DOI: 10.5061/dryad.kwh70rz7p
title: Seasonal distribution and fencing effect for water deer and wild boar in an urban forest fringe area

description: Forest fragmentation and habitat loss are increasing human–wildlife
 conflict. The use of fencing to reduce wildlife incursions into
 agricultural areas, particularly through optimizing fence locations, is
 gaining interest. However, the spatiotemporal movement patterns of animals
 relative to hotspot fencing have received scant study. This study
 evaluated how effectively a single fence reduces seasonal incursions into
 an agricultural zone by crop-damaging mammals. The seasonal distributions
 of wild boar (Sus scrofa Linnaeus) and water deer (Hydropotes inermis
 argyropus) were evaluated, along with the effect of a short fence on their
 spatial behavior at a junction of a forest fringe and fields during
 different seasons. Seasonal habitat suitability models were constructed
 using unmanned aerial vehicle and camera trap data for the environmental
 predictor variables. Connectivity analysis utilizing habitat suitability
 models was applied to assess the effects of fencing on wildlife. The
 seasonal movements of water deer and wild boar were found to be primarily
 constrained by trails and roads, while fencing was the most important
 variable for wild boar following fence erection. The fencing effect was
 evaluated using a connectivity model, which showed increases of 65.5% for
 wild boar and 100.9% for water deer from the growing season to the harvest
 season. However, a high permeability area of wild boar disappeared at the
 fencing location, while that of the water deer was maintained. This
 suggests that properly designed fencing, based on the behavior of target
 species, can significantly reduce their incursions. Knowledge of the
 fencing effects in fragmented areas may help allocate local and regional
 management resources to solve coexistence challenges and establish
 wildlife conservation strategies.
description: <strong>1. Study area</strong> The
 study site was in a forest fringe area of Baekbong mountain in Namyangju
 City (an area of 458.1 km<sup>2</sup>, located at 37° 30–46ʹ
 N, 127° 05–22ʹ E), Gyeonggi Province, South Korea (Figure 1). It was
 selected because it provides accessibility to agricultural fields for
 wildlife and is a fragmented forest with suitable conditions that enable
 identification of the effects of fence installation on wildlife movements
 through monitoring with high-density camera traps. The climate is hot and
 humid in summer, and cold and arid in winter, and is part of the East
 Asian monsoon system. The annual mean temperature is 21°C, and the monthly
 mean temperature fluctuates seasonally, ranging from −4.2°C in January
 (winter) to 25.0°C in July (summer). The vegetation is primarily composed
 of a mixed forest of deciduous broadleaf and needleleaf trees, including
 pine (<em>Pinus densiflora</em>) and nut pine (<em>Pinus
 koraiensis</em>). Grassland and cropland are the other dominant
 nonurban land cover types in the area. Lettuce, green onion, pepper, corn,
 potato, sweet potato, and beans were identified as main crops through
 manual investigation. <strong>2. Occurrence of
 data collection and spatial filtering</strong>
 Camera traps were deployed in 18 locations for 14 months (January
 2021–February 2022) and were separated within regular hexagonal cells with
 100-m-long sides (Figure 1). Camera trap locations were selected based on
 traces of wild boar, such as rubbing trees, mud pools, soil rooting, and
 feces, or along wild animal pathways, to capture multiple events during
 the period. Cameras were located at least 50 m from each other. Cameras
 with infrared flashes, types D3 and D9, were used (Hong Kong Ica Industry
 Co., China). These were tied high on tree trunks beyond the reach of wild
 boar (1.18 m, <em>SD</em> = 0.20 m) to ensure safety
 (Supplementary Table S1). Both camera types had an aperture angle of 90°
 and detected animal movement using a passive infrared sensor (PIR sensor)
 with a detection range of ≤20 m. Camera PIR sensitivity was set to the
 normal level from among three choices (low, normal, and high). When
 triggered, they recorded 30 s of continuous video (resolution: 1920 × 1080
 pixels). The cameras were checked and their memory cards replaced every 2
 weeks. During the months representing crop growth, the
 camera traps recorded 283, 382, 694, and 1209 water deer appearances
 during the seedling season (March), growing season (June), harvest season
