Reframing human–elephant conflict in India through context-dependent coexistence strategies

Abstract

Human–Elephant Conflict (HEC) has escalated into one of India's most serious conservation and socio-economic challenges, with high costs for both people and elephants. We examined national-level trends in HEC from 2009 to 2024 and evaluated the effectiveness of current policies in mitigating it. Over the past 16 years, a total of 7,868 human fatalities were recorded from elephant encounters, averaging nearly 500 deaths annually. Notably, four states, Odisha, Jharkhand, West Bengal and Assam, accounted for almost 70% of these incidents, underscoring their status as critical conflict hotspots. During the same period, 1,653 elephants died from anthropogenic causes, with electrocution, train collisions, poaching, and poisoning emerging as the most persistent threats. Together, these figures highlight the scale and urgency of addressing HEC in India, which supports over 60% of the global wild Asian elephant population. Our study also evaluated the effectiveness of India’s current mitigation measures by combining mortality data analysis with perception surveys of 428 stakeholders, including Forest Department managers, researchers, and NGOs across 33 Elephant Reserves and elephant range states. Findings revealed that solar-powered fences and mobile-based early warning systems were widely regarded as effective, particularly in fragmented landscapes. In contrast, structural barriers such as trenches and concrete walls were perceived as costly, difficult to maintain, and less effective in the long term. Compensation schemes, although critical, were undermined by delays, inadequate rates, and procedural hurdles that erode community trust. Rapid Response Teams were valued for their role in conflict management but remain constrained by insufficient training, workforce, and resources. Overall, India's HEC policy framework remains fragmented, with generalized strategies unable to capture the ecological heterogeneity and socio-political diversity of elephant landscapes. Addressing these challenges requires decentralization, regionally tailored interventions, streamlined compensation systems, and stronger community participation. Embedding mitigation into broader land-use planning, ecological restoration, and corridor connectivity is equally critical. Implementing adaptive, participatory, and technology-driven strategies can help India minimize human and elephant mortality while enabling long-term coexistence.

1 Introduction

The Asian elephant (Elephas maximus) is a keystone and culturally revered species that plays a vital role in seed dispersal and forest dynamics (Venkataraman et al., 2002; Naha et al., 2019; Pandey et al., 2024a). However, widespread agricultural expansion and infrastructure development across Asia have degraded habitat quality and connectivity, intensifying human–elephant interactions (Calabrese et al., 2017; Pandey et al., 2024b). Loss of habitat and shrinking corridors increasingly push elephants into conflict zones (Gubbi, 2012; Roy et al., 2025), resulting in economic losses, human injuries and deaths, and growing resistance to conservation efforts (Naha et al., 2019, Naha et al., 2020; Shaffer et al., 2019; Gross et al., 2021; Hu et al., 2021; Thant et al., 2021).

India holds over 60% of the world’s wild Asian elephant population, the largest share among the 13 range countries (Williams et al., 2020; Pandey et al., 2024a). Although some populations have remained stable or increased in recent decades (Haddad et al., 2015; Pandey et al., 2024a), elephants are increasingly fragmented and vulnerable to conflict with people (Budd et al., 2023). As elephants share space with more than 1.3 billion people in India (Pandey et al., 2024b), traditional coexistence (Pandey, 2022) is being undermined by rapid habitat loss, land-use change, and human expansion (De Silva et al., 2023). Consequently, Human–Elephant Conflict (HEC) now occurs across most elephant-bearing states, resulting in widespread crop damage, human injuries and deaths, and retaliatory elephant killings (Choudhury, 2004). Habitat fragmentation, expanding agriculture, and linear infrastructure have intensified HEC, while major causes of elephant mortality include electrocution, railway collisions, and direct conflict with humans (Williams et al., 2001; Palei et al., 2014; Roy and Sukumar, 2017; Sangma et al., 2025). The Government of India launched Project Elephant (PE) in 1992 through the Ministry of Environment, Forest and Climate Change (MoEF&CC) to support the conservation and management of the Asian elephant and its habitats. Although Project Elephant aims to reduce conflict, its impact has been constrained by limited region-specific planning, uneven implementation, and weak community engagement (Bisht, 2002). Growing overlap between elephants and dense human settlements has further complicated mitigation efforts (Natarajan et al., 2021), while unmanaged HEC risks human and elephant lives and also undermines public support for conservation (Sukumar, 2003; Desai and Riddle, 2015). Given the global relevance of HEC, India’s experiences are important for shared learning. Mitigation measures such as early warning systems, corridors, compensation, and community outreach have shown variable effectiveness across regions (Sukumar, 2003; Vasudev et al., 2021; Das et al., 2022; Puyravaud et al., 2024; Shameer et al., 2024). Many interventions remain largely top-down, with limited attention to local ecological contexts and community participation, reducing their long-term effectiveness and sustainability. Recently, physical measures such as solar fencing, crop protection, and elephant-proof trenches have become increasingly prominent (Das et al., 2022). These measures are often poorly maintained, RRTs (Rapid Response Teams) are under-resourced, and compensation is delayed, leading to low community trust and a fragmented HEC policy landscape in India (Nad et al., 2022).

