DOI: 10.5061/dryad.gb5mkkwx1
title: Data from: Hidden space use behaviors of a nonbreeding migratory bird: The role of environment and social context

description: Movement behavior strongly mediates species and environment interactions,
 yet our understanding is constrained by challenges tracking space use at
 fine spatiotemporal resolutions. Using an automated telemetry array, we
 quantified variation in and drivers of space use for a nonbreeding
 population of migratory bird, the American redstart Setophaga ruticilla.
 We identified three distinct and common behaviors – territoriality,
 floating, and transience – that were governed primarily by
 precipitation-driven resource availability. Within seasons, declines in
 weekly resources increased the prevalence of forays and the area of space
 utilized. Individuals adopting these alternative space use behaviors were
 less likely to maintain body condition throughout the nonbreeding season,
 which is expected to negatively influence fitness and survival. Our study
 demonstrates that nonbreeding migratory birds exhibit a high degree of
 plasticity in space use that is driven by both resource availability and
 the social environment. Ultimately, shifts in the distribution of space
 use behaviors are likely to occur due to broad-scale climatic changes,
 which are expected to have important implications on migratory
 populations.
description: <strong>Study System</strong> This
 study was conducted January-May of 2016-2019 on a nonbreeding population
 of American redstarts at the Font Hill Nature Preserve on the southwest
 coast of, St. Elizabeth Parish, Jamaica (18° 02’ N, 77° 57’ W, &lt; 5
 m above sea level). Redstarts are relatively easy to capture, tag, and
 monitor, allowing us to not only track their changes in space use within
 and between seasons but also to investigate the consequences of these
 behaviors. Resource availability in our Jamaican system can vary widely
 within and between nonbreeding seasons. Further, though most redstarts are
 territorial in this population [55], redstarts have been shown to exhibit
 a diverse mix of space use behaviors that range from territoriality,
 occupation of home ranges, mixed species flocking, and floating
 [55–58]. To quantify the movement behavior of
 individuals, we employed two tracking methodologies: one based on
 <em>manual hand tracking</em> and another <em>automated
 telemetry</em> using a network of 5 automated receivers
 (<em>sensorgnomes</em>). Hand tracking allowed us to follow
 individuals at a coarse temporal resolution (hourly-daily) but at a
 precise spatial resolution (&lt; 5 m). The network of automated
 receivers provided a very fine temporal resolution (detections every ~10
 seconds) but the coarse spatial resolution (~300 m detection range of each
 station; presence/absence). Coupling both approaches allowed us to gather
 a more complete understanding of both the spatial and temporal variability
 in movement behavior. Over the course of the study, we
 equipped a total of 141 redstarts
 (<em>N<sub>2016</sub></em> = 23,
 <em>N<sub>2017</sub></em> = 66,
 <em>N<sub>2018</sub></em> = 15,
 <em>N<sub>2019</sub></em> = 37) with a 0.29 g
 digitally coded VHF radio transmitters (NTQB1-1 & NTQB2-1, Lotek
 Wireless Inc., Newmarket, ON, Canada) using a modified leg-loop harness
 [59]. Transmitters operated continuously at a ~ 10.3-second cycle, which
 resulted in a battery lifespan of approximately ~30 - 45 days. Individuals
 were captured randomly across the study sites using a passive netting
 approach to avoid potentially biasing our tagged sample towards
 territorial individuals that are more susceptible to capture using
 playback. Further, to avoid the effects of the migratory preparation on
 individual movement behavior, we only included individuals tagged before
 April 1 of each year, which is about 4-5 weeks before May 5, the average
 date of departure for this population [60,61]. Of the 141 redstarts
 radio-tagged, a final sample of 74 individuals (39
 <em>males</em>, 35 <em>females</em>) were
 ultimately included in this study. Upon capture, we classified individuals
 into age and sex classes using plumage and feather characteristics [62]
 and then uniquely marked them with a combination of USGS aluminum band and
 two-color bands. We measured standard morphometrics, including body mass
 (g), fat score, pectoral muscle size [63], and tarsus length (mm). We
 estimated body condition using the scaled mass index following [64], where
 body mass was scaled by tarsus size and reported as standardized grams. To
 investigate the potential role that dominance status plays in influencing
 space use behaviors (territorial vs. floater), we relied on age and sex as
 well-established proxies of dominance since adult males have been
 demonstrated to be dominant over females and young males [65,66]. Further,
 to investigate the potential consequences of space use behaviors on the
 maintenance of individual condition across the nonbreeding season – an
 important contributor to an individual’s fitness [25,61,67,68] – we
 attempted to recapture as many individuals as possible throughout the
 season (<em>Mean Capture Window </em>= ) to
 quantify changes in body condition. Movement behaviors
 are often tied to changes in resource availability, and to explore the
 plasticity in space use behaviors, we attempted to link changes in the
 distribution in individual movement behavior to changing resource
 availability both between years and within a season at weekly intervals.
