Data from: Climate and predation drive variation of diel activity patterns in chacma baboons (Papio ursinus) across southern Africa
Data files
Apr 16, 2026 version files 2.75 MB
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Baboon_Activity_Data.csv
2.69 MB
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Environmental_Variables_Data.csv
54.05 KB
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README.md
5.31 KB
Abstract
Understanding how animals adjust their diel activity patterns in response to environmental gradients is crucial for uncovering ecological and evolutionary mechanisms shaping behavioral plasticity. While theory predicts that diel activity patterns are structured by trade-offs among thermoregulation, energy optimization, and predator avoidance, few studies have tested these dynamics across broad spatial scales within species. We used the widely distributed and adaptable chacma baboon (Papio ursinus) to explore how latitude, climate, vegetation, and predator activity influence diel behavioral schedules. Utilizing over 260,000 baboon camera trap detections collected between 2016 and 2022 across 29 sites and six biomes in South Africa and Zimbabwe, we quantified activity levels and patterns using kernel density estimations. Activity levels declined significantly by about 3% with increasing latitude, consistent with reduced resource stability and heightened thermal stress, while wake-up and sleep times remained synchronized across sites. Chacma baboons avoided midday heat, but unexpectedly increased activity at dawn and night in the presence of predators, indicating context-dependent trade-offs. These results demonstrate how daily activity emerges from interacting abiotic and biotic constraints and underscore temporal flexibility as an adaptive strategy in variable environments, a critical insight into how generalist mammals navigate changing landscapes amid global change.
Dataset DOI: 10.5061/dryad.vhhmgqp62
Description of the data and file structure
This dataset contains the data and accompanying documentation used to investigate the drivers of diel activity patterns in chacma baboons (Papio ursinus) across southern Africa, as presented in Dzingwena et al. (in review). The study tests the hypothesis that environmental variables (temperature, rainfall, NDVI) and predator presence (lions, leopards, and spotted hyenas) influence baboon activity patterns, and how these effects vary across 29 sites spanning different biomes in Zimbabwe and South Africa (34.46°S;20.38°E -17.70°S; 27.87°E ).
Camera trap data were collected between 2016 and 2022 and include detection records for baboons and their primary predators, along with associated spatial and temporal metadata (e.g., timestamp, site coordinates, season, and diel period). Environmental data are summarized at the seasonal scale and paired with baboon and predator activity estimates per site and diel period. The dataset supports beta regression models and diel overlap analyses that test variation in baboon activity by season and environmental condition, and quantify temporal overlap with each predator species.
Analyses revealed a consistent bimodal activity pattern in chacma baboons, with peaks in the morning and afternoon and reduced activity at midday and night. Overlap with predators varied by species and site, with lions typically showing lower overlap coefficients and spotted hyenas the highest. NDVI effects on baboon activity were strongest at midday. These findings provide evidence that both bottom-up (resource-driven) and top-down (predator-driven) forces shape baboon activity across heterogeneous landscapes.
Two CSV files are included:
- Baboon_Activity_Data.csv – a cleaned detection-level dataset for all species used in activity analyses.
- Environmental_variables_data.csv – seasonal environmental summaries for each site and time period.
This data package supports the manuscript “Climate and predation drive variation of diel activity patterns in chacma baboons (Papio ursinus) across southern Africa”.
Files and variables
File: Baboon_Activity_Data.csv
Description: This datasets include camera trap detections of chacma baboons, lions, leopards, and spotted hyenas across 29 sites.
Number of variables: 15
Number of cases/rows: 13 299
Variables
- capture_id: Unique identifier for each site, roll number and camera station
- subject_id: Unique identifier for each detection event
- capture_site: Camera station name
- lon: Longitude for the camera station
- lat: Latitude for the camera station
- date and time posix: Date and time of image capture
- origin: Source project for camera traps data
- species: Common name of species detected
- count: Number of individuals of the same species in an image
- Site: Study area where camera were deployed
- Roll: Continuous camera trap session from the time of camera deployment to battery replacement (unitless identifier for sampling occasion)
- Biome: Biome in which the study area is found; categories (savanna, fynbos, forest, thicket, nama-karoo, and grassland)
- season: season of observation; summer(December-February),autumn(March-May),winter(June-August),spring(September-November)
- period: Time of the day the image was captured; dawn (1 hr. before sunrise to sunrise), morning (sunrise–noon), midday (noon to 2 hrs. after), afternoon (post-midday to dusk), dusk (sunset to1 hr. after), and night (1 hr. after sunset to 1 hr. before sunrise).
Missing data codes: None
Specialized formats or other abbreviations used:
- APN: Associate Private Nature Reserves
File: Environmental_Variables_Data.csv
Description: This datasets include Environmental variables (temperature, rainfall and normalised difference vegetation index (NDVI)) across all seasons and day periods and GPS location for all sites
Number of variables: 8
Number of cases/rows: 550
Variables
- Site: Study area in which camera were deployed.
- season: season of observation; summer(December-February),autumn(March-May),winter(June-August),spring(September-November).
- period: Time of the day ; dawn (1 hr. before sunrise to sunrise), morning (sunrise–noon), midday (noon to 2 hrs. after), afternoon (post-midday to dusk), dusk (sunset to 1 hr. after), and night (1 hr. after sunset to 1 hr. before sunrise).
- temperature: Mean seasonal temperature (°C)
- mean_ppt: Mean seasonal rainfall (mm), calculated as the average of total rainfall within each season across all study years.
- mean_NDVI: Mean seasonal NDVI (unitless)
- Longitude: Longitude for the study area
- Latitude: Latitude for the study area
Missing data codes: None
Specialized formats or other abbreviations used:
- APN: Associate Private Nature Reserves
Code/software
No instrument or software specific information required to interpret these data sets
Access information
Other publicly accessible locations of the data:
- None
Data was derived from the following sources:
- None
