Climate suitability for European ticks: Assessing species distribution models against null models and projection under AR5 climate

Williams, H.W., Cross, D.E., Crump, H.L., Drost, C.J. and Thomas, C. (2015) Climate suitability for European ticks: Assessing species distribution models against null models and projection under AR5 climate. Parasites and Vectors, 8 (1). ISSN 1756-3305

Full content URL: http://doi.org/10.1186/s13071-015-1046-4

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Item Type:Article
Item Status:Live Archive

Abstract

Background
There is increasing evidence that the geographic distribution of tick species is changing. Whilst correlative Species Distribution Models (SDMs) have been used to predict areas that are potentially suitable for ticks, models have often been assessed without due consideration for spatial patterns in the data that may inflate the influence of predictor variables on species distributions. This study used null models to rigorously evaluate the role of climate and the potential for climate change to affect future climate suitability for eight European tick species, including several important disease vectors.

Methods
We undertook a comparative assessment of the performance of Maxent and Mahalanobis Distance SDMs based on observed data against those of null models based on null species distributions or null climate data. This enabled the identification of species whose distributions demonstrate a significant association with climate variables. Latest generation (AR5) climate projections were subsequently used to project future climate suitability under four Representative Concentration Pathways (RCPs).

Results
Seven out of eight tick species exhibited strong climatic signals within their observed distributions. Future projections intimate varying degrees of northward shift in climate suitability for these tick species, with the greatest shifts forecasted under the most extreme RCPs. Despite the high performance measure obtained for the observed model of Hyalomma lusitanicum, it did not perform significantly better than null models; this may result from the effects of non-climatic factors on its distribution.

Conclusions
By comparing observed SDMs with null models, our results allow confidence that we have identified climate signals in tick distributions that are not simply a consequence of spatial patterns in the data. Observed climate-driven SDMs for seven out of eight species performed significantly better than null models, demonstrating the vulnerability of these tick species to the effects of climate change in the future.

Additional Information:cited By 14
Divisions:College of Science
ID Code:38313
Deposited On:31 Oct 2019 10:46

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