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33 results for “Wildlife diseases”
Modeling management strategies for chronic disease in wildlife: predictions for the control of respiratory disease in bighorn sheep
<p>1. Controlling persistent infectious disease in wildlife populations is an on-going challenge for wildlife managers and conservationists worldwide.</p> <p>2. Here, we develop a dynamic pathogen transmission model capturing key features of M. ovipneumoniae infection, a major cause of population declines in North American bighorn sheep (Ovis canadensis). We explore the effects of model assumptions and parameter values on disease dynamics, including density versus frequency dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease-induced mortality and recovery rates, and the epidemic growth rate.</p> <p>3. We compare the effectiveness of a suite of management actions following an epidemic, including test-and-remove, depopulation-and-reintroduction, range expansion, herd augmentation, and density reduction.</p> <p>4. Our results suggest that test-and-remove, depopulation-and-reintroduction, and range expansion have the potential to facilitate recovery of persistently infected bighorn sheep herds post-epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Management that improves host survival or reduces disease-induced mortality are also likely to improve population size and persistence of chronically infected herds.</p> <p>5. Dynamic transmission models like the one employed here offer a structured, logical approach towards exploring hypotheses and can serve as a basis for planning field experiments and adaptive management. Models should be used iteratively with the field empirical approaches to triangulate on better approaches to wildlife management.</p>
Data from: Interacting effects of wildlife loss and climate on ticks and tick-borne disease
Both large-wildlife loss and climatic changes can independently influence the prevalence and distribution of zoonotic disease. Given growing evidence that wildlife loss often has stronger community-level effects in low-productivity areas, we hypothesized that these perturbations would have interactive effects on disease risk. We experimentally tested this hypothesis by measuring tick abundance and the prevalence of tick-borne pathogens (Coxiella burnetii and Rickettsia spp.) within long-term, size-selective, large-herbivore exclosures replicated across a precipitation gradient in East Africa. Total wildlife exclusion increased total tick abundance by 130% (mesic sites) to 225% (dry, low-productivity sites), demonstrating a significant interaction of defaunation and aridity on tick abundance. When differing degrees of exclusion were tested for a subset of months, total tick abundance increased from 170% (only mega-herbivores excluded) to 360% (all large wildlife excluded). Wildlife exclusion differentially affected the abundance of the three dominant tick species, and this effect varied strongly over time, likely due to differences among species in their host associations, seasonality, and other ecological characteristics. Pathogen prevalence did not differ across wildlife exclusion treatments, rainfall levels, or tick species, suggesting that exposure risk will respond to defaunation and climate change in proportion to total tick abundance. These findings demonstrate interacting effects of defaunation and aridity that increase disease risk, and they highlight the need to incorporate ecological context when predicting effects of wildlife loss on zoonotic disease dynamics.
Disease's hidden death toll: Using parasite aggregation patterns to quantify landscape-level host mortality in a wildlife system
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Data from: Decision making for mitigating wildlife diseases: from theory to practice for an emerging fungal pathogen of amphibians
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Data from: Reconstructing the emergence of a lethal infectious disease of wildlife supports a key role for spread through translocations by humans
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Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
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Modeling management strategies for chronic disease in wildlife: predictions for the control of respiratory disease in bighorn sheep
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Data from: Interacting effects of wildlife loss and climate on ticks and tick-borne disease
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Data from: The ecology of wildlife disease surveillance: demographic and prevalence fluctuations undermine surveillance
Wildlife disease surveillance is the first line of defence against infectious disease. Fluctuations in host populations and disease prevalence are a known feature of wildlife disease systems. However, the impact of such heterogeneities on the performance of surveillance is currently poorly understood. We present the first systematic exploration of the effects of fluctuations' prevalence and host population size on the efficacy of wildlife disease surveillance systems. In this study, efficacy is measured in terms of ability to estimate long-term prevalence and detect disease risk. Our results suggest that for many wildlife disease systems, fluctuations in population size and disease lead to bias in surveillance-based estimates of prevalence and overconfidence in assessments of both the precision of prevalence estimates and the power to detect disease. Neglecting such ecological effects may lead to poorly designed surveillance and ultimately to incorrect assessments of the risks posed by disease in wildlife. This will be most problematic in systems where prevalence fluctuations are large and disease fade-outs occur. Such fluctuations are determined by the interaction of demography and disease dynamics. Although particularly likely in highly fluctuating populations typical of fecund short-lived hosts, such fluctuations cannot be ruled out in more stable populations of longer-lived hosts. Synthesis and applications. Fluctuations in population size and disease prevalence should be considered in the design and implementation of wildlife disease surveillance, and the framework presented here provides a template for conducting suitable power calculations. Ultimately, understanding the impact of fluctuations in demographic and epidemiological processes will enable improvements to wildlife disease surveillance systems leading to better characterization of, and protection against endemic, emerging and re-emerging disease threats.
Data from: Population-scale treatment informs solutions for control of environmentally transmitted wildlife disease
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Data from: Differential impacts of vaccination on wildlife disease spread during epizootic and enzootic phases
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Data from: The ecology of wildlife disease surveillance: demographic and prevalence fluctuations undermine surveillance
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Fig. 3 in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 3. Factors contributing to the weight of evidence that parasites negatively affect bobwhite.
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OpenNeuro
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