Camera-based badger density estimation using the REM, CT-DS, and SMR

Accurate and precise assessment of population density plays a critical role in effective wildlife management, but reliable estimates are often difficult to obtain. Camera traps have emerged as valuable non-invasive tools for studying elusive species, offering cost-effective solutions for both marked and unmarked populations. We evaluated the consistency of badger (Meles meles) density estimates obtained from the random encounter model (REM) and camera trap distance sampling (CT-DS) with independent estimates from spatial mark-resight (SMR) models and quantified the bias in CT-DS arising from animals reacting to camera traps. Six camera trap surveys were conducted in Cornwall, UK, in 2019 and 2021, and data were used to estimate badger density using the REM and CT-DS. Four sites were included in a badger vaccination research project, providing an opportunity to mark badgers with uniquely identifiable fur clips to facilitate resighting within an SMR framework. We found consistency in the ..., Data collection
Data were collected from six camera trap surveys at five sites in Cornwall, UK, in 2019 and 2021. 
Data Analysis
Badger density was estimated using three methods: The Random Encounter Model (REM), Camera trap Distance Sampling (CT-DS), and Spatially Explicit Mark Resight (SEMR). Details of each method are given below.
REM Density Estimation

Density estimates were calculated from encounter rates using an equation involving variables like the number of independent badger encounters (y), temporal survey effort (t), and camera detection zone parameters (r and θ).
Model parameters were estimated from camera images, including badger position data, speed, activity level, and detection zone dimensions.
Density estimates were obtained using the 'camtools' package, including a nonparametric bootstrap of trap rate errors.
Where badgers showed reactive behaviour, 'reactive' sequences were removed from the estimation of animal speed and the camera detection zone.

CT-DS Density Esti..., , # **Camera-based badger density estimation using the REM, CT-DS, and SMR**

The data and code are provided for three methods used to estimate badger density - the Random Encounter Model (REM), Camera-Trap Distance Sampling (CT-DS), and Spatially-Explicit Mark Resight (SMR).

## **Description of the data and file structure**

For each method, data are organised into separate files representing the different sites (numbered 1-5). Any data containing location information has been omitted in line with privacy-sharing agreements so that participating landholders remain anonymous. As such, we have not included the shapefiles to generate the habitat mask for SMR or the coordinates of camera locations.

We have also included the code for the simulation of animal density using SMR across a range of *pID* values, reflecting the proportion of identifiable individuals. The values provided are similar to the observed detection conditions of the full dataset.  

Below we have outlined the methodology...