Dataset Information

Landscape genetics and the spatial distribution of chronic wasting disease.

ABSTRACT: Predicting the spread of wildlife disease is critical for identifying populations at risk, targeting surveillance and designing proactive management programmes. We used a landscape genetics approach to identify landscape features that influenced gene flow and the distribution of chronic wasting disease (CWD) in Wisconsin white-tailed deer. CWD prevalence was negatively correlated with genetic differentiation of study area deer from deer in the area of disease origin (core-area). Genetic differentiation was greatest, and CWD prevalence lowest, in areas separated from the core-area by the Wisconsin River, indicating that this river reduced deer gene flow and probably disease spread. Features of the landscape that influence host dispersal and spatial patterns of disease can be identified based on host spatial genetic structure. Landscape genetics may be used to predict high-risk populations based on their genetic connection to infected populations and to target disease surveillance, control and preventative activities.

SUBMITTER: Blanchong JA

PROVIDER: S-EPMC2412942 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Landscape genetics and the spatial distribution of chronic wasting disease.

Blanchong Julie A JA Samuel Michael D MD Scribner Kim T KT Weckworth Byron V BV Langenberg Julia A JA Filcek Kristine B KB

Biology letters 20080201 1

Predicting the spread of wildlife disease is critical for identifying populations at risk, targeting surveillance and designing proactive management programmes. We used a landscape genetics approach to identify landscape features that influenced gene flow and the distribution of chronic wasting disease (CWD) in Wisconsin white-tailed deer. CWD prevalence was negatively correlated with genetic differentiation of study area deer from deer in the area of disease origin (core-area). Genetic differen ...[more]

PMID: 18077240

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Project description:Many infectious diseases in wildlife occur under quantifiable landscape ecological patterns useful in facilitating epidemiological surveillance and management, though little is known about prion diseases. Chronic wasting disease (CWD), a fatal prion disease of the deer family Cervidae, currently affects white-tailed deer (Odocoileus virginianus) populations in the Mid-Atlantic United States (US) and challenges wildlife veterinarians and disease ecologists from its unclear mechanisms and associations within landscapes, particularly in early phases of an outbreak when CWD detections are sparse. We aimed to provide guidance for wildlife disease management by identifying the extent to which CWD-positive cases can be reliably predicted from landscape conditions. Using the CWD outbreak in Virginia, US from 2009 to early 2020 as a case study system, we used diverse algorithms (e.g., principal components analysis, support vector machines, kernel density estimation) and data partitioning methods to quantify remotely sensed landscape conditions associated with CWD cases. We used various model evaluation tools (e.g., AUC ratios, cumulative binomial testing, Jaccard similarity) to assess predictions of disease transmission risk using independent CWD data. We further examined model variation in the context of uncertainty. We provided significant support that vegetation phenology data representing landscape conditions can predict and map CWD transmission risk. Model predictions improved when incorporating inferred home ranges instead of raw hunter-reported coordinates. Different data availability scenarios identified variation among models. By showing that CWD could be predicted and mapped, our project adds to the available tools for understanding the landscape ecology of CWD transmission risk in free-ranging populations and natural conditions. Our modeling framework and use of widely available landscape data foster replicability for other wildlife diseases and study areas.

Project description:Abstract The European Commission requested an analysis of the Chronic Wasting Disease (CWD) monitoring programme in Norway, Sweden, Finland, Iceland, Estonia, Latvia, Lithuania and Poland (9 January 2017–28 February 2022). Thirteen cases were detected in reindeer, 15 in moose and 3 in red deer. They showed two phenotypes, distinguished by the presence or absence of detectable disease‐associated normal cellular prion protein (PrP) in lymphoreticular tissues. CWD was detected for the first time in Finland, Sweden and in other areas of Norway. In countries where the disease was not detected, the evidence was insufficient to rule out its presence altogether. Where cases were detected, the prevalence was below 1%. The data also suggest that the high‐risk target groups for surveillance should be revised, and ‘road kill’ removed. Data show that, in addition to differences in age and sex, there are differences in the prion protein gene (PRNP) genotypes between positive and negative wild reindeer. A stepwise framework has been proposed with expanded minimum background surveillance to be implemented in European countries with relevant cervid species. Additional surveillance may include ad hoc surveys for four different objectives, specific to countries with/without cases, focusing on parallel testing of obex and lymph nodes from adult cervids in high‐risk target groups, sustained over time, using sampling units and a data‐driven design prevalence. Criteria for assessing the probability of CWD presence have been outlined, based on the definition of the geographical area, an annual assessment of risk of introduction, sustained minimum background surveillance, training and engagement of stakeholders and a surveillance programme based on data‐driven parameters. All positive cases should be genotyped. Sample sizes for negative samples have been proposed to detect and estimate the frequency of PRNP polymorphisms. Double‐strand sequencing of the entire PRNP open reading frame should be undertaken for all selected samples, with data collated in a centralised collection system at EU level.

Dataset Information

Landscape genetics and the spatial distribution of chronic wasting disease.

Publications

Landscape genetics and the spatial distribution of chronic wasting disease.

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