Disturbance plays a key role in ecological structure and function. Two important and often studied components of disturbance are frequency and magnitude. Despite the potential for non‐linear interactions between frequency and magnitude, their effects are often assumed to combine in a linear manner. Additionally, studies of disturbance have mainly examined effects on species diversity and competitive interactions within a single trophic level, with less focus on exploitative interactions across trophic levels. Furthermore, while the effects of disturbance are often viewed in terms of reducing population abundance, disturbances can also alter demographic processes, ‘indirectly’ changing abundances. We analyzed several classic dynamic models of species interactions to examine the effects of varying disturbance frequency and magnitude on population persistence in predator–prey and competition systems. Our analysis revealed the potential for non‐linear interactions between frequency and magnitude and their effect on population persistence. Effects differed depending on the form of population dynamics and whether disturbance affected abundance or demographic rates. It is critical to management efforts aiming to improve chances of population persistence to further understand the effects of varying disturbances on interacting populations.

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges—as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions—potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

Ecologists and fisheries scientists are faced with forecasting the ecological responses of non-stationary processes resulting from climate change and other drivers. While much is known about temporal change, and resulting responses vis-à-vis species distributional shifts, less is known about how spatial variability in population structure changes through time in response to temporal trends in drivers. A population experiencing decreasing spatial variability would be expected to be more evenly spatially distributed over time, and an increasing trend would correspond to greater extremes or patchiness. We implement a new approach for modelling this spatiotemporal variability in the R package sdmTMB. As a real-world application, we focus on a long-term groundfish monitoring dataset, from the west coast of the USA. Focusing on the 36 species with the highest population densities, we compare our model with dynamic spatiotemporal variance to a model with constant spatiotemporal variance. Of the 36 species examined, 13 had evidence to support increasing patchiness, including darkblotched rockfish, lingcod, and petrale sole. Species appearing to be more uniformly spatially distributed over time included: Dover sole, Pacific ocean perch, and Dungeness crab. Letting spatiotemporal variation change through time generally results in small differences in population trend estimates, but larger estimated differences in precision.