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Title: Spatial methods for modelling species distributions
Author: Synes, Nicholas William
ISNI:       0000 0004 5922 3516
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2015
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Species distribution modelling methods are used for a variety of applications including: to assess current patterns of biodiversity, to make predictions about the impacts of environmental and climate change, and to assist in conservation planning. However, important factors are often neglected both in the pre-processing of data (e.g. ignoring sampling bias), and in the construction of models (e.g. ignoring ecological processes). In terms of the pre-processing of data, recent improvements in distance sampling methods are used to convert count data to abundance estimates, utilising both distance and habitat data from a previously conducted bird count survey. Biotic interactions are studied using MaxEnt and pairs of virtual species; a novel iterative method is demonstrated, using each species prediction as a subsequent variable for the partner species. Population dynamics and dispersal are studied using RangeShifter, a recently developed individual-based model. A number of climate change adaptation actions are applied to a section of UK landscape data, and the range shifting ability of a set of focal species is measured. Many previous studies have predicted climate change impacts on species; some have started to incorporate simple measures of dispersal ability. This work demonstrates the importance of considering both dispersal and population dynamics when predicting the future distributions of species and assessing their ability to track climate change. Finally, dynamic feedbacks between species and their environment are studied by coupling RangeShifter with CRAFTY, a recently developed agent-based model of land-use dynamics. Socio-ecological system dynamics are crucial in determining species distributions, but have rarely been studied as a truly coupled system. The coupled model presented here is the first of its kind, modelling both animals and land-use agents at an individual level. A case study is presented, demonstrating the feedback mechanisms that exist between pollinators and farms that rely on them, and the potential risk posed by agricultural intensification.
Supervisor: Osborne, Patrick Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available