Quantifying the Impacts of Land Use Transitions on Human-Elephant Conflict in Peninsular Malaysia: Implication for Sustainable Landscape Planning

Quantifying the Impacts of Land Use Transitions on Human-Elephant Conflict in Peninsular Malaysia: Implication for Sustainable Landscape Planning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying the Impacts of Land Use Transitions on Human-Elephant Conflict in Peninsular Malaysia: Implication for Sustainable Landscape Planning Anis Maisarah Fakhrulanuar, Kamaruddin Zainul Abidin, Mohammad Saiful Mansor, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7312791/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Asian elephants (Elephas maximus) are keystone species in Southeast Asia's tropical ecosystems yet are increasingly threatened by habitat loss due to land-use change. Rapid agricultural and urban expansion has caused major habitat fragmentation and increased human–elephant conflict (HEC) in Southern Peninsular Malaysia. This study quantifies how specific land-use transitions influence the spatial intensity and temporal patterns of HEC. Using kernel density mapping in ArcGIS and transition modeling with TerrSet’s Land Change Modeler, we developed a conflict risk framework integrated into a generalized linear mixed model (GLMM) to assess relationships between land transitions and conflict intensity. We predicted the short- and long-term impacts of key land-use transitions on HEC. Anthropogenic transitions, particularly forest to plantation, and idle land to plantation were found to significantly increase HEC risk, while transitions toward natural land cover, especially forest, tend to reduce conflict over time. Notably, recovery from plantation to forest requires nearly twice the time needed to offset the HEC impact caused by forest to plantation transitions. These findings highlight the role of land-use decisions in shaping conflict dynamics and underscore ecological restoration as a long-term mitigation strategy. The insights offer practical guidance for sustainable planning and targeted HEC management in conflict-prone landscapes. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Human–elephant conflict (HEC) Land-use change (LUC) Wildlife-landscape interaction Landscape planning GLMM Predictive spatial modelling Figures Figure 1 Figure 2 Figure 3 1. Introduction Asian elephants ( Elephas maximus ), the largest terrestrial mammals in Asia, play a crucial role as ecosystem engineers, shaping landscapes and maintaining biodiversity through their movements and feeding behaviours [ 1 ]. However, their survival is increasingly threatened by land-use changes that alter their habitats, forcing them into conflict with human populations. Human–elephant conflict (HEC), which includes crop raiding, infrastructure damage and human fatalities, has emerged as a major conservation and socio-economic challenge in regions where human settlements and elephant ranges overlap [ 2 ]. One of the most significant drivers of HEC is habitat loss and fragmentation, largely caused by agricultural expansion, urbanisation and infrastructure development. Studies in China and Sumatra have shown that as forests shrink and become fragmented, elephants are pushed into smaller, more isolated patches, increasing their likelihood of encountering human-dominated landscapes [ 3 ]. Similarly, in Myanmar, rapid human encroachment has led to increased poaching and HEC incidents, causing severe elephant-population declines [ 4 ]. In Peninsular Malaysia, the state of Johor has experienced significant land-use changes, particularly from the late 20th century onwards, a period marked by the rapid conversion of forest into plantations, urban areas and idle lands [ 5 ]. These transitions have largely been driven by economic development initiatives, industrial expansion and infrastructure projects. Between 1984 and 2015, the Johor River Basin (JRB) experienced rapid growth in oil palm plantations, with estate areas expanding by 47.98%. This was accompanied by substantial urban expansion, particularly after the launch of the Iskandar Malaysia special economic region in 2006, which accelerated development in southern Johor and downstream areas of the basin [ 6 , 7 ]. Further highlighting these trends, it is reported that between 2000 and 2015, forested areas in the JRB declined to 32.15%, while oil palm plantations increased to 11.88% of the total land area [ 8 ]. Agricultural lands other than oil palm also expanded by 11.07%, and urban areas grew by 9.82%, reflecting significant land use and land cover changes with important implications for the region’s hydrology and ecosystem services. This transition has fragmented elephant habitats, forcing the elephants to navigate human-dominated landscapes in search of food and water, and thereby intensifying HEC incidents [ 9 ]. The Endangered status of the Asian elephant [ 10 ] highlights the importance of understanding how land-use changes affect conflict occurrence and habitat use, which is critical for developing effective conservation and mitigation initiatives. To address these challenges, this study examines spatial-temporal patterns of HEC in Johor, focusing on how land-use transitions influence the occurrence and intensity of conflicts. Using spatial modelling techniques, the study analyses HEC hotspots, identifying key land transitions contributing to conflict risk. Investigating the impact of land-use changes on HEC is crucial as it helps identify how habitat alteration contributes to increased interactions and conflicts between humans and elephants. Considering elephants as a keystone species, their conservation contributes to the protection of broader ecosystems and biodiversity [ 11 ]. Integrating their ecological needs into sustainable land use planning promotes the development of more balanced landscapes that foster long-term coexistence of elephants, other wildlife, and local communities [ 12 ]. 2. Results 2.1 GLMM Estimates of Land-Use Transitions Influencing HEC Density The total area of land-use change (LUC) differed between the two transition periods (2010–2015 and 2015–2021), and these differences influenced the strength and direction of the relationship between land-use transitions and HEC density. A summary of the total area for each land-use transition during both periods is provided in Supplementary Table S1 . The GLMM results for land-use transitions from 2010–2015 in relation to HEC density from 2013–2017 revealed that not all 20 transitions were included in the final model. Some transitions were removed due to multicollinearity, identified using variance inflation factor (VIF) analysis, while others were excluded for being zero or near-zero variance predictors. Among the remaining transitions, several were found to be statistically significant (p P), idle land to settlement (I > S), idle land to plantation (I > P), plantation to idle land (P > I), forest to plantation (F > P) and forest to idle land (F > I) were significant predictors of HEC. These significant transitions suggest that changes involving plantations, settlements and forest loss play a key causal role in HEC. Conversely, transitions such as water body to forest (W > F), water body to idle land (W > I), plantation to settlement (P > S), forest to water body (F > W) and forest to settlement (F > S) were not statistically significant in influencing HEC density, (p > 0.01). These transitions were excluded from the next stage of analysis, particularly when calculating the rate of change. Table 1 Generalised linear mixed model (GLMM) results for Timeframe 1 (T1), covering land-use change (LUC) from 2010–2015 and human–elephant conflict (HEC) from 2013–2017, Timeframe 2 (T2), encompassing 2015–2021 LUC and 2018–2023 HEC, and Timeframe 3 (T3), for 2010–2015 LUC and 2018–2023 HEC. LUC Types Estimate Std. Error T1 T2 T3 T1 T2 T3 W > F -0.183 0.192 -0.867 *** 0.209 0.119 0.225 W > S -0.578 -0.681* 0.377 0.289 W > P 0.26 0.493*** 0.183 0.088 S > F -0.419* 0.119 -0.628 * 0.198 0.107 0.067 S > W -0.404 1.243** -0.727 * 0.266 0.452 0.333 S > I -0.009 -0.631*** 0.153 0.17 S > P 0.564*** -0.385*** 0.07 0.068 I > F -0.415* 0.052 -0.8 *** 0.193 0.101 0.195 I > S -0.974*** -0.844*** 0.231 0.15 I > P 0.587*** 0.807*** 0.083 0.049 I > W -0.423* 0.207 P > F -0.326*** 0.13* 0.142 * 0.096 0.064 0.070 P > W -1.044 -0.234* -0.314 ** 0.515 0.102 0.102 P > S 0.072 0.054 0.103 0.05 P > I 0.333*** 1.091*** 0.08 0.13 F > P 0.19** -0.227*** 0.069 0.059 F > W -0.807* 0.127 0.392 0.098 F > S 0.172. -0.236 0.103 0.172 F > I -0.473*** 0.435. 0.121 0.232 Note: Significance levels are indicated by asterisks, ranging from high statistical significance ‘***’ (p < 0.001), ‘**’ (p < 0.01), ‘*’ (p < 0.05), ‘.’ (p < 0.1), with no symbol indicating non-significance (p ≥ 0.1). Land use transitions are represented using abbreviations, where P = Plantation, F = Forest, I = Idle Land, S = Settlement, and W = Water Body. The symbol “>” denotes directional change; for example, P > F indicates a transition from plantation to forest. A second GLMM analysis was performed for land-use transitions from 2015 to 2021 in relation to HEC density in 2018–2023 (Table 1 ). As with the previous model, not all 20 land changes were retained due to multicollinearity and variance checks. The results indicate that several transitions remained significant, reinforcing the patterns observed in the earlier period and suggesting that land transitions involving plantations, settlements, and idle land are persistent drivers of HEC density. Several transitions that were significant in 2010–2015 retained their influence in 2015–2021, such as idle land to plantation (I > P), settlement to plantation (S > P), and plantation to idle land (P > I), maintaining their role in shaping conflict patterns. Conversely, transitions such as water body to forest (W > F), and plantation to settlement (P > S) were not statistically significant, (p > 0.01), and were also excluded from the next stage of analysis. 