Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer-resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Ecological data often violate common assumptions of traditional parametric statistics (e.g., that residuals are Normally distributed, have constant variance, and cases are independent). Modern statistical methods are well equipped to handle these complications, but they can be challenging for non-statisticians to understand and implement. Rather than default to increasingly complex statistical methods, resampling-based methods can sometimes provide an alternative method for performing statistical inference, while also facilitating a deeper understanding of foundational concepts in frequentist statistics (e.g., sampling distributions, confidence intervals, p-values). Using simple examples and case studies, we demonstrate how resampling-based methods can help elucidate core statistical concepts and provide alternative methods for tackling challenging problems across a broad range of ecological applications.

Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty‐two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data‐sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

I used a reaction-diffusion-advection framework to investigate the relative and combined damping impacts of generalist predation and habitat loss with reaction terms taken from the May and Rosenzweig-MacArthur models. The models were parameterized using data from snowshoe hare and Canada lynx field studies to generate similar cyclic dynamics in the center of a single patch in the absence of generalist predation. I found that generalist predation has strong stabilizing effects for both models and may represent a threat to the persistence of specialized predators. The magnitude of cycle damping due to habitat loss depends on movement rates and model choice, but ultimately results in the loss of cycles. Differences in model carrying capacity may explain differences in model sensitivity to habitat loss, and cycle amplitude may or may not decrease monotonically with habitat loss, depending on model choice. Elevated generalist predation rates at patch edges and in matrix habitat hasten cycle attenuation in situations that lead to increased prey exposure to generalists, including small patch size, higher movement rates into the matrix, and increased prey density at patch edges. Habitat disturbances may therefore have myriad consequences for cyclic systems depending in part on the nature of specialist predator-prey interactions and the extent to which the disturbances increase generalist access to prey. Field data that clarifies the relationships between habitat loss and fragmentation, generalist density and behavior, and cyclic activity would be invaluable in informing future modeling efforts.