DOI: 10.5061/dryad.15dv41p3k
title: Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?

description: Socioecological theory attributes social variation in female-bonded
 species to differences in within- and between-group competition, shaped by
 food distribution. Strong between-group contests are expected over large,
 monopolisable resources, but not when low-quality food is distributed
 across large, undefended home ranges. Within-group contests are expected
 to be more frequent with increasing heterogeneity in feeding sites. We
 tested these predictions in female Asian elephants, which show traits
 associated with infrequent contests – predominant graminivory, overlapping
 home ranges, and high fission-fusion. We examined how agonistic
 interactions within and between female elephant clans (social groupings)
 vary with food distribution and competitor density. We found stronger
 between-clan contests than that known from neighbouring forests and more
 frequent agonism between females between clans than within clans. Such
 strong between-clan contest is attributable to food patchiness as the
 Kabini grassland in the study area had three times the grass biomass as
 adjacent forests. Within-clan agonism was also frequent but was not
 influenced by food distribution, contradicting socioecological
 predictions. Contrary to recent claims, increasing within-clan agonism
 with group (party) size showed that ecological constraints operate despite
 high fission-fusion in Asian elephants. Thus, despite graminivory and
 fission-fusion, within-clan and between-clan agonism can be frequent,
 especially at high population density.
description: # Do food distribution and competitor density affect agonistic behaviour
 within and between clans in a high fission-fusion species?
 There are eight data files
 here, including data on a) grass biomass in the Kabini grassland area and
 Nagarahole forest, b) rates of agonistic interactions between and within
 females in Asian elephant clans, and c) the ecological correlates of
 within-clan and between-clan agonistic interactions. ## Description of the
 data and file structure **Description of data files linked with the
 article**: Gautam H and Vidya TNC. 2023 Do food distribution and
 competitor density affect agonistic behaviour within and between clans in
 a high fission–fusion species? R. Soc. Open Sci. 10: 230990 **Description
 of data files linked with the article**: Gautam H and Vidya TNC. 2023 Do
 food distribution and competitor density affect agonistic behaviour within
 and between clans in a high fission–fusion species? R. Soc. Open Sci. 10:
 230990 1\) grass_forest_vs_grassland.xls: Datasets to check habitat-level
 patchiness i.e. comparison of grass biomass (g/m.sq.; freshly harvested
 grass biomass, i.e. not oven dried) across habitat types (Kabini grassland
 vs forests of Nagarahole and Bandipur). There are six sets of grass
 biomass data from this region: 1) Kabini grassland 2015, 2) Kabini
 grassland 2016, 3) Nagarahole forests (end of wet season in 2013), 4)
 Bandipur forests (dry season in 1993), 5) Nagarahole forests (dry season
 in 2022) and 6) Kabini grassland (dry season in 2022). Summary statistics
 are provided below each dataset. In datasets 1, 2 and 3: the monthly
 average column has values in the last row for each month while other rows
 are not applicable and hence have n/a. In datasets 5 and 6:
 Avg_biomass_plot column has biomass averaged from 4 quadrats in the last
 row for each plot; other three rows are not applicable and hence have n/a.
