Projected climate zone shifts could undermine the effectiveness of global protected areas for biodiversity conservation by the mid-to-late century

Projected climate zone shifts could undermine the effectiveness of global protected areas for biodiversity conservation by the mid-to-late century | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Projected climate zone shifts could undermine the effectiveness of global protected areas for biodiversity conservation by the mid-to-late century Diyang Cui, Amy Frazier, Shunlin Liang, Patrick Roehrdanz, George Hurtt, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3992123/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 Climate change is driving broad-scale redistribution of species and is expected to accelerate in the coming decades, potentially undermining the effectiveness of protected areas (PAs) for biodiversity conservation. To assess exposure of global PAs to future climate risks, we develop a high-resolution climate change velocity measure to quantify climate zone shifts under future climate scenarios. We find that by mid-century, around 20% of global protected land area is projected to undergo climate zone shifts under all scenarios. Under RCP 8.5, the rate of climate zone velocity will continue to accelerate through the end of this century, potentially impacting 40% of existing PA land area. 15% of these climate zone shifts will terminate outside the existing PA network and into human-modified areas, and about 15% of protected land area will be exposed to novel and disappearing climates, potentially undermining the effectiveness of the existing network. Strategic and adaptive conservation planning that explicitly considers climate zone shifts will enable greater resilience for conservation interventions under climate change. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Earth and environmental sciences/Environmental sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As key drivers of ecological interactions and biological processes, climatic conditions determine global patterns of biomes and biodiversity 1,2 . In response to contemporary, unprecedented changes in climate, species are undergoing evolutionary adaptation 3,4 , changing phenology and abundance 5,6 , and shifting their distributions 7 . Regional changes in the availability and location of climatically suitable areas have resulted in observed latitudinal and elevational shifts in species distributions and rearrangement of species assemblages 5,8 . Consequently, ongoing changes in climatic conditions are driving a significant redistribution of life on Earth, leading to restructured biotic community compositions 9 , losses of ecosystem services, and increased threats to human welfare across the globe 1,10 . Protected areas (PAs) are essential for biodiversity conservation strategies 11,12 , evidenced by reduced rates of habitat loss 13 and enhanced species diversity within their boundaries 14 . PAs also provide various social and economic benefits by preserving natural resources, delivering ecosystem services, and supporting human livelihoods 15 . Expanding global PA coverage while maintaining the effectiveness of PA networks are key conservation targets agreed to in the most recent Convention on Biological Diversity (CBD) 16,17 . However, changes in climate and land use can undermine PA network effectiveness 11,18–20 . Habitat loss and fragmentation from land use changes can create barriers to species movement and disconnect PA networks 21–24 . As climate shifts, suitable climates for species may become less accessible, and species may move into unprotected or human-dominated areas as they attempt to track preferred climatic conditions, increasing extinction risk 18,20,25 . Current low PA connectivity 26,27 , and inadequate consideration of future biodiversity ranges 28,29 could further diminish the capacity of PA networks in protecting future biodiversity 30,31 , impeding progress toward global conservation goals. To enhance the effectiveness of global PAs and develop more strategic and adaptive PA conservation approaches, projected climate shifts need to be incorporated into PA conservation planning to anticipate biodiversity redistributions 10,12,32 . Given known gaps in species data and the uncertainties inherent in bioclimatic modeling 8,33 , recent studies assessing biodiversity exposure risks increasingly use climate change metrics 8 including local climate velocity 33,34 , climate analog velocity 9,35 , climate stability 32 , and novel and disappearing climates 8,20,36 as well as species-specific biotic velocities 37–39 . Velocity measures are valuable indicators for assessing the potential migration paths of species in response to changing climate conditions, and climate velocity, in particular, can provide a general understanding of exposure. Local climate velocities are based on a single climate variable (e.g., temperature only), while climate analog velocities are based on a climate space derived from a statistical multivariate climate index and may not be biologically relevant. Thus, each alone may not be indicative of the properties and functions of biomes and ecosystems, nor can they be indiscriminately applied in biodiversity risk evaluation 33 . To overcome these limitations, a metric is needed that combines multiple climate variables while providing a more biologically meaningful climate space for conservation. Climate zone velocity measures based on Köppen-Geiger climate (KGC) zones can be a valuable surrogate for the potential movement of species to maintain suitable climatic conditions because they overcome the limitations of single-variable measures (i.e., local climate velocity) while also being grounded in biome and ecosystem functioning. While other methods exist to map biomes and ecoregions 2,40–43 , the KGC scheme is the most commonly used, and it can be applied to examine shifts in climate zones and possible biome changes 44–46 as it presents standardized, globally comprehensive climate maps based on multiple climate variables and allows nested, hierarchical levels of analysis. KGC zones show strong correlations with biome distributions 47,48 , and studies have shown KGC climate zones shifting poleward and upslope under climate change, with significant area expansion of tropical (A) and arid (B) climate zones and shrinkage of polar (E) climates 44,45,47,49 . Together, forward and backward velocities can provide complementary information to support conservation. Forward velocity (i.e., present-to-future; Fig. 1 a) reflects the minimum path that species need to migrate to maintain suitable climatic conditions 18,50 with higher values implying high threats to a species or population inhabiting the site. Backward velocity (i.e., future-to-present; Fig. 1 b) represents the minimum path that species need to migrate to locate or colonize the new, suitable site, with higher values implying high threats to the ecosystem functioning and services of a site. Forward velocity can be interpreted as species exposure to climate change while backward velocity can be interpreted in terms of risks for ecosystem functioning and services. High spatial and temporal resolution assessments of climate velocity at multiple future time steps, including the near future, are urgently needed (see Table S1 ) to achieve the CBD’s 2030 biodiversity strategic plan and 2050 vision of ‘living in harmony with nature 16,17,51,52 . Existing PA exposure assessments derived from coarse spatial resolution climate data may overlook finer scale topo-climatic patterns 12,53–57 , leading to flawed estimates, especially in rugged terrain 58,59 , and limited ability to detect microrefugia 60,61 . Moreover, the use of coarse temporal resolution data (e.g., intervals of 80–100 year) can obscure the finer time scales over which climates may become less favorable for local species and populations. We develop a high spatial and temporal resolution climate zone velocity measure that conforms to climate-analog velocity algorithms 12,18,53,58 but leverages KGC classes along with topographically-informed paths (Fig. S1 ) to better model how species may move with climate change. We use this measure to examine the spatiotemporal patterns of climate zone shifts in the global PA network at 1-km spatial resolution for four future time periods (2020–2049, 2040–2069, 2060–2089, 2070–2099) and four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), using 1971–2000 as a baseline. We assess both forward and backward velocities using a global dataset of Köppen-Geiger climate projections (KGClim) 62 derived from the bias-corrected, downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations 63 and WorldClim Historical Climate Data (WorldClim V2) 64 . We assess climate zone shifts for 26,220 terrestrial protected areas (PAs) covering 16.9 million km² (13% of the global land surface) to identify the most vulnerable PAs projected to undergo climate zone changes by the end of this century (areas where the mean velocity is estimated to be greater than 1 km yr − 1 ). We also assess the effectiveness of the current PA network to absorb projected climate zone shifts by evaluating seven spatial patterns of climate movement (Fig. S2). Climates relocated within, among, or into other existing PAs 18 indicate sustained network effectiveness for absorbing climate shifts. Climates relocated outside existing PAs, into human-modified areas, or that disappear (from the PA and search area) altogether undermine network effectiveness. We characterize the climate vulnerability of PAs based on their elevation, terrain ruggedness, human footprint level, and species richness to identify larger-scale trends. Lastly, we present a case study to integrate climate shifts in a PA prioritization scheme to inform future climate adaptation planning and biodiversity conservation. Results and Discussion Projected climate zone shifts in protected areas Over 17,000 terrestrial PAs comprising 40% of global protected land area could experience shifts to different climate zones during this century, and 20% could see change as early as mid century (under RCP8.5; Fig. 2 , Table S2). Even under the low emissions pathway (RCP2.6), 19% of global protected land area is projected to undergo climate zone shifts by 2050. More than half of PAs (52–55%, depending on scenario) have already experienced climate zone shifts, and 26–28% are projected to continue to experience shifts throughout this century (Table S3). PAs exposed to both high forward and backward velocities are concentrated in North America, Europe, North Asia, the Amazon, southern Africa, and central Australia (Fig. 2 a). Of these areas, the highest median velocities are projected in Russia (0.53 km yr − 1 ), Canada (0.4 km yr − 1 ), Brazil (0.39 km yr − 1 ), and Europe (0.35 km yr − 1 ) (full results in Table S4). Species and populations in these PAs are likely to face serious climate threats and may need to migrate to track climates or colonize new areas. Notably, many PAs in North America, Europe, and Russia are projected to have a large proportion of their area shift to a different climate zone during this century (Fig. 2 b). Under RCP8.5, 38% of the terrestrial PAs (n = 8,667) are projected to have completely different climate zone distributions by the end of the century, with half of these located in Europe and one third in North America (Fig. 2 b). More than half of global terrestrial PAs are projected to experience climate shifts at mean velocities greater than 100 m yr − 1 (Fig. 3 a). These trends are consistent when analyzing results by continent (Fig. 3 b and Table S4) and IUCN category (Fig. 3 c and Table S5). Under the high emission scenario (RCP8.5), to which the current global warming trajectory closely aligns 65 , global PAs could undergo pronounced changes in climatic conditions with constantly accelerating rates during the century. Forward velocities are projected to intensify at an average rate of 0.64 km yr − 1 (median = 0.22 km yr − 1 ), which is slightly lower than the projected average backward velocity of 0.79 km yr − 1 (median = 0.25 km yr − 1 ) (Table S2 and S6). Similar trends