Research Paper

INTRODUCTION

Shrublands and young forest (i.e., early successional woody plant communities) are in steady decline throughout much of the eastern United States (Greenberg et al. 2011, King and Schlossberg 2014). Long-term suppression of natural disturbances, such as flooding and wildfire, as well as forest succession following agricultural land abandonment in the mid-1900s has largely driven the loss of these early successional communities (Thompson et al. 2013, King and Schlossberg 2014). Additionally, the use of timber harvests to promote the establishment of early successional communities on private lands has decreased in the eastern United States (Shifley et al. 2014). Prolonged population declines of many avian species, such as Prairie Warbler (Setophaga discolor) and Field Sparrow (Spizella pusilla), have paralleled the loss of early successional plant communities in the region (King and Schlossberg 2014, Sauer et al. 2019). As a result, several management programs aim to increase the availability of early successional communities for wildlife on public and private lands through practices, such as mowing, timber harvesting, prescribed burning, and herbicide treatments (Oehler 2003, Litvaitis et al. 2021).

Assessing avian species responses to young forest and shrubland management is important to describe effectiveness of and identify ways to improve conservation actions (i.e., Boves et al. 2013, Akresh et al. 2015, Buckardt Thomas et al. 2023). As such, several eastern shrubland bird species have been the focus of studies seeking to assess response to habitat management (Zuckerberg and Vickery 2006, Akresh et al. 2015, Bakermans et al. 2015). However, the Blue-winged Warbler (Vermivora cyanoptera), has received little attention despite its prevalence in the eastern United States (Fink et al. 2023). This Nearctic-Neotropical migratory passerine breeds in early successional woody communities of the eastern United States and southern Canada and has experienced population declines in several Bird Conservation Regions, such as the Piedmont and Mid-Atlantic Coast (Sauer et al. 2019, Gill et al. 2020). Studies examining Blue-winged Warbler response to habitat management are uncommon relative to the wealth of research attention paid to its congener, the Golden-winged Warbler (V. chrysoptera), which is experiencing an annual population decline of nearly 2% (Sauer et al. 2019, McNeil et al. 2020, Fiss et al. 2021, Buckardt Thomas et al. 2023). In fact, much of what is known about Blue-winged Warbler breeding ecology comes from studies that compared both Vermivora species in areas of sympatry, typically in the context of how Blue-winged Warbler presence may negatively impact Golden-winged Warblers via hybridization and/or competition (Confer and Knapp 1981, Confer et al. 2003, Confer et al. 2010, Patton et al. 2010). Studies that improve our understanding of the ecology of the Blue-winged Warbler and assess the species’ use of managed shrublands would be of conservation value for this relatively understudied species.

To understand species-habitat relationships, it is important to (1) accurately delineate the spatial scale of interest (i.e., individual core and total home ranges; Johnson 1980) and (2) quantify habitat features at a scale that matches individual resource selection behaviors (Kirol et al. 2012, Farallo and Miles 2016). Both of these can be difficult to achieve for small songbirds occupying structurally complex early successional communities where visual detections can be challenging (Wood et al. 2017). However, radio telemetry has provided insight regarding breeding season space use and habitat selection of birds in early successional communities at spatial and temporal resolutions that were previously unobtainable (King et al. 2006, Anich et al. 2009, Vitz and Rodewald 2011, Frantz et al. 2016). Indeed, radio-telemetry based studies have found that spot-mapping may underestimate space use estimates and overlook an individual’s use of variety of habitat conditions (Barg et al. 2005, Anich et al. 2009). For example, studies that used radio-telemetry to better understand the breeding season ecology of Golden-winged Warblers revealed considerable differences between male territory and peripheral home range sizes (Frantz et al. 2016) and between nesting and post-fledging habitats (Fiss et al. 2020, 2021).

