Upgrading protected areas to safeguard Kenya's herpetofauna under climate change

Upgrading protected areas to safeguard Kenya's herpetofauna under climate change | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 March 2025 V1 Latest version Share on Upgrading protected areas to safeguard Kenya's herpetofauna under climate change Authors : Ronnie Kimani 0000-0001-8335-7539 , Mi Chunrong 0000-0002-3350-8324 , Beryl Bwong , Patrick Malonza , and Wei-Guo Du 0000-0002-1868-5664 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174250805.50666790/v1 Published Ecology and Evolution Version of record Peer review timeline 434 views 158 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Climate change is a major driver of biodiversity loss, particularly for ectothermic species such as reptiles and amphibians (hereafter herpetofauna), which are highly sensitive to environmental changes. While extensive research has evaluated the effectiveness of protected areas (PAs) in conserving biodiversity under climate change in developed and rapidly developing countries, similar studies in Africa remain scarce despite the continent’s exceptional biodiversity. This study focuses on Kenya, home to over 110 amphibians and 290 reptile species, as a model to address this conservation gap in the face of climate change. We used species distribution models (SDMs) to predict herpetofauna distributions for 2050 under three climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our results indicate that 20 herpetofauna species (5 amphibians and 15 reptiles) are at risk of local extinction. Furthermore, over 80% of species in both groups currently have less than 30% of their range protected within existing PAs, a trend that persists under future scenarios. We applied a systematic conservation planning approach to address this shortfall to identify priority areas for future conservation efforts. Our findings suggest that Kenya’s PA network would need to expand by approximately 16–19% of the total land area to safeguard herpetofauna both now and in the future effectively. This study underscores the urgent need to optimize Kenya’s PA network to mitigate the effects of climate change on herpetofauna. A proactive approach to conservation planning is essential to enhance species resilience and ensure their long-term survival in a rapidly changing climate. Upgrading protected areas to safeguard Kenya’s herpetofauna under climate change Abstract Climate change is a major driver of biodiversity loss, particularly for ectothermic species such as reptiles and amphibians (hereafter herpetofauna), which are highly sensitive to environmental changes. While extensive research has evaluated the effectiveness of protected areas (PAs) in conserving biodiversity under climate change in developed and rapidly developing countries, similar studies in Africa remain scarce despite the continent’s exceptional biodiversity. This study focuses on Kenya, home to over 110 amphibians and 290 reptile species, as a model to address this conservation gap in the face of climate change. We used species distribution models (SDMs) to predict herpetofauna distributions for 2050 under three climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our results indicate that 20 herpetofauna species (5 amphibians and 15 reptiles) are at risk of local extinction. Furthermore, over 80% of species in both groups currently have less than 30% of their range protected within existing PAs, a trend that persists under future scenarios. We applied a systematic conservation planning approach to address this shortfall to identify priority areas for future conservation efforts. Our findings suggest that Kenya’s PA network would need to expand by approximately 16–19% of the total land area to safeguard herpetofauna both now and in the future effectively. This study underscores the urgent need to optimize Kenya’s PA network to mitigate the effects of climate change on herpetofauna. A proactive approach to conservation planning is essential to enhance species resilience and ensure their long-term survival in a rapidly changing climate. Keywords; Reptiles, Amphibians, species distribution, climate change, Protected Areas, Conservation prioritization, Species distribution models Introduction The global emission of greenhouse gases due to human activities has increased the average surface temperature by approximately 1°C over the past century (Calvin et al., 2023). This increase has triggered shifts in climate patterns and a heightened frequency of extreme weather events worldwide (Shivanna, 2022). If emissions are not significantly reduced in the coming decades, the Earth’s temperature is projected to rise by 1.5–2°C by the end of the 21st century (Intergovernmental Panel on Climate Change [IPCC], 