emerge under the RCP6.0 scenario, but the magnitude is smaller. However, under the lower forcing scenarios (RCP2.6 and RCP4.5), velocities may eventually decrease by 2100 (Table S2). The varying trends in the pace of climate zone shifts under different emission scenarios closely align with the projected dynamics of radiative forcing levels 66 (Fig. S3). Globally, there are pronounced latitudinal trends, with higher forward and backward velocities in northern high latitudes (> 60 o N) and southern low latitudes (10 o -30 o S; Fig. 4 a). These latitudes also host greater PA coverage (Fig. 2 ), potentially undermining area-based goals. The largest percentages of PA area exposed are in northern mid-latitudes (40 o N to 60 o N) and southern mid-latitudes (30 o S to 50 o S; Fig. 4 b). While a considerable proportion of these shifts will be absorbed within existing protected areas, large portions could still occur outside PAs or into disturbed areas (Fig. 4 b). The higher northern and lower southern latitudes are particularly susceptible to exposure to novel and disappearing climates, which implies potential aggregation and disaggregation of species assemblages 8,9,36 . These spatial patterns also provide insight into whether the global PA network will be able to absorb anticipated climate shifts. By the end of the century, projections under RCP 8.5 show that only 25% of the projected shifts will be absorbed within existing PAs, with an additional 1.4% absorbed by nearby PAs. About 7.6% (forward) and 8.8% (backward) of protected land area will have climates terminating outside of the current PA network (Table S7 and S8), while 8.3% of global protected land area will shift to a novel climate, with no existing, same climate zone within a 1,000 km radius, and 6.6% will experience disappearing climates. Meanwhile, outgoing and incoming climates will originate from surrounding unprotected lands for 4.6% and 3.3% of protected land area, respectively. Critically, shifting climates may terminate outside PAs into areas dominated by human land uses including crop, pasture, rangeland, or urban areas, posing increasing risks to species 67 . More than three quarters of terrestrial PAs (equal to 12.3% of global protected land area), will face these types of threats by the end of this century (Table S7 and S8). The PAs likely to be exposed to high climate zone velocities share certain characteristics, including larger areas, higher latitudes, lower elevations, and lower topographic heterogeneity (Fig. S4; Section D of SI). PAs in Oceania, in particular, are projected to experience high threats to local biodiversity (Fig. S4 and S5). These impacts are projected to be stronger during the first half of the century (Fig. S4 and S5). In addition, correlations between climate zone velocity and area change suggest that PAs with more area projected to experience climate zone shifts are also more likely to be exposed to faster rates of climate zone shifts (Section E of SI). Risk assessment for the most vulnerable protected areas While most PAs will have only a portion of their area exposed to climate shifts, 35 PAs are projected to have their entire area shift by the end of this century, with mean velocities greater than 1 km yr − 1 . These 35 PAs are located mainly in North America, Europe, and Russia with a few in the Kalahari region of southern Africa, the Amazon region of Brazil, and southern Australia (Fig. 5 a). Most of these PAs are small and characterized by low elevations and low topographic roughness (Fig. 5 b). Moreover, while the human footprint in these highly vulnerable PAs varies, many have high species richness (Fig. 5 b), suggesting that biodiversity losses could be considerable as climate shifts. We identified distinct temporal and spatial patterns of exposure risks for these PAs, with many seeing high velocities within and outside their boundaries by the end of the century (Fig. 5 c-d). These patterns offer valuable insights into the potential implications of the effectiveness of regional PA networks and can inform future conservation planning strategies. Taking Wood Buffalo National Park in Canada as an example (Fig. 5 a-#14), spatial pattern results indicate that considerable portions of the park will experience climate zone shifts to areas outside the PA boundary and into unprotected lands by mid-century (Fig. 5 d). Furthermore, the park is projected to undergo rapid climate zone shifts in the mid-century with average velocities exceeding 1 km yr − 1 (Fig. 5 c). These projections could result in substantial threats to the Wood Bison ( Bison bison ) and endangered whooping crane ( Grus americana ) that rely on the park for habitat. The findings underscore the urgency of addressing climate change impacts through PAs and emphasize the need for proactive conservation strategies to ensure the long-term viability of existing parks. Dynamic spatial prioritization for conservation planning To demonstrate how climate-induced biological movement and rearrangement of species assemblages can be incorporated into spatial prioritizations for conservation planning, we provide a regional case study of PAs in the Yellowstone National Park, USA region (Fig. 6 ). Large portions of the PAs in this region are projected to transition from polar (ET) and boreal cold summer (Dfc) climates to boreal warm summer (Dfb) climate by mid- to late-century. Using climate connectivity features computed for Yellowstone National Park and other PAs adjacent to the national park based on the density and percentage of velocities starting in, ending in, and passing through the given cells (Fig. 6 , top), we show that many areas along mountain ridges will be climate sinks (i.e., where a climate shift terminates), which may lead to increased local diversity but potential extinction for species with poor tolerance 55 . Areas with lower elevations adjacent to these sinks are primarily climate sources (i.e., where a climate shift starts), indicating potential sources of species movements and potentially a decline in local species richness. Regions with high passage density (purple) can be suggested for biodiversity corridors to advance connections for climate migrants. Climate spatial relationships assessed for Yellowstone NP can be used to identify potential biodiversity threats, which could reduce conservation capacity and undermine PA effectiveness. A large proportion of the short term shifts (expected by mid century) are projected to occur within the Yellowstone NP, particularly in flatter, lower elevation regions (Fig. 6 , main panel). The region is supported through many, well-connected PAs however, and the areas adjacent to Yellowstone where climate shifts are projected to occur among PAs (yellow) should be prioritized for strict management corridors to link existing PAs and assist climate-induced species range shifts 68 . A small proportion of areas surrounding Yellowstone will experience climate shifts originating from outside unprotected land and terminating inside the PA (brown), which could potentially introduce new species to the park, creating new management opportunities. Discussion Together, the climate zone velocities and shifts computed in this study can provide information on the continuity of suitable climatic conditions available for species as well as the paths along which species might move to track shifting climates across surrounding areas. To enhance the resilience of the PA network to the anticipated redistributions of biodiversity, it is critical for conservation planners to explicitly consider and address the spatiotemporal patterns of climate shifts over the planning horizon. Performed at a spatial resolution of 1-km and temporal intervals of 20 years, and encompassing four future periods and alternative scenarios, this study provides a fine scale and biologically relevant global database on climate risk. While we find 20% of global protected land area is projected to experience climate zone shifts by mid century, we also find that larger PAs will absorb these effects to a greater extent than smaller PAs (Fig. S8). This finding provides further support for expanding existing PAs or corridors to connect PAs to support species migration 12,33,69 rather than establishing new, small PAs in isolation. It will be particularly important to focus conservation planning efforts in areas where projected climate velocities might exceed the dispersal rates of species 26 . SInce the climate zone velocities developed here are species agnostic, and the actual effects of climate change on biodiversity will depend on species’ intrinsic abilities to respond to climate exposure and other threats, it is important to also integrate species-specific, biotic velocity measures into decision making. Climate velocity and biotic velocity have been shown to contribute unique information to understanding future shifts 37 . The climate zone velocity-based forecasts presented here are adaptive and can be combined with existing biotic velocity-based information to inform climate adaptation planning in specific regions or for specific species and to predict climatically suitable habitats using biologically relevant information 70,71 . In areas where biological data is lacking, the climate zone velocity measures presented here can be a valuable proxy for assessing general climatic threats to biodiversity, particularly in regions where biogeographic factors have large influence on species vulnerability. This study provides a fine-scale quantitative assessment of near-, mid-, and long-term climate change exposure for global terrestrial PAs. The results highlight the varying spatiotemporal patterns of climate zone shifts and implications for biodiversity conservation over multiple time steps. Shifts in climatic conditions and human-induced land-use degradation could drive large-scale biodiversity changes and undermine the conservation effectiveness of existing PA networks but targeted interventions to expand or link existing PAs can mitigate some of these projected impacts. To address the ongoing biodiversity crisis and achieve future conservation goals, anticipating climate shifts and climate-induced biological movement is crucial to developing strategic and adaptive PA conservation planning to ensure climatic connectivity and conservation capacity. Methods and Data Climate classification data We used the 1-km global dataset of historical and future Köppen-Geiger climate classification (KGClim) 1 to calculate climate zone velocity for a contemporary baseline (1970-2000) and four future (2020-2049, 2040-2069, 2060-2089, and 2070-2099) epochs across four representative concentration pathways (RCP2.6, 4.5, 6.0 and 8.5) (Table S9). The Köppen-Geiger climate classification scheme used in the KGClim dataset followed the classification criteria as described in Kottek et al. 2 and Rubel and Kottek 3 and first presented by Geiger 4 . The Köppen-Geiger climate classification characterizes climatic conditions based on warmth and aridity and distinguishes 30 climate types in tropical (A), arid (B), temperate (C), boreal (D), and polar (E) climate zones. The baseline (1970-2000) KGClim map was derived based on 30-year averages of monthly surface air temperature and precipitation from 1-km WorldClim Historical Climate Data V2 5 , which used a thin-plate smoothing spline with elevation, distance to the coast, and other satellite-derived independent variables to interpolate climate data from 9,000-60,000 weather stations 5 . The future maps (2020-2049, 2040-2069, 2060-2089, and 2070-2099) were derived based on Climate Change, Agriculture and Food Security (CCAFS) high-resolution (1-km) bias-corrected downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations 6 , which include 35 GCMs for RCP 2.6, 4.5, 6.0 and 8.5. CCAFS’s bias-corrected and downscaled climate projections are based on interpolated anomalies and apply the delta method to baseline climates to correct model biases 6 , which can effectively reduce bias effects from the threshold-based climate classification scheme 7 . The KGClim dataset used here selected the highest confidence climate class from an ensemble of future