DOI: 10.5061/dryad.xpnvx0kkf
title: Data for: Estimation of density distribution in unmarked populations using camera traps

description: Reliable estimates of species distribution and density are essential to
 ecology. Camera traps have revolutionized wildlife monitoring, and
 camera-trap data are increasingly used to study animal distribution and
 density.  We propose a general framework and present a
 statistical model to estimate the distribution and density of species for
 which individuals lack identifying marks. Numbers recorded at traps allow
 spatial variation in density to be modelled, while distances of detected
 animals from the cameras allow correction for missed animals in the
 detection sector, using distance sampling. We test the model by simulating
 a camera-trap survey of a population of single animals, and we apply the
 model to data from a field study of Reeves's muntjac. The simulation
 indicated that the estimates of population density were unbiased, and the
 model performed well in depicting spatial variation in density. In the
 field study, the model estimated that the overall population density of
 Reeves's muntjac was 4.1 ind/km2, and mapped its density distribution
 across the study area. We provide a method to estimate unmarked species’
 density distribution using camera-trap data. Application of the model can
 help investigate the distribution and density of many ground-dwelling
 solitary animal populations lacking individually recognizable markings. We
 expect our method to provide an effective means for wildlife monitoring.
description: The dataset contains two folders: (1) Folder
 "simulation study" include the data and codes in go language,
 aiming to generate the movement path of 5000 animal individuals; include
 the data and codes in r language, aiming to build a DDM to estimate the
 density distribution of the simulated population. (2)
 Folder "case study" include the data and codes in r language,
 aiming to build a DDM to estimate the density distribution of the
 Reeves's muntjac.
description: Readers can process the codes in go and R languages.
  * Go can be downloaded at https://go-language.org/.
  * R can be downloaded at https://www.r-project.org/.
  * If readers want to process the codes to repeat the work in our
 case study, please load the file "r_codes.r" in the folder
 "case study" using R language, which contains all the
 documentation for the other files under this folder. Then, readers can run
 the R codes and process the data with the help of the notes in this
 script.  * If readers want to process the codes to
 repeat the work in our simulation study of parameter estimation, please
 load the file "r_codes2.r" in the folder "/simulation
 study/parameter estimation" using R language, which contains all the
 documentation for the other files under this folder. Then, readers can run
 the R codes and process the data with the help of the notes in this
 script.   * If readers want to process the codes to
 repeat the work in our simulation study of animal movement, please open
 the file "codes and notes.txt" in the folder "/simulation
 study/animal movement simulation", which contains all the
 documentation for the other files under this folder. Then, readers can run
 the GO codes and process the data with the help of the notes in this text
 file. * We are planning to develop an R package for the
 model if this paper can be accepted.