2.2 Magnitude of Human-Elephant Conflict Impact The rate of change analysis for land-use transitions was conducted for both concurrent effects (5-year gap) and delayed effects (10-year gap) to assess their impact on HEC. The concurrent effect analysis focused on immediate land-use transitions within a 5-year period, while the delayed effect was applied to transitions leading to natural land types (forest and water body), where the impact on HEC is expected to manifest over a longer time scale. Table 2 presents the predicted impact and directional response of HEC intensity associated with key land use transitions over concurrent and delayed periods Table 2 Predicted impact and directional response on HEC for each key land use transitions over concurrent and delayed periods. LUC Rate of change (KDE per km²) Predicted Impact (KDE impact ) Response on HEC Concurrent effect (5 years) S > I 0.005 0.157 Increase S > P 0.001 0.290 Increase F > P 0.002 0.672 Increase I > S 0.001 0.180 Increase I > P 0.0002 0.164 Increase P > I -0.005 -0.197 Decrease W > P 0.004 0.221 Increase I > W -0.032 -0.523 Decrease W > S 0.006 0.178 Increase F > I 0.013 0.307 Increase Delayed effect (10 years) S > W 0.023 0.143 Increase S > F -0.007 -0.419 Decrease P > F -0.0007 -0.196 Decrease I > F -0.004 -0.415 Decrease P > W -0.010 -0.809 Decrease Note: Only land-use changes (LUCs) with statistically significant estimates (p < 0.01) from the generalised linear mixed model (GLMM) were included. For the concurrent effect (5-year timeline), several transitions were associated with an increased impact on HEC intensity. These include settlement to idle land (0.157 per km²), settlement to plantation (0.290 per km²), forest to plantation (0.672 per km²), idle land to settlement (0.180 per km²), and idle land to plantation (0.164 per km²). These findings indicate that urban expansion and the conversion of natural or idle land into more intensive land uses contribute to increased HEC occurrences. Conversely, some land-use transitions during the same period showed a decrease in HEC intensity, one of them being plantation to idle land (-0.197 per km²), suggesting that reducing anthropogenic pressure or converting land back into less-developed states may help reduce conflict. The results from the delayed-effect timeline showed that transitions such as settlement to forest (− 0.419 per km²), plantation to forest (− 0.196 per km²), idle land to forest (− 0.415 per km²), and plantation to water body (− 0.809 per km²) contributed to a reduction in HEC intensity over time. These outcomes highlight the importance of allowing longer timelines for habitat restoration strategies to take visible ecological effect. To further illustrate how the rate of change translates into predicted HEC intensity, Table 3 indicates how the rate of land-use change translates into predicted HEC intensity, showing that the increase in conflict associated with forest to plantation transition is consistently greater than the reduction observed from the reverse transition, even when the area of land involved is the same. This suggests that converting forest into plantation has a disproportionately higher impact on increasing conflict compared to the relatively modest decline in conflict resulting from reforestation of plantations. Figure 3 further illustrates the spatial distribution of HEC kernel density overlaid with these selected land-use transition areas. Table 3 Magnitude of predicted HEC impact of selected land-use transitions and translated impact into predicted counts under varying land conversion area scenarios. Area of changes (km 2 ) Forest to Plantation Plantation to Forest Predicted HEC impact (KDE impact ) Predicted HEC counts Predicted HEC impact (KDE impact ) Predicted HEC counts 100 0.224 161 -0.066 123 200 0.449 199 -0.132 116 300 0.673 245 -0.198 109 400 0.898 301 -0.264 103 500 1.122 374 -0.330 96 3. Discussion This study presents one of the few quantitative attempts to estimate the magnitude of impact that specific land-use transitions have on human–elephant conflict (HEC), offering a significant contribution to understanding how anthropogenic landscape change shapes conflict intensity over space and time. The results help us to examine the spatial and temporal relationships between land-use changes (LUC) and HEC, using both concurrent and delayed timeline approaches. Comparable findings were reported [ 13 ], with the usage of GIS-MCDA to map HEC hotspots and elephant habitat zones in Sri Lanka, showing that spatial models are useful for predicting and mitigating conflict. The results provide clear evidence that land transitions, particularly those involving plantations, settlements and idle land are key contributors to conflict intensity, while transitions towards forested areas and water bodies tend to support long-term conflict mitigation. The analysis showed that land transitions into anthropogenic uses, especially from forest to plantations and settlements, were consistently associated with high HEC intensity. These transitions likely increase conflict risk by reducing natural foraging and movement/homerange areas, thereby pushing elephants into human-dominated spaces in search of food and water. These patterns align with previous research demonstrating how habitat fragmentation increases the likelihood of human–elephant interactions and crop-raiding events [ 14 , 15 , 16 , 17 ]. One of the significant transitions, including settlement to plantation (S > P), idle land to plantation (I > P), and forest to plantation (F > P) exhibited significant positive associations with HEC across both concurrent and delayed models, this suggests that the impact of these transitions doesn’t just persist over time, it can actually become stronger as the changes spread or last longer on the landscape. These findings support the importance of managing plantation expansion and settlement encroachment in elephant ranges [ 18 ]. However, not all transitions showed immediate effects. Transitions involving forest regeneration and restoration, such as plantation to forest (P > F) and settlement to forest (S > F), did not immediately reduce conflict intensity despite showing a declining trend in HEC in the delayed-effect model. This lag is likely due to the time required for ecological recovery, vegetation growth and elephant re-familiarisation with restored habitats. These results suggest longer restoration periods (10-year lags/more than 5 years) should be considered when evaluating the benefits of restoration. Further, our predicted HEC counts between forest to plantation and plantation to forest transitions highlight that the impacts of forest loss are more severe and immediate than the slower benefits gained from reforestation. In other words, a substantially larger area must be restored to forest to compensate for the conflict increase caused by forest clearing for plantations. Forest regeneration helps mitigate conflict by gradually restoring habitat structure, vegetation complexity, and food availability that support elephant movement over time [ 19 ]. Similarly, transitions toward water bodies are generally associated with reduced HEC intensity, particularly in the study area, where many water bodies are located near or within natural habitats [ 1 ]. These areas provide essential resources such as hydration during dry periods and often function as natural buffers that limit elephant movement into human-modified landscapes. However, the results also show that transitions toward water bodies do not always reduce conflict. As marginal habitats neither fully natural nor completely anthropogenic, water bodies can produce mixed outcomes, especially when they occur outside of forested zones or in fragmented landscapes. Their ecological function may shift depending on seasonal resource availability, landscape configuration, and proximity to elephant movement corridors [ 20 ]. Poorly planned or uncoordinated land conversions, particularly those involving scattered or isolated water bodies, may inadvertently increase conflict by altering movement paths or drawing elephants toward human areas [ 1 ]. These findings underscore the need for sustainable land use planners to carefully consider the spatial arrangement and ecological context of water bodies when designing landscapes in elephant ranges. Over the short term (concurrent 5-year period), the transition from forest to plantation showed as the most influential predicted impact on human–elephant conflict (HEC) intensity, due to both ecological and land-use factors. This type of land conversion usually involves the clearing of forest for agricultural development, which reduces and fragments the natural habitat elephants rely on. With less available forest, elephants may be forced to move into areas closer to human activity [ 14 , 17 ]. There are also instances where elephants are drawn into plantations in search of young vegetation or water, which increases the risk of encounters with humans and damage to crops or infrastructure [ 21 ]. They are particularly attracted to shredded palm hearts, a preferred food source made easily accessible during new plantation rotation cycles that involve land clearing, removal of bole and roots, and shredding of felled palms [ 22 ]. This not only disrupts their natural movement but also creates more opportunities for conflict [ 23 ]. The forest to plantation transition, therefore, did not just represent a shift in land cover but also contributes directly to rising HEC intensity by reshaping how elephants move through and interact with the landscape. The most minimal impact which is the idle land to water body transition likely reflects the ecological and spatial characteristics of these areas. Idle land that transitions into water bodies is typically located in less accessible or lower elevation zones, which are not commonly used by elephants for movement or foraging [ 24 ]. Such areas are often farther from human activity and settlements, reducing the chances of direct encounters between elephants and people. Although water is essential for elephants, these newly formed or isolated water bodies may not provide sufficient vegetative resources or habitat connectivity, making them less attractive as movement corridors or foraging sites [ 25 ]. Overall, the result for this transition suggests that the spatial context of idle land whether it transitions into resource-rich or resource-poor areas can influence its impact on human–elephant conflict. It is important to note that the estimated magnitude of HEC impact in this study is based on conditions observed in the southern region of Peninsular Malaysia, which serves as a representative case for understanding broader conflict dynamics. While the data includes areas such as Johor, where HEC cases have been notably high, with an average of 150–200 incidents annually between 2015 and 2021 [ 26 ], the analytical approach can be adapted to other regions experiencing similar patterns of land-use change and human–elephant conflict. This framework supports a more generalizable understanding of how land transitions can influence conflict intensity and offers a valuable reference point for stakeholders, land-use planners, and wildlife authorities. By anticipating potential conflict escalation for this umbrella species, the study contributes to the development of more proactive, spatially targeted, and evidence-based land management and biodiversity conservation strategies at a broader scale. These findings also offer practical insights for improving land use and HEC management. From a forestry perspective, promoting forest regeneration and restoring secondary forests can help reduce conflict over the long term, especially in areas where forest has been converted to plantations. Establishing forest buffers and limiting further deforestation are also important steps to prevent elephants from being pushed into human-dominated areas. For the wildlife department, the spatial patterns and conflict impact values identified here can help prioritize surveillance and guide the development of elephant corridors that avoid high-conflict transitions [ 6 , 27 ]. In communities located near active land conversion zones, especially where settlements have shifted to idle land, targeted mitigation programs and community engagement will be important. At the planning level, integrating these findings into spatial policy can help prevent high-risk transitions while encouraging low-impact land uses. Plantation operators also have a role to play by adopting wildlife-friendly practices, restoring marginal lands, and contributing to monitoring efforts. Together, these approaches can support more balanced, sustainable and coexistence-oriented land management strategies. 