climate maps generated by multiple model projections 1 . Protected area data We used the World Database on Protected Area (WDPA) 8 (https://www.protectedplanet.net/, accessed June 2023) to delimit global terrestrial protected areas. We removed all PAs with marine coverage and filled in data gaps for China, South Africa, and India using alternate sources. For China, we used the PA data collected for Chinese national parks released by the Chinese government, as well as the WDPA Sept 2020 dataset. For South Africa, we used the South Africa Protected Areas Data (SAPAD) for the Protected and Conservation Areas (PACA), released by the Forestry, Fisheries & the Environment department in 2022 Q4 (https://egis.environment.gov.za/protected_and_conservation_areas_database). For India, we used PA data published by the National Park and Wildlife Sanctuary in India in June 2020, which was built with Open Street Map information and official Ministry of Environment, Forest and Climate Change (ENVIS) listings (https://geo.ejatlas.org/layers/geonode:Protected_Area_India_Final). To facilitate processing, we excluded a large PA in Greenland, Nationalparken I Nord-Og Østgrønland (972,000 km²), and retained only PA polygons with an area larger than 10km 2 . We dissolved overlapping polygons with the same IUCN category to avoid duplicates. As some PAs consist of multiple polygons, we used the unique ID (WDPAID) to identify each PA. Overall, we included 26,220 terrestrial PAs covering 16.9 million km² (13% of the global land surface). The 14,265 PAs designated as IUCN categories Ia, Ib, II, III, and IV, which have the strictest management objectives for biodiversity conservation, are estimated to comprise 6.1% of global land. Continental-level PA terrestrial coverage ranges from 8.9% (Africa) to 20.6% (Oceania), with some countries in Europe, America and Oceania having higher coverage (Table S10). Climate zone velocity calculation We developed an analytical measure of climate zone velocity (CZV) that conforms to climate-analog velocity algorithms 9–12 and quantifies the exposure changes in bioclimatic conditions if the climate moves beyond the physiological tolerance of a local population. This measure is based on climate-analog concepts and allows for the calculation of both forward and backward velocities to represent different change directions (Fig. 1) that correspond to different impacts on ecosystems or biota. Forward velocity indicates the distance from past climate locations to the nearest site with the same present climate, reflecting the minimum path that an organism would need to migrate to maintain a consistent climate condition. Backward velocity measures the distance from projected future climate cells back to analogous current climate locations, reflecting the minimum path that climatically adapted organisms would need to migrate to colonize the site. Forward velocity points to future destinations, while backward velocity points to origins that might feed into the destination. The two velocities provide complementary information and are useful to interpret together to prioritize different conservation management needs. To generate the CZV algorithm, we incorporated projected bioclimatic information and improved on past conceptualizations of climate-driven movement that considered only straight line (Euclidean distance) or least-cost paths, by incorporating topographic factors into the velocity calculation to enhance the use of the algorithm for conservation planning and biodiversity risk assessments. The forward and backward velocities can be calculated based on the nearest distance between climate types as follows, The CZV algorithm identifies the velocity origin, which is the targeting pixel, and a destination, which is the nearest pixel within the 1,000 km search radius with the same climate type (Fig. 1). Using these origin and destination cells and a 1-km topographic surface 13 , we generated topographic least cost paths and aggregated the climate zone velocity across the four future epochs to represent a continuous trajectory along which climate migrants will need to follow to track similar climatic conditions through time and space (Fig. S1e). We demonstrate through an example of a randomly selected pixel in the Selway-Bitterroot Wilderness in the United States (see SI: Section A) how the climate zone velocity algorithm developed here can better capture potential climate migrations over a fine spatial scale and across finer temporal time steps compared to the traditional Euclidean distance and topographic paths. Since discrepancies among GCMs can lead to uncertainties in climate projections, we assess the sensitivity of the CZV algorithm to GCMs by estimating climate zone velocities for 100 randomly selected PAs using model projections from 16 GCMs (Table S11) and report on inter-model variability. Individual models show varying results at the PA level, and our velocity results agree well with the multi-model mean levels (Fig. S9). Assessing PA exposure to climate zone shifts and expected spatial patterns For the global set of PAs, we assessed overall exposure to forward and backward climate zone velocities as well as the proportion of each PA that will be exposed to a climate shift under each RCP scenario for each time period. For each PA, we first assessed climate zone shifts according to 1) the area within each PA projected to undergo climate zone shifts, 2) the direction and speed of those shifts according to the velocities, and 3) the spatial relationships (i.e., patterns) of climate zone shifts in the PA network. We categorized the spatial patterns of each climate zone shift based on whether climates are relocated (i) within the same PA, (ii) among PAs (from one to another), (iii) into, or (iv) outside the PA network, or (v) into unprotected and human-dominated surroundings. We also categorized the emergence of (vi) novel or (vii) disappearing climate zones (within the 1,000 km search distance) for the global terrestrial PAs (Fig. S2). These spatial patterns allow us to assess the capacity of the existing PA network to absorb future climate changes. The framework for characterizing these spatial patterns was modified from Batllori et al. 9 . For each spatial relationship category, we quantified the area change in climate zones and climate zone velocity for each future epoch (2020-2049, 2040-2069, 2060-2089, and 2070-2099) under the four RCP scenarios. To identify spatial pattern shifts into human-dominated land uses, we used future land use projections from the Land-Use Harmonization (LUH2) project 14 . We used the annual, gridded fractions of land-use states derived from LUH2 v.2h historical land use forcing dataset for 1070-2000 and the LUH2 v.2f future harmonized dataset for 2020-2100 and four scenarios (RCP2.6 from IMAGE, RCP4.5 from MESSAGEix-GLOBIOM, RCP6.0 from GCAM, RCP8.5 from ReMIND-MAgPIE). We considered managed pasture, rangeland, urban land, and cropland (all functional groups) as human-dominated land use states. We aggregated the annual gridded fractions of land use states for each 30 year period (1970-2000, 2020-2049, 2040-2069, 2060-2089, and 2070-2099) and extracted the long-term states using the land use with the highest fraction for each pixel. We aggregated the pixel-level, climate velocity results to the PA level to assess the general exposure of each individual PA. The pixel level results (see Fig. S1e), can illustrate the finer-scale spatial patterns of climate zone velocities within the PA, while the PA level results provide an aggregate look at each PA. We compiled the PA level results for each future climate (RCP) scenario and parsed them by continent and IUCN category for RCP 8.5. We also mapped the forward and backward climate zone velocities as well as the percent of protected land area that will undergo climate zone shifts with different spatial relationship patterns in each time period by latitude. The latitudinal trends are based on the summarized pixel-level velocity values for 5 o latitudinal bands. To investigate the potential timing of the climate zone shifts, we also reported the time frame projected for the climate zone shifts for each PA globally (see SI: Section B). Assessing vulnerability of protected areas based on PA characteristics To assess whether certain PAs may be more vulnerable to climate zone shifts based on their characteristics, we used correlations analysis to identify PA attributes associated with vulnerability. The characteristics we assessed included topographic heterogeneity, human pressure, and biotic uniqueness. Topographic heterogeneity attributes, including elevation and terrain ruggedness, were extracted from a global, 1-km multivariate elevation data product 13 . The mean elevation within the PA is used to assess generally whether PAs are in highland or lowland regions. The Terrain Ruggedness Index (TRI) 15 , is the mean of the elevation differences between the center pixel and its eight surrounding pixels. We used mean TRI in the PA to characterize variability in elevation that may buffer climate change effects on ecosystems 16 . The human footprint (HFP) for each PA was calculated using 1-km human footprint data from 2009 17 . The biotic uniqueness of a PA quantifies its irreplaceability as the degree of overlap among the ranges of species 18 . We quantified the potential species richness level for each PA by overlapping area of habitat (AOH) estimates for 10,774 birds, 5219 mammals, 4462 reptiles, and 6254 amphibians (Fig. S10), produced using IUCN species range polygons and then modified with habitat associations with land cover and elevation limits for individual species 19 . We identified PAs that are projected to have their entire area shift climate zones by the end of this century, with mean velocities larger than 1km yr -1 . This velocity criterion is based on the rationale that estimated migration rates for tree species typically fall below 1 km per year, and the majority of animal species cannot keep pace with changes occurring at such high rates. We summarize the attributes (size, latitude, elevation, TRI, HFT) of these highly vulnerable PAs and report their mean forward and backward climate zone velocities, along with the spatial patterns. Case study for spatial planning prioritizations We demonstrate how the CZV algorithm can be used for spatial conservation planning through a case study of the Yellowstone, USA region, which is located in northwestern Wyoming, eastern Idaho, and southern Montana and includes 10 PAs, some of which are adjacent to each other. The PAs within this region span a diverse range of climatic zones and elevation profiles and provide habitats for distinct plant and animal species. Using the climate velocities, we classify climate connectivity features in the region using Burrow’s approach 20 , which assigns classes of source, sink, corridor, convergence, or divergence based on the proportions of trajectories starting from, ending in, and passing through a pixel during the period. Sources are cells where climate velocities start but none end. Sinks are cells where climate velocities terminate and none start. Corridors are cells with a high proportion of climate velocities passing through. Convergence is where more climate velocities end than start in a cell, and divergence is the opposite. We also categorized the climate spatial patterns within the Yellowstone region. Considerations for climate zone velocity algorithm use To facilitate a clearer interpretation of the climate zone velocity results and to better understand the implications of these velocities for biodiversity, a few key issues should be considered. First, the temporal and spatial scales at which velocity is measured can impact interpretations. The computations in this study are performed at 1-km spatial resolution and approximately 20-yr time steps. Using coarser spatial resolution data could inflate velocity estimates 21,22 , potentially overestimating exposure risks and overlooking microrefugia 23 . Using longer time frames could obscure fine scale temporal variations and confound findings on the timing of changes. The climate zones defined by the Köppen-Geiger system are based on thresholds, and climate shifts in regions above and below the thresholds can be underrepresented, while factors such as wind, atmospheric CO2, and solar radiation are not considered 24–26 . Climate zone velocities are species agnostic, and impacts of climate change are highly individualistic across species while also exhibiting large spatial and temporal heterogeneity. Decreased availability of climatically suitable areas, barriers created by human modifications and habitat fragmentation 27,28 , and varied adaptative and dispersal capacity can result in large variations in the biological responses of individual species 29,30 . Time lags in plant movement can further slow or impede the movements of species that depend on them 31,32 . Since species may not shift distributions in response to climate change but instead contract into microrefugia and maintain a smaller population 33 , the CZV measure used here should only be interpreted in terms of climate exposure and not specific biological responses 21 . Lastly, it is important to note that the results are constrained by the current distribution of existing PAs. We observe the most significant shifts in mid-to-high latitudes, where large proportions of PAs are distributed. Climate zone shifts and velocity occur regardless of whether an area is protected or not. Therefore, our findings can also inform future planning efforts aimed at establishing new protected areas in regions adjacent to existing PA locations and currently lacking such conservation measures. References Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. U.S.A. 104 , 5925–5930 (2007). Woodward, F. I., Lomas, M. R. & Kelly, C. K. Global climate and the distribution of plant biomes. Phil. Trans. R. Soc. Lond. B 359 , 1465–1476 (2004). Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol Appl 7 , 1–14 (2014). Parmesan, C. Ecological and Evolutionary Responses to Recent Climate Change. Annu. Rev. Ecol. Evol. Syst. 37 , 637–669 (2006). Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science (New York, N.Y.) 354 , (2016). Parmesan, C. & Yohe, G. 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Dinerstein, E. et al. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. Bioscience 67 , 534–545 (2017). Sayre, R. A New Map of Global Ecological Land Units - an Ecophysiographic Stratification Approach . (Association of American Geographers, Washington, DC, 2014). Papagiannopoulou, C., Miralles, D. G., Demuzere, M., Verhoest, N. E. C. & Waegeman, W. Global hydro-climatic biomes identified via multitask learning. Geosci. Model Dev. 11 , 4139–4153 (2018). Sayre, R. et al. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation 21 , e00860 (2020). Chan, D. & Wu, Q. Significant anthropogenic-induced changes of climate classes since 1950. Scientific reports 5 , 13487 (2015). Mahlstein, I., Daniel, J. S. & Solomon, S. Pace of shifts in climate regions increases with global temperature. Nature Clim Change 3 , 739–743 (2013). Rubel, F. & Kottek, M. Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. metz 19 , 135–141 (2010). Cui, D., Liang, S. & Wang, D. Observed and projected changes in global climate zones based on Köppen climate classification. WIREs Clim Change 14 , 484 (2021). Rohli, R. V., Joyner, T. A., Reynolds, S. J. & Ballinger, T. J. Overlap of global Köppen–Geiger climates, biomes, and soil orders. Physical Geography 36 , 158–175 (2015). Rohli, R. V., Andrew, J. T., Reynolds, S. J., Shaw, C. & Vázquez, J. R. Globally Extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. Physical Geography 36 , 142–157 (2015). Hamann, A., Roberts, D. R., Barber, Q. E., Carroll, C. & Nielsen, S. E. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21 , 997–1004 (2015). Watson, J. et al. Set a global target for ecosystems. 578 , (2022). Convention on Biological Diversity. First draft of the post-2020 global biodiversity framework. 12 doi:2021. Carroll, C., Parks, S. A., Dobrowski, S. Z. & Roberts, D. R. Climatic, topographic, and anthropogenic factors determine connectivity between current and future climate analogs in North America. Glob Change Biol 24 , 5318–5331 (2018). Dobrowski, S. Z. et al. The climate velocity of the contiguous United States during the 20th century. Glob Change Biol 19 , 241–251 (2013). Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507 , 492–495 (2014). Ordonez, A., Martinuzzi, S., Radeloff, V. C. & Williams, J. W. Combined speeds of climate and land-use change of the conterminous US until 2050. Nature Clim Change 4 , 811–816 (2014). Hannah, L. et al. Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends in Ecology & Evolution 29 , 390–397 (2014). Heikkinen, R. K. et al. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Sci Rep 10 , 1678 (2020). Dobrowski, S. Z. & Parks, S. A. Climate change velocity underestimates climate change exposure in mountainous regions. Nat Commun 7 , 1–8 (2016). Dobrowski, S. Z. A climatic basis for microrefugia: The influence of terrain on climate. Glob Change Biol 17 , 1022–1035 (2011). Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: Implications for species redistribution. Ecography 40 , 253–266 (2017). Cui, D., Liang, S., Wang, D. & Liu, Z. A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables. Earth Syst. Sci. Data 13 , 5087–5114 (2021). Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Scientific data 7 , 7 (2020). Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol 37 , 4302–4315 (2017). Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America 117 , 19656–19657 (2020). van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109 , 5–31 (2011). Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520 , 45–50 (2015). Venter, O. Corridors of carbon and biodiversity. Nature Clim Change 4 , 91–92 (2014). Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16 , 476–487 (2010). Tingley, M. W., Darling, E. S. & Wilcove, D. S. Fine- and coarse-filter conservation strategies in a time of climate change. Ann N Y Acad Sci 1322 , 92–109 (2014). Jones, K. R., Watson, J. E. M., Possingham, H. P. & Klein, C. J. Incorporating climate change into spatial conservation prioritisation. Biological Conservation 194 , 121–130 (2016). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3992123","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386345060,"identity":"1f662965-1ff8-4a30-8b06-1c89007c1be4","order_by":0,"name":"Diyang Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDCCwzAGM/MBIAnCCURrYUsgUssBOIvHgDgtfMd5DD8X/GKQNzjO803ix587DPzsOQZ4tUge5jGWntnHYLjhMO82yd62ZwySPW/wazE4zGMgzdvDkGAA1CLN2HCYweAGAVuAWox/Q7TwPJNm+HOYwZ4ILWbSPD/AWtikGdiAtkgQ9AtbmTVvA4PhzMNsxpZAv/BInHlWgFcL3/nDm2/z/GGQBzIe3gCGmBx/e/IGvFoYGDgMGBjb/sO5PASUgwD7AwaGP0SoGwWjYBSMgpELAG+9Ri/LlhpZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4729-841X","institution":"University of California, Santa Barbara","correspondingAuthor":true,"prefix":"","firstName":"Diyang","middleName":"","lastName":"Cui","suffix":""},{"id":386345061,"identity":"84cb7cd6-f67d-47bf-b61a-ffb6e1df797e","order_by":1,"name":"Amy Frazier","email":"","orcid":"https://orcid.org/0000-0003-4552-4935","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Frazier","suffix":""},{"id":386345062,"identity":"2a8923bd-56c1-4ee3-98d6-e8c41acf9313","order_by":2,"name":"Shunlin Liang","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Shunlin","middleName":"","lastName":"Liang","suffix":""},{"id":386345063,"identity":"66346f4a-adfb-4901-9e6a-37cfb2b2065e","order_by":3,"name":"Patrick Roehrdanz","email":"","orcid":"https://orcid.org/0000-0003-4047-5011","institution":"Conservation International","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Roehrdanz","suffix":""},{"id":386345064,"identity":"4904aefc-e137-46c6-99e8-5ac1eecff488","order_by":4,"name":"George Hurtt","email":"","orcid":"https://orcid.org/0000-0001-7278-202X","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Hurtt","suffix":""},{"id":386345065,"identity":"aa9868f8-4aa5-40b5-8813-a18a782b6d71","order_by":5,"name":"Zhiliang Zhu","email":"","orcid":"","institution":"U.S. Geological Survey","correspondingAuthor":false,"prefix":"","firstName":"Zhiliang","middleName":"","lastName":"Zhu","suffix":""},{"id":386345066,"identity":"d01a763e-2c29-4969-9b22-9dad66688744","order_by":6,"name":"Dongdong Wang","email":"","orcid":"","institution":"University of maryland","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-26 22:55:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3992123/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3992123/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70732889,"identity":"a8f69656-c6e6-40c1-9278-41b4b108dc10","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptualization of forward and backward climate zone velocities based on spatial and temporal changes in climate zones. \u003c/strong\u003eThe velocity algorithm identifies the distance and direction between the focal cell and the nearest cell within the search radius (1,000 km in this study) with the same climate type. \u003cstrong\u003e(a) \u003c/strong\u003eForward (present-to-future) velocity captures distance and direction for the climate in a cell in the present (t1) to the nearest cell in the future (t2) with the same Köppen-Geiger climate class. \u003cstrong\u003e(b)\u003c/strong\u003e Backward (future-to-present) velocity captures distance and direction from a projected future climate cell (t2) back to the nearest cell with the same climate in the present (t1) thereby capturing what climate class moved into a site and from where it came.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/0271c718eaeef9105b33d76f.png"},{"id":70732893,"identity":"ec75149e-7bcc-4128-9074-44316dbba4c3","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1336840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjected climate zone shifts within global terrestrial PAs \u003c/strong\u003efor \u003cstrong\u003e(a) \u003c/strong\u003emean climate zone velocities, and \u003cstrong\u003e(b)\u003c/strong\u003epercent of PA area projected to shift by the end of the 21st century under RCP8.5. Forward and backward climate zone velocity values are grouped into categories based on equal-area quartiles along each axis (n=26,220).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/401607a2ade103de0b4ab7ca.png"},{"id":70732892,"identity":"f30f18e0-68b6-40de-a4ef-f32ae601f631","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of climate zone shifts for global terrestrial PA. \u003c/strong\u003eDensity plots of (left) mean forward and (center) backward climate zone velocities within global terrestrial PAs, and (right) area percent of global PAs projected to undergo climate zone shifts during this century \u003cstrong\u003e(a)\u003c/strong\u003e under different RCP scenarios, \u003cstrong\u003e(b)\u003c/strong\u003e by continent (under RCP8.5), and \u003cstrong\u003e(c)\u003c/strong\u003e by IUCN categories (RCP8.5). n indicates the number of PAs that are projected to experience climate zone shifts in each category by the end of this century. Total number of PAs included in this study is 26,220.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/1cdfb53158a9a655331717a3.png"},{"id":70732894,"identity":"37c7435c-9412-408c-8dde-9d45539dbee5","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjected latitudinal distributions for climate zone shifts within global terrestrial protected areas (PAs) under RCP8.5 for (a) \u003c/strong\u003eclimate zone velocities, and \u003cstrong\u003e(b) \u003c/strong\u003epercent of global PA coverage projected to undergo climate zone shifts. Results are shown for forward (top) and backward (bottom) velocities in four future periods by latitudinal band (1km to 5\u003csup\u003eo\u003c/sup\u003e). In (a), black dots are median, pixel-level velocity values and error bars show first and third quartiles in the latitudinal bands. Blue lines are smoothed distributions. In (b), colored bars represent proportions of area in each latitudinal band represented by different spatial relationships of climate zone shifts (Fig. S2). Shifts within, among, and into protected areas will be absorbed by the current PA network. Shifts outside, into disturbed areas, or disappearing climates will not be absorbed by the existing PA network. Novel climates may also appear in protected areas. It should be noted that the time intervals across the four periods are not equal, so the magnitudes of the area percentages should not be directly compared across time periods.