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

Miles, Verity1

Published Aug 19, 2024 on Dryad. https://doi.org/10.5061/dryad.gb5mkkwwk

Data files

Aug 19, 2024 version files 10.62 MB

Abstract

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 density estimates across all methods, but the results had wide confidence intervals. Density estimates derived from CT-DS tended to be higher than those from the REM and were sensitive to the exclusion of reactive sequences, resulting in a two-fold decrease in the estimated density in one case. The REM tended to be the most precise method; however, where badger density was low, precision was low using all methods. Our findings suggest animal density can be assessed from camera traps in the absence of individual identification; however, it is important to account for reactive behaviours, especially where such behaviour is prevalent. In these circumstances, we recommend utilising the REM which offers a clear methodology for addressing bias arising from reactive sequences. In addition, we emphasise the need for improved precision to ensure the effectiveness of these methods in the context of wildlife management. We offer practical considerations to facilitate the broader application of these methods, drawing upon the example of disease control through badger vaccination.

README: 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 for obtaining the density estimates and provided references for further details of the methods.

REM - methods, data, and code

We obtained badger density estimates from all five sites using the REM following the methods and equation detailed in Rowcliffe* et al.* (2008). Data files used for the REM are: exifdata, deptable, deployment models, and cmods. Below is a description of each of these files.

Exifdata: contains all of the image metadata, including species, count, contact, and calibration tags. Exifdata is used to construct the animdat and postdat data files, which are used to estimate animal speed and the dimensions of the camera detection zone, respectively. The ‘contact’ data within exifdat is used to fit activity models and estimate trap rate. A ‘contact’ is defined as the first image of an animal sequence when an individual enters the field of view for the first time.

Cmods: camera calibration models built using an object of known size (a calibration pole), to estimate radius and angles in images. A separate camera model was built for each camera model and image size.

Deployment models: models for each camera deployment fitted using cmods to estimate real-world radius and angles in images.

Deptable: details of start and stop times of each camera placement. Used to build deployment calibration models and generate an estimate of temporal effort and trap rate.

Badger density estimates can be replicated using these files and the code provided. For further details on packages and code please see the CTtracking and camtools repositories at https://github.com/MarcusRowcliffe

CT-DS - methods, data, and code

Badger density estimates were obtained for all five sites using CT-DS following the methods and equation detailed in Howe* et al.* (2017). Data files used for CT-DS are: DS_data, DS_effort, and DS_radiantime. Below is a description of each of these files.

DS_data: the data file that follows the distance sampling data structure specified for use with the ‘Distance’ package in R. Each line represents a badger detection with distance and location (camera placement) recorded. Where no badgers were recorded, an ‘empty’ line for that camera trap is present in the data. These data are used to fit distance sampling models, following the code provided.

DS_effort: details of start and stop times of each camera placement. Used to estimate temporal effort.

DS_radiantime: file containing contact data with radian time of day to fit activity models.

Dimensions of the camera trapping zone (the angle of detection) are estimated as above for the REM and snapshot moments are estimated using the speed data from the REM.

SMR - methods, data, and code

Badger density estimates were obtained using SMR for three of the five sites where there were sufficient re-captures. Data files used for SMR are: Capdat_marked, Capdat_Tu, Capdat_Tm, Capdat_Tn, Decdat. Density estimates can be obtained using these files and the code provided, but please note that the location data (camera coordinates and shape file habitat masks) have been removed following landholder privacy agreements. Below is a description of each of these files.

For comprehensive instructions on how to use this data and code to generate SMR density estimates using the ‘secr’ package, please see Efford (2023).

Capdat_marked: capture histories of marked identifiable individuals.

Capdat_Tu: capture records of unmarked individuals.

Capdat_Tm: capture records of marked unidentifiable individuals.

Capdat_Tn: capture records of individuals with an unknown marking status. These data are not used for density analyses but can be provided to the model.

Decdat: detector information for badger traps and camera traps; location coordinates removed.

References

Efford, M. (2023). Vignette: Mark–resight in secr 4.6.

Howe, E.J., Buckland, S.T., Després-Einspenner, M.-L. & Kühl, H.S. (2017). Distance sampling with camera traps. Methods in Ecology and Evolution, 8, 1558-1565.

Rowcliffe, J.M., Field, J., Turvey, S.T. & Carbone, C. (2008). Estimating animal density using camera traps without the need for individual recognition. Journal of Applied Ecology, 45, 1228-1236.

Methods

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 Estimation

Point transect distance sampling methods adapted for still images were used.
Temporal and spatial effort calculations were adapted for continuous camera trapping.
Density was estimated using the number of badger observations, truncation distance, probability of detection, and activity level. We estimated density under two scenarios, where 'reactive' sequences were included or excluded from total badger observations.
Detection distances were determined through exploratory analysis and model selection.
Left truncation was applied to control bias arising from animals passing under the camera undetected.

SEMR Density Estimation

Individual badgers were identified by comparing marks in camera images with those taken during trapping and marking.
Retrospective capture histories of identifiable badgers were constructed.
SEMR models were fitted to the data using the 'secr' package in R.
Effective sampled area and buffer widths were determined based on the distance between capture and resighting locations.
Models with variable detection probability between marking and sighting occasions were considered.
Overdispersion was accounted for in models and standard errors.

All data analysis was performed in R (R Core Team, 2021).