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Darwin’s Naturalization Conundrum holds that both functional similarity and distinctiveness can facilitate biological invasions: invaders similar to natives may succeed through preadaptation to local abiotic conditions, whereas functionally distinct invaders may succeed by reducing competition. Yet the contexts in which either mechanism dominates are unclear. Prior research has primarily attributed variability in native–invasive differentiation to shifts in the balance between biotic and climatic barriers to invasion from local to regional scales. However, similarity and distinctiveness are frequent at both local and regional levels, indicating key drivers of native–invasive differentiation remain overlooked. Crucially, theory and evidence show that as climatic stress increases, competition weakens. This implies that harsh climates should favor invaders functionally similar to natives, whereas mesic climates should favor distinctiveness. However, this hypothesis has not been tested across climate regimes and functional traits. We addressed this gap by combining models of species distributions and flowering phenology for 2,810 species across the United States, estimating phylogenetic distance and phenological differentiation between natives and invasives relative to differentiation among co-occurring natives. In warm, humid regions, invasives flowered earlier, less synchronously, and were more distantly related to natives. In cold or dry regions, they flowered at similar or later times, more synchronously, and were more closely related than natives themselves. Across all climates, invasives consistently exhibited longer flowering durations, with little evidence of greater phenological plasticity. These findings reveal that Darwin’s Conundrum reflects a predictable continuum shaped by environmental context, highlighting climate as a key axis of invasion success. 

Climate change is altering the timing of species’ life-cycle events (i.e., phenology), but the rates of phenological shifts vary across taxa. These mismatches in phenological response may disrupt interactions between interdependent species, such as plants and their pollinators, which may lead to reduced plant reproduction via pollen limitation and thus contribute to secondary extinction risks for plants. However, secondary extinction risk is rarely assessed under future climate-change scenarios. Here, we used ca. 15,000 crowdsourced specimen records ofViolaspecies and their solitary bee pollinators, spanning 120 y across the eastern United States, and integrated climate data, phenological information, and species distribution models to quantify the risk of secondary plant extinction associated with phenological mismatch with their bee pollinators. We further examined geographical patterns in secondary extinction risk for plants and explored how their interactions between plants and generalist versus specialist pollinators influence such risk. Secondary local extinction risk ofViolaspp. increases with latitude, indicating that future climate change will pose a greater threat to plant–bee pollinator networks at northern latitudes. Additionally, the sensitivity of secondary local extinction risk to phenological mismatch with both generalist and specialist bee pollinators varies by latitude, with specialist bees showing a sharper decline at higher latitudes. Our findings demonstrate that existing conservation priorities based solely on primary extinction risk directly caused by climate change may be insufficient to support self-sustaining populations of plants. Thus, incorporating secondary extinction risk resulting from ecological mismatches between plants and pollinators into future global conservation frameworks should be carefully considered. 

Summary Reflectance spectroscopy is a rapid method for estimating traits and discriminating species. Spectral libraries from herbarium specimens represent an untapped resource for generating broad phenomic datasets across space, time, and taxa.We conducted a proof‐of‐concept study using trait data and spectra from herbarium specimens up to 179 yr old, alongside data from recently dried and pressed leaves. We validated model accuracy and transferability for trait prediction and taxonomic discrimination.Trait models from herbarium spectra predicted leaf mass per area (LMA) withR² = 0.94 and %RMSE = 4.86%. Models for LMA prediction were transferable between herbarium and pressed spectra, achievingR² = 0.88, %RMSE = 8.76% for herbarium to pressed spectra, andR² = 0.76, %RMSE = 10.5% for the reverse transfer. Discriminant models classified leaf spectra from 25 species with 74% accuracy, and classification probabilities were significantly associated with several herbarium specimen quality metrics.The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate that trait modeling can benefit from the complementary use of pressed‐leaf and herbarium‐leaf spectral datasets. These promising advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations. 

Abstract Global warming has caused widespread shifts in plant phenology among species in the temperate zone, but it is unclear how population‐level responses will scale to alter the structure of the flowering season at the community level. This knowledge gap exists largely because—while the climatic sensitivity of first flowering within populations has been studied extensively—little is known about the responsiveness of the duration of a population's flowering period. This limits our ability to anticipate how the entire flowering periods of co‐occurring species may continue to change under warming. Nonetheless, flowering sensitivity to temperature often varies predictably among species between and within communities, which may help forecast temperature‐related changes to a community's flowering season. However, no studies—empirical or theoretical—have assessed how patterns of variation in flowering sensitivity among species could scale to alter community‐level flowering changes under warming. Here, we provide a conceptual overview of how variation in the sensitivity of flowering onset and duration among species can mediate changes to a community's flowering season due to warming trends. Specifically, we focus on the effects of differences in (1) the mean sensitivity of flowering onset and duration among communities and (2) the sensitivity of flowering onsets and durations among species flowering sequentially through the season within a community. We evaluated the manner and degree in which these forms of between‐species variation in sensitivity might affect the structure of the flowering season—both independently and interactively—using simulations, which covered a wide but empirically informed range of parameter values and combinations representing distinct community‐level patterns. Our findings predict that communities across the temperate zone will exhibit varied and often contrasting flowering responses to warming across biomes, underscoring that accounting for the temperature sensitivity of both phenological onset and duration among species is essential for understanding community‐level flowering dynamics in a warming world. 

Abstract Anthropogenic pressures on biodiversity necessitate efficient and highly scalable methods to predict global species distributions. Current species distribution models (SDMs) face limitations with large-scale datasets, complex interspecies interactions, and data quality. Here, we introduce EcoVAE, a framework of autoencoder-based generative models trained separately on nearly 124 million georeferenced occurrences from taxa including plants, butterflies and mammals, to predict their global distributions at both genus and species levels. EcoVAE achieves high precision and speed, captures underlying distribution patterns through unsupervised learning, and reveals interspecies interactions viain silicoperturbation analyses. Additionally, it evaluates global sampling efforts and interpolates distributions without relying on environmental variables, offering new applications for biodiversity exploration and monitoring. 

Summary Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue. We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses.