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Globally, amphibians are one of the most threatened vertebrate groups and are hypersensitive to human-imposed habitat changes. Here we explore ways to conserve amphibians in two of the least-known biodiversity hotspots on earth, the Caucasus and Irano–Anatolian regions. We used two techniques: (ⅰ) combining species richness, endemism, and endangerness indices and (ⅱ) a species distribution model (SDM) to identify high-priority areas for Iran's seven endemic and/or threatened amphibian species. The identified amphibian high-priority areas were then targeted to assess the levels of protection granted by the network of conservation areas (CAs) in Iran. We also computed the species-specific extent of occurrence (EOO) and the area of occupancy (AOO) to detect conservation gaps for the targeted species. Our results indicate that amphibian high-priority areas in Iran are mostly distributed across the Hyrcanian forest in the north and Zagros Mountains in the west. The gap analysis revealed that based on the most optimistic metric, 40% of amphibian hotspots are covered by CAs in Iran. However, the species-specific gap analysis showed that Iran's CAs perform poorly at representing the EOO of all of the endemic and/or threatened amphibian species. These results suggest that expansion of CAs in Iran is essential for amphibian conservation. [ABSTRACT FROM AUTHOR]

Aim: Climate Change Vulnerability Assessment (CCVA) prescribes the quantification of species vulnerability based on three components: sensitivity, adaptive capacity and exposure. Such assessments should be performed through combined approaches that integrate trait‐based elements (e.g., measures of species sensitivity such as niche width) with correlative tools quantifying exposure (magnitude of changes in climate within species habitat). Furthermore, as land use alterations may increase climate impacts on biodiversity, CCVAs should focus on both climate and land use change effects. Unfortunately, most of such assessments have so far focused exclusively on exposure to climate change. Location: Himalaya. Methods: We evaluated the vulnerability of three otter species occurring in the Himalayan region, that is, Aonyx cinereus, Lutra lutra and Lutrogale perspicillata, to 2050 climate and land use change through the recently proposed Climate Niche Factor Analysis (CNFA) framework combined with Species Distribution Models. Results: Future climate and land use change will reduce (6%–15%) and shift (10%–18%) the geographical range of the three species in the Himalaya, with land use alterations exerting far more severe effects than climate change. Among vulnerability components, sensitivity played a greater role than exposure in determining the vulnerability of the otters. Specifically, the most specialist species, L. perspicillata, showed the highest vulnerability in comparison with the most generalist, L. lutra. Main conclusions: Our results underline how coupling climate and land use change components in CCVAs can generate diverging predictions of species vulnerability compared to approaches relying on climate change only. Moreover, intrinsic components, such as species sensitivity, proved significantly more important in determining vulnerability than extrinsic metrics such as habitat exposure. [ABSTRACT FROM AUTHOR]

• Background points are widely used in species distribution models. • The standard background point sampling method is inferior. • Sampling background points in environmental space yields more stable and more accurate models. • Stratified random in environmental space sampling yields the most accurate models. • Fully random in environmental space sampling yields the most stable models. Species distribution models (SDMs) have become central tools in ecology and biogeography. Although they can be fitted with different types of species data (e.g. presence-absence, abundance), the most common approach, based on data from large species repositories, is to use simple occurrences (i.e. presence-only) combined with background points (BP; also called pseudo-absences). But how should we sample these background points, and how does this choice affect SDMs? In most studies so far, BP were sampled randomly in geographic space, yet theory rather suggests, if a species is at equilibrium, that it is better to sample them in a stratified way in environmental space. However, this potential improvement of SDM predictions has never been tested. Furthermore, a typical assumption behind SDMs is that the modelled species are at equilibrium with their environment. But how do these models perform when species are in disequilibrium, as is the case for most invasive species? To answer these questions, we selected 30 different species (10 insects, 10 mammals and 10 plants; for each group 5 were invasive and 5 were considered at equilibrium) and for each we calibrated SDMs with different types of background selections: random in environmental space, random-stratified in environmental space, random in geographic space, and random-stratified in geographic space. For each SDM we assessed both predictive performance using standard metrics and their stability using a new approach that compares the model's habitat suitability projection with those of a SDM calibrated with virtual occurrence data generated from the most suitable areas. Finally, we compared the predictive performance of species distribution models of invasive alien (disequilibrium) species versus native (equilibrium) species by comparing model stability and performance metrics of the two groups. We found that sampling BP in a stratified-random way in environmental space yields the highest performance metrics, and that sampling fully randomly in environmental space yields the most stable models. This has implications for the use of SDMs in conservation, as the classical and frequently used fully random in geographic space BP are found to produce both less accurate and less stable models. Our results indicate that the best approach is to use stratified random in environmental space BP sampling if accuracy is essential, and fully random in environmental space BP sampling if model stability is essential. [ABSTRACT FROM AUTHOR]

