10.5061/DRYAD.RV15DV463
Fiss, Cameron
0000-0002-5649-6880
Indiana University of Pennsylvania
McNeil, Darin
Cornell University
Rodewald, Amanda
Cornell University
Duchamp, Joseph
Indiana University of Pennsylvania
Larkin, Jeffery
Indiana University of Pennsylvania
Golden-winged Warbler post-fledging movement and stand-scale habitat selection
Dryad
dataset
2020
2021-08-21T00:00:00Z
2021-08-21T00:00:00Z
en
4520094 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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 <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 <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. Movement and Space Use 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.
Cover Type Classification 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 (<10 years) timber
harvests on public lands in PA. In addition, technicians visited
>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 Stand Initiation (SI) cover
as stands that had recently (approximately <10 years) undergone
overstory removal harvest and were >50% stocked by trees <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 Stem Exclusion (SE) cover as older (approximately 10-25 years
post-harvest) even-aged stands >50% stocked by trees <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 >15
cm DBH. We divided mature forests into three sub-categories
(Shelterwood/Understocked, Mature Forest Wetland, and Mature Forest
Upland. We classified Shelterwood/Understocked (SH) cover as mature
forest <50% stocked. These stands were treated (e.g., shelterwood
harvest), or had experienced non stand-replacing natural disturbance. We
classified Mature Forest Upland (MU) as mature even- or uneven-aged stands
that were >50% stocked. These stands were approximately 60-90 years
old. We classified Mature Forest Wetland (MW; NE only) as mature
palustrine stands >50% stocked. Mature Forest Wetlands were
seasonally or perpetually inundated with water. We classified Shrub
Wetland (SW; NE only) as stands dominated by shrubs and trees <15
cm DBH and, in many cases, perpetually inundated with water. We classified
Upland Shrubland (US; NC only) as stands dominated by shrubs and
<50% stocked with trees <15 cm DBH being dominant. Shrub
cover in these stands was predominantly Vaccinium spp. or Gaylussacia spp.
Upland Shrubland cover was largely derived from a forest fire which
occurred in 1990. Statistical Analyses 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 75th 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 jagsUI (Kellner 2015) package. Because individuals can respond
differently to habitat, and because sub-broods occasionally had >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 R values <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). Model Fit Traditional goodness-of-fit (GOF)
methods are not appropriate for discrete-choice models (Womack et al.
2013), so we adopted the k-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 >25% suggesting adequate model fit (Bonnot et
al. 2009).