Light Detection and Ranging (LiDAR) is another technology that can align the scale at which habitat features are measured with the scale at which individual selection behavior occurs (Hardiman et al. 2018, Shanley et al. 2021). Although field surveys can provide useful information about Vermivora space-use patterns, LiDAR data have been demonstrated to perform better in describing certain aspects of habitat structure (McNeil et al. 2023). However, other important aspects of habitat, like plant species composition (Bellush et al. 2016) would require field surveys, as LiDAR cannot provide this information. LiDAR point clouds are three-dimensional representations of the physical world created by estimating return distances between a pulsed laser transmitted from and received by an airplane (in the case of airborne LiDAR; Verma et al. 2006). Several LiDAR-derived variables that quantify vegetation structure have been developed (Atkins et al. 2018). Importantly, LiDAR-derived variables can be used to describe structural attributes of a species’ habitat (e.g., variation in vegetation height and density) that are difficult to quantify by many other remotely sensed data or field-based vegetation sampling protocols. As such, the increased availability of LiDAR data has provided researchers with the ability to quantify and map vegetation structure for target species, and thus better inform conservation and management decisions (McNeil et al. 2023).

Here, we used radio telemetry, and a combination of ground-based vegetation surveys and LiDAR-derived habitat metrics to describe Blue-winged Warbler breeding season space use and to compare vegetation characteristics between individual core and peripheral home ranges. Specifically, we radio tracked male Blue-winged Warblers during the 2019 and 2020 breeding seasons in southwest Pennsylvania. We hypothesized that core home ranges would be similar in size and structure to those described in previous studies that used spot-mapping (e.g., Confer et al. 2003, Patton et al. 2010), but that peripheral home ranges would contain both distinct vegetation structure and cover type composition.

METHODS

Study area

Data collection for this study occurred from April to July of 2019 and 2020 at three sites in southwest Pennsylvania (Fig. 1). In 2019, we radio-tracked Blue-winged Warblers in a complex of managed shrublands associated with Forbes State Forest’s Mountain Streams Woodcock Habitat Management Area and adjacent private land (40.103° N, 79.322° W). Elevation within this area ranges from 470 to 530 m. Since 2015, this area has undergone management in small patches (often < 15 ha) that included canopy tree removal, mechanical mulching, and herbicide control of invasive grass and shrub species. In 2020, we monitored Blue-winged Warblers in managed shrublands in Yellow Creek State Park (YCSP; 40.569° N, 79.021° W) and State Game Lands 411 (SGL 411; 40.469° N, 79.318° W). Elevation in YCSP ranges from 390 to 430 m. YCSP contained a mosaic of vegetation communities including mixed deciduous forests, coniferous plantation forest, shrub wetlands, and upland old-field shrublands. We monitored birds in upland old-field shrublands that were maintained using various combinations of management practices including prescribed fire, herbicide, and mechanical removal of invasive species, such as honeysuckle (Lonicera spp.) and autumn olive (Elaegnus umbellata). SGL 411 was a mosaic of managed herbaceous fields and shrublands and mixed hardwood forest. Elevation in this site is nearly uniform at 300 m. Practices used to manage shrublands on SGL 411 included seasonal controlled flooding, mulching, and herbicide application. The managed shrublands we monitored across all three study sites were characterized by a patchwork of scattered deciduous trees, dense thickets of woody vegetation, such as dogwood (Cornus spp.), viburnums (Viburnum spp.), hawthorns (Crataegus spp.), brambles (Rubus spp.), honeysuckle, and autumn olive, as well as herbaceous vegetation including goldenrod (Solidago spp.) and graminoids (i.e., Poaceae spp. and Carex spp.). Additionally, shrublands included in our study were embedded within local landscapes dominated by closed-canopy deciduous forests, and thus all sites included forest-shrubland ecotones.

Capture and radio telemetry

Beginning in late April of each year, we regularly visited each study site to find and record locations of male Blue-winged Warblers arriving on territory. We revisited locations of territorial males starting the first week of May and used 30 mm mist-nets and a playback lure of Vermivora spp. songs and calls to capture males (Wood et al. 2017). After recording weight (g) and age (ASY or SY), we fitted each male weighing ≥ 8 g with a 0.35 g VHF radio-transmitter tag (Blackburn Transmitters, Nacogdaches, TX). We attached the tags using a modified figure-8 leg-loop harness (Rappole and Tipton 1991, Streby et al. 2015). In addition to the radio-transmitter tag, we fitted each male with a USGS band, and a unique combination of 2-3 color bands for visual identification of unique individuals. The combined weight of the transmitter, harness, and leg bands constituted < 5% of each bird’s mass. Handling time for each individual was ≤ 5 min and, upon completion of radio-tagging and banding, all birds were released at the place of capture.

