CCPDD Predator Prey Link Data

This file includes the data from the California Current Predator Diet Database (CCPDD) that was used for the analysis of forage species in predator diet in our publication. Each record (row) includes data for predator and prey taxonomy, citation info, location, date, observation type, sample size and units for measuring amount of prey consumed

CCPDD Location GIS files for polygons and points

These files can be used to link data from the CCPDD to GIS shape files using the field LocatNum

Appendix A. A table showing correlation coefficients among krill and forage fish within shelf, oceanic, and Monterey Bay regions, a correlation matrix showing detailed relationships–covariance among regional physical variables, and figures showing results of generalized additive models for assessing relationship between seabird reproductive success and regionalized abundance of forage species, 1990–2007.

A table showing correlation coefficients among krill and forage fish within shelf, oceanic, and Monterey Bay regions, a correlation matrix showing detailed relationships–covariance among regional physical variables, and figures showing results of generalized additive models for assessing relationship between seabird reproductive success and regionalized abundance of forage species, 1990–2007

Appendix F. Pairwise Pearson correlation coefficients (r) for comparisons of annual mean values of auklet egg-laying dates, breeding success for first and all breeding attempts, and chick fledging mass between populations.

Pairwise Pearson correlation coefficients (r) for comparisons of annual mean values of auklet egg-laying dates, breeding success for first and all breeding attempts, and chick fledging mass between populations

Appendix A. A map of the study sites.

A map of the study sites

Appendix G. Anomalies of ocean climate and ocean productivity variables during the breeding season at the three study sites.

Anomalies of ocean climate and ocean productivity variables during the breeding season at the three study sites

Appendix D. Rankings of mean and variance of local climate and ocean productivity variables at each study site relative to other study sites.

Rankings of mean and variance of local climate and ocean productivity variables at each study site relative to other study sites

Appendix B. Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables.

Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables

Appendix E. Covariation of ocean climate and ocean productivity variables across study sites.

Covariation of ocean climate and ocean productivity variables across study sites

DataSheet_1_Joint spatiotemporal models to predict seabird densities at sea.docx

IntroductionSeabirds 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.MethodsUsing 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. ResultsThe 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).DiscussionOur 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 renewable energy. </p