4. Materials and Methods 4.1 Study Area The study was conducted in Southern Peninsular Malaysia, Johor, including the districts of Mersing, Kota Tinggi and Kluang, which are known for their frequent incidents of human–elephant conflict (HEC) (Fig. 1). This region shares a border with Singapore to the south and is predominantly covered by rainforest situated above the equatorial zone. The rainforest is home to various wildlife species that are classified as endangered or vulnerable according to the IUCN Red List, including the Asian elephant ( Elephas maximus ), Malayan sun bear ( Helarctos malayanus ) and Malayan tiger ( Panthera tigris jacksoni ). Johor (latitude 1.9344°N and longitude 103.3587°E) occupies an area of approximately 19,166 km 2 and experiences a humid, rainy, equatorial climate with relatively constant temperatures throughout the year, receiving average annual rainfall of 2,600 mm [28]. In the study region, land-use transitions involving plantations, idle land, settlements, forests and water bodies have significantly altered the landscape over recent decades, leading to habitat fragmentation and the expansion of oil palm plantations [7]. 4.2 Methodological Framework Methodological framework for assessing the impact of land-use change on human–elephant conflict (HEC) is summarised in Fig. 2. The framework shows the overall process, starting from data collection and preprocessing, followed by kernel density estimation (KDE) of HEC and land-use transition analysis. It includes temporal alignment to assess short- and long-term effects, statistical modeling using GLMM, and estimation of the predicted impact based on land conversion and HEC intensity. 4.3 Human–Elephant Conflict Occurrence Data We compiled data on HEC incidents in Johor, from the Department of Wildlife and National Parks (DWNP), Peninsular Malaysia, focusing on the Asian elephant, a keystone species significantly impacted by land-use changes. The data spanned from 2013 to 2023, capturing a decade of conflict occurrences and trends. HEC data collection begins when the DWNP receives reports from the public regarding elephant-related conflicts, followed by site surveys and ground truthing. During site investigations, evidence such as the exact location of the conflict (XY coordinates), affected land-use type and complainant details are recorded. Each conflict point is documented with information describing the incident and its status. The HEC dataset was then filtered to remove redundant cases, incomplete records with ambiguous details, and points located outside the study area. This approach was based on the assumption that the recorded HEC occurrences were representative and minimally biased by sampling effort, ensuring a comprehensive dataset for spatial analysis. Out of the 3,991 recorded HEC occurrences, a total of 2,941 data points were retained for analysis after filtering, providing a substantial dataset for evaluating spatial patterns and trends. 4.4 Data Preprocessing for Statistical Modelling a) Spatial HEC Intensity We used ArcGIS to preprocess the conflict occurrence data. Kernel density estimation (KDE) was applied to generate a 250 m × 250 m resolution raster representing the density of HEC incidents. A 5-km search radius was set to smooth the HEC density estimates while capturing localised clustering of conflicts. This spatial dataset was used as the response variable in the generalised linear mixed model (GLMM). b) Land-Use Transition To assess the influence of land-use changes on HEC, land-cover maps for the years 2010, 2015 and 2021 were obtained from the Malaysian Government’s Survey and Mapping Department (Department of Survey and Mapping Malaysia, JUPEM). We prepared land-use transition datasets covering five major land-use types: settlement (S), idle land (I), water body (W), plantation (P) and forest (F). These land-use transitions were analysed over two temporal periods – 2010–2015 and 2015–2021 – resulting in a total of 20 possible land-use transitions per period. The spatial patterns of these 20 transition types are illustrated in Supplementary Fig. S1 and S2. The analysis was conducted using TerrSet version 2020, specifically employing the software’s ‘Land Change Modeler’ (LCM), which generated transition values for each land-use category change. The land-use maps for each time period were processed in ArcGIS, where raster datasets were standardised to a 250 m × 250 m resolution to match the kernel density raster of HEC occurrences. These transitions and HEC density values were then extracted and integrated into the statistical modelling to assess their effects on HEC occurrence patterns. The output data were represented in a binary format, indicating the presence or absence of a specific land-use transition within each 250-m cell (HEC occurrences), coded as 0 (absent) or 1 (present). By analysing land-use changes over different time periods, this approach was able to identify major landscape shifts and the amount of time they take to increase HEC risks. The transition potential values served as independent variables in the GLMM, allowing us to quantify the statistical relationship between land-use dynamics and conflict hotspots. c) Temporal Alignment Concurrent and delayed effects were analysed to capture these dynamics between land-use change (LUC) and HEC. To do this, we established three different timelines linking LUCs with HEC occurrences over different periods. The earlier concurrent timeline examined the immediate relationship between LUCs from 2010 to 2015 and HEC occurrences from 2013 to 2017, assuming that recent land-use modifications directly influence elephant movement and conflict patterns within a short timeframe. Meanwhile, the later concurrent timeline evaluated LUCs from 2015 to 2021 against HEC occurrences from 2018 to 2023, reflecting how more recent land-use transitions impacted elephant behaviour, habitat fragmentation and conflict trends. It is important to note that in both timelines, the LUC and HEC durations are not perfectly aligned, but are intentionally staggered to allow for a short lag between landscape transitions and its observable effect on elephant movement and conflict. This accounts for the ecological and behavioural latency in elephants responding to changes in their habitat [29]. To evaluate the potential delayed effects of LUC on HEC, LUCs from 2010 to 2015 were paired with HEC occurrences from 2018 to 2023. This delayed timeline was specifically applied to transitions involving shifts towards natural land-use types, such as forest and water bodies, where ecological recovery processes are expected to require more time before influencing elephant movement and conflict patterns [30]. The use of a longer temporal gap accounts for potential delayed ecological responses, allowing the analysis to capture delayed impacts that may not be immediately observable following the natural habitat alteration. This approach complements the concurrent timelines and enables a comparative assessment of both immediate and time-lagged relationships between land-use transitions and HEC. d) Statistical Modelling We used a GLMM with a negative binomial family in R programming [31] to analyse the spatial distribution of HEC in Johor. Following the GLMM script provided by Skewes, the model was modified to suit the HEC dataset's structure and the study area's specific spatial configuration. The negative binomial family was chosen to account for overdispersion in the conflict occurrence data, which is common in ecological datasets with count-based responses. This approach was also selected to address the overdispersion problem [32, 33, 34]. The GLMM approach allows for incorporating both fixed and random effects, improving model reliability in identifying the key factors influencing HEC patterns. In this model, the dependent variable was the HEC density value, while the independent variables consisted of binary LUC layers. The years of HEC occurrences was included as a random effect to account for potential spatial clustering and unobserved heterogeneity across administrative units. 4.5 Estimating the Magnitude of Impact To calculate the estimated magnitude of impact of LUC on HEC intensity, the rate of change was calculated to quantify the variation in the output estimate from GLMM between two time periods relative to the corresponding spatial extent of land-use transitions. This rate was then used to estimate the predicted impact of specific land transitions on conflict intensity. Two key calculations were employed in this analysis: a) Rate of Change (ROC) The rate of change quantifies how the estimated effect of a land-use transition on HEC varies over time, relative to the total area of land that underwent that transition, and was computed using equation (1). The resulting values are presented in Table 2 as ‘Rate of Change (KDE per km²).’ (1) Rate of change (KDE units per )= (KDET₂ − KDET₁) / (Landuse Area T₂ − Landuse Area T₁) where: · Kernel density estimates for timeline 1 (KDET 1 ) and timeline 2 (KDET 2 ) indicate the GLMM coefficients (in KDE units) for a particular land-use transition during two different periods, which are 2010–2015 and 2015–2021 · Landuse area T 1 (Timeline 1) and T 2 (Timeline 2) refer to the total area (km²) that experienced the respective land-use transition in each period. The equation above was applied to quantify the rate of change for each land-use transition, expressed in KDE units per km². This computation used the GLMM-derived coefficients (KDET₁ and KDET₂) for the two time periods (2010–2015 and 2015–2021) together with the corresponding total transition areas. The resulting values, calculated using this formula, are presented in Table 2 under the column “Rate of Change (KDE per km²).” KDET₂ and KDET₁ correspond to “Estimate T₂” and “Estimate T₁” in Table 1, respectively, while Landuse Area T₂ and Landuse Area T₁ correspond to “Land use transition area (km²) 2015–2021” and “2010–2015” in Supplementary Table S1. Readers may reproduce the calculation by applying equation (1) to these values or to other comparable datasets. b) Predicted Impact The predicted impact refers to the estimated level of HEC intensity that could result from a particular land type transition. It is calculated using the equation (2). The resulting values are presented in Table 2 as ‘Predicted Impact (KDE impact )’. (2) Predicted Impact (KDE impact ) = Rate of Change (KDE units per x Land Use Transition Area ( ) This calculation allows us to estimate how much HEC intensity may increase or decrease based on specific land-use transitions and their spatial extent. It also helps compare which types of LUCs have the highest impact on conflict levels. In this study, the predicted impact (in KDE units) was calculated for selected key land-use transitions