Data from: A behavioral syndrome of competitiveness in a non-social rodent

Erixon, Filippa1 ; Eccard, Jana A.1 ; Huneke, Rika1 ; Dammhahn, Melanie2

Research facility: University of Potsdam

Published Aug 06, 2024 on Dryad. https://doi.org/10.5061/dryad.dncjsxm61

Data files

Aug 06, 2024 version files 87.51 KB

body_metrics.xlsx

11.76 KB
emergence.xlsx

13.90 KB
open_field.xlsx

15.19 KB
README.md

10.05 KB
SDE.xlsx

22.88 KB
UMV.xlsx

13.73 KB

Abstract

Animals compete for limited resources and the outcome of intraspecific competition should be determined by individual variation in behavioral traits, such as aggressiveness, and dominance status. Consistent among-individual differences in behavior likely contribute to competitiveness and predispose individuals to acquire specific dominance ranks. In a step towards better understanding these functional links, we studied trait integration into behavioral syndromes, using 26 captive male bank voles (Myodes glareolus). We repeatedly assessed boldness in an emergence test, exploration in an open-field test, aggressiveness in staged dyadic encounters, and the among-individual correlations between these behaviors. We further related these personality traits to dominance rank, from quantifying urine marking value (UMV), as marking in bank voles is related to dominance rank. We found repeatable variations in boldness, exploration, aggressiveness, and UMV, which were correlated at the among-individual level. Aggressiveness tended to be negatively correlated with body condition, a proxy for fitness. Thus, key personality traits and social rank are functionally integrated into a behavioral syndrome of intraspecific competitiveness.

README: Data from: A behavioral syndrome of competitiveness in a non-social rodent

https://doi.org/10.5061/dryad.dncjsxm61

Description of the data and file structure

# Data from: A behavioral syndrome of competitiveness in a non-social rodent

[Access this dataset on Dryad](https://doi.org/10.5061/dryad.dncjsxm61)

The dataset contains four excel files of data from behavioral assessment of and one excel file of body metrics of male bank voles. All datasets have a “data” tab for the data and a “labels” tab explaining the data that can be found under the column names in the “data” tab. The individual identification “id” is the same across all files. The file names “emergence”, “open-field”, “SDE”, and “UMV” correspond to the assays explained in detail below. The “body metrics” dataset corresponds to the “body condition” section explained below. The body condition is also present in the SDE dataset. Statistical analysis used to analyze the data is described in the “Statistical analyses” section. This data can be used to evaluate among-individual correlations in behavior.

We collected the data for a study on captive animals from July-February of 2021 and 2022 in the animal facilities of the Animal Ecology Group in Potsdam (Germany). Study animals were male bank voles (Myodes glareolus) captured from the wild in a small forest patch near Potsdam (Germany)(n = 20) or born in captivity (n = 6) as F1 of wild-caught parents. Prior to the experiments, all animals spent 3 to 5 months in the laboratory and were sexually mature. Voles were housed in standard polycarbonate cages (type III and type IV) provided with wood shavings and hay as bedding, cardboard rolls as shelter, and a running disk for enrichment. Water and food pellets (Ssniff, NM, V 1244-0) were available ad libitum.

Files and variables

File: body_metrics.xlsx

Description: body metrics data of individuals in assessed for behavior.

Variables

id = the individual identification of male voles in the behavioral assays, same as the “id” in other data files.
width = the head width (mm)
comment = comments to the head width measurements, dead indicates that an animal was dead when the measurement was taken (blank cells have no comments)
closest_bm = body mass (g) of the temporally closest measurement taken to the head width measurement
date_bm = date for when the body mass was taken

File: UMV.xlsx

Description: data from paired trials to assess urine marking behavior

Variables

id = individual identification of the male voles in the experiment, same as the “id” in other data files.
date = date at which the test was conducted
UMV = the urine marking value scored as the number of squares with urine markings see paper for details (1-35)
total_squares_visible = the total number of squares that could be evaluated for UMV
UMV_factor = a factor based on the type of markings (D = dominant, S = subordinate)
test_time = the temporal length of the test in minutes
test.occasion = the orded of the test conducted, (first = 1, second = 2, third = 3 , fourth =4, time tested)
opponent = the identification of the opponent
comments = any comments to the body condition measurement

File: README.md

Description: README for datasets.

