp;68) all showing substantial conflict counts. As temperatures rise into summer (March\u0026ndash;May), conflicts peak in March (n\u0026thinsp;=\u0026thinsp;99), then decline through April (n\u0026thinsp;=\u0026thinsp;75) and drop sharply by May (n\u0026thinsp;=\u0026thinsp;43). With the onset of the monsoon (June\u0026ndash;September), clashes moderate: June records 52 incidents, July sees a slight rebound to 74, August falls to 50, and September reaches one of the year\u0026rsquo;s lowest counts (n\u0026thinsp;=\u0026thinsp;46). Finally, in the post-monsoon/autumn period (October\u0026ndash;November), conflicts dip to their nadir, with October at 47 and November at 40(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eSeasonal Dynamics of HGC (Monthly Means)\u003c/p\u003e \u003cp\u003eThe month-wise mean number of HGC incidents over the years showed the highest activity during the pre-monsoon period, with March recording the peak (11.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60), followed by April (8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63) and May (6.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85). During winter, January (9.75\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94) and February (9.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85) also exhibited elevated conflict levels. The southwest monsoon months (June\u0026ndash;September) saw moderate to low activity: June (5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80), July (9.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.83), August (6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46) and September (6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20). Conflict incidence reached its nadir in the post-monsoon autumn, October (5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02) and November (5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27), before rising again at the onset of winter in December (8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05). These results confirm a pronounced seasonal peak in the pre-monsoon period, with markedly lower conflict levels during the monsoon and post-monsoon months (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\u003eMonthly mean of conflict events and confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI_Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI_Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.06\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\u003e \u003cstrong\u003eConflict type\u003c/strong\u003e \u003c/p\u003e \u003cp\u003eCrop damage was the most prevalent type of HGC incident, accounting for 45.98% of the cases. This was followed by human injury, which comprised 30.70% of the total incidents. Livestock depredation and human deaths represented 11.66% and 8.98%, respectively. Property damage was the least reported conflict type, contributing to only 2.68% of the overall cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel performance\u003c/p\u003e \u003cp\u003eThe performance of the eight modeling algorithms varied based on AUC, COR, TSS, and deviance values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Random Forest (RF) showed the highest performance with an AUC of 0.96, COR of 0.85, TSS of 0.8, and the lowest deviance (0.49).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance metrics of different models used for predicting HGC Risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeviance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaxEnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\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\u003e \u003cstrong\u003eConflict risk\u003c/strong\u003e \u003c/p\u003e \u003cp\u003eThe predicted conflict risk map shows that very high and high conflict risk zones are mainly concentrated in the western and northwestern regions of Tamil Nadu, primarily aligned with the Western Ghats and adjoining landscapes (Fig.\u0026nbsp;8). In the Nilgiris District, very high-risk areas are observed around Gudalur and parts of Ooty, overlapping with important protected areas like the Mudumalai tiger reserve, Mukurthi National Park, and adjoining reserved forests. Further south, significant high-risk zones are detected around the Anaimalai Tiger Reserve, especially near Pollachi and Valparai.\u003c/p\u003e \u003cp\u003eMoving toward the east, Salem, Dharmapuri, and Attur regions exhibit notably high and very high conflict risk patches. Cauvery North Wildlife Sanctuary in Dharmapuri and Krishnagiri districts, and surrounding reserve forests in Salem district contribute to conflict hotspots in this belt. In the southwestern parts of the state, the Palani Hills region (Dindigul District) shows concentrated high-risk areas associated with the Kodaikanal Wildlife Sanctuary. Scattered but distinct high conflict patches are also visible along the Theni district boundary adjoining the Srivilliputhur\u0026ndash;Megamalai tiger reserve and Periyar tiger reserve corridors. The Nilgiris, Coimbatore, Erode, Salem, Dharmapuri, Dindigul, and Theni regions emerge as major zones of predicted high HGC. The conflict risk strongly overlaps with multiple protected areas, underlining the need for landscape-level conflict mitigation and corridor management in Tamil Nadu. The total area of the conflict risk zone is 18335 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVariable