DOI: 10.5061/dryad.ncjsxkt1g
title: Hot stops: Timing, pathways, and habitat selection of migrating Eastern Whip-poor-wills

description: Although miniaturized data loggers allow new insights into avian
 migration, incomplete knowledge of basic patterns persists, especially for
 nightjars. Using GPS data loggers, this study examined migration ecology
 of the Eastern whip-poor-will (Antrostomus vociferus), across three
 migration strategies: flyover, short-stay, and long-stay. We documented
 migration movements, conducted hotspot analyses, quantified land cover
 within 1-km and 5-km buffers at used and available locations, and modeled
 habitat selection during migration. From 2018-2020 we captured breeding
 whip-poor-wills from three study sites in Massachusetts and programmed GPS
 tags to collect data during fall and spring migration periods. Across 19
 individual males (nine of them with repeated years of data), GPS tags
 collected 479 locations, where 30% were classified as flyover points, 33%
 as short-stays, and 37% as long-stay locations. We documented seasonal
 flexibility in migration duration, routes, and stopover locations among
 individuals and between years. Analyses identified hotspot clusters in
 fall and spring migration in the Sierra de Tamaulipas in Mexico. Land
 cover at used locations differed across location types at the 5-km scale,
 where closed forest cover increased and crop cover decreased for flyover,
 short-stay, and long-stay locations, and urban cover was lowest at
 long-stay locations. Discrete choice modeling indicated that habitat
 selection by migrating whip-poor-wills differs depending on the scale and
 migration strategy. For example, at the 5-km scale birds avoided urban
 cover at long-stay locations and selected closed forest cover at
 short-stay locations. We suggest that whip-poor-wills may use land cover
 cues at large spatial scales, like 5-km, to influence rush or stay tactics
 during migration.
description: From 2018-2020, we captured
 breeding whip-poor-wills from three study sites in Massachusetts and
 programmed GPS tags to collect data during fall and spring migration
 periods. Across 19 individual males (nine of them with repeated years of
 data), GPS tags collected 479 locations, where 30% were
 classified as flyover points, 33% as short-stays, and 37% as long-stay
 locations. <strong>Data
 processing</strong> We filtered and retained
 migration data points when loggers connected to ≥ 4 satellites and points
 had dilution of precision values &lt; 5 to ensure a 3D fix of the
 location (Forrest et al. 2022, Bakermans et al. 2022). Using 30-m USGS DEM
 (digital elevation model; http://ned.usgs.gov)
 data, we generated the altitude of each point by converting the GPS tags’
 altitude to altitude above sea level and then subtracted the local
 elevation (from the DEM) from the bird’s altitude (A. Korpach, pers.
 communication). Next, we classified migration points based on altitude and
 number of points at a single location as either flyover, short-stay, or
 long-stay. Long-stays were locations with ≥ 2 GPS points within the same
 vicinity (i.e., &lt; 10 km). Short-stay and flyovers consisted of one
 GPS point at a single location. We differentiated short-stay versus
 flyover points by altitude based on the altitudes of birds at long-stay
 locations (mean = 17 m, range = 121 m). Short-stays were locations with
 elevations &lt; 100 m (mean = 15 m), and flyover locations had an
 altitude ≥ 100 m above the ground (mean = 800 m).
 <strong>Hotspot Analyses</strong>
 To identify areas of high or low use during migration, we ran an
 optimized hotspot analysis in ArcGIS 10.8.2 to identify statistically
 significant spatial clusters of high (hotspot) and low values (coldspot)
 of migration locations using the Getis-Ord Gi* statistic (Sussman et al.
 2019). This tool can “aggregate data, identify an appropriate scale of
 analysis, and correct for both multiple testing and spatial dependence”
 (ESRI 2021).  <strong>Land cover
 classification</strong> We used ArcGIS and quantified land cover types from
 2019 data using the 100-m Copernicus Global Land Service layer (Buchhorn
 et al. 2020). Land cover types were classified as (a) closed forest, (b)
 open forest, (c) shrubland, (d) herbaceous vegetation (hereafter,
 grassland), (e) herbaceous wetland, (f) cropland, (g) bare, (h) fresh- or
 saltwater, and (i) developed land (Buchhorn et al. 2020). Using the
 geoprocessing features of ArcMap, we quantified land cover at 5-km and
 1-km circle at an actual migration location (i.e., used) and random
 locations (i.e., available). 
