Efeito das mudanças climáticas na distribuição e abundância de anuros da família Hylidae na América do Sul - Rangel, Carolina (2024)
Climate change has been recognized as an emerging threat to biodiversity loss, and anurans comprise the most endangered group of vertebrates. Within this group, the most diverse family is Hylidae, which is widely distributed in South America, the continent predicted to experience the highest impact of climate change. Ecological Niche Modeling is a widely used tool to predict the impact of climate change on biodiversity. Recently, studies have suggested the use of abundance data to construct these models, known as Species Abundance Models. The aim of this study was to predict the future impacts of climate change on the distribution and abundance of anuran species of the family Hylidae in South America. We obtained georeferenced abundance and occurrence data of 12 hylid species from a literature review. We used two algorithms to model ecological niches and species abundance, Boosted Regression Trees and Random Forest, and projected them into the current scenario and three future scenarios for each species. The abundance and distribution models were developed separately, and consensus models were generated for each scenario and model. Subsequently, to identify areas of highest climatic suitability for both distribution and abundance of species, binary and categorical maps of distribution and abundance, respectively, were intersected. In general, climate change can affect the distribution and abundance of all the 12 species studied. Species with wide distributions have potential to benefit from climate change, while species with more restricted distributions tend to lose more area than gain, especially in terms of abundance. For some species, abundance tends to decline even though the areas of distribution are not projected to shrink. Our results can support decisions based on the identification of 'key habitats' where the climatic support capacity allows for maintaining viable population. The combined use of abundance and occurrence data can enhance the predictions of species' vulnerability to climate change.