Increasing environmental variability inhibits evolutionary rescue in a long-lived vertebrate.

Evolutionary rescue, whereby adaptive evolutionary change rescues populations from extinction, is theorized to enable imperiled animal populations to persist under increasing anthropogenic change. Despite a large body of evidence in theoretical and laboratory settings, the potential for evolutionary rescue to be a viable adaptation process for free-ranging animals remains unknown. Here, we leverage a 38-year dataset following the fates of 53,959 Magellanic penguins (Spheniscus magellanicus) to investigate whether a free-ranging vertebrate species can morphologically adapt to long-term environmental change sufficiently to promote population persistence. Despite strong selective pressures, we found that penguins did not adapt morphologically to long-term environmental changes, leading to projected population extirpation. Fluctuating selection benefited larger penguins in some environmental contexts, and smaller penguins in others, ultimately mitigating their ability to adapt under increasing environmental variability. Under future climate projections, we found that the species cannot be rescued by adaptation, suggesting similar constraints for other long-lived species. Such results reveal how fluctuating selection driven by environmental variability can inhibit adaptation under long-term environmental change. Our eco-evolutionary approach helps explain the lack of adaptation and evolutionary rescue in response to environmental change observed in many animal species.

Climate presses and pulses mediate the decline of a migratory predator.

Long-term climate changes and extreme climate events differentially impact animal populations, yet whether and why these processes may act synergistically or antagonistically remains unknown. Disentangling these potentially interactive effects is critical for predicting population outcomes as the climate changes. Here, we leverage the "press-pulse" framework, which is used to describe ecological disturbances, to disentangle population responses in migratory Magellanic penguins to long-term changes in climate means and variability (presses) and extreme events (pulses) across multiple climate variables and life history stages. Using an unprecedented 38-y dataset monitoring 53,959 penguins, we show for the first time that the presses and pulses of climate change mediate the rate of population decline by differentially impacting different life stages. Moreover, we find that climate presses and pulses can work both synergistically and antagonistically to affect animal population persistence, necessitating the need to examine both processes in concert. Negative effects of terrestrial heat waves (pulses) on adult survival, for example, were countered by positive effects of long-term changes in oceanographic conditions in migratory grounds (presses) on juvenile and adult survival. Taken together, these effects led to predicted population extirpation under all future climate scenarios. This work underscores the importance of a holistic approach integrating multiple climate variables, life stages, and presses and pulses for predicting the persistence of animals under accelerating climate change.

Predicting ecological outcomes using fuzzy interaction webs.

The past 100 years of empirical research in ecology have generated tremendous knowledge about the component interactions that structure ecological communities. Yet, we still lack the ability to reassemble these puzzle pieces to predict community responses to perturbations, a challenge that grows increasingly urgent given rapid global change. We summarize key advances in community ecology that have set the stage for modeling ecological systems and briefly review the evolution of ecological modeling efforts to identify critical hurdles to progress. We find that while Robert May demonstrated that quantitative models could theoretically predict community interactions nearly 50 years ago, in practice, we still lack the ability to predict ecological outcomes with reasonable accuracy for three reasons: (1) quantitative models require precise data for parameterization (often unavailable) and have restrictive assumptions that are rarely met; (2) estimating interaction strengths for all network components is extremely challenging; and (3) determining which species are essential to include in models is difficult (model structure uncertainty). We propose that fuzzy interaction webs (FIW), borrowed from the social sciences, hold the potential to overcome these modeling shortfalls by integrating quantitative and qualitative data (e.g., categorical data, natural history information, expert opinion) for generating reasonably accurate qualitative predictions sufficient for addressing many ecological questions. We outline recent advances developed for addressing model structure uncertainty, and we present a case study to illustrate how FIWs can be applied for estimating community interaction strengths and predicting complex ecological outcomes in a multitrophic (plants, herbivores, predators), multi-interaction-type (competition, predation, facilitation, omnivory) grassland ecosystem. We argue that incorporating FIWs into ecological modeling could significantly advance empirical and theoretical ecology.