Most studies evaluate individual tools or site-specific interventions rather than assessing policy effectiveness across diverse socio-ecological contexts (Van de Water et al., 2022). The absence of standardized monitoring limits evaluation of long-term outcomes. Field-level perspectives of managers and communities are essential to understand perceived drivers of HEC, trust in interventions, and policy gaps (Cabral de Mel et al., 2024; Su et al., 2020). Most existing work on HEC has focused heavily on the views of local communities, especially farmers and villagers living at the forest edge. These studies have documented crop loss, property damage, fear, and the economic and social pressures that shape attitudes toward elephants and conservation (Pant et al., 2016; Su et al., 2020). Although communities are often the most affected stakeholders, their participation in formal policy processes remains limited in many regions, highlighting the need for more inclusive governance mechanisms.

Perspectives of Forest Department managers remain underrepresented in HEC research despite their central role in implementing mitigation. Existing studies indicate that managers face operational constraints such as staff shortages, maintenance burdens, and fragmented habitats (Choudhury, 2004; Nayak and Swain, 2020), and often evaluate mitigation tools differently from communities and experts (Adams et al., 2020; Nguyen et al., 2021; Cabral de Mel et al., 2024). Such misalignments can weaken conflict management. To address this gap, we conducted a pan-India assessment of protected area managers and NGOs working in conflict-prone landscapes. While community perspectives are well documented, the views of these frontline implementers remain insufficiently studied. This study therefore prioritizes Forest Departments and NGOs to examine how key decision-makers perceive HEC drivers and mitigation effectiveness across India’s elephant landscapes.

Central India is a fragmented and rapidly changing elephant landscape shaped by dry deciduous forests, mining, and expanding infrastructure. Although historically connected to the state of Odisha (Roy et al., 2025), recent habitat loss and corridor disruption have increased elephant movement through human-dominated areas, intensifying HEC. Despite its growing importance, the region remains understudied compared to the Northeast and Western Ghats. We analyse HEC patterns and examine regional drivers using Chhattisgarh and Jharkhand as case studies. Both states are undergoing rapid land-use change, infrastructure expansion, and mining (Chatterjee, 2016; Padalia et al., 2019). Chhattisgarh is experiencing recent elephant recolonization and emerging conflict hotspots (Natarajan et al., 2023; Roy et al., 2025), while Jharkhand faces persistent conflict along fragmented forest edges and farmlands (Roy et al., 2025; Kumari et al., 2024). In both states, conflict is driven more by habitat fragmentation, edge effects, mining, and intersecting linear infrastructure than by overall forest loss.

In this study, we assess how stakeholders across different regions (in India) perceive and implement mitigation strategies including physical barriers, compensation schemes, RRTs, and emerging technologies, and whether these are adapted to local ecological and socio-economic contexts. By linking regional conflict drivers with managerial responses, the study explains why certain interventions succeed in some landscapes but not others, and identifies both systemic shortcomings and regionally grounded best practices to inform more adaptive, decentralized, and community-engaged HEC policy.

2 Methodology

2.1 Human - elephant conflict trends

Data on Human-Elephant Conflict were collected over 22 elephant range states of India over a period of 16 years (2009-2025) mainly from records maintained by Project Elephant, MoEF&CC. These data are collected every year through a reporting system where Divisional Forest Officers collect data on incident reports at the forest division level, which are then compiled by the Chief Wildlife Wardens of each state and then submitted to the Project Elephant Division. The data set includes state-wise and year-wise information on human deaths due to elephant attacks and elephant deaths due to human-related causes such as railway and road accidents, electrocution, poisoning, and poaching.

In addition, a structured narrative literature review was performed to provide context to the national trends and drivers of conflict. Literature searches were independently performed by three authors using Web of Science, Scopus, and Google Scholar for the period 2000-2024. The searches were performed using predefined keyword combinations related to human-elephant conflict and elephant mortality in India (e.g. human-elephant conflict, Asian elephant, elephant mortality, electrocution, poaching, train collisions, poisoning). The retrieved records were screened using predefined inclusion criteria (peer-reviewed studies conducted in India with a specific focus on HEC patterns, drivers, or elephant mortality) and exclusion criteria (non-Indian studies, grey literature, anecdotal reports, and studies with unclear methodology). Duplicates were removed before analysis. Due to high heterogeneity in the spatial, temporal, and reporting format of studies, published literature was not used for a quantitative meta-analysis of mortality data. Official records from Project Elephant were therefore used as the primary source of data, and the literature review was used to facilitate interpretation and discussion (Pandey et al., 2024a, Pandey et al., 2024b).

2.2 Understanding the influence of region-specific factors driving human elephant conflict

To understand how region-specific drivers shape patterns of human–elephant Conflict (HEC) and influence the way managers perceive and implement mitigation strategies, we selected two central Indian states - Chhattisgarh and Jharkhand as representative case studies. Data on HEC incidents (elephant and human mortality) were obtained from Forest Divisional Offices spanning over two decades, covering 19 divisions in Chhattisgarh and 21 in Jharkhand. Elephant mortality records were categorized by cause of death, sex, and age-class following Arivazhagan and Sukumar (2008): calves (0–1 years), juveniles (1–5 years), sub-adults (6–15 years), and adults (16+ years). Human casualties were classified by type of incident (fatality or injury). Temporal attributes, including year and season, were extracted from the dates of occurrence.

