DOI: 10.5061/dryad.9p8cz8ww5
title: Data integration improves species distribution forecasts under novel ocean conditions

description: Accurate forecasts of species distributions in response to a changing
 climate are essential for proactive management and conservation
 decision-making. However, species distribution models (SDMs) often have
 limited capacity to produce robust forecasts under novel environmental
 conditions, partly due to limitations in model training data. Model-based
 approaches that leverage diverse types of data have advanced over the last
 decade, yet their forecasting skill, especially during episodic climatic
 events, remains uncertain. Here, we develop a suite of SDMs for a
 commercially important fishery species, albacore tuna (Thunnus alalunga),
 to evaluate forecast skill under marine heatwave conditions. We compare
 models that use different methods to leverage data sources (data pooling
 vs. joint likelihood) and to address spatial dependence (environmental and
 spatial effects vs. environmental-only) to assess their relative
 performance in predicting species distributions under novel environmental
 conditions. Our results indicate model performance declined across all
 model types as environmental novelty increased, as expected. However,
 joint-likelihood approaches were more resilient to novel conditions,
 demonstrating greater predictive skill and ecological realism than
 traditional SDMs. These results suggest that ecological forecasts under
 novel environmental conditions are more skillful with a model framework
 that accounts for unmeasured spatial and temporal variability and uses
 model-based data integration to explicitly leverage diverse data types. As
 access to diverse data sources continues to increase, maximizing their
 utility will be key for delivering accurate forecasts of species
 distributions and advancing proactive, climate-ready management and
 conservation strategies.
description: # Data integration improves species distribution forecasts under novel
 ocean conditions This repository contains model outputs to help recreate
 visualizations and interpret results. Specifically, the repo contains: 1.
 Model performance outputs (i.e., AUC, MAE, Boyce, & Novelty metrics)
 from retrospective monthly forecast analysis 2. Each model's spatial
 forecast prediction for an example month (September 2015) 3. Response
 curves (i.e., marginal effects) from each model **Note:** The data
 provided in this repository do not contain the raw data, but only
 model-derived outputs, due to restrictions associated with both datasets
 used in the study. Specifically, logbook data for the U.S. albacore troll
 and pole-and-line fishery are confidential U.S. government data and are
 not publicly available. The raw data cannot be made public under the
 Magnuson–Stevens Fishery Conservation and Management Reauthorization Act
 of 2006, section 402(b), 16 U.S.C. 1881a. To request access to U.S. Highly
 Migratory Species albacore logbook data, please contact Charles Villafana
 ([Charles.Villafana@noaa.gov](mailto:Charles.Villafana@noaa.gov)).
 Additionally, the albacore archival tagging raw data are not publicly
 posted at the request of the American Fishermen’s Research Foundation
 (AFRF), which collaborated with the National Oceanic and Atmospheric
 Administration (NOAA) to implement the tagging program. However, these
 data are freely available for research purposes through AFRF and NOAA.
 Further information on how to obtain these data can be directed to Barbara
 Muhling ([bmuhling@ucsc.edu](mailto:bmuhling@ucsc.edu)). --- ##
 Description of the data and file structure ###
 `ModelPerformanceMetrics.csv` — refers to Figures 3 & 4 *
 **Description**: A comma-delimited file containing performance metrics for
 monthly forecasts, summarized by model type. * **Format**: `.csv` *
 **Size**: 8.59 KB * **Dimensions**: 121 rows × 8 columns * **Variables**:
 * `AUC`: Area under the Operating Curve * `MAE`: Mean Absolute Error *
 `Boyce`: Boyce index * `Hellingers_SST`: Hellinger's Distance (degree
 of novelty) of Sea Surface Temperature (SST) in the forecast month *
 `Hellingers_MLD`: Hellinger's Distance (degree of novelty) of Mixed
 Layer Depth (MLD) in the forecast month * `month_year`: Forecast month and
 year (format: mm/dd/yyyy) * `model`: Model type (`HE`: Habitat Envelope;
 `GF`: Gaussian Field; `iSDM`: integrated Species Distribution Model) *
 `count`: Number of data points used for evaluation that month (evenly
 split between presences and pseudo-absences) --- ###
 `VariableResponseCurves.csv` — refers to Figure 5 * **Description**: A
 comma-delimited file containing the estimated marginal effects of each
 environmental variable, including the mean, standard deviation, and 95%
 credible interval bounds. Results are summarized by model type. *
 **Format**: `.csv` * **Size**: 13.22 KB * **Dimensions**: 181 rows × 8
 columns * **Variables**: * `env_value`: Value or interval of the
 environmental variable at which the marginal effect is evaluated * `mean`:
 Posterior mean of the marginal effect * `sd`: Posterior standard deviation
 of the marginal effect * `0.025quant`: Lower bound of the 95% credible
 interval (2.5th percentile) * `0.5quant`: Posterior median (50th
 percentile) * `0.975quant`: Upper bound of the 95% credible interval
 (97.5th percentile) * `model`: Model type (`HE`: Habitat Envelope; `GF`:
 Gaussian Field; `iSDM`: integrated Species Distribution Model) *
 `Variable`: Environmental variable (`SST`: sea surface temperature; `MLD`:
 mixed layer depth; `Bathymetry`) --- ### `ModelSpatialPredictions.nc` —
 refers to Figure 6 * **Description**: A NetCDF file containing spatial
 predictions from three models stored as stacked layers. Each layer
 represents predicted habitat suitability over a common spatial grid of the
 Northeast Pacific Ocean. * **Format**: `.nc` * **Size**: 5.69 MB *
 **Dimensions**: 563 rows (latitude) × 840 columns (longitude) × 3 layers
 (`z`) * **Coordinate system**: WGS84 (`+proj=longlat +datum=WGS84
 +no_defs`) * **Resolution**: 0.083 × 0.083 * **Extent**: 179.9583,
 249.9583, 10.04167, 56.95833 (xmin, xmax, ymin, ymax) * **Variable**: *
 `variable`: Habitat suitability predictions * **Units**: Probability (0–1)
 * **Layers (`z` dimension)**: * `z = 1`: Habitat Envelope (HE) model * `z
 = 2`: Gaussian Field (GF) model * `z = 3`: integrated Species Distribution
 Model (iSDM)