Toward 30×30: Mapping in-situ Climate Refugia for Biodiversity Conservation across Mainland China

Toward 30×30: Mapping in-situ Climate Refugia for Biodiversity Conservation across Mainland China | 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 Toward 30×30: Mapping in-situ Climate Refugia for Biodiversity Conservation across Mainland China Qiyao Han, Pengzi Zhang, Shuyan Liu, Mingjuan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035425/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 Context Climate change poses significant threats to biodiversity by altering habitat conditions, thereby challenging the effectiveness of protected area (PA) networks. In-situ climate refugia—areas with stable climatic conditions—are increasingly recognized as essential for sustaining species persistence under future climate scenarios. Objectives This study aims to identify potential in-situ climate refugia across mainland China for 311 endangered terrestrial vertebrates under two future climate scenarios (SSP2-4.5 and SSP5-8.5). We further evaluate the spatial congruence between these refugia and China’s existing PA network to inform strategic conservation planning toward achieving the “30×30” target. Methods A climatic niche-based framework was developed to identify potential in-situ climate refugia by integrating species-specific climatic tolerance, climate change intensity, habitat suitability, and habitat quality. Climate change intensity was quantified using standardized Euclidean distance, and species-specific niche widths were used to define thresholds for climatic stability. Potential climate refugia were delineated by overlaying climate stability with habitat suitability and quality, and subsequently compared with existing PAs to identify conservation gaps. Results Our results reveal that 23.66% of China’s land area could function as climate refugia, but only 15.25% of these regions are currently protected. High-potential refugia, mainly located in the subtropical evergreen broadleaf and tropical monsoon forest zones, are severely underrepresented (8% coverage). Incorporating these refugia into PA networks could raise coverage from 18–25%, offering a realistic pathway to meet the 30×30 target of conservation. Conclusions The proposed framework offers a scalable approach for climate-informed conservation planning. To ensure biodiversity resilience, China should prioritize integrating high-potential refugia into national and regional PA strategies. This approach can significantly enhance ecological representativeness and climate adaptation capacity in PA networks. climate refugia climate stability climate change protected area biodiversity conservation conservation planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Anthropogenic climate change is increasingly undermining the long-term effectiveness of global protected areas (PAs) in conserving biodiversity (Garcia et al., 2014 ; Jones et al., 2023). As species respond to shifting climatic conditions, many existing PAs may no longer retain suitable environments for the species they were designed to protect, thereby diminishing their conservation effectiveness over time (Pacifici et al., 2018 ; Taheri et al., 2021 ; Williams and Blois, 2018 ). Expanding PAs has been widely advocated as a key strategy to safeguard biodiversity under climate change (Arneth et al., 2020 ). In particular, the global “30×30” target—protecting 30% of land and ocean areas by 2030—has emerged as a unifying international framework for halting biodiversity loss and maintaining ecosystem functions. However, while the ambition is clear, how and where to expand PAs in ways that enhance climate resilience remains a fundamental and unresolved question in conservation science (Wessely et al., 2017 ). In China, biodiversity conservation is particularly challenged by the rapid pace of climate change (Zhang et al., 2017 ). Projections indicate that China will experience an average increase in annual temperature of 1.87°C to 5.51°C and an increase in annual precipitation of 0.12 to 0.3 mm/day by the end of the century (Liang et al., 2018 ). As a response to the changing climate, many species will have to track their ecological niches through range shifts (Hällfors et al., 2024 ; Sillero et al., 2022 ), such as the giant panda (Gong et al., 2017 ), shrews (Hu et al., 2022 ), ungulates (Zhang et al., 2020 ). In this context, safeguarding species persistence under climate change has become a great challenge of the biodiversity conservation in China. Identifying and protecting potential climate refugia has gained increasing recognition as a key strategy for conserving biodiversity under climate change (Brambilla et al., 2022 ). Climate refugia are typically defined as regions that maintain relatively stable climatic conditions despite broader climatic shifts ( Reside et al., 2014 ; Morelli et al., 2020 ; Saunders et al., 2023 ). These areas could buffer species from climatic extremes and support long-term ecological persistence, thereby facilitating the survival of species in the long term (Robillard et al., 2015 ; Morelli et al., 2016 ; Brown et al., 2020a ; Hua et al., 2022 ; Zhao et al., 2024 ). Paleontological and biogeographical evidence has shown that climate refugia played a crucial role in sustaining species during the climatic fluctuations of the Quaternary glacial–interglacial cycles (Cain, 1944 ; Hewitt, 2004 ; Stebbins and Major, 1965 ). Building on this historical role, in-situ climate refugia—defined as areas currently inhabited by species and anticipated to remain suitable under future climate conditions—have gained prominence in modern conservation science (Beaumont et al., 2019 ). The in-situ refugia offer critical opportunities to preserve biodiversity and maintain ecosystem functions in a rapidly changing climate (Morelli et al., 2020 ), making them especially important for in-situ conservation strategies (Jianzhang et al., 2013 ). Accordingly, identifying and protecting in-situ refugia is likely to become an increasingly important component of effective biodiversity conservation under accelerating climate change (Morelli et al., 2016 ). Strategic conservation of these areas has the potential to substantially enhance the resilience of species and ecosystems to ongoing and future environmental changes (Ashcroft, 2010 ; Keppel et al., 2015 ; Peterson et al., 2011 ). Identifying climate refugia requires detecting areas likely to maintain relatively stable climatic conditions under future climate scenarios (Feng et al., 2016 ; Oliver et al., 2015 ; Trew and Maclean, 2021 ). A widely used approach for assessing climate stability is the calculation of climate velocity, which quantifies the rate at which climate conditions shift over space and time (Sachan et al., 2022 ). This method typically involves tracking the displacement of climate analogues between current and future climate scenarios. For instance, Lai et al. (2022) estimated forward and backward climate velocities across biogeographical regions of terrestrial Europe to identify conservation priority areas. Similarly, Kosanic et al. (2019) assessed seasonal climate velocity across Germany and used it to identify vulnerable species and potential climate refugia. Climate velocity reflects the speed at which species must migrate to remain within suitable climate niches (Haight and Hammill, 2019 ; Hamann et al., 2015 ). If species cannot keep pace with the climate velocity, they might be exposed to unfavourable climates (Serra Diaz et al., 2014 ). However, climate velocity-based methods do not capture the temporal persistence of climatic conditions at a given location—an essential characteristic of in-situ climate refugia. As a result, climate velocity alone may be insufficient for identifying areas capable of supporting long-term species persistence (Belote, Carroll et al. 2018 ). Other studies have assessed climate stability by quantifying the difference or similarity between current and future climate conditions using metrics such as Euclidean Distance (Zscheischler et al., 2012 ), Standardized Euclidean Distance (Williams et al., 2007 ) and Markov Distance (Mahony et al., 2017 ; Parks et al., 2022 ). These approaches effectively capture the temporal stability of climatic conditions at a given location, making them well-suited for identifying in-situ climate refugia (Palmer et al., 2015 ). Nevertheless, most of the studies used human-defined data classification methods (e.g., natural breaks) to determine thresholds for high climate stability, with little consideration of the differences among species in their sensitivity to climate change. It should be noted that species in different climate regions usually exhibit different tolerances to changes in climate (Calatayud et al., 2021 ) and may response differently to specific climate variables (Palmer et al., 2015 ). This could lead to a concern that human-defined thresholds might not reveal the realistic requirement of climate stability for species, thereby limiting the ecological realism of refugia identification. Here, we proposed a climatic niche-based approach to assess climate stability and identify potential climate refugia. A species’ climatic niche refers to the set of coarse-resolution climatic variables (e.g., temperature, precipitation) that influence its spatial and temporal persistence (Hutchinson, 1957 ; Pearman et al., 2008 ; Wang et al., 2020 ). With other abiotic and biotic factors, it defines where a species may occur and how it will respond to the changing climate (Di Marco et al., 2021 ). In particular, the width of a species’ climatic niche plays a pivotal role in shaping spatial pattern of species distributions. Recent studies have found that niche width is a strong predictor of climate-related local extinction and range shifts of species (Grinder and Wiens, 2023 ). It is therefore can be used to identify key climatic variables influencing species persistence, thereby enabling the calculation of climate stability and the establishment of species-specific thresholds for identifying in-situ refugia. We hypothesize that if predicted climate changes remain within the general climatic niche width of the species studied, the climate will be considered stable enough to support biodiversity persistence. The climatic niche-based approach was then applied to Mainland China to identify potential climate refugia for biodiversity conservation under climate change. Our study aims to answer three key questions: (1) What are the key bioclimatic variables and climatic niche widths (CNWs) that shape the distribution of species across Mainland China? (2) Where are the potential climate refugia in China under future climate scenarios? (3) Are these areas adequately covered by the existing PA network, and how can conservation planning be optimized to cope with future climate change? Our framework provides a scalable, climate-informed approach to conservation planning and identifies priority areas where future efforts can strengthen biodiversity resilience in the face of climate change. 2. Methods Our study followed a four-step approach: (1) identifying key bioclimatic variables shaping species distributions; (2) assessing the intensity of climate change; (3) calculating species-specific climatic niche widths to define climate stability thresholds; and (4) mapping potential climate refugia by integrating climate stability, habitat suitability, and habitat quality. This framework was applied to 311 endangered terrestrial vertebrate species (IUCN, 2023 )—comprising mammals, amphibians, reptiles, and non-migratory birds—under two future climate scenarios: SSP2-4.5 and SSP5-8.5. A subset of climate-sensitive species—species with high exposure, high sensitivity, and low adaptive capacity to climate change—was selected for detailed modeling based on IUCN Red List assessments (IUCN, 2023 ). Spatial analyses were conducted using high-resolution (1 km²) raster datasets within the ArcGIS and R environments. 