Mapping Leopard Conservation Priorities in Southeast Asia

Mapping Leopard Conservation Priorities in Southeast Asia | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 March 2026 V1 Latest version Share on Mapping Leopard Conservation Priorities in Southeast Asia Authors : Luciano Atzeni 0000-0002-4573-7431 , Jan Kamler 0000-0003-4148-2088 , Eric Ash , Pyae Kyaw 0000-0002-2763-5706 , Akchousanh Rasphone , Chanratana Pin , Cedric Tan , … Show All … , Susana Rostro-García , Patrick Jantz , Ivan Gonzalez , Dawn Burnham , Samuel Cushman , and Dawid W. Macdonald [email protected] Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.177331006.62561873/v1 310 views 168 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Indochinese leopard (Panthera pardus delacouri) is one of the most endangered leopard subspecies, restricted to less than 4% of its historical range across Southeast Asia. With severe population declines driven by poaching, habitat loss and fragmentation, and prey depletion, effective recovery requires urgent, spatially-informed conservation action. This study presents the first comprehensive connectivity analysis for the Indochinese leopard across its extant and former range, aiming to identify core habitats, connectivity corridors, and opportunities for population restoration. We used resistant kernel modelling through the CoLa Decision Support System to evaluate landscape connectivity across 14 forest complexes in the extant range and 11 potential reintroduction areas, considering scenarios of extant, reintroduction, and combined range connectivity. Importance to connectivity was assessed based on kernel extent, movement density, and protected area overlap. Our results confirmed the Dawna–Tenasserim landscape, spanning parts of Thailand and Myanmar, and Peninsular Malaysia as the most critical strongholds to conserve. In the former range, our analysis revealed potential for large-scale metapopulation structural connectivity across Cambodia and Lao PDR, despite recent local extirpations. Realizing reintroduction opportunities will require complementary actions such as reducing poaching, restoring prey, and improving habitat management. In some complexes, intensive landscape restoration may be needed to reconnect major core areas with smaller reintroduction complexes that could still be critical for meta-population connectivity. Despite increasing human pressure in the region, substantial habitat and connectivity potential remain. Our framework provides a spatial foundation to prioritize actions for preventing extinction and guiding large carnivore recovery across fragmented tropical landscapes. Mapping Leopard Conservation Priorities in Southeast Asia ABSTRACT The Indochinese leopard ( Panthera pardus delacouri ) is one of the most endangered leopard subspecies, restricted to less than 4% of its historical range across Southeast Asia. With severe population declines driven by poaching, habitat loss and fragmentation, and prey depletion, effective recovery requires urgent, spatially-informed conservation action. This study presents the first comprehensive connectivity analysis for the Indochinese leopard across its extant and former range, aiming to identify core habitats, connectivity corridors, and opportunities for population restoration. We used resistant kernel modelling through the CoLa Decision Support System to evaluate landscape connectivity across 14 forest complexes in the extant range and 11 potential reintroduction areas, considering scenarios of extant, reintroduction, and combined range connectivity. Importance to connectivity was assessed based on kernel extent, movement density, and protected area overlap. Our results confirmed the Dawna–Tenasserim landscape, spanning parts of Thailand and Myanmar, and Peninsular Malaysia as the most critical strongholds to conserve. In the former range, our analysis revealed potential for large-scale metapopulation structural connectivity across Cambodia and Lao PDR, despite recent local extirpations. Realizing reintroduction opportunities will require complementary actions such as reducing poaching, restoring prey, and improving habitat management. In some complexes, intensive landscape restoration may be needed to reconnect major core areas with smaller reintroduction complexes that could still be critical for meta-population connectivity. Despite increasing human pressure in the region, substantial habitat and connectivity potential remain. Our framework provides a spatial foundation to prioritize actions for preventing extinction and guiding large carnivore recovery across fragmented tropical landscapes. Keywords : Indochinese leopard; Panthera pardus delacouri; habitat connectivity; conservation translocation; reintroduction; CoLa DSS Introduction Large carnivores have experienced dramatic global population declines and range contractions, with leopards ( Panthera pardus ) being particularly affected across their distribution (Stein et al. 2020; Jacobson et al. 2016). Among the subspecies, the Indochinese leopard ( P. p. delacouri ) has undergone one of the most severe declines, now persisting in only 4% of its historical range (Jacobson et al. 2016; Rostro-García et al. 2016). This contraction mirrors the fate of other Asiatic leopard subspecies, each now occupying merely 2% of their former distributions (Jacobson et al. 2016), reflecting broader carnivore declines across Asia (Gray et al. 2018). The Critically Endangered Indochinese leopard has experienced a population decline greater than 80% over the last three generations (Rostro-García et al. 2019). Once widespread from southern China’s Pearl River to Singapore, with the Irrawaddy River in Myanmar marking its western limit (Kitchener et al. 2017), the subspecies is now extirpated from Vietnam (Rostro-García et al. 2016; Willcox et al. 2014), Lao People’s Democratic Republic (PDR), and Cambodia (Rostro-García et al. 2016, 2019, 2023; Rasphone et al. 2019). This decline reflects a broader conservation crisis in Southeast Asia, a region experiencing some of the world’s highest deforestation rates and biodiversity loss (Hughes 2017; Tilker et al. 2019; Gray et al. 2018). Habitat fragmentation, illegal wildlife trade, snaring for bushmeat and infrastructure development, threaten the subspecies’ survival (Tilker et al. 2019; Lam et al. 2023; Rostro-García et al. 2023; Rasphone et al. 2021), with snaring emerging as the primary threat to the persistence of large bodied terrestrial wildlife species (O’Kelly et al. 2018; Gray et al. 2018; Ripple et al. 2016). Currently, the Indochinese leopard persists in two major strongholds: the Northern Tenasserim Forest Complex (Thailand and Myanmar) and Peninsular Malaysia (Rostro-García et al. 2016, 2019). In Thailand, key populations occur in the Western Forest Complex and Kaeng Krachan-Kuiburi Complex, with others in Hala Bala Wildlife Sanctuary, Salawin National Park, and Namtok Huai Yang National Park along international borders (Rostro-García et al. 2016, 2019). Myanmar hosts populations in the Southern Forest Complex extending to Lenya National Park (Rostro-García et al. 2019), a potentially viable group in northern Karen State (Greenspan et al. 2021, 2022), and isolated populations in northern protected areas (Kyaw et al. 2021, 2024). In Malaysia, populations remain across major forest complexes (Belum-Temengor, Taman Negara National Park, Endau-Rompin National Park) and fragmented secondary forests (Rostro-García et al. 2016, 2019). With an estimated total of 114–1,130 individuals (including 77–766 mature; Rostro-García et al. 2019) and recent functional extirpations in Thailand’s Khlong Saeng–Khao Sok Complex (Rostro-García et al. 2024), urgent conservation action is needed. Population assessments reveal alarmingly low densities of Indochinese leopards across their remaining range. Salak Phra Wildlife Sanctuary recorded the lowest density at just 0.23 leopards/100 km² (Rostro-García et al. 2024). Similarly low figures were reported in Kweekoh Wildlife Sanctuary, eastern Myanmar (0.72–1.39 leopards/100 km²; Greenspan et al. 2023), while Malaysian protected areas showed densities from 0.98 in Pasir Raja to 2.64 leopards/100 km² in Taman Negara (Rostro-García et al. 2024). These numbers reflect dramatic declines across nearly all remaining subpopulations. Before its extirpation, Cambodia’s Srepok Wildlife Sanctuary population declined 82% over ten years (2009–2019), including a 72% decline in just five years (2009–2014), primarily due to poaching (Rostro-García et al. 2019, 2023). In Malaysia, Kenyir Wildlife Corridor experienced full population turnover and a 44% density decline between 2013 and 2015 (Hedges et al. 2015; Rimba unpubl. data; Rostro-García et al. 2019). Even well-protected sites like Huai Kha Khaeng Wildlife Sanctuary in Thailand saw a 38% decline in just three years (Simcharoen & Duangchantrasiri 2008; Rostro-García et al. 2019), indicating that no stronghold remains secure from anthropogenic pressures (e.g., Lam et al. 2023). For large carnivores like the Indochinese leopard, landscape connectivity is crucial for population persistence (Cushman et al. 2006; Kaszta et al. 2020a,b). Apex predators require extensive ranges and traverse diverse topographic and land-use conditions during dispersal (Fattebert et al. 2013; Elliot et al. 2014; Hussain et al. 2022). Dispersal habitat use may resemble that of resident individuals (Fattebert et al. 2015), yet other studies demonstrate movements across highly modified and fragmented landscapes, indicating that functional connectivity can persist even under strong human disturbance (Elliot et al. 2014; Wilkinson et al. 2024). At the same time, realized connectivity is often constrained by mortality risks and disturbance, even when structural corridors are available (Whittington et al. 2022; Day et al. 2020; Ash et al. 2023). Connectivity can also decline abruptly once habitat loss exceeds certain