Open Collections

Deith, Mairin

University of British Columbia

Humanity continues to depend on wild meat for subsistence and livelihoods, particularly in the Global South. Here, harvest sustainability is often undermined by ineffective wildlife management, exacerbated by limited data and research capacity. A key challenge to decision makers, then, is how to manage harvest systems in the face of data scarcity. The goal of this dissertation is to provide data-limited decision support tools for wild meat management, with particular focus on (1) predicting effort distribution across large scales, and (2) assessing spatial management strategies’ ability to robustly meet conservation and harvest goals. In my first chapter, I developed a novel hunter movement model based on landscape resistance and circuit theoretic algorithms. These algorithms build on commonly used accessibility metrics by incorporating diffusive, multi-path movement from settlements into surrounding landscapes. To facilitate broad application, I built the landscape-scale map from freely available geospatial datasets and open source software. Validating this approach with camera trap observations of hunters from Malaysian Borneo, I found that the new accessibility metric had far more explanatory power than any other accessibility-based or environmental predictor. To assess other drivers of spatial foraging patterns, in my second chapter I extended my scope across the tropics. Compiling images of foragers taken from cameras in 10 protected areas (PAs), I explored which types of variables—accessibility, wildlife value, or environmental features—best informed forager presence on the landscape. While the results of this chapter should be interpreted cautiously, given limited observations, I found no variable could consistently explain observed forager presence across PAs except for elevation. In my final chapter, I leveraged simulations and decision analysis to assess traditional spatial management. I found that large, fixed reserves could robustly maximize both harvest and conservation goals, especially compared to strategies with small or dynamic alternatives. However, outcomes were strongly influenced by the intensity of hunting effort, hunters’ adherence to spatial rules, and mammal dispersal patterns. While the models and simulations I apply here do not capture the full diversity of hunter behaviour and game responses, they can provide guidance and insights where local data collection is infeasible.

Text

2022-07-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81885

Zoology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/