Data from: Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity
Data files
Jan 17, 2025 version files 3 MB
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detectmat_231225.csv
51.50 KB
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effort_231225.csv
17.21 KB
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meshutm_0.5km_buff_land.cpg
5 B
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meshutm_0.5km_buff_land.dbf
1.70 MB
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meshutm_0.5km_buff_land.prj
389 B
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meshutm_0.5km_buff_land.qpj
645 B
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meshutm_0.5km_buff_land.shp
1.16 MB
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meshutm_0.5km_buff_land.shx
68.08 KB
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README.md
2.50 KB
Abstract
Spatial capture–recapture models (SCRs) provide an integrative statistical tool for analyzing animal movement and population patterns. Although incorporating home range formation with a theoretical basis of animal movement into SCRs can improve the prediction of animal space use in a heterogeneous landscape, this approach is challenging owing to the sparseness of recapture events.
In this study, we developed an advection–diffusion capture–recapture model (ADCR), which is an extension of SCRs incorporating home range formation with advection–diffusion formalism, providing a new framework to estimate population density and landscape permeability. we tested the unbiasedness of the estimator using simulated capture–recapture data generated by a step selection function. We also compared accuracy of population density estimates and home range shapes with those from an SCR incorporating the least-cost path. In addition, ADCR was applied to real dataset of Asiatic black bear in Japan to demonstrate the capacity of the ADCR to detect geographical barriers that constrain animal movements.
Population density, permeability, and home range estimates of ADCR were unbiased over randomly determined sets of true parameters. Although the accuracy of density estimates by ADCR was nearly identical to those of existing models, the home range shape could be predicted more accurately by ADCR than by an SCR incorporating the least-cost path. For the application to bear dataset, ADCR could detect the effect of water body as a barrier of movement which is consistent with previous population genetic studies.
ADCR provides unique opportunities to elucidate both individual- and population-level ecological processes from capture–recapture data. By offering a formal link with step selection functions to estimate animal movement, it is suitable for simultaneously modeling with capture–recapture data and animal movement data. This study provides a basis for studies of the interplay between animal movement processes and population patterns.
README: Data from: Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity
https://doi.org/10.5061/dryad.ksn02v7bq
Capture recapture dataset of Asiatic black bear (Ursus thibetanus) in eastern Toyama Prefecture, Japan in 2013-2015, and a shp file of 0.5km grid cells with the area ratio of agricultural land and water surface as factors affecting permeability of bears.
Description of the data and file structure
The capture recapture data of Asiatic black bears were obtained by authors using video-recording camera traps set to 86 locations. ID of individuals were given by matching shapes of body marks such as ring marks. The dataset consists of the following two .csv files and one shapefile:
1. Deployments (effort_231225.csv)
This is a data table defining location IDs (trapid
), camera-working days (effort
), years (year
), decimal longitudes (Lon
) and latitudes (Lat
), UTM coordinates (x
and y
), occasion IDs (effort_occ
) and deployment IDs (effortID
) of the camera trap deployments. Note that the coordinates are originally generalized by the centroids of grid cells defined by meshutm_0.5km_buff_land.shp. The geographical and projected coordinate systems are JGD2000 (epsg: 4612) and UTM Zone 54 (epsg: 3100), respectively.
2. Detections (detectmat_231225.csv)
This is a data matrix defining the number of detections for all the combinations of effortID
and individuals.
3. The shp file (meshutm_0.5km_buff_land.shp)
contains the geometry and attributes of geospatial features of grid cells used for landscape analysis. The file bundle contains the main file .shp and companion files including: .cpg, .dbf, .prj, .sbn, .sbx, .shx. The .dbf file contains a table with grid ID (id
), boundary coordinates (xmin
, xmax
, ymin
and ymax
), centroids (x
and y
), area (area
, in m^2) and area ratio of water surfaces (wtr_mean
) and agricultural lands (agri_mean
). Coordinate system is the same as Deployments
. The .shp file can be opened in QGIS and R (with function st_read() of package sf).
Sharing/Access information
Area ratios of water surfaces and agricultural lands were derived from the national vegetation map created by Biodiversity Center of Japan, Ministry of Environment (https://www.biodic.go.jp/index_e.html, accessed 24 May 2024) released in 2010s.
Methods
Study site
Our survey was conducted in the eastern Toyama prefecture, Japan. Our study site locates at the western foot of Tateyama mountains and partly overlapped to the Chubusangaku National Park. It contains a wide range of topography from lowland, hill to mountains. In the hilly area, agricultural lands along the rivers divide the forest landscape. The deciduous coniferous trees (Fagus crenata, Quercus crispula and Q. serrata) which offer food for bears in autumn are dominant species of the forest (Arimoto et al. 2011). As in other parts of Japan, a hard crop of acorns causes behavioral changes in black bears that increase conflicts with human (Ohnishi et al. 2011).
Survey design
From 2013 to 2015, we conducted a camera trap capture-recapture survey at 86 locations in the forest (Fig. S1). The survey were conducted from May to October, which is active season for bears. In each location, we set a camera trap (Trophycam ; Bushnell Outdoor Products, Overland Park) with video-recording mode. The duration of video was 30 seconds, and lag time after a trigger was set to minimal value. For efficient photographing of a chest mark as a key to individual recognition, we used an odor stimulant (mixture of honey and red wine) to encourage bears to stand up in front of camera by the protocol shown by Higashide et al. (2013). The odor stimulant was filled in a plastic bottle covered by a robust polyvinyl chloride tubing and fixed to the surrounding trees for protection from bear attachs. We visited each location every one to two months to replace batteries and SD cards and to refill the odor stimulant. The records of the same individual at a location within 60 minutes were grouped into a detection event. An image library of chest marks was developed from the video footage taken, and identical individuals were matched manually (Higashide et al. 2012). For fitting the capture recapture models, we aggregated the numbers of detections for each camera trap, individual and year.