identified as statistically significant in previous analyses. However, the same formula can be applied more broadly to estimate the predicted impact of any other land-use transition, making it a flexible tool for scenario modelling and land-use planning. These predicted values were then used to identify the transitions that most strongly contributed to raising or lowering HEC risk. c) Calibrating Predicted Impact to HEC Case Counts Following the calculation of predicted impact values for land-use transitions in KDE units, a calibration procedure was conducted to translate these intensity-based values into estimated HEC case counts. This calibration step was essential to translate the predicted impact values, which represent relative conflict intensity as event frequencies, thereby enabling the results to be interpreted practically, such as the estimated number of conflict cases under specific LUC scenarios. A simple linear regression model was employed to relate the predicted KDE impact values (independent variable) and the observed HEC case counts (dependent variable). Given the skewed distribution of the case data, a natural logarithmic transformation was applied to the observed HEC case counts to improve model fit and reduce variance. The regression analysis, conducted in R using a custom script, was performed to derive the coefficients of the linear equation; namely, the slope ( m ) and intercept ( c ), which were subsequently used in the calibration formula. The custom R script is openly available at Script. The derived model follows the basic linear regression form: Log (HEC Cases observed +1) = ( m x KDE impact )+ c The coefficients were analytically expressed as: m = [Log(HEC Cases observed +1) – c ] / KDE impact c = Log(HEC Cases observed +1) – ( m x KDE impact ) Where HEC Cases observed denotes the number of recorded conflict cases for each LUC, and m and c are the regression-derived slope and intercept, respectively. To estimate the predicted number of HEC cases, the log transformation was reversed using the equation (3): (3) HEC Cases predicted = exp( m x KDE impact + c ) – 1 This calibration enabled the KDE-based conflict intensity values to be interpreted as quantitative estimates of HEC case counts, facilitating comparisons across different land-use transition scenarios. For this study, the resulting equation was applied to the predicted impact values of land-use transitions from forest to plantation, and from plantation to forest. Declarations Acknowledgement We thank the Department of Wildlife and National Parks (DWNP), Peninsular Malaysia, for the support, approval, and the opportunity to conduct this study. The research was carried out under an official collaboration and permission with reference number JPHLTN.100-2/1/2 Jld 3(76) & JPHLTN.600-6/1/4 JLD2 (119) Author contributions: CRediT Anis Maisarah Fakhrulanuar: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. Kamaruddin Zainul Abidin: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. Mohammad Saiful Mansor: Validation, Writing – review & editing. Farah Shafawati Mohd-Taib: Validation, Writing – review & editing. Muhammad Fadlli Ab Yazi: Conceptualization, Validation, Writing – review & editing. Shukor Md-Nor: Conceptualization, Validation, Writing – review & editing. Data Availability The data supporting the findings of this study including human–elephant conflict (HEC) occurrence records are not publicly available due to confidentiality agreements and permit set by the Department of Wildlife and National Parks (DWNP) on the exact coordinate of the HEC. These datasets contain sensitive information related to the locations of endangered species and conservation enforcement activities. Ethics Declaration This study did not involve any direct handling or experimentation on animals or humans. All elephant movement data were obtained retrospectively from GPS collar datasets provided by the Department of Wildlife and National Parks, Peninsular Malaysia (PERHILITAN), under an official collaboration and permission with reference number JPHLTN.100-2/1/2 Jld 3(76) & JPHLTN.600-6/1/4 JLD2 (119). 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Diversity 14 , 320; https://doi.org/10.3390/d14050320 (2022). Additional Declarations No competing interests reported. Supplementary Files RevisedSupplementaryInformationScientificreports.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:56:34","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125039,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/1837d5f71396e6d2f117e6be.png"},{"id":92406074,"identity":"c0862781-5f2b-4fe2-9fd0-902c0de62656","added_by":"auto","created_at":"2025-09-29 11:12:34","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129263,"visible":true,"origin":"","legend":"","description":"","filename":"52002a5e23274dbb8f116a52be352a5a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/fb25adbc5193ecb50fb6c553.xml"},{"id":92404500,"identity":"07d41b15-b346-4879-80b8-a1e3bf5830a7","added_by":"auto","created_at":"2025-09-29 10:56:34","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142423,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/f577283fd7bf5b031a4f3ff9.html"},{"id":92404476,"identity":"3cb96144-ca8b-455e-a167-b29acd899530","added_by":"auto","created_at":"2025-09-29 10:56:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1094278,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and spatial distribution of human–elephant conflict (HEC) in Johor, Southern Peninsular Malaysia. The map highlights key districts (Kluang, Kota Tinggi, and Mersing) and shows HEC kernel density (5 km bandwidth), forest cover and water bodies. HEC density is classified into three levels (low, moderate, high) based on kernel estimates derived from conflict incident locations. Maps were generated in ArcGIS Vers. 10.8 (https://www.esri.com).\u003c/p\u003e","description":"","filename":"Fig.1.Scientificreports.png","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/b40bf9d76c04028f4d62b088.png"},{"id":92404479,"identity":"9adfee3c-1c20-4e1e-a367-4c355fe34e68","added_by":"auto","created_at":"2025-09-29 10:56:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1655754,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for assessing land-use change impacts on human–elephant conflict (HEC). Encompassing data compilation and preprocessing, kernel density estimation (KDE) analysis, land-use transitions, temporal alignment, GLMM modeling, and impact prediction.\u003c/p\u003e","description":"","filename":"Fig.2.Scientificreports.png","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/5c3ad95d13a9ebb75f8944cb.png"},{"id":92404902,"identity":"2af36beb-1690-4cd8-8c2e-e2d9836bb2bd","added_by":"auto","created_at":"2025-09-29 11:04:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3469545,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of human–elephant conflict (HEC) kernel density and major land-use transitions in Southern Peninsular Malaysia, from 2010 to 2015, with HEC density based on occurrence data from 2018 to 2023. Panel (a) shows HEC intensity overlaid with forest loss to anthropogenic land uses (plantation, settlement, idle land); panel (b) illustrates HEC intensity in areas of forest loss to plantation; panel (c) presents HEC intensity in areas undergoing plantation to forest recovery. Maps were generated in ArcGIS Vers. 10.8 (https://www.esri.com).\u003c/p\u003e","description":"","filename":"Fig.3.Scientificreports.png","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/917abeb99822ccfb15ddc47a.png"},{"id":93203427,"identity":"7818ccd8-0ead-4dfe-9e9a-3583525f06e2","added_by":"auto","created_at":"2025-10-10 07:24:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6908784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/7238e5e9-d1e3-4403-8f64-e6f4dd815f06.pdf"},{"id":92404477,"identity":"e3d23c16-f1cc-431f-b7e5-81fff0318962","added_by":"auto","created_at":"2025-09-29 10:56:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":328497,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedSupplementaryInformationScientificreports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7312791/v1/a15d0eab8d0b561383f93899.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying the Impacts of Land Use Transitions on Human-Elephant Conflict in Peninsular Malaysia: Implication for Sustainable Landscape Planning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAsian elephants (\u003cem\u003eElephas maximus\u003c/em\u003e), the largest terrestrial mammals in Asia, play a crucial role as ecosystem engineers, shaping landscapes and maintaining biodiversity through their movements and feeding behaviours [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, their survival is increasingly threatened by land-use changes that alter their habitats, forcing them into conflict with human populations. Human\u0026ndash;elephant conflict (HEC), which includes crop raiding, infrastructure damage and human fatalities, has emerged as a major conservation and socio-economic challenge in regions where human settlements and elephant ranges overlap [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the most significant drivers of HEC is habitat loss and fragmentation, largely caused by agricultural expansion, urbanisation and infrastructure development. Studies in China and Sumatra have shown that as forests shrink and become fragmented, elephants are pushed into smaller, more isolated patches, increasing their likelihood of encountering human-dominated landscapes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, in Myanmar, rapid human encroachment has led to increased poaching and HEC incidents, causing severe elephant-population declines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Peninsular Malaysia, the state of Johor has experienced significant land-use changes, particularly from the late 20th century onwards, a period marked by the rapid conversion of forest into plantations, urban areas and idle lands [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These transitions have largely been driven by economic development initiatives, industrial expansion and infrastructure projects.\u003c/p\u003e\u003cp\u003eBetween 1984 and 2015, the Johor River Basin (JRB) experienced rapid growth in oil palm plantations, with estate areas expanding by 47.98%. This was accompanied by substantial urban expansion, particularly after the launch of the Iskandar Malaysia special economic region in 2006, which accelerated development in southern Johor and downstream areas of the basin [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Further highlighting these trends, it is reported that between 2000 and 2015, forested areas in the JRB declined to 32.15%, while oil palm plantations increased to 11.88% of the total land area [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Agricultural lands other than oil palm also expanded by 11.07%, and urban areas grew by 9.82%, reflecting significant land use and land cover changes with important implications for the region\u0026rsquo;s hydrology and ecosystem services.