Spatial Multi-Criteria Modeling of Geophysical Drivers Shaping Human-Wildlife Conflict Vulnerability in Mindoro, Philippines

Spatial Multi-Criteria Modeling of Geophysical Drivers Shaping Human-Wildlife Conflict Vulnerability in Mindoro, Philippines | 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 Research Article Spatial Multi-Criteria Modeling of Geophysical Drivers Shaping Human-Wildlife Conflict Vulnerability in Mindoro, Philippines Luis Joaquin M. Raquiza, Jean-Matthew B. Bate, Nikki Heherson A. Dagamac This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9090745/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 Human–wildlife conflict (HWC) remains a growing challenge in tropical landscapes where expanding human land use overlaps with wildlife habitats. Identifying areas vulnerable to HWC is critical for guiding targeted conservation and land management strategies. This study applies a spatial multi-criteria approach to evaluate geophysical drivers shaping HWC vulnerability across Mindoro Island, Philippines. An Analytical Hierarchy Process (AHP) was used to integrate key environmental variables, including vegetation productivity (EVI), water availability (NDWI), land use and land cover (LULC), elevation, and climatic factors represented by temperature annual range (BIO7) and annual precipitation (BIO12). These drivers were combined within a GIS-based framework to generate a spatial vulnerability map identifying areas where environmental conditions and topographic characteristics may facilitate greater overlap between wildlife movement and human activities. The resulting spatial patterns indicate that HWC vulnerability tends to occur in heterogeneous landscapes where productive vegetation, accessible terrain, and water resources coincide with agricultural land use. Areas located along transitional zones between forested habitats and cultivated landscapes at low to mid elevations appear particularly susceptible to Human-Wildlife Interactions (HWI). These patterns highlight the importance of spatially explicit approaches for anticipating potential conflict-prone areas and informing proactive mitigation strategies, including landscape planning, habitat connectivity management, and targeted monitoring. By integrating geophysical drivers within a spatial decision-making framework, this study provides a practical tool for identifying potential HWC risk areas and supporting conservation planning in rapidly changing tropical landscapes. risk assessment landscape connectivity remote sensing decision-support mapping tropical island Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Human-wildlife conflict (HWC), defined as interactions between humans and wildlife that result in negative impacts on livelihoods, property, or wildlife populations, has increasingly been recognized not only as an ecological or spatial problem but also as a politically driven challenge embedded in broader bureaucratic governance and socioecological processes (Masse 2016; Frank et al. 2019 ; Hohbein and Abrams, 2022 ). Across many regions of the Global South, conflicts between humans and wildlife are shaped by historical land-use policies (Rudel and Hernandez 2017 ), uneven development trajectories (Fisher et al. 2018 ), and contested access to natural resources among social groups (Rodriguez and Inturias 2018; Radhuber and Radcliffe 2022 ). As conservation initiatives for biodiversity, agricultural expansion for food sustainability, and infrastructure development converge into shared landscape mosaics, HWC emerges as a tangible manifestation of tensions between livelihood security, state-led conservation agendas, and biodiversity protection. Understanding where such conflicts occur, therefore, requires analytical approaches that account for both material landscape conditions and the socio-political structures that organize human–environment relations. From a political ecology perspective, landscapes are not neutral backdrops but are actively produced through governance regimes (Buizer et al. 2015 ), land tenure arrangements (Unruh 2006 ), and development priorities (Lo Piccolo and Todaro 2021 ), which privilege certain land uses and actors over others. Therefore, HWC is not spatially random but is strongly mediated by landscape characteristics that shape both human activities and wildlife movement. Geomorphological features such as elevation, slope, terrain ruggedness, drainage networks, and valley systems influence habitat suitability, species dispersal corridors, and patterns of human settlement and accessibility. In many regions, lowland plains and riverine valleys attract intensive agriculture and infrastructure development (Kumar and Singh 2022 ), while adjacent uplands and forested slopes provide refuge for wildlife (Chen and Kirwan 2023 ; Punongbayan-Candelaria et al. 2025), creating ecotones where encounters are most likely to occur. Historically, the same geomorphological features have guided patterns of settlement building, agricultural intensification, and the assignment of conservation zoning (Olfato-Parojinog et al. 2023 ; Ding et al. 2025 ). These terrain-mediated policy decisions often determine who occupies marginal or high-risk areas and where wildlife habitats are fragmented or enclosed. As a result, geomorphology plays a critical yet underexamined role in structuring spatial inequalities in exposure to human–wildlife conflict (Adu-Boahen et al. 2023 ), particularly among rural and/or indigenous communities residing deeply in mountainous forests and protected ancestral domains. In the context of Mindoro Island, these geophysical characteristics provide the environmental backdrop that shapes patterns of HWC, influencing both human activities and wildlife movement across the landscape. Despite growing recognition of HWC as a governance issue, many spatial assessments remain largely technocratic, focusing primarily on incident locations or spot reports while overlooking the institutional contexts in which conflicts occur (Hodgson et al. 2020 ). While land cover and species distribution models provide valuable ecological insights, they often fail to capture how policy decisions, such as agricultural zoning, road placement, or protected area delineation, interact with terrain to concentrate conflict risk in specific localities (Guisan et al. 2013 ; Zvidzai 2023). This gap highlights the need for integrative spatial frameworks that facilitate informed and accountable decision-making, rather than just merely predictive mapping. Hence, an approach is via the usage of Multi-Criteria Decision Analysis (MCDA), which offers a methodological bridge between spatial science and governance-oriented inquiry by making explicit the values, priorities, and assumptions embedded in risk assessment (Radmehr 2025 ). Specifically, the AHP enables the systematic weighting of multiple criteria based on expert judgment and policy relevance, thereby rendering the decision-making process transparent and contestable (Digkoglou and Papathanasiou 2024 ). When operationalized within a Geographic Information System (GIS), AHP facilitates the production of spatial representations of conflict susceptibility that can be interrogated by planners, conservation practitioners, and local stakeholders alike. Such tools are increasingly important in governance contexts where decisions must balance conservation objectives with social equity and livelihood concerns (Zaheb et al. 2024 ; Bate and Dagamac 2025 ). Nevertheless, the application of AHP to human–wildlife conflict remains limited, especially in studies that explicitly foreground geomorphological landscapes and governance implications. Existing research has largely emphasized mainland or continental settings, leaving island environments underrepresented despite their heightened vulnerability to land-use pressures and governance constraints. Island systems often exhibit compressed socio-ecological gradients, where small changes in policy or land management can produce disproportionate effects on both human communities and wildlife populations (Domptail et al. 2013 ). Addressing these dynamics requires spatial approaches that are sensitive to scale, institutional context, and landscape heterogeneity. The Mindoro Islands in the Philippines exemplify these challenges (Buebos-Esteve et al. 2024 ; Komoda et al. 2025 ). As a biodiversity hotspot with high levels of endemism, Mindoro has been the focus of conservation interventions that intersect with agricultural development, indigenous land claims, and decentralized governance under local government units (Veridiano-de Castro et al. 2024 ; Racoma et al. 2025 ). The island’s rugged interior mountains, extensive river networks, and productive lowlands have shaped settlement patterns and resource access, often situating marginalized communities at the interface between protected areas and agricultural frontiers (Buebos-Esteve and Dagamac 2025 ). In this context, human–wildlife conflict reflects not only ecological overlap but also the outcomes of land-use governance, enforcement capacity, and development priorities across multiple administrative scales. In response, this research study applies the AHP within a GIS-based decision framework to assess the geomorphological landscapes prone to human–wildlife conflict in the Mindoro Islands. By integrating terrain attributes with land-use and accessibility factors, the analysis seeks to identify spatial patterns of conflict susceptibility while making explicit the criteria that underpin such assessments. Rather than presenting susceptibility maps as purely technical outputs, the study positions them as decision-support tools that can inform more inclusive, context-sensitive governance of human–wildlife interactions. In doing so, it contributes to ongoing debates in political ecology and environmental governance on how spatial knowledge can be mobilized to negotiate coexistence in contested landscapes for Mindoro, Philippines. 