We tracked tagged individuals daily between 0600 and 1600 hr from 4 May through 27 June using a Lotek STR 1000 receiver (Lotek Wireless, Newmarket, Ontario, Canada) with a 3-element Yagi antenna. We varied the time of day that we tracked each individual (morning = 0600-1100 hr, afternoon = 1100-1600 hr) to account for time-of-day effects on our observations (Shields 1977). We tracked individuals solely via homing rather than triangulation to avoid location error due to bird movements (Withey et al. 2001). We attempted to track each bird daily until transmitter battery failure (~28 days). After we located and confirmed an individual based on their color bands, we recorded their location with a Garmin eTrex 22x (accurate to < 5 m) and started a 30 min timer. Within those 30 min, we recorded as many locations as possible (based on “burst” sampling; Barg et al. 2005). We excluded locations we influenced (e.g., if we flushed a bird from its location, we did not record its next location), and if a bird remained stationary throughout the survey period, we did not record multiple GPS points for that day. We did record additional GPS points for the same location if they occurred on different days (i.e., if a bird repeatedly used the same song perch, foraging location, or nest area across multiple days), which allowed us to emphasize “hotspots” used by birds throughout the breeding season.

Delineation of core, peripheral and total home ranges

We imported all telemetry locations into program R ver. 4.1.0 (R Core Team 2021) and filtered to only include locations that were taken ≥ 2 min apart using the “filter” function in the package dplyr (Wickham et al. 2020). Blue-winged Warblers can traverse their entire home-range in under 2 min (K. Fuoco, personal observation), but we used this threshold to reduce spatial dependence of points (Barg et al. 2005). We used the adehabitatHR package (Calenge 2006) in R to estimate core, peripheral, and total home ranges for each individual. We used fixed kernel density estimates (KDE) and considered the 95% KDE as the total home range and the 50% KDE as the core home range (similar to Can et al. 2019). The peripheral home range is the area within the total home range but outside the core area (the 95% KDE area minus the 50% KDE area; Fig. 2).

Vegetation sampling and analysis

We sampled vegetation in core and peripheral portions of each home range to compare vegetation characteristics between these two space use scales. We used QGIS ver. 3.28.1 (QGIS.org 2021) to randomly generate 10 vegetation sample points within each bird’s core home range and 20 points within each peripheral home range to maximize the amount of area sampled. We then randomly assigned each point to either a “partial” or “full” vegetation survey so that there was an equal number of each survey type across each bird’s total home range (Fig. 2). For both survey types, we estimated average percent overhead cover using a spherical densiometer (Lemmon 1956), and measured proportion ground cover of leaf litter, grass, Rubus spp., Solidago spp., other forbs (not including Solidago spp.), and ferns using a GRS densitometer at plot center and at 5 m in each cardinal direction. These features are regularly recorded using these methods in Vermivora warbler research because of their influence on habitat use, nest site selection, nest success, and foraging (e.g., Bulluck and Buehler 2008, Bellush et al. 2016, Frantz et al. 2016, Leuenberger et al. 2017). For both partial and full surveys, we recorded the species and count of all saplings < 10 cm diameter at breast height (DBH) and shrubs ≥ 0.5 m tall within a 3-m radius of plot center. Full surveys differed from partial surveys in that, in addition to measuring proportion of cover variables and stem counts, we also included counts of snags and live trees > 10 cm DBH within an 11.3-m radius of plot center and measured their DBH.

We tested for differences in these vegetation variables between core and peripheral home ranges using a variety of methods based on data type. For proportion-based cover estimates (i.e., overhead cover, grass cover, etc.) we used the betareg package (Cribari-Neto and Zeileis 2010) to fit individual beta regression models for each variable. Beta regression is recommended for proportion-based data that are bounded by 0-1 where heteroscedasticity is of concern (Ferrari and Cribari-Neto 2004). Our models considered “area” (core or peripheral home range) as the predictor variable and proportion of each cover variable as the response variable. We altered values that equaled exactly 0 or exactly 1 to 0.01 and 0.99, respectively, to allow the models to capture all values in the data. For count-based variables (number of saplings, shrubs, trees, and snags) we used generalized linear mixed models fit with a Poisson distribution to test for differences between core and peripheral home ranges. We fit individual models with “area” as the predictor variable and each stem type as the response variable. We included “bird ID” as a random effect to account for variation among individuals. Last, for average basal area and average DBH, we used paired t-tests to examine whether values differed between core and peripheral home ranges. Basal area was log-transformed prior to the paired t-test to reach normality. We adjusted all p-values from these analyses using the Holm-Bonferroni adjustment method to account for multiple testing across variables (Holm 1979).

