William Henry Rycroft

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.

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

Identifying physical and ecological boundaries that limit where species can occur is important for predicting how those species will respond to global change. The island of Borneo encompasses a wide range of habitats that support some of the highest richness on Earth, making it an ideal location for…

Antibiotic resistance and viral diseases are rising around the world and are becoming major threats to global health, food security, and development. One measure that has been suggested to mitigate this crisis is the development of new antibiotics. Here, we provide a comprehensive evaluation of the …

The description of a new species is a key step in cataloguing the World’s flora. However, this is only a preliminary stage in a long process of understanding what that species represents. We investigated how long the species discovery process takes by focusing on three key stages: 1, the collection …

Aim: To determine the global position of tree line isotherms, compare it with observed local tree limits on islands and mainlands, and disentangle the potential drivers of a difference between tree line and local tree limit. Location: Global. Time period: 1979–2013. Major taxa studied: Trees. Method…