 In this system, changes in food availability mirror changes in habitat
 quality driven by rainfall [61,69,70] because drought negatively impacts
 leaf biomass and subsequently arthropod biomass declines [70]. As such, we
 utilized normalized difference vegetation index (NDVI) as a proxy for
 resource availability that provided both a seasonal metric of resource
 availability to facilitate comparisons across years but also captured
 changes in resource availability within season. We extracted NDVI across
 the study site from weekly Sentinel-2 multispectral imagery (10 m
 resolution) sourced using Google Earth Engine [71].
 <strong>Manual Hand Tracking</strong>
 Following the initial capture and tagging, we used a standardized
 protocol of localizing each individual daily through a mix of
 triangulation and homing localization techniques using an SRX-800 (Lotek
 Wireless Inc., Newmarket, ON, Canada) and a 3-element yagi. Each day, we
 searched for every active tag (once per round) for approximately 5-10
 minutes near the capture location or the last location the individual was
 detected. Upon detection, individuals were either localized via homing
 (identified visually via color bands), and a GPS location taken (&lt;1
 m) or triangulated by a single observer taking multiple bearings to the
 tag within a 3-minute period. Because of the close distances (&lt; 50
 m), triangulated positions were relatively precise (± 5m; LOAS software,
 Ecological Software Solutions). If an individual was not detected during
 the initial 5-10 minute search of that round, nor opportunistically while
 traversing the study site between tag detections, we expanded our search
 to include all areas of the study site that were not originally traversed
 for approximately 60 minutes. Because the nanotags are digitally encoded
 and transmitted on a single frequency, we were able to scan the 166.380
 mHz continuously while tracking, enabling us to locate individuals as we
 moved through the study site. Therefore, this protocol allowed us to
 confidently assess whether an individual was still alive, died, or
 departed the study site (~ 200 ha). Of the 74, 11 individuals were likely
 <em>transients</em> as they were only tracked a few times
 before ultimately relocating away from the study site and were excluded
 from further analysis. Following this protocol each season daily from ~ 6
 am - 1 pm, which resulted in an average of 26.5 locations per individual
 (range = 5 - 108) for the 63 individuals included in subsequent
 analyses We quantified space use and the variability in
 movement behavior in two complimentary ways using this temporally coarse
 but spatially precise hand-tracking data. First, to quantify an
 individual’s space use area, each individual’s ‘home range’ size was
 calculated as the area of the minimum convex polygon. While any estimate
 of home-range area is sensitive to sample size [72], our aims with this
 approach was to identify large deviations in home range area that amounted
 from individuals utilizing different space use tactics and not in
 relatively minute differences between individuals employing the same
 tactic. Further, as opposed to alternative home range estimators such as
 KDEs, MCPs typically underestimate home ranges at smaller sample sizes
 thus only likely underestimating home range estimates for floaters that
 already occupy areas that are orders of magnitude larger than average
 territory size. As such, MCP approach allowed us to compare the scale of
 space use in a continuous way across all individuals irrespective of their
 space use behavior because differences in home range area varied by orders
 of magnitude between territorial (&lt; 0.5 ha), floater (&gt; 2-10
 ha), and transient individuals (&gt; 10 ha). Second, , to draw
 comparisons between previous work and evaluate outcomes of space use
 behavior on individual performance (detailed below), we used net square
 displacement (NSD) to broadly categorize individuals into sedentary
 (‘territorial’) and alternative space use behaviors (‘floaters’ and
 ‘transients’). NSD is a frequently used movement metric that captures the
 scale and breadth of an individual’s trajectory as it measures the square
 of the Euclidean distance between subsequent locations. Distinct patterns
 in NSD time-series are expected from specific movement strategies with
 asymptotic patterns in NSD associated with a sedentary behavior (territory
 or home range) and NSD increasing over time representative of nomadic
 movements (floaters and transients). Although many studies have used
 non-linear parametric models to assign individuals to discrete movement
 strategies [73–75], patterns in net square displacement over time were
 distinct enough to visually assign individuals in this study (see
 Appendix, Figure S2). <strong>Automated
 Telemetry</strong> To track individuals at a fine
 temporal resolution across the study site, we utilized an automated radio
 tracking system that consisted of 5<strong>
 </strong>Sensorgnome receivers (Appendix, Figure S1).