\u003c/p\u003e\u003cp\u003eThis transition has fragmented elephant habitats, forcing the elephants to navigate human-dominated landscapes in search of food and water, and thereby intensifying HEC incidents [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The Endangered status of the Asian elephant [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] highlights the importance of understanding how land-use changes affect conflict occurrence and habitat use, which is critical for developing effective conservation and mitigation initiatives.\u003c/p\u003e\u003cp\u003eTo address these challenges, this study examines spatial-temporal patterns of HEC in Johor, focusing on how land-use transitions influence the occurrence and intensity of conflicts. Using spatial modelling techniques, the study analyses HEC hotspots, identifying key land transitions contributing to conflict risk. Investigating the impact of land-use changes on HEC is crucial as it helps identify how habitat alteration contributes to increased interactions and conflicts between humans and elephants. Considering elephants as a keystone species, their conservation contributes to the protection of broader ecosystems and biodiversity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Integrating their ecological needs into sustainable land use planning promotes the development of more balanced landscapes that foster long-term coexistence of elephants, other wildlife, and local communities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 GLMM Estimates of Land-Use Transitions Influencing HEC Density\u003c/h2\u003e\u003cp\u003eThe total area of land-use change (LUC) differed between the two transition periods (2010\u0026ndash;2015 and 2015\u0026ndash;2021), and these differences influenced the strength and direction of the relationship between land-use transitions and HEC density. A summary of the total area for each land-use transition during both periods is provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The GLMM results for land-use transitions from 2010\u0026ndash;2015 in relation to HEC density from 2013\u0026ndash;2017 revealed that not all 20 transitions were included in the final model. Some transitions were removed due to multicollinearity, identified using variance inflation factor (VIF) analysis, while others were excluded for being zero or near-zero variance predictors.\u003c/p\u003e\u003cp\u003eAmong the remaining transitions, several were found to be statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in influencing HEC density (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, transitions from settlement to plantation (S\u0026thinsp;\u0026gt;\u0026thinsp;P), idle land to settlement (I\u0026thinsp;\u0026gt;\u0026thinsp;S), idle land to plantation (I\u0026thinsp;\u0026gt;\u0026thinsp;P), plantation to idle land (P\u0026thinsp;\u0026gt;\u0026thinsp;I), forest to plantation (F\u0026thinsp;\u0026gt;\u0026thinsp;P) and forest to idle land (F\u0026thinsp;\u0026gt;\u0026thinsp;I) were significant predictors of HEC. These significant transitions suggest that changes involving plantations, settlements and forest loss play a key causal role in HEC. Conversely, transitions such as water body to forest (W\u0026thinsp;\u0026gt;\u0026thinsp;F), water body to idle land (W\u0026thinsp;\u0026gt;\u0026thinsp;I), plantation to settlement (P\u0026thinsp;\u0026gt;\u0026thinsp;S), forest to water body (F\u0026thinsp;\u0026gt;\u0026thinsp;W) and forest to settlement (F\u0026thinsp;\u0026gt;\u0026thinsp;S) were not statistically significant in influencing HEC density, (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01). These transitions were excluded from the next stage of analysis, particularly when calculating the rate of change.\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\u003eGeneralised linear mixed model (GLMM) results for Timeframe 1 (T1), covering land-use change (LUC) from 2010\u0026ndash;2015 and human\u0026ndash;elephant conflict (HEC) from 2013\u0026ndash;2017, Timeframe 2 (T2), encompassing 2015\u0026ndash;2021 LUC and 2018\u0026ndash;2023 HEC, and Timeframe 3 (T3), for 2010\u0026ndash;2015 LUC and 2018\u0026ndash;2023 HEC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLUC Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eStd. 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char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.243**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.727 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.631***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.564***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.385***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.415*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.8 ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.974***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.844***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.587***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.807***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.423*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.326***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.142 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.234*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.314 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.333***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.091***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.227***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.807*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.172.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.473***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.435.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSignificance levels are indicated by asterisks, ranging from high statistical significance \u0026lsquo;***\u0026rsquo; (p \u0026lt; 0.001), \u0026lsquo;**\u0026rsquo; (p \u0026lt; 0.01), \u0026lsquo;*\u0026rsquo; (p \u0026lt; 0.05), \u0026lsquo;.\u0026rsquo; (p \u0026lt; 0.1), with no symbol indicating non-significance (p \u0026ge; 0.1).\u003c/p\u003e\n\u003cp\u003eLand use transitions are represented using abbreviations, where \u003cem\u003eP\u003c/em\u003e = Plantation, \u003cem\u003eF\u003c/em\u003e = Forest, \u003cem\u003eI\u003c/em\u003e = Idle Land, \u003cem\u003eS\u003c/em\u003e = Settlement, and \u003cem\u003eW\u003c/em\u003e = Water Body. The symbol \u0026ldquo;\u0026gt;\u0026rdquo; denotes directional change; for example, \u003cem\u003eP \u0026gt; F\u003c/em\u003e indicates a transition from plantation to forest.\u003c/p\u003e\u003cp\u003eA second GLMM analysis was performed for land-use transitions from 2015 to 2021 in relation to HEC density in 2018\u0026ndash;2023 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As with the previous model, not all 20 land changes were retained due to multicollinearity and variance checks. The results indicate that several transitions remained significant, reinforcing the patterns observed in the earlier period and suggesting that land transitions involving plantations, settlements, and idle land are persistent drivers of HEC density. Several transitions that were significant in 2010\u0026ndash;2015 retained their influence in 2015\u0026ndash;2021, such as idle land to plantation (I\u0026thinsp;\u0026gt;\u0026thinsp;P), settlement to plantation (S\u0026thinsp;\u0026gt;\u0026thinsp;P), and plantation to idle land (P\u0026thinsp;\u0026gt;\u0026thinsp;I), maintaining their role in shaping conflict patterns. Conversely, transitions such as water body to forest (W\u0026thinsp;\u0026gt;\u0026thinsp;F), and plantation to settlement (P\u0026thinsp;\u0026gt;\u0026thinsp;S) were not statistically significant, (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01), and were also excluded from the next stage of analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Magnitude of Human-Elephant Conflict Impact\u003c/h2\u003e\u003cp\u003eThe rate of change analysis for land-use transitions was conducted for both concurrent effects (5-year gap) and delayed effects (10-year gap) to assess their impact on HEC. The concurrent effect analysis focused on immediate land-use transitions within a 5-year period, while the delayed effect was applied to transitions leading to natural land types (forest and water body), where the impact on HEC is expected to manifest over a longer time scale. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the predicted impact and directional response of HEC intensity associated with key land use transitions over concurrent and delayed periods\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\u003ePredicted impact and directional response on HEC for each key land use transitions over concurrent and delayed periods.\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRate of change (KDE per\u0026nbsp;km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePredicted Impact (KDE\u003csub\u003eimpact\u003c/sub\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResponse on HEC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eConcurrent effect\u003c/p\u003e\u003cp\u003e(5 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eW\u0026thinsp;\u0026gt;\u0026thinsp;P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eW\u0026thinsp;\u0026gt;\u0026thinsp;S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u0026thinsp;\u0026gt;\u0026thinsp;I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDelayed effect (10 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.0007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecrease\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\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003eOnly land-use changes (LUCs) with statistically significant estimates (p \u0026lt; 0.01) from the generalised linear mixed model (GLMM) were included.\u003c/p\u003e\u003cp\u003eFor the concurrent effect (5-year timeline), several transitions were associated with an increased impact on HEC intensity. These include settlement to idle land (0.157 per km\u0026sup2;), settlement to plantation (0.290 per km\u0026sup2;), forest to plantation (0.672 per km\u0026sup2;), idle land to settlement (0.180 per km\u0026sup2;), and idle land to plantation (0.164 per km\u0026sup2;). These findings indicate that urban expansion and the conversion of natural or idle land into more intensive land uses contribute to increased HEC occurrences. Conversely, some land-use transitions during the same period showed a decrease in HEC intensity, one of them being plantation to idle land (-0.197 per km\u0026sup2;), suggesting that reducing anthropogenic pressure or converting land back into less-developed states may help reduce conflict.\u003c/p\u003e\u003cp\u003eThe results from the delayed-effect timeline showed that transitions such as settlement to forest (\u0026minus;\u0026thinsp;0.419 per km\u0026sup2;), plantation to forest (\u0026minus;\u0026thinsp;0.196 per km\u0026sup2;), idle land to forest (\u0026minus;\u0026thinsp;0.415 per km\u0026sup2;), and plantation to water body (\u0026minus;\u0026thinsp;0.809 per km\u0026sup2;) contributed to a reduction in HEC intensity over time. These outcomes highlight the importance of allowing longer timelines for habitat restoration strategies to take visible ecological effect.