Evaluating the role of movement behaviour and habitat familiarity on translocated grizzly bear success using an agent-based modelling approach
| dc.contributor.author | Zubiria Perez, Alejandra | |
| dc.contributor.supervisor | Bone, Christopher | |
| dc.date.accessioned | 2020-09-11T05:57:36Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-09-10 | |
| dc.degree.department | Department of Geography | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | In North America, the grizzly bear (Ursus arcos) is one of many species increasingly threatened by the consequences of human-wildlife conflict, with human-bear encounters on the rise due to increased human activity near or in bear habitat. As a result, a growing number of bears are subjected to management measures such as translocations in which animals are moved to areas with lower risk of human conflict, although these measures are not always successful. Previous research has attempted to understand factors associated with translocation success, but new methods are needed to address the continuous and complex nature of issues related to how animals move and learn about their surroundings as well as how they adapt to novel environments. The objective of my MSc thesis is to develop and employ a novel agent-based computer simulation model to analyze how grizzly bears learn and respond following a translocation event. This modelling effort attempts to capture how bears make decisions based on multiple factors, and represent how grizzly bears interact with their environment and make movement decisions based on learned behaviours. First, an agent-based movement model was developed for female grizzly bears using GPS-location data for bears within a region in west-central Alberta, Canada. The model, which incorporates multi-scale decision-making and machine learning, generated movement patterns similar to those observed in radio-collared females in the study area. Home range sizes and movement metrics produced by the model were consistent with those observed in female grizzly bears in the area. The model was then used to simulate translocation events in which bears with varying “exploration” propensities were translocated to habitats with familiar or novel landscape characteristics. In general, bears translocated to habitats with similar landscape features to their original habitat were more likely to use high-quality habitat than bears moved to areas with very different landscape features. However, while increased exploration led to greater use of high-quality habitat in the long run, exploratory behaviour was found to be mostly detrimental during the first years following a translocation, the period considered critical for translocation success. Model results were found to be scale-dependent with results varying both in time and space, highlighting the need for a multi-scale approach to animal movement studies. The findings presented here also emphasize the need to account for behavioural traits of wildlife and habitat characteristics of the capture and release sites when selecting suitable translocation locations. This work highlights the potential for agent-based modelling as a tool to study animal movement as a continuous and complex process and evaluate conservation policies. | en_US |
| dc.description.embargo | 2021-08-24 | |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/12119 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | grizzly | en_US |
| dc.subject | bear | en_US |
| dc.subject | agent-based modelling | en_US |
| dc.subject | animal cognition | en_US |
| dc.subject | animal memory | en_US |
| dc.subject | animal learning | en_US |
| dc.subject | wildlife translocation | en_US |
| dc.subject | simulation | en_US |
| dc.title | Evaluating the role of movement behaviour and habitat familiarity on translocated grizzly bear success using an agent-based modelling approach | en_US |
| dc.type | Thesis | en_US |