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University of Reading (2018)

Modelling elephant movements and population dynamics using remote sensing of food availability

Boult, Victoria Louise

Titre : Modelling elephant movements and population dynamics using remote sensing of food availability

Auteur : Boult, Victoria Louise

Université de soutenance : University of Reading.

Grade : Doctor of Philosophy (PhD) 2018

Résumé
Biodiversity conservation has limited resources so must identify and prioritise the most critical threats facing species and ecosystems. This is especially apparent in light of current rates of global change which alter the abundance, distribution and resilience of species and habitats. Traditional approaches to understand the impacts of change have generally related variation in environmental factors to species’ dynamics, but these methods are unreliable when making predictions under novel conditions. Process-based approaches are built on fitness-maximising mechanisms that are robust to change and thus present the opportunity to both project the effects of global change and identify the most significant threats facing species. This PhD aimed to develop process-based models to simulate the impacts of environmental change on the elephants of Amboseli in Kenya. Food availability was considered a key driver of elephant movement decisions and demographic rates throughout. Satellite-derived measures of vegetation were calibrated with ground-based measures of biomass and used to estimate the food available to elephants through time and space. Elephant tracking data was used to confirm the importance of food availability as a key driver of elephant movement decisions and to identify additional explanatory variables, including risk and reproductive state, which mediated elephant space-use. An individualbased model (IBM) was developed and calibrated to accurately predict historic elephant population dynamics emerging from temporal variation in food availability. The IBM was subsequently used to project the impacts of changes in food availability resulting from anthropogenic climate change and habitat conversion on Amboseli’s elephants. Using climate projections for different greenhouse gas emissions scenarios and land use scenarios based on empirical data and stakeholder opinion, the model predicted elephant population size through the 21st century. Model results identified habitat conversion, rather than climate change, as the primary threat facing Amboseli’s elephants. Future model developments through the incorporation of behavioural mechanisms, spatially explicit landscapes and multiple stressors would provide more robust predictions of elephant population responses to environmental change. Nonetheless, the work presented here documents an early example of a process-based model developed to inform land management decisions and the conservation of Amboseli elephants

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