DataSheet_1_Joint spatiotemporal models to predict seabird densities at sea.docx

• Main Authors: Mayumi L. Arimitsu, John F. Piatt, James T. Thorson, Katherine J. Kuletz, Gary S. Drew, Sarah K. Schoen, Daniel A. Cushing, Caitlin Kroeger, William J. Sydeman
• Format: Dataset
• Language: unknown
• Published: 2023
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; species distribution models (SDM); marine bird distribution; marine bird surveys; detection factor; decadal scale change; Cook Inlet; Alaska; Gulf of Alaska; fratercula
• Online Access: https://doi.org/10.3389/fmars.2023.1078042.s001; https://figshare.com/articles/dataset/DataSheet_1_Joint_spatiotemporal_models_to_predict_seabird_densities_at_sea_docx/21980855

Introduction Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type. Methods Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions. Results The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters). Discussion Our results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and ...