importance\u003c/p\u003e \u003cp\u003eAmong the environmental and anthropogenic predictors analyzed, Elevation emerged as the most influential variable in explaining conflict risk (37.8%), followed by distance to road (18.9%) and distance to forest cover (17.5%). The human modification index (11.9%) and distance to water bodies (8.7%) also contributed significantly to the model. Moderate influence was observed for Annual mean temperature (9.0%), whereas variables such as annual precipitation (2.8%), distance to cropland (1.0%), NDVI (0.7%), and terrain ruggedness (0.7%) had minor contributions. Built-up Area (0.1%) and Isothermality (0%) had negligible effects on the predictions. These findings indicate that topographic and landscape features, especially elevation and road proximity, are central in predicting conflict risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVariable response\u003c/p\u003e \u003cp\u003eThe response curves for the variables predicting conflict risk reveal distinct influence patterns on the likelihood of HGC (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Annual mean temperature positively correlates with conflict risk, suggesting that areas with greater annual temperature may experience more frequent conflicts. Annual precipitation has a slight adverse effect, indicating that higher precipitation may reduce conflict risk. Isothermality demonstrates a non-linear effect, where moderate temperature variations between day and night are associated with lower conflict risk. Distance to built-up areas is negatively related to conflict risk. In contrast, the Digital Elevation Model shows a positive association, suggesting that higher elevation areas are linked to higher conflict risk. Distance to forest negatively correlates with conflict risk, highlighting that proximity to forests has higher conflict risk. The Human Modification Index positively correlates with conflict risk, which increases with highly modified areas. Normalized Difference Vegetation Index shows a slight positive trend, suggesting that areas with denser vegetation may have a high risk of conflict. Similarly, distance to roads shows an adverse effect, indicating that areas farther from roads tend to have lower conflict risks. The Terrain Ruggedness Index indicates that conflict risks are higher in areas with low to moderate terrain ruggedness, but these risks decrease as terrain ruggedness increases. Distance to water reveals a positive association, suggesting that areas near water sources are more prone to conflict risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study has revealed that high-altitude mountains such as the Nilgiris, Kodaikanal division, have the highest HGC in Tamil Nadu. Gaurs are now more likely to occur at higher elevations than in the past, probably because suitable lower habitat has been increasingly disturbed (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The Nilgiris are interspersed with plantations, woods and Shola forests. These regions also face a common HWC issue, with crop damage being the primary conflict reported (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Anthropogenic pressures, such as reduced grass biomass and habitat degradation, are key drivers of HWC in the Nilgiri Biosphere Reserve (NBR) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Another key issue in this area that impacts wildlife, often driving them into human habitations in search of food, is the spread of invasive alien species. NBR is one of India's most significant invasion impact hotspots by area, primarily invaded by \u003cem\u003eLantana camara\u003c/em\u003e, \u003cem\u003eProsopis juliflora\u003c/em\u003e, and \u003cem\u003eChromolaena odorata\u003c/em\u003e (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Historically, the NBR\u0026rsquo;s native grasslands were undervalued by both colonial and post-independence administrations, leading to their widespread conversion into plantations of exotic species like Eucalyptus and wattle, which were favoured for their commercial utility, especially in the paper industry. As a popular tourist destination, the Nilgiri Hills face growing pressure on wildlife habitats due to expanding infrastructure and increased human activity. The findings of (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) underscore the urgency, as they note that promoting tourism and industrial development has significantly altered the natural systems of the Nilgiris, making it the state's most industrialised and commercialised hill region. Deforestation, urbanization, and land use changes in the district have driven climate variability, marked by rising temperatures and a notable shift from forest cover to agricultural and built-up areas (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the Kodaikanal division, part of the Palani Hills, has lost native grasslands and forests over the last 40 years (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). A study based on 45 years of data (1973\u0026ndash;2017) revealed that the Western Ghats have lost approximately 38% of their shola grasslands and 3% of shola forests (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The analysis highlights significant habitat loss in mountain ranges such as the Nilgiris, Anamalais, and Palani Hills. The widespread loss of native grasslands is primarily attributed to introducing and expanding exotic tree species such as Acacia, Pine, and Eucalyptus in these regions. This large-scale invasion has reduced critical habitat and disrupted landscape connectivity. Therefore, the high levels of conflict in the Nilgiris and Kodaikanal (Palani Hills) divisions can be attributed to habitat degradation caused by invasive species and the resulting movement of wildlife into human-modified landscapes. Following this, the Dindigul division, which serves as a connecting landscape, has also shown high HGC.