File: SDE.xlsx

Description: data from staged dyadic encounters for assessing aggressiveness behavior.

Variables

id = individual identification of the male voles in the experiment, same as the “id” in other data files.
date = date at which the test was conducted
time = time at which the test was conducted
test.occasion = the orded of the test conducted, (first = 1, second = 2, third = 3 , fourth =4, time tested)
t*est_compartment* = which compartment (i.e. which side of the barrier) of the arena the individual was placed
arena = which arena out of two identical arenas the animal was placed in
opponent = the individual identification (id) of the opponent
body_mass = the body mass (g) immediately before the test was conducted
time_Z1 = time spent in Zone 1 (the zone closest to the barrier and the opponent)
time_Z1_opponent_Z1 = time spent in Zone 1 (the zone closest to the barrier and the opponent) when the opponent was also in Zone 1 of his compartment of the arena.
time_Z2 = time spent in zone 2 (the middle zone of the compartment of the arena)
time_Z3 = time spent in zone 3 (the zone furthest away from the barrier)
total_time = total time in test (s)
A2 = is the number of crossings by the focus individual into Zone 1 (the zone closest to the barrier and the opponent) disregarding the position (which zone or side of the arena) of the opponent (see online resource 1 Figure S2 from Erixon et al. 2024).
A1 = is the number of crossings into Zone 1 (the zone closest to the barrier and the opponent) when the opponent is on the same side (disregarding zone location) and when the focus individual crosses within Zone 1 to the side in which the opponent is located (disregarding zone location) (see online resource 1 Figure S2 from Erixon et al. 2024)
A4 = is the number of crossings by the focus individual into Zone 1 (the zone closest to the barrier and the opponent) when the opponent is in Zone 1 but disregarding which side of the arena the opponent is positioned in (see online resource 1 Figure S2 from Erixon et al. 2024).
A3 = is the number of crossings into Zone 1 (the zone closest to the barrier and the opponent) when the opponent is located in Zone 1 and crossings within Zone 1 to the side the opponent vole is located in (see online resource 1 Figure S2 from Erixon et al. 2024).
D2 = is the number of crossings by the focus individual out of Zone 1 (the zone closest to the barrier and the opponent) disregarding the position (which zone or side of the arena) of the opponent (see online resource 1 Figure S2 from Erixon et al. 2024).
D1 = is the number of crossings out of Zone 1 (the zone closest to the barrier and the opponent) when the opponent is on the same side (disregarding zone location) and when the focus individual crosses within Zone 1 away from (D) the side in which the opponent is located (disregarding zone location) (see online resource 1 Figure S2 from Erixon et al. 2024).
D4 = is the number of crossings by the focus individual out of Zone 1 (the zone closest to the barrier and the opponent) when the opponent is in Zone 1 but disregarding which side of the arena the opponent is positioned in (see online resource 1 Figure S2 from Erixon et al. 2024).
D3 = is the number of crossings out of Zone 1 (the zone closest to the barrier and the opponent) when the opponent is located in Zone 1 and crossings within Zone 1 away from the side the opponent vole is located in (see online resource 1 Figure S2 from Erixon et al. 2024).
total_crossings = total number of crossings between zones in the arena
comment = any comments to the test
body_condition = the body condition (g) at the start of the test, the index is calculated as the mass (g) of an individual standardized to the mean head width (mm)
comment_bc = any comments to the body condition measurement

File: open_field.xlsx

Description: data from open-field test.

