Estimating a physiological threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts

Abstract

Species distribution modeling is increasingly used to describe and anticipate consequences of a warming ocean. These models often identify statistical associations between distribution and environmental conditions such as temperature and oxygen, but rarely consider the mechanisms by which these environmental variables affect metabolism. Oxygen and temperature jointly govern the balance of oxygen supply to oxygen demand, and theory predicts thresholds below which population densities are diminished. However, parameterizing models with this joint dependence is challenging because of the paucity of experimental work for most species, and the limited applicability of experimental findings in situ. Here we ask whether the temperature-sensitivity of oxygen can be reliably inferred from species distribution observations in the field, using the U.S. Pacific Coast as a model system. We developed a statistical model that adapted the metabolic index — a compound metric that incorporates these joint effects on the ratio of oxygen supply and oxygen demand by applying an Arrhenius equation — and used a non-linear threshold function to link the index to fish distribution. Through simulation testing, we found that our statistical model could not precisely estimate the parameters due to inherent features of the distribution data. However, the model reliably estimated an overall metabolic index threshold effect. When applied to case studies of real data for two groundfish species, this new model provided a better fit to spatial distribution of one species, sablefish Anoplopoma fimbria, than previously used models, but did not for the other, longspine thornyhead Sebastolobus altivelis. This physiological framework may improve predictions of species distribution, even in novel environmental conditions. Further efforts to combine insights from physiology and realized species distributions will improve forecasts of species' responses to future environmental changes.