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Scott Gilliland

Publications and source records attributed to Scott Gilliland.

4 recordsLinked to original sources
USGS2024

Evaluating conservation units using network analysis: A sea duck case study

Conserving migratory wildlife requires understanding how groups of individuals interact across seasons and landscapes. Telemetry reveals individual movements at large spatiotemporal scales; however, using movement data to define conservation units requires scaling up from individual movements to species- and community-level patterns. We developed a framework to define flyways and identify important sites from telemetry data and applied it to long-term, range-wide tracking data from three species (640 individuals) of sea ducks: namely, North American scoters ( Melanitta spp). Our network of 88 nodes included both multispecies hotspots and areas uniquely important to individual species. We found limited spatial overlap between scoters wintering on the Atlantic and Pacific coasts of North America, with differing connectivity patterns between coasts. Finally, we identified four multispecies conservation units that did not correspond to traditional management flyways. From this approach, we show how individual movements can be used to quantify range-wide connectivity of migratory species and reveal gaps in conservation strategies.

Frontiers in Ecology and the Environment↗
USGS2015

Incorporating temporal variation in seabird telemetry data: time variant kernel density models

A key component of the Mid-Atlantic Baseline Studies project was tracking the individual movements of focal marine bird species (Red-throated Loon [Gavia stellata], Northern Gannet [Morus bassanus], and Surf Scoter [Melanitta perspicillata]) through the use of satellite telemetry. This element of the project was a collaborative effort with the Department of Energy (DOE), Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (USFWS), and Sea Duck Joint Venture (SDJV), among other organizations. Satellite telemetry is an effective and informative tool for understanding individual animal movement patterns, allowing researchers to mark an individual once, and thereafter follow the movements of the animal in space and time. Aggregating telemetry data from multiple individuals can provide information about the spatial use and temporal movements of populations. Tracking data is three dimensional, with the first two dimensions, X and Y, ordered along the third dimension, time. GIS software has many capabilities to store, analyze and visualize the location information, but little or no support for visualizing the temporal data, and tools for processing temporal data are lacking. We explored several ways of analyzing the movement patterns using the spatiotemporal data provided by satellite tags. Here, we present the results of one promising method: time-variant kernel density analysis (Keating and Cherry, 2009). The goal of this chapter is to demonstrate new methods in spatial analysis to visualize and interpret tracking data for a large number of individual birds across time in the mid-Atlantic study area and beyond. In this chapter, we placed greater emphasis on analytical methods than on the behavior and ecology of the animals tracked. For more detailed examinations of the ecology and wintering habitat use of the focal species in the midAtlantic, see Chapters 20-22.

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USGS2015

Wintering movements and habitat use of Surf Scoter (Melanitta perspicillata) in the mid-Atlantic U.S.

Wintering habitat use and migratory pathways are key issues facing sea ducks in the mid-Atlantic U.S. and elsewhere due to the potential for offshore wind energy development. A comprehensive understanding of important winter habitats and environmental characteristics determining sea duck abundance and distribution is paramount in advising marine spatial planning efforts in the region and identifying key resource areas for sea ducks. We captured and tracked 101 Surf Scoters to investigate the spatial patterns, temporal patterns, and environmental variation in migration and winter habitat use through a combination of satellite telemetry data and remotely collected environmental covariate information. We found that Surf Scoters in core-use areas utilized shallow (<40 m) areas within 4.5 km from shore. Resource selection models suggest that other dynamic variables such as sea surface temperatures, productivity, and salinity (and selected interactions among them) may also be important in determining valuable scoter habitat. Migration chronology of birds tracked in this study suggests that Surf Scoters wintering and migrating throughout the mid-Atlantic region could encounter future offshore wind energy facilities between mid-October and early May. Our analyses indicate Surf Scoters tagged along near-shore areas of the mid-Atlantic have a minimal likelihood of overlapping with current Wind Energy Areas (WEAs) in the mid-Atlantic, though activities associated with construction within WEAs, such as installation of transmission lines or vessel traffic within nearshore areas, or possible development of wind farms closer to shore and outside currently designated WEAs, may have a higher likelihood of overlapping with wintering Surf Scoters in the mid-Atlantic.

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