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Lesser Yellowlegs location data describing the occurrence of birds within harvest zones in the Caribbean and South America

Cite this dataset

McDuffie, Laura A. et al. (2021). Lesser Yellowlegs location data describing the occurrence of birds within harvest zones in the Caribbean and South America [Dataset]. Dryad.


Shorebirds have experienced a precipitous reduction in abundance over the past four decades. While some threats to shorebirds are widespread (e.g. habitat alteration), others are regional and may affect specific populations. Lesser Yellowlegs (Tringa flavipes) are long-distance migrants that breed across the North American boreal biome and have declined in abundance by 60-80% since the 1970s. The documented harvest of Lesser Yellowlegs in the Caribbean and northeastern South America during southward migration is a possible limiting factor for the species, but it is unknown to what extent birds from different breeding origins may be affected. To address the question of differential occurrence in harvest zones during southward migration, we used PinPoint GPS Argos transmitters to track the southward migrations of 85 adult Lesser Yellowlegs from across the species’ breeding range and 80° of longitude from Anchorage, Alaska, USA to the Mingan Archipelago, Quebec, Canada. We classified migratory locations as inside or outside three zones with high levels of harvest (Caribbean, coastal Guianas, and coastal Brazil) and then fit generalized additive mixed models to estimate the probability of occurrence of Lesser Yellowlegs in harvest zones according to their breeding origin. Individuals from the Eastern Canada population had a higher probability of occurrence within one or more harvest zones and remained in those zones longer than individuals breeding in Alaska and western Canada. Linear regressions also suggested that longitude of the breeding origin is an important predictor of occurrence in harvest zones during southward migration. Lastly, our findings, combined with other sources of evidence, suggest that current estimated harvest rates may exceed sustainable limits for Lesser Yellowlegs, which warrants further investigation.


Location data was collected using Lotek Argos PinPoint-75 transmitters attached to Lesser Yellowlegs using a leg-loop harness method. Lesser Yellowlegs were tracked from six geographically disparate populations. The PinPoint-75 model receives and transmits location data remotely through the Argos system, which allows data to be dowloaded without the need to recapture the bird. All data was accessed in ArgosWeb ( and processed through a proprietary sofware called Lotek Argos-GPS Data Processor V4.2. This processor outputs human-readable .csv files which include the latitude and longitude of locations in decimal degrees. To run the analyses outlined in the manuscript (probability of occurence in harvest zones and predictors of occurence in harvest zones) we truncated the data to only include locations from July 1 through October 21 of each year. We restricted our analysis to southward migration, ending on October 21 for three reasons. First, harvest policies in the jurisdictions of the harvest zones we examined indicate that most Lesser Yellowlegs harvest occurs during southward migration when flocks of shorebirds stop in the Lesser Antilles and northeastern South America due to seasonal weather events such as hurricanes and tropical depressions. Second, the predefined GPS tag schedules limited the number of transmissions received after October 21 in 2018, 2019, and 2020. Lastly, of the 69 (82%) individuals tracked throughout the non-breeding period, none of the individuals occurred in harvest zones after October 21 that had not already been previously observed there.

To run a continuous-time state-space random walk model we used package foieGras 0.4.0 in program R. To run a binomial generalized linear model we used package MASS 7.3-53 in program R. Code is provided in association with the location data .csv files.

Usage notes

The dataset does not include any missing values. The description of each column header for each spreadsheet is provided in an associated ReadMe file. The steps to follow for each analysis are provided in the R code files.


United States Air Force, Award: FXSB46058118

United States Fish and Wildlife Service, Award: T-32-1

Alaska Department of Fish and Game

Bird Studies Canada

Environment and Climate Change Canada

Parks Canada

Canadian Wildlife Service

ConocoPhillips (United States)

United States Air Force, Award: FXSB4658119

United States Air Force, Award: FXSBA53216120

United States Fish and Wildlife Service, Award: T-33-2020