To characterize landscape change and ecological context, Land Use–Land Cover (LULC) mapping was conducted over a 24-year period using satellite data at five-year intervals (2000, 2005, 2010, 2015, 2020, and 2024). Landsat 5 TM (2000-2005) and Landsat 8 OLI (2010-2015) imagery of 30 m resolution was classified into five LULC categories: (1) forest, (2) water bodies, (3) barren land, (4) crop land, and (5) built-up, using a Random Forest (RF) classifier from the “smileRandomForest” library. During classification, ground truthing was performed using the Google Earth Engine (GEE) selected using random sample technique in the homogenous classes through visual interpretation aided using high resolution google earth pro images and using different band combination, wherein at least 250 training samples were chosen for individual LULC classes. The collected sample data were divided into 70% and 30% for the training and validation periods, for 200 tree classification models (Supplementary Figure 1).

Landscape fragmentation was examined using FRAGSTATS (v4.2), which calculated landscape metrics. The input data for this analysis, derived from the LULC maps, consisted of forest and non-forest data. The LULC maps for 5 years were reclassified into forest and non-forest classes using the ArcGIS Spatial Analyst tool. Class-level metrics were used in this study because they measure the abundance, spatial distribution, and pattern of a particular LULC class in the landscape, in this study, Patch Density (PD), Edge Density (ED) and Largest Patch Index (LPI) respectively. These metrics are examined using moving window analysis of fixed size of 7km based on average elephant movement (Hassan et al., 2023). The window moves across the landscape and return the value to the centre cell and thereby create a continuous surface.

To identify environmental and anthropogenic factors influencing HEC, we selected 12 explanatory variables (distance to forests, croplands, built-up areas, roads, waterways, railways, protected areas, elephant reserves, and mines) along with three landscape metrics—largest patch index, edge density, and patch density (Supplementary Table 1). For this analysis forest, crop, and built-up area are extracted and converted into vector format from corresponding years of LULC layer. Generate near table tool is used to calculate the distance between conflict points and different environmental factors. The distance is calculated as the shortest separation between the features.

A Generalized Linear Modelling (GLM) framework with a binomial distribution and logit link was used to evaluate the relative influence of these variables on conflict occurrence. Mortality incidents (excluding natural deaths) were coded as 1, while pseudoabsence locations (coded as 0) were generated using the “Create Random Points” tool in ArcGIS Pro. Subsequently, any random points located within a 1 km buffer of actual incident points were removed to ensure spatial independence between random and incident locations.

Model selection was conducted through univariate analyses assessing the significance of each predictor, followed by collinearity checks to remove highly correlated variables (VIF > 5). Model performance was evaluated using Akaike Information Criterion (AIC), with models having ΔAIC ≤ 2 considered well-supported (Burnham and Anderson, 2002). The final model was selected based on the lowest AIC value, ensuring an optimal balance between explanatory power and parsimony. The “MuMIn” package in R was used for model ranking.

2.3 Are mitigation perspectives aligned with region-specific drivers of the conflict?

By integrating ecological drivers with managerial perspectives derived from stakeholder interviews, this analytical framework not only identifies the regional factors most strongly associated with conflict but also helps assess how managers interpret these drivers and whether their mitigation strategies such as barriers, compensation systems, or response mechanisms—are adapted to local conditions. This approach provides deeper insights into why specific interventions succeed or fail across regions, ultimately informing more context-sensitive and manager-responsive conflict mitigation policies.

To evaluate whether current mitigation strategies align with the region-specific drivers of Human–Elephant Conflict (HEC), this study employed a mixed-methods, perception-based survey design across India’s elephant range states. Our objective was to assess ground-level stakeholder perspectives on existing HEC strategies and identify priority areas for policy support, operational improvements, and strategic refinement. Recognizing the diversity of ecological, administrative, and socio-political contexts, the study was designed to capture both quantitative data and qualitative insights through a semi-structured questionnaire (Köpke et al., 2024).

A semi-structured questionnaire was developed using Google Forms to facilitate wide dissemination and accessibility. The instrument incorporated a combination of closed-ended questions (Likert scales, multiple-choice, and ranking formats) and open-ended prompts to capture nuanced feedback. The questionnaire was organized into eight domains (Kopke et al., 2024): a) Effectiveness of physical barriers (e.g., solar fencing, elephant-proof trenches) b) Compensation policies c) Rapid Response Teams (RRTs) and volunteer engagement d) Technology-based interventions (e.g., early warning systems, GPS tracking) e) Translocation and capture of elephants e) Community participation and local engagement f) Strategic prioritization and policy improvement suggestions. Questions within each domain were designed not only to assess perceived efficacy and operational challenges but also to capture how managers interpret regional conflict drivers such as land-use change, crop patterns, elephant movement behaviour, and administrative constraints and whether mitigation practices have been adapted accordingly. This approach provides a framework for understanding the alignment (or misalignment) between ecological realities and management responses, thereby informing more region-responsive and adaptive HEC mitigation strategies.

2.4 Data collection

The questionnaire was circulated via institutional networks, mailing lists, and direct outreach to State Forest Department personnel and conservation organizations. Google Forms enabled remote data collection and ensured accessibility across regions. All survey responses were anonymized prior to analysis. No personal identifiers were collected apart from optional demographic information such as state, age, gender, and years of professional experience.

2.4.1 Ethics statement

Participation was voluntary, and all respondents were informed about the study’s purpose and the intended use of the data for academic and policy research.

