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45 results for “Disease Ecology”
Iron Trace Elements Concentration in PM10 and Alzheimer's Disease in Lima, Peru: Ecological Study - dataset
<p>This dataset was created to evaluate the association between iron trace-elements concentration in PM10 with Alzheimer´s Disease cases in different districts in Lima, Peru. The database was constructed using open-access repositories of the Peruvian Ministry of Health and the Peruvian CDC.</p> <p>The uploaded datasets are in .dta and .csv formats.</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.
Ecological dataset from: 'Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia'
<p>This dataset includes the ecological and environmental data, a description of the analysis steps, and the related R code associated with the manuscript: "Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia" by Kim R. Blasdell, Serge Morand, Susan G.W. Laurance, Stephen L Doggett, Amy Hahs, David Perera, and Cadhla Firth, available at: https://www.pnas.org/doi/abs/10.1073/pnas.2112341119</p> <p>This dataset also relates to the preprint: "Rats in the city: implications for zoonotic disease risk in an urbanizing world" available at: https://www.biorxiv.org/content/10.1101/2021.03.18.436089v1</p> <p>A detailed description of the files can be found in README.txt</p>
Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease
<p><strong><em>Data associated with the paper "Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease".</em></strong></p> <p><strong>Content:</strong></p> <ul> <li><strong>Metagenomic curated data considering healthy and diseased state of the human individuals. Aligned against RefSeq with Kaiju.</strong></li> <li><strong>Curated metadata with anonymised physiological and medical information</strong></li> </ul> <p><strong>Paper authors</strong>: Jacopo Pasqualini, Sonia Facchin, Andrea Rinaldo, Amos Maritan, Edoardo Vincenzo Savarino, Samir Suweis</p> <p><strong>Paper preprint</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p> <p><strong>Data Curator</strong>: Jacopo Pasqualini.</p> <p><strong>Pipeline used to generate the data</strong>: https://github.com/jacopopasqualini/MetaGym</p> <p><strong>Complete description of data generation</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p>
Data from systematic review of uses of remote sensing in disease ecology
<p>These data accompany the paper "The potential of remote sensing for improved infectious disease ecology research and practice" by Teitelbaum, C., Ferraz, A., De La Cruz, S.E.W., Gilmour, M.E., and Brosnan, I.G.. Each .csv file contains data from primary articles that used remote sensing to study disease ecology. Studies are identified by a unique study ID in each table; relationships between tables are usually many-to-many, except for the biobliographic details, which contains only one entry per article. The metadata.csv file describes columns in all sheets.</p>
Data for: Capturing complex interactions in disease ecology with simplicial sets
<p>Here we provide archived code for: Code for Capturing Complex Interactions in Disease Ecology with Simplicial Sets. Ecology Letters. The code provided can be used to generate the figures used in the manuscript as well as to generate appendix 3 in the supplementary materials. In the paper, we describe how higher-order network approaches can be applied in disease ecology research. We explain <em>what</em> simplicial sets are; <em>why</em> their use would be beneficial in different subject areas; <em>where</em> these areas are: social, transmission, movement/spatial and ecological networks; and <em>when</em> using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population (see code for Appendix 3 in this repository). We also provide an overview of <em>how</em> to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we <em>synthesize</em> key research questions simplicial sets will help us answer and highlight the methodological developments required.</p>
Data from: Disease refuge or ecological trap: location-specific performance of amphibian hotspot shelters
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Disease or drought: Environmental fluctuations release zebra from a potential pathogen-triggered ecological trap
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Data for: Capturing complex interactions in disease ecology with simplicial sets
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Data from: Theory of infectious disease spillover at an ecological boundary: Impacts of seasonality and cross-boundary movement
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The epidemiology and clinical features of coccidioidomycosis (Valley fever) and disease ecology of Coccidioides spp. in New Mexico (2006-2023)
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Data from: Ecological and evolutionary effects of fragmentation on infectious disease
Ecological theory predicts that disease incidence increases with increasing density of host networks, yet evolutionary theory suggests that host resistance increases accordingly. To test the combined effects of ecological and evolutionary forces on host-pathogen systems, we analyzed the spatiotemporal dynamics of a plant (Plantago lanceolata)–fungal pathogen (Podosphaera plantaginis)relationship for 12 years in over 4000 host populations. Disease prevalence at the metapopulation level was low, with high annual pathogen extinction rates balanced by frequent (re-)colonizations. Highly connected host populations experienced less pathogen colonization and higher pathogen extinction rates than expected; a laboratory assay confirmed that this phenomenon was caused by higher levels of disease resistance in highly connected host populations.