thresholds, limiting opportunities for dispersal (Cushman et al. 2010; Zemanova et al. 2017). These examples emphasize the need to distinguish structural corridors derived from models from functional connectivity shaped by anthropogenic pressures (Pitman et al. 2016; Betts et al. 2015; Modi et al. 2025; Kanagaraj et al. 2013). While structural connectivity provides a foundation, long-term persistence ultimately depends on reducing persecution and ensuring sufficient prey and habitat quality (Bleyhl et al. 2021). In increasingly fragmented landscapes, maintaining connectivity has become central to conservation planning (Macdonald et al. 2024; Kaszta et al. 2024; Brennan et al. 2022). Recent studies in the Indochinese leopard’s range have identified critical corridors and bottlenecks affecting large felids’ movements (Greenspan et al. 2021; Kaszta et al. 2020a,b; Ash et al. 2025, Suttidate et al. 2021), while broader analyses highlighted the importance of smaller habitat patches in sustaining connectivity (Diniz et al. 2021; Rezaei et al. 2022; Kanagaraj et al. 2013). Connectivity is also relevant to conservation translocations, seen as crucial tools for species recovery (Betts et al. 2015; Jule et al. 2008; Wolf and Ripple 2018; Thomas et al. 2023), which require a thorough understanding of landscape connectivity patterns to ensure long-term success in highly-fragmented landscapes (Zemanova et al. 2017; Gantchoff et al. 2022; Greenspan et al. 2021; Wilkinson et al. 2024). Given the daunting conservation situation for the Indochinese leopard, understanding patterns of landscape connectivity becomes crucial (Cushman et al. 2018; Ash et al. 2025; Kaszta et al. 2020a). While recent research has documented population persistence in key strongholds (Rostro-García et al. 2019; Greenspan et al. 2021, 2022; Rostro-García et al. 2024), significant knowledge gaps remain regarding connectivity across the subspecies’ range. Connectivity has been investigated for other large felids in Southeast Asia (Kaszta et al. 2020a,b; Greenspan et al. 2021; Suttidate et al. 2021; Ash et al. 2025; Kanagarai et al. 2013; Modi et al. 2025), yet a comprehensive range-wide connectivity analysis for the Indochinese leopard is still lacking. This gap is particularly critical for identifying and evaluating prospective reintroduction areas that could establish new core populations and restore lost connectivity in historically occupied regions. Such efforts require a deep understanding of both current and potential connectivity patterns, essential for developing effective corridor networks (Kaszta et al. 2020a,b) and guiding reintroduction efforts that contribute to metapopulation recovery (Thomas et al. 2023; Jule et al. 2008; Betts et al. 2015). This analysis is urgent given Southeast Asia’s rapid landscape change, which undermines opportunities for maintaining and restoring functional connectivity. To address these knowledge gaps, we present the first comprehensive connectivity analysis for the Indochinese leopard using cumulative resistant kernel modelling (Compton et al. 2007). Our approach evaluates connectivity among existing populations in extant forest complexes and assesses how potential reintroduction areas could serve as stepping stones or establish new connected strongholds. By prioritizing core habitats and key corridors, we provide a systematic framework to guide urgent recovery efforts for this critically endangered subspecies. Methods Creation of the resistance surface The basis for the resistance surface was a 2015 250m land cover map of South-east Asia (Miettinen et al. 2016). We reclassified the original layer into five categories (Supplementary Table 1). This approach simplifies the thematic resolution to reduce land cover classification uncertainty and provides a conservative, transparent analogue to the resistance surface developed for Indochinese tigers ( Panthera tigris corbetti ) in Ash et al. (2020a, 2022, 2025), thereby ensuring methodological comparability across regional large-carnivore connectivity studies. We clipped the layer using the Indochinese leopard range polygon from Rostro-García et al. (2019) to match the IUCN-defined range, excluding southern China, which was absent from the original land-cover dataset (Miettinen et al. 2016). Road features of the range countries were downloaded from OpenStreetMap (OpenStreetMap, 2023). Primary and secondary roads were converted to rasters at the same resolution as the land-cover dataset and overlaid on the reclassified resistance surface with the values of 100 and 30, respectively, using a cell statistic operation with rule = max (Ash et al. 2020a, 2025). We reclassified the GHSL - Global Human Settlement Layer (GHS-BUILT-S - R2023A; Pesaresi and Politis, 2023) into a binary raster, assigning built-up areas a value of 100, and everything else 1. After re-sampling to match landcover resolution, we used a cell statistic operation with rule = max to incorporate built-up areas on the resistance surface (Figure 1). All operations were performed in ArcGis Pro 3.2.2. Creation of source locations in the extant Indochinese leopard range We downloaded protected area polygon shapefiles for all countries within the Indochinese leopard’s IUCN range from the WDPA (UNEP-WCMC and IUCN, 2024) and merged them with Myanmar’s official dataset (WCS Myanmar, unpublished; MONREC, 2020, unpublished). Protected areas and forest complexes were selected as potential leopard source locations based on Rostro-García et al. (2016, 2019) and unpublished data contributing to Stein et al. (2020) and Rostro-García et al. (2019). In Myanmar, selections also drew on Kyaw et al. (2021, 2024) and unpublished presence data (WCS Myanmar; MONREC, 2020). After identifying all protected areas with Indochinese leopard populations, we overlaid the extant range from Stein et al. (2020) and extracted portions located outside protected areas. Protected areas and portions of extant range outside protected areas were then grouped into forest complexes using a proximity-based clustering approach: patches within 20 km were iteratively merged until all within-group patches were separated by no more than 20 km. This threshold was based on documented leopard dispersal capabilities, as individuals typically disperse long distances at maturity (Fattebert et al. 2013) and can cross fragmented, human-dominated landscapes (Wilkinson et al. 2024). The 20 km threshold represents a precautionary threshold, remaining far below largest reported dispersal distances (~194.5 km, Fattebert et al. 2013; ~111 km, Liang et al. 2022), supporting the aggregation of patches into structurally connected forest complexes. This grouping criterion generated the following starting localities (Table 1; Figure 2): The Western Forest Complex - Taninthayi (WFCT) spans across both Thailand and Myanmar, and connects several protected areas, including Thung Yai Naresuan Wildlife Sanctuary, Huai Kha Khaeng Wildlife Sanctuary, Salak Phra Wildlife Sanctuary, Khuen Si Nakarin National Park, and Khao Laem National Park in Thailand with the Taninthayi Nature Reserve in Myanmar. Further south, the Kaeng Krachan Forest Complex - Lenya - Taninthayi (KKLT) links Namtok Huay Yang National Park, Kuiburi National Park, and Kaeng Krachan National Park Forest Complex in Thailand with Myanmar’s Lenya National Park system, including its extension and Taninthayi National Park. The Salawin complex (SAL) in Thailand comprises Salawin National Park and its associated Wildlife Sanctuary. In Malaysia, the Taman Negara Forest Complex (TNFC) incorporates several protected areas, including Jerangau Forest Reserve, Jengai Forest Reserve, Hulu Nerus Forest Reserve, and Taman Negara across Pahang, Kelantan, and Terengganu states, along with Sungai Ketiar Wildlife Sanctuary. The Royal Belum - Gunung - Temenggor complex (RBGT) includes Royal Belum State Park and Gunung Basor Forest Reserve in Malaysia, connecting with Hala Bala Wildlife Sanctuary in Thailand. We included previously identified extant populations in Malaysia, including Ulu Muda Forest Reserve (UM), Tengku Hassanal Wildlife Reserve (TH; formerly Krau Wildlife Reserve), Endau-Rompin complex (ER), Pasoh Forest Reserve (PAS) and Ayer Hitam Forest Reserve (AH). We also considered several smaller but significant protected areas, including the Mulayit Wildlife Sanctuary (MUL), North Zarmayi Wildlife Sanctuary (NZWS) and Panlaung Pyadalin Cave Wildlife Sanctuary (PPC) in Myanmar (Figure 2). The number of source points per area, which serve as starting locations for the cumulative resistant kernel model, was calculated by multiplying the average mean density across all study areas in mainland South-east Asia (1.532 individuals/100 km²; Rostro-García et al. 2024) by the area of each forest complex (in km²), then dividing by 100. To allow variability in the locations of the source points, we created 10 sets of random source points using ArcGis Pro 3.2.2. Creation of source locations in proposed reintroduction areas in the Indochinese leopard former range We hypothesised 11 potential reintroduction locations based on recent local extinction times (Rostro-García et al., 2016, 2019, 2023; Stein et al. 2020) (Table 2; Figure 2). These areas extend across Thailand, Cambodia, and Lao PDR. In Thailand, we identified the Khlong Saeng Wildlife Sanctuary - Khao Sok National Park complex (KSKS), the Dong Phayayen-Khao Yai Forest Complex (DPKY), and the Nam Nao National Park - Phu Kiew Wildlife Sanctuary complex (NNPK). In Cambodia, the Cardamom Mountains complex (CAR) encompasses the Cardamom Corridor, Central Kravanh National Park, and Southern Kravanh National Park regions. The Northern Plains Landscape (NPL) includes three interconnected areas: Chhep Wildlife Sanctuary, Kulen Promtep Wildlife Sanctuary, and Preah Roka Wildlife Sanctuary. The Eastern Plains Landscape (EPL) comprises Keo Seima Wildlife Sanctuary, Lumphat Wildlife Sanctuary, Phnom Prich Wildlife Sanctuary, and