2. Materials and Methods 2.1 Study Site Situated southwest of mainland Luzon, the island of Mindoro (Fig. 1 ) provides a unique setting for studying HWC due to its ecological diversity, high biodiversity, and complex socio-ecological landscape (Alviola et al. 2022 ; Buot and Buhay 2022 ). The island is divided into Occidental and Oriental regions by an extensive mountain range, which also delineates local administrative boundaries. Its landscapes include closed-canopy forests, bamboo forests, grasslands, croplands, built-up areas, and coastal mangroves, supporting numerous endemic and threatened species (Veridiano-de Castro et al. 2024 ). Indigenous communities inhabit portions of Mindoro, holding ancestral domains and protected areas where land-use rights and territorial claims have occasionally led to local disputes (Panahon 2025 ). Widespread human settlements and agricultural activities create frequent interfaces between people and wildlife, producing a dynamic environment for HWC (Rosales 2022 ). This combination of diverse ecosystems, landscape heterogeneity, and socio-cultural and territorial complexities provides a robust framework for examining how geophysical and ecological drivers shape HWC patterns, influencing species distribution, resource use, and the likelihood of human–wildlife encounters. Hence, once the study site was established, the methodological framework was developed (Fig. 2 ) 2.2 Layer Acquisition and Standardization Environmental predictor layers were obtained from publicly accessible satellite- and climate-based repositories, including WorldClim v2.1, the Copernicus Program, and the USGS Earth Explorer. Selected datasets represented vegetation productivity (EVI), surface moisture (NDWI), land use and land cover (LULC), elevation, and bioclimatic variables (BIO7 – Annual Temperature Range and BIO12 – Annual Precipitation), all of which are ecologically relevant to spatial patterns of HWC (Table 1 ). Collectively, these variables capture resource availability, habitat configuration, topographic accessibility, and climatic controls that shape wildlife distribution and mediate spatial overlap with human activities. All raster layers were clipped to Mindoro's administrative boundary in ArcGIS Pro 3.6.0 to ensure geographic consistency across predictors. Because source datasets differed in spatial resolution and projection, preprocessing was conducted to harmonize the layers prior to multi-criteria integration. All raster files were resampled to a uniform 100 m spatial resolution, selected to balance ecological interpretability and computational efficiency. This resolution captures landscape heterogeneity relevant to wildlife movement and agricultural land use while minimizing overgeneralization associated with coarser grids. Continuous variables (EVI, NDWI, Elevation, BIO7, BIO12) were resampled using bilinear interpolation to preserve gradient continuity, whereas the categorical LULC layer was resampled using nearest-neighbor assignment to maintain thematic class integrity. All layers were reprojected to WGS 84 / UTM Zone 51N (EPSG:32651), an appropriate projected coordinate reference system for Mindoro that preserves distance and area measurements necessary for spatial modeling. With all environmental layers harmonized in spatial extent, resolution, and coordinate system, their ecological relevance to HWC can now be examined, allowing hypotheses to be formulated for how each variable may influence wildlife distribution, resource use, and the likelihood of encounters with human activities. 2.3 Parameters for HWC Vulnerability 2.3.1 Enhanced Vegetation Index (EVI) Vegetation productivity likely exerts the strongest influence on human–wildlife conflict because it directly reflects the availability of food resources and structural habitat conditions (Nyhus 2016 ; Abrahms et al. 2023 ). High EVI values may correspond to dense forest vegetation that provides natural forage and shelter, or to intensively cultivated croplands characterized by concentrated and predictable food supply (Bautista et al. 2022 ). In agricultural landscapes, vigorous crop growth can attract wildlife, increasing the frequency of crop-raiding and retaliatory encounters (Kitratport and Takeuchi 2019). Conflict probability is therefore hypothesized to increase in moderately to highly productive areas, particularly along forest–cropland and cropland–settlement interfaces (Li et al. 2024 ; Liu et al. 2024 ). 2.3.2 Land Use and Land Cover (LULC) Patterns of land use and land cover play a central role in structuring wildlife–human interactions by defining how landscapes are functionally utilized (Syombua 2013 ; Behera et al. 2024 ). Agricultural mosaics, plantations, and peri-urban zones increase spatial overlap between wildlife habitat and cultivated lands, intensifying edge effects and encounter opportunities (Roth et al. 2024 ). Transitional areas, especially forest–agriculture boundaries, may serve as recurrent points of wildlife incursion (Khatri et al. 2024 ). Nevertheless, conflict incidence is hypothesized to concentrate within fragmented and human-modified land cover systems rather than in relatively intact forest interiors (Singh et al. 2025 ). 2.3.3 Elevation Topographic gradients influence conflict patterns primarily through their association with accessibility, settlement density, and agricultural expansion (Pathak et al. 2024 ). Lower elevations tend to support infrastructure development and intensive land conversion, increasing the probability of Human-Wildlife Interactions (HWI). In contrast, higher elevations are generally less densely populated and less intensively cultivated, limiting direct interactions (Sharma et al. 2020 ). 2.3.4 Normalized Difference Water Index (NDWI) Surface moisture availability shapes both wildlife distribution and agricultural viability (Ogutu et al. 2014 ; Calhoun et al. 2025 ). Areas with higher NDWI values—such as riparian corridors and irrigated croplands—may attract terrestrial wildlife seeking hydration and food resources (Maestas et al. 2023 ). Although water contributes to foraging opportunities, it plays a comparatively smaller role in providing structural shelter than vegetation density (Fehlmann et al. 2020 ). Conflict likelihood is therefore hypothesized to increase in moisture-rich environments where water availability enhances crop productivity and wildlife presence. 2.3.5 Temperature Annual Range (BIO 7) Thermal variability can influence seasonal wildlife movement and resource dynamics (Abrahms et al. 2021 ). Moderate temperature ranges may sustain stable agricultural production while maintaining suitable habitat conditions for wildlife persistence (Abbass et al. 2022 ). Seasonal fluctuations in temperature may also trigger shifts in foraging behavior, increasing wildlife movement across cultivated landscapes during resource-scarce periods (Sharma et al. 2020 ). The relationship between BIO7 and conflict probability is likely context-dependent and mediated by vegetation productivity and land use configuration. 2.3.6 Annual Precipitation (BIO12) Long-term precipitation regimes shape vegetation structure, crop yield, and water availability (Naha et al. 2019 ). Higher annual rainfall can sustain dense natural vegetation and productive agricultural systems, both of which may attract wildlife (Rendón-Sandoval et al. 2020 ). Enhanced rainfall may intensify resource concentration effects in croplands, increasing the likelihood of crop-raiding incidents (Tiller et al. 2021 ). However, because climatic conditions are temporally dynamic relative to more spatially stable landscape features, their influence on conflict patterns likely operates indirectly through vegetation productivity and land use structure (Hariohay et al. 2025 ). Table 1 The layers used for the spatial modeling, along with their respective year, resolution, source, and unit. Variable Year Native Resolution Standardized Resolution Source Unit EVI 2024 10m 100m Sentinel-2 MSI L2A (Copernicus) Index NDWI 2025 10m 100m Sentinel-2 MSI L2A (Copernicus) Index Elevation 2000 30m 100m SRTM v3 / NASA (USGS Earth Explorer) Meters above sea level (m.a.s.l.) LULC 2020 100m 100m Copernicus Global Land Cover Layers (CGLS-LC100) Categorical Temperature Annual Range (BIO7) 1970–2000 (Average) 30 arc-seconds (~ 1 km) 100m WorldClim v2.1 °C Annual Precipitation (BIO12) 1970–2000 (Average) 30 arc-seconds (~ 1 km) 100m WorldClim v2.1 mm 2.4 Multi-Criteria Decision Analysis A multi-criteria decision analysis (MCDA) was conducted using the AHP developed by Thomas L. Saaty ( 2008 ). Pairwise comparisons were guided by Saaty’s ( 2008 ) 1–9 fundamental scale, in which higher values indicate a stronger relative influence of one criterion over another; in this study, higher scores indicate greater contribution to the occurrence and spatial vulnerability of HWC in Mindoro. A pairwise comparison matrix (Table 2 ) was constructed in Microsoft Excel (Microsoft 365, Version 2601) to systematically evaluate each parameter against every other criterion within the set, based on structured expert judgment informed by contextual knowledge of species ecology, landscape configuration, and anthropogenic pressures operating in the study area. The expert panel consisted of a university-level terrestrial ecologist, a conservation wildlife ecologist, and an environmental impact assessor, all of whom possess extensive professional and field experience in Mindoro and have collectively conducted multiple research and conservation projects in the province. Consultations were conducted in successive stages to conceptualize the analytical framework, refine criteria definitions and assumptions, and collectively assign and validate weights to reduce individual bias and strengthen consensus. Matrix consistency was assessed using the Consistency Ratio (CR), where values below 0.10 are considered acceptable in AHP applications (Pant et al. 2022 ). The computed CR of 0.083 indicates that the Consistency Index (CI) accounts for 8.3% of the corresponding Random Index (RI) for the given matrix size, reflecting acceptable, reasonably consistent judgments and supporting the robustness of the derived weights. These normalized weights were then applied in a weighted linear combination (WLC) to generate a spatially explicit visualization of HWC vulnerability across Mindoro. Table 2 Pairwise comparison matrix of the parameters EVI EVI NDWI Elevation LULC BIO7 BIO12 1 8.00 8.00 4.00 8.00 8.00 NDWI 0.12 1 0.33 0.25 2.00 2.00 Elevation 0.12 3.00 1 0.12 3.00 3.00 LULC 0.25 4.00 8.00 1 9.00 9.00 BIO7 0.12 0.50 0.33 0.11 1 1.00 BIO12 0.12 0.50 0.33 0.11 1.00 1 2.5 Weighted Linear Combination The environmental layers were combined using a WLC in the Raster Calculator tool in ArcGIS Pro 3.6.0, with each layer multiplied by its AHP-derived normalized weight (Table 3 ). This process produced a composite surface highlighting landscapes most susceptible to HWC, reflecting the integrated influence of ecological and geophysical drivers. The resulting WLC raster was further refined in QGIS to improve symbology and layout, enhancing the visualization and interpretability of spatial patterns. By capturing the combined effects of all layers, this approach allows the identification of areas where environmental conditions and landscape characteristics jointly elevate conflict risk, providing a spatial framework for understanding the relative contribution of each driver and supporting evidence-based management and mitigation strategies. Table 3 Relative importance of geophysical variables used in the AHP model based on standardized weights and percentage contribution Variable Rank Standardized Weight (% Contribution) EVI 1 0.51 50.6 NDWI 4 0.05 5.4 Elevation 3 0.08 8.3 LULC 2 0.29 29.1 BIO7 5 0.03 3.3 BIO12 5 0.03 3.3 3. Results 3.1 Spatial Patterns of Geophysical Drivers The spatial distribution of the environmental layers used in the analysis is presented in Fig. 3 . These maps highlight how elevation, climate, surface moisture, vegetation productivity, and land use vary across the island, providing visual context for the environmental gradients that structure the landscape. Pronounced environmental gradients characterize the landscape, where elevation rises sharply from near sea level to 2,564 masl., forming a rugged central mountain range that anchors the island’s physical structure. This elevational backbone organizes clear climatic contrasts across space. The annual temperature range varies from 8.9 to 12.3°C, with relatively higher variability in inland and elevated areas, while annual precipitation (1,947–3,269 mm) varies markedly across the island, with comparatively wetter conditions in the southern and eastern sectors. These patterns highlight the strong influence of topography in structuring local climatic variability. Surface moisture, vegetation productivity, and land use reflect these underlying gradients. NDWI values (0–1) indicate increased surface moisture in low-lying areas and along drainage networks, contrasting with relatively drier upland zones. EVI values (0–1) delineate dense, contiguous vegetation across the montane interior, highlighting the concentration of forested habitats at higher elevations. The LULC layer further illustrates this spatial organization: tree cover predominates in the uplands, grasslands and croplands form a mosaic across transitional and low-elevation landscapes, and built-up areas are largely concentrated in accessible coastal plains and valley corridors. Collectively, these layers reveal a spatially structured and ecologically stratified system that provides the biophysical foundation for subsequent weighting procedures and HWC risk modeling. These spatial patterns also reflect the strong influence of topography and land use on environmental conditions across Mindoro Island, shaping the distribution of natural habitats and human-modified landscapes throughout the island. 