2.1 Identifying Key Climatic Variables We combined species occurrence records with current climate variables to identify the key bioclimatic variables shaping each species’ climatic niche. Endangered terrestrial species data were obtained from the IUCN Red List (IUCN, 2023 ). Based on threat sensitivity, our study focused on 77 endangered mammals (17 climate-sensitive), 162 amphibians (23 climate-sensitive), 42 reptiles (7 climate-sensitive), and 30 non-migratory birds—all considered climate-sensitive in this study. We converted IUCN species range SHP files to point data using the ArcGIS “Raster to Point” tool, and applied spatial thinning with “Subset Features” to reduce computational load while preserving distribution patterns. Thinning rates were scaled to range size: 1% for species > 100 million hectares (Mha), 5% for 10–100 Mha, and 10% for 1–10 Mha. No thinning was applied to species with ranges < 1 Mha. Current (1970–2000) and future (2081–2100) climate data under SSP2-4.5 and SSP5-8.5 scenarios were obtained from WorldClim ( https://worldclim.org/ ) at 30-arcsecond resolution. To reduce model uncertainty, we used the ensemble mean of 10 GCMs: ACCESS-CM2, CMCC-ESM2, EC-Earth3-Veg, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. For each species, we assessed collinearity among 19 bioclimatic variables using Pearson’s correlation (r ≥ 0.75) and excluded highly correlated predictors within its spatial range (Tan et al., 2018 ). We then evaluated multicollinearity using variance inflation factors (VIF < 10), calculated via the ‘cor’ function in R (Mason and Perreault Jr, 1991 ), and retained a final set of uncorrelated, independent climate variables for species-specific modeling. For each of the climate-sensitive species, we used MaxEnt v3.4.4 (Steven J. Phillips, 2023 ) to identify key bioclimatic variables defining species’ climatic niches. Species distribution records and corresponding climate layers (independent climate variables) were input into MaxEnt. Each model was run 10 times, and the average results were used. Variables with a cumulative contribution ≥ 90% (Zhang et al., 2020 ) were retained as key variables for each species. To facilitate group-level analysis, we subsequently aggregated and summarized these key variables by taxonomic group—mammals, amphibians, reptiles, and non-migratory birds—for downstream climate change intensity assessment. 2.2 Assessing Climate Change Intensity We assessed climate change intensity (CCI) across mainland China by calculating the standardized Euclidean distance between current and future (2081–2100) climate conditions (Fig. 1 a) for each taxonomic group—mammals, amphibians, reptiles, and non-migratory birds— using the set of key bioclimatic variables identified for each group in Section 2.1. All climate layers were resampled to a 1 km × 1 km resolution using the nearest-neighbor method and projected to the Krasovsky 1940 coordinate system. The calculation was based on the following standardized Euclidean distance formula: (1) where and represent the current and future values of the climate variable, and is the standard deviation of that variable across all grid cells. Lower CCI values indicate smaller differences between current and future climatic conditions, reflecting higher climatic stability. 2.3 Calculating Species-Specific Climatic Niche Widths To estimate the climatic niche width (CNW) for each species, we used the current distribution data and the key bioclimatic variables identified in Section 2.1. For each species, we randomly sampled point pairs within its distribution range and calculated the standardized Euclidean distances between their climatic values (Fig. 1 b). To ensure computational efficiency while maintaining representativeness, only 1% of grid cells were sampled for species with distributions larger than 10 million hectares. CNW was then defined as the maximum distance observed among these sampled pairs, representing the broadest climatic difference tolerated within the species’ current range. This approach captures the ecological breadth of a species’ climatic tolerance across its native habitat, while minimizing the influence of spatial or geographic outliers. 2.4 Identifying Climatically Stable Regions Climatically stable regions were identified at the habitat level for each of the four taxonomic groups. For each group, we calculated the mean CNW across all species occurring within a given habitat type to establish a baseline threshold for climatic stability. Grid cells of that habitat type with projected CCI values below this threshold were preliminarily classified as climatically stable. Within these stable zones, we refined the assessment using species-specific CNWs. For each species, grid cells of the climatically stable areas with CCI values below its CNW were marked as climatically suitable (assigned a value of 1), and 0 otherwise. A frequency score was then calculated by weighting these values according to species’ IUCN threat categories: using weights of 2 for Vulnerable (VU), 4 for Endangered (EN), and 8 for Critically Endangered (CR), following Shrestha et al. (2021). The resulting weighted scores represent the frequency with which each grid cell is deemed climatically stable for the persistence of threatened species. Finally, we multiplied the species frequency score by the inverse of CCI to generate a continuous climate stability index, where higher values indicate stronger climatic stability and greater conservation relevance. 2.5 Identifying Potential Climate Refugia To identify potential climate refugia, we integrated three key factors—climate stability (CS), habitat suitability (HS), and habitat quality (HQ)—which are critical for species persistence under climate change (Brown et al., 2020; Morelli et al., 2016 ; Robillard et al., 2015 ). HS was defined as the distribution of climatically suitable areas for endangered terrestrial vertebrates under current climate conditions. Using MaxEnt, we modeled suitable habitat ranges for mammals, amphibians, reptiles, and non-migratory birds across mainland China. The resulting habitat layers were weighted by species’ IUCN threat status and combined using the raster calculator in ArcGIS 10.8. HQ was assessed using the InVEST 3.14.3 (Integrated Valuation of Ecosystem Services and Tradeoffs)——a model developed by the United States Natural Capital Project team——which incorporates spatial interactions between anthropogenic threats (e.g., urban expansion, agricultural encroachment) and ecosystems. Parameters for threat intensity, distance decay, and habitat sensitivity were assigned based on previously published studies, as detailed in Appendix Tables S1-2. Based on the results of HS, HQ, and CS, we calculated a climate refugia value (CRV) for each grid cell within the identified climatically stable regions. CRV indicates the potential of a grid cell to function as a climate refugia. We first assessed the habitat condition of each cell by multiplying HS and HQ, producing a combined habitat score. This score highlights areas where ecological suitability and integrity are both high, under the assumption that habitat persistence depends on the co-occurrence of favorable climatic conditions and minimal anthropogenic disturbance. Next, we integrated this habitat score with CS to quantify the composite potential for long-term species persistence. All layers were normalized to a 0–1 scale prior to integration. Recognizing the complementary roles of habitat condition and climate stability in shaping species survival, we applied an equal-weighted additive approach: (2) Higher CRV values indicate areas that simultaneously offer stable climatic conditions and high-quality habitats, representing strong candidates for potential climate refugia under future climate change. We first classified the results of CRV into three categories—low, medium, and high potential—using the equal-interval (quantile) method. The continuous 0–1 CRV range was divided into three equal parts to facilitate spatial prioritization. To account for uncertainty across climate scenarios, we integrated results from both SSP2-4.5 and SSP5-8.5 pathways. Using a maximum-value principle, we retained the higher CRV for each grid cell between the two scenarios. This conservative approach ensured that areas identified as potential refugia under either scenario were preserved, minimizing the risk of underestimation under more severe future conditions. Finally, we combined the scenario-integrated outputs across the four taxonomic groups—mammals, amphibians, reptiles, and non-migratory birds—by applying a maximum-value overlay. Each grid cell was assigned the highest CRV observed among all groups, ensuring that areas critical for the persistence of any one group were retained as final potential climate refugia. 3. Results 3.1 Key Bioclimatic Variables, Niche Widths and Climate Change Intensity The key bioclimatic variables influencing species distributions varied across taxonomic groups (Appendix Table S3-6). MaxEnt modeling revealed that mammals and amphibians were influenced by a broader suite of bioclimatic variables, particularly annual mean temperature (BIO1), temperature seasonality (BIO4), and annual precipitation (BIO12). Reptiles, by contrast, responded primarily to temperature and precipitation extremes (e.g., precipitation of driest month and precipitation of coldest quarter), while non-migratory birds exhibited a wide climatic response spectrum involving both seasonal and annual indicators. CNWs also exhibited clear interspecific variation (Fig. 2 , Appendix Table S7-10). Amphibians had the narrowest average CNW (0.70), suggesting a greater sensitivity to climatic shifts, while birds (1.66) and mammals (1.45), followed by reptiles (1.10), displayed broader climatic tolerances. These values reflect differing capacities for climate adaptation across taxa. Detailed CNW values for each species are provided in Appendix Table S1 . Under the SSP2-4.5 scenario, CCI exhibited clear taxonomic and spatial variation (Fig. 3 a–d). Reptiles showed the lowest average CCI values across most regions, indicating relatively low climatic exposure, whereas mammals exhibited the highest average CCI, suggesting greater vulnerability to climatic shifts. Geographically, low CCI values were primarily concentrated in the southwestern part of the subtropical evergreen broadleaf forest (SEBF) zone, the tropical monsoon rainforest (TMR) zone, and the Qinghai Plateau alpine vegetation (QPAV) zone. In contrast, high CCI values were observed for most species in the warm-temperate deciduous broadleaf forest (WDBF), cold-temperate coniferous forest (CF), and the central part of the SEBF, indicating stronger climatic shifts in these areas. Under the more extreme SSP5-8.5 scenario, overall CCI values increased across all taxa and regions, with spatial heterogeneity becoming more pronounced (Fig. 3 e–h). Despite the general increase, the spatial distribution of high and low CCI values remained broadly consistent with the pattern observed under SSP2-4.5, suggesting persistent spatial trends in climatic change exposure. 3.2 Climate Stability Pattern The spatial distribution of climate stability—calculated by integrating species-specific CNWs, their threat-weighted frequencies, and projected CCI—exhibited marked heterogeneity across China under future climate scenarios. Under the SSP2-4.5 scenario, climate stability exhibited strong spatial variation across taxonomic groups (Fig. 4 ). For birds, climatically stable areas were widespread, spanning seven major biomes across China. In contrast, climatically stable regions for mammals and reptiles were primarily restricted to the southwestern corner of the country. For amphibians, however, almost no climatically stable areas were identified, indicating a severe mismatch between their climatic requirements and projected climate change. Overall, regions with high climate stability values were predominantly concentrated in southwestern China (Fig. 4 ). At the biome level, areas in southwestern SEBF exhibited the highest climate stability, indicating their potential to serve as long-term refugia under future climatic shifts. In contrast, low stability values were observed in northern and arid regions, including much of the WDBF, most of the QPAV, Temperate Desert (TD) and Temperate Grassland (TG) zones. These areas either exceeded the climatic thresholds of many species or overlapped with fewer threatened taxa, thus offering limited capacity for biodiversity retention. Under the SSP5-8.5 scenario, the distribution of areas with high climate stability remained largely consistent with that under SSP2-4.5 (Appendix Figure S1 ). For mammals and reptiles, the identified climatically stable regions were similar across both scenarios. In contrast, for birds, the extent of stable regions was noticeably reduced under SSP5-8.5. As observed under SSP2-4.5, no climatically stable areas were identified for amphibians under the SSP5-8.5 scenario, suggesting a persistent lack of refugial potential for this group. 