2023). In recent years, anthropogenic pressures, such as agricultural expansion, urban development, and logging, have contributed to habitat alterations and subsequent biodiversity loss (Harfoot et al., 2021). As global warming continues, these trends are expected to intensify, with climate change predicted to drive the future extinction of numerous species (Bellard et al., 2012). There is substantial evidence indicating that anthropogenic climate change is responsible for shifts in species’ distribution ranges (Lenoir et al., 2020), the occurrence of extirpations (Panetta et al., 2018), and, consequently, the broader loss of biodiversity (Cox et al., 2022). Climate change affects biodiversity distribution patterns in species-specific ways (Coelho et al., 2023), with some species facing suitable habitat loss threats while others expanding their ranges as previously uninhabitable areas become accessible (Wingfield et al., 2015). As ectotherms, herpetofauna species are particularly vulnerable to climate change, as they rely on environmental temperature to regulate physiological processes (Lopez-Alcaide & Macip-Ríos, 2011). An organism’s susceptibility to environmental changes depends on both the degree of exposure and its capacity to adapt and recover (Huey et al., 2012). The distribution of reptiles and amphibians is closely linked to rainfall patterns and ambient temperatures within their habitats (Mi et al., 2022, 2024). Consequently, climate alterations increase their vulnerability, significantly affecting their distribution and habitat suitability. Additionally, limited dispersal capabilities hinder their ability to track shifting climatic conditions, making them more prone to habitat loss than range expansion (Inman et al., 2022). Globally, more than 300 amphibian and 500 reptile species are projected to face extinction due to shrinking distribution ranges in the coming century (Mi et al., 2023). Moreover, over 41% of amphibian species are currently threatened, with climate change among the primary drivers of species decline (de Albuquerque et al., 2024; IUCN, 2024; Luedtke et al., 2023). Given these risks, herpetofauna provides valuable insights into how climate change influences species distributions and the effectiveness of existing protected areas (PAs) in safeguarding them. PAs are crucial for conserving herpetofauna populations (Nowakowski et al., 2023). Their primary function is to preserve natural ecosystems by mitigating or eliminating anthropogenic pressures within their boundaries (Joppa & Pfaff, 2010). However, in many developing countries, PAs often struggle to fulfil this mandate effectively (Fromont et al., 2024; Watson et al., 2014). Furthermore, studies by Dobrowski et al. (2021) and Parks et al. (2023) indicate that existing PA networks may be insufficient in protecting global biodiversity under changing climatic conditions despite their potential to serve as climate refugia for herpetofauna species (Mi et al., 2023). This highlights the need to assess the effectiveness of PAs in conserving biodiversity under localized climate change scenarios, particularly in developing regions such as Africa. Kenya, situated in the tropical region of Africa, harbours a rich diversity of herpetofauna, with over 110 amphibian and 290 reptile species recorded (Malonza & Bwong, 2023). The primary threats to these species are driven mainly by habitat loss and deforestation, with approximately 15% of Kenya’s herpetofauna classified as threatened (Luedtke et al., 2023). For instance, Arthroleptides dutoiti , an amphibian species endemic to Mount Elgon, has become locally extinct on the Kenyan slopes of the mountain (Ngwava et al., 2021). Climate change has been implicated in the loss of amphibian species (Lötters et al., 2023). There are geographical biases and scarcity in studies focusing on the effects of climate change on herpetofauna (Tan et al., 2023). In Kenya, research on the impacts of climate change on these species is minimal. Notably, species previously recorded in arid regions are now being observed in areas historically classified as wet forested highlands, suggesting shifts in distribution due to warming temperatures (Malonza & Bwong, 2023). Given these changes, further studies are needed to assess the effects of climate change on Kenya’s herpetofauna at a macro scale, particularly in the context of developing countries. This study sought to understand how climate change will impact the suitable habitats of herpetofauna at present and under future climate change scenarios in Kenya. It aims to provide decision-makers with comprehensive data and assessments to protect and conserve the underrepresented herpetofauna species and their habitats in Kenya. Precisely, our study employed species distribution modeling