DOI: 10.5061/dryad.rv15dv463
title: Golden-winged Warbler post-fledging movement and stand-scale habitat selection

description: Our understanding of songbird habitat needs during the breeding season
 stems largely from studies of nest success. However, growing evidence
 shows that nesting habitat and post-fledging habitat often differ.
 Management guidelines for declining species need to be revaluated and
 updated to account for habitat shifts that may occur across the full
 breeding cycle. The Golden-winged Warbler (Vermivora chrysoptera) is a
 declining songbird species for which best management practices (BMPs) are
 based overwhelmingly on nesting habitat. We studied stand-scale habitat
 selection by fledgling Golden-winged Warblers during May-July 2014-2017 in
 two landscapes (2 years of data for each landscape), 200 km apart in
 Pennsylvania. Across four years, we radio-tagged and tracked 156
 fledglings. We used discrete-choice models to evaluate habitat selection
 during two post-fledging time periods (days 1-5, days 6-28). Fledglings
 used a variety of cover types, but most telemetry relocations (i.e. 85%)
 occurred in forest in the stand initiation stage, stem exclusion stage, or
 mature forest upland. Fledglings primarily selected stand initiation
 forest during the first five days, but preferred habitats differed between
 regions during days 6-28 post-fledging. Fledglings in one landscape
 favored stands in the stem exclusion stage while fledglings in the other
 landscape continued to select stands in the initiation stage. Fledglings
 moved greater distances as they aged and dispersed approximately 750 m by
 day 28 post-fledging. These findings suggest the need to update
 Golden-winged Warbler BMPs to account for the broader habitat needs of
 fledglings during the breeding season. In addition, these results indicate
 that regional studies of habitat requirements can help guide management of
 dynamic forest landscapes for birds.
description: We searched for Golden-winged Warbler nests from May-June within
 early-successional forests and along edges of adjacent mature forest
 across both study areas. We used active searching techniques (e.g.,
 parental behavior cues) to locate nests. For each nest discovered, we
 conducted checks on a three-day interval to monitor progress and to ensure
 accurate estimates of nestling age (Martin and Geupel 2016). As nestlings
 approached fledging (eight days old; Confer et al. 2011), we monitored
 nests daily. Immature Golden-winged Warblers were
 usually marked as nestlings eight days after hatching. However,
 individuals that fledged prior to nest checks on day eight were caught by
 hand, typically within 10m of the nest. We randomly selected two members
 of each brood to be fitted with a VHF radio-transmitter (Blackburn
 Transmitters Inc., Nacogdoches, TX) with 95 mm antenna. Two fledglings
 were chosen because parents split broods shortly after fledgling (Peterson
 et al. 2016), and we wanted to increase the chance of monitoring separate
 sub-broods. Both birds received an aluminum USGS leg band and a radio
 transmitter affixed using the figure-eight harness method (Rappole and
 Tipton 1991). We constructed harnesses from &lt;1mm black elastic
 thread to allow for growth (Streby et al. 2015). VHF radio transmitters
 used in this study weighed either 0.35 g or 0.40 g; and when combined with
 a harness and leg band, constituted &lt;5% of each bird’s mass. There
 was no obvious indication that transmitters affected mobility or survival
 of fledglings, and radio-tagged individuals were often seen behaving in a
 similar fashion to brood-mates without radio transmitters. Handling time
 for each brood was ≤10 min and, upon completion of radio-tagging and
 banding, all birds were returned to their nest (nestlings) or perch
 (recently fledged young). In addition to fledglings from monitored nests,
 we opportunistically captured dependent fledglings that we encountered
 during nest searching and telemetry. We aged these birds to the nearest
 day by comparing their plumage characteristics to known-age
 fledglings. Each radio-tagged fledgling was tracked
 daily between 06:00 and 16:00 hours using a Lotek STR 1000 (Lotek Wireless
 Inc., Newmarket, ON) receiver and Yagi three-element antenna. We tracked
 each fledgling once per day using the homing technique until we visually
 confirmed its location. Upon arriving at a fledgling’s location, we
 recorded the presence and behaviors of siblings and parents to determine
 fledgling independence. We recorded coordinates at the first location the
 fledgling was observed using a Garmin eTrex 20 GPS unit (Garmin Intl.