 (September), and winter season (December), respectively. The appearances
 by wild boar numbered 176, 419, 86, and 97 for the same months,
 respectively. Local Moran’s I applied based on a distance weight of 160 m
 and a power value of 2 in GeoDa (version 1.20), and spatial
 autocorrelation of the records was removed (Kwon et al. 2016). After
 spatial filtering, 981 and 146 appearances by water deer and wild boar,
 respectively, were retained in the final dataset used for habitat
 suitability modeling (Figure 2). Occurrence points were randomly generated
 within a circular sector buffer that considered camera trap shooting
 range, 90° camera angle, and a detectable range of up to 20 m that
 included occurrence data in high spatial resolution. To decrease the
 spatial aggregation, one occurrence point was randomly selected per pixel
 per 1 m. <strong>3. Environmental predictors of
 habitat modeling</strong> To establish
 environmental variables, high-resolution UAV images were acquired in June
 2021 using a DJI Phantom 4 Pro, V2.0, (DJI, Shenzhen, China) equipped with
 a built-in ground control point (GCP) unit and a 1-in. 20 MP CMOS sensor
 (5480 × 3648 pixels) with an 84° field of view and storage of 4K images at
 60 fps. The ﬂight mission was planned using DJI Go 4 and the Pix 4D
 capture applications. Altitude was set at 150 m, and flying speed was set
 to 5 m/sec, yielding a ground sampling distance of 7.85 cm/pixel. To
 generate the orthomosaic, a front and side overlap of 80% was set between
 the images to ensure coverage of every part of the study area. This
 resulted in 162 collected images. Image correction was conducted using GCP
 coordinate values, which were manually measured using the Gaia GPS app.
 Non-natural constructs, such as grave headstones, centers of manhole
 covers, and corners of road paint that were in low-slope open spaces, were
 selected as reference locations that could be precisely distinguished. A
 total of 15 GCPs and 39 checkpoints (CPs) were used for quality assurance.
 The digital elevation model (DEM) and orthomosaic were produced based on
 the structure from motion algorithms using the UAV imagery and Pix4Dmapper
 software (www.pix4d.com). The resulting DEM and orthomosaic were aligned
 to each other and used to generate environmental variables.
 Before running the water deer and wild boar habitat models, all
 the layers created using drones were set at a resolution of 1 × 1 m, and
 13 distance-related variables in four types of habitats were selected.
 These consisted of the distances to the following items: oak trees,
 coniferous trees, and edge of forested area (forest); sweet potato and
 corn (distance to crops); distances to roads, informal trails, formal
 trails, cemeteries, building, and fences (human-related); and distances to
 mud pool and water bodies (hydrography). In addition, three site
 characteristics were selected: elevation, slope, and roughness
 (topography). To reduce the effect of potential multicollinearity on the
 models, Pearson’s correlation coefficient was calculated, and only the
 variables &lt;0.75 were selected (Dormann et al., 2013). Therefore,
 from the 16 environmental variables, 11 were selected to predict the
 distributions of the species. These consisted of oak tree, coniferous
 tree, and edge of forested area (forest); corn (crop); distances to roads,
 informal trails, and fences (human-related); DEM, slope, and roughness
 (topography); and distance to water bodies (hydrography) (for further
 details, see Supplementary Table S2 and S3).
 <strong>4. Maxent model setting and model
 validation</strong> To model seasonal water deer
 and wild boar distribution, maximum entropy species distribution modeling
 (Maxent, version 3.4.4) was used. This applies the principle of maximum
 entropy to relate presence-only data to environmental variables in order
 to model the likelihood of the presence of species without the need for
 absence data, which is difficult to collect (Phillips et al. 2009). In
 this study, presence data of water deer and wild boar were collected by
 camera traps as a representative value for each hexagon, and environmental
 layers were derived from high-resolution mapping resulting from the use of
 UAVs (Figure 2). The presence data was solely captured
 through camera traps; therefore, a bias grid file was created by assigning
 a sampling probability value of 1 within the camera trap shooting range
 (90° angle and 20 m detection distance) and the value of 0.01 to other
 areas to restrict the background sampling area (Vollering et al. 2019).
