The effect of trait choice on hybrid species distribution model projections under climate change

Species distribution models (SDMs) have been increasingly combined with thermal performance data to enhance their transferability and to provide a physiological explanation for the predicted geographic patterns. Yet, while it is widely acknowledged that thermal sensitivities may vary among biological traits, it remains largely unexplored to what extent predictions obtained using hybrid models depend on the choice of trait, and, hence, the type of thermal performance data considered. In this study, we examine the thermal performance of three fitness-related traits, namely spore germination rate, maximum quantum yield of photosystem II (Fv/Fm), and growth rate, in the brown seaweed Dictyota dichotoma, a common macroalga along European coastlines. To predict the species' current and future distributions, we constructed a traditional correlative SDM, fitting a comprehensive dataset of occurrence records and environmental data, as well as three distinct hybrid SDMs, incorporating the thermal responses of each fitness-related trait. Despite considerable differences in thermal performance among traits, with Fv/Fm and growth displaying the broadest and narrowest thermal performance curves, respectively, predictions made by the distinct hybrid SDMs were largely congruent under both current and future conditions. This seems to be linked to the models' ability to detect even small differences in trait performance, which are then used to fine-tune suitability predictions and achieve the best fit. In addition, predictions made by hybrid SDMs were similar to those made by the correlative SDM, demonstrating that when occurrence data are representative for the species' distribution, environmental predictors are relevant, and model responses are ecologically constrained, correlative and hybrid approaches can converge. Importantly, all models predicted range contractions for D. dichotoma at the warm-edge limit in the Mediterranean Sea under future climate change scenarios. This suggests that these populations may be particularly vulnerable, although cooler local microhabitats could mitigate some of these effects.