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Short-term social dynamics following anthropogenic and natural disturbances in a free-living mammal

Cite this dataset

Gall, Gabriella et al. (2022). Short-term social dynamics following anthropogenic and natural disturbances in a free-living mammal [Dataset]. Dryad.


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.


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.


National Science Foundation, Award: Graduate Research Fellowship to C.O.J

National Science Foundation, Award: DEB 1456730

Cota-Robles Fellowship

University of California, Davis, Award: Jastro Shields Award

Mills College, Award: Letts-Villard Endowed Professorship

W. M. Keck Foundation

Barrett Foundation

Mary Bowerman Research Fund