Variables

id = individual identification of the male voles in the experiment, same as the “id” in other data files.
test.occasion = the orded of the test conducted, (first = 1, second = 2 , time tested)
observer = initials of the observer of the test
date = date at which the test was conducted
time = time at which the test was conducted
latency_center = the latency to enter the center circle with the whole body (excluding the tail) (s)
crossings = number of crossings into the center circle with the whole body (excluding the tail)
sections = number of sections explored of the arena (1-16)
activity = number 10 s interval instances spent active in the arena (1-35)
inactivity = number 10 s interval instances spent inactive in the arena (1-35)
percentage_active = percentag eof 10 second interval instances spent active (the activity divided by 35 - or less if the activity was impossible to not at any 10 s instance)
days_between_tests = the number of days between tests occasions
comment = any comments to the test, Na’s and blank cells indicate that there are no comments to the test.

File: emergence.xlsx

Description: data from emergence test.

Variables

id = individual identification of the male voles in the experiment, same as the “id” in other data files.
test.occasion = the order of the test conducted, (first = 1, second = 2 , time tested)
observer = initials of the observer of the test
date = date at which the test was conducted, NA’s indicate when the data does not exist for the animal as the video recording gave up on three occasions.
time = time of day at which the test was conducted, NA’s indicate when the data does not exist for the animal as the video recording gave up on three occasions.
days_between_tests = the number of days between tests occasions, NA’s indicate are present when data from the second test is missing and there is no record of the number of days between tests.
latency_head = the latency to emerge from tube with the whole head (including the ears) (s) , NA’s indicate when the data does not exist for the animal as the video recording gave up on three occasions.
latency_body = the latency to emerge from the tube with the whole body (excluding the tail) (s), NA’s indicate when the data does not exist for the animal as the video recording gave up on three occasions.
comment = any comments to the test

Code/Software

All analyses were carried out in R, Version 4.3.1.

Methods

Study animals

We conducted this study on captive animals from July-February of 2021 and 2022 in the animal facilities of the Animal Ecology Group in Potsdam (Germany). Study animals were male bank voles (Myodes glareolus) captured from the wild in a small forest patch near Potsdam (Germany)(n = 20) or born in captivity (n = 6) as F1 of wild-caught parents. Prior to the experiments, all animals spent 3 to 5 months in the laboratory and were sexually mature. Voles were housed in standard polycarbonate cages (type III and type IV) provided with wood shavings and hay as bedding, cardboard rolls as shelter, and a running disk for enrichment. Water and food pellets (Ssniff, NM, V 1244-0) were available ad libitum.

Personality assessment

Emergence test and open-field test

We tested 26 males twice in two established behavioral tests to quantify among-individual differences in activity, exploration, and boldness (Mazza et al 2019, Schirmer et al 2019). A circular arena (100 cm diameter surrounded by 35 cm high walls) with a white floor connected with an opaque tube (11 x 30 cm) with a sliding door was used in both tests. By drawing on the floor, the arena was visually divided into a circular middle area (70 cm in diameter) where the vole was fully exposed and a border area (15 cm wide) by the wall, representing a safer area. Each area was additionally visually divided into eight sections.

We quantified boldness-related behaviors via an emergence test, also named dark-light test. The individual was placed in the tube for one minute of habituation, after which the door to the arena was opened by pulling a string from the neighboring room. Based on direct observation (or via video analysis), one observer (FE) measured two behavioral variables: the time to emerge with the head including ears (latency head) and the latency to emerge with the full body excluding the tail (latency body). We quantified activity- and exploration-related behaviors via open-field tests. In this test, individuals were placed in an inverted beaker at the edge of the arena and left to habituate for one minute. Thereafter the beaker was lifted from the neighboring room and the individual was observed for 10 minutes. We recorded the activity (running, jumping, scanning, grooming) instantaneously every 10 seconds, latency to enter the center part of the arena with the full body excluding the tail (latency center), number of crossings of the full body excluding the tail into the middle part (crossings), and the number of different sections entered with its full body excluding the tail (sections).