3.3 Identification of Potential Climate Refugia The identification of climate refugia, which combines the results of SC, HS (Fig. S2), and HQ (Fig. S3), revealed pronounced taxonomic and spatial differences (Fig. 5 ). High- and medium-potential refugia for mammals were large and continuous in the southwestern SEBF and southern TMR zones (Fig. 5 a), showing a similar spatial pattern to reptiles, albeit at a smaller scale and with greater fragmentation (Fig. 5 b). Non-migratory birds displayed more spatially dispersed refugia, consistent with their broader climatic tolerances (Fig. 5 c). Amphibians, however, lacked any identifiable climate refugia under either SSP2-4.5 or SSP5-8.5 scenarios, likely due to their narrow climatic niche widths and high exposure to projected climate change. Using a maximum-value integration approach, we combined the refugia identified for the four taxonomic groups to generate a composite climate refugia distribution map across mainland China (Fig. 5 d). In total, 227.1 Mha of climate refugia were identified, comprising 16.9 Mha of high-potential, 184.2 Mha of medium-potential, and 26.0 Mha of low-potential areas (Table S11). Low-potential refugia were fragmented and primarily distributed across the QPAV, eastern coastal WDBF, and northeastern SEBF zones. Medium-potential refugia were concentrated in most of the CF and central SEBF zones, with smaller, scattered patches in western WDBF, TG, and TD zones. High-potential refugia were found in the southwestern SEBF and southern coastal TMR zones, with almost no high-value areas in other vegetation zones. 3.4 Effectiveness of Protected Areas for Conserving Climate Refugia To address whether projected climate refugia are adequately covered by China’s existing PA network and to identify conservation gaps under future climate change, we evaluated the spatial congruence between current PA boundaries and the distribution of identified climate refugia (Fig. 6 ; Table 2). Our results show that only 15% (34.6 Mha) of the total 227.1 Mha of identified climate refugia are currently covered by existing PAs (Fig. 6 a). Protection coverage is uneven across refugia categories: 33% (8.55 Mha) of low-potential refugia are protected, largely due to extensive reserves in the QPAV, TD, and TG zones. In contrast, only 13% (24.64 Mha) of medium-potential and 8% (1.44 Mha) of high-potential refugia fall within current PA boundaries. The majority (~ 90%) of medium- and high-potential refugia remain outside the current PA system (Fig. 6 b). Notably, core high-value areas in the southwestern SEBF (14.56 Mha, 91%) and TMR (0.93 Mha, 93%), as well as medium-potential refugia in the CF (30.98 Mha, 87%) and WDBF (12.20 Mha, 86%) zones, are severely unprotected, highlighting urgent conservation gaps in these ecologically critical regions. Table 1 Area (million hectares) of potential climate refugia inside and outside PAs across biomes. Vegetation Zone Low potential Medium potential High potential In-PAs ex-PAS In-PAs ex-PAS In-PAs ex-PAS SEBF Area 1.29 5.52 12.43 90.70 1.37 14.56 Proportion 19% 81% 12% 88% 9% 91% WDBF Area 0.33 2.16 2.02 12.20 / / Proportion 13% 87% 14% 86% / / QPAV Area 5.35 5.24 1.98 3.87 / / Proportion 51% 49% 34% 66% / / TMR Area 0.12 0.51 1.41 9.37 0.07 0.95 Proportion 19% 81% 13% 87% 7% 93% TG Area 0.47 1.73 1.40 9.63 / / Proportion 21% 79% 13% 87% / / TD Area 0.95 2.18 0.89 2.76 / / Proportion 30% 70% 24% 76% / / CF Area 0.04 0.11 4.51 30.98 / 0 Proportion 24% 76% 13% 87% / / Total Area 8.55 17.46 24.64 159.52 1.44 15.50 Proportion 33% 67% 13% 87% 8% 92% Total area 26.00 184.15 16.94 Biomes: TMF—tropical monsoon forest, TCDBF—temperate coniferous & deciduous broadleaf forest, WDBF—warm-temperate deciduous broadleaf forest, CF—cold-temperate coniferous forest, SEBF—subtropical evergreen broadleaf forest, TD—temperate desert, TG—temperate grassland. To enhance climate adaptation, strategically expanding the protected area network is essential. We found that incorporating high-potential climate refugia alone could increase national terrestrial protection from 18–25%. Including both high- and medium-potential refugia would elevate this to 44%, while full integration of all identified refugia could raise coverage to 47%. These results highlight a critical need to realign China’s conservation strategy by prioritizing underrepresented, high-value climate refugia—particularly in the SEBF and TMR zones—to ensure long-term biodiversity persistence under accelerating climate change. 4. Discussion This study developed an integrative, climate-niche-based framework for the identification of in-situ climate refugia by explicitly linking species-specific climatic tolerance with macro-scale projections of climate change intensity and habitat condition (e.g., habitat suitability and quality). Unlike previous approaches that typically rely on generalized climatic envelopes or uniform thresholds of climate stability, our method quantifies CNWs for individual species and uses them to establish ecologically grounded thresholds for climate stability. This allows for a more biologically realistic delineation of climate refugia across diverse taxa and ecosystems. Importantly, our analysis shows that high climate stability does not always align with low climate change intensity. Instead, areas deemed stable are those where projected climate shifts fall within species-specific tolerances and where multiple climate-sensitive taxa co-occur. This highlights the inadequacy of climate-only approaches and emphasizes the need for integrating species-level ecological data to reliably identify long-term conservation strongholds. Our study in China identified climate refugia of varying conservation potential and reveals a significant spatial mismatch between these refugia and the existing PA network. Of the 227.1 million hectares identified as climate refugia, just 15% are currently protected. High-potential climate refugia are concentrated in biodiversity-rich zones such as the SEBF and TMR, whereas arid and temperate biomes exhibit much lower conservation potential under future climate conditions. Critically, we show that the current PA network provides only limited coverage of these high-value refugia (8%), while low-potential refugia, particularly those in the QPAV and TD zones, receive disproportionate protection. Our findings underscore a significant conservation gap and point to a tangible, actionable opportunity: incorporating just the high-potential refugia could increase China’s protected land coverage from 18–25%, offering a clear pathway toward achieving the 30×30 target. Moreover, the mismatch between existing PAs and high-value climate refugia underscores the urgency of optimizing national conservation strategies. Particularly, southwestern China emerges as a critical climate refugium core, suggesting it should be prioritized in future ecological redline revisions, national park designations, and climate-resilient ecological corridors. Effective climate-adaptive conservation requires anticipating not only where biodiversity will persist, but also how spatial protections can be realigned to safeguard those trajectories. Additionally, while formal PAs remain essential, complementary mechanisms such as Other Effective area-based Conservation Measures (OECMs)—including community-managed lands and traditional reserves—can play a critical role in safeguarding areas that lie outside formal protection but nonetheless contribute meaningfully to biodiversity persistence. Recognizing and supporting these informal conservation measures would expand the scope of climate-adaptive conservation without requiring entirely new designations. Nevertheless, several limitations should be considered when interpreting our findings. Firstly, the precision of species distribution data is constrained, relying largely on IUCN Red List shapefiles and applying random sampling for processing, which may oversimplify real patterns. We acknowledge that the use of species' distribution range data, rather than species occurrence data, to define climatic niche width may conflate climatic breadth with spatial distribution, potentially emphasizing distributional outliers. To enhance ecological realism, future studies could refine niche width estimation by using presence/absence point data or employing density-based or percentile-based climatic envelopes that better represent core climatic tolerances. Additionally, the assumption that species persistence is ensured when projected climatic changes remain within species’ niche widths is a simplification. Future research will need empirical validation of this assumption through paleoecological data, historical refugia mapping, or long-term monitoring datasets. We also recognize the limitations in combining climate stability, habitat suitability, and habitat quality using equal weights. Although this simplification was applied to ensure analytical clarity, future iterations of the framework could explore weight optimization based on species traits, conservation outcomes, or expert elicitation to improve the robustness of prioritization. Moreover, the study does not account for species’ adaptive or dispersal capacities, which could lead to over- or underestimation of refugial viability. Future research should incorporate finer-resolution species data, model dispersal dynamics, and integrate land-use and habitat transformation scenarios to enhance the robustness and applicability of climate refugia identification. Despite these limitations, our study fills a critical methodological gap by offering a scalable tool to guide climate-adaptive conservation. Importantly, it highlights ecologically valuable regions under climate change—such as the subtropical evergreen broadleaf and tropical monsoon forests—that merit strategic integration into protected area expansion plans. While grounded in a China-specific context, the core principles of this framework can inform conservation planning in other regions with comparable climate and biodiversity gradients. 5. Conclusions Contemporary climate change is causing significant challenges to the conservation of biodiversity in China. Identifying and protecting climate refugia is one of the most effective strategies to mitigate the adverse impacts of climate change on species persistence. This study developed a climatic niche-based framework that integrates future climate stability, species-specific tolerance thresholds, habitat suitability, and habitat quality to systematically identify potential climate refugia across mainland China. Our findings revealed substantial spatial and taxonomic variation in refugia distribution, with a total of 227.1 Mha (approximately 23.7% of China’s land area) identified as potential refugia. However, only 15.3% of these areas currently fall within the national PA network, and critically, just 8.5% of high-potential refugia—the most important for long-term biodiversity resilience—are protected. This spatial mismatch underscores the urgent need to incorporate climate-adaptive priorities into protected area planning. Our results offer a clear and achievable pathway toward realizing the 30×30 target. We recommend that high-potential climate refugia be formally prioritized in national and regional planning efforts. The proposed framework provides a scalable and replicable tool for identifying climate-resilient areas and optimizing conservation networks to meet both ecological and policy goals under accelerating climate change. Declarations Author Contribution Q.H. and P.Z. designed the study and wrote the manuscript text, Q.H. and M.Z. revised the manuscript text,P.Z. conducted data analyses and prepared figures and tables,S.L. prepared the initial data and figures. Data Availability The spatial dataset of identified climate refugia generated in this study is publicly available at Figshare DOI: 10.6084/m9.figshare.29388533 References Arneth, A., Shin, Y., Leadley, P., Rondinini, C., Bukvareva, E., Kolb, M., Midgley, G.F., Oberdorff, T., Palomo, I., Saito, O., 2020. Post-2020 biodiversity targets need to embrace climate change. Proceedings of the National Academy of Sciences 117, 30882-30891. Ashcroft, M.B. Identifying refugia from climate change. Author). Wiley Online Library; 2010. pp. 1407-1413. Beaumont, L.J., Esperón-Rodríguez, M., Nipperess, D.A., Wauchope-Drumm, M., Baumgartner, J.B., 2019. Incorporating future climate uncertainty into the identification of climate change refugia for threatened species. BIOL CONSERV 237, 230-237. Belote, R. T., C. Carroll, S. Martinuzzi, J. Michalak, J. W. Williams, M. A. Williamson and G. H. Aplet (2018). "Assessing agreement among alternative climate change projections to inform conservation recommendations in the contiguous United States." Scientific Reports 8 (1): 9441. Brambilla, M., Rubolini, D., Appukuttan, O., Calvi, G., Karger, D.N., Kmecl, P., Mihelič, T., Sattler, T., Seaman, B., Teufelbauer, N., 2022. 