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/0c657f06dac4e0a3b94eaed5.png"},{"id":70732891,"identity":"187ddd4b-81d9-4d43-a98a-1c216a34377c","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":321155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePAs exposed to large proportions of climate zone shifts and high climate velocity. (a) \u003c/strong\u003eGeographic locations of 35 terrestrial PAs projected to experience completely different climate zone distributions by the end of the century with mean estimated velocities greater than 1 km yr\u003csup\u003e-1\u003c/sup\u003e.\u003cstrong\u003e (b) \u003c/strong\u003eAssociated PA attributes for the first 15 PAs identified (full results in Fig. S6) including area, latitude, elevation, terrain ruggedness index (TRI), human footprint (HFP), and biodiversity.\u003cstrong\u003e (c) \u003c/strong\u003eSpatial and temporal patterns of climate zone shifts for four time periods (2030, 2050, 2070, 2080) that could potentially undermine the effectiveness of PA networks (including climate shifts outside the PA networks, into human dominated lands, and novel and disappearing climates) for both forward and backward mean velocities. \u003cstrong\u003e(d) \u003c/strong\u003eSpatial and temporal patterns of percent change in area of the 15 PAs for both forward and backward mean velocities for the same time periods. Please see Fig. S6 for complete results for all 35 PAs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/a2b44fad3d16753d59fba8d4.png"},{"id":70732890,"identity":"73161dfe-0982-441d-a603-2d4d66b97c74","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1152839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional case study of the use of climate zone velocity results for conservation prioritization in the Yellowstone, USA region. \u003c/strong\u003eProtected area boundaries are outlined in maroon.\u003cstrong\u003e \u003c/strong\u003eBlack solid lines show climate zone velocities derived based on topographic paths, with black dots marking destinations. We identify climate connectivity based on the density and percentage of velocities starting in, ending in, and passing through the given cells (top thumbnail) by adapting Burrow’s approach of mapping velocity pattern to indicate species distribution shifts\u003ca href=\"https://www.zotero.org/google-docs/?MnKyXJ\"\u003e\u003csup\u003e55\u003c/sup\u003e\u003c/a\u003e. Gray areas indicate no shifts. We also categorize the climate spatial relationship patterns based on whether climates are relocated within, among, into, or outside the PA networks based on the 1-km climate zone velocity metric and a modification of Batllori’s framework\u003ca href=\"https://www.zotero.org/google-docs/?jTYm2e\"\u003e\u003csup\u003e18\u003c/sup\u003e\u003c/a\u003e (bottom thumbnail).The results shown are for mid century (baseline to 2020-2049). Late century results (2070-2099) are in Fig. S7. Insights across multiple periods can inform conservation planners where to incorporate potential climate-induced biological movements into dynamic spatial prioritization, and how to adaptively expand and conserve PAs over the long term.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/b570136a9ffd4da34a80c9f9.png"},{"id":70733084,"identity":"d3080a86-71ce-4183-8f65-6d1fbfa9eebb","added_by":"auto","created_at":"2024-12-06 06:05:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3489125,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/66198843-d4b1-45fb-8510-6f21e5ed3aef.pdf"},{"id":70732895,"identity":"b02e82c8-f302-43cf-a835-119a8f90e3c3","added_by":"auto","created_at":"2024-12-06 05:57:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11718562,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3992123/v1/7710f92d97bc3ab313449faf.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Projected climate zone shifts could undermine the effectiveness of global protected areas for biodiversity conservation by the mid-to-late century","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs key drivers of ecological interactions and biological processes, climatic conditions determine global patterns of biomes and biodiversity\u003csup\u003e1,2\u003c/sup\u003e. In response to contemporary, unprecedented changes in climate, species are undergoing evolutionary adaptation\u003csup\u003e3,4\u003c/sup\u003e, changing phenology and abundance\u003csup\u003e5,6\u003c/sup\u003e, and shifting their distributions\u003csup\u003e7\u003c/sup\u003e. Regional changes in the availability and location of climatically suitable areas have resulted in observed latitudinal and elevational shifts in species distributions and rearrangement of species assemblages\u003csup\u003e5,8\u003c/sup\u003e. Consequently, ongoing changes in climatic conditions are driving a significant redistribution of life on Earth, leading to restructured biotic community compositions\u003csup\u003e9\u003c/sup\u003e, losses of ecosystem services, and increased threats to human welfare across the globe\u003csup\u003e1,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProtected areas (PAs) are essential for biodiversity conservation strategies\u003csup\u003e11,12\u003c/sup\u003e, evidenced by reduced rates of habitat loss\u003csup\u003e13\u003c/sup\u003e and enhanced species diversity within their boundaries\u003csup\u003e14\u003c/sup\u003e. PAs also provide various social and economic benefits by preserving natural resources, delivering ecosystem services, and supporting human livelihoods\u003csup\u003e15\u003c/sup\u003e. Expanding global PA coverage while maintaining the effectiveness of PA networks are key conservation targets agreed to in the most recent Convention on Biological Diversity (CBD)\u003csup\u003e16,17\u003c/sup\u003e. However, changes in climate and land use can undermine PA network effectiveness\u003csup\u003e11,18\u0026ndash;20\u003c/sup\u003e. Habitat loss and fragmentation from land use changes can create barriers to species movement and disconnect PA networks\u003csup\u003e21\u0026ndash;24\u003c/sup\u003e. As climate shifts, suitable climates for species may become less accessible, and species may move into unprotected or human-dominated areas as they attempt to track preferred climatic conditions, increasing extinction risk\u003csup\u003e18,20,25\u003c/sup\u003e. Current low PA connectivity\u003csup\u003e26,27\u003c/sup\u003e, and inadequate consideration of future biodiversity ranges\u003csup\u003e28,29\u003c/sup\u003e could further diminish the capacity of PA networks in protecting future biodiversity\u003csup\u003e30,31\u003c/sup\u003e, impeding progress toward global conservation goals.\u003c/p\u003e \u003cp\u003eTo enhance the effectiveness of global PAs and develop more strategic and adaptive PA conservation approaches, projected climate shifts need to be incorporated into PA conservation planning to anticipate biodiversity redistributions\u003csup\u003e10,12,32\u003c/sup\u003e. Given known gaps in species data and the uncertainties inherent in bioclimatic modeling\u003csup\u003e8,33\u003c/sup\u003e, recent studies assessing biodiversity exposure risks increasingly use climate change metrics\u003csup\u003e8\u003c/sup\u003e including local climate velocity\u003csup\u003e33,34\u003c/sup\u003e, climate analog velocity\u003csup\u003e9,35\u003c/sup\u003e, climate stability\u003csup\u003e32\u003c/sup\u003e, and novel and disappearing climates\u003csup\u003e8,20,36\u003c/sup\u003e as well as species-specific biotic velocities\u003csup\u003e37\u0026ndash;39\u003c/sup\u003e. Velocity measures are valuable indicators for assessing the potential migration paths of species in response to changing climate conditions, and climate velocity, in particular, can provide a general understanding of exposure. Local climate velocities are based on a single climate variable (e.g., temperature only), while climate analog velocities are based on a climate space derived from a statistical multivariate climate index and may not be biologically relevant. Thus, each alone may not be indicative of the properties and functions of biomes and ecosystems, nor can they be indiscriminately applied in biodiversity risk evaluation\u003csup\u003e33\u003c/sup\u003e. To overcome these limitations, a metric is needed that combines multiple climate variables while providing a more biologically meaningful climate space for conservation.\u003c/p\u003e \u003cp\u003eClimate zone velocity measures based on K\u0026ouml;ppen-Geiger climate (KGC) zones can be a valuable surrogate for the potential movement of species to maintain suitable climatic conditions because they overcome the limitations of single-variable measures (i.e., local climate velocity) while also being grounded in biome and ecosystem functioning. While other methods exist to map biomes and ecoregions\u003csup\u003e2,40\u0026ndash;43\u003c/sup\u003e, the KGC scheme is the most commonly used, and it can be applied to examine shifts in climate zones and possible biome changes\u003csup\u003e44\u0026ndash;46\u003c/sup\u003e as it presents standardized, globally comprehensive climate maps based on multiple climate variables and allows nested, hierarchical levels of analysis. KGC zones show strong correlations with biome distributions\u003csup\u003e47,48\u003c/sup\u003e, and studies have shown KGC climate zones shifting poleward and upslope under climate change, with significant area expansion of tropical (A) and arid (B) climate zones and shrinkage of polar (E) climates\u003csup\u003e44,45,47,49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTogether, forward and backward velocities can provide complementary information to support conservation. Forward velocity (i.e., present-to-future; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) reflects the minimum path that species need to migrate to maintain suitable climatic conditions\u003csup\u003e18,50\u003c/sup\u003e with higher values implying high threats to a species or population inhabiting the site. Backward velocity (i.e., future-to-present; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) represents the minimum path that species need to migrate to locate or colonize the new, suitable site, with higher values implying high threats to the ecosystem functioning and services of a site. Forward velocity can be interpreted as species exposure to climate change while backward velocity can be interpreted in terms of risks for ecosystem functioning and services.