\u003c/p\u003e\u003cp\u003eTo further illustrate how the rate of change translates into predicted HEC intensity, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates how the rate of land-use change translates into predicted HEC intensity, showing that the increase in conflict associated with forest to plantation transition is consistently greater than the reduction observed from the reverse transition, even when the area of land involved is the same. This suggests that converting forest into plantation has a disproportionately higher impact on increasing conflict compared to the relatively modest decline in conflict resulting from reforestation of plantations. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further illustrates the spatial distribution of HEC kernel density overlaid with these selected land-use transition areas.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMagnitude of predicted HEC impact of selected land-use transitions and translated impact into predicted counts under varying land conversion area scenarios.\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eArea of changes (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eForest to Plantation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePlantation to Forest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePredicted HEC impact (KDE\u003c/b\u003e\u003csub\u003e\u003cb\u003eimpact\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ePredicted HEC counts\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePredicted HEC impact (KDE\u003c/b\u003e\u003csub\u003e\u003cb\u003eimpact\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePredicted HEC counts\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study presents one of the few quantitative attempts to estimate the magnitude of impact that specific land-use transitions have on human\u0026ndash;elephant conflict (HEC), offering a significant contribution to understanding how anthropogenic landscape change shapes conflict intensity over space and time. The results help us to examine the spatial and temporal relationships between land-use changes (LUC) and HEC, using both concurrent and delayed timeline approaches. Comparable findings were reported [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with the usage of GIS-MCDA to map HEC hotspots and elephant habitat zones in Sri Lanka, showing that spatial models are useful for predicting and mitigating conflict. The results provide clear evidence that land transitions, particularly those involving plantations, settlements and idle land are key contributors to conflict intensity, while transitions towards forested areas and water bodies tend to support long-term conflict mitigation.\u003c/p\u003e\u003cp\u003eThe analysis showed that land transitions into anthropogenic uses, especially from forest to plantations and settlements, were consistently associated with high HEC intensity. These transitions likely increase conflict risk by reducing natural foraging and movement/homerange areas, thereby pushing elephants into human-dominated spaces in search of food and water. These patterns align with previous research demonstrating how habitat fragmentation increases the likelihood of human\u0026ndash;elephant interactions and crop-raiding events [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One of the significant transitions, including settlement to plantation (S\u0026thinsp;\u0026gt;\u0026thinsp;P), idle land to plantation (I\u0026thinsp;\u0026gt;\u0026thinsp;P), and forest to plantation (F\u0026thinsp;\u0026gt;\u0026thinsp;P) exhibited significant positive associations with HEC across both concurrent and delayed models, this suggests that the impact of these transitions doesn\u0026rsquo;t just persist over time, it can actually become stronger as the changes spread or last longer on the landscape. These findings support the importance of managing plantation expansion and settlement encroachment in elephant ranges [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, not all transitions showed immediate effects. Transitions involving forest regeneration and restoration, such as plantation to forest (P\u0026thinsp;\u0026gt;\u0026thinsp;F) and settlement to forest (S\u0026thinsp;\u0026gt;\u0026thinsp;F), did not immediately reduce conflict intensity despite showing a declining trend in HEC in the delayed-effect model. This lag is likely due to the time required for ecological recovery, vegetation growth and elephant re-familiarisation with restored habitats. These results suggest longer restoration periods (10-year lags/more than 5 years) should be considered when evaluating the benefits of restoration. Further, our predicted HEC counts between forest to plantation and plantation to forest transitions highlight that the impacts of forest loss are more severe and immediate than the slower benefits gained from reforestation. In other words, a substantially larger area must be restored to forest to compensate for the conflict increase caused by forest clearing for plantations.\u003c/p\u003e\u003cp\u003eForest regeneration helps mitigate conflict by gradually restoring habitat structure, vegetation complexity, and food availability that support elephant movement over time [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, transitions toward water bodies are generally associated with reduced HEC intensity, particularly in the study area, where many water bodies are located near or within natural habitats [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These areas provide essential resources such as hydration during dry periods and often function as natural buffers that limit elephant movement into human-modified landscapes. However, the results also show that transitions toward water bodies do not always reduce conflict. As marginal habitats neither fully natural nor completely anthropogenic, water bodies can produce mixed outcomes, especially when they occur outside of forested zones or in fragmented landscapes. Their ecological function may shift depending on seasonal resource availability, landscape configuration, and proximity to elephant movement corridors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Poorly planned or uncoordinated land conversions, particularly those involving scattered or isolated water bodies, may inadvertently increase conflict by altering movement paths or drawing elephants toward human areas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These findings underscore the need for sustainable land use planners to carefully consider the spatial arrangement and ecological context of water bodies when designing landscapes in elephant ranges.\u003c/p\u003e\u003cp\u003eOver the short term (concurrent 5-year period), the transition from forest to plantation showed as the most influential predicted impact on human\u0026ndash;elephant conflict (HEC) intensity, due to both ecological and land-use factors. This type of land conversion usually involves the clearing of forest for agricultural development, which reduces and fragments the natural habitat elephants rely on. With less available forest, elephants may be forced to move into areas closer to human activity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. There are also instances where elephants are drawn into plantations in search of young vegetation or water, which increases the risk of encounters with humans and damage to crops or infrastructure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. They are particularly attracted to shredded palm hearts, a preferred food source made easily accessible during new plantation rotation cycles that involve land clearing, removal of bole and roots, and shredding of felled palms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This not only disrupts their natural movement but also creates more opportunities for conflict [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The forest to plantation transition, therefore, did not just represent a shift in land cover but also contributes directly to rising HEC intensity by reshaping how elephants move through and interact with the landscape.\u003c/p\u003e\u003cp\u003eThe most minimal impact which is the idle land to water body transition likely reflects the ecological and spatial characteristics of these areas. Idle land that transitions into water bodies is typically located in less accessible or lower elevation zones, which are not commonly used by elephants for movement or foraging [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Such areas are often farther from human activity and settlements, reducing the chances of direct encounters between elephants and people. Although water is essential for elephants, these newly formed or isolated water bodies may not provide sufficient vegetative resources or habitat connectivity, making them less attractive as movement corridors or foraging sites [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Overall, the result for this transition suggests that the spatial context of idle land whether it transitions into resource-rich or resource-poor areas can influence its impact on human\u0026ndash;elephant conflict.\u003c/p\u003e\u003cp\u003eIt is important to note that the estimated magnitude of HEC impact in this study is based on conditions observed in the southern region of Peninsular Malaysia, which serves as a representative case for understanding broader conflict dynamics. While the data includes areas such as Johor, where HEC cases have been notably high, with an average of 150\u0026ndash;200 incidents annually between 2015 and 2021 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the analytical approach can be adapted to other regions experiencing similar patterns of land-use change and human\u0026ndash;elephant conflict. This framework supports a more generalizable understanding of how land transitions can influence conflict intensity and offers a valuable reference point for stakeholders, land-use planners, and wildlife authorities. By anticipating potential conflict escalation for this umbrella species, the study contributes to the development of more proactive, spatially targeted, and evidence-based land management and biodiversity conservation strategies at a broader scale.\u003c/p\u003e\u003cp\u003eThese findings also offer practical insights for improving land use and HEC management. From a forestry perspective, promoting forest regeneration and restoring secondary forests can help reduce conflict over the long term, especially in areas where forest has been converted to plantations. Establishing forest buffers and limiting further deforestation are also important steps to prevent elephants from being pushed into human-dominated areas. For the wildlife department, the spatial patterns and conflict impact values identified here can help prioritize surveillance and guide the development of elephant corridors that avoid high-conflict transitions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In communities located near active land conversion zones, especially where settlements have shifted to idle land, targeted mitigation programs and community engagement will be important. At the planning level, integrating these findings into spatial policy can help prevent high-risk transitions while encouraging low-impact land uses. Plantation operators also have a role to play by adopting wildlife-friendly practices, restoring marginal lands, and contributing to monitoring efforts. Together, these approaches can support more balanced, sustainable and coexistence-oriented land management strategies.