Monitoring animal populations with cameras using open, multistate, N-mixture models

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated 4 commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, 12; and years = 2, 5, 10) and evaluated the b..., For the simulation analysis, data were generated using base simulation functions in R (see code) and there are no traditional field data associated with this part of the manuscript.
The dataset (moose_data.rds) accompanies the manuscript: \"Monitoring animal populations with cameras using open, multistate, N-mixture models\". It is an rds file that includes counts of adult female and juvenile moose (Alces alces) captured on remote cameras. The file is a 4-dimensional table that includes sites (n = 225 [indexed as 257]), years (n = 6), age classes (n = 2; adult and juveniles), and surveys per year (n = 4). We have also included another file (moose_data_bulls.rds) that includes counts of adult female and male moose as well as juveniles. These data were not formally analyzed but mentioned in the discussion as a dataset for readers to explore using multistate DM models. The data were collected by Dr. Alexej Siren and the other co-authors (see dataset authors) in Vermont and New Hampshire, USA..., , # Monitoring animal populations with cameras using open, multistate, N-mixture models

[https://doi.org/10.5061/dryad.tqjq2bw76](https://doi.org/10.5061/dryad.tqjq2bw76)

Our dataset allows the user to replicate the results of the study (see description below). **We politely request to be contacted by parties interested in data reuse from the empirical moose study to discuss collaboration**.

## Description of the data and file structure

The simulation file `Multi-State DM Simulation.Rmd` does not contain external data (see below).

The empirical moose data (moose_data.rds) is a 4-dimensional array that contains count data of juvenile and adult female moose from 2014 to 2019. The structure of the data is as follows:

* first dimension of array = number of camera sites (n = 225)

* second dimension of array = number of years (n = 6; 2014 - 2019)

* third dimension of array = number of states (n = 2; \"Juveniles\", \"Adults\")

* fourth dimension of array = number of monthly surveys per year...