Southeast Asian forests are dominated by the tree family Dipterocarpaceae, whose abundance and diversity are key to maintaining the structure and function of tropical forests. Like most biodiversity, dipterocarps are threatened by deforestation and climate change, so it is crucial to understand the potential impacts of these threats on current and future dipterocarp distributions. We developed species distribution models (SDMs) for 19 species of dipterocarps in the Philippines, which were projected onto current and two 2070 representative concentration pathway (RCP) climate scenarios, RCP 4.5 and 8.5. Current land cover was incorporated as a post-hoc correction to restrict projections onto intact habitats. Land cover correction alone reduced current species distributions by a median 67%, and within protected areas by 37%. After land cover correction, climate change reduced distributions by a median 16% (RCP 4.5) and 27% (RCP 8.5) at the national level, with similar losses in protected areas. There was a detectable upward elevation shift of species distributions, consisting of suitable habitat losses below 300 m and gains above 600 m. Species-rich stable areas of continued habitat suitability (i.e., climate macrorefugia) fell largely outside current delineations of protected areas, indicating a need to improve protected area planning. This study highlights how SDMs can provide projections that can inform protected area planning in the tropics.

Complex distribution data can be summarised by grouping species with similar or overlapping distributions to unravel patterns in species distributions and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data-driven rigour of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices engender substantial variations in clustering outcome and performance. To facilitate robust decision making, we provide guidance on choosing methods appropriate to the study objective(s). As a case study, we apply the framework to model tree distributions in Borneo to evaluate the impact of land-cover change on separate species groupings. We identified 11 distinct clusters that unravelled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution-related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever-growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.

Understanding the relative contributions of historical and anthropogenic factors to declines in genetic diversity is important for informing conservation action. Using genome-wide DNA of fresh and historic specimens, including that of two species widely thought to be extinct, we investigated fluctuations in genetic diversity and present the first complete phylogenomic tree for all nine species of the threatened shorebird genus Numenius, known as whimbrels and curlews. Most species faced sharp declines in effective population size, a proxy for genetic diversity, soon after the Last Glacial Maximum (around 20,000 years ago). These declines occurred prior to the Anthropocene and in spite of an increase in the breeding area predicted by environmental niche modeling, suggesting that they were not caused by climatic or recent anthropogenic factors. Crucially, these genetic diversity declines coincide with mass extinctions of mammalian megafauna in the Northern Hemisphere. Among other factors, the demise of ecosystem-engineering megafauna which maintained open habitats may have been detrimental for grassland and tundra-breeding Numenius shorebirds. Our work suggests that the impact of historical factors such as megafaunal extinction may have had wider repercussions on present-day population dynamics of open habitat biota than previously appreciated.

Complex distribution data can be summarized by grouping species with similar or overlapping distributions to unravel spatial patterns and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data-driven rigor of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices drive substantial variations in clustering outcomes and performance. To facilitate robust decision-making, we provide guidance on choosing methods appropriate to one's study objective(s). As a case study, we apply our framework to modeled tree distributions in Borneo and subsequently evaluate the impact of land-cover change on separate species groupings. Based on the modeled distribution of 390 tree species prior to anthropogenic land-cover changes, we identified 11 distinct clusters that unraveled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution-related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever-growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.

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