Spatially explicit nutritional landscapes couple with environmental variables to shape nekton distribution patterns

Abstract Species distributions are traditionally explained by environmental gradients, yet the role of nutritional factors, particularly essential fatty acids (EFA), remains poorly understood. We test the hypothesis that zooplankton EFA form a nutritional landscape that couples with environmental variables to shape the biogeography of nekton. We integrated spatial data of zooplankton EFA (arachidonic acid (ARA, 20:4n‐6), eicosapentaenoic acid (EPA, 20:5n‐3), and docosahexaenoic acid (DHA, 22:6n‐3)) with environmental variables into joint species distribution models (JSDMs). We compared explanatory power across three model types: JSDM_Env (environmental variables only), JSDM_EFA (EFA variables only), and JSDM_EnvEFA (combined environmental and EFA variables). Spatial analysis revealed significant heterogeneity in EFA concentrations, with elevated levels of EPA and DHA concentrated in the northeastern region. Model results showed that both EFA and environmental variables were effective predictors of species distributions. The JSDM_EnvEFA model exhibited the greatest explanatory power (mean AUC = 0.96, mean R 2 = 0.40), with variance decomposition revealing that EFA explained more variance than environmental variables (EFA: 54%, environment: 32%). Notably, EPA contributed up to 37% of the total explained variance, surpassing the influence of sea surface temperature (14%). Incorporating EFA also changed the estimated strength of species associations, suggesting that co‐occurrence patterns may be shaped by shared nutritional dependencies. Our results demonstrate that EFA availability is as critical as environmental variables in determining nekton species distributions, which support the spatial coupling hypothesis, demonstrating that EFA landscapes couple with environmental variables to structure pelagic communities. These findings establish nutritional landscape as a critical dimension of marine biogeography, providing a novel framework for predicting community responses to global change.