Cover types and selection

We classified core and peripheral home ranges into three broad cover types to further describe habitat use and selection. Using imagery from National Agricultural Imagery Program (USDA Farm Service Agency 2019), we hand digitized forest, shrubland, and ecotone (defined as a 20 m-wide buffer at the forest and shrubland interface) within each core and peripheral home range. We based our classification of forest and shrubland based on imagery as well as our personal knowledge of each core and peripheral home range. Generally, areas with contiguous canopy cover and increased tree density were classified as forest, and all other open areas lacking those features were classified as shrubland. We opted to delineate our “ecotone” cover type using a 20-m buffer because of variations in abiotic factors and vegetation structure observed within 20 m from forest edges (Alignier and Deconchat 2013). We excluded water features and paved roads from delineation entirely.

We quantified the proportion of each cover type within each space use category (Schlossberg and King 2008). We then fit beta regression models separately for each cover type to test for differences between core and peripheral home ranges, again using “area” (core or peripheral home range) as the predictor variable and proportion of each cover type as the response variable. We adjusted p-values from model outputs to account for multiple testing (Holm 1979).

To describe use and selection of each of the three digitized cover types in both core and peripheral home ranges, we compared the proportion of “used” points (GPS locations) to the proportion of “available” points in each cover type. To attain available points, we used the sf package (Pebesma 2018) to randomly generate points within each core and peripheral home range, equivalent to the number of used locations per bird (i.e., 1:1 ratio). We then used generalized linear mixed models with a binary response (used vs available) to cover type (forest, shrubland, or ecotone) to describe probability of use of each cover type. We used the lme4 package (Bates et al. 2015) to fit models for core home ranges and peripheral home ranges separately using bird ID as a random effect, and evaluated scaled residuals for each bird to check for autocorrelation. Using model results, we were able to predict the relative probability of use for each cover type in both core and peripheral home ranges, wherein a value > 50% represents selection for a cover type, a value < 50% represents selection against a cover type, and a value equal to 50% represents random selection.

LiDAR data and processing

LiDAR metrics were derived from previous work creating 10-m² forest attribute rasters across most of Pennsylvania (McNeil et al. 2023) using datasets available from the USGS National Map. LiDAR datasets used to create these metrics were collected over multiple campaigns between spring 2015 and spring 2020 with the intent of targeting leaf-off conditions that may describe canopy diversity better than leaf-on conditions (Davison et al. 2020). LiDAR data for southwest Pennsylvania were collected during one campaign in March 2019. LiDAR metrics were calculated using the lidR package in R (Roussel et al. 2020), with all LiDAR return heights normalized based on a triangulated irregular network (TIN) algorithm to construct the digital terrain model from returns.

Normalized LiDAR point clouds can be distilled into many metrics aimed at quantifying vertical and horizontal vegetation structure (Hardiman et al. 2018). We chose metrics that would best describe structure of core home ranges and peripheral home ranges of Blue-winged Warblers based on previous studies highlighting the importance of vegetation density (e.g., Askins et al. 2007, Davis 2014, McNeil et al. 2023). In total, we selected six metrics: interquartile range (IQR), height below which 75% of returns occurred (p75), height below which 90% of returns occurred (p90), percent of all returns 1–5 m (perc5to1), percent of first returns 1–5 m (percfirst5to1), and standard deviation of p95 within a 30 m x 30 m neighborhood (p95 rugosity). We clipped each LiDAR raster using each bird’s peripheral home range and extracted all cell values from within the clipped boundary. This was repeated for the core home range and we summarized a mean value for each LiDAR metric for each bird’s peripheral and core home range. We then used paired t-tests to evaluate differences in IQR, p75, p90, and p95 rugosity between core and peripheral home ranges. For perc5to1 and percfirst5to1, we tested for differences between core and peripheral home ranges using beta regression models since the data values are proportion-based. We adjusted all p-values from t-tests and beta regression models to account for multiple testing (Holm 1979). We also calculated mean values and 95% confidence intervals for each of these variables for forest, shrubland, and ecotone cover types to identify potential structural differences between the three cover types.