to (1) Predict species distributions under future climatic conditions, (2) Quantify the representation of herpetofauna species in PAs, and (3) Identify nationwide conservation priority areas (CPAs) that ensure effective conservation of herpetofauna in Kenya under climate change scenarios. Materials and methods Occurrence records Occurrence data for herpetofauna in Kenya were collected using a multi-step approach. First, we extracted published literature from Web of Science and Google Scholar by searching with keywords such as “Species Distribution Models,” “Species richness,” and “Species diversity.” The results were filtered for “Kenya” and/or “Amphibians” or “Reptiles” and limited to articles published between 2000 and 2024. Second, we compiled data from online databases, including GBIF, VertNet, and iDigBio. Third, we incorporated information from field guides of Kenyan herpetofauna, geotagging the local names provided for each species using Google Earth Pro. After compiling the data, occurrence records were cleaned using the ‘CoordinateCleaner‘ package (Zizka et al., 2019) to eliminate records located in cities, institutes, and museums. The ‘spThin’ package in R was then applied to remove duplicate occurrences within a single 1 km × 1 km grid cell and to exclude species with fewer than five records, thereby reducing sampling biases (Aiello-Lammens et al., 2015). The cleaned dataset was subsequently visualized in ArcGIS to display the distribution of herpetofauna in Kenya, and the clip function was used to remove records outside Kenya’s administrative boundaries. This process yielded 2,329 occurrence records representing 97 (~88%) amphibian species and 6,948 records representing 243 (~84%) reptile species, which were fed to the species distribution models. Environmental variables Environmental variables representing current (1970–2000) and future (2041–2070) climatic conditions were obtained from the ‘WorldClim’ database (https://www.worldclim.org/). To address uncertainties in future projections (hereafter referred to as 2050), the raster data of five Global Circulation Models—GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL—were averaged. This dataset includes nineteen bioclimatic variables (BIO1–BIO19) at a 30-second spatial resolution. Additionally, three Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were considered while predicting future distributions. The environmental layers were reprojected to a Lambert Azimuthal Equal Area projection (Herkt et al., 2017), centred at 10°N, 38°E, with a grid cell resolution of 1 km × 1 km. Species distribution modeling Species distribution models (SDMs) are widely accepted and extensively used for predicting the potential geographic ranges of species in response to climate change (Elith & Leathwick, 2009). To develop the SDMs, we used the ‘sdm’ package in R statistical software (Naimi & Araújo, 2016). We employed an ensemble approach combining five widely used algorithms: Random Forest (RF), Generalized Linear Model (GLM), Maximum Entropy (MAXENT), Support Vector Machines (SVM), and Generalized Boosted Regression Models (GBR) (Elith et al., 2006; Mi et al., 2023; Naimi & Araújo, 2016). Pseudo-absence data were generated by randomly assigning points in unoccupied grid cells following the methods of Mi et al. (2023). We applied the ‘vif’ function from the ‘usdm’ R package to minimize multicollinearity, filtering out bioclimatic variables with a Variance Inflation Factor (VIF) greater than 10 (Naimi et al., 2014). The dataset was randomly split, with 80% allocated for model training and the remaining 20% used for cross-validation. Models with a True Skill Statistic (TSS) value greater than 0.7 were retained for the final ensemble model (Gallardo et al., 2017). Model performance was evaluated using the Area Under the Curve (AUC) and TSS. We then converted the habitat suitability maps into binary presence-absence maps using the Max-TSS threshold (Gallardo et al., 2017; Liu et al., 2016). Given the limited dispersal capabilities of most herpetofauna species, additional analyses were conducted under a no-dispersal assumption (Smith & Green, 2005). Estimating species richness and rarity-weighted richness The binary SDM maps generated for each species were used to estimate species richness under current and future climate scenarios. Species richness was calculated using the ’calc’ function from the raster package in R, determining the number of species present in each grid cell for both timeframes. Differences in species richness between current and future projections were then analysed to assess the impact of climate change on suitable habitats for herpetofauna species. Rarity-weighted richness (RWR) was calculated following the methodology outlined by Albuquerque