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigh spatial and temporal resolution assessments of climate velocity at multiple future time steps, including the near future, are urgently needed (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) to achieve the CBD\u0026rsquo;s 2030 biodiversity strategic plan and 2050 vision of \u0026lsquo;living in harmony with nature\u003csup\u003e16,17,51,52\u003c/sup\u003e. Existing PA exposure assessments derived from coarse spatial resolution climate data may overlook finer scale topo-climatic patterns\u003csup\u003e12,53\u0026ndash;57\u003c/sup\u003e, leading to flawed estimates, especially in rugged terrain\u003csup\u003e58,59\u003c/sup\u003e, and limited ability to detect microrefugia\u003csup\u003e60,61\u003c/sup\u003e. Moreover, the use of coarse temporal resolution data (e.g., intervals of 80\u0026ndash;100\u0026nbsp;year) can obscure the finer time scales over which climates may become less favorable for local species and populations. We develop a high spatial and temporal resolution climate zone velocity measure that conforms to climate-analog velocity algorithms\u003csup\u003e12,18,53,58\u003c/sup\u003e but leverages KGC classes along with topographically-informed paths (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) to better model how species may move with climate change. We use this measure to examine the spatiotemporal patterns of climate zone shifts in the global PA network at 1-km spatial resolution for four future time periods (2020\u0026ndash;2049, 2040\u0026ndash;2069, 2060\u0026ndash;2089, 2070\u0026ndash;2099) and four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), using 1971\u0026ndash;2000 as a baseline. We assess both forward and backward velocities using a global dataset of K\u0026ouml;ppen-Geiger climate projections (KGClim)\u003csup\u003e62\u003c/sup\u003e derived from the bias-corrected, downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations\u003csup\u003e63\u003c/sup\u003e and WorldClim Historical Climate Data (WorldClim V2)\u003csup\u003e64\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe assess climate zone shifts for 26,220 terrestrial protected areas (PAs) covering 16.9\u0026nbsp;million km\u0026sup2; (13% of the global land surface) to identify the most vulnerable PAs projected to undergo climate zone changes by the end of this century (areas where the mean velocity is estimated to be greater than 1 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). We also assess the effectiveness of the current PA network to absorb projected climate zone shifts by evaluating seven spatial patterns of climate movement (Fig. S2). Climates relocated within, among, or into other existing PAs\u003csup\u003e18\u003c/sup\u003e indicate sustained network effectiveness for absorbing climate shifts. Climates relocated outside existing PAs, into human-modified areas, or that disappear (from the PA and search area) altogether undermine network effectiveness. We characterize the climate vulnerability of PAs based on their elevation, terrain ruggedness, human footprint level, and species richness to identify larger-scale trends. Lastly, we present a case study to integrate climate shifts in a PA prioritization scheme to inform future climate adaptation planning and biodiversity conservation.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProjected climate zone shifts in protected areas\u003c/h2\u003e \u003cp\u003eOver 17,000 terrestrial PAs comprising 40% of global protected land area could experience shifts to different climate zones during this century, and 20% could see change as early as mid century (under RCP8.5; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S2). Even under the low emissions pathway (RCP2.6), 19% of global protected land area is projected to undergo climate zone shifts by 2050. More than half of PAs (52\u0026ndash;55%, depending on scenario) have already experienced climate zone shifts, and 26\u0026ndash;28% are projected to continue to experience shifts throughout this century (Table S3). PAs exposed to both high forward and backward velocities are concentrated in North America, Europe, North Asia, the Amazon, southern Africa, and central Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Of these areas, the highest median velocities are projected in Russia (0.53 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Canada (0.4 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Brazil (0.39 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and Europe (0.35 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (full results in Table S4). Species and populations in these PAs are likely to face serious climate threats and may need to migrate to track climates or colonize new areas. Notably, many PAs in North America, Europe, and Russia are projected to have a large proportion of their area shift to a different climate zone during this century (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Under RCP8.5, 38% of the terrestrial PAs (n\u0026thinsp;=\u0026thinsp;8,667) are projected to have completely different climate zone distributions by the end of the century, with half of these located in Europe and one third in North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMore than half of global terrestrial PAs are projected to experience climate shifts at mean velocities greater than 100 m yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These trends are consistent when analyzing results by continent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and Table S4) and IUCN category (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table S5). Under the high emission scenario (RCP8.5), to which the current global warming trajectory closely aligns\u003csup\u003e65\u003c/sup\u003e, global PAs could undergo pronounced changes in climatic conditions with constantly accelerating rates during the century. Forward velocities are projected to intensify at an average rate of 0.64 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (median\u0026thinsp;=\u0026thinsp;0.22 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which is slightly lower than the projected average backward velocity of 0.79 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (median\u0026thinsp;=\u0026thinsp;0.25 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Table S2 and S6). Similar trends emerge under the RCP6.0 scenario, but the magnitude is smaller. However, under the lower forcing scenarios (RCP2.6 and RCP4.5), velocities may eventually decrease by 2100 (Table S2). The varying trends in the pace of climate zone shifts under different emission scenarios closely align with the projected dynamics of radiative forcing levels\u003csup\u003e66\u003c/sup\u003e (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGlobally, there are pronounced latitudinal trends, with higher forward and backward velocities in northern high latitudes (\u0026gt;\u0026thinsp;60\u003csup\u003eo\u003c/sup\u003eN) and southern low latitudes (10\u003csup\u003eo\u003c/sup\u003e-30\u003csup\u003eo\u003c/sup\u003eS; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). These latitudes also host greater PA coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), potentially undermining area-based goals. The largest percentages of PA area exposed are in northern mid-latitudes (40\u003csup\u003eo\u003c/sup\u003eN to 60\u003csup\u003eo\u003c/sup\u003eN) and southern mid-latitudes (30\u003csup\u003eo\u003c/sup\u003eS to 50\u003csup\u003eo\u003c/sup\u003eS; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). While a considerable proportion of these shifts will be absorbed within existing protected areas, large portions could still occur outside PAs or into disturbed areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The higher northern and lower southern latitudes are particularly susceptible to exposure to novel and disappearing climates, which implies potential aggregation and disaggregation of species assemblages\u003csup\u003e8,9,36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese spatial patterns also provide insight into whether the global PA network will be able to absorb anticipated climate shifts. By the end of the century, projections under RCP 8.5 show that only 25% of the projected shifts will be absorbed within existing PAs, with an additional 1.4% absorbed by nearby PAs. About 7.6% (forward) and 8.8% (backward) of protected land area will have climates terminating outside of the current PA network (Table S7 and S8), while 8.3% of global protected land area will shift to a novel climate, with no existing, same climate zone within a 1,000 km radius, and 6.6% will experience disappearing climates. Meanwhile, outgoing and incoming climates will originate from surrounding unprotected lands for 4.6% and 3.3% of protected land area, respectively. Critically, shifting climates may terminate outside PAs into areas dominated by human land uses including crop, pasture, rangeland, or urban areas, posing increasing risks to species\u003csup\u003e67\u003c/sup\u003e. More than three quarters of terrestrial PAs (equal to 12.3% of global protected land area), will face these types of threats by the end of this century (Table S7 and S8).\u003c/p\u003e \u003cp\u003eThe PAs likely to be exposed to high climate zone velocities share certain characteristics, including larger areas, higher latitudes, lower elevations, and lower topographic heterogeneity (Fig. S4; Section D of SI). PAs in Oceania, in particular, are projected to experience high threats to local biodiversity (Fig. S4 and S5). These impacts are projected to be stronger during the first half of the century (Fig. S4 and S5). In addition, correlations between climate zone velocity and area change suggest that PAs with more area projected to experience climate zone shifts are also more likely to be exposed to faster rates of climate zone shifts (Section E of SI).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk assessment for the most vulnerable protected areas\u003c/h3\u003e\n\u003cp\u003eWhile most PAs will have only a portion of their area exposed to climate shifts, 35 PAs are projected to have their entire area shift by the end of this century, with mean velocities greater than 1 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These 35 PAs are located mainly in North America, Europe, and Russia with a few in the Kalahari region of southern Africa, the Amazon region of Brazil, and southern Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Most of these PAs are small and characterized by low elevations and low topographic roughness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Moreover, while the human footprint in these highly vulnerable PAs varies, many have high species richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), suggesting that biodiversity losses could be considerable as climate shifts.\u003c/p\u003e \u003cp\u003eWe identified distinct temporal and spatial patterns of exposure risks for these PAs, with many seeing high velocities within and outside their boundaries by the end of the century (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). These patterns offer valuable insights into the potential implications of the effectiveness of regional PA networks and can inform future conservation planning strategies. Taking Wood Buffalo National Park in Canada as an example (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-#14), spatial pattern results indicate that considerable portions of the park will experience climate zone shifts to areas outside the PA boundary and into unprotected lands by mid-century (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Furthermore, the park is projected to undergo rapid climate zone shifts in the mid-century with average velocities exceeding 1 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). These projections could result in substantial threats to the Wood Bison (\u003cem\u003eBison bison\u003c/em\u003e) and endangered whooping crane (\u003cem\u003eGrus americana\u003c/em\u003e) that rely on the park for habitat. The findings underscore the urgency of addressing climate change impacts through PAs and emphasize the need for proactive conservation strategies to ensure the long-term viability of existing parks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDynamic spatial prioritization for conservation planning\u003c/h2\u003e \u003cp\u003eTo demonstrate how climate-induced biological movement and rearrangement of species assemblages can be incorporated into spatial prioritizations for conservation planning, we provide a regional case study of PAs in the Yellowstone National Park, USA region (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Large portions of the PAs in this region are projected to transition from polar (ET) and boreal cold summer (Dfc) climates to boreal warm summer (Dfb) climate by mid- to late-century. Using climate connectivity features computed for Yellowstone National Park and other PAs adjacent to the national park based on the density and percentage of velocities starting in, ending in, and passing through the given cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, top), we show that many areas along mountain ridges will be climate sinks (i.e., where a climate shift terminates), which may lead to increased local diversity but potential extinction for species with poor tolerance\u003csup\u003e55\u003c/sup\u003e. Areas with lower elevations adjacent to these sinks are primarily climate sources (i.e., where a climate