Densities of Wintering Scoters in Relation to Benthic Prey Assemblages in a North Atlantic Estuary

Loring, Pamela H.; Paton, Peter W. C.; McWilliams, Scott R.; McKinney, Richard A.; Oviatt, Candace A.

doi:10.1675/063.036.0204

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…Distance to land, which was associated with both occupancy and abundance, tends to be positively associated with bathymetry and often has a strong influence on sea duck occupancy estimates (Flanders et al, ; Guillemette et al, ; Lewis, Esler, & Boyd, ; Winiarski et al, ). Sediment grain size can also affect prey availability for foraging sea ducks (Goudie & Ankney, ; Loring, Paton, McWilliams, McKinney, & Oviatt, ; Lovvorn, Grebmeier, Cooper, Bump, & Richman, ) and was associated with occupancy and conditional abundance in this study. In addition, topographic variability of the sea floor also influenced occupancy and conditional abundance, although its relationship to prey availability is less understood.…”

Section: Discussionmentioning

confidence: 54%

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Smith

Hofner

Lamb

et al. 2019

Ecology and Evolution

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Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.

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Section: Discussionmentioning

confidence: 54%

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Smith

Hofner

Lamb

et al. 2019

Ecology and Evolution

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“…During that same winter, surface-feeders did not respond to sediment grain size, but a few species did respond to gentle slope. Sediment grain size is considered a proxy for variations in benthic prey assemblages (Loring et al 2013), and the lack of its relationship with scoters in the second year may reflect a dynamic functional response to unstable sandy sediment (Dalyander et al 2013) or ephemeral secondary productivity (zooplankton) and benthic prey resources (Loring et al 2014).…”

Section: Distribution Abundance and Habitat Use Of Marine Birdsmentioning

confidence: 99%

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model

Goyert

Gardner

Sollmann

et al. 2016

Ecological Applications

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Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012-April 2014), along with associated remotely sensed habitat data, in the lower Mid-Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA. We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [SST], salinity, primary productivity). We expected that avian bottom-feeders would respond primarily to static covariates that characterize seafloor variability, and that surface-feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species-specific and community-level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom-feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface-feeders associated significantly with two dynamic covariates (SST, primary productivity). However, other cases revealed significant relationships between static covariates and surface-feeders (e.g., distance to shore) and between dynamic covariates and bottom-feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.

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“…Migrating ampeliscids are also available to Arctic phocid seals, which eat amphipods along with many other invertebrates and fish (Dehn et al 2007;Giraldo et al 2016). Benthic feeding seabirds either consume ampeliscids directly or benefit indirectly when the ampeliscid tube mats enhance growing conditions for their bivalve prey (MacKenzie et al 2006;Loring et al 2013). Because of their reliance on dense prey assemblages, top predators are sentinels of change in the Arctic and essential food resources for indigenous cultures (Moore et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Dense ampeliscid bed on the Canadian Beaufort Shelf: an explanation for species patterns

2018

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The first known ampeliscid (Amphipoda: Ampeliscidae) bed for the Canadian Arctic was reported in 2013 from the Canadian Beaufort Shelf (CBS), but species patterns were not examined. This study examines their distributions relative to differences in life strategies and environmental variables. The intent is to build a better understanding of this highly productive system in comparison with ampeliscid beds in the neighboring Bering and Chukchi Seas which are important resources for higher trophic level consumers. Data from 412 samples collected to 1000 m depth over 2002-2009 indicate that there are at least eight ampeliscid species on the CBS. Five occur elsewhere in polar and temperate latitudes and three may be new to science or are species complexes. All are limited to bottom temperatures < 0.41 °C. Congeners do not distribute coherently (Similarity Profiles analysis, p < 0.05). Resource-demanding Ampelisca macrocephala (max. 8467 ind. m −2) dominates Byblis spp. and Haploops laevis on the shelf enriched by wind-driven upwelling but dominance switches with depth to Haploops tubicola, Haploops sibirica and Haploops sp. Obligate suspension feeding with adaptations for fine particle capture enables deep living while abundance dominants supplement their diets with deposit feeding and predation. The ampeliscids may facilitate other amphipods by providing attachment sites on their tubes. Polychaetes may facilitate the ampeliscids by bringing buried resources to the surface. Given that the CBS is undergoing substantial environmental change, we recommend the CBS ampeliscids for monitoring its environmental regime to complement ongoing monitoring in other polar and temperate ampeliscid beds.

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Densities of Wintering Scoters in Relation to Benthic Prey Assemblages in a North Atlantic Estuary

Cited by 19 publications

References 34 publications

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model

Dense ampeliscid bed on the Canadian Beaufort Shelf: an explanation for species patterns

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