and Gregory (2017). This process involved two steps: First, we assigned a rarity score to each species, calculated as the inverse of the number of grid cells a species occupies. For instance, a species restricted to a single grid cell received the maximum score (1.0), while a species occurring in 100 grid cells received a lower score (0.01, calculated as 1/100). Secondly, the rarity scores for each species were then summed across grid cells using the formula: \begin{equation} RWR=\ \sum_{1}^{n}\frac{1}{c_{i}}\nonumber \\ \end{equation} Where c i represents the number of grid cells occupied by species i , which is summed for the n species occurring in a given grid cell. RWR was calculated for current and future scenarios to evaluate the potential impact of climate change on the rarity-weighted richness of herpetofauna species. Range of herpetofauna inside PAs To assess the effectiveness of PAs, geospatial data for PAs in Kenya were obtained from the World Database on Protected Areas (WPDA) (https://www.protectedplanet.net/en). PAs classified under IUCN management categories I–VI were considered, excluding marine PAs. The projected distribution maps were first reprojected into the Lambert Azimuthal Equal Area projection to calculate species range sizes. We calculated each species’ range size inside PAs using the binary maps for current and future scenarios. The species range sizes inside PAs were then categorized according to the following categories to show the representation of amphibians and reptiles inside the existing PAs network: Unprotected (UP) ≤ 10%; Inadequately Protected (IP) 50%; Adequately protected > 50% ≤ 80%; Protected > 80%. To evaluate the future effectiveness of the current PA network in conserving herpetofauna diversity, we used the projected range size maps for both current and future scenarios, calculating the percentage of species falling within each of the five representation categories. Conservation prioritization To optimize the existing PAs network in Kenya for the effective conservation of the 340 herpetofauna species, we used the Marxan software (Ball et al., 2009), a conservation planning tool designed to optimize resource allocation for biodiversity preservation while minimizing costs. The planning limits were set using Kenya’s administrative boundary, and planning units of 1 km² were created, resulting in a total of 588,327 planning units. Each unit was assigned a status: 2 (‘locked in’) for those that intersected with the current PA network, indicating their conservation status is unlikely to change; 3 (‘locked out’) for those within a 5 km radius of towns, municipalities, or city centres, marking them as unavailable for inclusion in conservation priority areas (CPAs); and 0 (‘available’) for the remaining planning units, which were considered available for inclusion in the Marxan solution. The cost estimates for each planning unit were derived from the Human Footprint Index (Venter et al., 2016), calculated by incorporating data on human pressures, such as population density, built environments, and land use, which compete with biodiversity conservation, thus providing a reliable baseline for cost. We incorporated the binary range maps for the 340 herpetofauna species generated by the SDMs, resulting in a matrix of planning units categorized by conservation features. Extensive literature has discussed the minimum protection required to ensure species’ long-term survival. One standard method is setting conservation targets based on species range size, which varies depending on geographic distribution. We adopted the criteria from Carwardine et al. (2008) and Mi et al. (2023): if a species’ predicted range is ≤ 1,000 km², the target is set at 100%; for ranges between ≥ 1,000 km² and ≤ 10,000 km², the target is 30%; and for species with ranges > 10,000 km², the conservation target was set at 10%. We ran four independent spatial prioritization scenarios to explore optimal conservation solutions for Kenyan herpetofauna under current and future scenarios. Each scenario was executed with 1,000 repetitions, constituting 10 million iterations. The ‘best solution’ output from Marxan was selected as the final solution for the CPAs. We performed a cluster and outlier analysis to identify planning units with high conservation values. We then overlapped the current and future CPAs to identify regions selected in both scenarios. The boundary length modifier was calibrated using the ArcMarxan Toolbox, with a value of the planning units and assign their statuses. At the same time, R software was employed to calculate each planning unit’s cost, feature value, and conservation target for each species. SDM performance Our models performed well for all 340 herpetofauna species included in the analysis (AUC = 