Abstract Species distribution models (SDMs) are powerful tools for addressing global ecological challenges. Many species, particularly invaders, exhibit dynamic niches in novel environments, which reduces SDM transferability, raising concerns about prediction accuracy. Improving model transferability and validating predictions with robust methods and independent occurrence data are essential to address these issues. However, the extent of transferability improvement required to achieve reliable predictions remains unclear. This work aims to quantitatively assess how niche dynamics influence model transferability and prediction accuracy, using freshwater invasive golden, zebra and quagga mussels as a case study. We categorized mussel occurrence data based on invasion histories and quantified niche dynamics using bioclimatic variables. We analysed the improvements in SDM transferability by optimizing occurrence data and various environmental variables. Finally, we established the relationship between niche dynamics and transferability‐related indices, identifying their threshold ranges necessary for accurate distribution predictions. Zebra mussels maintained high niche stability during invasions, while golden and quagga mussels exhibited niche expansion and unfilling. Incorporating invasive range data and diverse environmental variables improved model transferability and prediction accuracy for zebra mussels but was less effective for the other two species. We found positive correlations between SDM transferability and niche stability and negative correlations with niche expansion and unfilling. We also identified potential minimum thresholds for accurate mussel distribution predictions: a Continuous Boyce Index of 0.900, a Linear Correlation of Suitable Habitat Area of 0.800 and a Schoener's D of 0.200. Synthesis and applications . For freshwater invaders with non‐conservative niches, accurate predictions can be achieved by improving transferability when niche dynamics meet specific thresholds. Our findings therefore offer strategies for refining SDMs to improve prediction accuracy for species with dynamic niches and provide valuable insights for managing freshwater ecosystems in the context of global change.
Li et al. (Wed,) studied this question.