2.4.2 Target group and sampling

The survey was circulated among key stakeholders engaged in HEC mitigation and elephant conservation across India. The target population included individuals working within a) State Forest Departments (SFDs), b) Non-Governmental Organizations (NGOs), and c) Research institutions and conservation groups. A purposive sampling strategy was employed to reach professionals with direct experience in elephant conservation, wildlife management, and human-wildlife conflict resolution. Particular attention was given to forest field managers from India’s 33 designated Elephant Reserves, as these areas represent core conflict zones and conservation priorities. A total of 428 responses were collected, distributed across four major elephant range regions: a) Central India: 168 responses b) Southern India: 130 responses c) North-Eastern India: 78 responses d) Northern India: 52 responses. Respondents were further categorized by professional background into two primary expertise groups- Field Managers (e.g., Divisional Forest Officers, Range Forest Officers): 357 respondents and Research/NGO representatives: 71 respondents. This classification enabled comparative analysis of responses based on the nature of institutional roles and exposure to ground-level authenticity.

2.5 Data analyses

Along with descriptive statistics, a series of Chi-square tests of independence and an ordinal logistic regression were performed to examine associations between region, expertise group, compensation experiences, perceptions of the efficacy of physical barriers, and the Rapid Response Team. These inferential analyses allowed us to identify whether observed patterns were statistically significant and therefore unlikely to be due to chance alone.

3 Results

3.1 Trends and causes of elephant mortality

Anthropogenic mortality among elephants in India remained a significant conservation challenge, predominantly resulting from human-related factors. Over a 16-year period, a total of 1,653 elephant deaths were recorded (Figure 1), with electrocution, train collisions, and poaching identified as the primary causes. Electrocution constituted the leading cause, accounting for 1,105 fatalities (69.06& ±& 17.8 per year), with the highest incidences reported in Odisha (n=221), Karnataka (n=181), and Assam (n=172); Tamil Nadu (n=131) and West Bengal (n=86) also experienced considerable losses. Train collisions were responsible for 225 deaths (15.9& ±& 4.9 per year), predominantly affecting Assam (n=82; Supplementary Figure 2) and West Bengal (n=62). Other states, such as Odisha and Uttarakhand, experienced occasional incidents, whereas several states reported no cases or only a few. Poisoning resulted in 79 deaths, most notably in Assam (n=45) and Odisha (n=15), with Odisha reporting no poisoning cases in the past seven years. Isolated incidents occurred in Chhattisgarh, Jharkhand, and Kerala, while many other states recorded none. Poaching led to 214 deaths, with persistently elevated figures in Odisha (n=66) and recurrent instances observed in Assam (n=27), Kerala (n=24), Meghalaya (n=23), Karnataka (n=22), and Tamil Nadu (n=22).

Figure 1

Electrocution was the most frequent cause of anthropogenic elephant deaths (Supplementary Figure 3), followed by train accidents and poaching. Odisha and Assam reported higher annual mortality rates (>20 deaths/year), while Karnataka (13 deaths/year) and Tamil Nadu (10 deaths/year) have experienced moderate levels over the past 16 years. Odisha recorded a total of 345 elephant fatalities, with electrocution responsible for 221 deaths (about 64%), and poaching accounting for 66 deaths. In Assam, the second-highest number of deaths was recorded at 326. Alongside electrocution, train accidents contributed to 82 deaths, which is the highest among all the states, and poisoning accounted for 45 deaths in Assam.

3.2 Human fatalities from elephant attacks

Between 2009 and 2024, a total of 7,868 human deaths due to elephant attacks were reported across 16 elephant range states in India (Supplementary Figure 4). On average, this translated to 492 deaths per year. Odisha recorded the highest number of casualties (n = 1,495), followed by Jharkhand (n = 1,205), West Bengal (n = 1,306), and Assam (n = 1,161). These four states together accounted for nearly 70% of all reported human deaths during the study period. Other states with moderate numbers included Tamil Nadu (n = 747), Chhattisgarh (n = 782), and Karnataka (n = 520). States such as Kerala (n = 290), Meghalaya (n = 92), and Uttarakhand (n = 132) reported lower but persistent fatalities, while very few cases were documented from Nagaland (n = 9), Tripura (n = 13), and Maharashtra (n = 17).

The annual death toll fluctuated between 372 (2011–12) and 629 (2023–24), indicating substantial year-to-year variation. Central–eastern states (Odisha, Jharkhand, Chhattisgarh, and West Bengal) consistently recorded the highest levels of conflict. In contrast, southern states (Tamil Nadu, Karnataka, Kerala) showed moderate but steady levels of human casualties. North-eastern states such as Assam and Meghalaya also contributed significantly to the overall fatalities. These findings highlighted that central–eastern India constitutes the core conflict hotspot, with consistently high human fatalities, whereas southern and north-eastern states reflect regionally important but comparatively moderate levels of conflict.

3.3 Region-specific drivers of the conflict: factors influencing HEC in Chhattisgarh and Jharkhand

In Chhattisgarh, HEC patterns were found to be strongly influenced by natural landscapes and human-modified surroundings. The probability of human fatalities was found higher closer to water bodies (β = -0.142, p < 0.001), roads (β = −0.543, p < 0.001), croplands (β = -1.802, p < 0.001), built-up areas (β = -0.408, p < 0.001), and elephant reserves (β = -0.836, p < 0.001). In contrast, the probability of human mortalities increased with an increase in distance from forest patches (β = 1.487, p < 0.001) and protected areas (β = 0.233, p < 0.001). In addition, conflict incidents showed a decreasing trend in areas with larger forest patches (LPI) (β = -0.313, p < 0.001).

Elephant deaths were found to be significantly higher near elephant reserve boundary (β = -1.357, p = 0.001) (Supplementary Figures S5, S6; Supplementary Tables S2–S5). Additionally, deaths were found higher closer to croplands (β = -1.237, p = 0.016), built-up areas (β = -0.461, p = 0.074), and mines (β = -0.510, p = 0.064) and increased with increase in distance from forest (β = 0.487, p = 0.031) and protected areas (β = 0.364, p = 0.182; not significant). Proximity to water (β = -0.607, p = 0.096), and roads (β = -0.133, p = 0.594) did not exhibit significant relationships with mortality risk.

In Jharkhand, human casualty incidences were found higher closer to waterbodies (β = −0.007, p < 0.001), roads (β = −0.906, p < 0.001), and elephant reserves (β = −0.878, p < 0.001). Areas closer to forests also showed increased risk (β = −0.003, p < 0.001), emphasizing the importance of forest edges as conflict zones. Our analysis also revealed higher conflict probability near mines (β = −0.101, p < 0.001). However, conflict incidence increased with an increase in distance from built-up areas (β = 0.143, p < 0.001) and protected areas (β = 0.011, p < 0.001). Landscape configuration also influenced conflict patterns, with higher fragmented forest patch density (β = 0.220, p < 0.001) associated with increased conflict risk.

Elephant mortality incidents were found to be higher closer to water bodies (β = -1.080, p < 0.05), railways (β = -1.128, p = 0.0001), and forests (β = -7.419, p < 0.05) and roads (β = -1.079, p < 0.05), indicating greater risk in close proximity to these features. In contrast, mortality increased with increasing distance from built-up areas (β = 2.553, p = 0.0001), protected areas (β = 4.066, p = 0.0001), and mines (β = 3.298, p = 0.0001) (Supplementary Figures S7, S8; Supplementary Tables S6–S9).

3.4 Stakeholder response to the effectiveness of interventions

3.4.1 Efficacy of physical barriers

Analysis of stakeholder ratings of the effectiveness of physical barriers against HEC revealed apparent regional differences. Respondents from the North (OR = 1.93, CI: 1.49–2.51) were nearly twice as likely as those from Central India to give higher effectiveness scores. Similarly, respondents from the South (OR = 1.68, CI: 1.38–2.06) and North-East (OR = 1.50, CI: 1.19–1.89) were significantly more likely than Central respondents to rate barriers positively. When comparing barrier types, solar-powered fences were the most highly rated intervention. They were significantly more likely than trenches to be rated effective (OR = 1.28, CI: 1.02–1.62). In contrast, structural barriers such as walls received the lowest ratings, with a 35% lower likelihood of being judged effective compared to trenches (OR = 0.65, CI: 0.52–0.83). Railway-line fences were rated similarly to trenches (OR = 0.89, CI: 0.70–1.12), with no significant difference, suggesting mixed perceptions. Social fencing and manpower-based deterrents received slightly higher ratings than trenches (OR = 1.10, CI: 0.87–1.40), but these differences were not statistically significant, reflecting variation depending on regional contexts. However, no significant differences were detected between expertise groups (Field Managers vs RA_NGO representatives), nor were there significant interaction effects between region and expertise. This indicates broad consensus across professional backgrounds, with regional variation and barrier type driving most of the differences (Table 1).

3.4.2 Compensation adequacy and challenges

Analysis of responses on the rate of compensation in HEC cases revealed substantial variation across respondents (Figure 2). Overall, 62.3% reported that all cases received compensation, while 18.7% indicated that only half of the cases were compensated, 18.5% responded that less than half were compensated, and a small minority (0.5%) stated that no compensation was provided. When disaggregated by region (North, South, Central, North-East) and expertise (Field Managers, RA_NGOs), the differences were highly significant (χ² = 143.27, df = 21, p < 0.0001). This confirms that both geographic location and institutional background strongly influence perceptions of compensation adequacy. Our results highlight both regional disparities and professional divergences in stakeholder perceptions of compensation adequacy.

Figure 2

Significant regional heterogeneity in reported challenges to accessing compensation (χ² = 101.33, df = 12, p < 0.0001). Respondents from the North-East most frequently identified fund shortages, whereas those from the Central and North regions emphasized bureaucratic delays and procedural hurdles. In contrast, respondents from the South more often cited limited awareness of compensation schemes. A parallel analysis by expertise group also yielded significant differences (χ² = 14.00, df = 4, p = 0.007). Field Managers predominantly highlighted resource constraints and administrative bottlenecks, while RA_NGO representatives placed greater emphasis on deficits in community awareness and lack of transparency. In addition, the distribution of compensation methods (full payment, part payment, innovative mechanisms) showed no significant regional differences (χ² = 6.79, p = 0.34). A near-significant expertise effect was detected (χ² = 9.43, p ≈ 0.051), with NGOs reporting greater reliance on part payments compared to Field Managers. When disbursement times were considered, significant associations emerged. Besides, the crop insurance scheme was considered a positive alternative for mitigating HEC across all four regions.

