Deer abundance estimation at landscape-scales in heterogeneous forests

Abstract

Reliable estimates of deer abundance support effective management of source-sink population dynamics in complex landscapes and improve understanding of the relation between deer density and biodiversity impacts. Performance of distance sampling using thermal imaging of Reeves’ muntjac Muntiacus reevesi and roe deer Capreolus capreolus was examined across 123 km2 of conifer forest in Eastern England, sampling 1567 km in total. For muntjac distance sampling was compared to estimates from drive counts in 2007. For each of three subsequent winters (2008-2010), we compared the magnitude and precision of forest-wide abundance estimated from analytical designs that: i) ignored potential habitat-specific detectability, either with uneven or balanced sampling effort; ii) controlled for sampling effort and/or density among seven forest blocks (mean = 18.8 km2 ± 11.1 SD); iii) accounted for potential movement prior detection; iv) accounted for varying detectability among habitat classes (as a covariate), while controlling for differing densities among blocks. Detectability was further examined in models that stratified to estimate habitat-specific Effective Strip Width (ESW). Estimated muntjac densities from distance sampling were of similar magnitude to estimates from drive counts. Over 2008-2010, we observed 1926 muntjac and 921 roe groups; allowing robust abundance estimation and habitat-specific analysis. ESWs in open habitat were 31% and 27% greater than in mature and 45% and 46% greater than in dense habitat, for roe and muntjac respectively. Although differences in densities among model designs were not large, ignoring block or habitat effects gave higher estimates, while models that accounted for habitat-specific detectability gave lower (-8%) and more precise (38% reduction in CV) estimates (n = 3, muntjac: 5.3-7.5% CV; roe deer: 8.8-12.6% CV). The similarity of density estimates between ungrouped and grouped data and analysis of behaviour of detected deer support the conclusion that distance estimates were not biased by avoidance. We conclude that distance sampling using thermal imaging is a robust and powerful method for estimating deer density. In heterogeneous forest density estimates will be improved by accounting for varying detectability among growth stages or habitats

Detection probabilities and surveillance sensitivities for managing an invasive mammalian herbivore

“…difference between warm-bodied animals and the cool temperate forest floor. However, as canopy closure increases, presumably P(detec-tion|presence) (and derived SSe) for thermal imaging cameras will decrease as canopy vegetation obscures the ability to view objects on the forest floor (Graves et al 1972, Wäber and Dolman 2015, Hambrecht et al 2019. By the end of camera trap deployments at each site (about two months), camera traps had identified half to twothirds of the unique GPS-collared wallabies that were available to be detected.…”

Modelling parameter uncertainty reveals bushmeat yields versus survival trade-offs in heavily-hunted duiker Cephalophus spp.

“…These estimates of carrying capacity were quite variable, for example, ranging from 10.2 blue duikers km -2 in the Ituri Forest, north-eastern Democratic Republic of Congo [ 25 ] to around 70 blue duikers km -2 in north-eastern Gabon estimated by Feer [ 60 ]. The reasons for these discrepancies could be manifold: different measuring techniques [ 13 ], observation error [ 61 – 63 ], or a spatial gradient as suggested by Peres [ 64 ] in his comparison of hunted and non-hunted sites across the Amazonian rain forest. This makes cross-habitat generalisations about optimal harvesting rates more difficult.…”

Habitat quality, configuration and context effects on roe deer fecundity across a forested landscape mosaic

“…In the absence of large predators, deer populations are managed by wildlife rangers employed by Forestry Commission England (FC) to mitigate their impacts on forest crop establishment, biodiversity and road collisions. Forest-wide density of roe deer (estimated using nocturnal distance-sampling by thermal imaging [8]) remained low throughout the study period, fluctuating between ca. 2.6 to 4.7 deer/km 2 (Zini et al, unpublished data).…”

“…Deer populations are increasing in both North America and Europe [1–3], with important consequences for biodiversity, human health and traffic collisions [4–6]. Management effectiveness is improved by landscape-scale analysis of source-sink demography [7]; however, measuring the required parameters of density, fecundity, neonatal and adult mortality can be time consuming and challenging [7,8]. Local, context-specific, measures are required as deer fecundity can vary with both density [9,10] and landscape suitability [11–13].…”