Red Knot VHF tracking data

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AICc comparison of the statistical model for arrival in James Bay; Locations of groupings of automated telemetry receivers in North America; Estimates of the relationship between residual mass (relative body condition) and arrival dates to the sub-Arctic from Body condition explains migratory performance of a long-distance migrant

Table S1. AICc comparison of the statistical model for arrival in James Bay. The top 5 models are displayed, with the best model in boldface. Timing of arrival in James Bay is the response variable in all models. Departure refers to the last detection of an individual in Delaware Bay and tailwind refers to the tailwind the first 3h of the trajectory.; Fig S1. Locations of groupings of automated telemetry receivers in North America (see main text for details). The white dot indicates the capture site of Delaware Bay and the grey dot represents James Bay, located at the southern edge of the breeding grounds. The red dots indicate the fall detection sites of the Mingan Archipelago the Bay of Fundy. Maps created using R 3.3.3 using packages ggplot2, ggmap, raster and RgoogleMaps (image data providers: US Dept. of State Geographer © 2016); Fig S2. Estimates of the relationship between residual mass (relative body condition) and arrival dates to the sub-Arctic. Birds in a higher condition at the stopover site arrive earlier at the breeding grounds. Data points are estimates of linear mixed models (see main text for details), and the gray area represents 95% confidence intervals

Potential winter habitat for Bicknell’s Thrush using the 10 percentile training presence logistic threshold (≥0.25) from the best-fitting Maxent model.

<p>Potential winter habitat for Bicknell’s Thrush using the 10 percentile training presence logistic threshold (≥0.25) from the best-fitting Maxent model.</p

Response curves from a Maxent model created using only the corresponding variable for the model using GlobCov v2.2 (2004–2006) land cover data.

<p>These curves reflect the dependence of predicted suitability both on the selected variable and on dependencies induced by correlations between the selected variable and other variables.</p

Ocular estimates of canopy height (m; mean±SD ), understory and canopy density (% cover; mean±SD), and the frequency of forest type, seral stage, and moisture regime at presence/presumed absence survey points for Bicknell’s Thrush in the Dominican Republic.

<p>Ocular estimates of canopy height (m; mean±SD ), understory and canopy density (% cover; mean±SD), and the frequency of forest type, seral stage, and moisture regime at presence/presumed absence survey points for Bicknell’s Thrush in the Dominican Republic.</p

Maxent logistic estimates of probability of presence of Bicknell’s Thrush in the Greater Antilles.

<p>Black triangles indicate known locations Bicknell’s Thrush. Response variables included elevation, aspect (categorical), land cover (categorical), total winter precipitation, and winter mean minimum temperature.</p

Maximum entropy general and reduced models using Globcover (2004–2006) land cover data to estimate Bicknell’s Thrush winter habitat in the Greater Antilles.

<p>Values reported include training gain, test gain and test area under curve (AUC) averaged (95% confidence intervals) across 10 random partitions of presence data. Box indicates models that are not statistically different using the overlap between 95% confidence intervals. The best model based on parsimony is indicated in bold.</p

The amount of potential Bicknell’s Thrush habitat (km2) in protected and unprotected areas estimated from the most parsimonious Maxent model (land cover, wprecip, aspect, slope, and elev).

<p>We used the 10 percentile training presence logistic threshold (0.248) to create a distribution map of potential Bicknell’s Thrush winter habitat. The Nature Conservancy provided up-to-date protected areas boundaries for our study area. We used both officially designated and newly proposed protected areas for our analysis as a best-case scenario.</p

Training gain for each predictor variable alone (black) and the loss in training gain when the variable is removed from the full model (gray).

<p>Training gain for each predictor variable alone (black) and the loss in training gain when the variable is removed from the full model (gray).</p