Accuracy of geolocation estimates for flying seabirds

By R. Phillips, J. Silk, J. Croxall, V. Afanasyev, D. R. Briggs. 2004

552 citations recorded by the source corpus.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/f3920764330a5884928c7631585742902249f730

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In Ecology

Citations in the corpus

Extrinsic and intrinsic determinants of winter foraging and breeding phenology in a temperate seabird (2005, background): “(Phillips et al. 2004), our data suggest an even distribution around the colony during this period.”
Geolocation by light: accuracy and precision affected by environmental factors (2012, unknown): “Phillips et al. (2004) described an average deviation from the correct position of 186 ± 114 km (SD, great circle distance) after the usage of several ad hoc filters that reduce the error on positioning.”
The effects of geolocator drag and weight on the flight ranges of small migrants (2010, background): “These devices record light levels over time, which can be used to estimate the position of the animal on the globe to within a few 100 km (Hill 1994; Phillips et al. 2004); their size means that they can be placed on animals that are too small to carry satellite telemetry tags (Gaunt et al.”
Oceanographic characteristics of areas used by Cory’s shearwaters during short and long foraging trips in the North Atlantic (2010, background): “Loggers weight represented between 1.3 and 2.1% of birds body mass, which is believed to have no deleterious effect in seabird species ( Phillips et al. 2003; Igual et al. 2005).”
Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review (2009, methodology): “Geolocators record ambient light levels, which, following tag retrieval, are used to derive 2 positions d–1, with comparatively low mean accuracy of ~190 km (±110 km SD) (Phillips et al. 2004a).”
Multicolony tracking reveals the winter distribution of a pelagic seabird on an ocean basin scale (2012, unknown): “Positions derived from geolocation have an average error of c. 185 km (Phillips et al., 2004), and to reduce the influence of outliers when calculating distances and allocating positions to areas of interest, we smoothed positions using a threeposition moving average based on spherical trigonometry.”
Movements, at-sea distribution and behaviour of a tropical pelagic seabird: the wedge-tailed shearwater in the western Indian Ocean (2009, methodology): “Validated data were smoothed twice (Phillips et al. 2004).”
Combination of At-Sea Activity, Geolocation and Feather Stable Isotopes Documents Where and When Seabirds Molt (2016, methodology): “Processing of Positional and Immersion State Data from GLS Geolocators provide two positions per day based on light levels with a mean error ± SD of 186 ± 114 km (Phillips et al., 2004).”
Tracking seabirds to identify ecologically important and high risk marine areas in the western North Atlantic (2012, methodology): “The quality of light curves was validated on a case-by-case basis using standardized criteria (Phillips et al., 2004).”
Trans-equatorial migration and habitat use by sooty shearwaters Puffinus griseus from the South Atlantic during the nonbreeding season (2012, methodology): “Validated data were smoothed twice, with raw or fixed positions maintained around periods of missing data (Phillips et al. 2004).”
From hotspots to site protection: Identifying Marine Protected Areas for seabirds around the globe (2012, background): “, this issue), while data can be collected over several years using light-based geolocation devices (Phillips et al., 2004).”
Applying global criteria to tracking data to define important areas for marine conservation (2016, methodology): “For GLS data, we used 4 Diversity and Distributions, 1–10, ª 2015 John Wiley & Sons Ltd h = 186, which corresponds to the average error of the locations in kilometres (Phillips et al., 2004).”
GPS tracking a marine predator: the effects of precision, resolution and sampling rate on foraging tracks of African Penguins (2004, background): “Over the last few decades, devices have been developed to measure a wide range of parameters from meal size to heart rate (Wilson et al. 1995; Boyd 1997; Butler and Jones 1997; Peters 1997; Croll et al. 1998; Weimerskirch et al. 2000; Williams et al. 2000), but tracking the movement patterns of an”
Phenological matching across hemispheres in a long‐distance migratory bird (2013, background): “Light curves around sunrise and sunset from an open-habitat species like Bobolink are extremely ‘clean’, and we were able to easily identify aberrant curves; these days were deleted (Phillips et al., 2004).”