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Applications and limitations of spatially-explicit mechanistic models for animal conservation

Applications and limitations of spatially-explicit mechanistic models for animal conservation
Applications and limitations of spatially-explicit mechanistic models for animal conservation
We live in a world of human-induced rapid environmental change, where the frequency of extinctions and resulting loss in biodiversity has reached levels associated with a mass extinction event. At the same time, technological developments in computing have facilitated the growth of highly complex, mechanistic models across all scientific fields. The challenge for conservation biologists is then to develop models that can predict how organisms respond to conservation measures and increasing anthropogenic pressures. Here I explore the potential and limitations for conservation applications of spatially-explicit mechanistic models of habitat selection, by developing a simulation applicable to large felids. I demonstrate that initial choice of resolution may bias the parameterisation process of spatially-explicit models, when applied to spatially-explicit empirical data. I use mechanistic models to address two current problems in conservation biology: (a) efficient calculation of movement metrics from telemetry data, tested with a virtual ecology approach; and (b) accounting for interacting influences on populations, quantified with a model that controls for confounding variables. I identify the major caveats to accurately predicting the complex behaviour of large-bodied animals. The spatially-explicit mechanistic models developed here, and applied to real-world problems, demonstrate the potential of these types of simulation for confronting otherwise impossible questions in diverse areas of conservation biology.
University of Southampton
Ball, Alice, Elizabeth
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Ball, Alice, Elizabeth
eafbb4c5-b041-4492-ab9d-966316bd2813
Doncaster, Charles
0eff2f42-fa0a-4e35-b6ac-475ad3482047

Ball, Alice, Elizabeth (2018) Applications and limitations of spatially-explicit mechanistic models for animal conservation. University of Southampton, Doctoral Thesis, 154pp.

Record type: Thesis (Doctoral)

Abstract

We live in a world of human-induced rapid environmental change, where the frequency of extinctions and resulting loss in biodiversity has reached levels associated with a mass extinction event. At the same time, technological developments in computing have facilitated the growth of highly complex, mechanistic models across all scientific fields. The challenge for conservation biologists is then to develop models that can predict how organisms respond to conservation measures and increasing anthropogenic pressures. Here I explore the potential and limitations for conservation applications of spatially-explicit mechanistic models of habitat selection, by developing a simulation applicable to large felids. I demonstrate that initial choice of resolution may bias the parameterisation process of spatially-explicit models, when applied to spatially-explicit empirical data. I use mechanistic models to address two current problems in conservation biology: (a) efficient calculation of movement metrics from telemetry data, tested with a virtual ecology approach; and (b) accounting for interacting influences on populations, quantified with a model that controls for confounding variables. I identify the major caveats to accurately predicting the complex behaviour of large-bodied animals. The spatially-explicit mechanistic models developed here, and applied to real-world problems, demonstrate the potential of these types of simulation for confronting otherwise impossible questions in diverse areas of conservation biology.

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Alice Ball FINAL thesis - Version of Record
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Published date: 30 June 2018

Identifiers

Local EPrints ID: 428151
URI: https://eprints.soton.ac.uk/id/eprint/428151
PURE UUID: c24ba76c-0821-4af8-be43-87cda4adc8b0
ORCID for Charles Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

Catalogue record

Date deposited: 13 Feb 2019 17:30
Last modified: 14 Mar 2019 01:52

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