Data from: No safe refuge? Contrasting effects of hunting on rainforest mammal persistence and (re)colonisation
Data files
Apr 23, 2026 version files 358.59 KB
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Effort_minutes.csv
43.72 KB
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Hunting_Direct_sightings.csv
5.72 KB
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Hunting_Hunting_camps.csv
5.72 KB
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Hunting_Hunting_trails.csv
5.72 KB
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Hunting_Machette_cuts.csv
5.72 KB
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Hunting_Shotgun_cartridges.csv
5.72 KB
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Hunting_Wire_snares.csv
5.72 KB
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README.md
3.04 KB
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Site_covariates.csv
30.04 KB
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Species_1_Putty-nosed_monkey.csv
22.50 KB
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Species_10_Chimpanzee.csv
22.50 KB
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Species_11_Elephant.csv
22.50 KB
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Species_2_Crowned_monkey.csv
22.50 KB
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Species_3_Mona_monkey.csv
22.50 KB
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Species_4_Blue_duiker.csv
22.50 KB
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Species_5_Red_duikers.csv
22.50 KB
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Species_6_Red_river_hog.csv
22.50 KB
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Species_7_Read-eared_monkey.csv
22.50 KB
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Species_8_Red-capped_mangabey.csv
22.50 KB
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Species_9_Preuss_guenon.csv
22.50 KB
Abstract
Defaunation in tropical forests represents one of the most urgent biodiversity crises of our time, driven largely by unsustainable hunting. Understanding how species persist or disappear under intense hunting pressure is critical for designing effective conservation strategies, especially in unprotected areas. We quantified how hunting influences species dynamics in an unprotected Afrotropical rainforest by analysing monitoring data collected from 2016 to 2023 for eleven terrestrial and arboreal mammal species in the Ebo forest, Cameroon. Using Bayesian dynamic occupancy models with correlated detections, we assessed how species occupancy changes along environmental and anthropogenic gradients, explicitly including hunting occurrence and hunters’ travel cost as predictors for persistence and colonisation. Elevation emerged as the strongest predictor positively influencing the occupancy of all eleven species, with some species exhibiting non-linear trends along the elevation gradient. Most species generally occupied areas with high travel cost, especially elephant, chimpanzee, crowned monkey and red river hog. Meanwhile, terrain ruggedness had variable effects, with a strong positive effect on chimpanzee and a negative effect on mona monkey. Surprisingly, hunting showed a positive though weak association with the persistence probability of some species, suggesting that the areas used as refuges by wildlife were not exempt from hunting. Species persistence was generally unaffected by travel cost, except for chimpanzees. In parallel, colonisation probability was lower in areas with past hunting history, indicating that previously hunted areas were less likely to be recolonised by wildlife. Similarly, colonisation tended to be higher in areas with high travel cost, suggesting that more accessible areas were less likely for recolonisation. These results imply that hunting not only targets areas used as refuges by wildlife, but also reduces recolonisation potential, and may therefore alter source-sink dynamics. We recommend spatially targeted community-led actions to limit hunting where wildlife still persists through the creation of no-hunting areas to ensure the long-term persistence of wildlife within the forest.
Dataset DOI: 10.5061/dryad.rr4xgxdp3
Description of the data and file structure
Data collected across the Ebo forest, Cameroon from 2016 to 2023, on guided recce lines. the data cover eleven species, including African forest elephant, Nigeria-Cameroon chimpanzee, Putty-nosed monkey, Crowned monkey, Mona monkey, Red-eared monkey, Red-capped mangabey, Preuss's guenon, Red river hog, Blue duiker and Red duikers. It also covers a range of signs used as evidence of hunting activities.
Files and variables
Species files
Description: Range from species 1 (Putty-nosed monkey) to Species 11 (Elephant)
Variables
- Year: When the data were collected, ranges from 2016 to 2023. Some years (2019 and 2020) are missing because surveys were interrupted due to COVID-19.
- The first column of the files represents the sites which are 1 x 1 km grid cells.
- For each year and within each grid cells, we have five spatial replicates (2016.1 to 2016.5) which are 200-m segment along the ~1 km portion of the recce line falling within the grid cell.
Values
- 0/1 values in each cell denote the presence (1) or absence (0) of the species on each given spatial replicate (200-m segment) during each survey year.
- NA values represent spatial replicates that were not surveyed during a given year.
Hunting files
Description: These files contain data on each type of hunting sign recorded during the surveys. The data was turned into events (presence/absence) along 200-m segments and summed up for each grid cell.
Variables
- FID: grid cell ID.
- 2016 - 2023: Total number of events of each hunting sign within each specific grid cell each specific year.
File: Effort_minutes.csv
Description: This is the time spent surveying each 200-m segment each year. This file follows the exact same structure as the species files.
Variables
- 2016.1: Time in minutes spent surveying the first spatial replicate (200-m segment) in 2016
- 2016.2: Time in minutes spent surveying the second spatial replicate (200-m segment) in 2016
- etc.
- Spatial replicates with the value NA are those that were not surveyed during a given year
File: Site_covariates.csv
Description: Characteristics of each grid cell.
Variables
- FID: grid cell id, similar in all files.
- Effort_km: length in km of recce line within the grid cell.
- Elev: mean elevation of the grid cell in m.
- Rugg: mean terrain ruggedness index of the grid cell.
- VillDist: distance in km from the centre of the grid cell to the nearest village.
- RoadDist: distance from the centre of the grid cell to the nearest road.
- RivDist: distance from the centre of the grid cell to the nearest river.
- MovCost: mean travel cost from the village to the grid cell.
Code/software
All files are csv and can be open in Excel and in R.