 2\) Code_grass.R: R script to analyze variation in grass abundance
 (percentage cover, biomass, and height) within the Kabini grassland across
 different focal zones, months and year (tables in Supplement 1 for the
 paper). Data file to be used: grass_kabini.csv 3\) grass_kabini.csv: data
 file to analyze variation in grass abundance (percentage cover, biomass,
 and height) across different focal zones, months and year (tables in
 Supplement 1 for the paper). Description of columns in the csv file: *
 year = year of measurement (2015 or 2016); * month = names given to the
 four 30-day periods (broadly corresponding to Feb, Mar, Apr and May); *
 zone = name of the focal zones or large stretches of grasslands (six focal
 zones); * plot = ID of the four plot-clusters within each focal zone (see
 Figure 2c in paper); * gr_cvr_1m = average percentage cover of grass in
 the plot-cluster (across 5 constituent quadrats); * gr_biomass_1m =
 average grass biomass (g/m.sq.) in the plot-cluster (across 5 constituent
 quadrats); * gr_avheight_1m = average grass height (cm) in the
 plot-cluster (across 5 constituent quadrats); * StdDev - cover = standard
 deviation in grass cover in the plot-cluster (across 5 constituent
 quadrats); * StdDev - biomass (g/m.sq.) = standard deviation in grass
 biomass (g) in the plot-cluster (across 5 constituent quadrats); * StdDev
 - Mean height (cm) = standard deviation in grass height (cm) in the
 plot-cluster (across 5 constituent quadrats); * CV - cover = coefficient
 of variation (%) in grass cover in the plot-cluster (across 5 constituent
 quadrats); * CV - biomass (g/m.sq.) = coefficient of variation (%) in
 grass biomass in the plot-cluster (across 5 constituent quadrats); * CV -
 Mean height (cm) = coefficient of variation (%) in grass height in the
 plot-cluster (across 5 constituent quadrats). In row 73: the biomass
 values for plot D show the imputed means calculated from three other plots
 (A, B and C) in the zone since the abundance data for plot D could not be
 collected. The subsequent columns have n/a because the variability indices
 could not be calculated due to the lack of data from 5 constituent
 quadrats in plot D. 4\) Code_WGBG_diff_allclans.R: R script to 1) compare
 the rates of within-clan and between-clan agonism, keeping clan-identity
 as a random effect and 2) compare the slope of the relation between
 competitor density and rate of agonism for within- and between-clan
 agonism (this analysis was done using simple regression for each type and
 then comparing the slopes, see formula in Supplement 3 of this paper).
 Data file to use: diff_wcbc_allclans_grsize.csv \[For Fig. S11 and Table
 S6 in the Supplement, there is a similar code called 5\)
 “Code_WGBG_diff_5clans_agonism.R” for comparing within-clan and
 between-clan agonism for the top five common clans. For this script, use
 6\) diff_agonism_wcbc_5clans.csv for agonism per female (description
 similar to diff_wcbc_allclans_grsize.csv listed below), and 7\)
 NI_I_diff_agonism_wcbc_5clans.csv This file is to analyze NI/R ratio
 (similar description as the diff_wcbc_allclans_grsize.csv file below;
 excluded rows with n/a in NI_I_ratio column.] 8\)
 diff_wcbc_allclans_grsize.csv: Data file to compare the rates of
 within-clan and between-clan agonism. Use R script:
 Code_WGBG_diff_allclans.R Description of columns in the csv file: *
 type_wcbc = type of focal group observation of agonism (within- or
 between-clan agonism); * year = Year of observation; * month = Names given
 to the four 30-day periods that roughly correspond to February, March,
 April and May; * plot = ID of the unique plot-cluster near which agonims
 occurred; * focal_sno = ID of the focal group observation; * AF_sum_grsize
 = local competitor density (adult female group size for within-clan
 agonism, and sum of adult female group size of the two competing groups
 for between-clan agonism); * date = date of observation; * duration_obs =
 duration of observation (minutes); * I_count = total no. of independent
 individual-level agonistic interactions (see Figure 1 in the paper); *
 agonism_per_h = rate of agonism experienced per hour by an average female
 in the group (see Supplement table S2 and fig S7 for calculation); *
 NI_count = total no. of non-independent agonistic interactions (i.e. the
 interactions occurring within 15 min of other interactions) observed in
 the focal; * NI_I_ratio = NI:I ratio (number of non-independent
 interactions per independent interaction); * clan_id = ID of the clan
 observed; * clan_id_bg = ID of the clan-combination for between-clan
 agonism (valid only for between-clan agonism). n/a cells in the NI_I_ratio
 column: when there is no independent interaction (i.e. I_count is 0), NI/I
 ratio is not applicable. 9\) Quadr_Fig_grsize_WGC.R: R script to make
 Figure 4b 10\) Doublescatter_grsize_vs_WGBG.R: R script to make Figure 4a
 11\) Code_correlates_WGCagonism.R: R script to analyze 1) the correlates
 of within-clan agonism (Table 1 in the paper), and 2) the quadratic
 relationship between group size and within-clan agonism. Data file to be
 used: correlates_WGCagonism.csv. 12\) correlates_WGCagonism.csv: data file
 used in the code to analyse correlates of within-clan agonism (Table 1 in
 the paper). R script to be used: Code_correlates_WGCagonism.R Description
 of columns in the csv file: * year = year of observation; * month = names
 given to the four 30-day periods that roughly correspond to February,
 March, April and May; * zone = name of the focal zones or large stretches
 of grasslands (we sampled six zones, full names in the supplement); *
 Nearest Plot-Cluster = unique ID of the nearest plot cluster (of grass
 sampling) where the observation happened (~within 100m); * Focal S.No. =
 serial number of focal observation; * no._AF = number of adult females in
 the group; * date = date of observation; * focal_duration_obs = duration
 of the focal group observation (minutes); * no._I = total no. of
 independent agonistic interactions observed in the focal; * agonism_perAF
 = rate of agonism experienced per hour by an average female in the group
 (see Supplement table S2 and fig S7 for calculation); * no._NI = total no.