 The output format was set to cloglog as it provides a better result
 compared with logistic when bias correction is used (Phillips et al.
 2017). The final output provides relative suitability values from 0
 (unsuitable) to 1 (suitable). All other parameters remained on default
 settings. The K-fold cross-validation method was used to fit the model
 (Fielding and Bell 1997), with partitioning occurrences for training (75%
 of the total) to calibrate the model and testing (25%) to evaluate model
 performance. Models with the area under the curve (AUC) &gt;0.75
 corresponded to high discriminatory performances, while a value of 0.5
 indicated that the model prediction was only as good as a random guess
 (Fielding and Bell 1997). The final probability model was transformed into
 a binary map using the maximum of the sum of training sensitivity and
 specificity, with the value above for presence and under for absence
 (Manel et al. 2001). Thus, eight models representing the two species and
 four seasons (seedling, growing, harvest, winter) were obtained.
 <strong>5. Connectivity modeling</strong>
 Functional connectivity analysis can be applied to assess the
 effects of fencing on wildlife because it can represent their movement
 capacity within human-modified landscapes (Cushman et al. 2009).
 Open-source Julia software (version 1.6.5) was used to implement the
 Omniscape algorithm, a circuit theory approach for omnidirectional
 landscape connectivity modeling used to assess pathways across landscapes.
 Omniscape calculates current flow over the study area between all (or a
 regularly spaced subset of) pixels and the center pixel, treating the
 landscape as resistance to movement in a circular moving window within a
 user-specified radius (McRae et al. 2016). Species distribution maps,
 generated using Maxent, were imported into Omniscape to generate the input
 spatial source and resistance layer (see Section 2.4). The habitat
 suitability model was employed as the source layer and was transformed to
 produce the resistance layer by reversing the values in order to allocate
 high movement resistance to low-quality areas (1-suitability) (Costa et
 al. 2021). Two Omniscape outputs were generated, and
 within the cumulative connectivity values map, higher values represented
 higher connectivity and normalized flow potential, where cumulative
 connectivity was divided by current flow without resistance. The
 normalized flow potential map was reclassified into three categories: low
 (impeded &lt; 1), moderate (diffused = 1), and high (channeled or
 intensified &gt; 1). These better represented the most important areas
 for water deer and wild boar movement and illustrated the fencing impact
 upon them. To assess the similarity of intra- and interspecies functional
 connectivity according to the seasons, the spatial overlap in the
 cumulative connectivity map was assessed with pairwise Schoener’s
 <em>D</em> metric using ENMTools in R, version 4.1.3 (Warren
 et al. 2010). <em>D</em> statistic values range from 0 to 1,
 where 1 corresponds to identical niche overlap or close to the absence of
 overlap, and 0 corresponds to no niche overlap.
 <strong>6. Fence erection in the hotspot
 area</strong>  To observe the effect of fencing
 in water deer and wild boar hotspot areas, fencing was erected considering
 the following factors: (1) a location where the probability of presence
 and connectivity is relatively high before fencing erection; (2) the
 presence of a mud pool, which is a preferred habitat; and (3) consultation
 with the land owner. In addition, there were three aspects of fence
 construction that had to be considered in its design since large mammals
 pass through fences by three methods: jumping over the fence, lifting up
 and crawling under the fence, and penetrating weak areas of the fence
 material. Therefore, the fence was designed and installed considering
 these three aspects (Figure 3). Firstly, the upper part of the fence
 included an approximately 60° angle toward the direction from which
 animals mostly come. Secondly, the lower part of the fence net was tied
 with poles to prevent animals from bending or twisting the net and
 widening gaps. Lastly, the net was comprised of steel wire strong enough
 not to be broken by animals pushing against it. The erected fence was 200
 m in length and was installed from July 26 to August 6, 2021. From June 28
 to December 31, 2021, six camera traps facing the fence area were evenly
 deployed along the fence to observe approaching wild boar and water deer.
