Optimal Investment to Enable Evolutionary Rescue

'Evolutionary rescue' is the potential for evolution to enable population persistence in a changing environment. Even with eventual rescue, evolutionary time lags can cause the population size to temporarily fall below a threshold susceptible to extinction. To reduce extinction risk given human-driven global change, conservation management can enhance populations through actions such as captive breeding. To quantify the optimal timing of, and indicators for engaging in, investment in temporary enhancement to enable evolutionary rescue, we construct a model of coupled demographic-genetic dynamics given a moving optimum. We assume 'decelerating change', as might be relevant to climate change, where the rate of environmental change initially exceeds a rate where evolutionary rescue is possible, but eventually slows. We analyze the optimal control path of an intervention to avoid the population size falling below a threshold susceptible to extinction, minimizing costs. We find that the optimal path of intervention initially increases as the population declines, then declines and ceases when the population growth rate becomes positive, which lags the stabilization in environmental change. In other words, the optimal strategy involves increasing investment even in the face of a declining population, and positive population growth could serve as a signal to end the intervention. In addition, a greater carrying capacity relative to the initial population size decreases the optimal intervention. Therefore, a one-time action to increase carrying capacity, such as habitat restoration, can reduce the amount and duration of longer-term investment in population enhancement, even if the population is initially lower than and declining away from the new carrying capacity

The mechanisms of phenology: the patterns and processes of phenological shifts

Species across a wide range of taxa and habitats are shifting phenological events in response to climate change. While advances are common, shifts vary in magnitude and direction within and among species, and the basis for this variation is relatively unknown. We examine previously suggested patterns of variation in phenological shifts in order to understand the cue-response mechanisms that underlie phenological change. Here, we review what is known about the mechanistic basis for nine factors proposed to predict phenological change (latitude, elevation, habitat type, trophic level, migratory strategy, ecological specialization, species\u27 seasonality, thermoregulatory mode, and generation time). We find that many studies either do not identify a specific underlying mechanism or do not evaluate alternative mechanistic hypotheses, limiting the ability of scientists to predict future responses to global change with accuracy. We present a conceptual framework that emphasizes a critical distinction between environmental (cue-driven) and organismal (response-driven) mechanisms causing variation in phenological shifts and discuss how this distinction can reduce confusion in the field and improve predictions of future phenological change

Network metrics can guide nearly-optimal management of invasive species at large scales

Invasive species harm biodiversity and ecosystem services, with global economic costs of invasions exceeding $40 billion annually. Widespread invasions are a particular challenge because they involve large spatial scales with many interacting components. In these contexts, typical optimization-based approaches to management may fail due to computational or data constraints. Here we evaluate an alternative solution that leverages network science, representing the invasion as occurring across a network of connected sites and using network metrics to prioritize sites for intervention. Such heuristic network-guided methods require less data and are less computationally intensive than optimization methods, yet network-guided approaches have not been bench-marked against optimal solutions for real-world invasive species management problems. We provide the first comparison of the performance of network-guided management relative to optimal solutions for invasive species, examining the placement of watercraft inspection stations for preventing spread of invasive zebra mussels through recreational boat movement within 58 Minnesota counties in the United States. To additionally test the promise of network-based approaches in limited data contexts, we evaluate their performance when using only partial data on network structure and invaded status. Metric-based approaches can achieve a median of 100% of optimal performance with full data. Even with partial data, 80% of optimal performance is achievable. Finally, we show that performance of metric-guided management improves for counties with denser and larger networks, suggesting this approach is viable for large-scale invasions. Together, our results suggest network metrics are a promising approach to guiding management actions for large-scale invasions.Comment: 29 pages, 8 figures, 3 table

A community convention for ecological forecasting: output files and metadata

This document summarizes the open community standards developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast adoption, distribution, validation, and synthesis. For output files EFI has adopted a three-tiered approach reflecting trade-offs in forecast data volume and technical expertise. The preferred output file format is netCDF following the Climate and Forecast Convention for dimensions and variable naming, including an ensemble dimension where appropriate. The second-tier option is a semi-long CSV format, with state variables as columns and each row representing a unique issue date time, prediction date time, location, ensemble member, etc. The third-tier option is similar to option 2, but each row represents a specific summary statistic (mean, upper/lower CI) rather than individual ensemble members. For metadata, EFI expands upon the Ecological Metadata Language (EML), using additional Metadata tags to store information designed to facilitate cross-forecast synthesis (e.g. uncertainty propagation, data assimilation, model complexity) and setting a subset of base EML tags (e.g. temporal resolution, output variables) to be required. To facilitate community adoption we also provides a R package containing a number of vignettes on how to both write and read in the EFI standard, as well as a metadata validator tool.First author draf

