DOI: 10.5061/dryad.rn8pk0pgs
title: The role of neighbour proximity and context on meerkat close call acoustic structure

description: In many animal species, including humans, producer arousal state is
 considered a key modifier of vocal production and structure. Encoding of
 affective arousal state in vocalisations provides a rapid means of
 information transfer about an individual’s internal state, potentially
 reflecting its response to external stimuli. Meerkats, Suricata suricatta,
 are a highly vocal species. They use close calls to maintain group
 cohesion while foraging. Due to their patchily distributed prey,
 motivation for neighbour proximity varies; being too close results in
 competition (increased arousal - aggression), while too far results in
 risks of losing the group and predation threats (increased arousal -
 fear). We investigated how neighbour proximity, and also behavioural,
 social and environmental context, influence the acoustic structure of wild
 wild meerkats’ close calls. We found little effect of neighbour distance
 on the majority of the acoustic parameters measured. However, there was a
 consistent effect of the behavioural context in which the call was given
 across several acoustic parameters. Additionally, if the nearest neighbour
 was a pup calls became longer, lower and quieter. Overall, meerkat close
 calls potentially convey information on current behaviour and are modified
 in relation to social context. This highlights a potential mechanism in
 the diversification of acoustic signals. 
description: We recorded acoustic and
 behavioural data of subordinate adult (&gt;1 year) meerkats during
 foraging, noting every time the focal produced a close call, the context
 in which the call was given, the distance to the nearest neighbour and the
 neighbour identity. Individuals were recorded with a Sennheiser
 directional microphone (ME66/K6) connected to a Marantz PMD-670
 solid-state recorder (Marantz Japan Inc.; sampling frequency 48 kHz, 16
 bits accuracy). A windshield (Rainhardt, W200) was attached to the
 microphone to ensure good-quality recordings under variable wind
 conditions.  Vocalisations were imported at a sampling rate
 of 48 kHz and saved in WAV format at 16-bit amplitude resolution. Each
 recording was manually annotated in Adobe Audition for close calls and
 corresponding contextual information. Close calls were verbally indicated
 by the recording observer and confirmed visually by the spectrograms
 during annotation. We extracted each annotated close call from the
 recordings, and Hann band-pass filtered between 0.05 and 12 kHz for
 analysis. We only included calls confirmed to be from the focal
 individual, as indicated verbally during data collection by the recording
 observer, and only calls with no overlap with other meerkat or bird calls.
 We extracted and analysed 2,399 calls produced by 24 individuals (65-118
 per individual) over 1,893 minutes of recordings. We then extracted
 acoustic parameters in PRAAT (Boersma & Weenink 2023) using a
 custom script for spectral and temporal analysis (adapted from Reby
 & McComb 2003; Charlton et al. 2009; Briefer et al. 2019; Wyman et
 al. in prep. We conducted a statistical analysis in RStudio
 (version 2022.07.1). We began by running a Principal Component Analysis to
 eliminate redundancy within our set of variables and determine which
 parameters accounted the most for the differences observed between calls.
 We used the ‘broken stick’ method to select which Principal Components
 (PC) to keep as response variables in linear mixed models (LMMs; lmer
 function, lme4 library, (Kuznetsova et al. 2016)). The broken stick method
 selects PCs with observed variance (eigenvalue) greater than that of the
 total variance (sum of eigenvalues) when divided randomly amongst
 components, following a ‘broken stick distribution’ (King &
 Jackson 1999; Peres-Neto et al. 2005). For those PCs with eigenvalues
 greater than that of the broken stick distribution, we built models
 including the PC scores as a response variable and the following fixed
 effects; nearest neighbour distance (0-0.5, 1-2, 2-5, 6-9, 10+),
 behavioural context (scabble, forage, dig, eat, move, post-vigilance),
 nearest neighbour age category (pup, juvenile sub-adult, adult), nearest
 neighbour dominance status (subordinate or dominant) and sex (male or
 female), group size, and vegetation density (none, low, medium, dense).
 All fixed effects were categorical, with the exception of group size which
 was continuous. Interactions were not included as they were not supported
 by the model. Models contained individual ID nested within group ID as
 random effects, as previous work has confirmed individual- and
 group-signatures in meerkat close calls (Townsend et al. 2010, 2011a). We
 checked model assumptions for normality and heteroscedasticity, by testing
 model residuals distribution (KS test), dispersion and outliers
 (testResiduals function, DHARMa library (Hartig 2022)). All models’
 residuals fit a normal distribution and thus no transformation was
 necessary. An information theoretic (IT) approach was applied for model
 selection, using Akaike’s information criterion (AICc) to rank the models
 (model.sel function, MUMin library (Bartoń 2022)) following the approach
 used by Richards et al. (2011). Models within AICc ≤ 4 of the model with
 the lowest AICc value formed the ‘top set’. Post-hoc analyses were
 performed using estimated marginal means on the top models, to investigate
 the significance of the fixed effects factor levels (emmeans function,
 emmeans library (Lenth 2023)) and pairwise comparison (contrasts function,
 emmeans library (Lenth 2023)).