DOI: 10.5061/dryad.ksn02v7bq
title: Data from: Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity

description: 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.
description: 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
 (<em>Fagus crenata</em>, <em>Quercus crispula</em>
 and <em>Q. serrata</em>) 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. 
    
description: # Data from: Mechanistic home range capture–recapture models for the
 estimation of population density and landscape connectivity
 [https://doi.org/10.5061/dryad.ksn02v7bq](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](https://www.biodic.go.jp/index_e.html), accessed 24 May 2024) released in 2010s.