Srepok Wildlife Sanctuary. The Virachey National Park - Xe Pian National Protected Area complex (VXP) uniquely spans both Cambodia and Lao PDR. In Lao PDR, we identified several additional sites: the Nam Ha National Protected Area - Nam Kan National Park complex (NHNK), the Nakai-Nam Theun National Park area (NN), Nam Et-Phou Louey National Park (NEPL), and Xe Xap National Protected Area (XX). The number of points initialised in these complexes followed the same rationale adopted for the extant range (Table 2). As with extant location, we introduced stochasticity in the modelling by creating 10 sets of random source points using ArcGis Pro 3.2.2. Connectivity modelling We used the software CoLa DSS (Connecting Landscapes Decision Support System) implemented as an R package (Jantz et al. 2025) to run resistant kernel models (Compton et al., 2007). Cumulative resistant kernels provide spatially comprehensive and omnidirectional assessments of connectivity patterns. The cumulative resistant kernel approach estimates expected movement density patterns from source locations to all other cells across the raster landscape and has been shown to outperform many other methods in estimating actual movement patterns (Cushman et al. 2014), gene flow (Atzeni et al. 2024), and simulated movement density (Lumia et al. 2023). The cumulative resistant kernel method calculates the probability that organisms originating from source points can reach specific cells within user-defined distance parameters based on the landscape’s resistance, the density and placement of source points, and the organism’s dispersal capability. The software is a wrapper and extension of pre-existing tools for modelling landscape connectivity (UNICOR, (Landguth et al., 2012)) and landscape genetics (CDPOP; (Landguth and Cushman, 2010), including added functionalities for the creation and modification of resistance surfaces. We simulated resistant kernels (function cola::crk_py( ) ) for two scenarios: one using source points in the extant range (Extant Range scenario) and another using source points from hypothesized reintroduction areas (Reintroduction scenario). The maximum dispersal ability was set at 200 km, with dispersal probability regulated by a Gaussian function. This threshold was informed by the longest documented straight-line leopard dispersal (~194.5 km for African leopards, Fattebert et al., 2013), that serves as a conservative upper bound rather than a typical value. Asia-based estimates for the North China leopard ( P. p. japonensis ) indicate dispersal potential of value used in this analysis is a cautious ceiling rather than an assumption of routine movements. Because dispersal probability in the model follows a Gaussian curve, the intensity of movement is highest near the source and declines progressively with distance, and is near zero well before the 200 km maximum is reached. The resistant kernel surfaces from each set of random points within each forest complex and scenario were averaged to create final connectivity surfaces. To reduce computational time, we re-sampled the resistance surface to 1,000 m cell size through the aggregate() function from the terra package (Hijmans, 2023) using the mean as the function. All analyses were run in RStudio (RStudio Team 2022) using R v.4.2.2 (R Core Team 2022). Core Area and Connectivity Kernel Analysis - single forest complexes For each scenario, we identified Core Areas, defined as areas with greater than the 80 th percentile of resistant kernel values. These areas represent locations where the expected density of movement is highest and define the zones that would likely support stable populations given the potential dispersal ability. Additionally, we identified Connectivity Kernels above the 20 th percentile of the resistant kernel values. Unlike Core Areas, these encompass regions characterized by lower (albeit non-negligible) intensity of movement, and represent critical areas that may facilitate dispersal processes around and between stable areas. To rank these areas, we applied several key metrics, following Kaszta et al. (2020a) and Cushman et al. (2018). These included the total area of each patch (kernel extent), the sum of the kernel values within each patch (kernel sum), and the number of protected areas intersected by, or overlapping with, the core areas or connectivity kernel patches. The analyses were conducted separately for each forest complex in both Extant Range and Reintroduction scenarios. The relative ranking of each metric for each patch was determined as the ratio of the metric value to the highest values across all patches within the same percentile category and scenario (Ash et al. 2025). These relative rankings were then averaged to provide a final mean rank for each patch (Ash et al. 2025). Core Areas and Connectivity Kernels were created through a combination of raster, vector and dataframes operations using the R packages terra (Hijmans, 2023) and sf (Pebesma, 2018; Pebesma and Bivand, 2023). Core Area and Connectivity Kernel Analysis - overlaid outputs To create a composite kernel output across the entire Indochinese leopard range for both the Extant Range and Reintroduction scenarios, we employed a raster operation using the maximum value rule for overlapping cells. This approach was chosen over a summation rule to better represent the actual movement potential across the landscape. Using maximum values prevents the artificial inflation of movement intensity that would occur when summing values from adjacent forest complexes. This is important because movement intensity is fundamentally determined by landscape characteristics rather than the additive effect of neighbouring source areas. When two forest complexes are connected, an assessment of their combined influence on movement should reflect the underlying landscape connectivity rather than the sum of their individual effects. Percentile thresholds (80 th for Core Areas and 20 th for Connectivity Kernels) were applied to these composite outputs to create multi-polygon features. The ranking methodology remained consistent with our previous analysis, applying the same metrics (kernel extent, kernel sum, protected area intersection, average rank). Finally, we created a composite scenario by overlaying the outputs from both Extant Range and Reintroduction scenarios, applying the same threshold and ranking methodology to the merged raster. Results This section presents the results of our CoLa DSS connectivity analysis across mainland Southeast Asia based on the composite kernel outputs from both the Extant Range and Reintroduction scenarios, as well as their combined assessment. Detailed results for individual forest complexes within each scenario are provided as supplementary material (Supplementary Tables 2 and 3; Supplementary Figures 4 and 5). 3.1. Extant Range Scenario In the Extant Range scenario (Table 3, Figure 3; Supplementary Figure 1), we identified nine Connectivity Kernels (> 20 th percentile) across the region. The MUL-KKLT-WFCT cluster (Thailand-Myanmar) achieved the highest mean ranking (0.86), due to the largest proportions of kernel extent (39.01%, 67,386.46 km²) and kernel sum (57.94%), connecting 32 protected areas. The TH-TNFC-RBGT cluster (southern Thailand–northern Peninsular Malaysia) ranked second (mean rank 0.68), occupying 23.71% of total extent (40,958.31 km²) and 24.8% of kernel sum, while notably achieving the highest protected area coverage with 56 connected protected areas. The Salawin (SAL) complex ranked third (mean rank 0.34), contributing 20.84% of total extent (35,997.53 km²) and 12.57% of kernel sum, connecting 15 protected areas. Five Core Areas (> 80 th percentile) were identified. WFCT (Western Forest Complex - Taninthayi Nature Reserve) achieved the highest mean ranking (0.94), with 27.78% of kernel extent (11,996.17 km²), the highest kernel sum ranking (34.13%), and linking 10 protected areas. KKLT (Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park) ranked second (mean rank 0.82), showing the highest kernel extent ranking (30.18%, 13,032.35 km²) and substantial kernel sum contribution (31.12%), connecting 6 protected areas. TNFC (Taman Negara Forest Complex) ranked third (mean rank 0.72), with l19.22% of extent (8,300.70 km²) and 17.79% of kernel sum achieving the highest protected area coverage (11). Salawin complex (SAL) ranked fourth (mean rank 0.35; 14.09% of extent, 6,081.61 km²; 10.90% of kernel sum; 3 protected areas), and RBGT (Royal Belum - Gunung - Temenggor complex) ranked fifth (mean rank 0.31; 8.72% of extent, 3,766.58 km²; 5.87% of kernel sum; 5 protected areas). 