\u003c/p\u003e \u003cp\u003eThe next highest conflict was observed in the Dharmapuri division, which faces significant environmental challenges. These include the loss of landforms, reduced vegetation, and rising temperatures, all driven by rapid land use and land cover changes. These changes are further exacerbated by climatic shifts, population growth, and increased human encroachment. The urbanization and granite quarrying in this region, driven by the region\u0026rsquo;s granite deposits, have led to significant environmental changes. A Landsat imagery study (2000\u0026ndash;2017) found that granite quarries expanded by 2,562.78 ha, quarry lakes increased by 5.3 ha, and vegetation cover decreased by 1,521 ha. This highlights the impact of quarrying on land cover and the environment in the region (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe largest area of the Dharmapuri forest land is occupied by dry deciduous forests (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The dry season is long, and most trees remain leafless, which may attract wildlife to the crop cultivation areas. This district also cultivates mango, banana, tomato, Bhendi, Brinjal, Radish, Gourds, and Tapioca crops. Our data also shows that conflicts increase during the winter and early summer. The winter season in Tamil Nadu is a critical period for elephant crop raiding, as it coincides with crop harvesting, making agricultural fields attractive food sources for wildlife (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). As these months are typically dry seasons in Tamil Nadu, wildlife often migrates towards human settlements in search of water and food, resulting in increased HWC. Large-bodied mammals, requiring substantial food and possessing extensive home ranges, usually venture into human habitats during foraging or territorial movements, increasing the likelihood of HWC (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The gaurs' daily movements are influenced by habitat availability, food resources, and topography (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDistance to forest, road and digital elevation emerged as the most influential predictors of gaur conflict risk. Conflict risk was higher near forest edges, likely due to the proximity of preferred gaur habitats to human-modified landscapes. Elevation, conversely, influences both vegetation types and land-use patterns, with mid-elevation zones often comprising a mosaic of forest and agriculture, making them hotspots for HGC. The habitat suitability of gaur\u0026rsquo;s peaks in the mid-elevation regions, particularly around 1500 meters (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Similarly, the response curve of our study also indicated that conflict risk was higher within these elevation ranges, particularly in low-rugged terrain areas. Gaurs prefer flat or gently sloping terrain, which provides easier access to grazing areas and water while minimising energy expenditure during foraging. The partial response curve suggests that gaur conflict incidents are increasingly concentrated near the forest areas. The reserve forests tend to dry up during the summer, reducing water and forage availability. In response, gaurs are often forced to move beyond the forest boundary for sustenance, resulting in frequent crop raiding incidents along the forest periphery (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This also correlates with the other variables, such as distance to human settlements, roads, built-up areas and croplands, showing high conflict probabilities of HGC in its proximity. Roads have positively influenced the habitat suitability of gaur (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Gaur prefers living in the core area but travels through the road seasonally (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Also, highways often intersect protected areas in our study area, which may also be a reason for this relationship. The response curve for NDVI shows an initial rise in habitat suitability followed by stabilization, indicating a threshold beyond which vegetation density has minimal effect. The predicted conflict risks are also near agricultural or human-modified lands, which typically exhibit lower to moderate and more seasonal vegetation density than the dense and stable canopy of protected forest areas. Interestingly, conflict risk increased with greater distance from water bodies, contrasting with previous studies indicating a preference for proximity to water in habitat suitability assessments (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Gaurs may prefer habitats farther from water sources due to better forage availability along forest edges and open grasslands, which offer high-quality grazing opportunities (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Conflict risk was also higher in areas with