Stephen P. Ellner

Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck‐induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity.

Studies of eco‐evolutionary dynamics have integrated evolution with ecological processes at multiple scales (populations, communities and ecosystems) and with multiple interspecific interactions (antagonistic, mutualistic and competitive). However, evolution has often been conceptualised as a simple process: short‐term directional adaptation that increases population growth. Here we argue that diverse other evolutionary processes, well studied in population genetics and evolutionary ecology, should also be considered to explore the full spectrum of feedback between ecological and evolutionary processes. Relevant but underappreciated processes include (1) drift and mutation, (2) disruptive selection causing lineage diversification or speciation reversal and (3) evolution driven by relative fitness differences that may decrease population growth. Because eco‐evolutionary dynamics have often been studied by population and community ecologists, it will be important to incorporate a variety of concepts in population genetics and evolutionary ecology to better understand and predict eco‐evolutionary dynamics in nature.

Sensitivity analysis of ecological and epidemiological models is often used to identify the best targets of opportunity for control or management, for example, which subpopulations should be prioritised for vaccination or protection. However, effectiveness of a management action depends not just on which system component is targeted but also on when an action is taken. Traditional methods of model sensitivity analysis apply to time‐invariant parameter perturbations, which limits their ability to address questions about the timing of management interventions. A semianalytic method for performing time‐dependent sensitivity analysis (TDSA) has been recently introduced to address this need (Ng et al., in press). However, some of the steps typically require substantial time and effort. We have developed an R package, tdsa , that automates all steps required for TDSA by using numerical rather than analytic differentiation of model terms to evaluate the sensitivity equations. By avoiding analytic differentiation, the package substantially reduces user effort and risk of human error and increases the generality of TDSA by making it possible to analyse nonparametric or equation‐free models. The results of TDSA may be sensitive to assumptions of the system model. By reducing the effort needed to perform TDSA, the tdsa package facilitates comparison of results under different model assumptions, hence allowing for more robust conclusions. We illustrate this using an example involving the transmission of Morogoro virus in the Natal multimammate mouse, under different assumptions about how the contact rate depends on mouse population density. By automating most of the mathematical and programming steps of the semianalytical approach, the tdsa package makes TDSA accessible to a wide range of users with varying quantitative expertise.

Bottom-towed fishing gears produce significant amounts of seafood globally but can result in seafloor habitat damage. Spatial closures provide an important option for mitigating benthic impacts, but their performance as a fisheries management policy depends on numerous factors, including how fish respond to habitat quality changes. Spatial fisheries management has largely focused on marine protected areas with static locations, overlooking dynamic spatial closures that change through time. To investigate the performance of dynamic closures, we develop a spatial fishery model with fishing-induced habitat damage, where habitat quality can affect both fish productivity and movement. We find that dynamic spatial closures often achieve greater harvest and habitat protection than fixed marine protected areas or conventional nonspatial maximum sustainable yield management, especially under strong habitat–stock interactions. Determining optimal dynamic spatial closures may require considerable information, but we find that simple policies of fixed-schedule rotating closures also perform well. Dynamic spatial closures have received less attention as fisheries management tools, and our results demonstrate their potential value for addressing both harvest and habitat impacts from fishing.