 Inc., Olathe, KS). We followed this tracking protocol until fledgling
 mortality or radio-transmitter battery failure (~30 days). When
 radio-signal was lost for an individual, we conducted systematic searches
 to determine if the fledgling had moved outside the normal detection range
 of our equipment. Searches were centered on the fledgling’s last known
 location and extended along 1-km transects in each cardinal direction. If
 a fledgling remained undetected, we conducted daily searches from
 automobile throughout the study area for ≥1 week before ceasing
 searches. <b>Movement and Space
 Use</b> We assessed fledgling movements and space
 use separately for each study area. Because Golden-winged Warblers are a
 brood-splitting species and multiple radio-tagged fledglings occasionally
 went with the same parent, we treated sub-broods as a random effect. To
 assess movement rate, we averaged daily straight-line movements across all
 sub-broods during two periods (low survival [~70% of mortalities]: day 1-5
 post-fledging and high survival [~30% of mortalities]: day 6-28
 post-fledging, McNeil 2019). We averaged Euclidean distance from each
 sub-brood location to its nest of origin to determine dispersal distance.
 During the high-survival period, we compared fledgling dispersal range
 between study areas using a Student’s T-test.
 <b>Cover Type Classification</b>
 We classified cover types in both study areas with ArcGIS 10.3
 (Environmental Systems Research Institute 2015) using a combination of
 Pennsylvania State Forest and State Game Lands forest inventory data,
 ArcGIS online aerial imagery (Esri 2015), National Wetlands Inventory
 data, and records of recent (&lt;10 years) timber harvests on public
 lands in PA. In addition, technicians visited &gt;3800 randomly
 selected locations in our study areas and classified forest developmental
 stage. We used these ground-based samples to assist in classification of
 cover types. We classified most cover types based on tree size, stocking
 level (i.e. tree density relative to the stand’s capacity), and age class
 of the timber stand as described in the PA Department of Conservation and
 Natural Resources (DCNR) Bureau of Forestry Inventory Manual (PA DCNR
 2016). We classified <i>Stand Initiation</i> (SI) cover as
 stands that had recently (approximately &lt;10 years) undergone
 overstory removal harvest and were &gt;50% stocked by trees &lt;15
 cm DBH. Stand Initiation cover closely represented Golden-winged Warbler
 nesting habitat and contained substantial shrub and herbaceous ground
 cover in addition to a diverse mixture of regenerating seedlings/saplings.
 We defined <i>Stem Exclusion</i> (SE) cover as older
 (approximately 10-25 years post-harvest) even-aged stands &gt;50%
 stocked by trees &lt;15 cm DBH. These stands were distinct from SI
 cover, due to the dominance of a dense sapling layer such that herbaceous
 vegetation and most shrubs were shaded-out by the overstory. Mature forest
 (i.e. stands in the understory reinitiation stage) was characterized by
 the dominance of trees &gt;15 cm DBH. We divided mature forests into
 three sub-categories (Shelterwood/Understocked, Mature Forest Wetland, and
 Mature Forest Upland.  We classified <i>Shelterwood/Understocked
 </i>(SH) cover as mature forest &lt;50% stocked. These stands
 were treated (e.g., shelterwood harvest), or had experienced non
 stand-replacing natural disturbance. We classified <i>Mature Forest
 Upland </i>(MU) as mature even- or uneven-aged stands that were
 &gt;50% stocked. These stands were approximately 60-90 years old. We
 classified <i>Mature</i> <i>Forest Wetland</i>
 (MW; NE only) as mature palustrine stands &gt;50% stocked. Mature
 Forest Wetlands were seasonally or perpetually inundated with water. We
 classified<i> Shrub Wetland</i> (SW; NE only) as stands
 dominated by shrubs and trees &lt;15 cm DBH and, in many cases,
 perpetually inundated with water. We classified <i>Upland
 Shrubland</i> (US; NC only) as stands dominated by shrubs and
 &lt;50% stocked with trees &lt;15 cm DBH being dominant. Shrub
 cover in these stands was predominantly <i>Vaccinium</i> spp.
 or <i>Gaylussacia</i> spp. Upland Shrubland cover was largely
 derived from a forest fire which occurred in 1990.