higher temperatures. Temperature variables were most strongly associated with gaurs' habitat suitability (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). These regions also receive ample rainfall, supporting the observed pattern that conflict risk increases in areas with increasing precipitation. The precipitation seasonality has a potential impact on the habitat suitability of gaur (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data used in this study were sourced from the secondary database maintained by the forest department\u0026rsquo;s compensation records. However, some conflicts may not be reported or may not qualify for the compensation scheme, leading to potential underrepresentation. Therefore, the findings presented in this paper should be considered partial indicators of conflict intensity, particularly regarding spatial and temporal patterns. These limitations should be taken into account when interpreting the results.\u003c/p\u003e \u003cp\u003eOur study compiles and analyses long-term data on HGC across Tamil Nadu, identifying spatial patterns, key predictors, and high-risk zones. The conflict risk model provides a valuable decision-support tool for prioritizing areas that require immediate and targeted mitigation interventions. High-risk zones, especially in the northern, northwestern, and western districts, overlap with regions of dense human populations and intensive agriculture, particularly cultivation of gaur-attracting crops such as paddy and carrot. Seasonal trends revealed by the data underscore the need for season-specific management strategies, including crop protection measures and community awareness during peak conflict periods. The results highlight the importance of developing long-term, evidence-based mitigation plans that integrate ecological research, stakeholder collaboration, and habitat restoration, particularly the recovery of degraded grasslands and the management of invasive species. These proactive efforts, informed by scientific data, can prevent the escalation of HGC into more widespread and severe HWC scenarios across the state.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThe Tamil Nādu Forest Department funded the study\u003c/p\u003e \u003cp\u003eCompeting interests policy\u003c/p\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e \u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eThe data used in this study are included within the manuscript. Additional data, if required, can be obtained by contacting the corresponding author.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTTS conceptualised and designed the study. TTS and PR did the fieldwork and analysed the data. TTS constructed figures. TTS and PR wrote the manuscript. AU, RK, SS, SS, DVK, and SS supervised the work, mobilised the funds, procured permission, and provided field and logistical support.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements The authors acknowledge the Tamil Nadu Forest Department, the PCCF \u0026amp; Head of Forest Force and the PCCF \u0026amp; Chief Wildlife Warden for granting the necessary permissions and all the forest department officials for providing full cooperation and support during our field visits.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are included within the manuscript. Additional data, if required, can be obtained by contacting the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaranth K K, Gopalaswamy AM, Prasad PK, Dasgupta S (2013) Patterns of human\u0026ndash;wildlife conflicts and compensation: Insights from Western Ghats protected areas. Biological Conservation, 166, 175\u0026ndash;185. https://doi.org/https://doi.org/10.1016/j.biocon.2013.06.027\u003c/li\u003e\n\u003cli\u003eMekonen S (2020) Coexistence between human and wildlife: the nature, causes and mitigations of human wildlife conflict around Bale Mountains National Park, Southeast Ethiopia. BMC Ecology, 20(1), 51. https://doi.org/10.1186/s12898-020-00319-1\u003c/li\u003e\n\u003cli\u003eWhite PCL, Ward AI (2010) Interdisciplinary approaches for the management of existing and emerging human\u0026ndash;wildlife conflicts. 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International Journal of Geomatics and Geosciences, 2(3), 911\u0026ndash;923.\u003c/li\u003e\n\u003cli\u003eSrinivasan K, SebastianAnand, Bilyaminu H, Haritha S (2021) Assessment of dynamic land system in Nilgiri Biosphere Reserve using MODIS derived temporal data sets during 2001 to 2018. International Journal of Environment and Climate Change 11(6): 132\u0026ndash;149. https://doi.org/10.9734/IJECC/2021/v11i630430.\u003c/li\u003e\n\u003cli\u003eArasumani M, Khan D, Das A, Lockwood I, Stewart R, Kiran RA, Muthukumar M, Bunyan M, Robin VV (2018) Not seeing the grass for the trees: Timber plantations and agriculture shrink tropical montane grassland by two-thirds over four decades in the Palani Hills, a Western Ghats sky island. 