Aim: As a prominent geographical distribution centre for the dark coniferous forests, mountains of Southwest China (MSWC) is experiencing an unprecedented warming trend, posing severe challenges to the survival of dominant fir (Abies) species. Although plant's migration ability is a prerequisite for its survival in changing environments, it has often been ignored in species distribution models (SDMs). This study aimed to quantify the magnitude and direction of range changes by the year 2080 for six dominant fir species, that is Abies recurvata, Abies faxoniana, Abies squamata, Abies ernestii, Abies forrestii and Abies georgei, with an emphasis on exploring the relationship between migration ability and projected distributions. Location: The mountains of Southwest China. Methods: We applied the Maximum Entropy (Maxent) algorithm to calibrate ecological niche models and to project the climatically suitable areas (CSAs) of each species under two emission scenarios (RCP 4.5 and RCP 8.5). Additionally, we delimited future species ranges by three migration scenarios (full‐, no‐ and partial‐migration scenarios). Results: The simulations showed the distinctive responses of the six fir species to anthropogenic climate change (ACC). By 2080, the distribution areas of Abies recurvata were projected to decline only in the no‐migration scenario but increase under the full‐ and partial‐migration scenarios, while the other five species were projected to decline in the majority of emission × migration scenarios. Fir species in the southern region were predicted to be more vulnerable to ACC due to the larger losses in CSAs and a stronger effect of the partial‐migration scenario on the newly colonized areas of this group. The studied species showed a simulated migration trend (northward and westward) to the interior Qinghai‐Tibet Plateau under ACC. Main conclusions: Benefits or losses for species under ACC depended on the geographical location, their ecological niches and migration abilities, which provide essential insights for a spatial conservation assessment of biodiversity hotspots in the future. [ABSTRACT FROM AUTHOR]

The differential responses of co-occurring species in rich communities to climate change—particularly to drought episodes—have been fairly unexplored. Species distribution models (SDMs) are used to assess changes in species suitability under environmental shifts, but whether they can portray population and community responses is largely undetermined, especially in relation to extreme events. Here we studied a shrubland community in SE Spain because this region constitutes an ecotone between the Mediterranean biome and subtropical arid areas, and it has recently suffered its driest hydrological year on record. We used four different modeling algorithms (Mahalanobis distance, GAM, BRT, and MAXENT) to estimate species' climatic suitability before (1950-2000) and during the extreme drought. For each SDM, we related species' climatic suitability with their remaining green canopy as a proxy for species resistance to drought. We consistently found a positive correlation between remaining green canopy and species' climatic suitability before the event. This relationship supports the hypothesis of a higher vulnerability of populations living closer to their species' limits of aridity tolerance. Contrastingly, climatic suitability during the drought did not correlate with remaining green canopy, likely because the exceptional episode led to almost zero suitability values. Overall, our approach highlights climatic niche modeling as a robust approach to standardizing and comparing the behavior of different co-occurring species facing strong climatic fluctuations. Although many processes contribute to resistance to climatic extremes, the results confirm the relevance of populations' position in the species' climatic niche for explaining sensitivity to climate change. [ABSTRACT FROM AUTHOR]