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33 results for “Wildlife diseases”
The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area
<p>Data to</p> <p>"The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area"</p> <p>accepted in Ecological Solutions and Evidence</p>
Fig. 2 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 2. Map of Tasmania showing blood collection sites for the three native carnivore species and the introduced feral cat. Places identified are those referred to in the text.
Fig. 1 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 1. Map of Tasmania showing average cat densities from individual spotlighting districts over 8 years and blood collection sites for the Tasmanian pademelon. Positive T. gondii sites are those where at least one sample tested positive to IgG antibodies. Negative sites are those where no evidence of exposure to T. gondii was found in any sample.
Fig. 3 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 3. Prevalence of IgG antibodies of Tasmanian mammals to T. gondii by trophic level; n represents the total number of samples tested. Standard error bars are shown.
Fig. 2. Flow diagrams showing a in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 2. Flow diagrams showing a weight of evidence framework using the (A) 7 questions proposed by Burkhardt-Holm and Scheurer (2007) and the (B) modified questions for addressing disease(s) in wildlife.
Fig. 1 in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 1. Timeline depicting the history of wildlife diseases in the United States: blue boxes are for disease reports and outbreaks, green for improvements to disease research, and red for events that hindered disease research. Abbreviations: foot-and-mouth disease (FMD), Smoot-Hawley Tariff Act (SHTA), State-Federal Cooperative Brucellosis Eradication Program (SFCBER), Bear River Research Station (BRRS), Wildlife Disease Investigations Laboratory (WDIL), Southeastern Cooperative Wildlife Disease Study (SCWDS), epizootic hemorrhagic disease (EHD), World Organisation for Animal Health's (OIE), National Wildlife Research Center (NWRC), U. S. Fish and Wildlife Service (USFWS). References: 1. Antolin et al. (2002), 2. Creel (1941), 3. Anderson (1978), 4. Locke and Friend (1987), 5. McCoy and Chapin (1912), 6. Wherry and Lamb (1914), 7. Meagher and Meyer (1994), 8. Clements (2007), 9, Bachrach (1968), 10. Busch and Parker (1972), 11. USFWS (1991), 12. Tunnicliff and Marsh (1935), 13. Brooks and Buchanan (1970), 14. Elton (1931), 15. Brown (2007), 16. CDFW 2019, 17. Friend (2014), 18. SCWDS 2019, 19. Shope et al. (1960), 20. Cohen (2000), 21. Cross et al. (2013), 22. Samuel et al. (2007), 23. Carvalho et al. (2017), 24. Dobson and Hudson (1986), 25. Jones et al. (2008), 26. Berger et al. (1998), 27. Laurance et al. (1996), 28. Collins and Crump (2009), 29. OIE 2008, 30. Voyles et al. (2015), 31. Fagerstone (2014), 32. USFWS (2016), 33. Scheele et al. (2019). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data from: A novel approach to wildlife transcriptomics provides evidence of disease-mediated differential expression and changes to the microbiome of amphibian populations
Ranaviruses are responsible for a lethal, emerging infectious disease in amphibians and threaten their populations throughout the world. Despite this, little is known about how amphibian populations respond to ranaviral infection. In the United Kingdom, ranaviruses impact the common frog (Rana temporaria). Extensive public engagement in the study of ranaviruses in the UK has led to the formation of a unique system of field sites containing frog populations of known ranaviral disease history. Within this unique natural field system, we used RNA sequencing (RNA-Seq) to compare the gene expression profiles of R. temporaria populations with a history of ranaviral disease and those without. We have applied a RNA read filtering protocol that incorporates Bloom filters, previously used in clinical settings, to limit the potential for contamination that comes with the use of RNA-Seq in non-laboratory systems. We have identified a suite of 407 transcripts that are differentially expressed between populations of different ranaviral disease history. This suite contains genes with functions related to immunity, development, protein transport and olfactory reception amongst others. A large proportion of potential non-coding RNA transcripts present in our differentially expressed set provides first evidence of a possible role for long non-coding RNA (lncRNA) in amphibian response to viruses. Our read-filtering approach also removed significantly more bacterial reads from libraries generated from postitive disease history populations. Subsequent analysis revealed these bacterial read sets to represent distinct communities of bacterial species, which is suggestive of an interaction between ranavirus and the host microbiome in the wild.