Data from: Anthropogenic and ecological drivers of amphibian disease (Ranavirosis)
Ranaviruses are causing mass amphibian die-offs in North America, Europe and Asia, and have been implicated in the decline of common frog (Rana temporaria) populations in the UK. Despite this, we have very little understanding of the environmental drivers of disease occurrence and prevalence. Using a long term (1992-2000) dataset of public reports of amphibian mortalities, we assess a set of potential predictors of the occurrence and prevalence of Ranavirus-consistent common frog mortality events in Britain. We reveal the influence of biotic and abiotic drivers of this disease, with many of these abiotic characteristics being anthropogenic. Whilst controlling for the geographic distribution of mortality events, disease prevalence increases with increasing frog population density, presence of fish and wild newts, increasing pond depth and the use of garden chemicals. The presence of an alternative host reduces prevalence, potentially indicating a dilution effect. Ranavirosis occurrence is associated with the presence of toads, an urban setting and the use of fish care products, providing insight into the causes of emergence of disease. Links between occurrence, prevalence, pond characteristics and garden management practices provides useful management implications for reducing the impacts of Ranavirus in the wild.
Data from: Missing the people for the trees: identifying coupled natural-human system feedbacks driving the ecology of Lyme disease
1. Infectious diseases are rapidly emerging and many are increasing in incidence across the globe. Processes of land-use change, notably habitat loss and fragmentation, have been widely implicated in emergence and spread of zoonoses such as Lyme disease, yet evidence remains equivocal. 2. Here we discuss and apply an innovative approach from the social sciences, instrumental variables, that seeks to tease out causality from observational data. Using this approach, we revisit the effect of forest fragmentation on Lyme disease incidence, focusing on human interaction with fragmented landscapes. Though human interaction with infected ticks is of clear and fundamental importance to human disease incidence, human activities that influence exposure have been nearly universally overlooked in the ecology literature. 3. Using county-level land-use and Lyme disease incidence data for ~800 counties from the northeastern United States over the span of a decade, we illustrate (1) human interaction with fragmented forest landscapes reliably predicts Lyme disease incidence, while ecological measures of forest fragmentation alone are unreliable predictors and (2) that identifying the effect of forest fragmentation on human disease requires addressing the feedback between Lyme disease risk and human decisions to avoid interaction with high-risk landscapes. 4. Synthesis and applications. The innovative approach and novel results help to clarify the equivocal literature on forest fragmentation and Lyme disease, and illustrate the key role that human behavior may be playing in the ecology of Lyme disease in North America. Accounting for human activity and behavior in the ecology of disease more broadly may result in improved understanding of both the ecological drivers of disease, as well as actionable intervention strategies to reduce disease burden in a changing world. For example, our model results have practical implications for land-use policy aimed at disease reduction. Our model suggests land use regulations that reduce parcel size would be an actionable approach for policy makers concerned about increasing Lyme disease incidence in the northeastern US.10-Oct-2018
Data from: Cross-scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens
Communities of free-living organisms are shaped by processes operating within and among patches of habitat, while pathogen communities are shaped by analogous processes operating within and among hosts. Resource competition (R) theory can describe dynamics within patches or hosts, while metacommunity dynamics describe competition-colonization tradeoffs, extinction debts, and superinfection. However, models at this broader scale often assume instantaneous competitive exclusion in co-inhabited patches or coinfected hosts. Impacts of more gradual competitive exclusion on the abundance, distribution, and diversity of species are less clear. Here, we nest a general resource competition model within a metacommunity framework and manipulate the relative timescales for processes operating within and among patches/hosts. We focus on superinfection in pathogen communities. We compare cases where transmission depends on infection prevalence versus the abundance of pathogens within hosts. Surprisingly, slowing the relative pace of competitive exclusion within hosts can decrease infection prevalence of the inferior competitor and increase prevalence of the superior competitor, depending on transmission and virulence. Slower dynamics reduce the abundance of both pathogens within hosts and promote diversity at multiple scales: coinfections within individual hosts and co-occurrence in the host population. These results highlight surprising feedbacks that can emerge across scales and reinforce the rich cross-scale connections between community and disease ecology.
