| Summary: | Aim: Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. While various approaches can be used to integrate different datasets, studies comparing their performance, particularly for highly migratory and mobile species, are scarce. Here, we assess whether a model-based integrative framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types. Location: North Atlantic Ocean Time Period: 1993 - 2019 Major Taxa Studied: Blue shark ( Prionace glauca ) Methods: We trained traditional, correlative SDMs and integrated SDMs (iSDMs) with three distinct data types: fishery-dependent marker tags, fishery observer records, and fishery-independent electronic tag data. We evaluated data pooling and ensemble approaches in a correlative SDM framework and compared performance to an iSDM approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset. Results: While each integration approach yielded robust models, model performance varied among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model's ability to explain the spatiotemporal dynamics of the training data. iSDMs that explicitly accounted for seasonal variability yielded the most accurate and ecologically realistic estimates. However, such approaches are computationally intensive and warrant identifying model purpose as an important step in the data-integration process. Main Conclusions: Our findings reveal important trade-offs among the current techniques for integrating data in SDMs, including variability in accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, maximizing our ability to leverage available ...
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