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Climate change is expected to cause irreversible changes to biodiversity, but predicting those risks remains uncertain. I synthesized 485 studies and more than 5 million projections to produce a quantitative global assessment of climate change extinctions. With increased certainty, this meta-analysis suggests that extinctions will accelerate rapidly if global temperatures exceed 1.5°C. The highest-emission scenario would threaten approximately one-third of species, globally. Amphibians; species from mountain, island, and freshwater ecosystems; and species inhabiting South America, Australia, and New Zealand face the greatest threats. In line with predictions, climate change has contributed to an increasing proportion of observed global extinctions since 1970. Besides limiting greenhouse gases, pinpointing which species to protect first will be critical for preserving biodiversity until anthropogenic climate change is halted and reversed. 

Abstract Graduate students across disciplines are eager for experiential training that enables them to address real-world environmental challenges. Simultaneously, communities across the world face numerous environmental challenges, including increased frequency of extreme heat in summer and poor air quality, and could benefit from the expertise and engagement of graduate students with the requisite skills and interests to address these challenges. In this paper we bring together lessons learned from three interdisciplinary graduate training programs focused on preparing graduate students to contribute to urban environmental solutions by working in partnerships with non-academic organizations. We discuss the multiple elements required for partnerships to be mutually beneficial, including using a T-shaped approach to training that incorporates bothdepthandbreadthwhile making strong efforts to broaden participation. We share lessons with the goal of enhancing graduate programs to improve training of students to address urban environmental challenges globally. This training aligns with the United Nations Sustainable Development Goal 17, “Partnership for the Goals,” which aims to achieve sustainable development goals through partnerships among entities. 

Projecting ecological and evolutionary responses to variable and changing environments is central to anticipating and managing impacts to biodiversity and ecosystems. Current modeling approaches are largely phenomenological and often fail to accurately project responses due to numerous biological processes at multiple levels of biological organization responding to environmental variation at varied spatial and temporal scales. Limited mechanistic understanding of organismal responses to environmental variability and extremes also restricts predictive capacity. We outline a strategy for identifying and modeling the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation. A central component of this strategy is quantifying timescales and magnitudes of climatic variability and how organisms experience them. We highlight recent empirical research that builds this information and suggest how to design future experiments that can produce more generalizable principles. We discuss how to create biologically informed projections in a feasible way by combining statistical and mechanistic approaches. Predictions will inform both fundamental and practical questions at the interface of ecology, evolution, and Earth science such as how organisms experience, adapt to, and respond to environmental variation at multiple hierarchical spatial and temporal scales. 

The finding that adaptive evolution can often be substantial enough to alter ecological dynamics challenges traditional views of community ecology that ignore evolution. Here, we propose that evolution might commonly alter both local and regional processes of community assembly. We show how adaptation can substantially affect community assembly and that these effects depend on regional (metacommunity) factors, including environmental heterogeneity and its spatial structure. In particular, early colonists can often arrive from a nearby community, adapt to local conditions, and subsequently alter the establishment or abundance of late-arriving species, often producing an evolutionary priority effect. We also discuss how interaction type and relative rates of colonization, evolution, and community interactions determine divergent community outcomes. We describe new conceptual approaches that provide insights into these dynamics and statistical methods that can better evaluate their importance. Overall, we demonstrate that accounting for adaptation during community assembly opens up novel ways for making progress on fundamental questions in community ecology. 

Abstract Predicting if, when, and how populations can adapt to climate change constitutes one of the greatest challenges in science today. Here, we build from contributions to the special issue on evolutionary adaptation to climate change, a survey of its authors, and recent literature to explore the limits and opportunities for predicting adaptive responses to climate change. We outline what might be predictable now, in the future, and perhaps never even with our best efforts. More accurate predictions are expected for traits characterized by a well-understood mapping between genotypes and phenotypes and traits experiencing strong, direct selection due to climate change. A meta-analysis revealed an overall moderate trait heritability and evolvability in studies performed under future climate conditions but indicated no significant change between current and future climate conditions, suggesting neither more nor less genetic variation for adapting to future climates. Predicting population persistence and evolutionary rescue remains uncertain, especially for the many species without sufficient ecological data. Still, when polled, authors contributing to this special issue were relatively optimistic about our ability to predict future evolutionary responses to climate change. Predictions will improve as we expand efforts to understand diverse organisms, their ecology, and their adaptive potential. Advancements in functional genomic resources, especially their extension to non-model species and the union of evolutionary experiments and “omics,” should also enhance predictions. Although predicting evolutionary responses to climate change remains challenging, even small advances will reduce the substantial uncertainties surrounding future evolutionary responses to climate change. 

Abstract Urbanization is a persistent and widespread driver of global environmental change, potentially shaping evolutionary processes due to genetic drift and reduced gene flow in cities induced by habitat fragmentation and small population sizes. We tested this prediction for the eastern grey squirrel (Sciurus carolinensis), a common and conspicuous forest‐dwelling rodent, by obtaining 44K SNPs using reduced representation sequencing (ddRAD) for 403 individuals sampled across the species' native range in eastern North America. We observed moderate levels of genetic diversity, low levels of inbreeding, and only a modest signal of isolation‐by‐distance. Clustering and migration analyses show that estimated levels of migration and genetic connectivity were higher than expected across cities and forested areas, specifically within the eastern portion of the species' range dominated by urbanization, and genetic connectivity was less than expected within the western range where the landscape is fragmented by agriculture. Landscape genetic methods revealed greater gene flow among individual squirrels in forested regions, which likely provide abundant food and shelter for squirrels. Although gene flow appears to be higher in areas with more tree cover, only slight discontinuities in gene flow suggest eastern grey squirrels have maintained connected populations across urban areas in all but the most heavily fragmented agricultural landscapes. Our results suggest urbanization shapes biological evolution in wildlife species depending strongly on the composition and habitability of the landscape matrix surrounding urban areas.