Furthermore, the majority of respondents (54.7%) reported that compensation was received within 1 week–15 days, followed by 26.6% reporting 15–30 days. A smaller proportion (11.4%) received compensation within 48 hours, while only 7.2% reported delays of 1–3 months. Regional comparison showed variation in disbursement times, though not statistically significant (χ² = 13.33, df = 9, p = 0.14). The Central and South regions recorded the highest disbursement counts within 1 week–15 days. In contrast, longer delays of 1–3 months were more common in the South than in other regions. When comparing responses between Field Managers and RA_NGO representatives, the overall pattern remained similar, with no significant difference observed (χ² = 1.38, df = 3, p = 0.711). Field Managers most frequently reported compensation within 1 week–15 days (55.7%), while RA_NGO respondents also showed the same dominant category (53.6%). Both groups reported only a small fraction of cases receiving compensation within 48 hours. However, regional differences were highly significant (χ² = 67.23, df = 12, p < 0.0001), with the North-East and South reporting longer delays (>6 months), while the North and Central more often fell within 1–6 months. Expertise differences were also significant (χ² = 36.65, df = 4, p < 0.001), with NGOs reporting longer delays than Field Managers.

The assessment of whether the current compensation policy for HEC was adequate to address losses revealed significant variation across regions and expertise groups. Overall, a majority of respondents (74.2%) reported that the policy was not adequate, while only 25.8% considered it adequate. The Chi-square test confirmed that these differences were statistically significant (χ² = 31.31, df = 7, p < 0.0001), indicating that perceptions of policy adequacy were strongly influenced by both the regional context and respondents’ professional backgrounds. Notably, while some regions, such as the Central and South, showed relatively higher support for the policy’s adequacy, responses from the North and North-East were predominantly negative, with field managers and NGO representatives often diverging in their assessments.

Subsequently, the compensation rate for crop damage elicited diverse opinions among respondents and varied significantly across regions and expertise groups (χ² = 50.12, df = 21, p = 0.0003). A large share indicated that compensation rates either vary based on crop type and growth stage (38.2%) or require revision (36.1%), while smaller proportions felt that rates remain the same for all crops and stages (22.3%) or apply only to fully grown crops (3.5%).

3.4.3 Engagement of rapid response teams

Perceptions of RRT and volunteer team effectiveness were generally positive, though significant variation was observed across groups (χ² = 14.13, df = 7, p = 0.049). Central Field Manages reported more substantial support than expected, while North-East NGOs were more skeptical. On the other hand, the delivery of challenges to engage RRT and Village Volunteer Teams did not vary across regions and expertise groups (χ² = 27.88, df = 21, p = 0.144). The most common issue across all groups remained the lack of trained and skilled manpower, followed by logistics and the availability of teams.

Analysis of regional responses to the most effective way for HEC mitigation showed apparent variation in perceived effectiveness of HEC mitigation strategies (χ² = 135.67, df = 28, p < 0.0001). Rapid Response Teams were the most preferred option in the South (67.2%), North-East (61.0%), and North (63.5%), whereas in the Central region, respondents favoured Hathi Mitra initiatives (50.6%) over Rapid Response Teams (34.7%). Social barriers received notable support in the South (22.1%), while radio telecast and Hulla Party were rarely selected across all regions.

3.4.4 Regional and expertise-based variation in technology interventions

The ordinal logistic regression indicated that region and expertise significantly influenced perceptions of technology efficacy. Respondents from the North were 1.33 times more likely to assign higher ratings than those from the Central region (p = 0.029), while RA_NGO respondents were 1.73 times more likely than Field Managers to rate technologies favourably (p < 0.001). Among technologies, mobile-based early warning systems were most effective, with 1.56 times higher odds of receiving favourable ratings compared to AI-based camera traps (p < 0.001). By contrast, radio telecast/communication was less preferred (0.77 times odds, p = 0.039), while drones and satellite telemetry showed no significant differences. Overall, mobile-based solutions were consistently rated highest, especially by NGO respondents, whereas radio communication was least valued (Table 2).

3.4.5 Translocation of conflict elephants

Most respondents (49.4%; Figure 3) considered translocation necessary in the case of a problem animal, followed by human deaths (18.7%) and repeated crop raids (14.0%), with significant variation across region × Expertise groups (χ² = 58.02, df = 35, p = 0.009). Human deaths were also the most cited reason for permanent captivity, though regional differences were not significant (χ² = 24.45, df = 21, p = 0.272). Perceptions of translocation effectiveness in reducing HEC were divided, with significant contextual variation across groups (χ² = 19.01, df = 7, p = 0.008). Many respondents further believed translocation may shift conflict to new areas, although this pattern did not differ significantly across groups (χ² = 20.21, df = 14, p = 0.124). Most stakeholders believe the State Forest Department’s current response time to Human–Elephant Conflict (HEC) was inadequate, with a smaller proportion asserting it was adequate. Although responses varied somewhat by region and type of expertise, the variation was not statistically significant (χ² = 12.84, df = 7, p = 0.076). Most respondents reported that the SFD typically takes 30 minutes to 1 hour to respond to HEC incidents (32.7%). However, there was substantial variation across regions and expertise groups (χ² = 72.43, df = 35, p < 0.001).

Figure 3

More than half of the respondents (53.0%) considered within 30 minutes to be the appropriate response time for addressing HEC incidents. Expectations differed significantly across regions and expertise groups (χ² = 44.91, df = 28, p = 0.028), highlighting the importance of regional context and stakeholder experience in shaping perceptions of adequate response time.