3.2 HWC Vulnerability and Hotspot Delineation Application of the AHP framework produced a spatially structured pattern of HWC vulnerability characterized by clear regional contrasts and clustered hotspots. In both scenarios, higher vulnerability classes are concentrated predominantly in the south-central to southwestern portions of Mindoro Island, while much of the northern sector remains dominated by very low to low classes, resulting in a discernible north–south gradient (Fig. 4 ). On the other hand, Table 4 shows that under the equal-weight model, moderate vulnerability represents the largest proportion of the landscape (29.5%), followed by low (28.3%) and high (18.0%) classes. High and very high categories collectively account for 25.0% of the total area, with very high vulnerability covering 7.0% (696.7 km²). When WLC-AHP–derived weights are applied, the spatial pattern becomes more consolidated and polarized. The proportion of very high vulnerability increases from 7.0% to 9.6% (954.5 km²), while high vulnerability slightly increases to 18.6%, resulting in a combined 28.2% of the island classified as high to very high risk. Conversely, moderate vulnerability declines substantially from 29.5% to 23.8%, while very low areas expand from 17.2% to 20.4%. These shifts indicate a reduction in intermediate-risk zones and a redistribution toward both lower and higher extremes, producing a more differentiated and spatially coherent vulnerability surface under the weighted model. This redistribution suggests that applying variable weights enhances the influence of key geophysical drivers in the model, allowing areas with similar environmental conditions to emerge as more clearly defined clusters of vulnerability across the landscape. Table 4 Comparison of total land area (sqkm.) and area coverage (%) for each class in the equal-weighted scenario and WLC-AHP assisted scenario Class Equal-Weighted WLC-AHP assisted Area (sqkm.) Area Coverage (%) Area (sqkm.) Area Coverage (%) Very Low 1705.9 17.2 2023.4 20.4 Low 2813.3 28.3 2743.5 27.6 Moderate 2926.6 29.5 2364.2 23.8 High 1794.8 18.0 1851.7 18.6 Very High 696.7 7.0 954.5 9.6 4. Discussion AHP modeling reveals that landscape vulnerability to HWC in Mindoro arises from the interplay of multiple environmental drivers rather than a single factor. For instance, vegetation productivity, captured by EVI, plays a central role. Areas with moderate to high productivity often occur along forest margins, secondary growth, and agricultural mosaics where human cultivation meets wildlife habitat. These areas provide abundant forage for wildlife while simultaneously supporting crops, creating a spatial overlap that facilitates HWC (Fortin et al. 2020 ). Importantly, EVI highlights not just the presence of vegetation, but also its quality and density, which influence wildlife foraging behavior, movement patterns, and seasonal habitat selection (Sun et al. 2021 ). In Poland, similar mixed forest–agriculture interfaces attract wild boars and deer, which preferentially feed in high-productivity cropland patches adjacent to forests (Kuka et al. 2022 ). Similarly, in India, elephants exploit mosaic landscapes where croplands and forests coincide, showing how productivity gradients shape both resource use and conflict potential (Anoop et al. 2023 ). In Mindoro, high-EVI areas along forest edges likely function as biodiversity corridors connecting upland refugia with lowland feeding areas and making these zones predictable HWC hotspots. Water availability, indicated by NDWI, further shapes HWC risk by acting as both a resource and a movement attractor (Rumiano et al. 2020 ). Streams, ponds, wetlands, and irrigated fields concentrate wildlife activity because animals rely on consistent water sources for drinking, foraging, and thermoregulation (Bate and Dagamac 2025 ). NDWI identifies these moisture-rich areas across the landscape, allowing us to see where wildlife is most likely to overlap spatially with human land use. Climatic variables, represented by BIO7 and BIO12, add another layer of influence: temperature ranges and annual precipitation govern vegetation growth cycles, seasonal water availability, and resource distribution, indirectly guiding wildlife movement and habitat use (Gutiérrez-Hernández and García 2021 ). In Namibia, human–elephant conflict has been observed to increase during periods of drought, higher temperatures, and reduced precipitation, as elephants expand their movements in search of alternative sources of shelter, food, and water for thermoregulation during seasonal climatic variation (Shiweda et al. 2023 ). By combining NDWI with climatic variables, the model identifies zones where water resources, habitat suitability, and human activity overlap, effectively highlighting areas where HWC is likely to occur seasonally or persistently. These drivers underscore that HWC is not random; it is a predictable outcome of resource-driven movement and human presence. LULC and elevation together define the physical canvas on which HWC unfolds, shaping accessibility, connectivity, and movement pathways (Sharma et al. 2022 ; Pavithra et al. 2025 ). Low- and mid-elevation zones tend to host settlements, roads, and agricultural fields, while forested uplands serve as refugia for wildlife. In Mindoro, landscapes where forest edges transition into agricultural land and other modified LULC types at accessible elevations are likely to experience greater spatial overlap between wildlife habitat and human land use. Such configurations can facilitate wildlife movement from forested areas into nearby cultivated landscapes, increasing the probability of encounters between wildlife and local communities (Li et al. 2024 ; Ma et al. 2024 ). Elevation, when considered alongside LULC, therefore influences landscape permeability and connectivity by shaping how wildlife move between habitat patches and human-dominated areas (Sharma et al. 2022 ; Ranjan et al. 2025 ). These spatial relationships highlight how HWC vulnerability is often concentrated in transitional zones where ecological connectivity and agricultural land use intersect, a pattern widely documented in studies examining human–wildlife interactions across mixed-use landscapes (Buccholtz et al. 2020; Shrestha et al. 2026 ). 4.1 Management and Conservation Implications In the central mountainous regions of Mindoro, Indigenous Peoples (IPs) inhabit forested areas deep within the mountains and have coexisted with wildlife in these core habitats for generations (Ishihara et al. 2014 ). Yet the introduction of human-mediated croplands within these landscapes can attract species such as the endemic tamaraw, leading to localized HWC. These social and land-use dynamics highlight the importance of understanding HWC within both ecological and human contexts. The spatial patterns of HWC identified in Mindoro further emphasize the need for management strategies that are both spatially explicit and ecologically informed. Specifically, areas where high EVI, NDWI, and favorable climatic conditions intersect with accessible LULC and moderate elevations represent predictable hotspots, suggesting that interventions should focus on these landscapes. Maintaining landscape connectivity in the central and southern portions of Mindoro Island, particularly across heterogeneous mosaics of forest patches, croplands, and grasslands, may help reduce HWC by guiding wildlife movement along safe corridors and away from human settlements. In this context, strengthening ecological linkages among forest fragments can channel wildlife movement through permeable landscapes, reducing encounters with nearby settlements and helping limit crop raiding in adjacent agricultural areas. Additionally, establishing buffer zones or agroforestry strips along forest–agriculture interfaces can provide transitional habitats that discourage wildlife from entering cultivated fields. Water resources, captured by NDWI, are critical nodes for intervention, as wildlife tend to congregate around streams, ponds, and irrigated fields; protecting these within natural habitats and designing irrigation systems that minimize attractants near settlements can further reduce HWC. Seasonal climatic variability, as indicated by Annual Temperature Range (BIO7) and Annual Precipitation (BIO12), underscores the need for time-sensitive mitigation efforts, with increased monitoring during dry periods when water scarcity drives wildlife into human-dominated areas, and during wet seasons when lowland fields are more accessible. Elevation and LULC together highlight the importance of terrain-sensitive planning, where maintaining upland forest refugia while improving permeability in mid- and low-elevation zones through wildlife-friendly corridors can facilitate safe movement. Finally, community engagement remains central, particularly in lowland areas where agriculture and settlements abut forests; education, early-warning systems, crop protection techniques, and compensation schemes for damage can enhance coexistence. By integrating AHP-derived vulnerability maps into conservation and land-use planning, local authorities and stakeholders can prioritize interventions, optimize resources, and anticipate high-risk areas before conflicts escalate (Pandit et al. 2025 ; Thammaboribal et al. 2025 ). 4.2 Study Limitations and Future Research The AHP-based framework applied in this study provides a spatial perspective on landscape vulnerability to HWC in Mindoro Island, yet several limitations should be considered when interpreting the results. The model primarily incorporates geophysical drivers, including EVI, NDWI, elevation, LULC, Annual Temperature Range (BIO7), and Annual Precipitation (BIO12), which capture environmental conditions influencing wildlife habitat suitability and movement but do not explicitly account for social or behavioral factors that also shape HWC dynamics. Variables such as settlement density, wildlife distribution, and local mitigation practices may influence where conflicts ultimately occur, and their exclusion may limit the model’s ability to fully represent the complexity of human–wildlife interactions across the landscape. The AHP weighting process also involves an element of expert judgment, meaning that the relative importance assigned to each driver reflects informed interpretation rather than purely empirical relationships that other covariates may generate. Although widely applied in spatial decision-making, alternative weighting schemes or the inclusion of additional variables could alter the model's spatial distribution of vulnerability. Data resolution and temporal variability also present constraints. Satellite-derived indices such as EVI and NDWI capture environmental conditions over specific time periods, whereas climatic variables such as Annual Temperature Range (BIO7) and Annual Precipitation (BIO12) represent long-term averages that may obscure seasonal fluctuations that influence wildlife movement and resource availability. In addition, the resulting vulnerability map represents potential landscape susceptibility to HWC rather than confirmed conflict occurrences and should therefore be interpreted as indicative patterns of risk rather than direct predictions of conflict events. Future research could strengthen predictive capacity by incorporating higher-resolution environmental data, species-specific ecological information, and socio-economic variables that reflect patterns of human land use. Integrating AHP with complementary spatial approaches, such as species distribution modeling or analyses based on documented HWC incidents, may further refine hotspot identification and enable more robust validation of vulnerability patterns. Expanding field-based monitoring and community reporting of HWC events would also improve empirical datasets that can support future spatial modeling efforts and contribute to more adaptive, evidence-based strategies for managing HWC across Mindoro Island. 