Using remote imaging and machine learning tools to quantify ecological effects of red deer (Cervus elephas) in Scotland

Studentship strategic priority area:Biodiversity
Keywords: Deer, management, abundance, population dynamics

Abstract:
The spatial distribution and movement of animal populations shapes a variety of ecological processes, including resource-consumer interactions, inter-specific competition and parasite/disease dynamics. However, quantifying animal occurrence over large landscapes can be difficult, particularly in systems where animals move heterogeneously with respect to habitat. Thus, a key limitation in understanding the drivers of ecological interactions involving mobile species is our ability to reliably measure animal occurrence across time and space. This issue is particularly relevant to Scottish red deer populations, which occur in increasingly high densities in many areas. High deer abundance is of concerns due to a range of ecological consequences, including increased abundance of ticks, which commonly use deer as reproduction hosts (Millins et al, 2017). Standard census methods for deer are either extremely costly (e.g. helicopter surveys), and thus are difficult to repeat multiple times within a season, or are dependent on indirect indices (e.g. dung surveys) which are plagued by methodological biases and detection issues. When linking distribution and movement patterns of deer to highly dynamic ecological processes, such as tick abundance and distribution, much finer temporal resolution, high-quality data are needed.
Capitalising on ongoing studies of deer, livestock and ticks in the Uist Islands, and building on partnerships with deer management stakeholders, this PhD project will use novel imaging technologies to quantify the distribution and movement of red deer and interactions with livestock and human presence, which are key to understanding exposure to parasites (ticks) and tick-borne diseases (Lyme borreliosis). Using wildlife survey techniques developed at the University of Glasgow (Torney et al. 2019), this project will enhance methods for counting deer from aerial drone video and camera traps across two areas (the Uists and an area of the Scottish mainland with well-monitored deer abundances). These sites are ideal for the development of these methods because deer occur in relatively open vegetation and populations are relatively small and isolated. We will deploy machine learning algorithms to efficiently classify and census animals from imagery. The relatively fine temporal scale over which data will be collected will allow a better understanding of the dynamic nature of deer-habitat relationships over time. Information generated through this project will directly inform management actions aimed at reducing tick prevalence. For instance, both sheep and deer are important hosts for ticks, yet there are often conflicting views about which host should be the target of management interventions, such as lethal control, fencing, reduction of stocking densities and habitat manipulation. Thus, one broader aim of this PhD project is to shed new light on the relative roles of deer versus sheep in driving the spatiotemporal dynamics of tick populations in rural Scotland. Control of deer densities is also essential for improving sapling survival in regenerating forests, reducing vehicle collisions on roadways and optimizing harvest quotas. Accurate estimates of deer population densities are needed, and this project has the potential to transform the tools and our ability to make ecological inferences related to deer distribution and movement in a variety of habitats (e.g. farmland, heath, bog, woodland) in Scotland.