Social network dynamics under experimental manipulations of predation risk and food abundance in wild rock hyraxes

Understanding how animals respond to ecological constraints is crucial for interpreting the dynamics of social networks in the wild. We investigated how experimentally induced changes in perceived predation risk and food abundance influence the social behaviour of wild rock hyraxes (Procavia capensis), using experimental manipulations and a meta-analytical framework. We used proximity sensors, trail cameras, and observations to record multiple aspects of social interactions.
Elevated predation risk caused hyraxes to prioritize spatial adjustments over social rewiring, leading to a slight decrease in sociality and increased social stability within groups. Bachelor males and adult females exhibited greater behavioural adjustments, with solitary individuals interacting more with social groups to mitigate predation risk.
In contrast, increased food abundance led to forced proximity at feeding patches, promoting social interactions, and clustering within groups. Bachelor males connected soci..., , # Data from: Social network dynamics under experimental manipulations of predation risk and food abundance in wild rock hyraxes

Dataset DOI: [10.5061/dryad.wdbrv162g](10.5061/dryad.wdbrv162g)

## Description of the data and file structure

This study investigates how wild rock hyrax (*Procavia capensis*) social networks respond to short-term changes in perceived predation risk and food availability. Between 2017 and 2020, we conducted 15 experimental trials in two neighboring canyons at the Ein Gedi Nature Reserve, Israel—nine “fear-related” experiments using wolf call playbacks or simulated aerial attacks by drones, and six “food-related” experiments using artificial food patches. Each trial included pre-treatment, treatment, and post-treatment stages. Social data were collected using proximity biologgers, direct behavioural observations, and trail cameras, from which we derived proximity-based social networks. To account for differences in data types, resolution, and sampling effort,..., ,