shift starts), indicating potential sources of species movements and potentially a decline in local species richness. Regions with high passage density (purple) can be suggested for biodiversity corridors to advance connections for climate migrants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClimate spatial relationships assessed for Yellowstone NP can be used to identify potential biodiversity threats, which could reduce conservation capacity and undermine PA effectiveness. A large proportion of the short term shifts (expected by mid century) are projected to occur within the Yellowstone NP, particularly in flatter, lower elevation regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, main panel). The region is supported through many, well-connected PAs however, and the areas adjacent to Yellowstone where climate shifts are projected to occur among PAs (yellow) should be prioritized for strict management corridors to link existing PAs and assist climate-induced species range shifts\u003csup\u003e68\u003c/sup\u003e. A small proportion of areas surrounding Yellowstone will experience climate shifts originating from outside unprotected land and terminating inside the PA (brown), which could potentially introduce new species to the park, creating new management opportunities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eTogether, the climate zone velocities and shifts computed in this study can provide information on the continuity of suitable climatic conditions available for species as well as the paths along which species might move to track shifting climates across surrounding areas. To enhance the resilience of the PA network to the anticipated redistributions of biodiversity, it is critical for conservation planners to explicitly consider and address the spatiotemporal patterns of climate shifts over the planning horizon. Performed at a spatial resolution of 1-km and temporal intervals of 20 years, and encompassing four future periods and alternative scenarios, this study provides a fine scale and biologically relevant global database on climate risk. While we find 20% of global protected land area is projected to experience climate zone shifts by mid century, we also find that larger PAs will absorb these effects to a greater extent than smaller PAs (Fig. S8). This finding provides further support for expanding existing PAs or corridors to connect PAs to support species migration\u003csup\u003e12,33,69\u003c/sup\u003e rather than establishing new, small PAs in isolation.\u003c/p\u003e \u003cp\u003eIt will be particularly important to focus conservation planning efforts in areas where projected climate velocities might exceed the dispersal rates of species\u003csup\u003e26\u003c/sup\u003e. SInce the climate zone velocities developed here are species agnostic, and the actual effects of climate change on biodiversity will depend on species\u0026rsquo; intrinsic abilities to respond to climate exposure and other threats, it is important to also integrate species-specific, biotic velocity measures into decision making. Climate velocity and biotic velocity have been shown to contribute unique information to understanding future shifts\u003csup\u003e37\u003c/sup\u003e. The climate zone velocity-based forecasts presented here are adaptive and can be combined with existing biotic velocity-based information to inform climate adaptation planning in specific regions or for specific species and to predict climatically suitable habitats using biologically relevant information\u003csup\u003e70,71\u003c/sup\u003e. In areas where biological data is lacking, the climate zone velocity measures presented here can be a valuable proxy for assessing general climatic threats to biodiversity, particularly in regions where biogeographic factors have large influence on species vulnerability.\u003c/p\u003e \u003cp\u003eThis study provides a fine-scale quantitative assessment of near-, mid-, and long-term climate change exposure for global terrestrial PAs. The results highlight the varying spatiotemporal patterns of climate zone shifts and implications for biodiversity conservation over multiple time steps. Shifts in climatic conditions and human-induced land-use degradation could drive large-scale biodiversity changes and undermine the conservation effectiveness of existing PA networks but targeted interventions to expand or link existing PAs can mitigate some of these projected impacts. To address the ongoing biodiversity crisis and achieve future conservation goals, anticipating climate shifts and climate-induced biological movement is crucial to developing strategic and adaptive PA conservation planning to ensure climatic connectivity and conservation capacity.\u003c/p\u003e"},{"header":"Methods and Data","content":"\u003ch2\u003eClimate classification data\u003c/h2\u003e\n\u003cp\u003eWe used the 1-km global dataset of historical and future K\u0026ouml;ppen-Geiger climate classification (KGClim)\u003csup\u003e1\u003c/sup\u003e to calculate climate zone velocity for a contemporary baseline (1970-2000) and four future (2020-2049, 2040-2069, 2060-2089, and 2070-2099) epochs across four representative concentration pathways (RCP2.6, 4.5, 6.0 and 8.5) (Table S9). The K\u0026ouml;ppen-Geiger climate classification scheme used in the KGClim dataset followed the classification criteria as described in Kottek et al.\u003csup\u003e2\u003c/sup\u003e and Rubel and Kottek\u003csup\u003e3\u003c/sup\u003e and first presented by Geiger\u003csup\u003e4\u003c/sup\u003e. The K\u0026ouml;ppen-Geiger climate classification characterizes climatic conditions based on warmth and aridity and distinguishes 30 climate types in tropical (A), arid (B), temperate (C), boreal (D), and polar (E) climate zones.\u003c/p\u003e\n\u003cp\u003eThe baseline (1970-2000) KGClim map was derived based on 30-year averages of monthly surface air temperature and precipitation from 1-km WorldClim Historical Climate Data V2\u003csup\u003e5\u003c/sup\u003e, which used a thin-plate smoothing spline with elevation, distance to the coast, and other satellite-derived independent variables to interpolate climate data from 9,000-60,000 weather stations\u003csup\u003e5\u003c/sup\u003e. The future maps (2020-2049, 2040-2069, 2060-2089, and 2070-2099) were derived based on Climate Change, Agriculture and Food Security (CCAFS) high-resolution (1-km) bias-corrected downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations\u003csup\u003e6\u003c/sup\u003e, which include 35 GCMs for RCP 2.6, 4.5, 6.0 and 8.5. CCAFS\u0026rsquo;s bias-corrected and downscaled climate projections are based on interpolated anomalies and apply the delta method to baseline climates to correct model biases\u003csup\u003e6\u003c/sup\u003e, which can effectively reduce bias effects from the threshold-based climate classification scheme\u003csup\u003e7\u003c/sup\u003e. The KGClim dataset used here selected the highest confidence climate class from an ensemble of future climate maps generated by multiple model projections\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProtected area data\u003c/h2\u003e\n\u003cp\u003eWe used the World Database on Protected Area (WDPA)\u003csup\u003e8\u003c/sup\u003e (https://www.protectedplanet.net/, accessed June 2023) to delimit global terrestrial protected areas. We removed all PAs with marine coverage and filled in data gaps for China, South Africa, and India using alternate sources. For China, we used the PA data collected for Chinese national parks released by the Chinese government, as well as the WDPA Sept 2020 dataset. For South Africa, we used the South Africa Protected Areas Data (SAPAD) for the Protected and Conservation Areas (PACA), released by the Forestry, Fisheries \u0026amp; the Environment department in 2022 Q4 (https://egis.environment.gov.za/protected_and_conservation_areas_database). For India, we used PA data published by the National Park and Wildlife Sanctuary in India in June 2020, which was built with Open Street Map information and official Ministry of Environment, Forest and Climate Change (ENVIS) listings (https://geo.ejatlas.org/layers/geonode:Protected_Area_India_Final). To facilitate processing, we excluded a large PA in Greenland, Nationalparken I Nord-Og \u0026Oslash;stgr\u0026oslash;nland (972,000 km\u0026sup2;), and retained only PA polygons with an area larger than 10km\u003csup\u003e2\u003c/sup\u003e. We dissolved overlapping polygons with the same IUCN category to avoid duplicates. As some PAs consist of multiple polygons, we used the unique ID (WDPAID) to identify each PA. Overall, we included 26,220 terrestrial PAs covering 16.9 million km\u0026sup2; (13% of the global land surface). The 14,265 PAs designated as IUCN categories Ia, Ib, II, III, and IV, which have the strictest management objectives for biodiversity conservation, are estimated to comprise 6.1% of global land. Continental-level PA terrestrial coverage ranges from 8.9% (Africa) to 20.6% (Oceania), with some countries in Europe, America and Oceania having higher coverage (Table S10).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eClimate zone velocity calculation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe developed an analytical measure of climate zone velocity (CZV) that conforms to climate-analog velocity algorithms\u003csup\u003e9\u0026ndash;12\u003c/sup\u003e and quantifies the exposure changes in bioclimatic conditions if the climate moves beyond the physiological tolerance of a local population. This measure is based on climate-analog concepts and allows for the calculation of both forward and backward velocities to represent different change directions (Fig. 1) that correspond to different impacts on ecosystems or biota. Forward velocity indicates the distance from past climate locations to the nearest site with the same present climate, reflecting the minimum path that an organism would need to migrate to maintain a consistent climate condition. Backward velocity measures the distance from projected future climate cells back to analogous current climate locations, reflecting the minimum path that climatically adapted organisms would need to migrate to colonize the site. Forward velocity points to future destinations, while backward velocity points to origins that might feed into the destination. The two velocities provide complementary information and are useful to interpret together to prioritize different conservation management needs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo generate the CZV algorithm, we incorporated projected bioclimatic information and improved on past conceptualizations of climate-driven movement that considered only straight line (Euclidean distance) or least-cost paths, by incorporating topographic factors into the velocity calculation to enhance the use of the algorithm for conservation planning and biodiversity risk assessments. The forward and backward velocities can be calculated based on the nearest distance between climate types as follows,\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58894_9946feeafa4c1df7/58894_custom_files/img1733463082.png\" width=\"765\" height=\"224\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe CZV algorithm identifies the velocity origin, which is the targeting pixel, and a destination, which is the nearest pixel within the 1,000 km search radius with the same climate type (Fig. 1). Using these origin and destination cells and a 1-km topographic surface\u003csup\u003e13\u003c/sup\u003e, we generated topographic least cost paths and aggregated the climate zone velocity across the four future epochs to represent a continuous trajectory along which climate migrants will need to follow to track similar climatic conditions through time and space (Fig. S1e). We demonstrate through an example of a randomly selected pixel in the Selway-Bitterroot Wilderness in the United States (see SI: Section A) how the climate zone velocity algorithm developed here can better capture potential climate migrations over a fine spatial scale and across finer temporal time steps compared to the traditional Euclidean distance and topographic paths.