0.951 ± 0.034, TSS = 0.883 ± 0.085; see supplementary data). This performance is highlighted by our prediction that 11 herpetofauna species will experience local extinction, losing all their suitable habitats, under the SSP2-4.5 scenario by 2050 (see supplementary Table 1 for additional scenarios and the endemicity of the locally extinct species). Our ensemble models also revealed that, under the current scenario, 4 amphibian and 5 reptile species have suitable habitats entirely outside the existing PAs network. This number increases to 7 reptile and 4 amphibian species under the SSP2-4.5 scenario (see supplementary Table 2 for other scenarios). Species richness Our SDMs predicted similar distribution patterns for both reptile and amphibian species in Kenya (Pearson correlation: r = 0.881, p < 0.001). The current scenario shows that species richness is concentrated in the central, western, and southeastern regions and along the humid coastal belt. Similar distribution patterns were observed in future predictions (Pearson correlation: r = 0.968 for amphibians, r = 0.966 for reptiles), with these regions also facing habitat loss (see Fig. 1C and Supplementary Figs. 1 and 2 for other scenarios). In contrast, the northern and eastern areas exhibited lower species richness. Our maps indicated regions experiencing significant species richness loss were predominantly outside the existing PAs (Fig. 1C and F). However, results from the Wilcoxon test showed no significant difference in proportional range loss between areas located inside and outside PAs (see supplementary Figs. 9–11). Notably, the interquartile range for proportional range loss in areas inside protected areas was slightly lower for both taxa across all scenarios. Rarity Weighted Richness Our analysis revealed that high RWR regions (RWR > 0.3) for both taxa were concentrated along the coastal strip, Taita Hills toward the Eastern Arc Mountains (southern part of the country), the western region near Kakamega Forest, Nandi Hills, through Mau Forest to the Aberdare Ranges, and the slopes of Mount Kenya (central region of the country) (Fig. 1). In the future, some reptile species are predicted to become rare as regions with relatively high RWR values emerge. For amphibians, we observed a reduction in areas with high RWR values. This was evident as both taxa exhibited narrower interquartile ranges (Q3) in the boxplots of all future scenarios compared to the current scenario (Fig. 1I and L for SSP2-4.5 and supplementary Figs. 3 and 4). In all scenarios, regions along the Taita Hills displayed high RWR values for both taxonomic groups. However, they do not fall within any of the four IUCN management categories (IUCN I – VI) of PAs. Effectiveness of current PAs and proposed CPAs In the current scenario, approximately 38% of amphibian species fall under the ‘Unprotected’ (≤ 10%) category, a proportion that increases to about 40% by 2050. For reptiles, this proportion increases from around 26% to approximately 31% (see Fig. 2 and Supplementary Figs. 12 and 13). The percentage of species in the ‘Inadequately protected’ category (≤ 30%) decreased for both groups: from approximately 45% to 42% for amphibians and from 59% to 52% for reptiles. The ‘Protected’ category also saw a modest increase, with amphibians rising from 3.1% to 4.1% and reptiles from 3.3% to 3.7% under the SSP2-4.5 scenario. An increase in the percentage of species in the ‘Protected’ category was also observed in the other scenarios (see Supplementary Figs. 12 and 13). Our findings indicated that, currently, four (one endemic) amphibian species and seven (two endemic) reptile species have their range sizes entirely outside any form of protection (Table 2 in supplementary materials). In the future, this number is projected to rise to seven (two endemic) amphibians and sixteen (four endemic) reptiles (Table 2 in supplementary materials). No significant difference was observed in the relative range change of both amphibians and reptiles inside versus outside protected areas (Wilcox test; p = 0.93, Z = 0.08 for amphibians; p = 0.78, Z = 0.28 for reptiles; see supplementary Figs. 9–11 for other two scenarios). Spatial optimization of existing PAs We observed that 83% of amphibian species and 85% of reptile species have less than 30% of their range size within protected areas (PAs), falling below the minimum conservation target set by the Kunming-Montreal Global Biodiversity Framework (GBF). In all future scenarios, the proportion of species’ range sizes inside existing PAs declined (see Fig. 3 and Supplementary Figs. 5 and 6). Currently, PAs cover approximately 10.2% of Kenya’s total area, equivalent to around 62,247 km². However, optimization using the Marxan solution increased this coverage to approximately 