3.2. Reintroduction scenarios The Reintroduction scenario (Table 4, Figure 4; Supplementary Figure 2) revealed nine Connectivity Kernels (> 20 th percentile) distributed across the region. The VXP-EPL-XX cluster (Lao PDR- Cambodia) achieved the highest mean ranking, due to the largest kernel extent (70,238.75 km², 38.96%) and kernel sum (43.52% of total), while also connecting the highest number of protected areas (34). The Cardamom Mountains (CAR) complex ranked second (mean rank 0.37), encompassing 19,425.49 km² (10.78% of kernel extent) and 18.79% of kernel sum, linking 14 protected areas. Nakai-Nam Theun (NN) ranked third (mean rank 0.36), with 27,105.38 km² (15.04% of kernel extent) and 12.4% of kernel sum, connecting 14 protected areas. The Northern Plains Landscape (NPL) followed (mean rank 0.28), covering 17,971.67 km² (9.97% of extent) and 5.11% of kernel sum, with 16 connected protected areas. NHNK (Nam Ha - Nam Kan complex), DPKY (Dong Phayayen-Khao Yai Forest Complex), and KSKS (Khlong Saeng - Khao Sok Complex) each had a mean rank of 0.15. NHNK covered 13,373.22 km² (7.42% of extent) and 5.26% of sum (5 protected areas), DPKY encompassed 8,028.69 km² (4.45% of extent) and 5.57% of sum, (7 protected areas), and KSKS covered 6,503.76 km² (3.61% of extent) and 1.78% of sum, (11 protected areas). Nam Nao - Phu Khiew complex (NNPK) ranked eighth (mean rank 0.14; 5,344.55 km², 2.96%; 3.66% of the sum; 9 protected areas), and Nam Et-Phou Louey (NEPL) completed the ranking (mean rank 0.11; 12,280.60 km², 6.81%; 2 protected areas). Thresholding kernels at the 80 th percentile identified nine Core Areas. The Eastern Plains Landscape (EPL) complex achieved maximum rankings across all metrics, with 11,758.01 km² (26.11%), 31.4% of kernel sum, and connecting 8 protected areas. The Cardamom Mountains (CAR) maintained strong connectivity potential (mean rank 0.85), covering 9,331.23 km² (20.72% of total extent) and 23.44% of kernel sum, with 8 connected areas. Virachey - Xe Pian complex (VXP) followed with a mean ranking of 0.57, spanning 7,617.82 km² (16.92% of extent) and 14.14% of sum, incorporating 5 protected areas. DPKY (Dong Phayayen-Khao Yai Forest Complex) ranked fourth (mean rank 0.43; 3,815.12 km², 8.47%; 6.48%; 6 protected areas), and Nakai-Nam Theun (NN) fifth (mean rank 0.42; 5,520.62 km², 12.26%; 13.21%; 3 protected areas). Nam Nao - Phu Khiew complex (NNPK) ranked sixth (mean rank 0.32; 2,404.20 km², 5.34%; 4.17%; 5 protected areas), followed by Nam Ha - Nam Kan complex (NHNK) (mean rank 0.17; 1,006.83 km², 2.24%; 1.37%; 3 protected areas). The Northern Plains Landscape (NPL) and Nam Et-Phou Louey (NEPL) completed the ranking (mean ranks 0.16 and 0.12; extents 3.97% and 3.98%, with 2 and 1 protected areas, respectively). 3.3. Combined scenarios In the Combined scenario (Table 5, Figure 5; Supplementary Figure 3), eighteen Connectivity Kernels (> 20 th percentile) were identified across both regions. The MUL-KKLT-WFCT cluster emerged as the most important area with the highest mean ranking (0.84), driven by the maximum kernel sum ranking (32.64%) and by its kernel extent (67,392.10 km², 19.09%), connecting 32 protected areas. The VXP-EPL-XX cluster ranked second (mean rank 0.73), with the largest kernel extent (70,233.10 km², 19.90%), 18% of kernel sum, connecting 34 protected areas. The TH-TNFC-RBGT cluster ranked third (mean rank 0.67), with 40,959.44 km² (11.6% of extent) and 13.97% of kernel sum, while achieving the highest protected area coverage (56). Salawin complex (SAL) followed (mean rank 0.33; 10.2% of extent; 7.08% of kernel sum; 15 protected areas), then Nakai-Nam Theun (NN) (mean rank 0.27; 7.68% extent; 5.41% kernel sum; 14 protected areas) and the Cardamom Mountains (CAR) (mean rank 0.26; 5.5% extent; 8.21% kernel sum; 14 protected areas). Four areas each had a mean rank of 0.1: NPL (Northern Plains Landscape) (17,964.90 km², 5.09% of extent), KSKS (Khlong Saeng - Khao Sok Complex) (6,502.63 km², 1.84% of extent), ER (Endau-Rompin complex) (5,728.32 km², 1.62% of extent), and NNPK (Nam Nao - Phu Khiew complex) (5,343.42 km², 1.51% of extent), connecting 16, 11, 11, and 9 protected areas respectively. Thirteen Core Areas (> 80 th percentile) were identified. WFCT (Western Forest Complex - Taninthayi Nature Reserve) ranked first (mean rank 0.97), with the highest kernel sum share (17.22%) and protected area coverage (12), and 12,742.27 km² (14.46%) of kernel extent. KKLT (Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park) ranked second (mean 0.84), with the largest kernel extent (13,895.82 km², 15.77%) and 18.22% of the sum, connecting 7 protected areas. TNFC (Taman Negara Forest Complex) ranked third (mean 0.73; 9,144.98 km², 10.38% of extent ; 16.58% of sum) linking 12 protected areas. EPL (Eastern Plains Landscape) ranked fourth (mean 0.72; 11,229.77 km², 12.74% extent; 13.31% kernel sum; 8 protected areas) and CAR (Cardamom Mountains) fifth (mean 0.61; 8,992.62 km², 10.2% extent; 9.97% sum; 8 protected areas). The next positions were VXP (Virachey - Xe Pian complex, mean 0.40), SAL (Salwin Complex, 0.37), RBGT (Royal Belum - Gunung - Temenggor complex, 0.32), and NN (Nakai-Nam Theun, 0.31), connecting 5, 3, 5, and 3 PAs, respectively. DPKY (Dong Phayayen-Khao Yai Forest Complex, mean 0.29), NNPK (Nam Nao - Phu Khiew complex, mean 0.22), NPL (Northern Plains Landscape, mean 0.10) and NEPL (Nam Et-Phou Louey, mean 0.08) completed the ranking. Discussion The successful conservation of the Indochinese leopard will require differentiated, region-specific strategies. This need is especially pressing given the recent regional extinctions of several leopard subpopulations (Rostro-García et al. 2016, 2019, 2024), as well as parallel extinction trajectories observed for Indochinese tigers (Goodrich et al. 2022; Sanderson et al. 2023), and the complex suite of threats that continue to drive leopard declines (Rostro-García et al. 2019, 2024). This analysis provides the first range-wide evaluation of structural connectivity for the Indochinese leopard, one of Asia’s most threatened large carnivores. Using cumulative resistant kernel modelling (Compton et al. 2007), we identified opportunities to maintain or restore meta-population processes. Despite severe range contractions and local extinctions, our results show that significant structural linkages persist between strongholds in Thailand, Myanmar, and Peninsular Malaysia, as well as across key forest complexes in Cambodia and Lao PDR. Mapping these Connectivity Kernels is crucial because structural connectivity forms the foundation for functional connectivity, but long-term persistence depends on suitable habitat, adequate prey, and the absence of anthropogenic threats such as poaching and persecution (Bleyhl et al. 2021; Day et al. 2020; Ghoddousi et al. 2020, 2021; Rostro-García et al. 2019, 2024 ) , all of which must be addressed within a broader socio-ecological context. Our framework complements demographic and ecological studies by offering a transparent spatial baseline that highlights where landscape conservation and/or recovery efforts may be most impactful. Comparable landscape-level connectivity analyses have informed conservation planning for other wide-ranging carnivores (Ash et al. 2025; Kaszta et al. 2020a; Rabinowitz & Zeller 2010; Pratzer et al. 2023; Calderon et al. 2024; Khosravi et al. 2018), demonstrating how structural maps can prioritize both core habitats and connectivity linkages in fragmented systems. Resistant kernel modelling captures gradients of movement probability across heterogeneous landscapes, avoiding arbitrary corridor delineation and instead identifying broad zones of opportunity (Unnithan Kumar & Cushman 2022; Jantz et al. 2025; Kaszta et al. 2020a,b; Cushman et al. 2018). This enables managers to target anti-poaching, prey recovery, and habitat management where they can most effectively reinforce connectivity, tailored to local contexts and prevailing threats. In this way, our analysis using CoLa DSS (Jantz et al. 2025) provides not only an academic contribution but also a practical tool to guide where conservation actions will yield the greatest leverage for preventing extinction and restoring a lost predator. Stronghold systems in extant range Our analysis supported two major strongholds for the Indochinese leopard, in agreement with previous research (Rostro-García et al. 2016, 2019; Greenspan et al. 2021).The Thai-Myanmar complex, encompassing the Western Forest Complex-Taninthayi (WFCT) and Kaeng Krachan-Lenya-Taninthayi (KKLT) areas, emerged as the most important stronghold, with WFCT achieving the highest mean ranking among Core Areas and the MUL-KKLT-WFCT cluster dominating Connectivity Kernels (Table 3; Figure 3). This supports previous research confirming substantial structural connectivity across the Dawna–Tenasserim landscape, a region critical for multiple large carnivores (Sanderson et al. 2023; Ash et al. 2025; Duangchantrasiri et al. 2024; Greenspan et al. 2020, 2021, 2023; Rostro-García et al. 2016, 2019). The structural integrity of the MUL–KKLT–WFCT system has been consistently demonstrated, from Indochinese leopards (Greenspan et al. 2021) to clouded leopards (Kaszta et al. 2020a,b) and Indochinese tigers (Ash et al. 2025; Suttidate et al. 2021), with recent long-distance tiger dispersal further validating its regional importance (Simcharoen et al. 2022). Within WFCT, Huai Kha Khaeng Wildlife Sanctuary remains a key site for the subspecies (Rostro-García et al. 2016, 2019), though past surveys documented density declines (Simcharoen & Duangchantrasiri 2008) and current trends are unknown. Patterns of low density characterize Mae Wong, and Khlong Lan National Parks (Phumanee et al. 2021), and Salak Phra (Rostro-García et al. 2024), with leopard presence ascertained in Thung Yai Naresuan Wildlife Sanctuary (Vinitpornsawan et al. 2024). In the KKLT region, Kuiburi National Park maintained relatively recent stable populations (Pliosungnoen 2023), while density declines were reported from Kweekoh Wildlife Sanctuary in Myanmar’s Karen State (Greenspan et al. 2023). Our analysis identified additional areas of potential meta-population connectivity despite current isolation. Although Salawin (SAL) achieved moderate connectivity metrics, it likely lacks a viable leopard population (Rostro-García et al. 2016). In contrast, North Zarmayi Wildlife Sanctuary (NZWS) and Panlaung Pyadalin Cave Wildlife Sanctuary (PPC) exhibited limited connectivity, consistent with the scarcity of leopard records in northern Myanmar since 2005 (Rostro-García et al. 2016; Kyaw et al. 2024). In peninsular Malaysia, the TH-TNFC-RBGT cluster, represented the second most important stronghold (Table 3; Figure 3), characterised by high protected areas coverage and encompassing two important Core Areas (Taman Negara Forest Complex, TNFC and Royal Belum - Gunung - Temenggor complex, RBGT). Taman Negara historically supported robust populations (Kawanishi and Sunquist 2004; Lynam et al. 2007), with recent density estimates of 2.64 leopards/100 km² (Rostro-García et al. 2024). Kenyir Wildlife Corridor (within TNFC) showed comparable densities, ranging from 2.92 to 3.30 leopards/100 km² (Hedges et al. 2015). Within the RBGT