The power of forecasts to advance ecological theory

Ecological forecasting provides a powerful set of methods for predicting short- and long-term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight

Causes and Consequences of Evolutionary Rescue in Noisy Environments

Anthropogenic global change poses a grand challenge for management of wild populations. Environmental change affects two fundamental processes — selection and phenotypic plasticity— that shape variation within populations. Because variation within populations in turn affects population dynamics, incorporating understanding of how these processes shape trait dynamics into forecasts and management planning is an urgent need. In this dissertation I address that need, focusing on phenotypic plasticity and varying selection and their implications for evolutionary rescue, which occurs when adaptive evolution enables population persistence in a changed environment. A key element in both of these processes is stochasticity.<div><br></div><div>Submitted in 2016.</div

Response of fish biota to dams in the Lower Colorado River Basin: empirical findings and utility for predicting responses to climate and water use change

<p>Report for Ecogeomorphology field class, UC Davis. Spring 2014</p> <p> </p> <p>Abstract:</p> <p>Over the past century, development of the Colorado River Basin for water resources changed a dynamic river into a managed system. Seasonality in flow, temperature, and turbidity is reduced, and formerly riverine habitats are now reservoirs. These changes have played out in the fish fauna. Native species have declined and non-natives have increased. The key alteration, relative to the past, is increased competition and predation, especially at juvenile stages, which is likely mediated by more stable flow and resource regimes. A dataset (SONFISHES) assembled by W. L. Minckley and covering 150 years of fish occurrence in the Lower Colorado River (below Glen Canyon Dam) has provided insight into patterns in extirpation among native fishes and range expansion among non-native fishes. Several studies have combined these data with measures of extinction risk to explain how range fragmentation and species’ traits correlate with observed extinctions or threats in native species. Another study analyzed range shifts within a strategy space of potential fish life histories (originally introduced by K. O. Winemiller and K. A. Rose) to understand how human activity has created and removed ecological niches. Together, these studies support a causal link between environmental and faunal change. This strongly suggests that continuing the status quo will result in further extirpation and extinction of native fishes. I argue, however, that current knowledge is not yet sufficient to quantitatively forecast the magnitude and timescale of the response in fish biodiversity to any projected future environment. Such forecasting requires a model that quantitatively relates the environment to persistence in the strategy-space of fish life histories. Unfortunately, we do not need such a model to make the qualitative prediction: threatened “big-river” fishes will eventually go extinct without intervention.</p> <p> </p> <p>Note: a version of this paper prior to peer review was published at the course website (link below).</p

Causes and Consequences of Evolutionary Rescue in Noisy Environments

Anthropogenic global change poses a grand challenge for management of wild populations. Environmental change affects two fundamental processes — selection and phenotypic plasticity— that shape variation within populations. Because variation within populations in turn affects population dynamics, incorporating understanding of how these processes shape trait dynamics into forecasts and management planning is an urgent need. In this dissertation I address that need, focusing on phenotypic plasticity and varying selection and their implications for evolutionary rescue, which occurs when adaptive evolution enables population persistence in a changed environment. A key element in both of these processes is stochasticity.<div><br></div><div>Submitted in 2016.</div

Data from: Stochastic evolutionary demography under a fluctuating optimum phenotype

Many natural populations exhibit temporal fluctuations in abundance that are consistent with external forcing by a randomly changing environment. As fitness emerges from an interaction between the phenotype and the environment, such demographic fluctuations probably include a substantial contribution from fluctuating phenotypic selection. We study the stochastic population dynamics of a population exposed to random (plus possibly directional) changes in the optimum phenotype for a quantitative trait that evolves in response to this moving optimum. We derive simple analytical predictions for the distribution of log population size over time both transiently and at stationarity under Gompertz density regulation. These predictions are well matched by population- and individual-based simulations. The log population size is approximately reverse gamma distributed, with a negative skew causing an excess of low relative to high population sizes, thus increasing extinction risk relative to a symmetric (e.g., normal) distribution with the same mean and variance. Our analysis reveals how the mean and variance of log population size change with the variance and autocorrelation of deviations of the evolving mean phenotype from the optimum. We apply our results to the analysis of evolutionary rescue in a stochastic environment and show that random fluctuations in the optimum can substantially increase extinction risk by both reducing the expected growth rate and increasing the variance of population size by several orders of magnitude