 All tagged individuals were initially caught within the detection
 range of any given receiver ensuring that most individuals but because
 individuals often moved from their point of capture we only included
 individuals that were detected consistently for at least 5 days to
 accurately capture space use behaviors. Of the 63 individuals captured as
 part of this study, 36 were detected frequently enough across the array to
 include in this foray analysis. Each receiver was
 equipped with four horizontally polarized omnidirectional antennas
 positioned 9 meters high on a galvanized steel mast. These receivers
 continuously collected incoming signals from any nearby transmitter and
 logged the tag ID, timestamp, signal strength, and antenna port for each
 detection. Data collected by the automated telemetry system was uploaded
 to the Motus Wildlife Tracking System network for preliminary processing
 [46] and we used the R packages <em>Motus</em> [76] &
 <em>tidyverse</em> [77] to download, filter, and analyze the
 data. Although detections occurred approximately every 10 seconds, we
 smoothed the detection data at one-minute intervals by calculating the
 median signal strength from a series of consecutive detections for each
 receiver and then converted this into a detection history (1s and
 0s). Given our array configuration (Appendix, Figure
 S1), we were able to unambiguously determine that detections across
 different receivers of the same individual were indicative of relatively
 long-distance forays. First, these omni-directional antennas have reduced
 detection range compared to the typical directional yagis conventionally
 used. Based on the average distance at which an individual with known
 position was not detected, each receiver had an approximate detection
 range of ~300 m when individuals were moving at or below the canopy level.
 Further, the spatial configuration of receivers was such that they were
 distributed over an a large area () and separated by distances
 between , which are orders of magnitude larger than the average
 territory size of a redstart (; [78]). Taken together,
 sequential detections across different towers were indicative of
 exploratory forays outside of the territory or home range of an
 individual. Conventional hand-tracking approaches do not offer the
 temporal resolution needed to effectively capture these relatively rare or
 infrequent movements and therefore underrepresent the diversity of
 movement behaviors. As such, quantifying median daily forays enabled us to
 quantify the extent to which individuals (territorial or floaters)
 explored the study site on a continuous scale which allowed us to estimate
 the effect that seasonally variable resource availability, such as NDVI,
 had on the extent and prevalence of these movement behaviors.
 Forays can be quantified in two ways using the automated
 telemetry array: movements between one receiver and another
 (<em>Transitions</em>) or movements out of range of one
 receiver and back (<em>Recursions</em>). <em>Transitions
 </em>are unambiguous because, with this array configuration,
 departures from one receiver’s detection range into another’s reflect
 true, relatively large-scale, movements. <em>Recursions,
 </em>however, are more ambiguous. They can either represent the
 movement of individuals temporarily out of range of the receiver but still
 within its home range or territory, or they can represent forays outside
 of their respective home range but in areas not covered by our array
 (Appendix, Figure S1). Given their ambiguity, in this manuscript, we chose
 to exclude <em>recursions</em> and therefore our
 quantification of daily forays represents a conservative
 estimate. <strong>Evaluating the Consequences of
 Space Use Tactics on Nonbreeding Season Condition</strong>
 Nonbreeding season condition – defined as how well an individual
 maintains or improves their body condition throughout the nonbreeding
 season – is a key trait that underlies the overall performance of an
 individual and is inherently tied to fitness in this species through a
 seasonal interaction on departure time [61,69]. However, body condition is
 an inherently multifaceted trait that is difficult to measure and rarely
 captured with single metrics. Because we were primarily interested in the
 relative mass and muscle an individual accumulates or maintains throughout
 the season, we keyed in on the change in scaled mass index (~size
 corrected mass, g) [64] and change in pectoral muscle size (mm)[63,79] as
 response variables in linear models that included space use tactic
 (territorial vs. floaters) as well as age, sex, and habitat type to
 account for their confounding effects. Of the 63 individuals tracked in
 this study, we recaptured 46 at least twice within a season (&gt; 10
 days apart), which allowed us to assess how space use strategy influenced
 changes in nonbreeding season condition. Key positive indicators of
 condition were positive changes in scaled mass index (increases in body
 mass) and increases in muscle size. Poor performers, on the other hand,
 would expect to lose or maintain mass throughout the season and decrease
