DOI: 10.5061/dryad.vhhmgqp3z
title: The effects of female group-changing behavior on female-female aggression, female rank, and female hierarchy stability

description: It is unclear how habitat features alter animal response to social
 instability. Only by uncovering such interactions can we fully understand
 the evolutionary drivers and fitness consequences of sociality. We
 capitalize on a management-induced manipulation of social stability in an
 island population of free-ranging feral horses (Equus caballus), living
 across three distinct habitat types. We tested whether female
 group-changing behaviour (a reliable measure of social instability)
 affected 1) female-female aggression, 2) rank within female dominance
 hierarchies, 3) stability of female hierarchies (in the groups they joined
 and/or left), and 4) how habitat characteristics shaped these responses.
 Female group-changing behaviour positively predicted aggression from other
 females, but only when habitat features such as visibility and freshwater
 distribution were considered. We found no strong association among female
 group changing behaviour and the aggression initiated, female rank, or the
 stability of female dominance hierarchies. Our work reveals that animal
 responses to social instability are nuanced and impacted by the
 surrounding habitat. A better understanding of these impacts can help
 elucidate evolutionary drivers of sociality and mitigate unintended
 effects of management practices.
description: Behavioural and demographic sampling
 Comparable to previous studies, data were collected across 7.5
 weeks in 2021 (June – July) and 8 weeks in 2023 (June – August), totaling
 697.15 hours of behavioural observation (379.9 hours, 2021; 317.25 hours,
 2023), averaging 4.72 ± 0.61 hours per mare (range = 0.5-14.25 hours). We
 recorded all instances of female-female aggression including
 displacements, bites, kicks, and pushes [25]. Each female’s aggression
 rate was calculated as the total number of aggressive interactions (either
 initiated or received) / the total number of hours she was observed. Only
 reproductive mares (aged 4 years and older) for which we collected at
 least three hours of data were included in our analyses (n = 51). When
 possible, animals were observed during both years of the study; two main
 factors precluded this for some animals: 1) field logistics (finding and
 then observing animals on foot) required that only mares that could be
 located on a consistent basis were observed each year and 2) several
 animals did not meet our age cutoff in 2021 but did in 2023. Consequently,
 35 mares were observed in both seasons; the remaining were observed in
 only 2021 (n = 10) or 2023 (n = 6). Observed animals represented 89% and
 91% of all reproductive females in 2021 and 2023, respectively. We
 excluded 2021 data for one mare that changed groups and initiated
 aggression at rates that were 4.86 and 5.41 standard deviations above the
 mean, respectively (Grubbs test: group changes per hour, G = 4.87, U =
 0.72, <em>P</em> = 6.63 x 10<sup>-6</sup>;
 aggression initiated per hour, G = 5.42, U = 0.66, <em>P</em>
 = 1.03 x 10<sup>-7</sup>). On average, we
 located each study group once every 7.39 ± 1.09 (range = 3-17) days and
 once every 9.72 ± 1.14 (range = 5-23) days in 2021 and 2023, respectively.
