DOI: 10.5061/dryad.prr4xgxw4
title: Spur-winged lapwings show spatial behavioral types with different mobility and exploration between urban and rural individuals

description: Understanding how wildlife responds to the spread of human-dominated
 habitats is a major challenge in ecology. It is still poorly understood
 how urban areas affect wildlife space-use patterns and consistent
 intra-specific behavioral differences (i.e., behavioral types; BTs), which
 in turn shape various ecological processes. To address these questions, we
 investigated the movements of a common resident wader, the spur-winged
 lapwing (Vanellus spinosus), hypothesizing that urban individuals will be
 more mobile than rural ones. We used an ATLAS tracking system to track
 many (n=135) individuals at a high resolution (8 second-fix interval) over
 several months each. We first established that daily movement indices show
 consistent differences among individuals, acting as spatial-BTs. Then
 focusing on the two main principle-components of lapwings’ daily-movements
 – mobility and position along the exploration-exploitation gradient– we
 investigated how these BTs are shaped by urbanization, season (nesting vs.
 non-nesting), and sex. We found that urban lapwings were indeed more
 mobile in both seasons. Furthermore, urban females were less explorative
 than rural females, especially during the nesting season. These results
 highlight how urbanization affects wildlife behavior, even of apparently
 urban-resilient avian residents. This underscores the need to consider
 possible behavioral consequences that are only apparent through advanced
 tracking methods.
description: For movement tracking we captured nesting lapwings during
 2019-2022. We searched the study area for nests and placed a walk-in trap
 on them. When possible, sex was determined from spur length (males
 &gt; 10 mm, females &lt; 7 mm), and otherwise by means of genetic
 markers from a commercial provider, using feather samples collected from
 all individuals. We used 7g tracking device tags attached in a leg-loop
 configuration (&lt; 4% of body weight, including harness). A total of
 211 transmitters were deployed on 194 individual adult lapwings (2020 =
 68, 2021 = 74 and 2022 = 69), including 17 re-captures in subsequent
 years. To obtain thetracking data at the
 high-frequency we used ATLAS (Advanced Tracking and Localization of
 Animals in real-life Systems). This system is based on a reverse-GPS, in
 which the bird-borne tags emit a unique-ID signal at set intervals that is
 detected by an array of fixed base-stations (tower-mounted antennas)
 deployed at high vantage points in the region. Signals detected by three
 or more base stations enable localization of the tag from their
 differential times of arrival. The tags were set to transmit at 8-sec
 intervals (1/8Hz), with a practical lifetime of up to 315 days. The
 deployment of our particular ATLAS system comprised 19 base-stations
 covering ~350 km<sup>2</sup>. Tracking always started during
 the nesting season (March-August), but many of the trajectories extended
 into the non-nesting season (September-February), allowing us to include
 seasonal differences in the analyses, while addressing individual
 consistency in behavior, on an urbanization gradient.
 To determine whether lapwing movements present spatial-BTs, we
 characterized their daily movements using several movement indices. Center
 of activity (COA) was defined with a 100x100 m grid, as the area where the
 lapwings spent more than 75% of their total tracking time. Using the
 flight segments we calculated daily: (1) total time in COA (minutes); (2)
 maximum displacement from the COA (the distance to the furthest location
 in the daily trajectory, in meters); (3) total flying distance (in
 meters); (4) number of flying segments, (5)  flying speed (meters/second);
 (6) max diameter (the distance between the two most distant locations in a
 daily trajectory, in meters); (7) daily locations visited; (8) daily
 unique locations visited; (9) re-visitation rate (total visited
 locations/unique locations visited on the map); and (10) total time on the
 ground. To determine whether the affinity to
 urbanization affects the lapwings' movement behaviors, we estimated
 the coverage of urban habitat within their core home range (HR). As an
 index of their core HR (where they spent most of the time) we used the
 median kernel density estimation (KDE50%). For our analysis we consider
 urban habitats as areas where people live, such as small villages or
 cities including lawns and roundabouts, or open areas within settlement
 border. Rural habitats, in contrast, include fields and ponds with much
 lower human infrastructure (e.g. buildings) and activity.
 For determining the core HR (KDE50%) we subsampled our data into
 1-hour intervals. We considered the non-flight locations only. The KDE
 analysis was implemented with the package
 '<em>adehabitatHR</em>' with parameters of h = href,
 extent = 1 and grid = 500. We combined habitat polygon layers of urban
 areas and fish ponds, with agricultural fields, and calculated the
 percentage of built-up in each lapwing’s core home-range. This index
 (ranging from 0 to 100%) was used as a proxy for the bird's level of
 urbanization, and individuals could readily be classified into ‘urban’ and
 ‘rural’ individuals. The index showed a clear bi-modal distribution with
 one peak below 30% (rural lapwings) and one above 60% (urbans).
 We calculated each one with repeatability using the package
 '<em>rptR</em>', using 1000 bootstraps for
 confidence interval estimation, including the individual identity as a
 random effect.  To reduce the dimensionality of movement, we then
 performed a Principal Component Analysis (PCA) that included all indices
 that were significantly repeatable (<em>P</em> &lt; 0.05).
