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Climate change has led animal species to shift their ranges to greater elevations, latitudes, and depths, tracking their preferred abiotic niche. However, there is extensive variation in these shifts, and some species have not shifted their ranges at all. Some of this variation arises because species’ distributions not only align with the abiotic environment but are also shaped by biotic factors and movement. Through facilitating rapid adaptive responses to climate-mediated changes to abiotic, biotic, and movement factors, behavioral plasticity allows populations to survive environmental change by persisting in place, while also enabling successful establishment in novel habitats when shifting in space. 

Abstract Populations declining due to climate change may need to evolve to persist. While evolutionary rescue has been demonstrated in theory and the lab, its relevance to natural populations facing climate change remains unknown. Here we link rapid evolution and population dynamics in scarlet monkeyflower,Mimulus cardinalis, during an exceptional drought. We leverage whole-genome sequencing across 55 populations to identify climate-associated loci. Simultaneously we track demography and allele frequency change throughout the drought. We establish range-wide population decline during the drought, geographically variable rapid evolution, and variable population recovery that is predictable by both standing genetic variation and rapid evolution at climate-associated loci. These findings demonstrate evolutionary rescue in the wild, showing that genomic variability at adaptive, but not neutral loci, predicts population recovery. 

ABSTRACT Predicting the effects of climate change on plant and animal populations is an urgent challenge for understanding the fate of biodiversity under global change. At the surface, quantifying how climate drives the vital rates that underlie population dynamics appears simple, yet many decisions are required to connect climate to demographic data. Competing approaches have emerged in the literature with little consensus around best practices. Here we provide a practical guide for how to best link vital rates to climate for the purposes of inference and projection of population dynamics. We first describe the sources of demographic and climate data underlying population models. We then focus on best practices to model the relationships between vital rates and climate, highlighting what we can learn from mechanistic and phenomenological models. Finally, we discuss the challenges of prediction and forecasting in the face of uncertainty about climate‐demographic relationships as well as future climate. We conclude by suggesting ways forward to build this field of research into one that makes robust forecasts of population persistence, with opportunities for synthesis across species.