 We recorded each group’s GPS location and composition, noting the presence
 or absence of individual mares. Horses were identified individually by
 colour, sex, physical condition, and other distinguishing markings,
 including scars and freeze brands. Ages are known from long-term records
 and from NPS data (Stuska, unpublished data). We observed groups from at
 least 15-20 meters away (as per NPS guidelines). Though such distances may
 be disruptive with other animal populations, the horses of Shackleford
 Banks are tolerant of human activity. Upon our approach, horses typically
 continued their activity or looked to the observer for several seconds
 before returning to their previous behaviour (personal observation,
 C.M.V.N.). Mare group changes were rarely witnessed
 directly (2021, n = 1; 2023, n = 1); therefore, mare absence from a band
 was an important metric with which we measured the number of changes
 between groups. We remained with each group for at least 30 minutes to
 ensure that individuals recorded as absent were not nearby, but out of
 sight. Group-changing behaviour was confirmed by the mares’ presence in
 new bands. In 2021, confirmation was made within 6.62 ± 1.03 days on
 average (range = 0-24); in 2023, confirmation was made within 8.75 ± 1.17
 days on average (range = 0-34). Typically, females changed groups
 individually. In one case, it seemed that two females had changed groups
 together, though this could not be confirmed as the change was not
 witnessed directly. Data regarding that change were excluded from our
 analyses. Dominance Hierarchies and Elo
 scores For each aggressive interaction, a “loser” and
 “winner” were determined based on whether a female maintained her position
 or left the immediate area. From these data, we calculated Elo ratings
 using the randomization process in the aniDom package in R (version
 4.2.0)  to determine females’ dominance ranks within each band in each
 year of the study. To compare ranks across bands of different sizes, we
 calculated individual ranks relative to the highest-ranking female in each
 band. We also estimated the stability of female hierarchies within each
 band using Elo uncertainty scores [45, 46]. Overall, hierarchies were
 steep, <em>i.e.</em> had a high probability that higher
 ranking animals would win a given interaction (mean probability = 0.94 ±
 0.02, range = 0.67 - 1), and stable (mean Elo uncertainty = 0.91 ± 0.18,
 range = 0.71 - 0.99 [45]).  Body condition
 Because physical condition can play a role in aggression and
 dominance, we measured mare condition with rump scores, assessing the
 curvature of the line between the tailbone and the point of the hip. Only
 one observer (C.M.V.N.) measured animal condition, referencing a rump
 score guide regularly to ensure consistency of measurement during the
 study. Scores were based on a scale from 1 to 5; a score of 1 being the
 poorest. Females were scored an average of 3.46 ± 0.58 times (range =
 2.0-4.9) and 3.11 ± 0.59 times (range = 1.0-4.1) in 2021 and 2023,
 respectively.
description: # The effects of female group-changing behavior on
 female-femaleaggression, female rank, and female hierarchy stability
 [https://doi.org/10.5061/dryad.vhhmgqp3z](https://doi.org/10.5061/dryad.vhhmgqp3z) ## Description of the data and file structure ### Files and variables #### File: CHANGES\_AND\_ELO\_BIO\_LET\_20241025.R **Description:** R code for analysis regarding the effects of female group-changing behavior on female dominance hierarchy stability #### File: CHANGES\_WITH\_RANK\_AND\_AGG\_BIO\_LET\_20241115.R **Description:** R code for analysis regarding the effects of female group-changing behavior on female-female aggression and female rank #### File: agg\_and\_rank\_data\_BIOL\_LETT\_20241025.csv **Description:** Data for analyzing the effects of female group-changing behavior on female-female aggression and female rank ##### Variables * year: * horse: * mareage: * region: * no_trts: * no_changes: * total_agg_given: * total_agg_rcd: * ch_per_hour: * agg_given_per_hrs_obs: * agg_rcd_per_hrs_obs: * total_time_watched_min: * total_time_watched_hrs: * avg_rel_rank: * ave_rump: #### File: agg\_and\_rank\_data\_dictionary\_20241025.csv **Description:** Data dictionary for analyzing the effects of female group-changing behavior on female-female aggression and female rank ##### Variables * mareage: * numeric: * 4-30: * mare age in years: #### File: elo\_data\_BIO\_LETT\_20241025.csv **Description:** Data for analyzing the effects of female group-changing behavior on female hierarchy stability ##### Variables * year: * male: * male_year: * elo_uncertainty: * joins: * leaves: * total_changes: * total_sightings: * total_changes_per_sighting: #### File: elo\_data\_dictionary\_BIO\_LETT\_20241025.csv **Description:** Data dictionary for analyzing the effects of female group-changing behavior on female hierarchy stability ##### Variables * variable name: * measurement unit: * allowed values: * variable definition: ## Code/software The code for our analysis can be viewed with R, a free software environment for statistical computing and graphics. Our datasets and data dictionaries are saved as CSV files and can be opened at text files so do not require any proprietary software.