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Abstract Generalization is difficult to quantify, and many classifications exist. A beta diversity framework can be used to establish a numeric measure of generalist tendencies that jointly describes many important features of species interactions, namely spatiotemporal heterogeneity. This framework is promising for studying generalized symbiotic relationships of any form.We formulated a novel index, turnover importance (T).Tdescribes spatiotemporal heterogeneity in interactor assemblages, an inherent feature of generalist relationships that is not captured by available metrics. We simulated the behaviour ofTrelative to other available metrics, calculatedTfor native North American orchid‐insect relationships, and tested correlations betweenTand eco‐geographic variables. We performed case studies to demonstrate applications ofTfor conservation and eco‐evolutionary studies.Tbehaves predictably across simulations, and dynamically interacts with site number, gamma diversity, and species range sizes.Tis moderately sensitive to sampling depth. Orchids with higherTscores occupy larger ranges and broader climatic niches.Alternative interactor‐specific measures of generalism are best employed for local‐level community networks over short timespans. While these interactor metrics can assess use versus availability in local communities,Tcan be used to measure spatiotemporal patterns of variation in interactor assemblages across a focal species' range. This study provides a roadmap for future work focused on better understanding the patterns and consequences of generalized relationships. 

Summary Poales are one of the most species‐rich, ecologically and economically important orders of plants and often characterise open habitats, enabled by unique suites of traits. We test six hypotheses regarding the evolution and assembly of Poales in open and closed habitats throughout the world, and examine whether diversification patterns demonstrate parallel evolution.We sampled 42% of Poales species and obtained taxonomic and biogeographic data from the World Checklist of Vascular Plants database, which was combined with open/closed habitat data scored by taxonomic experts. A dated supertree of Poales was constructed. We integrated spatial phylogenetics with regionalisation analyses, historical biogeography and ancestral state estimations.Diversification in Poales and assembly of open and closed habitats result from dynamic evolutionary processes that vary across lineages, time and space, most prominently in tropical and southern latitudes. Our results reveal parallel and recurrent patterns of habitat and trait transitions in the species‐rich families Poaceae and Cyperaceae. Smaller families display unique and often divergent evolutionary trajectories.The Poales have achieved global dominance via parallel evolution in open habitats, with notable, spatially and phylogenetically restricted divergences into strictly closed habitats. 

Abstract Species interactions drive ecosystem processes and are a major focus of global change research. Among the most consequential interactions expected to shift with climate change are those between insect herbivores and plants, both of which are highly sensitive to temperature. Insect herbivores and their host plants display varying levels of synchrony that could be disrupted or enhanced by climate change, yet empirical data on changes in synchrony are lacking. Using evidence of herbivory on herbarium specimens collected from the northeastern United States and France from 1900 to 2015, we provide evidence that plant species with temperature‐sensitive phenologies experience higher levels of insect damage in warmer years, while less temperature‐sensitive, co‐occurring species do not. While herbivory might be mediated by interactions between warming and phenology through multiple pathways, we suggest that warming might lengthen growing seasons for phenologically sensitive plant species, exposing their leaves to herbivores for longer periods of time in warm years. We propose that elevated herbivory in warm years may represent a previously underappreciated cost to phenological tracking of climate change over longer timescales. 

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. 

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth. 

Abstract Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.