5. Conclusion This study demonstrates the value of spatial multi-criteria approaches for identifying landscape vulnerability to HWC across Mindoro Island. Using AHP, the analysis integrates several geophysical drivers that influence wildlife movement and habitat conditions, including vegetation productivity (EVI), water availability (NDWI), terrain structure, land-use patterns (LULC), and climatic variability such as Annual Temperature Range (BIO7) and Annual Precipitation (BIO12). HWC vulnerability, therefore, emerges from the spatial convergence of these conditions, particularly in heterogeneous landscapes where forest habitats, agricultural lands, and accessible terrain occur in close proximity. The resulting vulnerability patterns highlight the importance of elevational gradients, landscape connectivity, and resource distribution in shaping wildlife movement between upland refugia and lowland agricultural areas. Mapping these spatial relationships provides a basis for anticipating where HWC risk may intensify and supports more proactive management strategies for the island of Mindoro. Integrating AHP-derived vulnerability maps into conservation planning and land-use management can help prioritize intervention areas, guide mitigation efforts, and improve the allocation of conservation resources for managing HWC in Mindoro Island. Declarations Ethics Declaration This study did not involve human participants, animals, or the collection of primary field data. The research was based solely on the Analytic Hierarchy Process (AHP) and secondary environmental spatial datasets obtained from publicly available sources. All data were used in accordance with their respective licensing and citation requirements. No ethical approval was required for this study. Author Contribution L.J.M.R.: Methodology, Formal analysis, Validation, Visualization, Data Curation, Writing, Scoring - original draft. J.M.B.: Conceptualization, Supervision, Writing, Scoring, Review & Editing. N.H.A.D.: Conceptualization, Supervision, Writing, Scoring, Review & Editing.All authors read and approved the final manuscript. Acknowledgement L.J.M.R. acknowledges DOST-ASTHRDP for the scholarship grant. N.H.A.D. would like to thank DOST-PCAARRD for the Balik Scientist grants. Data Availability The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Remote sensing layers, including Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were derived from Sentinel-2 L2A imagery (Copernicus, 2024–2025). Land use and land cover (LULC) data were obtained from the Copernicus Land Monitoring Service (CLC, 2020). Elevation data were sourced from the Shuttle Radar Topography Mission (SRTM, 30 m resolution), and bioclimatic variables (BIO7: temperature annual range; BIO12: annual precipitation) were acquired from WorldClim v2.1. Processed GIS layers, AHP weightings, and the resulting HWC vulnerability maps for Mindoro Island can be provided in standard GIS formats (e.g., raster, shapefile) upon reasonable request. References Abbass K, Qasim MZ, Song H et al (2022) A review of the global climate change impacts, adaptation, and sustainable mitigation measures. 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J Geogr Reg Plann 6(5):193–199. ttps://doi.org/10.5897/jgrp2013.0365 Thammaboribal P, Triapthti N, Lipiloet S (2025) Using of Analytical Hierarchy Process (AHP) in Disaster Management: A Review of Flooding and Landslide Susceptibility Mapping. Int J Geoinformatics 21(4). ttps://doi.org/10.52939/ijg.v21i4.4091 Tiller LN, Humle T, Amin R, Deere NJ, Lago BO, Leader-Williams N, Sinoni FK, Sitati N, Walpole M, Smith RJ (2021) Changing seasonal, temporal and spatial crop-raiding trends over 15 years in a human-elephant conflict hotspot. Biol Conserv 254:108941. ttps://doi.org/10.1016/j.biocon.2020.108941 Unruh JD (2006) Land Tenure and the Evidence Landscape in Developing Countries. Ann Assoc Am Geogr 96(4):754–772. ttps://doi.org/10.1111/j.1467-8306.2006.00515.x Veridiano-de Castro NA, Almadrones-Reyes KJ, Redeña-Santos JC, Limbo-Dizon JE, Dagamac NHA (2024) Applying geomatic analyses using landsat imagery: implications for ecosystem management in Occidental Mindoro, the Philippines. GeoJournal 89(5). ttps://doi.org/10.1007/s10708-024-11215-2 Zaheb H, Obaidi O, Mukhtar S, Shirani H, Ahmadi M, Yona A (2024) Comprehensive Analysis and Prioritization of Sustainable Energy Resources Using Analytical Hierarchy Process. Sustainability 16(11):4873. ttps://doi.org/10.3390/su16114873 Zvidzai M, Mawere K, Ndaimani N’anduR, Chenjerai Zanamwe H, Fadzai Zengeya (2023) Application of maximum entropy (MaxEnt) to understand the spatial dimension of human–wildlife conflict (HWC) risk in areas adjacent to Gonarezhou National Park of Zimbabwe. Ecol Soc 28(3). ttps://doi.org/10.5751/es-14420-280318 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9090745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625747795,"identity":"3822fbb5-47aa-4a11-862c-958f3eedcca6","order_by":0,"name":"Luis Joaquin M. 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Bate","email":"","orcid":"","institution":"Manila Central University","correspondingAuthor":false,"prefix":"","firstName":"Jean-Matthew","middleName":"B.","lastName":"Bate","suffix":""},{"id":625747797,"identity":"ff2d066f-0be9-4c1e-84e2-993b59ba70c4","order_by":2,"name":"Nikki Heherson A. Dagamac","email":"","orcid":"","institution":"D’ABOVILLE Foundation and Demo Farm Inc","correspondingAuthor":false,"prefix":"","firstName":"Nikki","middleName":"Heherson A.","lastName":"Dagamac","suffix":""}],"badges":[],"createdAt":"2026-03-11 06:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9090745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9090745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107490082,"identity":"733f0e59-857f-47fc-bbac-c2d1d33e074a","added_by":"auto","created_at":"2026-04-22 02:50:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91304,"visible":true,"origin":"","legend":"\u003cp\u003eStudy site map highlighting the Land-Use and Land Cover of Mindoro, mainly composed of tree cover, grasslands, and croplands\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9090745/v1/8003c9bffad468d79fae6ef6.png"},{"id":107490239,"identity":"2dec83a6-5b1a-4750-b3d3-2af058673140","added_by":"auto","created_at":"2026-04-22 02:51:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22367,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological workflow of the study adapted from Benti et al. (2023)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9090745/v1/fd384f7f7fc3a2082c5e9a17.png"},{"id":107446572,"identity":"14b65c12-1de2-4180-b33d-552b6d090e85","added_by":"auto","created_at":"2026-04-21 14:46:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":404418,"visible":true,"origin":"","legend":"\u003cp\u003eLayers used for the weighted linear combination, namely, elevation, annual temperature range, annual precipitation, NDWI, EVI, and LULC\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9090745/v1/373ff0678de9df409ad54509.png"},{"id":107487418,"identity":"49ceb2aa-c679-452d-b54f-6c8ef40be4da","added_by":"auto","created_at":"2026-04-22 02:41:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189242,"visible":true,"origin":"","legend":"\u003cp\u003eHWC vulnerability map of Mindoro using (a) the equal weighted scenario and (b) the WLC-AHP assisted scenario\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9090745/v1/e66b6e47074380cb786cdaad.png"},{"id":107710212,"identity":"3f46d418-f56b-4391-8d6b-fa864c2fd3f3","added_by":"auto","created_at":"2026-04-24 09:40:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9090745/v1/3a841484-1850-45cf-a456-e3a671c4a857.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Multi-Criteria Modeling of Geophysical Drivers Shaping Human-Wildlife Conflict Vulnerability in Mindoro, Philippines","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHuman-wildlife conflict (HWC), defined as interactions between humans and wildlife that result in negative impacts on livelihoods, property, or wildlife populations, has increasingly been recognized not only as an ecological or spatial problem but also as a politically driven challenge embedded in broader bureaucratic governance and socioecological processes (Masse 2016; Frank et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hohbein and Abrams, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Across many regions of the Global South, conflicts between humans and wildlife are shaped by historical land-use policies (Rudel and Hernandez \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), uneven development trajectories (Fisher et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and contested access to natural resources among social groups (Rodriguez and Inturias 2018; Radhuber and Radcliffe \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As conservation initiatives for biodiversity, agricultural expansion for food sustainability, and infrastructure development converge into shared landscape mosaics, HWC emerges as a tangible manifestation of tensions between livelihood security, state-led conservation agendas, and biodiversity protection. Understanding where such conflicts occur, therefore, requires analytical approaches that account for both material landscape conditions and the socio-political structures that organize human\u0026ndash;environment relations. From a political ecology perspective, landscapes are not neutral backdrops but are actively produced through governance regimes (Buizer et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), land tenure arrangements (Unruh \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and development priorities (Lo Piccolo and Todaro \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which privilege certain land uses and actors over others. Therefore, HWC is not spatially random but is strongly mediated by landscape characteristics that shape both human activities and wildlife movement. Geomorphological features such as elevation, slope, terrain ruggedness, drainage networks, and valley systems influence habitat suitability, species dispersal corridors, and patterns of human settlement and accessibility. In many regions, lowland plains and riverine valleys attract intensive agriculture and infrastructure development (Kumar and Singh \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while adjacent uplands and forested slopes provide refuge for wildlife (Chen and Kirwan \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Punongbayan-Candelaria et al. 2025), creating ecotones where encounters are most likely to occur. Historically, the same geomorphological features have guided patterns of settlement building, agricultural intensification, and the assignment of conservation zoning (Olfato-Parojinog et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These terrain-mediated policy decisions often determine who occupies marginal or high-risk areas and where wildlife habitats are fragmented or enclosed. As a result, geomorphology plays a critical yet underexamined role in structuring spatial inequalities in exposure to human\u0026ndash;wildlife conflict (Adu-Boahen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), particularly among rural and/or indigenous communities residing deeply in mountainous forests and protected ancestral domains. In the context of Mindoro Island, these geophysical characteristics provide the environmental backdrop that shapes patterns of HWC, influencing both human activities and wildlife movement across the landscape.