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ISBN: 978-93-49520-77-6\u003c/li\u003e\n\u003cli\u003ePoudel S, Pokhrel B, Neupane B, Miya MS, Kc N, Basyal CR, Neupane A, Dhami B (2024) Ecological and anthropogenic factors influencing the summer habitat use of Bos gaurus and its conservation threats in Chitwan National Park, Nepal. PeerJ, 12, e18035. https://doi.org/10.7717/peerj.18035\u003c/li\u003e\n\u003cli\u003eDhakal A, Neupane D, Pun S, Ghimire S, Sah MK, Adhikari B, Gautam J (2025) Habitat mapping of Bos gaurus in Parsa National Park, Nepal: Ensemble modeling approach. Ecology and Evolution, 15(3), e71148. https://doi.org/10.1002/ece3.71148\u003c/li\u003e\n\u003cli\u003eBhattarai BP, Katuwal HB, Regmi S, Aryal B, Tamang K, KC S, Nepali A, Bhandari S, Basnet A, Kandel P, Paneru C, Subedi B, Regmi N, Koirala S, Acharya H, Belant JL, Sharma HP (2025) Factors affecting the occupancy of gaur (Bos gaurus) during winter season in Parsa National Park, Nepal. Ecology and Evolution, 15(4), e71189. https://doi.org/10.1002/ece3.71189\u003c/li\u003e\n\u003cli\u003eTrisurat Y, Pattanavibool A, Gale GA, Reed DH (2010) Improving the viability of large-mammal populations by using habitat and landscape models to focus conservation planning. Wildlife Research, 37(5), 401\u0026ndash;412. https://doi.org/10.1071/WR09110\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-animals","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Animals](https://link.springer.com/journal/44338)","snPcode":"44338","submissionUrl":"https://submission.springernature.com/new-submission/44338/3","title":"Discover Animals","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human–wildlife conflict, Human–Gaur Conflict, Bos gaurus, ensemble modeling, conflict risk prediction, elevation, Tamil Nadu","lastPublishedDoi":"10.21203/rs.3.rs-6600101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6600101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman\u0026ndash;wildlife conflict (HWC) is one of the most pressing conservation challenges, particularly in shared landscapes where humans and wildlife are adversely affected. Despite various mitigation efforts globally, the frequency of HWC continues to rise. Among the conflict-prone species, the Indian gaur (Bos gaurus) has increasingly been involved in such interactions across southern India. To support the development of long-term mitigation strategies for Human\u0026ndash;Gaur Conflict (HGC), we conducted a comprehensive study using data collected from compensation records across 48 forest divisions in Tamil Nadu between 2016 and 2024. We analyzed spatial and temporal trends, predicted conflict risk zones using ensemble modeling, and identified the key drivers influencing HGC. Our findings reveal that conflict intensity was highest in the Nilgiri division, followed by Dharmapuri and Kodaikanal. Crop damage was the predominant conflict type, followed by human injuries, with incident peaks observed during December to March. Elevation emerged as the most influential predictor in the risk models, with a clear positive correlation showing that the conflict risk increased with rising elevation. The model also predicted that 18,335 km\u0026sup2; of the state falls under conflict risk zones, accounting for approximately 14.1% of Tamil Nadu's total geographical area. This study provides critical insights into the spatial ecology of HGC and highlights the utility of predictive modeling in identifying high-risk zones. The outcomes can inform conservationists and forest managers in designing and implementing proactive mitigation measures, especially in areas predicted to have a high likelihood of future conflict.\u003c/p\u003e","manuscriptTitle":"Data-Driven Insights into Human–Gaur Conflicts: Spatiotemporal Trends and Risk Mapping Across Tamil Nadu, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 11:17:49","doi":"10.21203/rs.3.rs-6600101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-28T09:09:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T11:36:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26852456244333073980605620283956439745","date":"2025-05-26T08:53:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141990278892521153849256461016422322012","date":"2025-05-25T17:07:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T18:50:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108098050954161390326344949747123729882","date":"2025-05-23T21:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337666954018683163815251440950941612842","date":"2025-05-21T04:27:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290180880155546785522750282836550542964","date":"2025-05-20T10:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307348082958465983814840464618208808950","date":"2025-05-20T09:15:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309768868069425805281076634234587048776","date":"2025-05-16T14:14:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T08:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T11:11:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-08T11:09:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Animals","date":"2025-05-06T07:15:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-animals","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Animals](https://link.springer.com/journal/44338)","snPcode":"44338","submissionUrl":"https://submission.springernature.com/new-submission/44338/3","title":"Discover Animals","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49221314-84e3-4383-901d-55d4abb1d976","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-27T13:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 11:17:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6600101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6600101","identity":"rs-6600101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}