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Feng, G., Mao, L., Sandel, B., Swenson, N.G., Svenning, J.C., 2016. High plant endemism in China is partially linked to reduced glacial‐interglacial climate change. J BIOGEOGR 43, 145-154. Garcia, R.A., Cabeza, M., Rahbek, C., Araújo, M.B., 2014. Multiple dimensions of climate change and their implications for biodiversity. SCIENCE 344, 1247579. Gong, M., Guan, T., Hou, M., Liu, G., Zhou, T., 2017. Hopes and challenges for giant panda conservation under climate change in the Qinling Mountains of China. ECOL EVOL 7, 596-605. Grinder, R.M., Wiens, J.J., 2023. Niche width predicts extinction from climate change and vulnerability of tropical species. GLOBAL CHANGE BIOL 29, 618-630. Haight, J., Hammill, E., 2019. Protected areas as potential refugia for biodiversity under climatic change. BIOL CONSERV 241, 108258. Hällfors, M.H., Heikkinen, R.K., Kuussaari, M., Lehikoinen, A., Luoto, M., Pöyry, J., Virkkala, R., Saastamoinen, M., Kujala, H., 2024. 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GS SEARCH. IUCN. The IUCN Red List of threatened species Version. Author); 2023. Jianzhang, M., Ke, R., Kun, C., 2013. Research and practice on biodiversity in situ conservation in China: pro-gress and prospect. Biodiversity Science 20, 551-558. Keppel, G., Mokany, K., Wardell-Johnson, G.W., Phillips, B.L., Welbergen, J.A., Reside, A.E., 2015. The capacity of refugia for conservation planning under climate change. FRONT ECOL ENVIRON 13, 106-112. Liang, Y., Yan, X., Huang, L., Lu, H., Jin, S., 2018. PREDICTION AND UNCERTAINTY OF CLIMATE CHANGE IN CHINA DURING 21ST CENTURY UNDER RCPS. J TROP METEOROL 24, 102-110. Mahony, C.R., Cannon, A.J., Wang, T., Aitken, S.N., 2017. A closer look at novel climates: new methods and insights at continental to landscape scales. GLOBAL CHANGE BIOL 23, 3934-3955. Mason, C.H., Perreault Jr, W.D., 1991. Collinearity, power, and interpretation of multiple regression analysis. J MARKETING RES 28, 268-280. Morelli, T.L., Barrows, C.W., Ramirez, A.R., Cartwright, J.M., Ackerly, D.D., Eaves, T.D., Ebersole, J.L., Krawchuk, M.A., Letcher, B.H., Mahalovich, M.F., 2020. Climate‐change refugia: Biodiversity in the slow lane. FRONT ECOL ENVIRON 18, 228-234. Morelli, T.L., Daly, C., Dobrowski, S.Z., Dulen, D.M., Ebersole, J.L., Jackson, S.T., Lundquist, J.D., Millar, C.I., Maher, S.P., Monahan, W.B., 2016. Managing climate change refugia for climate adaptation. PLOS ONE 11, e159909. Oliver, T.H., Heard, M.S., Isaac, N.J., Roy, D.B., Procter, D., Eigenbrod, F., Freckleton, R., Hector, A., Orme, C.D.L., Petchey, O.L., 2015. Biodiversity and resilience of ecosystem functions. TRENDS ECOL EVOL 30, 673-684. Pacifici, M., Visconti, P., Rondinini, C., 2018. A framework for the identification of hotspots of climate change risk for mammals. GLOBAL CHANGE BIOL 24, 1626-1636. Palmer, G., Hill, J.K., Brereton, T.M., Brooks, D.R., Chapman, J.W., Fox, R., Oliver, T.H., Thomas, C.D., 2015. Individualistic sensitivities and exposure to climate change explain variation in species’ distribution and abundance changes. SCI ADV 1, e1400220. Parks, S.A., Holsinger, L.M., Littlefield, C.E., Dobrowski, S.Z., Zeller, K.A., Abatzoglou, J.T., Besancon, C., Nordgren, B.L., Lawler, J.J., 2022. Efficacy of the global protected area network is threatened by disappearing climates and potential transboundary range shifts. ENVIRON RES LETT 17, 54016. Pearman, P.B., Guisan, A., Broennimann, O., Randin, C.F., 2008. Niche dynamics in space and time. TRENDS ECOL EVOL 23, 149-158. Peterson, D.L., Millar, C.I., Joyce, L.A., Furniss, M.J., Halofsky, J.E., Neilson, R.P., Morelli, T.L., 2011. Responding to climate change in national forests: a guidebook for developing adaptation options. Gen. Tech. Rep. PNW-GTR-855. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland 109. Reside, A.E., Welbergen, J.A., Phillips, B.L., Wardell Johnson, G.W., Keppel, G., Ferrier, S., Williams, S.E., VanDerWal, J., 2014. Characteristics of climate change refugia for A ustralian biodiversity. AUSTRAL ECOL 39, 887-897. Robillard, C.M., Coristine, L.E., Soares, R.N., Kerr, J.T., 2015. Facilitating climate‐change‐induced range shifts across continental land‐use barriers. CONSERV BIOL 29, 1586-1595. Sachan, D., Kumar, P., Saharwardi, M.S., 2022. Contemporary climate change velocity for near-surface temperatures over India. CLIMATIC CHANGE 173, 24. Saunders, S.P., Grand, J., Bateman, B.L., Meek, M., Wilsey, C.B., Forstenhaeusler, N., Graham, E., Warren, R., Price, J., 2023. Integrating climate-change refugia into 30 by 30 conservation planning in North America. FRONT ECOL ENVIRON 21, 77-84. Serra Diaz, J.M., Franklin, J., Ninyerola, M., Davis, F.W., Syphard, A.D., Regan, H.M., Ikegami, M., 2014. Bioclimatic velocity: the pace of species exposure to climate change. DIVERS DISTRIB 20, 169-180. Sillero, N., Ribeiro-Silva, J., Arenas-Castro, S., 2022. Shifts in climatic realised niches of Iberian species. OIKOS 2022, e8505. Stebbins, G.L., Major, J., 1965. Endemism and speciation in the California flora. ECOL MONOGR 35, 2-35. Steven J. Phillips, M.D.R.E. Maxent software for modeling species niches and distributions (Version 3.4.1). Author); 2023. Taheri, S., Naimi, B., Rahbek, C., Araújo, M.B., 2021. Improvements in reports of species redistribution under climate change are required. SCI ADV 7, e1110. Tan, X., Zhang, L., Zhang, A.P., Wang, Y., Huang, D., Wu, X.G., Sun, X.M., Xiong, Q.L., Pan, K.W., 2018. The suitable distribution area of Tsuga longibracteata revealed by a climate and spatial constraint model under future climate change scenarios. Acta Ecol. Sin 38, 8934-8945. Trew, B.T., Maclean, I.M., 2021. Vulnerability of global biodiversity hotspots to climate change. GLOBAL ECOL BIOGEOGR 30, 768-783. Wang, W., Feng, C., Liu, F., Li, J., 2020. Biodiversity conservation in China: A review of recent studies and practices. Environmental Science and Ecotechnology 2, 100025. Wessely, J., Hülber, K., Gattringer, A., Kuttner, M., Moser, D., Rabitsch, W., Schindler, S., Dullinger, S., Essl, F., 2017. Habitat-based conservation strategies cannot compensate for climate-change-induced range loss. NAT CLIM CHANGE 7, 823-827. Williams, J., Blois, J., 2018. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J BIOGEOGR 45. Williams, J.W., Jackson, S.T., Kutzbach, J.E., 2007. Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences 104, 5738-5742. Zhang, H., Zhao, H.X., Wang, H., 2020. Potential geographical distribution of Populus euphratica in China under future climate change scenarios based on Maxent model. Acta Ecol. Sin 40, 6552-6563. Zhang, L., Pacifici, M., Li, B.V., Gibson, L., 2020. Drought vulnerability among China's ungulates and mitigation offered by protected areas. Conservation Science and Practice 2, e177. Zhang, R., Yang, L., Ai, L., Yang, Q., Chen, M., Li, J., Yang, L., Luan, X., 2017. Geographic characteristics of sable (Martes zibellina) distribution over time in Northeast China. ECOL EVOL 7, 4016-4023. Zhao, H., Cheng, H., Wang, N., Bai, L., Chen, X., Liu, X., Qiao, B., 2024. Identifying climate refugia for wild yaks (Bos mutus) on the Tibetan Plateau. J ENVIRON MANAGE 366, 121655. Zscheischler, J., Mahecha, M.D., Harmeling, S., 2012. Climate classifications: the value of unsupervised clustering. Procedia Computer Science 9, 897-906. Additional Declarations No competing interests reported. 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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-7035425","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485472944,"identity":"bbb24ec3-1a83-43a5-b086-9c2735126cbb","order_by":0,"name":"Qiyao Han","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Qiyao","middleName":"","lastName":"Han","suffix":""},{"id":485472945,"identity":"5d82eb1f-712d-46eb-b09d-144dfe3013fd","order_by":1,"name":"Pengzi Zhang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pengzi","middleName":"","lastName":"Zhang","suffix":""},{"id":485472946,"identity":"1b95d651-3d41-4446-9ae3-d6125485d69b","order_by":2,"name":"Shuyan Liu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shuyan","middleName":"","lastName":"Liu","suffix":""},{"id":485472947,"identity":"33594a80-a708-4d8c-9631-9ff588be63d5","order_by":3,"name":"Mingjuan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RMWrDMBTG8c8InEWQ9Rk35AoGgUOh5CwqAmfxXDIasmd2aA7hKbMg0F7B4A7N4qlDoaUEAk2eyS4nWyD6L08C/ZBAgM93i9F5iCGgu0VQXEyi4lqCxJ5nPxm/LtrdLzYD1RS7H4mnUWVF++kiwfptoh7QiPTDmlgiU5UNJ4mLCNJpHP0zqbVmsn2urAzJRUKa/cXEt6hSm4PEsZ9IytPom0lCOuNbbD8hyl9iMKFaZ4/rxKjVNkydZFzONtEejRmWuam/5tPR8n3ROkmXkIDhN2r+nW7bd54L9sAUGNgLzvp8Pt89dgIEm0LFEuvUsgAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Mingjuan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-03 07:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035425/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87031227,"identity":"0c15db75-3472-4a71-abdd-e9a6a5adecb9","added_by":"auto","created_at":"2025-07-18 12:50:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":574725,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual diagrams illustrating the calculation of standardized Euclidean distance for the assessment of climate change intensity (CCI) and climatic niche width (CNW) based on key bioclimatic variables. (a) CCI for each grid cell is calculated as the standardized Euclidean distance between current (T\u003csub\u003ec\u003c/sub\u003e) and future (T\u003csub\u003ef\u003c/sub\u003e) climate conditions of the cell. (b) CNW for a given species is defined as the maximum standardized Euclidean distance between the current climate conditions of randomly sampled point pairs (e.g., the climate condition of point a—T\u003csub\u003ea\u003c/sub\u003e, and point b—T\u003csub\u003eb\u003c/sub\u003e) within its distribution range.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/a6dd432df2a0a4c7131b34dd.png"},{"id":87031228,"identity":"3e5ebdd9-4b9c-4253-97a3-796aec34e57c","added_by":"auto","created_at":"2025-07-18 12:50:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50038,"visible":true,"origin":"","legend":"\u003cp\u003eClimatic niche widths (CNWs) for four taxonomic groups: mammals, reptiles, non-migratory birds, and amphibians. Boxes represent the interquartile range, whiskers indicate the full data range excluding outliers, and black dots denote the mean CNW for each group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/03d480be2340579b8d470827.png"},{"id":87031240,"identity":"8617fef5-2a59-4cbe-b587-ebbdd2d47d07","added_by":"auto","created_at":"2025-07-18 12:50:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22773107,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of climate change intensity (CCI) for four taxa (mammals, reptiles, non-migratory birds, and amphibians) in China under the SSP2–4.5 and SSP5–8.5 scenarios. Biomes: TMF—tropical monsoon forest, TCDBF—temperate coniferous \u0026amp; deciduous broadleaf forest, WDBF—warm-temperate deciduous broadleaf forest, CF—cold-temperate coniferous forest, SEBF—subtropical evergreen broadleaf forest, TD—temperate desert, TG—temperate grassland.