\u003c/p\u003e\n\u003cp\u003eSince discrepancies among GCMs can lead to uncertainties in climate projections, we assess the sensitivity of the CZV algorithm to GCMs by estimating climate zone velocities for 100 randomly selected PAs using model projections from 16 GCMs (Table S11) and report on inter-model variability. Individual models show varying results at the PA level, and our velocity results agree well with the multi-model mean levels (Fig. S9).\u003c/p\u003e\n\u003ch2\u003eAssessing PA exposure to climate zone shifts and expected spatial patterns\u003c/h2\u003e\n\u003cp\u003eFor the global set of PAs, we assessed overall exposure to forward and backward climate zone velocities as well as the proportion of each PA that will be exposed to a climate shift under each RCP scenario for each time period. For each PA, we first assessed climate zone shifts according to 1) the area within each PA projected to undergo climate zone shifts, 2) the direction and speed of those shifts according to the velocities, and 3) the spatial relationships (i.e., patterns) of climate zone shifts in the PA network. We categorized the spatial patterns of each climate zone shift based on whether climates are relocated (i) within the same PA, (ii) among PAs (from one to another), (iii) into, or (iv) outside the PA network, or (v) into unprotected and human-dominated surroundings. We also categorized the emergence of (vi) novel or (vii) disappearing climate zones (within the 1,000 km search distance) for the global terrestrial PAs (Fig. S2). These spatial patterns allow us to assess the capacity of the existing PA network to absorb future climate changes. The framework for characterizing these spatial patterns was modified from Batllori et al.\u003csup\u003e9\u003c/sup\u003e. For each spatial relationship category, we quantified the area change in climate zones and climate zone velocity for each future epoch (2020-2049, 2040-2069, 2060-2089, and 2070-2099) under the four RCP scenarios.\u003c/p\u003e\n\u003cp\u003eTo identify spatial pattern shifts into human-dominated land uses, we used future land use projections from the Land-Use Harmonization (LUH2) project\u003csup\u003e14\u003c/sup\u003e. We used the annual, gridded fractions of land-use states derived from LUH2 v.2h historical land use forcing dataset for 1070-2000 and the LUH2 v.2f future harmonized dataset for 2020-2100 and four scenarios (RCP2.6 from IMAGE, RCP4.5 from MESSAGEix-GLOBIOM, RCP6.0 from GCAM, RCP8.5 from ReMIND-MAgPIE). \u0026nbsp; We considered managed pasture, rangeland, urban land, and cropland (all functional groups) as human-dominated land use states. We aggregated the annual gridded fractions of land use states for each 30 year period (1970-2000, 2020-2049, 2040-2069, 2060-2089, and 2070-2099) and extracted the long-term states using the land use with the highest fraction for each pixel.\u003c/p\u003e\n\u003cp\u003eWe aggregated the pixel-level, climate velocity results to the PA level to assess the general exposure of each individual PA. The pixel level results (see Fig. S1e), can illustrate the finer-scale spatial patterns of climate zone velocities within the PA, while the PA level results provide an aggregate look at each PA. We compiled the PA level results for each future climate (RCP) scenario and parsed them by continent and IUCN category for RCP 8.5. We also mapped the forward and backward climate zone velocities as well as the percent of protected land area that will undergo climate zone shifts with different spatial relationship patterns in each time period by latitude. The latitudinal trends are based on the summarized pixel-level velocity values for 5\u003csup\u003eo\u0026nbsp;\u003c/sup\u003elatitudinal bands. To investigate the potential timing of the climate zone shifts, we also reported the time frame projected for the climate zone shifts for each PA globally (see SI: Section B).\u003c/p\u003e\n\u003ch2\u003eAssessing vulnerability of protected areas based on PA characteristics\u003c/h2\u003e\n\u003cp\u003eTo assess whether certain PAs may be more vulnerable to climate zone shifts based on their characteristics, we used correlations analysis to identify PA attributes associated with vulnerability. The characteristics we assessed included topographic heterogeneity, human pressure, and biotic uniqueness. Topographic heterogeneity attributes, including elevation and terrain ruggedness, were extracted from a global, 1-km multivariate elevation data product\u003csup\u003e13\u003c/sup\u003e. The mean elevation within the PA is used to assess generally whether PAs are in highland or lowland regions. The Terrain Ruggedness Index (TRI)\u003csup\u003e15\u003c/sup\u003e, is the mean of the elevation differences between the center pixel and its eight surrounding pixels. We used mean TRI in the PA to characterize variability in elevation that may buffer climate change effects on ecosystems\u003csup\u003e16\u003c/sup\u003e. The human footprint (HFP) for each PA was calculated using 1-km human footprint data from 2009\u003csup\u003e17\u003c/sup\u003e. The biotic uniqueness of a PA quantifies its irreplaceability as the degree of overlap among the ranges of species\u003csup\u003e18\u003c/sup\u003e. We quantified the potential species richness level for each PA by overlapping area of habitat (AOH) estimates for 10,774 birds, 5219 mammals, 4462 reptiles, and 6254 amphibians (Fig. S10), produced using IUCN species range polygons and then modified with habitat associations with land cover and elevation limits for individual species\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe identified PAs that are projected to have their entire area shift climate zones by the end of this century, with mean velocities larger than 1km yr\u003csup\u003e-1\u003c/sup\u003e. This velocity criterion is based on the rationale that estimated migration rates for tree species typically fall below 1 km per year, and the majority of animal species cannot keep pace with changes occurring at such high rates. We summarize the attributes (size, latitude, elevation, TRI, HFT) of these highly vulnerable PAs and report their mean forward and backward climate zone velocities, along with the spatial patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase study for spatial planning prioritizations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe demonstrate how the CZV algorithm can be used for spatial conservation planning through a case study of the Yellowstone, USA region, which is located in northwestern Wyoming, eastern Idaho, and southern Montana and includes 10 PAs, some of which are adjacent to each other. The PAs within this region span a diverse range of climatic zones and elevation profiles and provide habitats for distinct plant and animal species. Using the climate velocities, we classify climate connectivity features in the region using Burrow\u0026rsquo;s approach\u003csup\u003e20\u003c/sup\u003e, which assigns classes of source, sink, corridor, convergence, or divergence based on the proportions of trajectories starting from, ending in, and passing through a pixel during the period. Sources are cells where climate velocities start but none end. Sinks are cells where climate velocities terminate and none start. Corridors are cells with a high proportion of climate velocities passing through. Convergence is where more climate velocities end than start in a cell, and divergence is the opposite. We also categorized the climate spatial patterns within the Yellowstone region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsiderations for climate zone velocity algorithm use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo facilitate a clearer interpretation of the climate zone velocity results and to better understand the implications of these velocities for biodiversity, a few key issues should be considered. First, the temporal and spatial scales at which velocity is measured can impact interpretations. The computations in this study are performed at 1-km spatial resolution and approximately 20-yr time steps. Using coarser spatial resolution data could inflate velocity estimates \u003csup\u003e21,22\u003c/sup\u003e, potentially overestimating exposure risks and overlooking microrefugia\u003csup\u003e23\u003c/sup\u003e. Using longer time frames could obscure fine scale temporal variations and confound findings on the timing of changes. The climate zones defined by the K\u0026ouml;ppen-Geiger system are based on thresholds, and climate shifts in regions above and below the thresholds can be underrepresented, while factors such as wind, atmospheric CO2, and solar radiation are not considered\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClimate zone velocities are species agnostic, and impacts of climate change are highly individualistic across species while also exhibiting large spatial and temporal heterogeneity. Decreased availability of climatically suitable areas, barriers created by human modifications and habitat fragmentation\u003csup\u003e27,28\u003c/sup\u003e, and varied adaptative and dispersal capacity can result in large variations in the biological responses of individual species\u003csup\u003e29,30\u003c/sup\u003e. Time lags in plant movement can further slow or impede the movements of species that depend on them\u003csup\u003e31,32\u003c/sup\u003e. Since species may not shift distributions in response to climate change but instead contract into microrefugia and maintain a smaller population\u003csup\u003e33\u003c/sup\u003e, the CZV measure used here should only be interpreted in terms of climate exposure and not specific biological responses\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLastly, it is important to note that the results are constrained by the current distribution of existing PAs. We observe the most significant shifts in mid-to-high latitudes, where large proportions of PAs are distributed. Climate zone shifts and velocity occur regardless of whether an area is protected or not. Therefore, our findings can also inform future planning efforts aimed at establishing new protected areas in regions adjacent to existing PA locations and currently lacking such conservation measures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKreft, H. \u0026amp; Jetz, W. 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To assess exposure of global PAs to future climate risks, we develop a high-resolution climate change velocity measure to quantify climate zone shifts under future climate scenarios. We find that by mid-century, around 20% of global protected land area is projected to undergo climate zone shifts under all scenarios. Under RCP 8.5, the rate of climate zone velocity will continue to accelerate through the end of this century, potentially impacting 40% of existing PA land area. 15% of these climate zone shifts will terminate outside the existing PA network and into human-modified areas, and about 15% of protected land area will be exposed to novel and disappearing climates, potentially undermining the effectiveness of the existing network. Strategic and adaptive conservation planning that explicitly considers climate zone shifts will enable greater resilience for conservation interventions under climate change.\u003c/p\u003e","manuscriptTitle":"Projected climate zone shifts could undermine the effectiveness of global protected areas for biodiversity conservation by the mid-to-late century","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-06 05:56:56","doi":"10.21203/rs.3.rs-3992123/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":"5a85ca58-228a-45d0-a02f-10a79ff42dbf","owner":[],"postedDate":"December 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41167564,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"},{"id":41167565,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"},{"id":41167566,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2024-12-06T05:56:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-06 05:56:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3992123","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3992123","identity":"rs-3992123","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}