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| Funder | Natural Environment Research Council |
|---|---|
| Recipient Organization | University of Aberdeen |
| Country | United Kingdom |
| Start Date | Sep 30, 2022 |
| End Date | Mar 30, 2026 |
| Duration | 1,277 days |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2773526 |
Wildlife play an important role in the epidemiology of some of the most important pathogens of livestock and humans, either as reservoirs or vectors of infection.
The presence of wildlife hosts can complicate disease management as traditional control measures, such as vaccination, are challenging in wildlife species.
Despite the abundance and widespread distribution of both red (Cervus elaphus) and roe (Capreolus capreolus) deer in the UK, and increasing awareness that deer may play a role in the epidemiology of numerous zoonotic and livestock pathogens, little is known about how these roles may differ between deer species, whose ecology can differ significantly, and to what extent land use and bioclimatic conditions drive infection risk in wild deer.
Globally, two environmentally transmitted bacterial pathogens, Leptospira spp. and Mycobacterium avium subsp. paratuberculosis (Map), have been shown to infect livestock and wild deer species.
Both pathogens are economically important, causing significant disease and productivity losses in livestock and Leptospira spp. are also one of the most common and widespread causes of zoonotic disease.
Bioclimatic conditions that favour the survival of these pathogens in the environment, and/or land use changes that facilitate contact between wildlife and livestock are likely to play an important role in driving infection risk.
To advance our understanding of the role of wildlife in the maintenance and transmission of these environmentally transmitted pathogens, this project aims to: i) quantify exposure and carriage in wild deer, ii) explore how the epidemiology of these pathogens differs between deer species, and iii) identify and quantify the effects of land use and bioclimatic conditions on infection probability.
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