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/2541a18227a4a5d4dbbc3198.jpeg"},{"id":87031229,"identity":"2486a1d0-4810-4c3d-9e1d-9e2d68817414","added_by":"auto","created_at":"2025-07-18 12:50:31","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9698933,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of climate stability for four taxa—a. mammals, b. reptiles, c. non-migratory birds, and d. amphibians—in China under the SSP2–4.5 scenario. A higher value indicates stronger climatic stability. Biomes: TMF—tropical monsoon forest, TCDBF—temperate coniferous \u0026amp; deciduous broadleaf forest, WDBF—warm-temperate deciduous broadleaf forest, CF—cold-temperate coniferous forest, SEBF—subtropical evergreen broadleaf forest, TD—temperate desert, TG—temperate grassland.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/440d03be2c2efb8979743e49.jpeg"},{"id":87031235,"identity":"8fdb1060-6664-4417-95aa-2af51bb5c62e","added_by":"auto","created_at":"2025-07-18 12:50:31","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12646412,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of potential climate refugia for three taxa —a. mammals, b. reptiles, and c. non-migratory birds, as well as d. combined result across Mainland China. Biomes: TMF—tropical monsoon forest, TCDBF—temperate coniferous \u0026amp; deciduous broadleaf forest, WDBF—warm-temperate deciduous broadleaf forest, CF—cold-temperate coniferous forest, SEBF—subtropical evergreen broadleaf forest, TD—temperate desert, TG—temperate grassland.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/18b22db0714f40fd86b211fe.jpeg"},{"id":87032392,"identity":"596656ba-4ce1-4453-982e-38d7e103fbd7","added_by":"auto","created_at":"2025-07-18 12:58:31","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6329166,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of potential climate refugia inside and outside protected areas (PAs) under future climate conditions. Biomes: TMF—tropical monsoon forest, TCDBF—temperate coniferous \u0026amp; deciduous broadleaf forest, WDBF—warm-temperate deciduous broadleaf forest, CF—cold-temperate coniferous forest, SEBF—subtropical evergreen broadleaf forest, TD—temperate desert, TG—temperate grassland.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/4de88a7269edb28fa6454d70.jpeg"},{"id":88479528,"identity":"3c350b2b-4d30-4e39-b840-514814692666","added_by":"auto","created_at":"2025-08-07 00:01:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":52861900,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/44bd3214-549a-40af-a17f-61a3b1007101.pdf"},{"id":87031250,"identity":"bec7b46b-41c1-4670-9c8f-fc5fc7ee8cdb","added_by":"auto","created_at":"2025-07-18 12:50:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37642378,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixLAE.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035425/v1/dcf9c64fda2f42ee0aae8b5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Toward 30×30: Mapping in-situ Climate Refugia for Biodiversity Conservation across Mainland China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAnthropogenic climate change is increasingly undermining the long-term effectiveness of global protected areas (PAs) in conserving biodiversity (Garcia et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jones et al., 2023). As species respond to shifting climatic conditions, many existing PAs may no longer retain suitable environments for the species they were designed to protect, thereby diminishing their conservation effectiveness over time (Pacifici et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Taheri et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Williams and Blois, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Expanding PAs has been widely advocated as a key strategy to safeguard biodiversity under climate change (Arneth et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, the global \u0026ldquo;30\u0026times;30\u0026rdquo; target\u0026mdash;protecting 30% of land and ocean areas by 2030\u0026mdash;has emerged as a unifying international framework for halting biodiversity loss and maintaining ecosystem functions. However, while the ambition is clear, how and where to expand PAs in ways that enhance climate resilience remains a fundamental and unresolved question in conservation science (Wessely et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn China, biodiversity conservation is particularly challenged by the rapid pace of climate change (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Projections indicate that China will experience an average increase in annual temperature of 1.87\u0026deg;C to 5.51\u0026deg;C and an increase in annual precipitation of 0.12 to 0.3 mm/day by the end of the century (Liang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a response to the changing climate, many species will have to track their ecological niches through range shifts (H\u0026auml;llfors et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sillero et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), such as the giant panda (Gong et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), shrews (Hu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ungulates (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, safeguarding species persistence under climate change has become a great challenge of the biodiversity conservation in China.\u003c/p\u003e\u003cp\u003eIdentifying and protecting potential climate refugia has gained increasing recognition as a key strategy for conserving biodiversity under climate change (Brambilla et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Climate refugia are typically defined as regions that maintain relatively stable climatic conditions despite broader climatic shifts ( Reside et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saunders et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These areas could buffer species from climatic extremes and support long-term ecological persistence, thereby facilitating the survival of species in the long term (Robillard et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Brown et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Hua et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePaleontological and biogeographical evidence has shown that climate refugia played a crucial role in sustaining species during the climatic fluctuations of the Quaternary glacial\u0026ndash;interglacial cycles (Cain, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1944\u003c/span\u003e; Hewitt, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Stebbins and Major, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). Building on this historical role, \u003cem\u003ein-situ\u003c/em\u003e climate refugia\u0026mdash;defined as areas currently inhabited by species and anticipated to remain suitable under future climate conditions\u0026mdash;have gained prominence in modern conservation science (Beaumont et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The \u003cem\u003ein-situ\u003c/em\u003e refugia offer critical opportunities to preserve biodiversity and maintain ecosystem functions in a rapidly changing climate (Morelli et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), making them especially important for \u003cem\u003ein-situ\u003c/em\u003e conservation strategies (Jianzhang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Accordingly, identifying and protecting \u003cem\u003ein-situ\u003c/em\u003e refugia is likely to become an increasingly important component of effective biodiversity conservation under accelerating climate change (Morelli et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Strategic conservation of these areas has the potential to substantially enhance the resilience of species and ecosystems to ongoing and future environmental changes (Ashcroft, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Keppel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIdentifying climate refugia requires detecting areas likely to maintain relatively stable climatic conditions under future climate scenarios (Feng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oliver et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trew and Maclean, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A widely used approach for assessing climate stability is the calculation of climate velocity, which quantifies the rate at which climate conditions shift over space and time (Sachan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This method typically involves tracking the displacement of climate analogues between current and future climate scenarios. For instance, Lai et al. (2022) estimated forward and backward climate velocities across biogeographical regions of terrestrial Europe to identify conservation priority areas. Similarly, Kosanic et al. (2019) assessed seasonal climate velocity across Germany and used it to identify vulnerable species and potential climate refugia. Climate velocity reflects the speed at which species must migrate to remain within suitable climate niches (Haight and Hammill, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hamann et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). If species cannot keep pace with the climate velocity, they might be exposed to unfavourable climates (Serra Diaz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, climate velocity-based methods do not capture the temporal persistence of climatic conditions at a given location\u0026mdash;an essential characteristic of in-situ climate refugia. As a result, climate velocity alone may be insufficient for identifying areas capable of supporting long-term species persistence (Belote, Carroll et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther studies have assessed climate stability by quantifying the difference or similarity between current and future climate conditions using metrics such as Euclidean Distance (Zscheischler et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Standardized Euclidean Distance (Williams et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Markov Distance (Mahony et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Parks et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These approaches effectively capture the temporal stability of climatic conditions at a given location, making them well-suited for identifying in-situ climate refugia (Palmer et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nevertheless, most of the studies used human-defined data classification methods (e.g., natural breaks) to determine thresholds for high climate stability, with little consideration of the differences among species in their sensitivity to climate change. It should be noted that species in different climate regions usually exhibit different tolerances to changes in climate (Calatayud et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and may response differently to specific climate variables (Palmer et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This could lead to a concern that human-defined thresholds might not reveal the realistic requirement of climate stability for species, thereby limiting the ecological realism of refugia identification.