From pictures to policy: camera-based estimation of animal density to inform disease control
File(s)
Author(s)
Miles, Verity
Type
Thesis
Abstract
Reliable density estimates are essential for effective wildlife management, yet difficult to obtain. Camera traps have revolutionised density estimation for elusive species where individuals are recognisable, but until recently could not be used for other species. The random encounter model (REM) and camera trap distance sampling (CT-DS) are novel approaches which fill this methodological gap.
The management of European badgers (Meles meles) for the control of bovine tuberculosis (bTB) offers an ideal system for testing these methods because counting badgers is difficult, and abundance data are crucial for informing policy decisions. This thesis aimed to assess the REM and CT-DS for estimating animal density to inform disease management, focusing on badger vaccination for the control of bTB.
Density estimates aligned well between methods (Chapters 2, 4, and 5), and with independent reference estimates (Chapters 2 and 4). The REM was the more precise method, but both approaches were imprecise where density was low (Chapters 2 and 4). Precision improved with the use of more cameras, with both methods achieving adequate precision with ≥100 cameras (Chapter 4).
Where badgers were not culled, mean vaccination coverage ranged from 50.6% to 74.2%, surpassing the 30% threshold likely to be necessary for bTB elimination in badgers (Chapter 3). At two post-cull sites, point estimates of coverage also exceeded 30%, although confidence intervals were wide (Chapter 5), likely due to the small geographical scale of the study.
These findings show the utility of the REM and CT-DS for estimating the density of individually unidentifiable species. Additionally, the results suggest that badger vaccination can attain high levels of coverage, highlighting this as a feasible strategy for bTB management. However, the imprecision of estimates immediately post-cull calls for larger-scale evaluations. These findings are highly relevant to current bTB policy in England which is prioritising post-cull badger vaccination.
The management of European badgers (Meles meles) for the control of bovine tuberculosis (bTB) offers an ideal system for testing these methods because counting badgers is difficult, and abundance data are crucial for informing policy decisions. This thesis aimed to assess the REM and CT-DS for estimating animal density to inform disease management, focusing on badger vaccination for the control of bTB.
Density estimates aligned well between methods (Chapters 2, 4, and 5), and with independent reference estimates (Chapters 2 and 4). The REM was the more precise method, but both approaches were imprecise where density was low (Chapters 2 and 4). Precision improved with the use of more cameras, with both methods achieving adequate precision with ≥100 cameras (Chapter 4).
Where badgers were not culled, mean vaccination coverage ranged from 50.6% to 74.2%, surpassing the 30% threshold likely to be necessary for bTB elimination in badgers (Chapter 3). At two post-cull sites, point estimates of coverage also exceeded 30%, although confidence intervals were wide (Chapter 5), likely due to the small geographical scale of the study.
These findings show the utility of the REM and CT-DS for estimating the density of individually unidentifiable species. Additionally, the results suggest that badger vaccination can attain high levels of coverage, highlighting this as a feasible strategy for bTB management. However, the imprecision of estimates immediately post-cull calls for larger-scale evaluations. These findings are highly relevant to current bTB policy in England which is prioritising post-cull badger vaccination.
Version
Open Access
Date Issued
2024-12-20
Date Awarded
01/04/2025
License URL
Advisor
Donnelly, Christl
Rowcliffe, Marcus
Woodroffe, Rosie
Brotherton, Peter
Sponsor
UK Research and Innovation
Imperial College London
Natural England (Agency)
Zoological Society London
The Cornwall Badger Project
Grant Number
NE/S007415/1
Publisher Department
Department of Infectious Disease
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)