Fine-scale spatial patterns of wildlife disease are common and understudied
<p>1. All parasites are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we sought to identify and examine spatial dependence of wildlife disease across a wide range of systems.</p> <p>2. Conducting a broad literature search, we collated 31 such datasets featuring 89 replicates and 71 unique host-parasite combinations, only 51% of which had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models, and we then meta-analysed the results to identify generalised determinants of the scale and magnitude of spatial autocorrelation.</p> <p>3. We detected spatial autocorrelation in 48/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups. Even some very small study areas (under 0.01km2) exhibited substantial spatial variation.</p> <p>4. Despite the common manifestation of spatial variation, our meta-analysis was unable to identify host-, parasite-, or sampling-level determinants of this heterogeneity across systems. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are potentially as important in spatially structuring disease as are environmental drivers of transmission efficiency.</p> <p>5. Our findings demonstrate that fine-scale spatial patterns of infection manifest frequently and across a range of wild animal systems, and many studies are able to investigate them – whether or not the original aim of the study was to examine spatially varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology. Ultimately, this may pave the way for an a priori predictive framework for spatial variation in novel host-parasite systems.</p>
Large scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases
<p>Multiple parasites and pathogens cause disease in aquatic wildlife and in aquaculture species, generating a need for monitoring and management. Conventional disease monitoring methods involve laborious, costly and invasive capture and examination of host species, and require specialised expertise for every host and pathogen of interest. These restrictions could be alleviated by using pathogen detection techniques based on environmental DNA that provide simultaneous surveys of multiple aquatic pathogens across different host taxa. This would also be valuable for approaches employing parasite diversity as bioindicators of ecosystem disturbance, which suffer from similar restrictions. Here, we tested the potential for simultaneous detection of four wildlife pathogens in water samples from 280, mainly riverine, sites across Switzerland. We targeted the crayfish pathogen <em>Aphanomyces astaci, </em>the amphibian pathogen <em>Batrachochytrium dendrobatidis, </em>and the fish pathogens <em>Saprolegnia parasitica</em> and <em>Tetracapsuloides bryosalmonae</em>. The eDNA detection showed a widespread distribution of <em>A. astaci</em>, <em>S. parasitica</em> and <em>T. bryosalmonae</em>, although <em>A. astaci </em>and <em>T. bryosalmonae</em> were not detected in some alpine river catchments. <em>B. dendrobatidis</em> was detected only rarely, which was expected since the sampling did not target amphibian breeding sites. Co-detection rates were higher in rivers than in lakes, likely reflecting the habitat preferences and distributions of the host species. We discuss the advantages and limitations of eDNA-based pathogen monitoring and list a set of recommendations for managers. Our study illustrates how eDNA-based techniques can monitor several pathogen species concurrently, thus facilitating more comprehensive disease monitoring schemes. Combined with metabarcoding approaches in the future, eDNA based sampling and detection can facilitate the incorporation of parasite and pathogen occurrence and diversity as an indicator for aquatic ecosystem health, and for revealing the hidden biodiversity and structure of parasite communities.</p>
Proactive management outperforms reactive actions for wildlife disease control
<p>Finding effective pathogen mitigation strategies is one of the biggest challenges humans face today. In the context of wildlife, emerging infectious diseases have repeatedly caused widespread host morbidity and population declines of numerous taxa. In areas yet unaffected by a pathogen, a proactive management approach has the potential to minimize or prevent host mortality. However, we typically lack critical information on the disease dynamics in a novel host system, have limited empirical evidence on efficacy of management interventions, and lack validated predictive models. As such, quantitative support for identifying effective management interventions is largely absent, and the opportunity for proactive management is often missed. Here, we consider the potential invasion of the chytrid fungus, <em>Batrachochytrium salamandrivorans</em>, whose expected emergence in North America poses a severe threat to hundreds of salamander species in this global salamander biodiversity hotspot. We developed and parameterized a dynamic multi-state occupancy model to forecast host and pathogen occurrence, following expected emergence of the pathogen, and evaluated the response of salamander populations to different management scenarios. Our model forecasts that taking no action is expected to be catastrophic to salamander populations. We also show that proactive action is expected to maximize host occupancy outcomes compared to 'wait and see' reactive management, thus providing quantitative support for proactive management opportunities. Additionally, we found that Bsal eradication is unlikely under any evaluated management options. Contrary to our expectations, even early pathogen detection had little effect on Bsal or host occupancy outcomes. Our analysis provides quantitative support that proactive management is the optimal strategy for promoting persistence of disease-threatened salamander populations. Our approach fills a critical gap by defining a framework for evaluating management options prior to pathogen invasion and can thus serve as a template for addressing novel disease threats that jeopardize wildlife and human health.</p>
The effectiveness of harvest for limiting wildlife disease: insights from 20 years of chronic wasting disease in Wyoming
<p>Data and code accompanying Moss et al. "The effectiveness of harvest for limiting wildlife disease: insights from 20 years of chronic wasting disease in Wyoming" (Ecological Applications). </p>
wild_expression: gene expression data on wildlife hosts of emerging infectious diseases
<p>Publicly accessible data and code supporting the manuscript:</p> <p>"Host gene expression in wildlife disease: making sense of species-level responses"</p>
Data for: Sex-biased infections scale to population impacts for an emerging wildlife disease
<p>Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios, and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity, and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behavior. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.</p>
Data for: Sex-biased infections scale to population impacts for an emerging wildlife disease
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Large scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases
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Quantitative support for the benefits of proactive management for wildlife disease control
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Fine-scale spatial patterns of wildlife disease are common and understudied
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Data from: A novel approach to wildlife transcriptomics provides evidence of disease-mediated differential expression and changes to the microbiome of amphibian populations
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Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
Predicting wildlife susceptibility to infectious diseases atglobal scales
<p>Dataset included as supplementary material of the paper entitled https://doi.org/10.5281/zenodo.4914750. It contains phylogenetic, geographical and environmental distance for birds and bats, counts of incidence of <em>Plasmodium relictum</em> on birds, counts of incidence of West Nile Virus on birds and counts of incidence of coronavirus in bats, and susceptibility calculated by the random forest algorithm. Also we include the r scripts to run the models, and the outputs of the models after 1000 runs. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.