16.4% for the current conservation priority areas (CPAs) and 16.2% for future CPAs (Supplementary Table 3). We identified several ‘overlapped’ regions—areas selected as CPAs in both current and future scenarios—including the Lower Tana River delta, Shimba Hills National Reserve, the coastal belt, and the Lake Victoria basin. In the current scenario, additional CPAs were proposed along the eastern and northwestern borders, Laikipia, the Aberdare slopes, and the Mount Kenya region. In contrast, future additional CPAs included areas along the Lake Turkana region, Arabuko Sokoke Forest, and Kajiado County. Notably, regions along the coastal, southern, and northwestern areas, such as those surrounding Sibiloi National Park, were consistently selected as CPAs across all future scenarios, highlighting their importance for herpetofauna conservation (see Fig. 3 and Supplementary Figs. 7 and 8). Our spatial prioritization analysis demonstrated improved herpetofauna representation within the proposed CPAs. The proportion of species with less than 30% of their relative range size inside CPAs decreased to below 30% in both current and future scenarios. Furthermore, the percentage of species in the ‘Unprotected’ (≤ 10%) category was reduced to 0% across all future scenarios. Cumulatively, the proportion of species classified as ‘Partially Protected,’ ‘Adequately Protected,’ and ‘Protected’ increased significantly—from 14.6% and 16.3% to 67.82% and 69.56% for reptiles and amphibians, respectively, in the current scenario, and from 15.87% and 17.4% to 70.36% and 69.23% under SSP2-4.5 (see Fig. 2 and Supplementary Figs. 12 and 13). Discussion Our study revealed high herpetofauna species richness and RWR to be distributed across Kenya’s central, western, and coastal regions. Future projections indicate that many areas at risk of biodiversity loss fall outside the existing PA network, with over seven species expected to lose all their suitable habitats. Our findings also highlight the inefficacy of Kenya’s current PAs in protecting herpetofauna, both now and in the future, as more than 80% of species have less than 30% of their range within these protected boundaries. To address this conservation gap, we used Marxan analysis to identify CPAs, which reduced the proportion of species with less than 30% range protection to below 35%. Our results underscore the inadequacy of the existing PA network in safeguarding herpetofauna under current and future climate change scenarios. A significant number of species fall into the ‘Unprotected’ category (<10% of their range within PAs) or the ‘Inadequately Protected’ category (10–30% intersection), and this trend persists across future projections (see Fig. 2 and Supplementary Figs. 12 and 13). This indicates that many species remain vulnerable as climate change further restricts their suitable habitats. We found no significant difference in range loss inside versus outside PAs. This suggests that the current PA network does not align with predicted range shifts and is ineffective as climate refugia for herpetofauna. This may also imply that existing PAs do not provide sufficient climate-buffering effects for herpetofauna species. We also identified a critical conservation gap, as four amphibian species (one endemic) and five reptile species (one endemic) currently have their entire ranges outside Kenya’s existing PAs, with this number projected to increase under future climate scenarios (Supplementary Table 2). These species face heightened extinction risks due to the absence of alternative habitats within protected areas (IŞIK, 2011). Endemic species are particularly vulnerable, as their restricted ranges limit their ability to seek refuge, further amplifying their extinction risk. Our findings reinforce concerns about the inadequacy of Kenya’s current PA network in protecting herpetofauna. This aligns with previous studies indicating that only 5% of the critically endangered Pancake tortoise population occurs within existing PAs (Eustace et al., 2021). Similarly, low representation within PAs has been observed for other vertebrate species in Kenya (Ogutu et al., 2016; Onditi et al., 2021; Tyrrell et al., 2020), as well as for African lions (Robson et al., 2022) and global mammal populations (Williams et al., 2022), underscoring the urgent need for PA expansion to mitigate biodiversity loss. Using Marxan for spatial prioritization, we identified key areas that would increase the overall area of PAs to approximately 16.4% under current and future SSP2-4.5 scenarios (Supplementary Table 3). Marxan consistently selected critical regions as CPAs across all scenarios, including the Lower Tana River, Shimba Hills, the Lake Victoria basin, and areas around Kitobo Forest in Tsavo West National Park (Fig. 