\u003c/p\u003e\u003cp\u003eHere, we proposed a climatic niche-based approach to assess climate stability and identify potential climate refugia. A species\u0026rsquo; climatic niche refers to the set of coarse-resolution climatic variables (e.g., temperature, precipitation) that influence its spatial and temporal persistence (Hutchinson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Pearman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With other abiotic and biotic factors, it defines where a species may occur and how it will respond to the changing climate (Di Marco et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, the width of a species\u0026rsquo; climatic niche plays a pivotal role in shaping spatial pattern of species distributions. Recent studies have found that niche width is a strong predictor of climate-related local extinction and range shifts of species (Grinder and Wiens, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is therefore can be used to identify key climatic variables influencing species persistence, thereby enabling the calculation of climate stability and the establishment of species-specific thresholds for identifying in-situ refugia. We hypothesize that if predicted climate changes remain within the general climatic niche width of the species studied, the climate will be considered stable enough to support biodiversity persistence.\u003c/p\u003e\u003cp\u003eThe climatic niche-based approach was then applied to Mainland China to identify potential climate refugia for biodiversity conservation under climate change. Our study aims to answer three key questions: (1) What are the key bioclimatic variables and climatic niche widths (CNWs) that shape the distribution of species across Mainland China? (2) Where are the potential climate refugia in China under future climate scenarios? (3) Are these areas adequately covered by the existing PA network, and how can conservation planning be optimized to cope with future climate change? Our framework provides a scalable, climate-informed approach to conservation planning and identifies priority areas where future efforts can strengthen biodiversity resilience in the face of climate change.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eOur study followed a four-step approach: (1) identifying key bioclimatic variables shaping species distributions; (2) assessing the intensity of climate change; (3) calculating species-specific climatic niche widths to define climate stability thresholds; and (4) mapping potential climate refugia by integrating climate stability, habitat suitability, and habitat quality. This framework was applied to 311 endangered terrestrial vertebrate species (IUCN, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026mdash;comprising mammals, amphibians, reptiles, and non-migratory birds\u0026mdash;under two future climate scenarios: SSP2-4.5 and SSP5-8.5. A subset of climate-sensitive species\u0026mdash;species with high exposure, high sensitivity, and low adaptive capacity to climate change\u0026mdash;was selected for detailed modeling based on IUCN Red List assessments (IUCN, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Spatial analyses were conducted using high-resolution (1 km\u0026sup2;) raster datasets within the ArcGIS and R environments.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Identifying Key Climatic Variables\u003c/h2\u003e\n \u003cp\u003eWe combined species occurrence records with current climate variables to identify the key bioclimatic variables shaping each species\u0026rsquo; climatic niche. Endangered terrestrial species data were obtained from the IUCN Red List (IUCN, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on threat sensitivity, our study focused on 77 endangered mammals (17 climate-sensitive), 162 amphibians (23 climate-sensitive), 42 reptiles (7 climate-sensitive), and 30 non-migratory birds\u0026mdash;all considered climate-sensitive in this study. We converted IUCN species range SHP files to point data using the ArcGIS \u0026ldquo;Raster to Point\u0026rdquo; tool, and applied spatial thinning with \u0026ldquo;Subset Features\u0026rdquo; to reduce computational load while preserving distribution patterns. Thinning rates were scaled to range size: 1% for species\u0026thinsp;\u0026gt;\u0026thinsp;100\u0026nbsp;million hectares (Mha), 5% for 10\u0026ndash;100 Mha, and 10% for 1\u0026ndash;10 Mha. No thinning was applied to species with ranges\u0026thinsp;\u0026lt;\u0026thinsp;1 Mha.\u003c/p\u003e\n \u003cp\u003eCurrent (1970\u0026ndash;2000) and future (2081\u0026ndash;2100) climate data under SSP2-4.5 and SSP5-8.5 scenarios were obtained from WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldclim.org/\u003c/span\u003e\u003c/span\u003e) at 30-arcsecond resolution. To reduce model uncertainty, we used the ensemble mean of 10 GCMs: ACCESS-CM2, CMCC-ESM2, EC-Earth3-Veg, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. For each species, we assessed collinearity among 19 bioclimatic variables using Pearson\u0026rsquo;s correlation (r\u0026thinsp;\u0026ge;\u0026thinsp;0.75) and excluded highly correlated predictors within its spatial range (Tan et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). We then evaluated multicollinearity using variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10), calculated via the \u0026lsquo;cor\u0026rsquo; function in R (Mason and Perreault Jr, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), and retained a final set of uncorrelated, independent climate variables for species-specific modeling.\u003c/p\u003e\n \u003cp\u003eFor each of the climate-sensitive species, we used MaxEnt v3.4.4 (Steven J. Phillips, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) to identify key bioclimatic variables defining species\u0026rsquo; climatic niches. Species distribution records and corresponding climate layers (independent climate variables) were input into MaxEnt. Each model was run 10 times, and the average results were used. Variables with a cumulative contribution\u0026thinsp;\u0026ge;\u0026thinsp;90% (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) were retained as key variables for each species. To facilitate group-level analysis, we subsequently aggregated and summarized these key variables by taxonomic group\u0026mdash;mammals, amphibians, reptiles, and non-migratory birds\u0026mdash;for downstream climate change intensity assessment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Assessing Climate Change Intensity\u003c/h2\u003e\n \u003cp\u003eWe assessed climate change intensity (CCI) across mainland China by calculating the standardized Euclidean distance between current and future (2081\u0026ndash;2100) climate conditions (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) for each taxonomic group\u0026mdash;mammals, amphibians, reptiles, and non-migratory birds\u0026mdash; using the set of key bioclimatic variables identified for each group in Section 2.1. All climate layers were resampled to a 1 km \u0026times; 1 km resolution using the nearest-neighbor method and projected to the Krasovsky 1940 coordinate system. The calculation was based on the following standardized Euclidean distance\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eformula:\u003c/p\u003e\n \u003cp\u003e\u003cimg width=\"140\" height=\"52\" src=\"data:image/wmf;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n \u003cp\u003ewhere \u003cimg width=\"18\" height=\"18\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;and \u003cimg width=\"18\" height=\"18\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAASCAYAAABWzo5XAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAAKNSURBVDhPY2lsbGSgBmChhiEgM0YNIhyS9AujXInDvELpe74Ak8n/iLdvWR9NnMh6rKnpG7obWXJTVTMOLL9fc+rGBcZD+7b++KBV/qtwctnaFHmOyUDFr98u8C8INuINqW/tYVsSOPnFNAaGKUDxtRgG/fr18fPGY4zFXdPatT0WfCwIqZjKeyMlvvqGrPz5VS1NQA3GzQn6hypPsThzGvp5vAe6BsMQcDqaufDVUiU9BoYZM5YyFHHfexph9HrG4uU7GLb/TH8dDlRQZXOrLn2HJKtVbPPHiVHineIMdTxnq1RDShdyOhjP2ciQ56nYPL+p6S5KYPOERbNYPNzCMGXVAYaQuKSguipF1anxO+peO3X+LcrQbJ/Z1LSxrq5OTc3MXefZhD1M7lxs3JIMDMyYKVs284m21s1Hiq+PyHF/PJK/ZeZRhtW8MQw2gb72h9qajtZJbJKoc++Wad71ZYFaw5G/Pw/Ovd52EBJaKC5qapq5sYD3uIKX5dcJvQkBDOu9Ol9mzMyo+zq96ShY9ft7DBZybJFuynIpknICDBbBdREnJjStxDAI5Oy3K2/5Pv91ncEpv5zBPCwsBGjIEXgMlb/j0Jjpyc1xlZuBVUhmhxsfw8UTUEkWR9aDIRtt9jL0CE9haLKWCHqj6Oj8xbflcl2tZ5UtA8OJpq1IEf2w3eTSvW+RL1VCGKRs+Oc0NTXdgMmyvL9+afX6LX4Mr9X4GJ4IJTAwOWf37E6UbQcqercfLbG8XjOV4foJYGjofGDQ3T1h+h0BR99TxrZ1bnubHrHs3vcm4t7TzQw/pScyrNycd2tvU9P5pibseUs0MPiY5dPj4VX9JQwsTFO59EM0jh21ZnjitpeBAQAyaADOPtWaTQAAAABJRU5ErkJggg==\" alt=\"image\"\u003e\u0026nbsp;represent the current and future values of the \u003cimg width=\"18\" height=\"18\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;climate variable, and \u003cimg width=\"35\" height=\"18\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the standard deviation of that variable across all grid cells. Lower CCI values indicate smaller differences between current and future climatic conditions, reflecting higher climatic stability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Calculating Species-Specific Climatic Niche Widths\u003c/h2\u003e\n \u003cp\u003eTo estimate the climatic niche width (CNW) for each species, we used the current distribution data and the key bioclimatic variables identified in Section 2.1. For each species, we randomly sampled point pairs within its distribution range and calculated the standardized Euclidean distances between their climatic values (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). To ensure computational efficiency while maintaining representativeness, only 1% of grid cells were sampled for species with distributions larger than 10\u0026nbsp;million hectares. CNW was then defined as the maximum distance observed among these sampled pairs, representing the broadest climatic difference tolerated within the species\u0026rsquo; current range. This approach captures the ecological breadth of a species\u0026rsquo; climatic tolerance across its native habitat, while minimizing the influence of spatial or geographic outliers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Identifying Climatically Stable Regions\u003c/h2\u003e\n \u003cp\u003eClimatically stable regions were identified at the habitat level for each of the four taxonomic groups. For each group, we calculated the mean CNW across all species occurring within a given habitat type to establish a baseline threshold for climatic stability. Grid cells of that habitat type with projected CCI values below this threshold were preliminarily classified as climatically stable.\u003c/p\u003e\n \u003cp\u003eWithin these stable zones, we refined the assessment using species-specific CNWs. For each species, grid cells of the climatically stable areas with CCI values below its CNW were marked as climatically suitable (assigned a value of 1), and 0 otherwise. A frequency score was then calculated by weighting these values according to species\u0026rsquo; IUCN threat categories: using weights of 2 for Vulnerable (VU), 4 for Endangered (EN), and 8 for Critically Endangered (CR), following Shrestha et al. (2021). The resulting weighted scores represent the frequency with which each grid cell is deemed climatically stable for the persistence of threatened species.\u003c/p\u003e\n \u003cp\u003eFinally, we multiplied the species frequency score by the inverse of CCI to generate a continuous climate stability index, where higher values indicate stronger climatic stability and greater conservation relevance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Identifying Potential Climate Refugia\u003c/h2\u003e\n \u003cp\u003eTo identify potential climate refugia, we integrated three key factors\u0026mdash;climate stability (CS), habitat suitability (HS), and habitat quality (HQ)\u0026mdash;which are critical for species persistence under climate change (Brown et al., 2020; Morelli et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Robillard et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHS was defined as the distribution of climatically suitable areas for endangered terrestrial vertebrates under current climate conditions. Using MaxEnt, we modeled suitable habitat ranges for mammals, amphibians, reptiles, and non-migratory birds across mainland China. The resulting habitat layers were weighted by species\u0026rsquo; IUCN threat status and combined using the raster calculator in ArcGIS 10.8.\u003c/p\u003e\n \u003cp\u003eHQ was assessed using the InVEST 3.14.3 (Integrated Valuation of Ecosystem Services and Tradeoffs)\u0026mdash;\u0026mdash;a model developed by the United States Natural Capital Project team\u0026mdash;\u0026mdash;which incorporates spatial interactions between anthropogenic threats (e.g., urban expansion, agricultural encroachment) and ecosystems. Parameters for threat intensity, distance decay, and habitat sensitivity were assigned based on previously published studies, as detailed in Appendix Tables S1-2.