RESULTS

Space use and telemetry

During the 2019 and 2020 breeding seasons, we radio-tagged and tracked a total of 27 Blue-winged Warblers (2019: 13 in Forbes State Forest, 2020: 10 in Yellow Creek State Park, and 4 in State Game Lands 411). On average, transmitter batteries lasted 27 ± 4 days (mean ± standard deviation) and each bird had at least one location recorded for 80% of their active battery days. After filtering locations to only those ≥ 2 min apart, the mean number of locations used to generate the 50% and 95% KDEs per bird was 65 ± 17 (min = 28, max = 104). The median core home range size was 2.9 ha (50% KDE; min = 0.6 ha, max = 16.5 ha) and the median total home range size was 12.9 ha (95% KDE; min = 2.3 ha, max = 90.6 ha). Total home ranges were more likely to overlap among birds than core home ranges, as 16 of 27 (59%) total home ranges overlapped, whereas only 6 of 27 (22%) core home ranges overlapped.

Vegetation composition

In total, we sampled vegetation cover and stem densities at 793 plots, 526 of which were in peripheral home ranges and 267 were in core home ranges. On average, core home ranges had greater grass (68.5% vs 58.6%) and Solidago cover (43.9% vs 27.3%) than peripheral home ranges. However, peripheral home ranges had greater overhead (74.1% vs 61.9%) and leaf litter cover (51.9% vs 32.2%) than core home ranges. Forb, fern, and Rubus cover did not differ between core and peripheral home ranges. Core home ranges had greater average shrub density than peripheral home ranges (9872 stems/ha vs 7106 stems/ha), whereas peripheral home ranges had greater average sapling density (6052 stems/ha vs 5114 stems/ha), tree density (237 trees/ha vs 170 trees/ha), and snag density (39 snags/ha vs 31 snags/ha) than core home ranges. Further, on average, peripheral home ranges had greater basal area than core home ranges (13.6 m²/ha vs 6.8 m²/ha), which is likely due to increased number of trees versus size of trees, as average DBH did not differ between core and peripheral home ranges (p = 0.74). Mean values and 95% confidence intervals can be found in Table 1.

Cover types and selection

Core and peripheral home ranges both contained all three cover types (forest, shrubland, and ecotone; Fig. 3). On average, peripheral home ranges had greater forest cover than core home ranges (43.9% vs 24.8%, p < 0.001). Core and peripheral home ranges showed no difference in ecotone cover (22.4% vs 16.8% respectively, p = 0.20) and no difference in shrubland cover (52.6% vs 39% respectively, p = 0.09).

Males used all three cover types in both core and peripheral home ranges, but based on the relative probability of use analyses, they selected for and against cover types differently in core and peripheral home ranges (Fig. 4). In core home ranges, males selected shrubland and ecotone cover at random, but selected against forest cover (relative probability of use = 45.4%). In peripheral home ranges, males selected shrubland cover at random, selected for ecotone cover (relative probability of use = 58.3%), and selected against forest cover (relative probability of use = 45.4%).

LiDAR metrics

Core and peripheral home ranges showed significant differences in five of the six LiDAR metrics evaluated (Table 1). Peripheral home ranges had greater average IQR (4.41vs 5.24) and p75 (5.28 vs 4.44) values than core home ranges. Core home ranges had greater p95 rugosity (3.45 vs 2.53), as well as greater perc5to1 (18.46% vs 12.85%) and percfirst5to1 (20.94% vs 14.3%) values. Only p90 showed no significant difference between core and peripheral home ranges (Table 1).

Based on evaluation of means and 95% confidence intervals, forest, ecotone, and shrubland cover types exhibited variable vegetation structure. The three cover types all differed in IQR, p75 and p90 values, but showed no differences in perc5to1 and percfirst5to1. Of particular interest related to our observed selection results, ecotone areas had greater p95 rugosity on average than forests (4.46 vs 2.83), but not greater than shrubland areas (means, standard deviation, and 95% confidence interval listed in Table 2).

