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.

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.

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.