 in muscle size as expected from a previous food-reduction experiment in
 this study system [25]. <strong>Data
 Analysis</strong> We fit general linear models
 that included age class (second year and after second year) and sex (male
 and female) to investigate how home range area of an individual, its space
 use tactic (territorial vs floater), and its median daily forays was
 influenced by its dominance status. We also included year as a fixed
 effect to assess how the average space use behavior of the population
 differed among years. With the home range area models, we opted to weight
 the variance by the number of locations used to estimate the MCP to
 account for the fact that home range area is sensitive to the number of
 locations used to estimate the MCP. As such observations that included
 more locations were given more weight than observations with fewer
 locations. While the home range area and forays were modelled using normal
 distributions, individual space use tactic was modeled using a logistic
 distribution. We initially fit all three models (home range area, space
 use tactic, and forays) with two-way interactions between habitat and age
 and sex but found that those interactions were non-significant. As such,
 we dropped those terms and report only results of the additive
 models. We explored within-year changes in space use
 behavior (mean daily forays & mean daily displacement) averaged
 across a week using linear mixed models that included individual as a
 random effect and weekly NDVI at the study site level as the predictor. We
 included age class, sex, and habitat type (dry scrub vs. wet mangrove)
 along with their respective interactions with NDVI to explore how an
 individual’s space use response to changes in environmental conditions
 varied by dominance status and habitat type. Because both mean daily
 forays and mean daily displacement were quite variable and spanned orders
 of magnitude, we log transformed these variables. In the case of mean
 daily forays which included 0’s, we added 1 to each value. All analyses
 were run in R and mixed models were fit using the lme4 package [80]. We
 assessed significance of all parameters at the level and where
 appropriate used likelihood ratio tests on reduced model varieties for
 models that included random effects.
description: # Data from: Hidden space use behaviors of a nonbreeding migratory bird:
 The role of environment and social context
 [https://doi.org/10.5061/dryad.gb5mkkwx1](https://doi.org/10.5061/dryad.gb5mkkwx1) ## Description of the data and file structure ### Files and variables #### File: analysis.r **Description:** R script for analyzing space use patterns and foraging behavior of American Redstarts. The script requires R (version 3.5 or higher) and the following packages: tidyverse, lme4, sjPlot, ggstance, patchwork, ggdist, emmeans. The analysis includes: 1. Data processing: Summarizes seasonal NDVI (weeks 1-12, pre-migratory period), calculates median daily forays per individual, and merges environmental and movement data with individual metadata. 2. Descriptive visualizations: Generates density plots of home range sizes and foray frequencies across years, and creates boxplots with dot distributions comparing space use metrics by sex, age, and habitat type (dry scrub vs. wet mangrove). 3. Between-year variation: Linear regression examining the relationship between seasonal median NDVI and mean home range area across years. 4. Space use modeling: Linear models testing effects of year, sex, age, and habitat on home range area (hectares), with post-hoc pairwise contrasts between years. Interaction terms (habitat × age, habitat × sex, age × sex) were tested but not retained in final models. 5. Foray behavior modeling: Linear models examining effects of year, sex, age, and habitat on median daily forays, with tests for two-way interactions. 6. Within-season plasticity: Linear mixed-effects models examining the relationship between weekly NDVI and median daily forays, with individual as a random effect. Models test for interactions between NDVI and habitat, age, and sex to assess whether movement responses to changing resource availability vary by demographic group. 7. Physiological consequences: Linear models examining the relationship between space use strategy (territorial vs. floating, based on net squared displacement thresholds) and changes in body mass and body condition across the season, controlling for age, sex, and habitat. #### File: individual.rds **Description:** Banding for each individual at each capture occasion. Data contain, unique identifier (band number), and common morphometrics (mass, tarsus, winglength, tail lengthfat score, breast muscle score, breast muscle size) in addition to each individuals age and sex assessed using plumage characteristics. ## Columns ### Study Design & Identification * **motusID**: Unique identifier in the Motus Wildlife Tracking System database (range: 14933-34050) * **year**: Study year (2016, 2017, 2018, 2019) * **band**: USGS aluminum band number (unique identifier for each individual bird) * **id**: Manufacturer tag ID combined with year (format: mfgID_year, e.g., "52_2017") * **id_year**: Combined