DOI: 10.5061/dryad.qbzkh18w5
title: Data from: Wetter and more forested nonbreeding areas result in later departures but a faster spring migration in Vermivora chrysoptera (Golden-winged Warblers)

description: Declines in migratory landbird populations require targeted research to
 identify population-limiting factors acting throughout the annual cycle.
 Tracking studies provide data on migration timing and speed, which have
 been used to describe carryover effects of nonbreeding habitat use. To
 explore how variation in precipitation and forest cover among nonbreeding
 sites influenced departure dates and migration speed in Vermivora
 chrysoptera (Golden-winged Warbler), we fitted 69 birds with radio
 transmitters in Costa Rica and tracked them via the Motus Wildlife
 Tracking System. Capture sites covered a broad gradient of precipitation
 and forest cover and were separated by &lt;100 km. The datasets
 contained herein include departure dates and migration durations for birds
 tracked via Motus and related data for each individual, including capture
 location, age, sex, and Motus tag ID. For each capture location in Costa
 Rica, we extracted precipitation values from Worldclim and estimated
 forest cover around locations in QGIS (see methods below).
description: <em>Study regions and sites</em>
 Five study regions were selected in central Costa Rica that were
 representative of the gradient of precipitation and forest cover within
 the nonbreeding range of <em>V. chrysoptera</em>. Within each
 of the five study regions, one to three discrete study sites were selected
 for capturing <em>V. chrysoptera</em>.
 <em>Captures and deployment of radio
 transmitters</em> <em>V.
 chrysoptera</em> were captured at each of the study sites between
 February 26 and March 31 in 2022 and February 24 and March 26 in 2023.
 Birds were captured using 9 or 12 m long mist nets (30 mm mesh) in
 combination with conspecific playback of <em>V.
 chrysoptera</em> song and chip calls to lure birds toward nets at
 ground-level or elevated 1-2 m using net pole extensions. On capture,
 birds were fitted with a numbered aluminum band (Porzana Ltd, reporting
 address www.aselva.co) and a unique color band combination for future
 resightings. Birds were aged and sexed following the criteria in Pyle
 (1997). For radio tracking, birds were fitted with 0.26 g NTQB2-1-M coded
 VHF nanotags (Lotek, Ontario, Canada) registered with Motus. Tags were set
 up with an interval of 15.9 seconds between pulses in 2022 and 15.7 s in
 2023, and had an expected life span of 72-108 days, which was sufficient
 to cover the pre-migratory and migratory period between late February and
 May. Tags were attached using a leg-loop harness (Naef-Daenzer 2007)
 constructed with a 0.5 mm black polyester and polyurethane thread (Stretch
 Magic). Total harness length was adjusted in line with unflattened wing
 chord as follows: wing 56-60 mm - 68 mm harness, wing 61-64 mm - 70 mm
 harness, and wing &gt;65 mm - 72 mm harness. Total tag and harness
 weight was ~0.30 g, equivalent to 3.0%-3.9% of the mass of birds at
 capture. <em>Defining departure dates using Motus
 automated telemetry data</em> To determine
 departure dates, birds were tracked using existing permanent Motus
 stations (4 stations: Finca Cristina; Caracara; Casa Carballo, Fortuna;
 Paraiso Quetzal) or through stations mounted temporarily within capture
 regions (7 stations: La Paz, San Ramon; Rio Macho; Naciente Mero; Jardin
 Botanico Lankester; San Antonio, Turrialba; Valle Los Quetzales; Finca
 Galeón). Permanent stations typically consisted of two 9-element Yagi
 antennas connected to a SensorStation (Cellular Tracking Technologies, New
 Jersey, U.S.) or a SensorGnome (www.sensorgnome.org), while temporary
 stations were fitted with one or two 3-element Yagi antennas to maximize
 local detections. At the end of each study period, detection data were
 downloaded from Motus and processed to identify individual departure dates
 following the recommendations in Taylor et al. (2017). We examined
 individual detection histories using the <em>Motus</em>
 package (Birds Canada 2024) for Program R (version 4.1.3; R Development
 Core Team 2017) and identified reliable departure dates using one or more
 of the following criteria (criteria are listed in order of decreasing
 confidence): A) Birds with continuous detections at nearby stations,
 followed by a clear nocturnal departure signal, e.g. a spike in signal
 strength as illustrated in Taylor et al. (2017) (n = 24); B) Birds with
 discontinuous or sporadic detections at a “local” station, followed by a
 clear nocturnal departure signal (n = 4). C. Birds with few or no
 detections on local stations but with a clear departure signal (n = 9). D.