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cp\u003e4.1 Study Area\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Southern Peninsular Malaysia, Johor, including the districts of Mersing, Kota Tinggi and Kluang, which are known for their frequent incidents of human\u0026ndash;elephant conflict (HEC) (Fig. 1). This region shares a border with Singapore to the south and is predominantly covered by rainforest situated above the equatorial zone. The rainforest is home to various wildlife species that are classified as endangered or vulnerable according to the IUCN Red List, including the Asian elephant (\u003cem\u003eElephas maximus\u003c/em\u003e), Malayan sun bear (\u003cem\u003eHelarctos malayanus\u003c/em\u003e) and Malayan tiger (\u003cem\u003ePanthera tigris jacksoni\u003c/em\u003e). Johor (latitude 1.9344\u0026deg;N and longitude 103.3587\u0026deg;E) occupies an area of approximately 19,166 km\u003csup\u003e2\u003c/sup\u003e and experiences a humid, rainy, equatorial climate with relatively constant temperatures throughout the year, receiving average annual rainfall of 2,600 mm [28]. In the study region, land-use transitions involving plantations, idle land, settlements, forests and water bodies have significantly altered the landscape over recent decades, leading to habitat fragmentation and the expansion of oil palm plantations [7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.2 Methodological Framework\u003c/p\u003e\n\u003cp\u003eMethodological framework for assessing the impact of land-use change on human\u0026ndash;elephant conflict (HEC) is summarised in Fig. 2. The framework shows the overall process, starting from data collection and preprocessing, followed by kernel density estimation (KDE) of HEC and land-use transition analysis. It includes temporal alignment to assess short- and long-term effects, statistical modeling using GLMM, and estimation of the predicted impact based on land conversion and HEC intensity.\u003c/p\u003e\n\u003cp\u003e4.3 Human\u0026ndash;Elephant Conflict Occurrence Data\u003c/p\u003e\n\u003cp\u003eWe compiled data on HEC incidents in Johor, from the Department of Wildlife and National Parks (DWNP), Peninsular Malaysia, focusing on the Asian elephant, a keystone species significantly impacted by land-use changes. The data spanned from 2013 to 2023, capturing a decade of conflict occurrences and trends. HEC data collection begins when the DWNP receives reports from the public regarding elephant-related conflicts, followed by site surveys and ground truthing. During site investigations, evidence such as the exact location of the conflict (XY coordinates), affected land-use type and complainant details are recorded. Each conflict point is documented with information describing the incident and its status.\u003c/p\u003e\n\u003cp\u003eThe HEC dataset was then filtered to remove redundant cases, incomplete records with ambiguous details, and points located outside the study area. This approach was based on the assumption that the recorded HEC occurrences were representative and minimally biased by sampling effort, ensuring a comprehensive dataset for spatial analysis. Out of the 3,991 recorded HEC occurrences, a total of 2,941 data points were retained for analysis after filtering, providing a substantial dataset for evaluating spatial patterns and trends.\u003c/p\u003e\n\u003cp\u003e4.4 Data Preprocessing for Statistical Modelling\u003c/p\u003e\n\u003cp\u003ea)\u0026nbsp; \u0026nbsp;Spatial HEC Intensity\u003c/p\u003e\n\u003cp\u003eWe used ArcGIS to preprocess the conflict occurrence data. Kernel density estimation (KDE) was applied to generate a 250 m \u0026times; 250 m resolution raster representing the density of HEC incidents. A 5-km search radius was set to smooth the HEC density estimates while capturing localised clustering of conflicts. This spatial dataset was used as the response variable in the generalised linear mixed model (GLMM).\u003c/p\u003e\n\u003cp\u003eb)\u0026nbsp; \u0026nbsp;Land-Use Transition\u003c/p\u003e\n\u003cp\u003eTo assess the influence of land-use changes on HEC, land-cover maps for the years 2010, 2015 and 2021 were obtained from the Malaysian Government\u0026rsquo;s Survey and Mapping Department (Department of Survey and Mapping Malaysia, JUPEM). We prepared land-use transition datasets covering five major land-use types: settlement (S), idle land (I), water body (W), plantation (P) and forest (F). These land-use transitions were analysed over two temporal periods \u0026ndash; 2010\u0026ndash;2015 and 2015\u0026ndash;2021 \u0026ndash; resulting in a total of 20 possible land-use transitions per period. The spatial patterns of these 20 transition types are illustrated in Supplementary Fig. S1 and S2. The analysis was conducted using TerrSet version 2020, specifically employing the software\u0026rsquo;s \u0026lsquo;Land Change Modeler\u0026rsquo; (LCM), which generated transition values for each land-use category change.\u003c/p\u003e\n\u003cp\u003eThe land-use maps for each time period were processed in ArcGIS, where raster datasets were standardised to a 250 m \u0026times; 250 m resolution to match the kernel density raster of HEC occurrences. These transitions and HEC density values were then extracted and integrated into the statistical modelling to assess their effects on HEC occurrence patterns. The output data were represented in a binary format, indicating the presence or absence of a specific land-use transition within each 250-m cell (HEC occurrences), coded as 0 (absent) or 1 (present).\u003c/p\u003e\n\u003cp\u003eBy analysing land-use changes over different time periods, this approach was able to identify major landscape shifts and the amount of time they take to increase HEC risks. The transition potential values served as independent variables in the GLMM, allowing us to quantify the statistical relationship between land-use dynamics and conflict hotspots.\u003c/p\u003e\n\u003cp\u003ec)\u0026nbsp; \u0026nbsp;Temporal Alignment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcurrent and delayed effects were analysed to capture these dynamics between land-use change (LUC) and HEC. To do this, we established three different timelines linking LUCs with HEC occurrences over different periods.\u003c/p\u003e\n\u003cp\u003eThe earlier concurrent timeline examined the immediate relationship between LUCs from 2010 to 2015 and HEC occurrences from 2013 to 2017, assuming that recent land-use modifications directly influence elephant movement and conflict patterns within a short timeframe. Meanwhile, the later concurrent timeline evaluated LUCs from 2015 to 2021 against HEC occurrences from 2018 to 2023, reflecting how more recent land-use transitions impacted elephant behaviour, habitat fragmentation and conflict trends. It is important to note that in both timelines, the LUC and HEC durations are not perfectly aligned, but are intentionally staggered to allow for a short lag between landscape transitions and its observable effect on elephant movement and conflict. This accounts for the ecological and behavioural latency in elephants responding to changes in their habitat [29].\u003c/p\u003e\n\u003cp\u003eTo evaluate the potential delayed effects of LUC on HEC, LUCs from 2010 to 2015 were paired with HEC occurrences from 2018 to 2023. This delayed timeline was specifically applied to transitions involving shifts towards natural land-use types, such as forest and water bodies, where ecological recovery processes are expected to require more time before influencing elephant movement and conflict patterns [30]. The use of a longer temporal gap accounts for potential delayed ecological responses, allowing the analysis to capture delayed impacts that may not be immediately observable following the natural habitat alteration. This approach complements the concurrent timelines and enables a comparative assessment of both immediate and time-lagged relationships between land-use transitions and HEC.\u003c/p\u003e\n\u003cp\u003ed)\u0026nbsp; \u0026nbsp;Statistical Modelling\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used a GLMM with a negative binomial family in R programming [31] to analyse the spatial distribution of HEC in Johor. Following the GLMM script provided by Skewes, the model was modified to suit the HEC dataset\u0026apos;s structure and the study area\u0026apos;s specific spatial configuration. The negative binomial family was chosen to account for overdispersion in the conflict occurrence data, which is common in ecological datasets with count-based responses. This approach was also selected to address the overdispersion problem [32, 33, 34]. The GLMM approach allows for incorporating both fixed and random effects, improving model reliability in identifying the key factors influencing HEC patterns. In this model, the dependent variable was the HEC density value, while the independent variables consisted of binary LUC layers. The years of HEC occurrences was included as a random effect to account for potential spatial clustering and unobserved heterogeneity across administrative units.\u003c/p\u003e\n\u003cp\u003e4.5 Estimating the Magnitude of Impact\u003c/p\u003e\n\u003cp\u003eTo calculate the estimated magnitude of impact of LUC on HEC intensity, the rate of change was calculated to quantify the variation in the output estimate from GLMM between two time periods relative to the corresponding spatial extent of land-use transitions. This rate was then used to estimate the predicted impact of specific land transitions on conflict intensity.\u003c/p\u003e\n\u003cp\u003eTwo key calculations were employed in this analysis:\u003c/p\u003e\n\u003cp\u003ea)\u0026nbsp; \u0026nbsp;Rate of Change (ROC)\u003c/p\u003e\n\u003cp\u003eThe rate of change quantifies how the estimated effect of a land-use transition on HEC varies over time, relative to the total area of land that underwent that transition, and was computed using equation (1). The resulting values are presented in Table 2 as \u0026lsquo;Rate of Change (KDE per km\u0026sup2;).\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e(1)\u0026nbsp; Rate of change (KDE units per\u0026nbsp;\u0026nbsp;)= (KDET₂ \u0026minus; KDET₁) / (Landuse Area T₂ \u0026minus; Landuse Area T₁)\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Kernel density estimates for timeline 1 (KDET\u003csub\u003e1\u003c/sub\u003e) and timeline 2 (KDET\u003csub\u003e2\u003c/sub\u003e) indicate the GLMM coefficients (in KDE units) for a particular land-use transition during two different periods, which are 2010\u0026ndash;2015 and 2015\u0026ndash;2021\u003c/p\u003e\n\u003cp\u003e\u0026middot; Landuse area T\u003csub\u003e1\u003c/sub\u003e (Timeline 1) and T\u003csub\u003e2\u003c/sub\u003e (Timeline 2) refer to the total area (km\u0026sup2;) that experienced the respective land-use transition in each period.