Spatial and temporal niche overlap of aardwolves and aardvarks in Serengeti National Park, Tanzania
Data files
Nov 01, 2023 version files 224.90 KB
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README.md
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sitecovs.csv
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vark_2s.csv
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varkobs.csv
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wolf_2s.csv
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wolfobs.csv
Abstract
Species interactions can influence species distributions, but mechanisms mitigating competition or facilitating positive interactions between ecologically similar species are often poorly understood. Aardwolves (Proteles cristata) and aardvarks (Orycteropus afer) are nocturnal, insectivorous mammals that co-occur in eastern and southern Africa, and knowledge of these species is largely limited to their nutritional biology. We used aardwolf and aardvark detections from 106 remote cameras during 2016–2018 to assess their spatial and temporal niche overlap in the grasslands of Serengeti National Park, Tanzania. Using a multispecies occupancy model, we identified a positive interaction between occupancy probabilities for aardwolves and aardvarks. Slope, proportion of grassland, and termite mound density did not affect occupancy probabilities of either species. Probability of aardwolf, but not aardvark, occupancy increased with distance to permanent water sources, which may relate to predation risk avoidance. Diel activity overlap between aardwolves and aardvarks was high during wet and dry seasons, with both species being largely nocturnal. Aardwolves and aardvarks have an important ecological role as termite consumers, and aardvarks are suggested to be ecosystem engineers. Our results contribute to a better understanding of the spatial and temporal niche of insectivores like aardwolves and aardvarks, suggesting high spatial and temporal niche overlap in which commensalism occur, whereby aardwolves benefit from aardvark presence through increased food accessibility.
README: Spatial and temporal niche overlap of aardwolves and aardvarks in Serengeti National Park, Tanzania
https://doi.org/10.5061/dryad.9kd51c5qb
This dataset contains the raw camera trap data and site covariate data needed to run the multispecies occupancy model in R, by use of the Unmarked package. This consists of binary data on aardwolf and aardvark presence, split by camera-period, the site covariates of season, distance to water, slope, elevation, proportion of grassland cover, number of termite mounds, and number of trees present at the site. It also contains the R script used to run the analysis.
Secondly, the dataset contains the raw camera data and the script needed to run the diel activity analysis used to analyze temporal niche overlap btween the species, by use of the Overlap package in R. This consists of camera data on aardwolf and aardvark presence at camera sites, associated with specific dates and times, and the number of animals present. It also contains the R script used to run the analysis.
Description of the data and file structure
FILES FOR THE MULTISPECIES OCCUPANCY ANALYSIS
-sitecovs.csv contains the following columns, in this exact order:
- Camera site ID ('ID')
- Longitude ('long', in WGS84)
- Latitude ('lat', in WGS84)
- Distance to water - continuous covariate in meters ('waterdist')
- Slope - continuous covariate in angular units ('slope')
- Elevation - continuous covariate in meters, not included in final model ('elev')
- Proportion of grassland cover - continuous covariate ranging 0–100 ('grass')
- Number of termite mounds - continuous covariate with termite mounds as unit ('mounds')
- Season - categorical covariate with factors Dry (June–October) / Wet (November–May) ('season')
- Number of trees present at site - continuous covariate with trees as unit ('trees')
-vark_2s.csv and wolf_2s.csv contain presence/absence data for aardvarks and aardwolves, respectively:
The first column is the camera site ID, formatted "xxx.x" whereby the characters before the dot indicate the actual camera ID,
the number after the dot indicates a specific camera-period at a pseudo-site, as we split camera data into multiple camera-periods using a stacking approach. The other columns range d1–d70, each representing one out of 70 days of data collection per camera-period, with three responses 0 = absent, 1=present, NA= no data collection
-occupancymodel.R is an annotated R-script to guide replication of the analysis using the datasets above.
Packages required to run the analysis are mentioned at the beginning of the script.
FILES FOR THE DIEL ACTIVITY ANALYSIS
-vark_obs.csv and wolf_obs.csv contain presence/absence data for aardvarks and aardwolves, respectively:
The first column is the camera site ID where the presence was recorded. The next columns are, in order:
- The species recorded (species)
- Number of individuals of this species (number)
- The date of observation (date)
- The time of observation (time)
- Date and time pasted (datetime)
-dielanalysis.R is an annotated R-script to guide replication of the analysis using the datasets above.
Packages required to run the analysis are mentioned at the beginning of the script.
Sharing/Access information
Site covariate data was derived from the following sources:
- Buchorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Herold, M., Fritz, S. (2020). Copernicus global land service: land cover 100m: collection 3, epoch 2017. https://doi.10.5281/zenodo.3518036
- Maliti, H., & von Hagen, C., Frankfurt Zoological Society, Tanzania National Parks Authority & Hopcraft, J. G. C. (2008). Serengeti Park rivers. Available at: https://serengetidata.weebly.com/rivers-and-lakes.html
- NASA, METI, AIST, Japan Space Systems & U.S./Japan ASTER Science Team (2018). ASTER global digital elevation model V003. Distributed by NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/ASTER/ASTGTM.003
Code/Software
All code is annotated to aid informed replication.
The R packages needed to run the analyses are found at the beginning of each R script.
Methods
We collected data during August 2016–June 2018 using 106 remote cameras (Stealth Cam, model N45NG; Irving, Texas, USA). Nearest distance between cameras was 3,000 m for 63 cameras and 4,225 m for 43 cameras (Figure 1). We attached cameras to metal stakes 50-cm above ground and cleared vegetation in front of cameras. We programmed cameras to record 3-image bursts at each detection with a 30-second delay and inspected each about every 6 weeks. Because of staggered camera installations, we extracted data for a period of 70 consecutive days from each camera (hereafter a “camera-period”) during 26 August 2016–1 January 2018, and a second 70-day camera-period for 77 cameras during 22 January 2017–30 June 2018. Overlap between the two periods of data collection occurred due to staggered camera installation. Most (95%) data were obtained during September 2016–February 2018. Each 70-day camera-period was associated with a season (wet season, November-May; dry season, June-October). If a camera-period overlapped two seasons, we split the data by season into two separate, shorter camera-periods each entirely with one season.