area, leopard presence in the late 1990 s was confirmed in Temenggor, Jengai, and Ulu Temiang (Lynam et al. 2007), with records also documented in Hala Bala Wildlife Sanctuary (Thailand) (Kitamura et al. 2010; Kawanishi et al. 2010; Ngoprasert et al. 2012; Hambali et al. 2021), forming a larger trans-boundary landscape (Rostro-García et al. 2016). Several smaller areas with recorded leopard presence remained isolated from this main complex. These include the Endau Rompin complex (ER) (Rayan et al. 2013; Rayan, 2007), Pasoh Forest Reserve (PAS), Ulu Muda Forest Reserve (UM) (Tan et al. 2015), and Ulu Temiang Forest Reserve (UT). Recent corridor assessments detected leopard movement between Taman Negara and Sungai Yu, but no evidence around Tengku Hassanal Wildlife Reserve (TH) (Magintan et al. 2022). However, occasional detections in previously unsurveyed areas like Ulu Sat Forest Reserve suggest some residual landscape permeability (Hizam et al. 2024). The vulnerability of isolated populations is exemplified by Ayer Hitam, where four leopards were recorded in 2008 (Sanei et al. 2011); this likely represent an unrecoverable relict population, if not already locally extinct. Recovery Potential Several promising areas for potential Indochinese leopard recovery were identified across its former eastern range. The VXP-EPL-XX complex emerged as the most structurally intact landscape based on our connectivity metrics, encompassing two of the three most important Core Areas (Eastern Plains Landscape, EPL and Virachey - Xe Pian, VXP) (Table 4; Figure 4). The Northern Plains Landscape (NPL) and Nakai-Nam Theun National Park (NN) stood as complementary component of this recovery network, demonstrating potential for structural connectivity with the VXP-EPL-XX complex, despite low-ranking core areas. The Cardamom Mountains (CAR) complex, with no leopard records since 2016 (Gray et al. 2017), although isolated, showed significant potential due to extensive protected area coverage and strong core and connectivity habitat metrics (Table 4; Figure 4). The EPL exemplifies the rapid collapse of leopard populations across the region. In 2009, Srepok Wildlife Sanctuary supported 3.6 individuals/100 km² (Gray and Prum 2012), but densities fell to 1.0 leopard/100 km² by 2014 (Rostro-García et al. 2018) and became extirpated by 2021 (Rostro-García et al. 2023). Phnom Prich Wildlife Sanctuary, which showed densities of 0.88-0.92 leopard/100 km² in 2016, saw its leopard population vanish by 2021 despite increased patrolling efforts (Rostro-García et al. 2023, 2024). Similar patterns of decline characterized other candidate reintroduction areas. Virachey National Park in Cambodia, part of the VXP-EPL-XX system, lost its leopards between 2001 and 2014 (Gray et al. 2012; Rostro-García et al. 2016), while both Xe Xap National Protected Area (XX) and Xe Pian National Protected Area (within VXP), in southern Lao PDR, showed no recent records despite presence in the 1990 s (Timmins et al. 1993; Steinmetz et al. 1999; Rostro-García et al. 2016). The Northern Plains Landscape (NPL) recorded leopards in Cheep Wildlife Sanctuary from 2012 to 2014 (Suzuki et al. 2017), with just two individuals in Preah Vihear between 2012 and 2015, but none detected in the final year (A. Suzuki, unpubl. data). Additional sightings were reported from Siem Pang Wildlife Sanctuary in 2002 and 2011, along with unconfirmed signs (BirdLife International, 2012). Nakai-Nam Theun (NN)’s population collapsed from an estimated 30-40 individuals in the mid-1990 s (Timmins and Evans 1996) to zero detections during intensive camera trapping from 2006-2011 (Coudrat et al. 2014). In Thailand, the Nam Nao National Park – Phu Khiew Wildlife Sanctuary complex (NNPK) exhibited limited Connectivity Kernel and Core Area potential. Although leopards were historically present (Borries & Koenig 2014), recent surveys have failed to detect them (Rostro-García et al. 2016). In northern Lao PDR, the Nam Ha National Protected Area – Nam Kan National Park complex (NHNK) and Nam Et–Phou Louey National Park (NEPL) showed low values for Connectivity Kernels and Core Areas. NHNK supported leopards until the late 1990 s (Duckworth et al. 1999) and may serve as the basis for a future northern meta-population, though the status of leopards in southern China remains uncertain (Laguardia et al. 2017; Rostro-García et al. 2016). NEPL, despite its status as Lao PDR’s largest and best protected area, last recorded leopards in 2004 (Johnson et al. 2006), with no detections in subsequent surveys (Rasphone et al. 2019). In Thailand, the Khlong Saeng Wildlife Sanctuary - Khao Sok National Park Complex (KSKS) retained a Connectivity Kernel, but lacked a Core Area and showed no structural connectivity to the larger Thai-Myanmar complexes (MUL-KKLT-WFCT). Leopards were last recorded in KSKS in 2014 (Rostro-García et al. 2016) and were absent in recent surveys (Rostro-García et al. 2024; Petersen et al. 2020). The Dong Phayayen-Khao Yai Forest Complex (DPKY), despite moderate Core Area potential, achieved only limited connectivity Kernel Values (cf. Ash et al. 2025). Historical records documented leopard presence at Ta Phraya National Park (Ngoprasert et al. 2012), but recent camera-trap surveys suggested leopards are now likely extirpated (Rostro-García et al. 2016). Institutional management Our analysis reveals complex and varying relationships between landscape structural integrity and protected area coverage across the Indochinese leopard’s range. These patterns reflect broader challenges in protected area management across Southeast Asia, where institutional capacity, funding, and enforcement effectiveness vary significantly between and within countries, shaping conservation outcomes (Rostro-García et al. 2016, 2019). The Thai–Myanmar system exemplifies this complexity. Much of the landscape within Myanmar’s portion of the MUL–KKLT–WFCT Connectivity Kernel remains outside formal protection (Greenspan et al. 2020). The central Dawna Range, a critical corridor linking the Western Forest Complex with the Karen Hills (Ash et al. 2025), lacks designated conservation status despite its ecological importance (Greenspan et al. 2020). Even where protection exists, as in Kweekoh Wildlife Sanctuary, key connecting habitats remain vulnerable to encroachment and development (Greenspan et al. 2020, 2021). This shortfall reflects broader governance challenges in Myanmar. Only about 26.8% of protected areas in Karen State receive active management (Greenspan et al. 2020). Proposed areas like Lenya National Park face jurisdictional overlap and competing mandates (Greenspan et al. 2020). In regions such as Taninthayi, conservation governance is further complicated by contested land tenure and divergent stakeholder visions for land use (Paul 2018; Greenspan et al. 2020). The Malaysian system demonstrates comprehensive protected area coverage, with TH–TNFC–RBGT complex spanning 56 protected areas and providing strong coverage of both Core Areas and Connectivity Corridors (Tables 3 and 5; Figures 3 and 5). The system likely benefits from Malaysia’s higher socioeconomic status and strict penalties for wildlife crime, which may help reduce local involvement in poaching (Rostro-García et al. 2024). However, some forest reserves in this key stronghold are less protected and susceptible to illegal activities, including cross-border encroachment (Kawanishi and Sunquist 2004; Lam et al. 2023). Recent assessments highlighted emerging threats even in long-established protected areas such as Taman Negara, particularly from illegal hunting and deforestation (Clements et al. 2020; Lam et al. 2023). In potential reintroduction areas across Cambodia and Lao PDR, mismatches between ecological potential and protected areas coverage are more pronounced. The VXP–EPL–XX complex shows high habitat quality and nominal protected area coverage, but limited institutional effectiveness, particularly in safeguarding core habitats (Tables 4 and 5; Figures 4 and 5). This gap is evident in the historical trajectory of population collapse across these landscapes (Rostro-García et al. 2023). Despite international support since 2005, Nam Et–Phou Louey National Park (NEPL), one of Lao PDR’s flagship protected areas, failed to sustain its leopard and tiger populations (Johnson et al. 2016; Rasphone et al. 2019). In Cambodia, management effectiveness varies considerably across reserves. While Phnom Prich Wildlife Sanctuary and Srepok Wildlife Sanctuary have benefitted from government and NGO support, others such as Virachey National Park have seen minimal conservation investment (Gray et al. 2012). However, even enhanced protection has not ensured success, as leopards are now functionally extinct if not fully extirpated from both Phnom Prich and Srepok, despite expanded anti-poaching initiatives (Rostro-García et al. 2023). These outcomes suggest that current protected area models, especially in poaching hotspots, may require fundamental restructuring to address the intensity and scale of illegal hunting. These regional contrasts highlight the critical importance of transboundary coordination (Farhadinia et al. 2021; Rostro-García et al. 2021), particularly in the Thai–Myanmar complex. Thailand’s protected areas benefit from consistent national and international funding, with notable successes such as reduced poaching in Huai Kha Khaeng Wildlife Sanctuary (Duangchantrasiri et al. 2016; Simcharoen and Duangchantrasiri 2008). By comparison, Myanmar’s protected areas receive less support, and their governance is often complicated by disputes between central and autonomous authorities (Greenspan et al. 2020, 2021). Political instability and ongoing civil unrest further limit conservation capacity and can pose indirect threats to biodiversity and habitats (Greenspan et al. 2020, 2021; Woods 2021). Furthermore, deforestation and poaching are expected to intensify as stability returns