\u003c/p\u003e \u003cp\u003eDespite growing recognition of HWC as a governance issue, many spatial assessments remain largely technocratic, focusing primarily on incident locations or spot reports while overlooking the institutional contexts in which conflicts occur (Hodgson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While land cover and species distribution models provide valuable ecological insights, they often fail to capture how policy decisions, such as agricultural zoning, road placement, or protected area delineation, interact with terrain to concentrate conflict risk in specific localities (Guisan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zvidzai 2023). This gap highlights the need for integrative spatial frameworks that facilitate informed and accountable decision-making, rather than just merely predictive mapping. Hence, an approach is via the usage of Multi-Criteria Decision Analysis (MCDA), which offers a methodological bridge between spatial science and governance-oriented inquiry by making explicit the values, priorities, and assumptions embedded in risk assessment (Radmehr \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Specifically, the AHP enables the systematic weighting of multiple criteria based on expert judgment and policy relevance, thereby rendering the decision-making process transparent and contestable (Digkoglou and Papathanasiou \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When operationalized within a Geographic Information System (GIS), AHP facilitates the production of spatial representations of conflict susceptibility that can be interrogated by planners, conservation practitioners, and local stakeholders alike. Such tools are increasingly important in governance contexts where decisions must balance conservation objectives with social equity and livelihood concerns (Zaheb et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bate and Dagamac \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, the application of AHP to human\u0026ndash;wildlife conflict remains limited, especially in studies that explicitly foreground geomorphological landscapes and governance implications. Existing research has largely emphasized mainland or continental settings, leaving island environments underrepresented despite their heightened vulnerability to land-use pressures and governance constraints. Island systems often exhibit compressed socio-ecological gradients, where small changes in policy or land management can produce disproportionate effects on both human communities and wildlife populations (Domptail et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Addressing these dynamics requires spatial approaches that are sensitive to scale, institutional context, and landscape heterogeneity.\u003c/p\u003e \u003cp\u003eThe Mindoro Islands in the Philippines exemplify these challenges (Buebos-Esteve et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Komoda et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a biodiversity hotspot with high levels of endemism, Mindoro has been the focus of conservation interventions that intersect with agricultural development, indigenous land claims, and decentralized governance under local government units (Veridiano-de Castro et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Racoma et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The island\u0026rsquo;s rugged interior mountains, extensive river networks, and productive lowlands have shaped settlement patterns and resource access, often situating marginalized communities at the interface between protected areas and agricultural frontiers (Buebos-Esteve and Dagamac \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, human\u0026ndash;wildlife conflict reflects not only ecological overlap but also the outcomes of land-use governance, enforcement capacity, and development priorities across multiple administrative scales.\u003c/p\u003e \u003cp\u003eIn response, this research study applies the AHP within a GIS-based decision framework to assess the geomorphological landscapes prone to human\u0026ndash;wildlife conflict in the Mindoro Islands. By integrating terrain attributes with land-use and accessibility factors, the analysis seeks to identify spatial patterns of conflict susceptibility while making explicit the criteria that underpin such assessments. Rather than presenting susceptibility maps as purely technical outputs, the study positions them as decision-support tools that can inform more inclusive, context-sensitive governance of human\u0026ndash;wildlife interactions. In doing so, it contributes to ongoing debates in political ecology and environmental governance on how spatial knowledge can be mobilized to negotiate coexistence in contested landscapes for Mindoro, Philippines.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Site\u003c/h2\u003e \u003cp\u003eSituated southwest of mainland Luzon, the island of Mindoro (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) provides a unique setting for studying HWC due to its ecological diversity, high biodiversity, and complex socio-ecological landscape (Alviola et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Buot and Buhay \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The island is divided into Occidental and Oriental regions by an extensive mountain range, which also delineates local administrative boundaries. Its landscapes include closed-canopy forests, bamboo forests, grasslands, croplands, built-up areas, and coastal mangroves, supporting numerous endemic and threatened species (Veridiano-de Castro et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Indigenous communities inhabit portions of Mindoro, holding ancestral domains and protected areas where land-use rights and territorial claims have occasionally led to local disputes (Panahon \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Widespread human settlements and agricultural activities create frequent interfaces between people and wildlife, producing a dynamic environment for HWC (Rosales \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This combination of diverse ecosystems, landscape heterogeneity, and socio-cultural and territorial complexities provides a robust framework for examining how geophysical and ecological drivers shape HWC patterns, influencing species distribution, resource use, and the likelihood of human\u0026ndash;wildlife encounters. Hence, once the study site was established, the methodological framework was developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Layer Acquisition and Standardization\u003c/h2\u003e \u003cp\u003eEnvironmental predictor layers were obtained from publicly accessible satellite- and climate-based repositories, including WorldClim v2.1, the Copernicus Program, and the USGS Earth Explorer. Selected datasets represented vegetation productivity (EVI), surface moisture (NDWI), land use and land cover (LULC), elevation, and bioclimatic variables (BIO7 \u0026ndash; Annual Temperature Range and BIO12 \u0026ndash; Annual Precipitation), all of which are ecologically relevant to spatial patterns of HWC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Collectively, these variables capture resource availability, habitat configuration, topographic accessibility, and climatic controls that shape wildlife distribution and mediate spatial overlap with human activities.\u003c/p\u003e \u003cp\u003eAll raster layers were clipped to Mindoro's administrative boundary in ArcGIS Pro 3.6.0 to ensure geographic consistency across predictors. Because source datasets differed in spatial resolution and projection, preprocessing was conducted to harmonize the layers prior to multi-criteria integration. All raster files were resampled to a uniform 100 m spatial resolution, selected to balance ecological interpretability and computational efficiency. This resolution captures landscape heterogeneity relevant to wildlife movement and agricultural land use while minimizing overgeneralization associated with coarser grids.\u003c/p\u003e \u003cp\u003eContinuous variables (EVI, NDWI, Elevation, BIO7, BIO12) were resampled using bilinear interpolation to preserve gradient continuity, whereas the categorical LULC layer was resampled using nearest-neighbor assignment to maintain thematic class integrity. All layers were reprojected to WGS 84 / UTM Zone 51N (EPSG:32651), an appropriate projected coordinate reference system for Mindoro that preserves distance and area measurements necessary for spatial modeling. With all environmental layers harmonized in spatial extent, resolution, and coordinate system, their ecological relevance to HWC can now be examined, allowing hypotheses to be formulated for how each variable may influence wildlife distribution, resource use, and the likelihood of encounters with human activities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Parameters for HWC Vulnerability\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Enhanced Vegetation Index (EVI)\u003c/h2\u003e \u003cp\u003eVegetation productivity likely exerts the strongest influence on human\u0026ndash;wildlife conflict because it directly reflects the availability of food resources and structural habitat conditions (Nyhus \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abrahms et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). High EVI values may correspond to dense forest vegetation that provides natural forage and shelter, or to intensively cultivated croplands characterized by concentrated and predictable food supply (Bautista et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In agricultural landscapes, vigorous crop growth can attract wildlife, increasing the frequency of crop-raiding and retaliatory encounters (Kitratport and Takeuchi 2019). Conflict probability is therefore hypothesized to increase in moderately to highly productive areas, particularly along forest\u0026ndash;cropland and cropland\u0026ndash;settlement interfaces (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Land Use and Land Cover (LULC)\u003c/h2\u003e \u003cp\u003ePatterns of land use and land cover play a central role in structuring wildlife\u0026ndash;human interactions by defining how landscapes are functionally utilized (Syombua \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Behera et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Agricultural mosaics, plantations, and peri-urban zones increase spatial overlap between wildlife habitat and cultivated lands, intensifying edge effects and encounter opportunities (Roth et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Transitional areas, especially