DOI: 10.5061/dryad.mpg4f4rcf
title: Data from: Identifying timescales of change in vulture social networks

description: Animal social interaction patterns change over time, but the continuous
 nature of social interactions makes selecting a timescale for temporal
 analysis challenging. We applied both a heuristic approach and a
 multilayer reducibility analysis approach to study timescales of change in
 social networks of free-ranging griffon vultures. We analyzed social
 networks in two behavioral situations: in-flight interactions, such as
 during foraging movements, which we expected to fluctuate seasonally but
 to exhibit a relatively constant pattern of change over the course of a
 season; and diurnal ground interactions, such as interactions while
 feeding, which we expected to show a pulsed temporal pattern that followed
 the pattern of carcass availability on the landscape. The heuristic method
 confirmed the suitability of a 3-10 day aggregation window for studying
 temporal change in vulture social networks. It also highlighted how
 different timescales of aggregation offer different insights about
 longer-term patterns of change. Multilayer reducibility analysis confirmed
 that substantial change was happening at every aggregation timescale we
 tested, with no redundancy in network layers; that is, social interactions
 in this population were not oversampled. However, it revealed more
 similarity between non-adjacent layers in the flight networks as compared
 to the feeding networks, further supporting the influence of carcasses as
 drivers of social network structure. Multilayer reducibility analysis over
 a multi-season timescale did not reveal seasonal similarities in network
 structure extensive enough to override the substantial demographic and tag
 coverage turnover between seasons. While multilayer analysis of temporal
 dynamics in social networks may prove useful for the study of change in
 links among a fixed subset of individuals, we highlight its limitations as
 a tool for studying long-term social network structural change, especially
 in free-living animal populations.
description: # Data from: Identifying timescales of change in vulture social networks
 Dataset DOI: [10.5061/dryad.mpg4f4rcf](10.5061/dryad.mpg4f4rcf) ##
 Description of the data and file structure Data associated with the paper
 "**Identifying timescales of change in vulture social
 networks"**, by Kaija Gahm, Elvira D'Bastiani, Nili Anglister,
 Gideon Vaadia, Marta Acácio, Orr Spiegel, and Noa Pinter-Wollman.
 Collected from GPS-tagged griffon vultures (*Gyps fulvus*). For details on
 how the GPS data were cleaned and processed, see the methods and
 supplementary material for [Gahm et al. (link to
 preprint)](https://flamechallenge.authorea.com/users/920464/articles/1292712-social-interactions-in-an-avian-scavenger-are-driven-by-social-preference-and-movement-depending-on-the-social-situation) For details on how the roost locations were defined, see the methods for [Acacio et al. 2024](https://www.pnas.org/doi/full/10.1073/pnas.2407298121) ### Files and variables #### File: roostPolygons_shifted.kml **Description:** Polygons used to define roost areas. Used in this analysis to exclude social interactions that occurred during the day inside these areas, to distinguish probable feeding interactions from in-roost diurnal ground interactions. These polygons have been shifted in the X and Y directions by a randomly-selected amount to protect the location of this sensitive species. #### File: data_seasons_simplified_shifted_1.csv **Description:** Season 1 (Pre-breeding 2020) movement data used to derive proximity-based social interactions. ##### Variables (these descriptions apply to subsequent seasons as well) * ground_speed: speed at which the vulture was moving, in m/s. Used to threshold for flying/stationary points * location_lat: latitude * location_long: longitude * timestamp: timestamp of the GPS fix * dateOnly: date of the GPS fix * Nili_id: ID (name) of the vulture * seasonUnique: Full name of the season (e.g. 2020 fall, 2023 summer) * geometry: SF geometry columns. To be used when converting data back into sf objects. #### File: data_seasons_simplified_shifted_2.csv **Description:** Season 2 (Breeding 2020-2021) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_3.csv **Description:** Season 3 (Post-breeding 2021) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_4.csv **Description:** Season 4 (Pre-breeding 2021) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_5.csv **Description:** Season 5 (Breeding 2021-2022) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_6.csv **Description:** Season 6 (Post-breeding 2022) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_7.csv **Description:** Season 7 (Pre-breeding 2022) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_8.csv **Description:** Season 8 (Breeding 2022-2023) movement data used to derive proximity-based social interactions. #### File: data_seasons_simplified_shifted_9.csv **Description:** Season 9 (Post-breeding 2023) movement data used to derive proximity-based social interactions. #### File: graphs_feeding_seasons.RDS RDS file containing season-scale social networks for the feeding situation. Can be loaded into R with `readRDS()` #### File: graphs_flight_seasons.RDS RDS file containing season-scale social networks for the flight situation. Can be loaded into R with `readRDS()` #### File: graphs_feeding.RDS RDS file containing sub-season scale social networks (summer/post-breeding 2023) for the feeding situation. Can be loaded into R with `readRDS()` #### File: graphs_flight.RDS RDS file containing sub-season scale social networks (summer/post-breeding 2023) for the flight situation. Can be loaded into R with `readRDS()` ## Code/software Code associated with this manuscript can be found at github.com/kaijagahm/vultureTemporal. The data used in the analysis is prepared using the targets pipeline, which is outlined in \_targets.R. The data files provided here are not the original raw data, but they represent various steps in the pipeline. To reproduce the analysis, you could edit the targets pipeline or follow it in a new script. * `roostPolygons_shifted` will take the place of `roostPolygons` in the targets pipeline. * the data files `data_seasons_simplified_shifted_*` need to be read into R (e.g. using read_csv()) and then bound together into a list, in order. Then that list will take the place of `alldata`. * Alternatively, you can read in the social networks directly from the four RDS files provided, skipping the preliminary analysis steps and proceeding straight to the multilayer reducibility analyses. It is possible that some analyses may not run seamlessly with the shifted data. Access to the original, un-shifted data will be available upon request.