School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemBuddhist beliefs, value orientations, and human well-being: an investigation of the interactions between human and wildlife in the Himalayan Kingdom of Bhutan( 2022)Human-wildlife conflict (HWC) is a growing concern globally, it is however more prevalent in developing countries where subsistence farmers pursue livelihoods in the context of high and endangered biodiversity. In common with many Asian and African countries, subsistence farmers in Bhutan are increasingly experiencing HWC which threatens the food security of the rural population and the success of biodiversity conservation. Most existing research lacks the conceptual charity of the underlying drivers of HWC and integration required to inform practical policy formulation for conflict mitigation in subsistence contexts. This research aimed to build on existing studies and fill this knowledge gap. Framed within a social-ecological system, guided by a post-positivist research tradition, this research used mixed methods across four districts in Bhutan to explore the social, the economic and the ecological dimensions of HWC in relation to livelihood types, land tenure and HWC mitigation practices. Both quantitative and qualitative data was collected from diverse and complex socio-economic circumstances and was analyzed and interpreted by using qualitative and quantitative analytical tools and techniques. The findings showed that religious aspects played a significant role in this setting. The Buddhists beliefs of rural people in Bhutan are important in shaping peoples’ value orientations, attitudes and behaviours toward wildlife protection and conflict management. In situations of high economic loss and a lack of alternative livelihood options, this foundation of Buddhist beliefs however is at risk with a general lack of compassion towards wildlife leading to cognitive dissonance between the need to adhere to societal norms and values and the need to provide for livelihoods. Another important finding was that subsistence farmers are impacted directly and indirectly by wildlife and not only economically and physically but also psychologically. These impacts are unevenly distributed across the studied landscapes. Adaptative capacity and vulnerability to wildlife impacts are to a large extent influenced by socio-economic and socio-demographic factors. This thesis adds at least four new insights to the broader HWC literature and strengthens and expands our understanding of HWC in relation to gender and wealth. Firstly, the thesis adds new insights into how Buddhists beliefs shape value orientations, attitudes and behaviours towards wildlife and HWC management and how the negative wildlife impacts lead to personal and community dissonance between societal religious beliefs and value orientations. Secondly, another contribution from this thesis is that the research has demonstrated how psychological capital can be considered alongside traditional livelihood capitals. The poor psychological health of farmers resulting from: the on-going loss of sleep, loss of peace of mind, persistent fear and worry, and frustration; and, feeling of stress and insecurity, drive farmers into situations of anxiety and depression, highlighting that psychological capital must be considered along with other livelihood capitals as a significant facet of long-term HWC management in communities impacted by HWC. Third, this is the first research demonstrating how HWC impacts affect several sustainable development goals (SDG) and domains of gross national happiness (GNH), highlighting achievement of these broader development goals in subsistence livelihood contexts in communities impacted by HWC can be challenging. Fourth, compared to the usual attention given to the flagship species such as large predators, this research found meso-scale predators (e.g., Asiatic wild dog, Tibetan wolf, wild pigs) have caused the greatest total economic losses. The research also demonstrates how elephants represent a “pulse” impact on some farmers’ livelihoods, while smaller wildlife species (e.g., wild pig, Asiatic wild, dog) represent a “press” impact on livelihoods. Finally, the thesis adds insights into how gender and wealth influence severity of wildlife impacts, further compounding their vulnerability to HWC and food insecurity. Overall, this thesis contributes an integrative framework that clarifies the underlying drivers of the conflict, and empirical advancement of our understanding of HWC in subsistence livelihood contexts. Findings from this thesis have implications beyond Bhutan to landscapes in other regions where subsistence farmers share landscapes with wildlife.
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ItemImproving species distribution models using extreme value theory and climate dataset ensembles( 2020)The development of climate datasets at fine spatial and temporal scales has commonly been driven by the need to better understand vegetation distributions and ecological systems. While a wide range of global, national and regional climate datasets have been developed over the last two decades, they are rarely compared directly in the ecological literature. This thesis evaluates a range of climate interpolation techniques and investigates how the spatial and temporal characteristics of climate datasets may be utilised to improve the predictive performance of plant species distribution models (SDM). A series of spline-based and geostatistical methods for interpolating temperature variables are first compared across Victoria, southeast Australia. Secondary predictors (thermal remote sensing data and local topographic indices) which indirectly capture mesoscale microclimate and cold air drainage regimes were found to improve monthly mean minimum temperature interpolations by up to 39%. Thermal remote sensing data only reduced root mean square error (RMSE) by up to 6% for maximum temperature across Victoria and was most effective during the summer months. The interpolation methods used in southeast Australia were subsequently transferred to the Royal Himalayan Kingdom of Bhutan to validate their effectiveness in a novel climate. In Bhutan, the predictive performance of minimum temperature interpolations was also improved considerably (up to 23% reduction in RMSE) when using thermal remote sensing data and local topographic indices as spatial covariates. Thermal remote sensing data also reduced the RMSE for maximum temperature interpolations by up to 16% in Bhutan. Interannual variability of climate extremes were used to evaluate how the temporal characteristics of climate may be used to improve the predictive performance of SDMs. Generalised Extreme Value (GEV) distributions were fitted to monthly climate data to generate variables which account for the skewed distribution of extremes. Models incorporating interannual variability (drawn from a range of expected return intervals) improved predictive performance compared to models using seasonal extremes only for 28 of 37 species assessed. Iteratively fitting models using alternate expected return intervals typically acted on the leading and trailing edges of current distributions, indicating that such methods may be useful for model calibration and characterising climate-driven source-sink population dynamics. The impact of spatial disparities in climate on the predictive performance of plant SDMs was evaluated using three distinct datasets developed for Victoria as part of this research, in addition to two global datasets (WorldClim v1 and v2). Individual models were compared against one another and as ensembles to explore the potential for alternate predictions to complement one another. The Victorian datasets demonstrated a significant improvement over the original WorldClim dataset (up to 17.3% mean increase in D2) and trended towards an improvement relative to WorldClim v2; however, no significant differences were found when comparing the alternate Victorian datasets. Multi-model ensembles achieved a mean increase of up to 13.8% and 29.2% in D2 relative to individual models when using regional and global datasets, respectively. Ensembles provide a pragmatic method to improve the predictive performance of SDMs and allow a trade-off between the uncertainties and potential biases embedded in competing climate datasets.