Integrating diverse data for robust species distribution models in a dynamic ocean

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 ...,
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, , # Data and code for the article \"Integrating diverse data for robust species distribution models in a dynamic ocean\"

[https://doi.org/10.5061/dryad.7sqv9s51c](https://doi.org/10.5061/dryad.7sqv9s51c)

This repository contains data and code to:

1. run cross-validation for each integration approach (data pooling, ensemble, integrated SDM with constant spatial effect, and integrated SDM with seasonal spatial effects) and measure performance metrics (predictive skill, ecological realism, computational demand)
2. Develop full models for each integration approach using all the data
3. Visualize and interpret results

The data in this repository include the raw data, representing species presence and pseudo-absences, used to construct the different integration approaches presented in the paper. 

Note that the raw data sets in this repository only include the fishery-dependent marker tag and fishery-independent electronic tag data sets. The fishery dependent observer dataset used in this st...