DISCUSSION

Our study provides one of the most in-depth examinations of Blue-winged Warbler breeding habitat requirements to date, and the first to be conducted in areas of little to no overlap with the closely related Golden-winged Warbler. Of particular importance, our results revealed significant complexity in space-use patterns by Blue-winged Warblers not previously described for the species. Total home ranges were influenced by use outside of core home ranges, which likely would not have been perceptible without the use of radio-telemetry. By evaluating core and peripheral home ranges separately, our results also suggest unique habitat relationships previously undocumented for the species. Our findings suggest that basing habitat management decisions for Blue-winged Warblers solely upon vegetation relationships within core, high-use areas (such as those most often delineated by visual observations of color-banded birds) could ultimately overlook important habitat components that the species selects in peripheral home ranges during the breeding season. Understanding such habitat relationships and space-use patterns will be helpful for informing conservation decisions that improve habitat availability and reproductive success for this species (Sherry and Holmes 1995).

Daily telemetry monitoring of individuals throughout the breeding season allowed us to identify core home ranges and locations regularly visited by individual males, as well as elucidate movements beyond core home ranges. Our telemetry-derived core home range estimates were similar in size to territories described by spot-mapping methods for Blue-winged Warblers elsewhere (i.e., 0.5–5 ha; Patton et al. 2010, Gill et al. 2020). Using a larger kernel density estimate, we also highlight a total home range size on average four times larger than core home ranges. Studies of other passerine species had comparable results, such as Prairie Warblers (Setophaga discolor; Can et al. 2019), Swainson’s Warblers (Limnothlypis swainsonii; Anich et al. 2009), Cerulean Warblers (Setophaga cerulea; Connare and Islam 2023), and Golden-winged Warblers (Streby et al. 2012, Frantz et al. 2016). Further, our finding that total home ranges commonly overlapped while core home ranges were more exclusive is consistent with findings from a study of Golden-winged Warblers in Pennsylvania (Frantz et al. 2016), as well as a study of overlapping Golden-winged Warbler and Blue-winged Warbler territories in Kentucky (Patton et al. 2010). Shared areas around core home ranges may provide opportunity for extra-pair copulation (Griffith et al. 2002, Vallender et al. 2007, Whitaker and Warkentin 2010), as well as increased foraging opportunity (Fedy and Stutchbury 2004, Whitaker and Warkentin 2010, Evens et al. 2018). It is also possible that higher likelihood of overlap in total home ranges may be related to the lower density of bird locations recorded in peripheral home ranges. If males have lower frequency of use in the peripheral home range, they may be less likely to encounter another male and defend the area for exclusive use, similar to the findings of Naef-Daenzer (1994) that described high likelihood of total home range overlap in low densities of tits (Parus spp.).

Although our results are largely consistent with previous studies that described vegetation in Blue-winged Warbler territories in the broad sense (e.g., Patton et al. 2010, Davis 2014, Gill et al. 2020), we are the first to document movements into and incorporation of extensive forest cover in peripheral portions of home ranges at a microhabitat scale. Blue-winged Warbler core home ranges in our study showed similar characteristics to those described in previous studies: low basal area (Patton et al. 2010), high shrub density (Askins et al. 2007, Davis 2014), and extensive goldenrod (Wood et al. 2016) and grass cover (Confer et al. 2010). Additionally, core home ranges were mostly shrubland cover (vs ecotone or forest cover) with a patchy mosaic of vegetation between 1 and 5 m in height (as depicted via LiDAR), further corroborating previously described preference for patchy, early successional communities for territory use (Confer et al. 2003, Patton et al. 2010, Davis 2014). In contrast, peripheral home ranges intermittently visited by males in this study were typically more forested, with greater overhead cover, basal area, tree density, and vegetation height as described by LiDAR. By combining field data with remotely sensed LiDAR data, we not only provide the most comprehensive assessment of Blue-winged Warbler breeding season habitat ecology to date, but also provide a highly detailed glimpse into the habitat needs for the species in multidimensional space (Fig. 5).