 During the winter, when crops are not generally damaged by wildlife, a
 door installed in the middle of the fence was opened.
description: # Seasonal distribution and fencing effect for water deer and wild boar in
 an urban forest fringe area --- ## Summary of Experimental Efforts This
 dataset is part of a study investigating the seasonal spatial behavior of
 wild boar (*Sus scrofa*) and water deer (*Hydropotes inermis*) at the
 forest-agriculture boundary of Baekbong Mountain, Namyangju City, South
 Korea. A 200-meter wildlife fence was installed during the summer of 2021
 to assess its effect on wildlife incursions into agricultural zones. The
 study utilized camera trap monitoring, unmanned aerial vehicle (UAV)
 imagery, Maxent species distribution modeling, and Omniscape connectivity
 analysis to evaluate seasonal habitat suitability and connectivity shifts
 before and after fencing installation. The data collection spanned 14
 months, covering all four seasons, and environmental variables were
 derived from high-resolution orthomosaics and digital elevation models.
 The fence was erected in an identified movement hotspot and monitored with
 additional camera traps. This dataset includes wildlife presence records,
 environmental rasters, habitat models, and connectivity outputs for reuse
 and replication. ## File Structure and Contents ### 1. `present_data/`
 Seasonal wildlife presence data from camera traps. - `seed.xlsx`: Spring
 presence data - `grow.xlsx`: Summer presence data - `harvest.xlsx`: Fall
 presence data - `winter.xlsx`: Winter presence data Each Excel file
 includes: - `ID`: Camera trap identifier - `Species`: "deer"
 (water deer) or "boar" (wild boar) - `X`, `Y`: UTM coordinates
 of detection (Zone 52N, WGS84) Filtered for spatial autocorrelation using
 Moran’s I to avoid spatial bias. ### 2. `environment_data/` Raster files
 are used as environmental predictors for modeling. #### Distance-Based
 Environmental Variables (unit: meters): - `chestnut.tif`: Distance to
 chestnut trees - `conifer.tif`: Distance to coniferous trees - `corn.tif`:
 Distance to cornfields - `edge.tif`: Distance to forest edges -
 `road.tif`: Distance to paved roads - `trackside.tif`: Distance to unpaved
 trails - `water.tif`: Distance to water bodies - `fence.tif`: Distance to
 pre-existing fences - `fence_new.tif`: Distance to the newly installed
 fence #### Topographic Variables: - `dem.tif`: Elevation (meters above sea
 level) - `slope.tif`: Slope angle (degrees) - `roughness.tif`: Surface
 roughness index All rasters have a 1m × 1m resolution. ### 3.
 `species_distribution_model_result/` Maxent model results showing habitat
 suitability for each species and season. - Eight `.tif` files (GeoTIFF)
 for two species × four seasons - `.csv` summaries of model performance
 (AUC, threshold values) - Output values range from 0 (unsuitable) to 1
 (high suitability), cloglog scale ### 4. `connectivity_result/` Omniscape
 outputs for landscape connectivity analysis. - Subfolders: `deer_seed/`,
 `boar_grow/`, etc. (species + season) Each includes: - `cumulative.tif`:
 Total current flow - `flow.tif`: Raw current flow - `normalization.tif`:
 Normalized flow potential (scaled) Normalized values were reclassified as:
 - `&lt;1`: Low (impeded flow) - `=1`: Moderate (diffused flow) - `&gt;1`:
 High (channeled flow) ## Other Data Sources - UAV imagery acquired via DJI
 Phantom 4 Pro V2.0 and processed using Pix4DMapper. - Ground Control
 Points (GCPs) and Check Points (CPs) measured using Gaia GPS. -
 Environmental layers derived from manual interpretation of UAV
 orthomosaics. ## Data Availability All data in this package are original,
 created as part of this study. No third-party data was used.