Honey bee habitat suitability: Unveiling spatial and temporal variations, predicting futures and mitigating natural hazard impacts
PhD Thesis
Title | Honey bee habitat suitability: Unveiling spatial and temporal variations, predicting futures and mitigating natural hazard impacts |
---|---|
Type | PhD Thesis |
Authors | Mudiyanselage, Sarasie Tennakoon |
Supervisor | |
1. First | Prof Armando Apan |
2. Second | Prof Tek Maraseni |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 197 |
Year | 2024 |
Publisher | University of Southern Queensland |
Place of Publication | Australia |
Abstract | Honey bees (Apis mellifera) are pivotal for global agriculture and ecosystem services, contributing significantly to pollination and the sustainability of crop production. Despite their significance, studies assessing the spatial and temporal variations in land suitability for honey bees and evaluating the impact of climate change and natural hazards are limited. This study in Southern Queensland, Australia, aimed to create a GIS-based framework for assessing apiary land suitability, predicting future suitability under changing climate, and identifying priority habitats for conservation against natural hazards. The specific objectives encompass the following: 1) to assess land suitability for beekeeping, considering spatial and temporal variations in criteria, using GIS-based multi-criteria decision analysis (MCDA); 2) to predict honeybee distribution using bioclimatic and environmental variables for two future time spans: 2020-2039 and 2060-2079; and 3) to pinpoint high-priority areas for protection from bushfires and floods, implementing effective mitigation strategies. The assessment conducted using fuzzy Analytical Hierarchy Process (fuzzy AHP) and fuzzy overlay with apiary site locations, environmental, and bioclimatic variables, reveals insights into seasonal land suitability. In spring, fuzzy AHP deems 67.8% of the study area as moderately suitable, while fuzzy overlay indicates 69.4% as marginal to moderate. Fuzzy AHP's validity (60-70%) outperforms fuzzy overlay (80% in spring, <60% in other seasons). Through ensemble modelling conducted using honey bee presence and pseudo absence data, the research identifies key bioclimatic and environmental variables shaping honey bee habitats, emphasising the critical synergy between climate and environment in determining suitability. Projections for the future (2060-2079) are concerning, with a 100% transition of highly suitable land into moderately (0.5%), marginally (17.6%), or not suitable areas (81.9%) for honey bees, necessitating urgent conservation efforts and policy implementation. The study also pioneers an investigation into threats faced by honey bees in the form of bushfires and floods. Results show that a significant portion of honeybee suitable areas is threatened by bushfires (97.6%). On the other hand, 5% of honeybee habitats are under the threat of flood hazard, while 1% face threats from both hazards. This study urges safeguarding honeybee habitats during natural disasters, offering vital insights and actionable strategies. Future research suggestions encompass examining the long-term effects of climate change on floral resources for honey bees. A cornerstone in honeybee protection, this study provides a robust framework for sustainable apiary management amid climate change and environmental threats. |
Keywords | fuzzy Analytical Hierarchy Process; Species distribution modelling; European honey bee, Apis mellifera; GIS; Fuzzy overlay |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 3002. Agriculture, land and farm management |
3003. Animal production | |
3103. Ecology | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author/creator. |
Byline Affiliations | School of Surveying and Built Environment |
https://research.usq.edu.au/item/z7903/honey-bee-habitat-suitability-unveiling-spatial-and-temporal-variations-predicting-futures-and-mitigating-natural-hazard-impacts
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