Modelling disease spread in real landscapes: Squirrelpox spread in Southern Scotland as a case study
Andrew White 1  
,  
Jenny Bryce 3
,  
Mel Tonkin 4
,  
Karen Ramoo 4
,  
 
 
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1
Department of Mathematics and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh
2
Lurzengasse 3, D-97236 Randersacker
3
Scottish Natural Heritage, Great Glen House, Leachkin Road, Inverness
4
Scottish Wildlife Trust, Harbourside House, 110 Commercial Street, Edinburgh
5
Forest Enterprise Scotland, Galloway Forest District, Newton Stewart
6
Department of Integrative Biology, University of California, Berkeley, 3040 Valley Life Sciences Building # 3140, Berkeley
Publish date: 2016-06-11
 
Hystrix It. J. Mamm. 2016;27(1)
KEYWORDS
ABSTRACT
There is increasing evidence that invading species can gain an advantage over native species by introducing novel disease. A clear understanding of the role of disease in the expansion of introduced and invading species is therefore essential for the conservation of native species. In this study we focus on the case study system of the UK red and grey squirrel system in which disease-mediated competition has facilitated the replacement of red squirrels by greys. We modify a deterministic model of the squirrel system in which the competition and infection dynamics are well understood to produce a stochastic model which includes a realistic representation of the heterogeneous habitat in Southern Scotland. The model is used to examine the potential spread of infection (squirrelpox virus) through the squirrel system and to examine the impact of conservation measures that control grey squirrel numbers in an attempt to contain disease spread. The results have direct implications for conservation management and we discuss how they have helped shape current and future policy for red squirrel conservation in Scotland. The methods in this study can be readily adapted to represent different systems and since the stochastic population and disease dynamics are underpinned by classical deterministic modelling frameworks the results are applicable in general.
eISSN:1825-5272
ISSN:0394-1914