 of non-independent agonistic interactions (i.e. the interactions occurring
 within 15 min of other interactions) observed in the focal; * NI_I_ratio =
 NI:I ratio (not applicable entered in rows with zero I interactions); *
 clan_id = ID of the clan observed; * cover_plot = average percentage cover
 of grass in the plot-cluster (across the 5 constituent quadrats); *
 biomass_plot = average grass biomass (g/m.sq.) in the plot-cluster (across
 the 5 constituent quadrats); * height_plot = average grass height (cm) in
 the plot-cluster (across the 5 constituent quadrats); * sd_cover_plot =
 standard deviation in grass cover in the plot-cluster (across 5
 constituent quadrats); * sd_biomass_plot = standard deviation in grass
 biomass (g) in the plot-cluster (across 5 constituent quadrats); *
 sd_height_plot = standard deviation in grass height (cm) in the
 plot-cluster (across 5 constituent quadrats); * CV_cover_plot =
 coefficient of variation (%) in grass cover in the plot-cluster (across 5
 constituent quadrats); * CV_biomass_plot = coefficient of variation (%) in
 grass biomass in the plot-cluster (across 5 constituent quadrats); *
 CV_height_plot = coefficient of variation (%) in grass height in the
 plot-cluster (across 5 constituent quadrats). 13\)
 Code_corr_glm_clan-levelBGC.R: R script to analyze the rate of clan-level
 between-clan agonistic encounters in focal zones using a generalized
 linear model (Poisson structure). Data file to be used:
 clan-level_BGC_encounters.csv 14\) clan-level_BGC_encounters.csv: Data
 file to analyze the rate of clan-level between-clan agonistic encounters
 in focal zones. R script to be used: Code_corr_glm_clan-levelBGC.R.