and development resumes (Connette et al. 2017), reinforcing the urgency of coordinated transboundary planning (Greenspan et al. 2021). This disparity in institutional resources and governance effectiveness critically undermines the ecological functionality of what should operate as unified conservation landscapes (Rostro-García et al. 2016, 2021). Implementation priorities and challenges Despite recent population losses, our analysis demonstrates the persistence of high connectivity and core area potential across the extant range and candidate reintroduction landscapes (Table 3 - 5; Figures 3 - 5). The fundamental habitat structure supporting connectivity remains largely intact, but functional persistence and recolonization ultimately depend on whether mortality and poaching pressures can be reduced, prey base restored and broader landscape restoration implemented (Montes-Rojas et al. 2024; Day et al. 2020; Bleyhl et al. 2021; Ghoddousi et al. 2020, 2022). Among the reintroduction areas, structural continuity is strongest across eastern Cambodia and Lao PDR. Large-scale analogues in other eco-systems (Rabinowitz & Zeller 2010; Calderón et al. 2024; Khosravi et al. 2018; Montes-Rojas et al. 2024), show how structurally continuous landscapes can sustain meta-population processes. Expanding this network northward would require targeted management intervention linking Nakai-Nam Theun National Park (NN), Nam Et-Phou Louey National Park (NEPL), and Nam Ha - Nam Kan complex (NHNK), potentially re-establishing continuity toward China, echoing evidence that restoration corridors and stepping-stone patches enhance connectivity in fragmented systems (Montes-Rojas et al. 2024; Kanagaraj et al. 2013; Suraci et al. 2020; Rodrigues et al. 2022; Diniz et al. 2021; Stweart et al. 2019). By contrast, Nam Nao - Phu Khiew complex (NNPK) and Dong Phayayen - Khao Yai forest complex (DPKY) appear too isolated and embedded in human-dominated matrices to represent viable reintroduction landscapes under current conditions (Ash et al. 2025), making it unlikely that these complexes could function as part of a broader meta-population network. The same considerations apply to the Cardamom Mountains complex (CAR), that although standing as important on its own, may reflect broader cases where viable carnivore populations are effectively isolated by intense human footprint (Iannella et al. 2024; Castilho et al. 2015; Kaszta et al. 2020a). Within this broader configuration, Khao Sok - Khlong Saeng complex (KSKS) appears critical for creating a larger interconnected landscape in the western range, connecting with the MUL–KKLT–WFCT cluster (Rostro-García et al. 2016), though realizing this potential will depend on national-scale landscape restoration and conservation interventions (Montes-Rojas et al. 2024; Rodrigues et al. 2022) and its re-connection with Malaysian strongholds appears unlikely due to extensive landscape modification in southern Thailand. In the same way, the intervening landscapes linking North Zarmayi Wildlife Sanctuary (NZWS), Panlaung Pyadalin Cave Wildlife Sanctuary (PPC), and Salawin complex (SAL) to MUL–KKLT–WFCT (Thailand - Myanmar stronghold), and Endau Rompin complex (ER) to the TH-RBGT–TNFC cluster (Malaysian stronghold), are heavily modified by human land use and would require extensive restoration and active management to re-establish connectivity with the major strongholds. While substantial habitat remains (Gray et al. 2023), restoration will require long-term commitments and context-specific adaptation. Reintroduction into unoccupied but suitable areas is possible (Sanderson et al. 2023), but only if the drivers of past extinctions are resolved (Rostro-García et al. 2019, 2023, 2024). In the Thai–Myanmar stronghold, deforestation (Tun et al. 2011; Connette et al. 2017; Greenspan et al. 2021), infrastructure expansion (Helsingen et al. 2015; Greenspan et al. 2021), poaching (Greenspan et al. 2020; Clements et al. 2020; Rostro-García et al. 2024), and political instability (Greenspan et al. 2021, 2022; Woods 2021; McEvoy et al. 2022) all constrain enforcement and recovery. These threats are especially concerning in a landscape where many protected areas are too small to sustain viable carnivore populations (Greenspan et al. 2020, 2021). Large-scale projects such as the Dawei Special Economic Zone and the Belt and Road Initiative threaten to sever existing habitat networks, particularly through planned transportation corridors across the Dawna–Tenasserim landscape (Bassi et al. 2016; Ng et al. 2020; Kaszta et al. 2020b; Helsingen et al. 2015). In Malaysia, similar fragmentation pressures are evident, caused by roads, plantations, dams, and urban expansion (Lynam et al. 2007; Clements et al. 2020). Although selectively logged forests can still support leopards under proper management (Rostro-García et al. 2024), increasing development has made connectivity between protected areas more difficult (Magintan et al. 2022). While religious beliefs and distance from major wildlife trade markets may reduce local poaching incentives (Belecky and Gray 2020; McEvoy et al. 2022), threats persist from encroachment and foreign exploitation (Clements et al. 2020; Lam et al. 2023). Reintroduction areas, particularly in eastern Cambodia and Lao PDR, face even greater challenges. Their proximity to Vietnam and China exposes them to heavy poaching driven by wildlife trade demand (Jiao et al. 2021). Eastern Southeast Asia is a global snaring hotspot, with densities exceeding 110 snares/km² in some areas (Belecky and Gray 2020). In Srepok Wildlife Sanctuary, over 3,700 snares were removed in a single year (Rostro-García et al. 2024), likely representing just 11–30% of the total deployed (O’Kelly et al. 2018). Prey depletion is another critical barrier to recovery. In the Eastern Plains Landscape and Yok Don National Park (Vietnam), key prey species such as ungulates have been severely reduced (Groenenberg et al. 2020, 2023). In many areas, prey declines drive carnivore loss, often as a result of commercial poaching and subsistence hunting (Rostro-García et al. 2019, 2023; Shairp et al. 2016; Sandalj et al. 2016). These conditions underscore the importance of re-establishing viable prey bases before attempting leopard reintroductions (IUCN/SSC, 2013). To address these challenges, conservation efforts must be phased, localised, and multi-scale. Core priorities include expanding and effectively managing protected area coverage (Greenspan et al. 2021; Gray et al. 2023), strengthening law enforcement, and improving cross-border cooperation, especially in landscapes where inconsistent protection across national borders undermines regional connectivity (Farhadinia et al. 2021; Rostro-García et al. 2016, 2024). Methodological Limitations While our analysis provides a novel and spatially comprehensive assessment of potential connectivity for the Indochinese leopard, several limitations should be acknowledged. First, our modelling framework is static and does not incorporate temporal dynamics such as population movement, colonization, or extinction processes. This limits our ability to capture how connectivity may evolve under changing ecological or management conditions (Barros et al. 2019; Ash et al. 2020a; Fletcher et al. 2019). The use of dynamic, individual-based simulations, such as agent-based models (Schumaker and Brookes 2018; Unnithan-Kumar et al. 2022) or demographic and genetic simulators like CDPOP (Landguth and Cushman, 2010) could improve realism by simulating demographic processes and dispersal over multiple generations. Second, our resistance surface is based on land cover does not explicitly incorporate ecological mortality risks such as poaching pressure or prey availability (Ghoddousi et al. 2020, 2021; Titus and Jachowski, 2024). These factors, particularly snaring, are among the leading causes of leopard decline across Southeast Asia and play a critical role in determining population viability and functional connectivity (Rostro-García et al. 2024; O’Kelly et al. 2018; Ghoddousi et al. 2020, 2021). Third, our land cover data may not accurately represent future habitat conditions. Landscape change in the region is rapid, with ongoing infrastructure expansion and land-use conversion potentially fragmenting even currently intact habitats (Ng et al. 2020; Hughes 2017). Incorporating projected development scenarios into dynamic connectivity models would help identify areas at risk of future isolation (Ash et al. 2020a). Additionally, our modelling resolution, based on medium-scale land cover and anthropogenic feature data, may overestimate connectivity in some regions. For example, areas within the MUL–KKLT-WFCT kernel, predicted as highly connected, have shown reduced functional connectivity (Greenspan et al. 2021). High-resolution data on roads, settlements, and edge effects would improve accuracy and better reflect barriers to leopard dispersal. Empirical validation is another key gap (Creech et al. 2024). Our connectivity predictions would benefit from validation with telemetry or genetic data (Lehnen et al. 2021; Calderón et al. 2024; Zeller et al. 2018; Cushman et al. 2014), which could refine resistance values and help distinguish between habitat preferences of resident versus dispersing individuals (i.e.; Elliott et al. 2014; Zeller et al. 2018). These datasets are also critical for identifying functional thresholds of connectivity relevant to conservation interventions. Our understanding of current leopard populations is also incomplete. Reliable population estimates exist only for a few regions (e.g. Rostro-García et al. 2024; Greenspan et al. 2023; Clements et al. 2020), with many others lacking recent assessments. Survey gaps are especially pronounced in Myanmar, where political instability limits monitoring (Greenspan et al. 2021). Outdated presence data may have affected our selection of source points. However, recent work by Ash