Potential effects of future climate change on global reptile distributions and diversity

Abstract

Aim Until recently, complete information on global reptile distributions has not been widely available. Here, we provide the first comprehensive climate impact assessment for reptiles on a global scale. Location Global, excluding Antarctica. Time period 1995, 2050 and 2080. Major taxa studied Reptiles. Methods We modelled the distribution of 6296 reptile species and assessed potential global and realm‐specific changes in species richness, the change in global species richness across climate space, and species‐specific changes in range extent, overlap and position under future climate change. To assess the future climatic impact on 3768 range‐restricted species, which could not be modelled, we compared the future change in climatic conditions between both modelled and non‐modelled species. Results Reptile richness was projected to decline significantly over time, globally but also for most zoogeographical realms, with the greatest decreases in Brazil, Australia and South Africa. Species richness was highest in warm and moist regions, with these regions being projected to shift further towards climate extremes in the future. Range extents were projected to decline considerably in the future, with a low overlap between current and future ranges. Shifts in range centroids differed among realms and taxa, with a dominant global poleward shift. Non‐modelled species were significantly stronger affected by projected climatic changes than modelled species. Main conclusions With ongoing future climate change, reptile richness is likely to decrease significantly across most parts of the world. This effect, in addition to considerable impacts on species range extent, overlap and position, was visible across lizards, snakes and turtles alike. Together with other anthropogenic impacts, such as habitat loss and harvesting of species, this is a cause for concern. Given the historical lack of global reptile distributions, this calls for a re‐assessment of global reptile conservation efforts, with a specific focus on anticipated future climate change.

Global projection of terrestrial vertebrate food webs under future climate and land-use changes

“…In particular, warming effects under no-dispersal scenarios are predicted to greatly reduce species distributions and cause strong declines in the size of large equatorial food webs (Figs. 5, S8, S9) 29,34,35 .…”

Human activity and climate change accelerate the extinction risk to non‐human primates in China

“…These impacts are projected to have a considerable effect on the extent and location of species' geographical ranges and the availability of suitable habitat. Thus, to prevent large‐scale declines in primate richness, it is crucial that China, the US, EU, and other industrial countries dramatically and immediately lower CO 2 emissions and that China expand PAs in regions and habitats with wild primate populations (Biber et al., 2023; Li et al., 2018; Zhang et al., 2022; Zhao et al., 2022).…”

“…We repeated the split sampling 10 times to account for the uncertainty associated with data partitioning (Mi et al., 2023). In sum, we used 16 commonly used high model performance SDM algorithms in the ensemble models: maximum entropy (MaxEnt) (Zhang et al., 2018), random forests (Williams et al., 2009), generalized additive model (Biber et al., 2023), generalized linear model, GLMPOLY, and GLMNET (Williams et al., 2009), support vector machine, Maxlike, multivariate adaptive regression spline, classification and regression trees (Naimi & Araújo, 2016), multilayer perceptron (Munoz‐Mas et al., 2017), radial basis function (Aldossari et al., 2022), mixture discriminant analysis (Marmion et al., 2009), recursive partitioning and regression trees, flexible discriminant analysis (Mugo & Saitoh, 2020), DOMAIN (Mugo & Saitoh, 2020; Tsoar et al., 2007), and boosted regression trees (Elith et al., 2008). Then, we used true skill statistics (Allouche et al., 2006) and the values of the area under a receiver operating characteristic curve (AUC) to calibrate and validate the robustness of the evaluation using the 16 models (model selection) (Mi et al., 2023).…”

“…To address these limitations and present a nation‐wide focus, we analyzed and integrated the most comprehensive set of environmental data, including indicators of human activity, human population density (HPD, a measure of human impact on the natural environment), gross domestic product (GDP), roads, the normalized difference vegetation index (NDVI), location and size of PAs, and expected changes in temperature and rainfall, on the occurrence and future distribution of 26 primate species in China. For each species, we used ensemble species distribution models (SDMs) to predict how species' range distributions are expected to shift in response to future human activity, climate change, and modification in land cover (Biber et al., 2023; Li, Ru, et al., 2022; Mi et al., 2023; Pinto et al., 2023; Struebig et al., 2015). In the case of China, previous studies using SDMs were conducted on a single or a small number of species and relied on now outdated IPCC climate emission scenarios (Li, Chen, et al., 2022; Li, Ru, et al., 2022; Yu et al., 2022; Zhao et al., 2019, 2022).…”

From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to Bryophytes

“…By combining historical occurrence data with environmental data, SDMs can be also used to make predictions about the potential distribution of species, even in regions where data are lacking. And last but not least SDMs can be used to explore the potential impacts of future climate change on species distributions, helping to inform adaptation and mitigation efforts [35].…”