Staged dyadic encounters (SDE)

We ran staged dyadic encounters (SDE) between various dyads formed by the 26 male bank voles, to measure among-individual variation in behavior towards a conspecific. A total of 26 took part in three SDE against three different opponents, except for one male for which only two encounters (with two different opponents) could be performed. To create dyads, we matched individuals of similar weight allowing a maximal weight difference of 6 g (corresponding to ≤ 25% of the body mass).

SDEs were performed in a rectangle box (56.5 x 36.5 x 59.5 cm) divided by a wall into two similar-sized compartments (27 x 36.5 x 59.5 cm). The dividing wall had a wire grid and a sliding door at the bottom to allow visual, acoustic, and olfactory contact between the two compartments after the sliding door was pulled up (see Figure SI1 in online resource 1). To simplify the video analysis of the placement of voles in the arena, we divided the area into three equally big zones (with zone one closest to the divider, zone two in the middle, and zone three furthest away from the divider) with lines on a paper placed at the bottom of the arena. Animals were weighed immediately before testing. We placed dyads in the setup for one minute of habituation with the divider down. The arena was then moved to the test room for another five minutes of habituation. The observer then raised the sliding door from the neighboring room and the voles were left to have visual, acoustic, and olfactory contact for 30 minutes.

One observer (FE) quantified movements of the focal vole between the three zones of the arena, where movements towards the divider and the opponent vole were defined as ‘approaches’ (Figure SI1.A1-A4 in online resource 1) and movements away from the divider and the opponent as ‘departures’ (Figure SI1.D1-D4 in online resource 1). We quantified, approaches and departures by counting crossings of the focal vole between zones with the whole body excluding the tail. For each movement between the zones, we additionally specified if the vole moved to or from the side of the arena in which the opponent vole was located (left or right of the vertical middle), i.e. if the vole crossed from one side over the arena, over the midline, to the other side of the arena with its full body (excluding the tail). For each of these crossings, we also noted down the zone position of the opponent vole. Furthermore, we quantified the time spent with its whole body (excluding the tail) in the section closest to the divider (‘zone one seeker’) and time spent in this section while the opponent also occupies zone one in his compartment (‘opponent seeker’). We combined variables into fewer variables explaining departures and approaches to opponen, including four variables of number of approaches, four variables of number of departures, and one variable for all crossings (‘totcross’). The four approach variables were: A1) number of all approaches to zone one (closest to the divider) disregarding opponent position, A2) number of approaches to opponent (to zone one when the opponent is on the same side and approaches to the same side as the opponent within zone one), A3) number of approaches to zone one only when the opponent also is in zone one, A4) number of approaches to opponent (to zone one when the opponent is in the same side of zone one and approaches within zone one to the side the opponent vole is located in zone one). The four departure variables are the opposite movement (i.e. from zone one or opponent) to the above-described approach movements (from here on referred to as ‘D1’, ‘D2’, ‘D3’, ‘D4’). See figure SI2 in online resource 1 for a visual demonstration on how the variables were combined.

No observer was present in the room of the setup during the emergence test, open-field, and SDE. All tests were recorded using cameras (ABUS, Mini Dome Camera HDCC35560) and analyzed half blinded to the vole identity after tests had been conducted.

Urine marking value

In addition to the SDE described above, we ran a series of repeated paired trials to asses urine making behavior of 24 males. Males were tested in the same dyads as in the behavioral SDE test. This setup consisted of two boxes (27.5 x 40 x 19 cm) attached side by side without a floor. The joint walls were perforated with small holes (ca. 0.5 cm diameter) to allow olfactory but only limited visual contact between the voles in each box. Filter paper was placed under the boxes to capture urine markings. We placed dyads in the setup, and left them to interact for two hours. Using UV light, we evaluated the filter paper from each vole by dividing it into 5 cm x 5 cm squares (35 in total) and counted how many of these squares were marked with a few large blots of marking (typical for subordinate status) or with many squiggly lines (typical for dominant status). An individual only marks typical for subordinate status or dominant status. We calculated a urine marking value (UMV) expressed as the proportion of the number of squares marked with urine markings of any style, subordinate or dominant urine marking patterns, divided by the total number of squares. We used the original quantitative variable of the proportion of squares marked.