et al. (2025) suggests that model outputs are more strongly driven by resistance structure than initialization parameters, providing some confidence in the robustness of our results. Finally, while our Connectivity Kernel and Core Area thresholds (20 th and 80 th percentiles, respectively) follow accepted practices (Cushman et al. 2013; Kaszta et al. 2020a), they remain somewhat arbitrary. Threshold selection may influence the perceived extent of connectivity, especially in the absence of species-specific dispersal validation (Zeller et al. 2018). Conclusions This study, using the CoLa DSS system (Jantz et al. 2025), provides the first comprehensive assessment of functional connectivity for the Indochinese leopard across Southeast Asia. Our analysis confirms the Thai-Myanmar and the Peninsular Malaysian systems as critical strongholds (Rostro-García et al. 2016, 2019), with the MUL-KKLT-WFCT and TH-TNFC–RBGT clusters supporting substantial structural habitat networks and Core Areas. However, these areas face mounting pressures from infrastructure development, deforestation, and poaching (Helsingen et al. 2015; Greenspan et al. 2020, 2021, 2023; Rostro-García et al. 2024; Clements et al. 2020; Lam et al. 2023), highlighting the urgent need for enhanced protection and transboundary cooperation (Farhadinia et al. 2021; Rostro-García et al. 2021). In the eastern portion of the range, our findings reveal the VXP-EPL-XX cluster as the most promising candidate for population recovery, supported by strong structural connectivity and potential for creating a larger meta-population with adjacent landscapes like Nakai-Nam Theun National Park (NN) and the Northern Plains Landscape (NPL). However, the recent leopard extirpation despite extensive landscape structural integrity underscores that connectivity alone is insufficient for population persistence (Rostro-García et al. 2023). The widespread snaring crisis and collapse of prey communities continue to be the most urgent conservation action in these areas (Rostro-García et al. 2024; O’Kelly et al. 2018; Gray et al. 2018; Jiao et al. 2021; Belecky and Gray 2020). Conservation success will require strengthening protection in existing strongholds while simultaneously preparing potential reintroduction sites through intensive anti-poaching efforts and prey recovery programs. Transboundary coordination will be essential, particularly in landscapes where political boundaries and different governance intersect areas of ecological importance (Greenspan et al. 2020, 2021; Rostro-García et al. 2024; Farhadinia et al. 2021). The Indochinese leopard’s decline reflects broader conservation challenges for large carnivores in Southeast Asia. However, the presence of substantial intact habitat networks able to maintain connectivity, combined with clear priorities for protection and restoration, offer cautious optimism for species recovery. Acknowledgements We are deeply grateful to the many individuals and organizations who contributed to the development of the CoLa Decision Support System. We especially thank the national and international participants of our first-generation workshops held between 2018 and 2022 in Lao PDR, Myanmar, Bhutan and Sabah as well as those involved in our more recent workshops in 2024 in Lao PDR, Thailand, Bhutan, Taiwan, Brunei and Sabah. Their insights, collaboration, and continued engagement have played an inspirational role in shaping the final design and functionality of the CoLa DSS. Funding sources Development of the CoLa Decision Support System (DSS) was initially supported through a series of three grants from the Robertson Foundation to DWM (2012–2024) focused on Felid Landscapes. Subsequent support was provided by the NASA Biodiversity and Ecological Conservation Applied Sciences Program (grant 80NSSC21K1942), which enabled the integration and enhancement of CDPOP and UNICOR within CoLa and the advancement of the DSS framework. Additional funding for the fieldwork underpinning this research was provided by the Recanati-Kaplan Foundation and, more recently, by Panthera. CRediT authorship contribution statement Atzeni Luciano: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing Kamler Jan: Conceptualization, Data curation, Methodology, Validation, Writing – review and editing Ash Eric: Data curation, Methodology, Validation, Writing – review and editing Kyaw Pyae Phyoe: Data curation, Methodology, Validation, Writing – review and editing Rasphone Akchousan: Data curation, Methodology, Validation, Writing – review and editing Pin Chanratana: Data curation, Methodology, Validation, Writing – review and editing Tan Cedric Kai Wei: Data curation, Methodology, Validation, Writing – review and editing Rostro-García Susana: Data curation, Methodology, Validation, Writing – review and editing Jantz Patrick: Conceptualization, Funding acquisition, Methodology, Software, Supervision, Validation, Writing – review and editing Gonzalez Ivan: Conceptualization, Methodology, Software, Validation, Writing – review and editing Burnham Dawn: Funding acquisition, Project administration, Software, Validation, Writing – review and editing Cushman Samuel A.: Conceptualization, Funding acquisition, Methodology, Software, Supervision, Validation, Writing – review and editing Macdonald David W.: Conceptualization, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Writing – review and editing Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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In: Spatial Complexity, Informatics, and Wildlife Conservation, Eds. Cushman S.A. and Huettman F. pp. 369–385. https://doi.org/10.1007/978-4-431-87771-4_20 TABLES Table 1. Forest complexes in the extant range of the Indochinese leopard. Sites (Field ID) represent either forest complexes or single Protected Areas. WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex; AH = Ayer Hitam. Area (in km 2 ) refers to the cumulative sum of the extent of Protected Areas plus any polygon classified as extant in Rostro-García et al. (2019), which is considered part of a complex. Points were initialised based on a simple relationship between area and the average density (1.532 individuals/100 km 2 ), calculated for the Indochinese leopard by Rostro-García et al. (2024). Country labels: MMR = Myanmar; MYS = Malaysia; THA = Thailand. ID Protected Areas Country Area Points WFCT Taninthayi Nature Reserve MMR 13701.53 210 Thung Yai Naresuan Wildlife Sanctuary THA Huai Kha Khaeng Wildlife Sanctuary THA Salak Phra Wildlife Sanctuary THA Khuen Si Nakarin National Park THA Khao Laem National Park THA KKLT Taninthayi National Park MMR 12544.56 192 Lenya National Park MMR Lenya National Park (Extension) MMR Namtok Huay Yang National Park THA Kuiburi National Park THA Kaeng Krachan National Park Forest Complex THA SAL Salawin National Park THA 5990.39 92 Salawin Wildlife Sanctuary THA TNFC Jengai Forest Reserve MYS 5981.31 92 Jerangau Forest Reserve MYS Hulu Nerus Forest Reserve MYS Taman Negara National Park (Pahang) MYS Taman Negara National Park (Kelantan) MYS Taman Negara National Park (Terengganu) MYS Sungai Ketiar Wildlife Sanctuary MYS RBGT Hala Bala Wildlife Sanctuary THA 3684.91 56 Royal Belum State Park MYS Gunung Basor Forest Reserve MYS MUL Mulayit Wildlife Sanctuary MMR 2114.95 32 UM Ulu Muda Forest Reserve MYS 1256.86 19 NZWS North Zarmayi Wildlife Sanctuary MMR 998.163 15 ER Endau Rompin (Johor) National Park MYS 910.76 14 Endau Rompin (Pahang) State Park MYS TH Tengku Hassanal Wildlife Reserve MYS 635.79 10 PPC Panlaung Pyadalin Cave Wildlife Sanctuary MMR 364.32 6 UT Ulu Temiang Forest Reserve MYS 152.13 2 PAS Pasoh Forest Reserve MYS 136.30 2 AH Ayer Hitam Forest Reserve MYS 16.78 1 Table 2. Proposed reintroduction areas in the Indochinese leopard range. Sites (Field ID) represent either forest complexes or single Protected Areas. KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap. Area (in km 2 ) refers to the cumulative sum of the Protected Areas extent. Points were initialised based on a simple relationship between area and the average density (1.532 individuals/100 km 2 ), calculated for the Indochinese leopard by Rostro-García et al. (2024). Country labels: KHM = Cambodia; LAO = Lao PDR; THA = Thailand. ID Protected Areas Country Area Points KSKS Khao Sok National Park THA 1629.70 24 Khlong Saeng Wildlife Sancturay THA CAR Cardamom Corridor KHM 9848.47 150 Central Kravanh National Park KHM Southern Kravanh National Park KHM DPKY Dong Phayayen - Khao Yai Forest Complex THA 6218.58 95 NNPK Nam Nao National Park THA 2558.70 39 Phu Khiew Wildlife Sanctuary THA NPL Chhep Wildlife Sanctuary KHM 6930.68 105 Kulen Promtep Wildlife Sanctuary KHM Preah Roka Wildlife Sanctuary KHM VXP Virachey National Park KHM 5931.19 90 Xe Pian National Protected Area LAO EPL Keo Seima Wildlife Sanctuary KHM 11410.89 173 Lumphat Wildlife Sanctuary KHM Phnom Prich Wildlife Sanctuary KHM Srepok Wildlife Sanctuary KHM NHNK Nam Ha National Protected Area LAO 3671.08 56 Nam Kan National Park LAO NN Nakai-Nam Theun National Park LAO 4828.46 65 NEPL Nam Et-Phou Louey National Park LAO 4256.15 74 XX Xe Xap National Protected Area LAO 1538.41 23 Table 3. Summary statistics for the Connectivity Kernels (> 20 th percentile of kernel values distribution; P = 0.2) and Core Areas (> 80 th percentile of kernel values distribution; P = 0.8) calculated on the cumulative output of the average kernels from each forest complex in the extant range. Kernel Extent = Area in km 2 ; Kernel Sum = Sum of the kernel values inside the patch; PAs = number of Protected Areas intersecting the patch. Relative ranks for the three metrics are shown along with the average rank (Mean). WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex. Site Kernel Extent Kernel Sum PAs Kernel Extent rank Kernel Sum rank PAs rank Mean P MUL-KKLT-WFCT 67386.46 1512425.49 32 1.00 1.00 0.57 0.86 0.2 TH-TNFC-RBGT 40958.31 647320.81 56 0.61 0.43 1.00 0.68 SAL 35997.53 