DOI: 10.5061/dryad.7sqv9s51c
title: Data from: Integrating diverse data for robust species distribution models in a dynamic ocean

description: 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 data with robust
 analytical approaches will be instrumental in enhancing conservation and
 management efforts and for understanding current and future species
 distributions in a dynamic ocean.
description: see manuscript for details
description: # 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 study are considered confidential under the U.S. Magnuson-Stevens Act: qualified researchers may request these data from the NOAA Pelagic Observer Program office by contacting [popobserver@noaa.gov](mailto:popobserver@noaa.gov); we requested data representing all pelagic longline sets between the years 1993 and 2019. Additionally, each of these data sets have about 1:3 presence:pseudo-absence ratio to ensure there was enough pseudo-absences for a 1:1 ratio. Filtering to a 1:1 ratio is done before every analysis  which can be found at the beginning of each script.  ## Description of the data and file structure *Data File Details* Details for: `bsh_marker_tag.csv` * Description: a comma-delimited file containing the raw data for the fishery-dependent marker tags for blue sharks in the North Atlantic Ocean.  * Format(s): .csv * Size(s): 4.608 Mb * Dimensions: 62894 rows by 8 columns * Variables: * long: longitude * lat: latitude * pres_abs: a binary integer where 1 indicates presence and 0 indicates absence * year_mon: year-month of presence or absence * sst: sea surface temperature (degrees C) * sst_sd: spatial standard deviation of sst * bathy:  bathymetry, bottom depth (in meters) * dataset: character value of the data type Details for: `bsh_electronic_tag.csv` * Description: a comma-delimited file containing the raw data for the fishery-independent electronic tags for blue sharks in the North Atlantic Ocean.  * Format(s): .csv * Size(s): 1.959 Mb * Dimensions: 26798 rows by 8 columns * Variables: * long: longitude * lat: latitude * pres_abs: a binary integer where 1 indicates presence and 0 indicates absence * year_mon: year-month of presence or absence * sst: sea surface temperature (degrees C) * sst_sd: spatial standard deviation of sst * bathy:  bathymetry, bottom depth (in meters) * dataset: character value of the data type *Script File Details* Details for: `ModelPerformanceAnalysis` * Description: contains all code to run the cross-validation to evaluate model performance for each integration approach. Scripts in the folder call on functions from the `functions` folder to run analyses * `BRT_Ensemble_Pooling_Analysis.r` - runs cross-validation for BRT data pooling and ensemble model approaches * `iSDM_Constant_Analysis.r` - runs cross-validation for iSDM constant model * `iSDM_Seasonal_Analysis.r` - runs cross-validation for iSDM seasonal model Details for: `FullModels` * Description: contains all the code to run the model with all the data for each integration approach. Scripts in the folder call on functions from the `functions` folder to run * `Full_Models.r` - fits each integration approach with all the data. BRT pooling and ensemble functions returns the BRT model. INLA functions for the iSDM constant and seasonal models returns spatial predictions, spatial predictions, Gaussian Markov Random Field (GMRF) values, marginal effect values for covariates, summary outputs, and range and variance parameters for the GMRFs Details for: `functions` * Description: contains wrapper functions to execute analysis in the `ModelPerformanceAnalysis` and `FullModels` folder ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Links to other publicly accessible locations of the data: * Blue shark marker tag data: [https://iccat.int/en/accesingdb.html](https://iccat.int/en/accesingdb.html) under “BSH” in the “Tagging” section. Environmental data was derived from the following sources: * GLORYS oceanographic data: [https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description) * Bathymetry: [https://www.ngdc.noaa.gov/mgg/global/global.html](https://www.ngdc.noaa.gov/mgg/global/global.html) ## Code/Software Model performance analysis was conducted in R version 4.2.3 on the University of California, Davis high-performance computing cluster, Farm ([https://hpc.ucdavis.edu/farm-cluster](https://hpc.ucdavis.edu/farm-cluster)). Model fitting for pooling and ensemble approaches were conducted using default computational specifications in R without explicit user-defined alterations to CPU cores (i.e. 1 CPU core utilized) or RAM. For both iSDMs, model fitting during cross-validation was run in parallel on 20 CPUs with 2 - 5 GB of memory per CPU on a single node (depending on model).