4, Supplementary Figs. 7 and 8). These regions play a crucial ecological role for Kenyan herpetofauna and could serve as essential refugia against climate change. Expanding PAs to encompass these areas would help establish a climate-resilient network, enhancing long-term species survival. Additionally, new CPAs were identified in northern Kenya (near Lake Turkana), along the coastal belt (around Arabuko Sokoke), and in the southern regions (Kajiado near Mount Kilimanjaro). These areas are currently underrepresented in the PA network but are critical for herpetofauna conservation as species range shifts in response to climate change. Notably, Sibiloi National Park is the only existing PA in northwestern Kenya, yet the region has been identified as vital for herpetofauna conservation in future scenarios, emphasizing the need for proactive conservation planning to safeguard these habitats. Our findings emphasize the necessity of expanding existing PAs to address conservation gaps for Kenyan herpetofauna and enhance their resilience to habitat loss. Specifically, our analysis suggests extending Maasai Mara, Tsavo East and West National Parks, Mount Kenya National Park, and Mwingi National Reserve to incorporate species ranges currently outside their boundaries. Such expansions would improve habitat connectivity, facilitating species dispersal and genetic flow in alignment with global conservation strategies (Beger et al., 2022). For instance, expanding Maasai Mara could enhance connectivity with Chepalungu Forest Reserve, benefiting herpetofauna and other vertebrates responding to climate pressures. Scientific consensus supports the expansion of PA networks as a means to preserve natural ecosystems, safeguard biodiversity, and enhance ecosystem services (Dinerstein et al., 2019; Watson et al., 2020). Additionally, our study highlights that small and isolated CPAs identified through Marxan spatial analysis will serve as climate refugia in future scenarios, making them essential for the conservation of Kenyan herpetofauna under climate change. The importance of such micro-reserves has been emphasized in recent research (Steigerwald et al., 2024), reinforcing the need to integrate them into Kenya’s conservation strategy, particularly in climate-sensitive regions of eastern Kenya. Our study mapped hotspots of RWR and species richness for Kenyan herpetofauna, particularly in the central, western, and coastal regions. These areas are characterized by diverse vegetation, complex topography, and favorable microhabitats, which support high species richness and endemism. However, projections indicate that these regions could experience significant species loss by 2050 due to climate change (Fig. 1, Supplementary Figs. 3 and 4). Narrower interquartile ranges for projected future RWR values suggest that regions with high RWR may contract as species’ ranges shrink, leading to a decline in range-restricted species within their habitats. The potential loss of rare species could contribute to increased species homogenization in these areas (Montràs-Janer et al., 2024). By identifying regions at risk of species loss, our study provides valuable insights for targeted conservation efforts. Endemic species are particularly vulnerable, with our analysis indicating that four herpetofauna species may face extinction under various scenarios (Table S1). Many of these species are already threatened by anthropogenic pressures, and climate change will likely exacerbate their risk of extinction. A notable example is Ancylodactylus mathewsensis , endemic to the hilltop montane forests of the Mathews Range, which is already experiencing habitat modification due to human activities (de Jong & Butynski, 2010). This underscores the urgent need for targeted conservation actions within CPAs to mitigate biodiversity decline and maintain ecosystem resilience (Valiente-Banuet et al., 2015). Kenya is committed to the Kunming-Montreal Global Biodiversity Framework, which aims to protect 30% of terrestrial and marine areas by 2030. However, human and financial constraints limit conservation efforts, highlighting the need for systematic planning in conservation strategies. Protected areas are often established without explicitly incorporating conservation objectives, mainly due to land acquisition challenges (Rodrigues et al., 2004). As a result, many PAs are designated in marginal lands with lower conversion pressures, limited biodiversity importance, or low conservation urgency. Instead, countries should assess whether designated PAs align with the highest conservation priorities before their establishment (Eckert et al., 2023). Kenya is not exempt from this spatial mismatch, as most government-managed national parks were initially converted from game-hunting reserves