\u003c/p\u003e\n \u003cp\u003eBased on the results of HS, HQ, and CS, we calculated a climate refugia value (CRV) for each grid cell within the identified climatically stable regions. CRV indicates the potential of a grid cell to function as a climate refugia. We first assessed the habitat condition of each cell by multiplying HS and HQ, producing a combined habitat score. This score highlights areas where ecological suitability and integrity are both high, under the assumption that habitat persistence depends on the co-occurrence of favorable climatic conditions and minimal anthropogenic disturbance. Next, we integrated this habitat score with CS to quantify the composite potential for long-term species persistence. All layers were normalized to a 0\u0026ndash;1 scale prior to integration. Recognizing the complementary roles of habitat condition and climate stability in shaping species survival, we applied an equal-weighted additive approach:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cp\u003e\u003cimg width=\"262\" height=\"22\" src=\"data:image/wmf;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (2)\u003c/p\u003e\n \u003cp\u003eHigher CRV values indicate areas that simultaneously offer stable climatic conditions and high-quality habitats, representing strong candidates for potential climate refugia under future climate change. We first classified the results of CRV into three categories\u0026mdash;low, medium, and high potential\u0026mdash;using the equal-interval (quantile) method. The continuous 0\u0026ndash;1 CRV range was divided into three equal parts to facilitate spatial prioritization. To account for uncertainty across climate scenarios, we integrated results from both SSP2-4.5 and SSP5-8.5 pathways. Using a maximum-value principle, we retained the higher CRV for each grid cell between the two scenarios. This conservative approach ensured that areas identified as potential refugia under either scenario were preserved, minimizing the risk of underestimation under more severe future conditions. Finally, we combined the scenario-integrated outputs across the four taxonomic groups\u0026mdash;mammals, amphibians, reptiles, and non-migratory birds\u0026mdash;by applying a maximum-value overlay. Each grid cell was assigned the highest CRV observed among all groups, ensuring that areas critical for the persistence of any one group were retained as final potential climate refugia.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Key Bioclimatic Variables, Niche Widths and Climate Change Intensity\u003c/h2\u003e\u003cp\u003eThe key bioclimatic variables influencing species distributions varied across taxonomic groups (Appendix Table S3-6). MaxEnt modeling revealed that mammals and amphibians were influenced by a broader suite of bioclimatic variables, particularly annual mean temperature (BIO1), temperature seasonality (BIO4), and annual precipitation (BIO12). Reptiles, by contrast, responded primarily to temperature and precipitation extremes (e.g., precipitation of driest month and precipitation of coldest quarter), while non-migratory birds exhibited a wide climatic response spectrum involving both seasonal and annual indicators.\u003c/p\u003e\u003cp\u003eCNWs also exhibited clear interspecific variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Appendix Table S7-10). Amphibians had the narrowest average CNW (0.70), suggesting a greater sensitivity to climatic shifts, while birds (1.66) and mammals (1.45), followed by reptiles (1.10), displayed broader climatic tolerances. These values reflect differing capacities for climate adaptation across taxa. Detailed CNW values for each species are provided in Appendix Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnder the SSP2-4.5 scenario, CCI exhibited clear taxonomic and spatial variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;d). Reptiles showed the lowest average CCI values across most regions, indicating relatively low climatic exposure, whereas mammals exhibited the highest average CCI, suggesting greater vulnerability to climatic shifts. Geographically, low CCI values were primarily concentrated in the southwestern part of the subtropical evergreen broadleaf forest (SEBF) zone, the tropical monsoon rainforest (TMR) zone, and the Qinghai Plateau alpine vegetation (QPAV) zone. In contrast, high CCI values were observed for most species in the warm-temperate deciduous broadleaf forest (WDBF), cold-temperate coniferous forest (CF), and the central part of the SEBF, indicating stronger climatic shifts in these areas.\u003c/p\u003e\u003cp\u003eUnder the more extreme SSP5-8.5 scenario, overall CCI values increased across all taxa and regions, with spatial heterogeneity becoming more pronounced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee\u0026ndash;h). Despite the general increase, the spatial distribution of high and low CCI values remained broadly consistent with the pattern observed under SSP2-4.5, suggesting persistent spatial trends in climatic change exposure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Climate Stability Pattern\u003c/h2\u003e\u003cp\u003eThe spatial distribution of climate stability\u0026mdash;calculated by integrating species-specific CNWs, their threat-weighted frequencies, and projected CCI\u0026mdash;exhibited marked heterogeneity across China under future climate scenarios.\u003c/p\u003e\u003cp\u003eUnder the SSP2-4.5 scenario, climate stability exhibited strong spatial variation across taxonomic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For birds, climatically stable areas were widespread, spanning seven major biomes across China. In contrast, climatically stable regions for mammals and reptiles were primarily restricted to the southwestern corner of the country. For amphibians, however, almost no climatically stable areas were identified, indicating a severe mismatch between their climatic requirements and projected climate change. Overall, regions with high climate stability values were predominantly concentrated in southwestern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the biome level, areas in southwestern SEBF exhibited the highest climate stability, indicating their potential to serve as long-term refugia under future climatic shifts. In contrast, low stability values were observed in northern and arid regions, including much of the WDBF, most of the QPAV, Temperate Desert (TD) and Temperate Grassland (TG) zones. These areas either exceeded the climatic thresholds of many species or overlapped with fewer threatened taxa, thus offering limited capacity for biodiversity retention.\u003c/p\u003e\u003cp\u003eUnder the SSP5-8.5 scenario, the distribution of areas with high climate stability remained largely consistent with that under SSP2-4.5 (Appendix Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For mammals and reptiles, the identified climatically stable regions were similar across both scenarios. In contrast, for birds, the extent of stable regions was noticeably reduced under SSP5-8.5. As observed under SSP2-4.5, no climatically stable areas were identified for amphibians under the SSP5-8.5 scenario, suggesting a persistent lack of refugial potential for this group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Identification of Potential Climate Refugia\u003c/h2\u003e\u003cp\u003eThe identification of climate refugia, which combines the results of SC, HS (Fig. S2), and HQ (Fig. S3), revealed pronounced taxonomic and spatial differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). High- and medium-potential refugia for mammals were large and continuous in the southwestern SEBF and southern TMR zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), showing a similar spatial pattern to reptiles, albeit at a smaller scale and with greater fragmentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Non-migratory birds displayed more spatially dispersed refugia, consistent with their broader climatic tolerances (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Amphibians, however, lacked any identifiable climate refugia under either SSP2-4.5 or SSP5-8.5 scenarios, likely due to their narrow climatic niche widths and high exposure to projected climate change.\u003c/p\u003e\u003cp\u003eUsing a maximum-value integration approach, we combined the refugia identified for the four taxonomic groups to generate a composite climate refugia distribution map across mainland China (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). In total, 227.1 Mha of climate refugia were identified, comprising 16.9 Mha of high-potential, 184.2 Mha of medium-potential, and 26.0 Mha of low-potential areas (Table S11). Low-potential refugia were fragmented and primarily distributed across the QPAV, eastern coastal WDBF, and northeastern SEBF zones. Medium-potential refugia were concentrated in most of the CF and central SEBF zones, with smaller, scattered patches in western WDBF, TG, and TD zones. High-potential refugia were found in the southwestern SEBF and southern coastal TMR zones, with almost no high-value areas in other vegetation zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Effectiveness of Protected Areas for Conserving Climate Refugia\u003c/h2\u003e\u003cp\u003eTo address whether projected climate refugia are adequately covered by China\u0026rsquo;s existing PA network and to identify conservation gaps under future climate change, we evaluated the spatial congruence between current PA boundaries and the distribution of identified climate refugia (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eOur results show that only 15% (34.6 Mha) of the total 227.1 Mha of identified climate refugia are currently covered by existing PAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Protection coverage is uneven across refugia categories: 33% (8.55 Mha) of low-potential refugia are protected, largely due to extensive reserves in the QPAV, TD, and TG zones. In contrast, only 13% (24.64 Mha) of medium-potential and 8% (1.44 Mha) of high-potential refugia fall within current PA boundaries.\u003c/p\u003e\u003cp\u003eThe majority (~\u0026thinsp;90%) of medium- and high-potential refugia remain outside the current PA system (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Notably, core high-value areas in the southwestern SEBF (14.56 Mha, 91%) and TMR (0.93 Mha, 93%), as well as medium-potential refugia in the CF (30.98 Mha, 87%) and WDBF (12.20 Mha, 86%) zones, are severely unprotected, highlighting urgent conservation gaps in these ecologically critical regions.\u003c/p\u003e\u003cp\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\u003eArea (million hectares) of potential climate refugia inside and outside PAs across biomes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVegetation Zone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLow potential\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMedium potential\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eHigh potential\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIn-PAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eex-PAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIn-PAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eex-PAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIn-PAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eex-PAS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWDBF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eQPAV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e159.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e26.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e184.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e16.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBiomes: TMF\u0026mdash;tropical monsoon forest, TCDBF\u0026mdash;temperate coniferous \u0026amp; deciduous broadleaf forest, WDBF\u0026mdash;warm-temperate deciduous broadleaf forest, CF\u0026mdash;cold-temperate coniferous forest, SEBF\u0026mdash;subtropical evergreen broadleaf forest, TD\u0026mdash;temperate desert, TG\u0026mdash;temperate grassland.