Even though forest cover was present in both core and peripheral home ranges albeit to different extents, it is most important to note that males selected against forest cover in both areas. These results align with those described at coarser scales that showed Blue-winged Warblers inhabit landscapes with less forest cover and more urban and agricultural development than its congener (Crawford et al. 2016, Wood et al. 2016). It may seem paradoxical for males to incorporate forest cover while simultaneously selecting against it, however selection against a habitat feature does not necessarily suggest the feature is irrelevant (Garshelis 2000). The only cover type males selected for in this study was the forest-shrubland ecotone in peripheral home ranges. Thus, despite the paradox between presence of forest and yet selection against it, having forest cover is a requisite for having a forest-shrubland edge, which males in this study sought out in the peripheral home range.

While some studies have provided evidence of forest edge avoidance by Blue-winged Warbler (e.g., Rodewald and Vitz 2005, Fink et al. 2006, Schlossberg and King 2008, Wood et al. 2016), our selection results more closely align with those that showed use of forest edge (e.g., Patton et al. 2010, LeBrun et al. 2012, Davis 2014). LiDAR data in this study illustrated that forest edges had more structural heterogeneity than forested areas and provided intermediate vegetation height compared to forests and shrublands. Of particular interest to Vermivora warblers, forest edge in our study may also have increased Lepidopteran prey availability relative to interior forests (Grow et al. 2013). Further, Blue-winged Warblers may have originated in the prairie-forest ecotones of the Central Hardwoods region of the United States before spreading eastward in the early 1900s (Gill 1980, Confer 2006). Thus, results that describe selection of these ecotonal areas even now may be indicative of the species historical preferences. All of these factors illustrate that incorporating and maintaining forest edge, and therefore forest and shrubland cover by association, when managing for Blue-winged Warbler breeding habitat is critical.

Although not a primary goal of this study, our analyses also provide an exciting example of how researchers might use LiDAR to model bird habitat relationships in eastern forests. Other sources of remotely sensed data, like the National Land Cover Database (Dewitz 2021) are recognized to poorly capture cover types that shrubland birds are dependent on (i.e., old fields, young forest, etc.; Bulluck et al. 2022). Recent work by McNeil et al. (2023) assessed the value of LiDAR data in Pennsylvania for predicting the occupancy of Golden-winged Warblers in the Pocono Mountains region. Their study found that LiDAR predicted occupancy far better than field-measured vegetation data, alone. Our study adds to a growing body of literature suggesting that LiDAR can be valuable for explaining patterns like space use and occupancy (Goetz et al. 2007, Lesak et al. 2011). Furthermore, LiDAR can also be used to predict space-use patterns across broad spatial extents using statistical models parameterized with these covariates (McNeil et al. 2023). Still, although LiDAR were highly valuable for our purposes, it remains important to acknowledge that efforts that require species-composition data at any structural layer, or any aspect of ground vegetation, which are important for a variety of eastern forest warbler species (Bellush et al. 2016, Bocetti et al. 2020, Wessels and Boves 2021, etc.), still rely upon field sampling.

Despite being a disturbance-dependent species experiencing a considerable decline in the eastern United States, the Blue-winged Warbler still remains an underdog in the story of Vermivora warblers. The species is frequently vilified, most likely because it hybridizes and may compete with its congener, the Golden-winged Warbler (Confer et al. 2020), overshadowing its own conservation need (Oliver et al. 2023). Our work adds to a small but growing body of literature describing breeding habitat requirements of Blue-winged Warblers, particularly in an area of little to no overlap with Golden-winged Warblers. Early successional communities provide habitat for a wide variety of wildlife species including Blue-winged Warblers, thus it is imperative that land managers continue to create and maintain such conditions (e.g., Bailey et al. 2011, Akresh and King 2016, Wagner et al. 2019). Management for Blue-winged Warblers in our study area should focus efforts in landscapes at < 600 m elevation, with 40–60% forest cover interspersed with sufficient shrubland cover that is adjacent to forest (Fink et al. 2006, Crawford et al. 2016, Wood et al. 2016). Within appropriately identified sites, management practices should aim to create and/or maintain a mosaic of grasses, forbs (especially Solidago spp.), and dense islands of woody vegetation for Blue-winged Warbler territory requirements. Such sites should also be adjacent to deciduous forest patches that provide the important edge structure males selected for in this study. There is ample opportunity for future research to build on our work and provide land managers a more complete understanding of the requirements of this disturbance-dependent species, including potential behavioral differences between core and peripheral home ranges, and habitat selection during the critical post-fledging period.

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