forest\u0026ndash;agriculture boundaries, may serve as recurrent points of wildlife incursion (Khatri et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, conflict incidence is hypothesized to concentrate within fragmented and human-modified land cover systems rather than in relatively intact forest interiors (Singh et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Elevation\u003c/h2\u003e \u003cp\u003eTopographic gradients influence conflict patterns primarily through their association with accessibility, settlement density, and agricultural expansion (Pathak et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Lower elevations tend to support infrastructure development and intensive land conversion, increasing the probability of Human-Wildlife Interactions (HWI). In contrast, higher elevations are generally less densely populated and less intensively cultivated, limiting direct interactions (Sharma et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Normalized Difference Water Index (NDWI)\u003c/h2\u003e \u003cp\u003eSurface moisture availability shapes both wildlife distribution and agricultural viability (Ogutu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Calhoun et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Areas with higher NDWI values\u0026mdash;such as riparian corridors and irrigated croplands\u0026mdash;may attract terrestrial wildlife seeking hydration and food resources (Maestas et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although water contributes to foraging opportunities, it plays a comparatively smaller role in providing structural shelter than vegetation density (Fehlmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conflict likelihood is therefore hypothesized to increase in moisture-rich environments where water availability enhances crop productivity and wildlife presence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Temperature Annual Range (BIO 7)\u003c/h2\u003e \u003cp\u003eThermal variability can influence seasonal wildlife movement and resource dynamics (Abrahms et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moderate temperature ranges may sustain stable agricultural production while maintaining suitable habitat conditions for wildlife persistence (Abbass et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seasonal fluctuations in temperature may also trigger shifts in foraging behavior, increasing wildlife movement across cultivated landscapes during resource-scarce periods (Sharma et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relationship between BIO7 and conflict probability is likely context-dependent and mediated by vegetation productivity and land use configuration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Annual Precipitation (BIO12)\u003c/h2\u003e \u003cp\u003eLong-term precipitation regimes shape vegetation structure, crop yield, and water availability (Naha et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Higher annual rainfall can sustain dense natural vegetation and productive agricultural systems, both of which may attract wildlife (Rend\u0026oacute;n-Sandoval et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Enhanced rainfall may intensify resource concentration effects in croplands, increasing the likelihood of crop-raiding incidents (Tiller et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, because climatic conditions are temporally dynamic relative to more spatially stable landscape features, their influence on conflict patterns likely operates indirectly through vegetation productivity and land use structure (Hariohay et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe layers used for the spatial modeling, along with their respective year, resolution, source, and unit.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSentinel-2 MSI L2A (Copernicus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNDWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSentinel-2 MSI L2A (Copernicus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElevation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSRTM v3 / NASA (USGS Earth Explorer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeters above sea level (m.a.s.l.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCopernicus Global Land Cover Layers (CGLS-LC100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature Annual Range (BIO7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970\u0026ndash;2000 (Average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 arc-seconds (~\u0026thinsp;1 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorldClim v2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePrecipitation (BIO12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970\u0026ndash;2000 (Average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 arc-seconds (~\u0026thinsp;1 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorldClim v2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emm\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 \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Multi-Criteria Decision Analysis\u003c/h2\u003e \u003cp\u003eA multi-criteria decision analysis (MCDA) was conducted using the AHP developed by Thomas L. Saaty (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Pairwise comparisons were guided by Saaty\u0026rsquo;s (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) 1\u0026ndash;9 fundamental scale, in which higher values indicate a stronger relative influence of one criterion over another; in this study, higher scores indicate greater contribution to the occurrence and spatial vulnerability of HWC in Mindoro. A pairwise comparison matrix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was constructed in Microsoft Excel (Microsoft 365, Version 2601) to systematically evaluate each parameter against every other criterion within the set, based on structured expert judgment informed by contextual knowledge of species ecology, landscape configuration, and anthropogenic pressures operating in the study area.\u003c/p\u003e \u003cp\u003eThe expert panel consisted of a university-level terrestrial ecologist, a conservation wildlife ecologist, and an environmental impact assessor, all of whom possess extensive professional and field experience in Mindoro and have collectively conducted multiple research and conservation projects in the province. Consultations were conducted in successive stages to conceptualize the analytical framework, refine criteria definitions and assumptions, and collectively assign and validate weights to reduce individual bias and strengthen consensus. Matrix consistency was assessed using the Consistency Ratio (CR), where values below 0.10 are considered acceptable in AHP applications (Pant et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The computed CR of 0.083 indicates that the Consistency Index (CI) accounts for 8.3% of the corresponding Random Index (RI) for the given matrix size, reflecting acceptable, reasonably consistent judgments and supporting the robustness of the derived weights. These normalized weights were then applied in a weighted linear combination (WLC) to generate a spatially explicit visualization of HWC vulnerability across Mindoro.\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\u003ePairwise comparison matrix of the parameters\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBIO7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBIO12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNDWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElevation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIO7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIO12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\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 \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Weighted Linear Combination\u003c/h2\u003e \u003cp\u003eThe environmental layers were combined using a WLC in the Raster Calculator tool in ArcGIS Pro 3.6.0, with each layer multiplied by its AHP-derived normalized weight (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This process produced a composite surface highlighting landscapes most susceptible to HWC, reflecting the integrated influence of ecological and geophysical drivers. The resulting WLC raster was further refined in QGIS to improve symbology and layout, enhancing the visualization and interpretability of spatial patterns. By capturing the combined effects of all layers, this approach allows the identification of areas where environmental conditions and landscape characteristics jointly elevate conflict risk, providing a spatial framework for understanding the relative contribution of each driver and supporting evidence-based management and mitigation strategies.\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\u003eRelative importance of geophysical variables used in the AHP model based on standardized weights and percentage contribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(% Contribution)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNDWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElevation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIO7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIO12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\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. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial Patterns of Geophysical Drivers\u003c/h2\u003e \u003cp\u003eThe spatial distribution of the environmental layers used in the analysis is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These maps highlight how elevation, climate, surface moisture, vegetation productivity, and land use vary across the island, providing visual context for the environmental gradients that structure the landscape. Pronounced environmental gradients characterize the landscape, where elevation rises sharply from near sea level to 2,564 masl., forming a rugged central mountain range that anchors the island\u0026rsquo;s physical structure. This elevational backbone organizes clear climatic contrasts across space. The annual temperature range varies from 8.9 to 12.3\u0026deg;C, with relatively higher variability in inland and elevated areas, while annual precipitation (1,947\u0026ndash;3,269 mm) varies markedly across the island, with comparatively wetter conditions in the southern and eastern sectors. These patterns highlight the strong influence of topography in structuring local climatic variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurface moisture, vegetation productivity, and land use reflect these underlying gradients. NDWI values (0\u0026ndash;1) indicate increased surface moisture in low-lying areas and along drainage networks, contrasting with relatively drier upland zones. EVI values (0\u0026ndash;1) delineate dense, contiguous vegetation across the montane interior, highlighting the concentration of forested habitats at higher elevations. The LULC layer further illustrates this spatial organization: tree cover predominates in the uplands, grasslands and croplands form a mosaic across transitional and low-elevation landscapes, and built-up areas are largely concentrated in accessible coastal plains and valley corridors. Collectively, these layers reveal a spatially structured and ecologically stratified system that provides the biophysical foundation for subsequent weighting procedures and HWC risk modeling. These spatial patterns also reflect the strong influence of topography and land use on environmental conditions across Mindoro Island, shaping the distribution of natural habitats and human-modified landscapes throughout the island.