After each behavioral test (emergence, open-field, SDE, UMV assessment), the animal was transferred back to its home cage, and the test arenas were cleaned with ethanol. Each vole was exposed to any of the setups only once per day.

Body condition

At the end of the dyadic encounters, one observer (FE) measured the head width (mm) of all animals and calculated body condition using the scaled body mass index suggested by Peig and Green (2009).

Statistical analyses

To analyze among-individual correlations between behaviors expressed in the different tests we first estimated repeatability of all behavioral variables. Due to it's biomodal distribution, we first transformed the time spent in zone one in the SDE into a binomial variable using the function “cutoff” from the package “cutoff” (Choisy, 2015). We log-transformed all approaches, departures, and total crossings from the SDE as well as latency to emerge head and body from the emergence test, and arcsine square root transformed the proportion spent active in the open-field test and proportion of squares with urine marking (UMV). We estimated repeatability using the rpt function from the package rptR (Nakagawa and Schielzeth, 2013; Stoffel et al., 2017), using 1000 simulations to estimate confidence intervals and 1000 permutations to estimate p-values. We used Gaussian family distribution for variables from the SDE which were zero-inflated.

Second, we reduced repeatable variables in separate principal component analyses (PCA) for each test (emergence, open-field, and SDE) with oblimin rotation for the open-field test and SDE. PCA assumptions were checked by inspecting the correlation matrix, Bartlett test, and Kaiser-Mayer-Olkin (KMO) criterion (Field et al., 2012). We included two variables for the PCA of the emergence test, three variables in the PCA for the open-field test, and two variables in the PCA of the SDE (Table SI2). If one of these behavioral variables had a better distribution than both the principal component and the other behavioral variable, we used this original values for in the subsequent bivariate mixed models. We only included components from the PCA with Eigenvalues > 1 in subsequent bivariate mixed models (BMMs).

Third, to estimate the fixed effects we ran univariate linear mixed effect models using the lmer function from the lme4 package (Bates et al., 2015) or generalized mixed effect models using the glmmTMB function from the glmmTMB package (Brooks et al., 2017) for each response variable with individual identity as a random effect. We evaluated the effect of controlling for test occasion (i.e. first, second or third time tested) on all response variables, and for body condition on the composite variable from the SDE. Due to the large percentage of animals (38%) that did not move in the SDE, we additionally ran a GLMM on a binomial variable of ‘aggressiveness’, and an LMM on a subset of data with only individuals that move as well as controlling for zero-inflation to further assess this relationship.

Lastly, to investigate among-individual correlations between pairs of variables we ran bivariate mixed models (BMMs), using the ‘mcmcglmm’ function from the MCMCglmm package (Hadfield, 2010; following procedures described in Dingemanse and Dochtermann, 2013 and Houslay and Wilson, 2017). We ran six BMMs to evaluate pair-wise correlations between two composite variables obtained from the PCAs, crossings, and UMVs. All models included individual identity as a random effect and variable-specific fixed effects (test occasion for crossings and body condition and test occasion for component from SDE). To retain all data in the SDE, we used the observations of both individuals in a dyad as independent observation. We fixed the within-individual variance to 0 because we did not estimate all variables at the same time. We ran all models with different priors as suggested by Hadfield 2010 (two informative, two non-informative priors). Reported model results are based on a flat uninformative prior (V=diag(2), nu=1.002). We used 250,000 iterations, a thinning interval of 100, and a burn-in of 50,000. Finally, using the posterior distributions from the bivariate mixed models, we calculated repeatability for each dependent variable, pairwise among-individual correlations, and their credibility intervals based on Houslay and Wilson (2017).