unique identifier with band number and year (format: band_year, e.g., "2670-21573_2017") ### Capture Metadata * **CAPDATE**: Capture date (format: DD-Mon-YY, e.g., "17-Jan-17") * **HABITAT**: Habitat type where individual was captured * "Wet Mangrove" = wet mangrove habitat * "Dry Scrub" = dry scrub habitat ### Individual Characteristics * **AGE**: Age class at capture * "Adult" = after second year (ASY) * "Juvenile" = second year (SY) * "Unknown" = age could not be determined * **SEX**: Sex determined from plumage characteristics * "Male" = male * "Female" = female * "Unknown" = sex could not be determined ### Morphometric Measurements * **CAPMAS**: Capture mass in grams * **TARSUS**: Tarsus length in millimeters * **WING**: Wing chord length in millimeters ### Physiological Condition Indicators * **CAPFAT**: Fat score at capture, indicating subcutaneous fat deposits (Scale: 0, T, LT, HT, 1,2,3,4,5) * Numeric values: 0-5 (standardized fat scoring scale, where 0 = no visible fat, 5 = maximum fat) * Character values: "T" (trace), "LT" (light trace), "HT" (heavy trace) denote fractional differences in fat deposits between scores 0 and 1 to provide better low fat score resolution. * **CAPBMS**: breast muscle size measured with calipers (mm; range -3.0 to 3.8 mm) using breast muscle sizer.  * Higher values indicate greater muscle mass/better condition #### File: mcp.rds **Description:** Home range data calculated using minimum convex polygons (MCP) for each tracked individual (N = 62 individuals). This file contains nested spatial data structures from the `amt` R package, combining telemetry location data with calculated home range polygons. **Columns:** * `id`: Individual identifier (mfgID_year format, e.g., "516_2016") * `data`: Nested list column containing tracking data for each individual. Each element is a `track_xyt` object with the following sub-columns: * `x_`: UTM easting coordinate (m) * `y_`: UTM northing coordinate (m) * `t_`: Timestamp of location (POSIXct format) * `HABITAT`: Habitat type (M = wet mangrove, L = dry scrub) * `AGE`: Age class (SY = second year, ASY = after second year) * `SEX`: Sex (M = male, F = female) * `n`: Number of location points used to calculate home range * `hr_mcp`: Nested list column containing the MCP home range object. Each element is an `mcp` object from the `amt`package with spatial geometry * `level`: Confidence level for MCP calculation (0.95 = 95% MCP for all individuals) * `what`: Type of estimate ("estimate" for all records) * `area`: Home range area in square meters #### File: change.rds **Description:** Contains data on change in body condition of each individual recaptured twice across the season. Change in breast muscle size, fat score, mass, and size corrected mass. Quantifies physiological consequences of different space use strategies. **Columns:** * `id_year`: Combined identifier with band number and year (e.g., "2550-39485_2017") * `mass_change`: Change in body mass between captures (g; range -0.5 to 1.3 g) * `bc_change`: Change in body condition (scaled mass index; range -3.7 to 3.0 in scaled grams) * `bm_change`: Change in breast muscle score (integer scale; -1 = decreased, 0 = no change, 1 = increased) * `bms_change`: Change in breast muscle size measured with calipers (mm; range -3.0 to 3.8 mm) using breast muscle sizer.  * `jdiff`: Number of days between first and last capture (Julian days; range 8-77 days, mean 40 days) #### File: ndvi.rds **Description:** Normalized Difference Vegetation Index (NDVI) values as a proxy for habitat quality and resource availability (N = 110 week-year combinations covering weeks 1-22 of the non-breeding season across 5 years: 2016-2020). NDVI extracted from Sentinel-2 multispectral satellite imagery at 10m resolution. **Columns:** * `year`: Study year * `week`: Week number of the non-breeding season (1-22, where week 1 starts early January) * `pred`: Predicted/smoothed NDVI value from temporal GAM model (range 0.24-0.58) * `ndvi`: Raw observed NDVI value for that week. #### File: forays.rds **Description:** Foray behavior data quantifying movement between automated telemetry receivers for tracked individuals. Forays represent excursions outside core areas detected by transitions between different automated receivers in the network. **Columns:** * `year`: Study year (2017-2019) * `week`: Week number of the non-breeding season (1-22) * `pred`: Predicted/smoothed NDVI value for that week-year (from ndvi dataset; range 0.31-0.58) * `motusID`: Unique Motus tracking system identifier for individual. * `HABITAT`: Habitat type (character; "Dry Scrub" = dry scrub, "Wet Mangrove" = mangrove habitat) * `AGE`: Age class (character; "Juvenile" = second-year bird, "Adult" = after second-year bird) * `SEX`: Sex (character; "Male" or "Female") * `median_transitions`: Median number of daily transitions between receivers during that weeK. * `total_transitions`: Total number of transitions recorded during that weeK. ## Access information Other publicly accessible locations of the data: NA Data was derived from the following sources: * NDVI estimated using data collected from Sentinel 2 hyperspectral imagery accessed through google earth engine.