4 Discussion

4.1 National patterns of elephant and human fatalities

Elephant and human fatalities in India are predominantly shaped by anthropogenic pressures (Palei et al., 2014; Roy and Sukumar, 2017; Dasgupta and Ghosh, 2015). National patterns indicate that mortality risks and human casualties are unevenly distributed and are not proportional to elephant population size, highlighting the importance of landscape configuration and human–elephant interfaces (Sukumar, 2003; Gubbi, 2012). Mortality of elephants primarily driven by anthropogenic causes, especially electrocution and train collisions (Palei et al., 2014; Roy and Sukumar, 2017). Since 2009, electrocution accounted for 67% of recorded elephant deaths, followed by train collisions (15%), poaching (13%), and poisoning (5%). Electrocution has accounted for over 1,100 deaths in 16 years, particularly in Odisha, Karnataka, and Assam, while rail accidents remain severe in Assam and West Bengal (Palei et al., 2014; Dasgupta and Ghosh, 2015). Although reduced in some areas, poaching continues to disproportionately affect male elephants in regions such as Odisha and the Nilgiris (Mishra and Bisht, 2022; Baskaran et al., 2024).The Government of India has implemented multiple measures to reduce train–elephant collisions, identifying 77 high-risk railway stretches across 14 states and introducing interventions such as speed restrictions, warning signage, vegetation clearance, and wildlife crossings (PE-MoEFCC-WII, 2025). Pilot technologies including sensor-based detection systems, fibre-optic DAS (Distributed Acoustic Sensing), and AI-enabled thermal cameras are being tested in hotspot areas to provide real-time alerts (Kannan et al., 2024). However, inconsistent enforcement, high maintenance costs, and technical reliability continue to limit effectiveness. Stronger coordination between forest and railway departments, and integration of mitigation into railway planning and corridor conservation, are critical to reducing elephant mortality.

Between 2009 and 2024, nearly 8,000 human fatalities from elephant attacks were recorded in India (Figure 4), with Odisha, Jharkhand, West Bengal, and Assam forming a core conflict zone driven by habitat fragmentation, land-use change, and frequent human–elephant encounters. Importantly, human fatalities were not proportional to elephant population size, as states with relatively smaller elephant populations such as Odisha, Jharkhand, West Bengal, and Chhattisgarh experienced disproportionately high deaths, unlike southern states with larger elephant populations. However, the interpretation of conflict trends must consider potential limitations associated with institutional datasets. National mortality and conflict databases, although indispensable for large-scale assessments, may be affected by underreporting, inconsistent detection probabilities, and variation in classification of mortality causes across states (Palei et al., 2014). Underreporting may be particularly relevant in remote landscapes, cases lacking clear attribution of cause of death, or regions with limited monitoring infrastructure. Consequently, national estimates are more appropriately interpreted as indicators of relative spatial and temporal patterns rather than absolute measures of mortality magnitude.

Figure 4

4.2 Spatial context of HEC: factors influencing HEC in Chhattisgarh and Jharkhand

Across both Chhattisgarh and Jharkhand, HEC and elephant mortality were concentrated in forest human edge zones, highlighting the role of fragmentation and anthropogenic pressure. Across Asia and Africa, studies have linked habitat loss, fragmentation, and natural factors to increased elephant mortality and human-elephant conflict (Ladue et al., 2021; Yu et al., 2024). In both states, risk increased near forest edges, water bodies, croplands, and settlements, identifying ecotonal areas as primary conflict hotspots (Palei et al., 2013). Habitat configuration differed between states. In Chhattisgarh, larger contiguous forest patches reduced human and elephant mortality. In contrast, Jharkhand’s fragmented forest patches showed strong positive associations between conflict and fragmentation metrics (patch and edge density), indicating greater exposure in fragmented landscapes (Gubbi, 2012; Karanth et al., 2012). Infrastructure effects were stronger in Jharkhand, where proximity to roads, railways, and mining areas significantly increased mortality, supporting earlier findings (Sukumar, 2003; Rani et al., 2024). Agricultural and settlement proximity consistently increased conflict in both states. Protected areas had different effects in the two states. In Jharkhand, deaths were higher outside protected areas, showing they act as safe shelters. In Chhattisgarh, conflicts were more common near reserve boundaries, where elephants move into nearby human-used areas. Overall, HEC and elephant deaths occur where natural resources and human activities overlap, and the level of risk depends on how the landscape is structured.

4.3 Manger’s perspective on the efficacy of various HEC management tools and strategies

India has adopted a multi-pronged approach to mitigate HEC, combining habitat and corridor protection, physical barriers, community engagement, compensation schemes, and technology-based monitoring (PE-WII-MoEF&CC, 2024a). Despite this comprehensive framework, implementation remains uneven, constrained by coordination gaps, inadequate funding, and weak institutional capacity (Pandey et al., 2024a).

Among physical interventions, solar-powered fencing is consistently perceived as the most effective in fragmented agricultural landscapes, while rigid structures such as concrete walls perform poorly due to high costs and maintenance challenges (Fernando et al., 2005; Das et al., 2022). Compensation schemes are essential but often undermined by delays, procedural complexity, and inconsistent coverage, eroding community trust and occasionally heightening conflict (Karanth et al., 2013). Rapid Response Teams (RRTs) are widely valued, yet their effectiveness is limited by manpower, training, and logistical constraints, highlighting the need for sustained investment and community integration.