\u003c/p\u003e\n\u003cp\u003eThe equation above was applied to quantify the rate of change for each land-use transition, expressed in KDE units per km\u0026sup2;. This computation used the GLMM-derived coefficients (KDET₁ and KDET₂) for the two time periods (2010\u0026ndash;2015 and 2015\u0026ndash;2021) together with the corresponding total transition areas. The resulting values, calculated using this formula, are presented in Table 2 under the column \u0026ldquo;Rate of Change (KDE per km\u0026sup2;).\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eKDET₂ and KDET₁ correspond to \u0026ldquo;Estimate T₂\u0026rdquo; and \u0026ldquo;Estimate T₁\u0026rdquo; in Table 1, respectively, while Landuse Area T₂ and Landuse Area T₁ correspond to \u0026ldquo;Land use transition area (km\u0026sup2;) 2015\u0026ndash;2021\u0026rdquo; and \u0026ldquo;2010\u0026ndash;2015\u0026rdquo; in Supplementary Table S1. Readers may reproduce the calculation by applying equation (1) to these values or to other comparable datasets.\u003c/p\u003e\n\u003cp\u003eb)\u0026nbsp; \u0026nbsp;Predicted Impact\u003c/p\u003e\n\u003cp\u003eThe predicted impact refers to the estimated level of HEC intensity that could result from a particular land type transition. It is calculated using the equation (2). The resulting values are presented in Table 2 as \u0026lsquo;Predicted Impact (KDE\u003csub\u003eimpact\u003c/sub\u003e)\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003e(2)\u0026nbsp;\u0026nbsp;Predicted Impact (KDE\u003csub\u003eimpact\u003c/sub\u003e) = Rate of Change (KDE units per\u0026nbsp;\u0026nbsp; x Land Use Transition Area (\u0026nbsp;)\u003c/p\u003e\n\u003cp\u003eThis calculation allows us to estimate how much HEC intensity may increase or decrease based on specific land-use transitions and their spatial extent. It also helps compare which types of LUCs have the highest impact on conflict levels. In this study, the predicted impact (in KDE units) was calculated for selected key land-use transitions identified as statistically significant in previous analyses. However, the same formula can be applied more broadly to estimate the predicted impact of any other land-use transition, making it a flexible tool for scenario modelling and land-use planning. These predicted values were then used to identify the transitions that most strongly contributed to raising or lowering HEC risk.\u003c/p\u003e\n\u003cp\u003ec)\u0026nbsp; \u0026nbsp;Calibrating Predicted Impact to HEC Case Counts\u003c/p\u003e\n\u003cp\u003eFollowing the calculation of predicted impact values for land-use transitions in KDE units, a calibration procedure was conducted to translate these intensity-based values into estimated HEC case counts. This calibration step was essential to translate the predicted impact values, which represent relative conflict intensity as event frequencies, thereby enabling the results to be interpreted practically, such as the estimated number of conflict cases under specific LUC scenarios.\u003c/p\u003e\n\u003cp\u003eA simple linear regression model was employed to relate the predicted KDE impact values (independent variable) and the observed HEC case counts (dependent variable). Given the skewed distribution of the case data, a natural logarithmic transformation was applied to the observed HEC case counts to improve model fit and reduce variance. The regression analysis, conducted in R using a custom script, was performed to derive the coefficients of the linear equation; namely, the slope (\u003cem\u003em\u003c/em\u003e) and intercept (\u003cem\u003ec\u003c/em\u003e), which were subsequently used in the calibration formula. The custom R script is openly available at Script.\u003c/p\u003e\n\u003cp\u003eThe derived model follows the basic linear regression form:\u003c/p\u003e\n\u003cp\u003eLog (HEC\u0026nbsp;Cases\u003csub\u003eobserved\u0026nbsp;\u003c/sub\u003e+1) = (\u003cem\u003em\u003c/em\u003e x KDE\u003csub\u003eimpact\u0026nbsp;\u003c/sub\u003e)+ \u003cem\u003ec\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe coefficients were analytically expressed as:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003em\u003c/em\u003e= [Log(HEC\u0026nbsp;Cases\u003csub\u003eobserved\u0026nbsp;\u003c/sub\u003e+1) \u0026ndash; \u003cem\u003ec\u003c/em\u003e] / KDE\u003csub\u003eimpact\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ec\u003c/em\u003e= Log(HEC\u0026nbsp;Cases\u003csub\u003eobserved\u0026nbsp;\u003c/sub\u003e+1) \u0026ndash; (\u003cem\u003em\u003c/em\u003e x KDE\u003csub\u003eimpact\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003eWhere HEC\u0026nbsp;Cases\u003csub\u003eobserved\u0026nbsp;\u003c/sub\u003edenotes the number of recorded conflict cases for each LUC, and m and c are the regression-derived slope and intercept, respectively. To estimate the predicted number of HEC cases, the log transformation was reversed using the equation (3):\u003c/p\u003e\n\u003cp\u003e(3)\u0026nbsp;\u0026nbsp;HEC\u0026nbsp;Cases\u003csub\u003epredicted\u0026nbsp;\u003c/sub\u003e= exp(\u003cem\u003em\u003c/em\u003e x KDE\u003csub\u003eimpact\u003c/sub\u003e + \u003cem\u003ec\u003c/em\u003e) \u0026ndash; 1\u003c/p\u003e\n\u003cp\u003eThis calibration enabled the KDE-based conflict intensity values to be interpreted as quantitative estimates of HEC case counts, facilitating comparisons across different land-use transition scenarios. For this study, the resulting equation was applied to the predicted impact values of land-use transitions from forest to plantation, and from plantation to forest.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Department of Wildlife and National Parks (DWNP), Peninsular Malaysia, for the support, approval, and the opportunity to conduct this study. The research was carried out under an official collaboration and permission with reference number JPHLTN.100-2/1/2 Jld 3(76) \u0026amp; JPHLTN.600-6/1/4 JLD2 (119)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: CRediT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnis Maisarah Fakhrulanuar:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing \u0026ndash; original draft. \u003cstrong\u003eKamaruddin Zainul Abidin:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMohammad Saiful Mansor:\u0026nbsp;\u003c/strong\u003eValidation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eFarah Shafawati Mohd-Taib:\u0026nbsp;\u003c/strong\u003eValidation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMuhammad Fadlli Ab Yazi:\u003c/strong\u003e Conceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eShukor Md-Nor:\u0026nbsp;\u003c/strong\u003eConceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study including human\u0026ndash;elephant conflict (HEC) occurrence records are not publicly available due to confidentiality agreements and permit set by the Department of Wildlife and National Parks (DWNP) on the exact coordinate of the HEC. These datasets contain sensitive information related to the locations of endangered species and conservation enforcement activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any direct handling or experimentation on animals or humans. All elephant movement data were obtained retrospectively from GPS collar datasets provided by the Department of Wildlife and National Parks, Peninsular Malaysia (PERHILITAN), under an official collaboration and permission with reference number JPHLTN.100-2/1/2 Jld 3(76) \u0026amp; JPHLTN.600-6/1/4 JLD2 (119). All original data collection procedures involving animal collaring were conducted by PERHILITAN in accordance with relevant institutional guidelines, regulations, and approved ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by \u003cb\u003eDepartment of Wildlife and National Parks, Peninsular Malaysia\u003c/b\u003e, under grant number \u003cb\u003e100-TNCPI/GOV 16/6/2 (043/2023)\u003c/b\u003e. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang, J., Yan, F., Su, F., Lyne, V. \u0026amp; Wang, X. Response of habitat quality to land use changes in the Johor River Estuary. \u003cem\u003eInt. J. Digit. 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An overview of modern applications of negative binomial modelling in ecology and biodiversity. \u003cem\u003eDiversity\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 320; https://doi.org/10.3390/d14050320 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human–elephant conflict (HEC), Land-use change (LUC), Wildlife-landscape interaction, Landscape planning, GLMM, Predictive spatial modelling","lastPublishedDoi":"10.21203/rs.3.rs-7312791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7312791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAsian elephants (Elephas maximus) are keystone species in Southeast Asia's tropical ecosystems yet are increasingly threatened by habitat loss due to land-use change. Rapid agricultural and urban expansion has caused major habitat fragmentation and increased human\u0026ndash;elephant conflict (HEC) in Southern Peninsular Malaysia. This study quantifies how specific land-use transitions influence the spatial intensity and temporal patterns of HEC. Using kernel density mapping in ArcGIS and transition modeling with TerrSet\u0026rsquo;s Land Change Modeler, we developed a conflict risk framework integrated into a generalized linear mixed model (GLMM) to assess relationships between land transitions and conflict intensity. We predicted the short- and long-term impacts of key land-use transitions on HEC. Anthropogenic transitions, particularly forest to plantation, and idle land to plantation were found to significantly increase HEC risk, while transitions toward natural land cover, especially forest, tend to reduce conflict over time. Notably, recovery from plantation to forest requires nearly twice the time needed to offset the HEC impact caused by forest to plantation transitions. These findings highlight the role of land-use decisions in shaping conflict dynamics and underscore ecological restoration as a long-term mitigation strategy. The insights offer practical guidance for sustainable planning and targeted HEC management in conflict-prone landscapes.\u003c/p\u003e","manuscriptTitle":"Quantifying the Impacts of Land Use Transitions on Human-Elephant Conflict in Peninsular Malaysia: Implication for Sustainable Landscape Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 10:56:29","doi":"10.21203/rs.3.rs-7312791/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4601c5ec-7298-42ef-b55c-666a575b207f","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55453803,"name":"Biological sciences/Ecology"},{"id":55453804,"name":"Earth and environmental sciences/Ecology"},{"id":55453805,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55453806,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-10-10T07:24:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 10:56:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7312791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7312791","identity":"rs-7312791","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}