 Description of columns: * Sno. = Serial number; * date = Date of
 observation; * year = Year of observation; * zone = Name of the focal
 zones or large stretches of grasslands (we sampled six focal zones); *
 month = names given to the four 30-day periods that roughly correspond to
 February, March, April and May; * interval_id = time periods for the
 2.5-hour intervals during which observations were made; * area_zone = area
 of each zone in sq.km.; * clan_count = Total number of clans present in
 the focal zone; * no._AF = Total number of adult females in the focal
 zone; * no._BG_encounter = Number of clan-level agonistic between-clan
 encounters observed in the focal zone; * zone_cover = average percentage
 cover of grass in the focal zone (average from the averages of four
 plot-clusters); * zone_biomass = average grass biomass (g/m2) in the focal
 zone (average calculated from the four constituent plot-clusters); *
 zone_height = average grass height (cm) in the focal zone (average
 calculated from the four constituent plot-clusters); * zone_sd_cover =
 standard deviation of the percentage cover of grass (across four
 plot-clusters) in the focal zone; * zone_sd_biomass = standard deviation
 of the grass biomass (across four plot-clusters) in the focal zone; *
 zone_sd_height = standard deviation of the height of grass (across four
 plot-clusters) in the focal zone; * zone_CV_cover = coefficient of
 variation (%) of the percentage cover of grass (across four plot-clusters)
 in the focal zone; * zone_CV_biomass = coefficient of variation (%) of
 grass biomass (across four plot-clusters) in the focal zone; *
 zone_CV_height = coefficient of variation (%) of grass height (across four
 plot-clusters) in the focal zone. 15\) Code_correlates_BGC_duratn.R: R
 script to analyze the duration of clan-level between-clan encounters. Data
 file to be used: corr_duration_clan-level_BGC.csv 16\)
 corr_duration_clan-level_BGC.csv: Data file to analyze the duration of
 clan-level between-clan encounters. R script to be used:
 Code_correlates_BGC_duratn.R. Description of columns: * year = Year of
 observation; * month = names given to the four 30-day periods that roughly
 correspond to February, March, April and May; * zone = name of the focal
 zones or large stretches of grasslands (we sampled six focal zones: the
 full names are in the supplementary material associated with this paper; *
 focal_s_no = ID of the focal group observation; * sum_AF = sum of adult
 female group size of the two competing groups; * diff_AF = difference in
 the adult female group size of the two competing groups; * date = date of
 observation; * BGD_duration_min = duration of the between-clan encounter
 (minutes); * no._I = total no. of independent individual-level
 between-clan agonistic interactions observed in the focal (see Figure 1 in
 the paper); * no._NI = total no. of non-independent individual-level
 between-clan agonistic interactions (occuring within 15 min of another
 interaction) observed in the focal; * Clan_combinat = ID of the
 combination of the two competing clans; * plot_id = unique ID of the
 nearest plot cluster (of grass sampling) where the observation happened
 (~within 100m); * plot_cluster = ID of the plot-cluster within focal zone;
 * cover_plot = average percentage cover of grass in the plot-cluster
 (across the 5 constituent quadrats); * biomass_plot = average grass
 biomass (g/m.sq.) in the plot-cluster (across the 5 constituent quadrats);
 * height_plot = average grass height (cm) in the plot-cluster (across the
 5 constituent quadrats); * sd_cover_plot = standard deviation in grass
 cover in the plot-cluster (across 5 constituent quadrats); *
 sd_biomass_plot = standard deviation in grass biomass (g) in the
 plot-cluster (across 5 constituent quadrats); * sd_height_plot = standard
 deviation in grass height (cm) in the plot-cluster (across 5 constituent
 quadrats); * CV_cover_plot = coefficient of variation (%) in grass cover
 in the plot-cluster (across 5 constituent quadrats); * CV_biomass_plot =
 coefficient of variation (%) in grass biomass in the plot-cluster (across
 5 constituent quadrats); * CV_height_plot = coefficient of variation (%)
 in grass height in the plot-cluster (across 5 constituent quadrats); *
 cover_zone = average percentage cover of grass in the focal zone (average
 from the averages of four plot-clusters); * biomass_zone = average grass
 biomass in the focal zone (average calculated from the four constituent
 plot-clusters); * height_zone = average grass height in the focal zone
 (average calculated from the four constituent plot-clusters); *
 sd_cover_zone = standard deviation of the percentage cover of grass
 (across four plot-clusters) in the focal zone; * sd_biomass_zone =
 standard deviation of the grass biomass (across four plot-clusters) in the
 focal zone; * sd_height_zone = standard deviation of the height of grass
 (across four plot-clusters) in the focal zone; * CV_cover_zone =
 coefficient of variation (%) of the percentage cover of grass (across four
 plot-clusters) in the focal zone; * CV_biomass_zone = coefficient of
 variation (%) of grass biomass (across four plot-clusters) in the focal
 zone; * CV_height_zone = coefficient of variation (%) of grass height
 (across four plot-clusters) in the focal zone. ## Sharing/Access
 information Links to other publicly accessible locations of the data:
 None. Data was derived from the following sources: None. These are primary
 data collected from the field. ## Code/Software The R codes used to
 analyse data are also attached. Descriptions are above.