 The PCA was implemented with the <em>'prcomp</em>'
 package from a base '<em>stats</em>' package in R.
 The first two principal components (PC1 and PC2) were used as the main
 dependent variables. For each PC we used a set of
 generalized linear mixed models (GLMMs), in a model comparison framework.
 All models included bird ID as random factors. Fixed effects (predictors)
 varied among models and included (1) our main predictor of interest - the
 urbanization level (urban or rural), as well as the additional predictors
 of: (2) sex (male or female); (3) season (nesting or non-nesting); and (4)
 initial weight (scaled). We also considered the date in the model as
 temporal autocorrelation, by using '<em>corAR1</em>'
 in the model for each ID. Because the effect of urbanization on movement
 can be mediated by these predictors, we also included models with two- and
 three-way interactions of urbanization with the other three predictors. 
 We implemented model selection with the
 '<em>MuMIn</em>' package, and included only models
 with Delta<sub>AICc</sub> ≤ 4  in the model
 averaging.
description: # Vanellus\_spinosus\_Movement\_Behavior\_Metadata: This dataset contains
 daily movement indices used in the manuscript titled: *"Spur-winged
 lapwings show spatial behavioral types with different mobility and
 exploration between urban and rural individuals"* **Research
 Approach**:\ We focused on the spur-winged lapwing (*Vanellus spinosus*),
 a common resident species in diverse habitats. We tagged breeding lapwings
 residing in the Harod Valley, northeastern Israel, and calculated their
 urbanization scores by analyzing the percentage of built-up area around
 their nests within their 50% KDE home range. Birds were classified as
 urban if 60% or more of their core home range consisted of built-up areas.
 We then analyzed daily movement patterns for each bird, assessing the
 repeatability of these behaviors. Using Principal Component Analysis
 (PCA), we extracted two key behavioral indices: mobility (PC1) and
 exploration (PC2). Finally, we explored how movement behaviors differed
 between birds inhabiting distinct habitat types. ## Description of the
 Data and file structure * **Col A, "Ring_ID"**: Bird ring
 identification number. * **Col B, "Date"**: Date of the
 experiment (dd-mm-yyyy). * **Col C, "PC1_Mobility_37"**: PC1
 scores per bird per date, representing 37% of the variance explained by
 the PCA. * **Col D, "PC2_Exploration_25"**: PC2 scores per bird
 per date, representing 25% of the variance explained by the PCA. * **Col
 E, "Urbanization_Score"**: Percentage of urban area within the
 bird's core home range, calculated via KDE, using 1 location per hour
 subsampling. * **Col F, "Flying_Segments"**: Number of times the
 bird flew during the day. * **Col G,
 "Total_Flying_Distance_m"**: Total distance flown in meters. *
 **Col H, "Flying_Speed_m/sec"**: Average flight speed in meters
 per second. * **Col I, "Daily_Locations_Visited"**: Total number
 of locations visited in a day. * **Col J,
 "Total_Time_On_Ground_Min"**: Total time spent on the ground, in
 minutes. * **Col K, "Max_Diameter_m"**: Maximum distance
 traveled in a day, in meters. * **Col L,
 "Daily_Unique_Locations_Visited"**: Number of unique locations
 visited in a day within a 100-meter radius. * **Col M,
 "Total_Time_in_COA_Min"**: Total time spent in the core area of
 activity (COA), defined as the area the bird spent most time within during
 the tracking period (100-meter radius). * **Col N,
 "Max_Displacement_From_COA_m"**: Maximum daily distance from the
 COA, in meters. * **Col O, "Land_Use"**: Ratio of daily unique
 locations to total locations visited. * **Col P, "Season"**:
 Nesting season (March-August) or non-nesting season (September-February).
 * **Col Q, "Urbanization_Factor"**: Habitat preference,
 categorized as urban (&gt;60% built area) or rural (&lt;30% built area). *
 **Col R, "Spur_Length_mm"**: Length of the bird's spur, in
 millimeters. * **Col S, "Wing_Length_mm"**: Wing length, in
 millimeters. * **Col T, "Tail_Length_mm"**: Tail length, in
 millimeters. * **Col U, "Tarsal_Length_mm"**: Tarsal length, in
 millimeters. * **Col V, "Weight_g"**: Bird’s weight at the time
 of capture, in grams. * **Col W, "Sex"**: Bird’s sex (male or
 female), determined via feather sampling. * **Col X,
 "Date_Capture"**: Date the bird was tagged. * **Col Y,
 "Start_Hour"**: Time the bird was tagged. * **Col Z,
 "Nest_Lat"**: Latitude of the bird's nest. * **Col AA,
 "Nest_Lon"**: Longitude of the bird's nest. * **Col AB,
 "ATLAS_Tag"**: ATLAS tag number, where a bird may have multiple
 tags if captured in sequential years. **Note**: "NA" indicates
 missing data.