 <strong>Habitat selection</strong>
 We used discrete choice modeling
 to determine habitat selection of Eastern whip-poor-will during migration.
 Discrete choice models examine the probability that an individual chooses
 a location based on a choice set of alternative available locations
 (Cooper and Millspaugh 1999). Choice sets included one used location based
 on the GPS fix and ten available locations. We constructed separate models
 for each type of migration point (i.e., flyover, short-stay, and
 long-stay) and spatial scale (i.e., 1 km and 5 km) with individual as a
 random effect. We used package jagsUI (Kellner 2021) with the software
 JAGS 4.3.1 (Plummer 2003). 
description: # Data from: Hot stops: Timing, pathways, and habitat selection of
 migrating Eastern Whip-poor-wills
 [https://doi.org/10.5061/dryad.ncjsxkt1g](https://doi.org/10.5061/dryad.ncjsxkt1g) These data were used to increase knowledge of the migration ecology of Eastern whip-poor-wills. In particular, we 1) generated descriptive information (timing, duration, route) surrounding fall and spring migration patterns, 2) identified hot and coldspots during migration to locate critical regions to focus conservation efforts, 3) examined if land cover differed among migration strategies for actual locations, and 4) investigated if birds selected specific land cover types across multiple spatial scales for different migration strategies. ## Description of the data and file structure Data are arranged in several csv files. Two files contain land cover data and information used in discrete choice modeling. The other two files contain location information used in hotspot analyses and to classify migration strategies. Datasets included: 5kmLandCoverDisChoice.csv and 1kmLandCoverDisChoice.csv * These datasets are used in discrete choice modeling at different spatial scales (5km and 1km scales). * Variables included: Use (indicates actual location (1) or available location (0)), Alts (alternates in the discrete choice sets, 1-11), nsets (the choice set number), MigTime (migration time; FM= fall migration, SM = spring migration), PtType (migration strategy of either flyover, short-stay, or long-stay location), BirdID (unique individual identifier for each bird), and percentage landcover variables of Bare (bare ground), ClosedForest (closed forest), Cropland, HerbVeg (herbaceous vegetation- grassland), HerbWet (herbaceous wetland), OpenForest (open forest), Water (fresh- or saltwater), Shrubs (shrubland), and Urban (developed land). FallMigrationLocations.csv and SpringMigrationLocations.csv * These datasets are used in hotspot analyses and include the classification of migration strategies for each location. * Variables included: BandNo (last 6 digits of each band number), GPStag (a unique identifier for each GPS tag), PtType (migration strategy of either flyover, short-stay, or long-stay location), Latitude, Longitude, Altitude, MigTime (migration time; FM= fall migration, SM = spring migration), Date (year/month/day format of date). * A value of n/a indicates that altitude was not calculated for that particular location. This only occurred for long-stay locations not retained in land cover analyses. Only one point per long-stay stopover was randomly selected and included in land cover analyses to avoid pseudoreplication. ## Sharing/Access information These data are licensed CC0. * ## Code/Software **Hotspot Analyses:** We used ArcGIS 10.8.2 to identify statistically significant spatial clusters of high (hotspot) and low values (coldspot) of migration locations using the Getis-Ord G<sub>i</sub>\* statistic (Sussman et al. 2019). **Land cover**: We used ArcGIS 10.8.2 and quantified land cover types from 2019 data using the 100-m Copernicus Global Land Service layer (Buchhorn et al. 2020). **Habitat selection:** For discrete choice modeling, we followed the code presented by Beatty et al. 2014. We used package *jagsUI* (Kellner 2021) with the software JAGS 4.3.1 (Plummer 2003). Beatty, W.S., Webb, E.B., Kesler, D.C. *et al.* Landscape effects on mallard habitat selection at multiple spatial scales during the non-breeding period. *Landscape Ecol* **29**, 989–1000 (2014). https://doi.org/10.1007/s10980-014-0035-x