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HWC Vulnerability and Hotspot Delineation\u003c/h2\u003e \u003cp\u003eApplication of the AHP framework produced a spatially structured pattern of HWC vulnerability characterized by clear regional contrasts and clustered hotspots. In both scenarios, higher vulnerability classes are concentrated predominantly in the south-central to southwestern portions of Mindoro Island, while much of the northern sector remains dominated by very low to low classes, resulting in a discernible north\u0026ndash;south gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that under the equal-weight model, moderate vulnerability represents the largest proportion of the landscape (29.5%), followed by low (28.3%) and high (18.0%) classes. High and very high categories collectively account for 25.0% of the total area, with very high vulnerability covering 7.0% (696.7 km\u0026sup2;). When WLC-AHP\u0026ndash;derived weights are applied, the spatial pattern becomes more consolidated and polarized. The proportion of very high vulnerability increases from 7.0% to 9.6% (954.5 km\u0026sup2;), while high vulnerability slightly increases to 18.6%, resulting in a combined 28.2% of the island classified as high to very high risk. Conversely, moderate vulnerability declines substantially from 29.5% to 23.8%, while very low areas expand from 17.2% to 20.4%. These shifts indicate a reduction in intermediate-risk zones and a redistribution toward both lower and higher extremes, producing a more differentiated and spatially coherent vulnerability surface under the weighted model. This redistribution suggests that applying variable weights enhances the influence of key geophysical drivers in the model, allowing areas with similar environmental conditions to emerge as more clearly defined clusters of vulnerability across the landscape.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of total land area (sqkm.) and area coverage (%) for each class in the equal-weighted scenario and WLC-AHP assisted scenario\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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEqual-Weighted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWLC-AHP assisted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (sqkm.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea Coverage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (sqkm.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea Coverage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1705.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2813.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2743.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2926.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2364.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1794.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1851.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e696.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e954.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.6\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":"4. Discussion","content":"\u003cp\u003eAHP modeling reveals that landscape vulnerability to HWC in Mindoro arises from the interplay of multiple environmental drivers rather than a single factor. For instance, vegetation productivity, captured by EVI, plays a central role. Areas with moderate to high productivity often occur along forest margins, secondary growth, and agricultural mosaics where human cultivation meets wildlife habitat. These areas provide abundant forage for wildlife while simultaneously supporting crops, creating a spatial overlap that facilitates HWC (Fortin et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Importantly, EVI highlights not just the presence of vegetation, but also its quality and density, which influence wildlife foraging behavior, movement patterns, and seasonal habitat selection (Sun et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Poland, similar mixed forest\u0026ndash;agriculture interfaces attract wild boars and deer, which preferentially feed in high-productivity cropland patches adjacent to forests (Kuka et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, in India, elephants exploit mosaic landscapes where croplands and forests coincide, showing how productivity gradients shape both resource use and conflict potential (Anoop et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Mindoro, high-EVI areas along forest edges likely function as biodiversity corridors connecting upland refugia with lowland feeding areas and making these zones predictable HWC hotspots.\u003c/p\u003e \u003cp\u003eWater availability, indicated by NDWI, further shapes HWC risk by acting as both a resource and a movement attractor (Rumiano et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Streams, ponds, wetlands, and irrigated fields concentrate wildlife activity because animals rely on consistent water sources for drinking, foraging, and thermoregulation (Bate and Dagamac \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). NDWI identifies these moisture-rich areas across the landscape, allowing us to see where wildlife is most likely to overlap spatially with human land use. Climatic variables, represented by BIO7 and BIO12, add another layer of influence: temperature ranges and annual precipitation govern vegetation growth cycles, seasonal water availability, and resource distribution, indirectly guiding wildlife movement and habitat use (Guti\u0026eacute;rrez-Hern\u0026aacute;ndez and Garc\u0026iacute;a \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Namibia, human\u0026ndash;elephant conflict has been observed to increase during periods of drought, higher temperatures, and reduced precipitation, as elephants expand their movements in search of alternative sources of shelter, food, and water for thermoregulation during seasonal climatic variation (Shiweda et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By combining NDWI with climatic variables, the model identifies zones where water resources, habitat suitability, and human activity overlap, effectively highlighting areas where HWC is likely to occur seasonally or persistently. These drivers underscore that HWC is not random; it is a predictable outcome of resource-driven movement and human presence.\u003c/p\u003e \u003cp\u003eLULC and elevation together define the physical canvas on which HWC unfolds, shaping accessibility, connectivity, and movement pathways (Sharma et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pavithra et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Low- and mid-elevation zones tend to host settlements, roads, and agricultural fields, while forested uplands serve as refugia for wildlife. In Mindoro, landscapes where forest edges transition into agricultural land and other modified LULC types at accessible elevations are likely to experience greater spatial overlap between wildlife habitat and human land use. Such configurations can facilitate wildlife movement from forested areas into nearby cultivated landscapes, increasing the probability of encounters between wildlife and local communities (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Elevation, when considered alongside LULC, therefore influences landscape permeability and connectivity by shaping how wildlife move between habitat patches and human-dominated areas (Sharma et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ranjan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These spatial relationships highlight how HWC vulnerability is often concentrated in transitional zones where ecological connectivity and agricultural land use intersect, a pattern widely documented in studies examining human\u0026ndash;wildlife interactions across mixed-use landscapes (Buccholtz et al. 2020; Shrestha et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Management and Conservation Implications\u003c/h2\u003e \u003cp\u003eIn the central mountainous regions of Mindoro, Indigenous Peoples (IPs) inhabit forested areas deep within the mountains and have coexisted with wildlife in these core habitats for generations (Ishihara et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Yet the introduction of human-mediated croplands within these landscapes can attract species such as the endemic tamaraw, leading to localized HWC. These social and land-use dynamics highlight the importance of understanding HWC within both ecological and human contexts. The spatial patterns of HWC identified in Mindoro further emphasize the need for management strategies that are both spatially explicit and ecologically informed. Specifically, areas where high EVI, NDWI, and favorable climatic conditions intersect with accessible LULC and moderate elevations represent predictable hotspots, suggesting that interventions should focus on these landscapes. Maintaining landscape connectivity in the central and southern portions of Mindoro Island, particularly across heterogeneous mosaics of forest patches, croplands, and grasslands, may help reduce HWC by guiding wildlife movement along safe corridors and away from human settlements. In this context, strengthening ecological linkages among forest fragments can channel wildlife movement through permeable landscapes, reducing encounters with nearby settlements and helping limit crop raiding in adjacent agricultural areas. Additionally, establishing buffer zones or agroforestry strips along forest\u0026ndash;agriculture interfaces can provide transitional habitats that discourage wildlife from entering cultivated fields. Water resources, captured by NDWI, are critical nodes for intervention, as wildlife tend to congregate around streams, ponds, and irrigated fields; protecting these within natural habitats and designing irrigation systems that minimize attractants near settlements can further reduce HWC. Seasonal climatic variability, as indicated by Annual Temperature Range (BIO7) and Annual Precipitation (BIO12), underscores the need for time-sensitive mitigation efforts, with increased monitoring during dry periods when water scarcity drives wildlife into human-dominated areas, and during wet seasons when lowland fields are more accessible. Elevation and LULC together highlight the importance of terrain-sensitive planning, where maintaining upland forest refugia while improving permeability in mid- and low-elevation zones through wildlife-friendly corridors can facilitate safe movement. Finally, community engagement remains central, particularly in lowland areas where agriculture and settlements abut forests; education, early-warning systems, crop protection techniques, and compensation schemes for damage can enhance coexistence. By integrating AHP-derived vulnerability maps into conservation and land-use planning, local authorities and stakeholders can prioritize interventions, optimize resources, and anticipate high-risk areas before conflicts escalate (Pandit et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thammaboribal et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Study Limitations and Future Research\u003c/h2\u003e \u003cp\u003eThe AHP-based framework applied in this study provides a spatial perspective on landscape vulnerability to HWC in Mindoro Island, yet several limitations should be considered when interpreting the results. The model primarily incorporates geophysical drivers, including EVI, NDWI, elevation, LULC, Annual Temperature Range (BIO7), and Annual Precipitation (BIO12), which capture environmental conditions influencing wildlife habitat suitability and movement but do not explicitly account for social or behavioral factors that also shape HWC dynamics. Variables such as settlement density, wildlife distribution, and local mitigation practices may influence where conflicts ultimately occur, and their exclusion may limit the model\u0026rsquo;s ability to fully represent the complexity of human\u0026ndash;wildlife interactions across the landscape. The AHP weighting process also involves an element of expert judgment, meaning that the relative importance assigned to each driver reflects informed interpretation rather than purely empirical relationships that other covariates may generate. Although widely applied in spatial decision-making, alternative weighting schemes or the inclusion of additional variables could alter the model's spatial distribution of vulnerability.