DOI: 10.5061/dryad.ngf1vhhwd
title: Short-term social dynamics following anthropogenic and natural disturbances in a free-living mammal

description: Anthropogenic disturbances are widely recognized for their far-reaching
 consequences on the survival and reproduction of wildlife, but we
 understand comparatively little about their effects on the social lives of
 group-living animals. Here we examined these short-term changes in
 affiliative behavior as part of a long-term study on a human-tolerant and
 socially flexible population of California ground squirrels
 (Otospermophilus beecheyi). We used social network analysis to examine
 short-term changes in affiliative behavior and individual consistency in
 response to disturbances by humans, domestic dogs, or a natural predator
 (the coyote). Overall, juveniles were more involved than adults in
 affiliative interactions, but the short-term directional effects of these
 acute disturbances on social cohesion varied by disturbance type. Human
 and dog presence reduced aboveground connectivity, particularly for
 juveniles, whereas disturbances by coyotes generally promoted it. Beyond
 these effects, we also detected non-random responses to disturbances,
 though individuals were not very consistent in their directional response
 to different disturbance types. Our results demonstrate the flexible
 changes in social behavior triggered by short-term disturbances imposed by
 humans and other threats. More generally, our findings elucidate the
 underappreciated sensitivity of animal social interactions to short-term
 ecological disturbances, raising key questions about their consequences on
 the social lives of animals.
description: California ground squirrels
 have been live-trapped, marked, and released at Briones Regional Park in
 Contra Costa Country, California (39.956769 N, 122.124304 W) since 2013
 (Smith et al.
 2018). The park is located
 in the San Francisco Bay area and is frequently visited by humans and
 their dogs. During captures of squirrels, individual age, sex, and
 reproductive status were assigned with high accuracy; adults were rarely
 reproductively active during the summer months (when data was collected)
 of this long-term study (Smith et al. 2018). 
 The data presented here,
 focuses on observational data collected from 2013-2019 at a
 moderately-disturbed study site. Trained observers monitored
 aboveground affiliative
 exchanges and disturbances within the study area primarily in the mornings (0800
 to 1200 h) and some afternoons (1200 to 1400 h) from multiple locations
 within the study site. Observers recorded all aboveground greetings
 (two individuals touching
 nose to nose, touching nose to cheek, or otherwise sniffing each
 other), proximity
 maintenance (in body contact or within less than 1 m), social foraging
 (consuming seeds or fresh vegetation within less than 1 m), play (Smith et
 al. 2016). Disturbances were defined as a specific disturber (human, dog,
 coyote, etc.) getting within 15 m of a landmark within the squirrel field
 site. Landmarks included natural and anthropogenic features and were used
 to approximate the location of squirrels during social interactions at the
 study site. For each disturbance, the type, and the number of disturbers
 were recorded. In this study, we focused on disturbances caused by humans,
 humans with dogs on leash, off-leash dogs, and coyotes in this analysis.
  For each disturbance event, we calculated two
 social networks: a ‘pre-disturbance’ network and a ‘post-disturbance’
 network. In these networks, nodes represented individuals and edges the
 number of interactions between individuals. The pre-disturbance network
 was comprised of the social interactions observed in the period 10 minutes
 before the disturbance event, while the post-disturbance network was built
 using all interactions occurring in the period 10 minutes after the
 disturbance event. Each pair of ‘pre-disturbance’ and ‘post-disturbance’
 networks were separated by a one-minute (or greater for merged
 disturbances, see below) time interval during which the subjects
 experienced one (or more) disturbance(s). Determining the exact duration
 of disturbances experienced by our subjects was generally difficult for
 human observers to ascertain because this would assume that we share the
 same risk perception as our study subjects. Instead, we assumed each
 disturbance event lasted roughly one minute because this is the typical
 duration of alarm calling in response to a disturbance, a reliable measure
 of behavioral arousal to threats at Briones (mean ± Standard Error (S.E.)
 call duration is 1.48 ± 0.08 minutes, n = 1839 alarm calls; unpublished
 data). Disturbances were excluded from the analysis if
 they occurred within 20 minutes of each other, as this would lead to an
 overlap between the post-disturbance period of one with the
 pre-disturbance period of the other. For merged events, we defined the
 disturbance type as “multiple” in the analysis if a merged event was
 comprised of disturbances caused by different disturber types.
 In these cases, the
 pre-disturbance network for merged events was calculated before the first
 disturbance and the post-disturbance network after the last disturbance.
 Because we did not have
 detailed information on the location of disturbers, we focused on the
 effects of disturbances on social interactions collected from the whole
 study site (~ 9596 m2; Ortiz et al. 2019) rather than on localized effects at the area
 first disturbed.  Density Data:
 Gives the network type (Full
 Affiliative (including all of the behaviours recorded), Proximity,
 Foraging, Greeting and Play), a counter for the disturbance, the
 disturbance type (Pre disturbance, coyote Dog off leash, Dog on leash,
 human, multiple) and the network density
 Sum weighted degree by age
 category data: To evaluate whether juveniles and adults
 responded differently to disturbances, we calculated the sum of weighted
 degrees of all individuals in an age category within each pre- and
 post-disturbance event network and for each network type, where the
 weighted degree is the sum of all edge weights connected to a node. Note
 that because adult squirrels very rarely play, we did not include them in
 the play network and analysed it for juveniles
 only. This dataset includes the network type
 (Full Affiliative
 (including all of the behaviours recorded), Proximity, Foraging, Greeting
 and Play), a counter for the disturbance, the percent of Juveniles in the
 network, the disturbance type (Pre disturbance, coyote Dog off leash, Dog
 on leash, human, multiple), the age category (P – Pup, A- Adult) and the
 sum weighted degree by age category
 Individual consistency in
 behaviour data: To test whether
 individuals showed consistency in their response across disturbance
 events, we calculated the change in individual weighted degree for each
 disturbance event by subtracting an individual’s ’pre-disturbance’
 weighted degree from their ’post-disturbance’ weighted degree. Because the
 relationship between network size and the weighted degree was non-linear
 (Figure S2), we focused on the direction of a change and not the size (the
 value would be strongly affected by network size, which varied among
 disturbance events, range 5-62 mean ±S.D.= 24.2 ± 14.4). Specifically, the
 direction of a change was one if weighted degree increased after a
 disturbance event, zero when weighted degree remained consistent over
 time, and negative one when weighted degree decreased after a disturbance
 event. We then calculated the mean and S.D. of directional change for each
 individual. The mean indicates the overall direction of change for each
 individual while the standard deviation reflects how consistent an
 individual is. Thus, more consistent individuals had smaller standard
 deviations than less consistent individuals. As we were interested in
 investigating whether individual repeatability depended on an individual’s
 ontogenetic stage, we calculated the mean and standard deviation for each
 individual and each life stage separately. Given the longitudinal nature
 of the study, we calculated two separate means/standard deviations if an
 individual was observed as both a juvenile and an adult. In addition, as
 many squirrels did not interact at all during our short observation
 periods, we also calculated the means and standard deviation excluding
 instances where individuals did not interact at all, neither pre- nor
 post-disturbance. This dataset includes the ID of the
 individual, its sex and age category (P – Pup, A – Adult), the number of
 disturbances for which an individual was observed, the Mean weighted
 degree, the standard deviation of the weighted degree, the network type
 (Full Affiliative (including all of the
 behaviours recorded), Proximity, Foraging, Greeting and Play) and an
 indication whether the zero nodes (i.e. instances were an individual did
 not interact) where included or not.