Distribution and Abundance of the Kittlitz\u27s Murrelet \u3ci\u3eBrachyramphus brevirostris\u3c/i\u3e in Selected Areas of Southeastern Alaska

We conducted boat-based surveys for the Kittlitz’s Murrelet Brachyramphus brevirostris during the breeding season in southeastern Alaska from 2002 to 2009. We completed a single survey in seven areas and multiple annual surveys in three areas. Although surveys spanned a broad geographic area, from LeConte Bay in the south to the Lost Coast in the north (~655 km linear distance), roughly 79% of the regional population of Kittlitz’s Murrelet was found in and between Icy and Yakutat bays (~95 km linear distance). The congeneric Marbled Murrelet B. marmoratus outnumbered the Kittlitz’s Murrelet in all areas surveyed except Icy Bay; in fact, Kittlitz’s Murrelet abundance constituted a relatively small proportion (7%) of the total Brachyramphus murrelet abundance in our survey areas. In areas for which there are multiple years of survey data, Kittlitz’s Murrelet abundance varied considerably, whereas Marbled Murrelet abundance was comparatively stable during the same time period. Since the southern distribution of this species has likely narrowed over the last 50 years, and the distribution of the Kittlitz’s Murrelet appears to be restricted to glacially influenced marine waters in southeastern Alaska, we expect that any future changes in glacial extent will likely affect this species and its long-term persistence in the region

Joint spatiotemporal models to predict seabird densities at sea

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 renewable energy

Juvenile Marbled Murrelet Nurseries and the Productivity Index

Volume: 111Start Page: 257End Page: 26

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