\u003c/p\u003e\u003cp\u003eTo enhance climate adaptation, strategically expanding the protected area network is essential. We found that incorporating high-potential climate refugia alone could increase national terrestrial protection from 18\u0026ndash;25%. Including both high- and medium-potential refugia would elevate this to 44%, while full integration of all identified refugia could raise coverage to 47%. These results highlight a critical need to realign China\u0026rsquo;s conservation strategy by prioritizing underrepresented, high-value climate refugia\u0026mdash;particularly in the SEBF and TMR zones\u0026mdash;to ensure long-term biodiversity persistence under accelerating climate change.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study developed an integrative, climate-niche-based framework for the identification of in-situ climate refugia by explicitly linking species-specific climatic tolerance with macro-scale projections of climate change intensity and habitat condition (e.g., habitat suitability and quality). Unlike previous approaches that typically rely on generalized climatic envelopes or uniform thresholds of climate stability, our method quantifies CNWs for individual species and uses them to establish ecologically grounded thresholds for climate stability. This allows for a more biologically realistic delineation of climate refugia across diverse taxa and ecosystems. Importantly, our analysis shows that high climate stability does not always align with low climate change intensity. Instead, areas deemed stable are those where projected climate shifts fall within species-specific tolerances and where multiple climate-sensitive taxa co-occur. This highlights the inadequacy of climate-only approaches and emphasizes the need for integrating species-level ecological data to reliably identify long-term conservation strongholds.\u003c/p\u003e\u003cp\u003eOur study in China identified climate refugia of varying conservation potential and reveals a significant spatial mismatch between these refugia and the existing PA network. Of the 227.1\u0026nbsp;million hectares identified as climate refugia, just 15% are currently protected. High-potential climate refugia are concentrated in biodiversity-rich zones such as the SEBF and TMR, whereas arid and temperate biomes exhibit much lower conservation potential under future climate conditions. Critically, we show that the current PA network provides only limited coverage of these high-value refugia (8%), while low-potential refugia, particularly those in the QPAV and TD zones, receive disproportionate protection.\u003c/p\u003e\u003cp\u003eOur findings underscore a significant conservation gap and point to a tangible, actionable opportunity: incorporating just the high-potential refugia could increase China\u0026rsquo;s protected land coverage from 18\u0026ndash;25%, offering a clear pathway toward achieving the 30\u0026times;30 target. Moreover, the mismatch between existing PAs and high-value climate refugia underscores the urgency of optimizing national conservation strategies. Particularly, southwestern China emerges as a critical climate refugium core, suggesting it should be prioritized in future ecological redline revisions, national park designations, and climate-resilient ecological corridors. Effective climate-adaptive conservation requires anticipating not only where biodiversity will persist, but also how spatial protections can be realigned to safeguard those trajectories. Additionally, while formal PAs remain essential, complementary mechanisms such as Other Effective area-based Conservation Measures (OECMs)\u0026mdash;including community-managed lands and traditional reserves\u0026mdash;can play a critical role in safeguarding areas that lie outside formal protection but nonetheless contribute meaningfully to biodiversity persistence. Recognizing and supporting these informal conservation measures would expand the scope of climate-adaptive conservation without requiring entirely new designations.\u003c/p\u003e\u003cp\u003eNevertheless, several limitations should be considered when interpreting our findings. Firstly, the precision of species distribution data is constrained, relying largely on IUCN Red List shapefiles and applying random sampling for processing, which may oversimplify real patterns. We acknowledge that the use of species' distribution range data, rather than species occurrence data, to define climatic niche width may conflate climatic breadth with spatial distribution, potentially emphasizing distributional outliers. To enhance ecological realism, future studies could refine niche width estimation by using presence/absence point data or employing density-based or percentile-based climatic envelopes that better represent core climatic tolerances. Additionally, the assumption that species persistence is ensured when projected climatic changes remain within species\u0026rsquo; niche widths is a simplification. Future research will need empirical validation of this assumption through paleoecological data, historical refugia mapping, or long-term monitoring datasets. We also recognize the limitations in combining climate stability, habitat suitability, and habitat quality using equal weights. Although this simplification was applied to ensure analytical clarity, future iterations of the framework could explore weight optimization based on species traits, conservation outcomes, or expert elicitation to improve the robustness of prioritization. Moreover, the study does not account for species\u0026rsquo; adaptive or dispersal capacities, which could lead to over- or underestimation of refugial viability. Future research should incorporate finer-resolution species data, model dispersal dynamics, and integrate land-use and habitat transformation scenarios to enhance the robustness and applicability of climate refugia identification.\u003c/p\u003e\u003cp\u003eDespite these limitations, our study fills a critical methodological gap by offering a scalable tool to guide climate-adaptive conservation. Importantly, it highlights ecologically valuable regions under climate change\u0026mdash;such as the subtropical evergreen broadleaf and tropical monsoon forests\u0026mdash;that merit strategic integration into protected area expansion plans. While grounded in a China-specific context, the core principles of this framework can inform conservation planning in other regions with comparable climate and biodiversity gradients.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eContemporary climate change is causing significant challenges to the conservation of biodiversity in China. Identifying and protecting climate refugia is one of the most effective strategies to mitigate the adverse impacts of climate change on species persistence. This study developed a climatic niche-based framework that integrates future climate stability, species-specific tolerance thresholds, habitat suitability, and habitat quality to systematically identify potential climate refugia across mainland China. Our findings revealed substantial spatial and taxonomic variation in refugia distribution, with a total of 227.1 Mha (approximately 23.7% of China\u0026rsquo;s land area) identified as potential refugia. However, only 15.3% of these areas currently fall within the national PA network, and critically, just 8.5% of high-potential refugia\u0026mdash;the most important for long-term biodiversity resilience\u0026mdash;are protected. This spatial mismatch underscores the urgent need to incorporate climate-adaptive priorities into protected area planning. Our results offer a clear and achievable pathway toward realizing the 30\u0026times;30 target. We recommend that high-potential climate refugia be formally prioritized in national and regional planning efforts. The proposed framework provides a scalable and replicable tool for identifying climate-resilient areas and optimizing conservation networks to meet both ecological and policy goals under accelerating climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.H. and P.Z. designed the study and wrote the manuscript text, Q.H. and M.Z. revised the manuscript text,P.Z. conducted data analyses and prepared figures and tables,S.L. prepared the initial data and figures.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe spatial dataset of identified climate refugia generated in this study is publicly available at Figshare DOI: 10.6084/m9.figshare.29388533\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArneth, A., Shin, Y., Leadley, P., Rondinini, C., Bukvareva, E., Kolb, M., Midgley, G.F., Oberdorff, T., Palomo, I., Saito, O., 2020. 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Procedia Computer Science 9, 897-906.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"climate refugia, climate stability, climate change, protected area, biodiversity conservation, conservation planning","lastPublishedDoi":"10.21203/rs.3.rs-7035425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e\u003cp\u003eClimate change poses significant threats to biodiversity by altering habitat conditions, thereby challenging the effectiveness of protected area (PA) networks. \u003cem\u003eIn-situ\u003c/em\u003e climate refugia\u0026mdash;areas with stable climatic conditions\u0026mdash;are increasingly recognized as essential for sustaining species persistence under future climate scenarios.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis study aims to identify potential in-situ climate refugia across mainland China for 311 endangered terrestrial vertebrates under two future climate scenarios (SSP2-4.5 and SSP5-8.5). We further evaluate the spatial congruence between these refugia and China\u0026rsquo;s existing PA network to inform strategic conservation planning toward achieving the \u0026ldquo;30\u0026times;30\u0026rdquo; target.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA climatic niche-based framework was developed to identify potential in-situ climate refugia by integrating species-specific climatic tolerance, climate change intensity, habitat suitability, and habitat quality. Climate change intensity was quantified using standardized Euclidean distance, and species-specific niche widths were used to define thresholds for climatic stability. Potential climate refugia were delineated by overlaying climate stability with habitat suitability and quality, and subsequently compared with existing PAs to identify conservation gaps.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur results reveal that 23.66% of China\u0026rsquo;s land area could function as climate refugia, but only 15.25% of these regions are currently protected. High-potential refugia, mainly located in the subtropical evergreen broadleaf and tropical monsoon forest zones, are severely underrepresented (8% coverage). Incorporating these refugia into PA networks could raise coverage from 18\u0026ndash;25%, offering a realistic pathway to meet the 30\u0026times;30 target of conservation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe proposed framework offers a scalable approach for climate-informed conservation planning. To ensure biodiversity resilience, China should prioritize integrating high-potential refugia into national and regional PA strategies. This approach can significantly enhance ecological representativeness and climate adaptation capacity in PA networks.\u003c/p\u003e","manuscriptTitle":"Toward 30×30: Mapping in-situ Climate Refugia for Biodiversity Conservation across Mainland China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 12:50:26","doi":"10.21203/rs.3.rs-7035425/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":"23090b93-9843-4812-bd86-b5d220423ffe","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T23:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 12:50:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7035425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035425","identity":"rs-7035425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}