DOI: 10.5061/dryad.8931zcrzk
title: Hunting data collected in the Ebo forest, Cameroon from 2008 to 2023

description: The long-term survival of many mammal populations relies on how
 effectively we mitigate the threat from unsustainable hunting. Yet,
 hunting activities are often cryptic, especially in unprotected forests.
 Here, we investigate whether hunting signs can help understand the
 spatiotemporal dynamics of hunting activities in an unprotected African
 rainforest and examine how landscape characteristics predict various
 indicators of hunting. We recorded hunting signs (e.g., shotgun
 cartridges, wire snares, direct sightings) systematically on 23 parallel
 recce lines across the Ebo forest, Cameroon from 2008 to 2023. We assigned
 hunting data and spatial covariates (e.g., elevation, distance to the
 village) to 1×1 km grid cells and applied generalized linear mixed models
 to predict the effects of these covariates on hunting. We found that
 hunting was commonplace across the entire Ebo forest. The best-fitting
 models for each hunting sign differed considerably. Shotgun cartridges and
 all hunting signs combined increased significantly from 2016 to 2023 and
 varied non-linearly along the village-distance gradient. We found a
 progressive inversion of hunting trends along the anthropogenic gradient;
 between 2016 and 2018, wire snares declined with the distance to the road
 but from 2021, they increased along the road distance gradient. Wire
 snares showed a similar pattern along the river distance gradient. Our
 results also revealed differences between shotgun hunting and snaring
 along the altitudinal gradient; the effect of elevation was positive on
 shotgun cartridges and negative on wire snares. Hunting signs and trails
 decreased significantly with increasing terrain ruggedness. Using
 long-term monitoring data, we show how hunting patterns change dynamically
 with respect to human and landscape-related features. We also demonstrate
 complex hunting patterns along the gradient of human influence, therefore
 questioning the use of proxies such as the distance to human settlements
 and even topography to account for hunting pressure. Overall, we show that
 hunting sign data can reveal the spatiotemporal patterns of hunting,
 crucial in evaluating the effectiveness of conservation interventions and
 guiding the prioritisation of limited conservation resources.
description: <strong>Data collection</strong>
 We established twenty-three parallel recce lines placed 4 km
 apart and totalling c. 345 km across the entire Ebo forest, Cameroon in
 2008 (see Fig. 1 in the publication). The recce lines were oriented
 perpendicularly to the main rivers and were surveyed in 2008, 2012, 2016,
 2017, 2018, 2019 (partially due to the COVID-19 pandemic), 2021, 2022, and
 2023. The surveys generally took place from October to April in the
 subsequent year, with the initial year regarded as the year in which the
 survey occurred. The survey team consisted of 2-3 trained observers
 followed by 3 porters, all moving at about 1 km per hour, using a handheld
 GPS (Garmin) and a manual compass to navigate along the predefined recce
 lines. The team used only pruning shears (secateurs) to ease movement,
 minimise damage to vegetation, minimise the risk of hunters using the
 recces, and distinguish their cuts from hunters’ machete cuts. We recorded
 different types of signs indicating human activities, including locations
 of wire snares (both active and inactive), hunting camps, trails regularly
 used by humans, cuttings (machete cuts and shrub breakings made by hunters
 while moving across the forest), human footprints, audible gunshots, and
 shotgun cartridges encountered on both sides of the
 recce lines. Perennial Permanent signs such as hunting
 camps and hunting trails were recorded when it was evident that they were
 used after the previous survey. All detected shotgun cartridges were
 collected and later destroyed to avoid double counting during subsequent
 surveys. We associated covariates related to the configuration of the
 landscape and anthropogenic pressures (distance to village, distance to
 road, distance to river, elevation, terrain ruggedness index, movement
 cost, human footprint index) to analyse the spatiotemporal patterns of
 hunting activities across the landscape.
 <strong>Data Preparation</strong>
 We placed 1 x 1 km grid cells (hereafter referred to as the site)
 across the entire study area using QGIS version 3.28.5 (QGIS Development
 Team, 2022; Whytock et al., 2021), and extracted a total of 346 sites
 which intersected with recce lines. We considered each site as a sampling
 unit (Wessling et al., 2020; Whytock et al., 2021) and assigned the data
 collected on recce lines across the years to their respective sites using
 their coordinates. We also calculated the survey effort (i.e., the
 kilometres walked) in each site. We only used hunting signs (shotgun
 cartridge, wire snare, hunters’ sign of passage) that were assumed to be
 less than one year old. The age of hunting signs was not documented in
 2008 and 2012; we therefore excluded them from in-depth analyses. Since
 there were no law enforcement activities undertaken in the area during the
 period of surveys, we assumed that the number of shotgun cartridges
 displaced from the original location where they were produced was
 negligible, as hunters would not attempt to hide their traces.
 For each site, following Soofi et al. (2018), we created
 secondary sampling units by dividing the recce line into segments of
 approximately 200 m each. On these segments, for each survey year, we
 recorded each hunting sign only once per segment to obtain presence and
 absence data (Karanth et al., 2011; Laurance et al., 2008; Soofi et al.,
 2018). Then, we summed the results from the segments within each site and
 obtained the number of events for each hunting sign which could vary from
 0 to 5 given that each site had a maximum of five 200 m segments. This
 allowed us to reduce potential pseudoreplication and increase the
 independence of the signs (Soofi et al., 2018) which may arise from
 multiple signs created by the same hunter at the same place (e.g. group of
 snares deployed by the same hunter, multiple shotgun cartridges produced
 by a hunter when attempting to kill one animal).
description: # Hunting data collected in the Ebo forest, Cameroon from 2008 to 2023
 [https://doi.org/10.5061/dryad.8931zcrzk](https://doi.org/10.5061/dryad.8931zcrzk) This dataset contains hunting data from the Ebo landscape, as well as other landscape features derived from GIS sources as indicated below. For the sake of the analyses, the data were assigned to 1 x 1 km grid cells, resulting in 346 cells. ## Description of the data and file structure The data contained in the CSV file is organized in columns as follows: Site: this represents the ID of the 1 x 1 km grid cells. There are a total of 346 grid cells, therefore, the column "site" varies from 1 to 346. Year: represents the year during which the survey took place. Each year is coded as a number as follows: 1 = 2008, 2 = 2012, 3 = 2016, 4 = 2017, 5 = 2018, 6 = 2019, 7 = 2021, 8 = 2022, 9 = 2023. In 2019, most grid cells were not surveyed; this year should therefore not be used in analyses. Effort: represents the distance in km walked in each grid cell every year. Elev: is the mean elevation (altitude asl) of each 1*1 km grid cell obtained from a Shuttle Radar Topography Mission (SRTM) layer of 30 m resolution obtained from [https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov) (download: May 2023) Rugg: is the terrain ruggedness index obtained from QGIS using the above-mentioned STRM layer. MovCost: is the mean anisotropic cumulative cost of moving from villages to different grid cells. It was obtained using the r.walk.points function in QGIS, taking into consideration the STRM layer and the location of the villages. VillDist: The Euclidean distance (km) from the centre of grid cells to the nearest village RoadDist: The Euclidean distance (km) from the centre of grid cells to the nearest road RivDist: Euclidian distance (km) from the centre of grid cells to the nearest river HFI: Mean Human Footprint Index within each grid cell