 <b>Statistical Analyses</b> We
 used mixed-effects conditional logistic regression (i.e. discrete-choice)
 to model stand-scale habitat selection by fledgling Golden-winged Warblers
 and their parents (Thomas et al. 2006). As such, we created daily choice
 sets for fledglings beginning on the first day an individual was
 radio-tracked. Choice sets contained the fledgling’s observed location
 (used) and 19 available points. Similar ratios of used to available points
 have been used in local-scale habitat selection studies (Bonnot et al.
 2011, Cheeseman et al. 2018). Available points were generated in ArcGIS
 using the “Create Random Points” tool. We restricted available points to a
 circle centered on a fledgling’s last used location, the radius of which
 was equal to the 75<sup>th</sup> percentile of all fledgling
 movements for a particular age, similar to Streby et al. (2016). As such,
 the range of available points expanded as fledglings developed and became
 more mobile. We measured Euclidean distance from all used and available
 points to each cover type to explain habitat selection (Conner et al.
 2003). Specifically, use of a given alternative in the choice set acted as
 a binary response that varied as a function of the distance (continuous)
 to each cover type variable. Additionally, we included distance to edge to
 measure the influence of ecotones on habitat selection. Edge was
 calculated as the distance to the closest intersection between an
 early-successional stand (SI, SE, US, SW) and a mature stand (MU, MW,
 SH). We fit habitat selection models within a Bayesian
 framework using JAGS (Plummer 2003) run from program R 3.5.1 (R Core Team
 2018) with the <i>jagsUI</i> (Kellner 2015) package. Because
 individuals can respond differently to habitat, and because sub-broods
 occasionally had &gt;1 radio-tagged fledgling, sub-broods were treated
 as random effects. We modeled each study area separately, and we modeled
 the post-fledging period in two parts for each study area (day 1-5 and day
 6-28). Prior to model fitting we assessed collinearity using Pearson’s
 correlation coefficient with a cutoff of 0.6. One variable (distance to
 edge) was removed from the NC day 1-5 model due to collinearity. Because
 we were interested in evaluating habitat preferences for each cover type
 and edge, we constructed models for each study area and age class that
 included all variables, resulting in four models (O’Hara and Sillanpää
 2009, Cheeseman et al. 2018; Appendix 1). We ran three concurrent
 Markov-chains for each model for 100 000 iterations of which 20 000 were
 allocated to a burn-in period. We assessed model convergence based on
 <i>R</i> values &lt;1.1 (Gelman and Rubin 1996). We inferred selection for or against cover types based on regression coefficients with 95% credible intervals not overlapping zero (Kéry 2010). <b>Model Fit</b> Traditional goodness-of-fit (GOF) methods are not appropriate for discrete-choice models (Womack et al. 2013), so we adopted the <i>k</i>-fold cross validation approach to test the fit of our models (Boyce et al. 2002, Bonnot et al. 2009). Briefly, for each model, we randomly subset the data into a training set (80%) and a testing set (20%). We fit each model using the training set and then evaluated the rate at which the fit model accurately predicted used locations in the testing set versus 3 randomly selected available locations. We repeated this process 5 times for each model and report the average predictive-success as a measure of GOF. Given that we evaluated 4 choices, we would expect 25% predictive-success to be due to chance alone and predictive-success &gt;25% suggesting adequate model fit (Bonnot et al. 2009).