and primarily established to protect large, tourist-attraction mammals such as elephants, lions, rhinos, buffalos, and leopards (Caro, 2003). However, there has been a global shift toward systematic conservation planning (Kareiva et al., 2014), which aims to achieve cost-effective and strategic PA establishment by identifying biodiversity hotspots for conservation prioritization (Carwardine et al., 2008). Systematic planning has proven to be an effective approach for identifying CPAs, as it explicitly seeks to meet conservation goals by representing a broad range of biodiversity features, ensuring their long-term persistence. To enhance the effectiveness of Kenya’s PA network in conserving herpetofauna, expansion and prioritization should focus on regions with high herpetofauna endemism (Rodrigues & Gaston, 2001). Conclusion Our findings emphasize the significant impact of climate change on Kenyan herpetofauna and the urgent need for targeted conservation efforts. Climate change is reducing suitable habitats and contracting species’ ranges, exacerbating their vulnerability. Additionally, our study identified a critical conservation gap in the representation of herpetofauna within existing PAs, both now and in future climate scenarios. These findings underscore the need for systematic conservation planning to enhance species resilience and mitigate biodiversity loss. While our study provides robust conclusions based on climatic variables, it has some limitations. We did not incorporate other biotic factors (e.g., species-specific traits, biotic interactions) or abiotic variables (e.g., land use and land cover change) in our analysis. This omission may have led to an underestimation of climate change impacts on the distribution of Kenyan herpetofauna and their integration into conservation strategies. Future research should address these gaps by developing mechanistic models to understand herpetofauna responses to climate change better. Despite these limitations, our study offers critical insights into adaptive and targeted conservation strategies for Kenyan herpetofauna in the face of a rapidly changing climate. Acknowledgements References Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography , 38 (5), 541–545. https://doi.org/10.1111/ecog.01132Albuquerque, F. S. de, & Gregory, A. (2017). The geography of hotspots of rarity-weighted richness of birds and their coverage by Natura 2000. 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The 1 st and 2 nd columns show species richness and RWR for current and future scenarios, while the 3 rd column shows the change in species richness in the first 2 rows (C and F). (G, H, J, K); RWR for reptiles and amphibians under current and future SSP2-4.5 climatic scenarios by 2050. The color scale indicates lower values in yellow and higher in dark red. I) and L); Box plots showing the difference between current and SSP2-4.5 RWR values. (Wilcoxon test; Z = 73.8, p-value <0.001 and, Z = 389, p-value <0.001) for I) and L), respectively. The box plots were zoomed in while plotting because more RWR values were < 0.02, making the boxplots to be squeezed in. Figure 2; The graph shows the proportion of herpetofauna species protected by A) the current PAs network and B) Marxan’s CPAs network for Reptiles and Amphibians for the four-conservation status under SSP2-4.5 by 2050 Figure 3; Inter-quantile range (q1, median, and q3) of the percentage of species range distributed inside existing PAs. ‘Red’ is for the current scenario, and ‘blue’ is for the SSP2-4.5 scenario by 2050 Figure 4; CPAs proposed by spatial prioritization analysis Information & Authors Information Version history V1 Version 1 20 March 2025 Peer review timeline Published Ecology and Evolution Version of Record 22 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords amphibians climate change conservation prioritization protected areas reptiles species distribution models Authors Affiliations Ronnie Kimani 0000-0001-8335-7539 Institute of Zoology Chinese Academy of Sciences View all articles by this author Mi Chunrong 0000-0002-3350-8324 Princeton University View all articles by this author Beryl Bwong National Museums of Kenya View all articles by this author Patrick Malonza National Museums of Kenya View all articles by this author Wei-Guo Du 0000-0002-1868-5664 [email protected] Key Laboratory of Biodiversity and Ecological Engineering of the Ministry of Education View all articles by this author Metrics & Citations Metrics Article Usage 434 views 158 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ronnie Kimani, Mi Chunrong, Beryl Bwong, et al. Upgrading protected areas to safeguard Kenya's herpetofauna under climate change. Authorea . 20 March 2025. 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