Data from: Invasion of two tick-borne diseases across New England: harnessing human surveillance data to capture underlying ecological invasion processes
Modelling the spatial spread of vector-borne zoonotic pathogens maintained in enzootic transmission cycles remains a major challenge. The best available spatio-temporal data on pathogen spread often take the form of human disease surveillance data. By applying a classic ecological approach—occupancy modelling—to an epidemiological question of disease spread, we used surveillance data to examine the latent ecological invasion of tick-borne pathogens. Over the last half-century, previously undescribed tick-borne pathogens including the agents of Lyme disease and human babesiosis have rapidly spread across the northeast United States. Despite their epidemiological importance, the mechanisms of tick-borne pathogen invasion and drivers underlying the distinct invasion trajectories of the co-vectored pathogens remain unresolved. Our approach allowed us to estimate the unobserved ecological processes underlying pathogen spread while accounting for imperfect detection of human cases. Our model predicts that tick-borne diseases spread in a diffusion-like manner with occasional long-distance dispersal and that babesiosis spread exhibits strong dependence on Lyme disease.
Evolution of Castanea in North America: RADseq and ecological modeling reveal a history of radiation, range shifts, and disease
<p><b>Premise of the Study: </b>Chestnuts and chinquapins are some of the best known and most widely loved of any plants in North America. Despite the fame of this clade, relatively little genomic sequencing has been done, and much is still unknown about their evolution. </p> <p><b>Methods: </b>Here we use ddRAD data to infer the species-level phylogeny for <i>Castanea </i>and assess the phylogeography of the North American species using samples collected from populations that span the full extent of the species' ranges. We also construct species distribution models using digitized herbarium specimens and observational data from field surveys. </p> <p><b>Key Results: </b>We identified strong population structure within <i>Castanea dentata</i> (American Chestnut) that reflects a stepwise northern migration since the last glacial maximum. Our species distribution models further confirm this scenario and match closely with the <i>Castanea</i> fossil pollen record. We also found significant structure within the <i>Castanea pumila</i> lineage, most notably a genetic cluster that corresponds to the frequently recognized "<i>Castanea pumila var. ozarkensis</i>."</p> <p><b>Conclusions: </b>The two North American <i>Castanea</i> species have contrasting patterns of population structure, but each is typical of plant phylogeography in North America. Within the <i>C. pumila</i> complex we find novel genetic structure that provides new insights to <i>C. pumila</i> taxonomy. Our results also identify a series of distinctive populations that will be valuable in on going efforts to conserve and restore the Chestnuts and Chinquapins in North America.</p>
Data from: Host and pathogen ecology drive the seasonal dynamics of a fungal disease, white-nose syndrome
Seasonal patterns in pathogen transmission can influence the impact of disease on populations and the speed of spatial spread. Increases in host contact rates or births drive seasonal epidemics in some systems, but other factors may occasionally override these influences. White-nose syndrome, caused by the emerging fungal pathogen Pseudogymnoascus destructans, is spreading across North America and threatens several bat species with extinction. We examined patterns and drivers of seasonal transmission of P. destructans by measuring infection prevalence and pathogen loads in six bat species at 30 sites across the eastern United States. Bats became transiently infected in autumn, and transmission spiked in early winter when bats began hibernating. Nearly all bats in six species became infected by late winter when infection intensity peaked. In summer, despite high contact rates and a birth pulse, most bats cleared infections and prevalence dropped to zero. These data suggest the dominant driver of seasonal transmission dynamics was a change in host physiology, specifically hibernation. Our study is the first, to the best of our knowledge, to describe the seasonality of transmission in this emerging wildlife disease. The timing of infection and fungal growth resulted in maximal population impacts, but only moderate rates of spatial spread.
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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.