328196.76 15 0.53 0.22 0.27 0.34 ER 5727.20 31577.86 11 0.08 0.02 0.20 0.10 UM 4604.10 30656.30 9 0.07 0.02 0.16 0.08 NZWS 10869.69 53068.65 1 0.16 0.04 0.02 0.07 PPC 6258.82 6360.49 3 0.09 0.00 0.05 0.05 PAS 372.48 327.18 5 0.01 0.00 0.09 0.03 UT 570.01 187.10 1 0.01 0.00 0.02 0.01 WFCT 11996.17 608115.00 10 0.92 1.00 0.91 0.94 0.8 KKLT 13032.35 558171.84 6 1.00 0.92 0.55 0.82 TNFC 8300.70 316962.83 11 0.64 0.52 1.00 0.72 SAL 6081.61 194200.63 3 0.47 0.32 0.27 0.35 RBGT 3766.58 104552.95 5 0.29 0.17 0.45 0.31 Table 4. Summary statistics for the Connectivity Kernels (> 20 th percentile of kernel values distribution; P = 0.2) and Core Areas (> 80 th percentile of kernel values distribution; P = 0.8) calculated on the cumulative output of the average kernels from each forest complex of the suggested reintroduction areas. Kernel Extent = Area in km 2 ; Kernel Sum = Sum of the kernel values inside the patch; PAs = number of Protected Areas intersecting the patch. Relative ranks for the three metrics are shown along with the average rank (Mean). KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap. Site Kernel Extent Kernel Sum PAs Kernel Extent rank Kernel Sum rank PAs rank Mean P VXP-EPL-XX 70238.75 880573.52 34 1.00 1.00 1.00 1.00 0.2 CAR 19425.49 380212.30 14 0.28 0.43 0.41 0.37 NN 27105.38 250796.92 14 0.39 0.28 0.41 0.36 NPL 17971.67 103398.03 16 0.26 0.12 0.47 0.28 NHNK 13373.22 106331.99 5 0.19 0.12 0.15 0.15 KSKS 6503.76 36022.63 11 0.09 0.04 0.32 0.15 DPKY 8028.69 112716.49 7 0.11 0.13 0.21 0.15 NNPK 5344.55 74095.96 9 0.08 0.08 0.26 0.14 NEPL 12280.60 79144.87 2 0.17 0.09 0.06 0.11 EPL 11758.01 431247.87 8 1.00 1.00 1.00 1.00 0.8 CAR 9331.23 322032.38 8 0.79 0.75 1.00 0.85 VXP 7617.82 194287.00 5 0.65 0.45 0.63 0.57 DPKY 3815.12 89040.45 6 0.32 0.21 0.75 0.43 NN 5520.62 181442.13 3 0.47 0.42 0.38 0.42 NNPK 2404.20 57317.53 5 0.20 0.13 0.63 0.32 NHNK 1006.83 18816.36 3 0.09 0.04 0.38 0.17 NPL 1785.65 38650.67 2 0.15 0.09 0.25 0.16 NEPL 1790.17 40757.60 1 0.15 0.09 0.13 0.12 Table 5. Summary statistics for the Connectivity Kernels (> 20 th percentile of kernel values distribution; P = 0.2) and Core Areas (> 80 th percentile of kernel values distribution; P = 0.8) calculated on the cumulative output of the average kernels from each forest complex of the suggested reintroduction areas and extant Indochinese leopard range. Kernel Extent = Area in km 2 ; Kernel Sum = Sum of the kernel values inside the patch; PAs = number of Protected Areas intersecting the patch. Relative ranks for the three metrics are shown along with the average rank (Mean). KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap; WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex. Site Kernel Extent Kernel Sum PAs Kernel Extent rank Kernel Sum rank PAs rank Mean P MUL-KKLT-WFCT 67392.10 1512425.74 32 0.96 1.00 0.57 0.84 0.2 VXP-EPL-XX 70233.10 880573.27 34 1.00 0.58 0.61 0.73 TH-TNFC-RBGT 40959.44 647320.86 56 0.58 0.43 1.00 0.67 SAL 36008.82 328197.26 15 0.51 0.22 0.27 0.33 NN 27103.12 250796.82 14 0.39 0.17 0.25 0.27 CAR 19425.49 380212.30 14 0.28 0.25 0.25 0.26 NPL 17964.90 103397.73 16 0.26 0.07 0.29 0.20 NHNK 13370.96 106331.89 5 0.19 0.07 0.09 0.12 DPKY 8026.43 112716.39 7 0.11 0.07 0.13 0.10 KSKS 6502.63 36022.58 11 0.09 0.02 0.20 0.10 ER 5728.32 31577.91 11 0.08 0.02 0.20 0.10 NNPK 5343.42 74095.91 9 0.08 0.05 0.16 0.10 NEPL 12279.48 79144.82 2 0.17 0.05 0.04 0.09 UM 4607.48 30656.45 9 0.07 0.02 0.16 0.08 NZWS 10869.69 53068.65 1 0.15 0.04 0.02 0.07 PPC 6258.82 6360.49 3 0.09 0.00 0.05 0.05 PAS 372.48 327.18 5 0.01 0.00 0.09 0.03 UT 570.01 187.10 1 0.01 0.00 0.02 0.01 WFCT 12742.27 623994.12 12 0.92 1.00 1.00 0.97 0.8 KKLT 13895.82 576551.16 7 1.00 0.92 0.58 0.84 TNFC 9144.98 334877.17 12 0.66 0.54 1.00 0.73 EPL 11229.77 421148.11 8 0.81 0.67 0.67 0.72 CAR 8992.62 315592.76 8 0.65 0.51 0.67 0.61 VXP 6800.61 178771.22 5 0.49 0.29 0.42 0.40 SAL 7227.28 218647.90 3 0.52 0.35 0.25 0.37 RBGT 4657.15 123493.20 5 0.34 0.20 0.42 0.32 NN 5337.76 177980.20 3 0.38 0.29 0.25 0.31 DPKY 3292.51 79116.40 6 0.24 0.13 0.50 0.29 NNPK 2128.79 52078.45 5 0.15 0.08 0.42 0.22 NPL 1242.73 28346.47 2 0.09 0.05 0.17 0.10 NEPL 1444.78 34212.58 1 0.10 0.05 0.08 0.08 Figure 1. The 1,000 meters cell size resistance surface employed in this analysis. Zoomed areas illustrate the level of details. The layer extent has been clipped to the Indochinese leopard ( Panthera pardus delacouri ) range as in Rostro-García et al. (2019), with the exclusion of Southern China. Figure 2. Map illustrating the extant range and suggested reintroduction areas of the Indochinese leopard ( Panthera pardus delacouri ) across Southeast Asia. Extrant Range: WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex; AH = Ayer Hitam. Reintroduction areas: KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap. Figure 3. Map and bar graphs showing core areas and connectivity among forest complexes within the Indochinese leopard ( Panthera pardus delacouri ) extant range. The left panel highlights the labels and the spatial distribution of Core Areas (> 80 th percentile of kernel values distribution; in red). Connectivity Kernels (> 20 th percentile of kernel values distribution) are illustrated in the legend through different colours. Panel A reports the summary statistics for the Connectivity Kernels, while panel B illustrates the summary statistics for the Core Areas. Bar plots represent relative ranks of kernel extent (area in km 2 ), kernel sum (sum of kernel values inside the patch) and Protected Areas (number of protected areas intersected by a patch). Mean represents the average ranking among the three metrics. WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex. Figure 4. Map and bar graphs showing core areas and connectivity kernels among forest complexes within the Indochinese leopard ( Panthera pardus delacouri ) suggested reintroduction areas. The left panel highlights the labels and the spatial distribution of Core Areas (> 80 th percentile of kernel values distribution; in red). Connectivity Kernels (> 20 th percentile of kernel values distribution) are illustrated in the legend through different colours. Panel A reports the summary statistics for the Connectivity Kernels, while panel B illustrates the summary statistics for the Core Areas. Bar plots represent relative ranks of kernel extent (area in km 2 ), kernel sum (sum of kernel values inside the patch) and Protected Areas (number of protected areas intersected by a patch). Mean represents the average ranking among the three metrics. KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap. Figure 5. Map and bar graphs showing core areas and connectivity kernels among forest complexes within the Indochinese leopard ( Panthera pardus delacouri ) extant range and suggested reintroduction areas. The left panel highlights the labels and the spatial distribution of Core Areas (> 80 th percentile of kernel values distribution; in red). Connectivity Kernels (> 20 th percentile of kernel values distribution) are illustrated in the legend through different colours. Panel A reports the summary statistics for the Connectivity Kernels, while panel B illustrates the summary statistics for the Core Areas. Bar plots represent relative ranks of kernel extent (area in km 2 ), kernel sum (sum of kernel values inside the patch) and Protected Areas (number of protected areas intersected by a patch). Mean represents the average ranking among the three metrics. WFCT = Western Forest Complex - Taninthayi Nature Reserve; KKLT = Kaeng Krachan Forest Complex - Lenya National Park - Taninthayi National Park; SAL = Salawin complex; TNFC = Taman Negara Forest Complex; RBGT = Royal Belum - Gunung - Temenggor complex; MUL = Mulayit complex; UM = Ulu Muda; NZWS = North Zarmayi Wildlife Sanctuary; ER = Endau Rompin complex; TH = Tengku Hassanal Wildlife Reserve; PPC = Panlaung Pyadalin Cave Wildlife Sanctuary; UT = Ulu Temiang; PAS = Pasoh complex; KSKS = Khlong Saeng - Khao Sok Complex; CAR = Cardamom Mountains; DPKY = Dong Phayayen-Khao Yai Forest Complex; NNPK = Nam Nao - Phu Khiew complex; NPL = Northern Plains Landscape; VXP = Virachey - Xe Pian complex; EPL = Eastern Plains Landscape; NHNK = Nam Ha - Nam Kan complex; NN = Nakai-Nam Theun; NEPL = Nam Et-Phou Louey; XX = Xe Xap. Information & Authors Information Version history V1 Version 1 12 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords comparative description none of the above terrestrial vertebrate Authors Affiliations Luciano Atzeni 0000-0002-4573-7431 University of Oxford WildCRU View all articles by this author Jan Kamler 0000-0003-4148-2088 University of Oxford WildCRU View all articles by this author Eric Ash University of Oxford WildCRU View all articles by this author Pyae Kyaw 0000-0002-2763-5706 Wildlife Conservation Society Myanmar Program View all articles by this author Akchousanh Rasphone World Wide Fund for Nature (WWF-Laos) View all articles by this author Chanratana Pin Ministry of Environment, Cambodia View all articles by this author Cedric Tan University of Nottingham Malaysia View all articles by this author Susana Rostro-García Panthera Corp View all articles by this author Patrick Jantz Northern Arizona University View all articles by this author Ivan Gonzalez Northern Arizona University View all articles by this author Dawn Burnham University of Oxford WildCRU View all articles by this author Samuel Cushman University of Oxford WildCRU View all articles by this author Dawid W. Macdonald [email protected] University of Oxford WildCRU View all articles by this author Metrics & Citations Metrics Article Usage 310 views 168 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Luciano Atzeni, Jan Kamler, Eric Ash, et al. Mapping Leopard Conservation Priorities in Southeast Asia. Authorea . 12 March 2026. DOI: https://doi.org/10.22541/au.177331006.62561873/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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