 Birds with few or no detections on local stations but a clear nocturnal
 flyby signal on one or more stations &lt;50 km to the north of the
 capture site (n = 7); E. Birds with regular daily detections for at least
 a month but no clear departure signal (n = 2). Twenty-three birds did not
 meet any of these criteria and were excluded from subsequent
 analysis. <em>Modeling the relationship between
 precipitation, forest cover, and departure date</em>
 We evaluated whether precipitation or forest cover was correlated
 with departure date by running a series of Generalized Linear Models, in
 which possible effects of age, sex, and year on departure date were
 controlled for (Turcotte-van de Rydt et al. 2023). For precipitation and
 forest cover, two variables were assessed for each as described below. We
 also included elevation in the model set given its relation to occupancy
 probabilities (Bennett et al. 2019a). To create
 precipitation variables, monthly precipitation values were extracted from
 the WorldClim global precipitation 1 km resolution rasters (Fick and
 Hijmans 2017) for the capture location of each individual bird. Monthly
 values were then summed to create two new variables based on the
 assumption that resource availability (Wolda 1978) may be influenced by
 either Annual Precipitation (sum of values over 12 months) or by
 Nonbreeding Precipitation: sum of values between November and March
 (months in which <em>V. chrysoptera</em> were present at study
 sites). For forest cover, we estimated the area of forest within a 500 m
 and 1000 m buffer around individual capture locations using a 2023 forest
 layer developed specifically for Costa Rica (SNIT 2024, resolution 50 m)
 in the program QGIS version 3.40 (QGIS Development Team 2024). The layer
 classified forests into six types, and we selected those relevant to
 <em>V. chrysoptera</em> – mature forest and secondary forest
 (Chandler and King 2011). <em>Estimating
 migration speed based on detections in the Motus
 array</em> To create a variable representative of
 migration speed, we determined the number of days elapsed between a
 reliable departure signal from Costa Rica and the first reliable detection
 in a narrow migration pathway through the US Midwest bounded by 36° and
 44° latitude and -88° and -96° longitude (note, our estimate of migration
 speed includes both flight and stopover phases following Hedenström and
 Alerstam (1998)). Given that the exact route taken and the number of
 kilometers flown cannot be directly estimated from detections in the Motus
 array, the time taken between point A and point B is considered an
 appropriate proxy for migration speed (Gómez et al. 2017).
description: # Data from: Wetter and more forested nonbreeding areas result in later
 departures but a faster spring migration in *Vermivora chrysoptera*
 (Golden-winged Warblers) Dataset DOI:
 [10.5061/dryad.qbzkh18w5](10.5061/dryad.qbzkh18w5) ## Description of the
 data and file structure The following data were collected to understand
 how variability in precipitation and forest cover between non-breeding
 sites occupied by *Vermivora chrysoptera* (Golden-winged Warbler) in Costa
 Rica influences departure timing on spring (pre-breeding) migration and
 how subsequent migration speed is related to departure date. ### Files and
 variables #### File: Departure\_date.csv **Description:** Departure dates
 for individual Vermivora chrysoptera alongside a series of variables
 expected to influence departure decisions. ##### Variables * Band_number:
 Unique identifier for each bird. * Motus_Tag_ID: Unique identifier for the
 radiotransmitter installed on each bird. * Ddate: Departure date for each
 bird, where 1 = 1st April. * Sex: M = male; F = female. * Age: Adult =
 birds in at least their second year of life; Imm = immature birds in their
 first year of life. * Year: A = 2022, B = 2023 * Site: Study region in
 Costa Rica where the bird was captured. * Rain_NB: Total rainfall during
 the non-breeding period (Nov-Mar) extracted from WorldClim in mm. *
 Rain_AN: Total annual rainfall, sum of monthly values extracted from
 WorldClim in mm. * Forest500: Forest cover in a 500 m radius around
 capture locations (hectares) * Forest1000: Forest cover in a 1000 m radius
 around capture locations (hectares) * Elevation: Elevation at capture
 site, masl. * AgeSex: Combined variable for each age and sex combination *
 Latitude: Latitude of capture site. WGS84. * Longitude: Longitude of
 capture site. WGS84. * Ddate_original: Full format date for departure
 dates (dd/mm/yyyy). * Ddate_confidence: Confidence level for departure
 date, A = highest (see methods) #### Files x 3: Migration\_speed\_36.csv /
 Migration\_speed\_40.csv / Migration\_speed\_All.csv
 **Description:** Three files, each with data for examining the
 relationship between migration speed (duration) and departure date. The
 files include data for individuals detected within different latitudinal
 bands as follows: 1) 36 and 40 degrees in North America; 2) 40 and 44
 degrees; 3) 36.9 and 42.8 degrees. ##### Variables * Band_number: Unique
 identifier for each bird. * Age: ad = adult, imm = immature. *
 Latitude_exact: Exact latitude of detection on Motus station. WGS84. *
 Latitude_band: Latitudinal band of detection. WGS84. * Ddate: Departure
 date, where 1 = April 1st. * Days: Number of days elapsed between the
 departure day and the first detection between 36 and 40 degrees. * Year :
 A = 2022, B = 2023. #### File: GWWA\_Costa\_Rica.R **Description:** R
 script for running the main analyses exploring departure dates and
 migration speed in the manuscript. #### File: GWWA\_Movement\_data.csv
 **Description:** Dataset compiling all movement data, e.g., filtered
 detections of *Vermivora chryosptera* away from their original capture
 site. ##### Variables * Motus_tagID: Unique identifier for the
 radiotransmitter. * Band_number: Unique identifier for each bird. *
 Detection_Date: Date of detection on Motus station (dd/mm/yyy hh:mm) *
 Detection_type: Capture - Detection_Date = capture date; Departure_Date -
 Detection_Date = date of final detection on local Motus station at capture
 site; Migration - Detection_Date = date of detection at Motus stations
 beyond the capture site during spring migration. * Latitude: Latitude of
 Motus station where the bird was detected. WGS84. * Longitude: Longitude
 of Motus station where the bird was detected. WGS84 * Sex: M = male; F =
 female. * Age: ad = adult; imm = immature * Year: Year of capture. #### ##
 Code/software The following file requires the Program R for reading and
 running the script. Packages required are included within the script. ####
 File: GWWA\_Costa\_Rica.R **Description:** R script for running the main
 analyses exploring departure dates and migration speed in the manuscript.
 ## Access information Other publicly accessible locations of the data: *
 [www.motus.org](http://www.motus.org)