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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.

Understanding the dynamic relationship between marine species and their changing environments is critical for ecosystem based management, particularly as coastal ecosystems experience rapid change (e.g., general warming, marine heat waves). In this paper, we present a novel statistical approach to robustly estimate and track the thermal niches of 30 marine fishes along the west coast of North America. Leveraging three long-term fisheries-independent datasets, we use spatiotemporal modeling tools to capture spatiotemporal variation in species densities. Estimates from our models are then used to generate species-specific estimates of thermal niches through time at several scales: coastwide and for each of the three regions. By synthesizing data across regions and time scales, our modeling approach provides insights into how these marine species may be tracking or responding to changes in temperature. While we did not find evidence of consistent temperature-density relationships among regions, we are able to contrast differences across species: Dover sole and shortspine thornyhead have relatively broad thermal niche estimates that are static over time, whereas several semi-pelagic species (e.g., Pacific hake, walleye pollock) have niches that are both becoming warmer over time and simultaneously narrowing. This illustrates how several economically and ecologically valuable species are facing contrasting fates in a changing environment, with potential consequences for fisheries and ecosystems. Our modeling approach is flexible and can be easily extended to other species or ecosystems, as well as other environmental variables. Results from these models may be broadly useful to scientists, managers, and stakeholders—monitoring trends in the direction and variability of thermal niches may be useful in identifying species that are more susceptible to environmental change, and results of this work can form quantitative metrics that may be included in climate vulnerability assessments, estimation of dynamic essential fish habitat, and assessments of climate risk posed to fishing communities.