Technological tools such as mobile early-warning systems, AI-enabled monitoring, and railway intrusion detection systems show considerable promise in reducing risky encounters and train collisions, but require stronger inter-departmental coordination for large-scale deployment. Perceptions of translocation remain mixed, and national guidelines emphasize that it should be used only as a last resort following rigorous ecological assessment (PE-MoEFCC-WII, 2024b).

Recent efforts from Project Elephant have increasingly emphasized broader analytical evaluations of HEC, including assessments of long-term trends and drivers across multiple states. Analyses conducted in regions such as Assam, Jharkhand, and Chhattisgarh have provided state-specific insights into conflict dynamics and associated ecological and anthropogenic factors (Habib et al., 2025a, Habib et al., 2025b, Habib et al., 2025c). While these assessments contribute to a more evidence-informed understanding of conflict processes, their effectiveness ultimately depends on how findings are incorporated into regionally adapted management strategies and supported by sustained monitoring frameworks. Importantly, these assessments identify state-specific drivers of conflict and generate village-level hotspot prioritization to guide focused interventions. Complementing this, Project Elephant has initiated Regional Action Plans (RAPs) for mitigating HEC in South India and the North-Eastern states, explicitly incorporating inter-state dynamics such as elephant movement across administrative boundaries and highlighting the need for collective action at the landscape scale to reduce human and elephant fatalities. In addition, recent policy and management initiatives have also included revisions to financial support mechanisms, increased emphasis on community-based early warning systems, and expanded training efforts for frontline personnel. These measures aim to improve institutional responsiveness and operational capacity in conflict-prone landscapes. However, the effectiveness and consistency of these interventions remain dependent on implementation, maintenance, and long-term evaluation across regions (PE-MoEFCC, 2024c). In parallel under the Indo-German Cooperation Project HEC guidelines has been developed, indicating a continued shift toward adaptive management grounded in learning, improved operational protocols, and scalable implementation across diverse socio-ecological contexts (PE- MoEFCC, 2024c).

Overall, HEC in India is shaped by complex, region-specific interactions among elephant ecology, habitat fragmentation, infrastructure, and human livelihoods (Van De Water and Matteson, 2018). Consequently, one-size-fits-all policies are ineffective; mitigation must be landscape-specific and adaptive. Future strategies should prioritize community-managed solar fencing in high-conflict agricultural frontiers, institutionalize time-bound and decentralized compensation systems, and strengthen RRT capacity through training and incentives. Independent monitoring of compensation and barrier performance is needed to improve accountability.

Effective coexistence requires integrating HEC mitigation with broader land-use planning, corridor restoration, and rural livelihoods, while categorizing landscapes into: (a) elephant core areas, (b) coexistence zones, and (c) areas where elephant presence should be actively discouraged. Coexistence zones demand rapid financial support, decentralized governance, and strong community leadership (Bhuyan, 2025; Kei et al., 2025; Pandey et al., 2024b; Tripathy et al., 2021).

National-level conservation frameworks and legal mechanisms in India have created a strong foundation for elephant protection, but sustainable coexistence requires tailoring these policies to the socio-cultural and ecological diversity of local regions (Pandey et al., 2024b). Therefore, by aligning national policies with local realities, India can move toward more equitable and robust model for human–elephant coexistence (Pandey et al., 2024b; Kulkarni et al., 2023; Natarajan et al., 2025).

This study synthesizes national HEC patterns using compiled datasets, but several important aspects remain beyond its scope. Future work should incorporate multi-stakeholder perspectives through structured, multilingual field surveys; conduct a state-wise evaluation of compensation delivery and its role in shaping tolerance; and quantitatively assess how land-use change, mining, and linear infrastructure expansion drive habitat loss and conflict escalation, alongside independent monitoring of mitigation effectiveness and sustainability.

5 Conclusion

The current study showed that HEC in India is fundamentally a governance and planning problem as much as an ecological one. National mortality trends, Central Indian landscape analyses, and perceptions from frontline stakeholders reveal that conflict is concentrated in fragmented agro-forest mosaics where institutional capacity, infrastructure regulation, and compensation delivery remain uneven. Central–eastern states experience disproportionate human fatalities despite smaller elephant populations, indicating that land-use configuration and administrative response not elephant abundance largely determine conflict risk.

Despite the presence of a national policy framework, mitigation effectiveness varies across regions, which may be attributed to limitation in governance, funding, and coordination. Our findings highlight the need for three policy shifts: (a) decentralized conflict governance, with time-bound, locally administered compensation and greater community oversight; (b) mainstreaming HEC into development planning, ensuring that roads, railways, mines, and power infrastructure incorporate elephant-safe design from the outset; and (c) performance-based mitigation, with independent monitoring of solar fencing, Rapid Response Teams, and early-warning systems and other mitigation strategies d) Project Elephant is being strengthened as a national, evidence-based planning framework for HEC, using long-term analyses to identify drivers and village-level hotspots across priority states and guide targeted interventions e) Regional Action Plans and upgraded support systems (ex-gratia, early-warning, training, revised guidelines) are improving inter-state coordination, frontline capacity, and scalable implementation to reduce human and elephant fatalities. Finally, India’s coexistence strategy must move beyond episodic crisis response toward integrated landscape governance aligning conservation, agriculture, rural livelihoods, and infrastructure within a single policy framework.

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