AbstractAnimal-attached data loggers are used increasingly to collect movement and behaviour information to advise species management and answer key ecological questions about their habitat use, activity, and energetics. Biologgers are especially important for monitoring cryptic animals such as carnivores, when direct observations are impractical or might influence behaviour. In combination with environment data, these loggers can enhance our understanding of how animals survive in changing, anthropogenic landscapes.
In this thesis, I test the methodologies used when deploying biologgers and processing the data to improve accuracy when monitoring free-roaming animals. Furthermore, I examine how urban environments and seasons influence the roaming behaviour of domestic cats (Felis catus), and quantify how large wild predators, mountain lions (Puma concolor), minimise energetic costs in increasingly isolated, challenging terrains.
I found that the accuracy of animal routes derived from GPS decrease with longer intervals between fixes and with faster animal speeds, but ground-truthed dead-reckoning maintains accuracy. Additionally, random forest models more accurately identify animal behaviours from accelerometer data after simple processing such as calculating extra variables, improving data evenness, and matching data frequencies to behaviours.
In the field, I investigate how urbanisation and seasons interact to affect cat roaming and provide recommendations for tailored management to decrease predation of native fauna through habitat-specific buffer zones and limited cat confinement. Finally, I measure the energetic cost of incline locomotion of pumas and estimate the daily energy expenditure of wild pumas in steep terrains. I show that wild pumas adapt their behaviours to maintain low locomotion costs through traversing, to decrease their path angle, and by walking slowly on steep inclines.
This thesis recommends methods to increase biologging accuracy that should be implemented when monitoring free-roaming animals and investigates the impact of urban expansion on carnivore behaviour and energy expenditure, with implications for conservation and habitat management.
|Date of Award||Dec 2020|
|Sponsors||Northern Ireland Department for the Economy|
|Supervisor||Michael Scantlebury (Supervisor), Nikki Marks (Supervisor) & Aaron Maule (Supervisor)|
- random forest model
- energy landscape