CR2025_55: Using biologging to prioritise conservation actions for an endangered African carnivore

*Please note that this PhD will be hosted at the Institute of Zoology*

Human-wildlife overlap will increase dramatically under current global change (1), with critical consequences for species extinctions. Wildlife conservation is likely to be most effective if it based upon a clear understanding of precisely how human-induced environmental change causes populations to decline. If threatening processes are well-understood, they can be simulated in mathematical models, and then these models can be used assess the consequences of different conservation actions under different scenarios, to identify the most promising conservation management options. This PhD entails taking this approach using the endangered African wild dog (Lycaon pictus, Figure 1), a charismatic flagship species, as a case study.

Wild dogs face a range of threats which vary in importance between populations (2). Previous studies suggest that this highly social predator lives on the edge of an energetic precipice. Wild dog hunts entail wide-ranging searches for prey, culminating in high-speed pursuits which sometimes cover long distances, a hunting strategy which is costly in terms of both energy and time (3). Anything which makes hunting less cost-effective is predicted to reduce individuals’ survival prospects and, ultimately, population persistence. Some such factors are natural elements of wild dog biology, including variation in pack size (4), and theft of kills by competing predators (termed “kleptoparasitism”, 3). However, human-induced environmental changes may also influence wild dogs’ hunting efficiency. For example, poaching depletes antelope numbers (5), potentially requiring wild dogs to spend longer searching for prey, while climate change is shortening the period of each day when it is cool enough to hunt (6). Recognising which key processes threaten specific populations can be difficult (7,8), potentially leading to ineffective conservation recommendations.

Because wild dogs range widely, through dense bush and rugged terrain, it is near-impossible to gain an unbiased picture of their hunting behaviour based on visual monitoring alone. To overcome this obstacle, researchers deployed collar-mounted GPS-enabled multi-sensor biologgers called “Daily Diaries” (9), which measure speed and direction of movement in three dimensions, 20 time per second. Behaviours such as walking, running, and feeding generate characteristic collar readings, meaning that collar data can be used to infer behaviour even when the animal is out of sight (10).

This project will draw upon over 3,000 collar-days of monitoring 21 free-ranging African wild dogs at six sites in Kenya, Zimbabwe, and South Africa, representing a range of environmental conditions, as well as data from four wild dogs collared and observed in captivity (11). Additional data may become available through ongoing collaring, although the PhD will still be achievable using existing data. Using this dataset, the student will be able to address several related questions:

(1) What are the “signatures” of key wild dog behaviours recorded by Daily Diaries? The student will use data collected simultaneously from Daily Diary collars and visual observations, both in the wild and in captivity, to characterise key behaviours which make up wild dog time budgets. Simple behaviours such as walking, running, and resting have been characterised already, but more complex behaviours (such as feeding) require more work.

(2) How does the profitability of hunting vary in space and time? Once component behaviours have been characterised, the student would be able to estimate, for each hunt, the cost (in terms of time and movement-related energy expenditure) and benefit (in terms of time spent feeding, a proxy for energy intake), to generate a measure of hunting profitability which is likely to vary between packs, sites, and seasons.

(3) How does hunting profitability vary in relation to temperature? The student could then explore how ambient temperature influences both overall hunting profitability, and the costs of different components of the hunt (e.g., searching, chasing), potentially resolving different assessments of threatening processes (7,8).

(4) How are prey loss and climate change projected to impact wild dog populations, and what can be done to reduce these impacts? Ultimately, the student would use their statistical analysis results to parameterise a dynamic energy budget model (12) centred on wild dog hunting. Within this model, threats (e.g., prey loss, climate warming) could be simulated, as could conservation interventions (e.g., prey conservation), to help identify conservation solutions.

Training opportunities:

A key opportunity is the CASE partnership with ZSL-Kenya, including an optional visit to field projects in Kenya to observe the study species, contribute to ongoing data collection, and interact with conservation practitioners, providing on-the-ground experience of tackling the challenges of conserving large carnivores in human-dominated landscapes. (Alternatively, the CASE partnership could be fulfilled through placement with conservation practitioners at ZSL’s London headquarters).

Training from the Institute of Zoology will include animal behaviour, ecology, and conservation, while training from Swansea University will cover processing big data, state-of-the-art modelling of sensor data and animal movements, and advanced statistical and mathematical modelling.

Student profile:        

This project would suit a student interested in animal behaviour and conservation, with strong quantitative skills and an interest in data analysis and modelling. Such a student might have degrees in biological sciences, mathematics, statistics, geography, or computer science. They should be committed to inclusive approaches to wildlife research and conservation.

Please note: For international candidates, this project will be hosted at Swansea University under lead supervision of Luca Börger.

Co-Sponsorship details:

This project will receive a CASE award from the Mpala Research Centre, Kenya

How to apply

Applications are made through our Online Portal, Good Grants: https://crocus-dla.grantplatform.com/