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  1. Free, publicly-accessible full text available August 1, 2023
  2. Free, publicly-accessible full text available June 1, 2023
  3. Interactions between species can influence access to resources and successful reproduction. One possible outcome of such interactions is reproductive character displacement. Here, the similarity of reproductive traits – such as flowering time – among close relatives growing in sympatry differ more so than when growing apart. However, evidence for the overall prevalence and direction of this phenomenon, or the stability of such differences under environmental change, remains untested across large taxonomic and spatial scales. We apply data from tens of thousands of herbarium specimens to examine character displacement in flowering time across 110 animal-pollinated angiosperm species in the eastern USA. We demonstrate that the degree and direction of phenological displacement among co-occurring closely related species pairs varies tremendously. Overall, flowering time displacement in sympatry is not common. However, displacement is generally greater among species pairs that flower close in time, regardless of direction. We additionally identify that future climate change may alter the nature of phenological displacement among many of these species pairs. On average, flowering times of closely related species were predicted to shift further apart by the mid-21st century, which may have significant future consequences for species interactions and gene flow.Competing Interest StatementThe authors have declared no competingmore »interest.« less
  4. Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079–189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3–85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253–10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. Thesemore »results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.« less
  5. Summary Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon‐specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro‐environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.
  6. Abstract Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.