\u003c/p\u003e \u003cp\u003eData resolution and temporal variability also present constraints. Satellite-derived indices such as EVI and NDWI capture environmental conditions over specific time periods, whereas climatic variables such as Annual Temperature Range (BIO7) and Annual Precipitation (BIO12) represent long-term averages that may obscure seasonal fluctuations that influence wildlife movement and resource availability. In addition, the resulting vulnerability map represents potential landscape susceptibility to HWC rather than confirmed conflict occurrences and should therefore be interpreted as indicative patterns of risk rather than direct predictions of conflict events. Future research could strengthen predictive capacity by incorporating higher-resolution environmental data, species-specific ecological information, and socio-economic variables that reflect patterns of human land use. Integrating AHP with complementary spatial approaches, such as species distribution modeling or analyses based on documented HWC incidents, may further refine hotspot identification and enable more robust validation of vulnerability patterns. Expanding field-based monitoring and community reporting of HWC events would also improve empirical datasets that can support future spatial modeling efforts and contribute to more adaptive, evidence-based strategies for managing HWC across Mindoro Island.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates the value of spatial multi-criteria approaches for identifying landscape vulnerability to HWC across Mindoro Island. Using AHP, the analysis integrates several geophysical drivers that influence wildlife movement and habitat conditions, including vegetation productivity (EVI), water availability (NDWI), terrain structure, land-use patterns (LULC), and climatic variability such as Annual Temperature Range (BIO7) and Annual Precipitation (BIO12). HWC vulnerability, therefore, emerges from the spatial convergence of these conditions, particularly in heterogeneous landscapes where forest habitats, agricultural lands, and accessible terrain occur in close proximity.\u003c/p\u003e \u003cp\u003eThe resulting vulnerability patterns highlight the importance of elevational gradients, landscape connectivity, and resource distribution in shaping wildlife movement between upland refugia and lowland agricultural areas. Mapping these spatial relationships provides a basis for anticipating where HWC risk may intensify and supports more proactive management strategies for the island of Mindoro. Integrating AHP-derived vulnerability maps into conservation planning and land-use management can help prioritize intervention areas, guide mitigation efforts, and improve the allocation of conservation resources for managing HWC in Mindoro Island.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eEthics Declaration\u003c/h2\u003e \u003cp\u003eThis study did not involve human participants, animals, or the collection of primary field data. The research was based solely on the Analytic Hierarchy Process (AHP) and secondary environmental spatial datasets obtained from publicly available sources. All data were used in accordance with their respective licensing and citation requirements. No ethical approval was required for this study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.J.M.R.: Methodology, Formal analysis, Validation, Visualization, Data Curation, Writing, Scoring - original draft. J.M.B.: Conceptualization, Supervision, Writing, Scoring, Review \u0026amp; Editing. N.H.A.D.: Conceptualization, Supervision, Writing, Scoring, Review \u0026amp; Editing.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eL.J.M.R. acknowledges DOST-ASTHRDP for the scholarship grant. N.H.A.D. would like to thank DOST-PCAARRD for the Balik Scientist grants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Remote sensing layers, including Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were derived from Sentinel-2 L2A imagery (Copernicus, 2024\u0026ndash;2025). Land use and land cover (LULC) data were obtained from the Copernicus Land Monitoring Service (CLC, 2020). Elevation data were sourced from the Shuttle Radar Topography Mission (SRTM, 30 m resolution), and bioclimatic variables (BIO7: temperature annual range; BIO12: annual precipitation) were acquired from WorldClim v2.1. Processed GIS layers, AHP weightings, and the resulting HWC vulnerability maps for Mindoro Island can be provided in standard GIS formats (e.g., raster, shapefile) upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbass K, Qasim MZ, Song H et al (2022) A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environ Sci Pollut Res 29:42539\u0026ndash;42559. ttps://doi.org/10.1007/s11356-022-19718-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbrahms B, Aikens EO, Armstrong JB et al (2021) Emerging Perspectives on Resource Tracking and Animal Movement Ecology. 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Global Ecol Conserv 24:e01284. ttps://doi.org/10.1016/j.gecco.2020.e01284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P, Gurung J, Wangchuk K, Uddin K, Chettri N (2022) Changing Landscape and Escalating Human-Wildlife Conflict: Introspection from a Transboundary Landscape. Springer EBooks 459\u0026ndash;476. ttps://doi.org/10.1007/978-3-030-98233-1_17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiweda M, Shivute F, Raquel A, Pereira MJ (2023) Climate Change and Anthropogenic Factors Are Influencing the Loss of Habitats and Emerging Human\u0026ndash;Elephant Conflict in the Namib Desert. Sustainability 15(16):12400\u0026ndash;12400. ttps://doi.org/10.3390/su151612400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha S, Shrestha UB, Shrestha P, Joshi S (2026) Livelihood vulnerability and human wildlife conflict in Nepal\u0026rsquo;s lowland protected areas. 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J Geogr Reg Plann 6(5):193\u0026ndash;199. ttps://doi.org/10.5897/jgrp2013.0365\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThammaboribal P, Triapthti N, Lipiloet S (2025) Using of Analytical Hierarchy Process (AHP) in Disaster Management: A Review of Flooding and Landslide Susceptibility Mapping. Int J Geoinformatics 21(4). ttps://doi.org/10.52939/ijg.v21i4.4091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiller LN, Humle T, Amin R, Deere NJ, Lago BO, Leader-Williams N, Sinoni FK, Sitati N, Walpole M, Smith RJ (2021) Changing seasonal, temporal and spatial crop-raiding trends over 15 years in a human-elephant conflict hotspot. Biol Conserv 254:108941. ttps://doi.org/10.1016/j.biocon.2020.108941\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnruh JD (2006) Land Tenure and the Evidence Landscape in Developing Countries. Ann Assoc Am Geogr 96(4):754\u0026ndash;772. ttps://doi.org/10.1111/j.1467-8306.2006.00515.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeridiano-de Castro NA, Almadrones-Reyes KJ, Rede\u0026ntilde;a-Santos JC, Limbo-Dizon JE, Dagamac NHA (2024) Applying geomatic analyses using landsat imagery: implications for ecosystem management in Occidental Mindoro, the Philippines. GeoJournal 89(5). ttps://doi.org/10.1007/s10708-024-11215-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaheb H, Obaidi O, Mukhtar S, Shirani H, Ahmadi M, Yona A (2024) Comprehensive Analysis and Prioritization of Sustainable Energy Resources Using Analytical Hierarchy Process. Sustainability 16(11):4873. ttps://doi.org/10.3390/su16114873\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZvidzai M, Mawere K, Ndaimani N\u0026rsquo;anduR, Chenjerai Zanamwe H, Fadzai Zengeya (2023) Application of maximum entropy (MaxEnt) to understand the spatial dimension of human\u0026ndash;wildlife conflict (HWC) risk in areas adjacent to Gonarezhou National Park of Zimbabwe. Ecol Soc 28(3). ttps://doi.org/10.5751/es-14420-280318\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"risk assessment, landscape connectivity, remote sensing, decision-support mapping, tropical island","lastPublishedDoi":"10.21203/rs.3.rs-9090745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9090745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman\u0026ndash;wildlife conflict (HWC) remains a growing challenge in tropical landscapes where expanding human land use overlaps with wildlife habitats. Identifying areas vulnerable to HWC is critical for guiding targeted conservation and land management strategies. This study applies a spatial multi-criteria approach to evaluate geophysical drivers shaping HWC vulnerability across Mindoro Island, Philippines. An Analytical Hierarchy Process (AHP) was used to integrate key environmental variables, including vegetation productivity (EVI), water availability (NDWI), land use and land cover (LULC), elevation, and climatic factors represented by temperature annual range (BIO7) and annual precipitation (BIO12). These drivers were combined within a GIS-based framework to generate a spatial vulnerability map identifying areas where environmental conditions and topographic characteristics may facilitate greater overlap between wildlife movement and human activities. The resulting spatial patterns indicate that HWC vulnerability tends to occur in heterogeneous landscapes where productive vegetation, accessible terrain, and water resources coincide with agricultural land use. Areas located along transitional zones between forested habitats and cultivated landscapes at low to mid elevations appear particularly susceptible to Human-Wildlife Interactions (HWI). These patterns highlight the importance of spatially explicit approaches for anticipating potential conflict-prone areas and informing proactive mitigation strategies, including landscape planning, habitat connectivity management, and targeted monitoring. By integrating geophysical drivers within a spatial decision-making framework, this study provides a practical tool for identifying potential HWC risk areas and supporting conservation planning in rapidly changing tropical landscapes.\u003c/p\u003e","manuscriptTitle":"Spatial Multi-Criteria Modeling of Geophysical Drivers Shaping Human-Wildlife Conflict Vulnerability in Mindoro, Philippines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 14:46:41","doi":"10.21203/rs.3.rs-9090745/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":"07696e5e-f7e6-4758-bff1-372f97e441be","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:13:42+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:46:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 14:46:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9090745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9090745","identity":"rs-9090745","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}