Estimating Baselines and Identifying Anomalies in Beached Bird Patterns on the Outer Coasts of Washington and Oregon, USA

Thesis (Master's)--University of Washington, 2016-06The marine ecosystem is a dynamic environment that has been altered globally through direct and indirect human activity. As high trophic predators, seabirds have been used as indicators of marine ecosystem health because their productivity and mortality rates may reflect offshore conditions, particularly when mass mortality events occur. This study created methods to estimate baselines of monthly seabird carcass encounter rates, and statistically identify unusual mortality events (UMEs) at the survey level using beached bird data collected on the outer coasts of Washington and Oregon by the citizen science program, the Coastal Observation and Seabird Survey Team (COASST). We used a generalized additive model (GAM) to estimate beach-specific baselines of carcass encounter rate and an iterative approach to subsequently identify 165 monthly surveys as UMEs (4.49% of all surveys). These anomalous events ranged in encounter rate from 0.667 to 122.5 carcasses km-1; 45.5% of all UMEs had at most 5 carcasses km-1 recorded, therefore subsequent analyses were broken down as low encounter rate UMEs (≤5 carcasses km-1), high encounter rate UMEs (10 carcasses km-1). Across all regions, UMEs had higher proportions of intact carcasses and carcasses found in the wrack zone of the beach when compared to the baseline, but this signal was inconsistent when comparing carcass proportions in low encounter rate UMEs with those in high encounter rate UMEs. The natural history of prevalent seabirds drove beached bird patterns; in particular, major peaks in the baseline encounter rate pattern were associated with post-breeding mortality of resident breeders (i.e. Common Murres Uria aalge), and winter kill of migratory species (i.e. Northern Fulmars Fulmarus glacialis). Only a third of high encounter rate UMEs aligned with the time and taxon peaks of natural mortality (i.e. “right species, right time” signal). Other UMEs occurred at times of unexpectedly heightened mortality (e.g. atypical spring mortality) and/or involved uncommon species as compared to the entire dataset species distribution. Overall, this study provides methods to estimate statistically estimate baselines and identify UMEs and a set of tools that can be used to characterize them