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13 results for “animated map”
Indicative distribution map for Ecosystem Functional Group M1.5 Photo-limited marine animal forests
<p>This archive contains indicative distribution maps and profiles for <strong>M1.5 Photo-limited marine animal forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates
<p>EFSA was requested to assess the impact on effectiveness of <em>post-mortem</em> inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals.</p> <p>The following information is indicated for each disease/condition in the disease map:</p> <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at <em>ante mortem </em>inspection;</li> <li>The lesions associated with the disease that could be detected at <em>post mortem</em> inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> <p>From the disease map, the list of organs to be considered at <em>post mortem</em> inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
Counting animals in aerial images with a density map estimation model
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Describing and mapping of the main existing structures and systematic initiatives and academic activities for surveillance in the EU for zoonoses (transboundary, emerging and re-emerging) in domestic animals and wildlife
<p>These annexes refer to a report which describes and maps the main existing structures and systematic initiatives and academic activities for surveillance in the EU for transboundary, emerging and re-emerging zoonoses in domestic animals, wildlife, and the environment, developed by the different sectors, namely human, domestic animal, wildlife and environmental, under One Health approach. This is essential to provide scientific and technical advice and improve future schemes of surveillance. A questionnaire was compiled by MSs and the information collected was complemented by literature reviews about (i) the main existing structures and systematic initiatives or activities, and (ii) academic activities for surveillance in the EU for zoonoses in domestic animals and wildlife. The annexes to the report are as follows:</p> <p>Annex 1. Questionnaire survey on official zoonotic disease surveillance activities in the EU and neighbouring countries.</p> <p>- Sheet 1: PART 1 – Surveillance. This part explores the general organization of the surveillance plan</p> <p>- Sheet 2: PART 2 – Pathogens. This part aims to identify target pathogen and species and methods for surveillance</p> <p>Annex 2. Characteristic of surveillance plans.</p> <p>- Sheet “Pathogens”, where not primary but also a wide range of hosts are summarized.</p> <p>- Sheet “active/passive surveillance” by country</p> <p>- Sheet “origin of funding” (the proportion and number) of surveillance plans</p> <p>Annex 3. More detailed distribution of active and passive surveillance planaccording to countries and pathogen is presented in this Annex.</p> <p>Annex 4. Standardized data model (to extract key information to characterize the surveillance systems in the literature review on systematic surveillance. The data model was divided into two parts:</p> <p>- Sheet 1: PART 1 – Surveillance system (explores the general organization)</p> <p>- Sheet 2: PART 2 – Pathogens (identifies the target pathogen, species, and methods)</p> <p>Annex 5. Standardized data model used during the literature review to extract key information to characterize the surveillance performed by the academia</p>
Integrating animal tracking datasets at a continental scale for mapping wildlife habitat
<div><em>Aim:</em></div> <div> </div> <div>The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable detailed and consistent habitat mapping at biogeographic scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).</div> <div> </div> <div> <em>Location: </em>Europe</div> <div> </div> <div><em>Methods:</em></div> <div> </div> <div>We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modeling approaches, comparing (1) global strategies that pool all data for training vs. building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection, and (3) different modeling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms.</div> <div> </div> <div><em>Results:</em></div> <div> </div> <div>Both global and local modeling strategies allowed building transferable habitat models with overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habitat models. Our habitat maps identified large areas of suitable, but currently unoccupied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.</div> <div> </div> <div><em>Main conclusions:</em></div> <div> </div> <div>We demonstrate that global and local modeling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.</div>
Data and code for: A novel method for mapping high-precision animal locations using high-resolution imagery
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Mapping the prevalence of household-scale livestock ownership by animal taxon in low- and middle-income countries
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Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat
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Data from: Resource-Area-Dependence Analysis: inferring animal resource needs from home-range and mapping data
An animal's home-range can be expected to encompass the resources it requires for surviving or reproducing. Thus, animals inhabiting a heterogeneous landscape, where resource patches vary in size, shape and distribution, will naturally have home-ranges of varied sizes, so that each home-range encompasses a minimum required amount of a resource. Home-range size can be estimated from telemetry data, and often key resources, or proxies for them such as the areas of important habitat types, can be mapped. We propose a new method, Resource-Area-Dependence Analysis (RADA), which uses a sample of tracked animals and a categorical map to i) infer in which map categories important resources are accessible, ii) within which home range cores they are found, and iii) estimate the mean minimum areas of these map categories required for such resource provision. We provide three examples of applying RADA to datasets of radio-tracked animals from southern England: 15 red squirrels Sciurus vulgaris, 17 gray squirrels S. carolinensis and 114 common buzzards Buteo buteo. The analyses showed that each red squirrel required a mean (95% CL) of 0.48 ha (0.24-0.97) of pine wood within the outermost home-range, each gray squirrel needed 0.34 ha (0.11-1.12) ha of mature deciduous woodland and 0.035-0.046 ha of wheat, also within the outermost home-range, while each buzzard required 0.54 ha (0.35-0.82) of rough ground close to the home-range center and 14 ha (11-17) of meadow within an intermediate core, with 52% of them also relying on 0.41 ha (0.29-0.59) of suburban land near the home-range center. RADA thus provides a useful tool to infer key animal resource requirements during studies of animal movement and habitat use.
Data from: Resource-Area-Dependence Analysis: inferring animal resource needs from home-range and mapping data
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Genome-wide maps of H3K9me3 and H3K4me3 state in failing heart with or without chaetocin in animal model
GEO Series GSE69194. Rattus norvegicus. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide maps of chromatin state for LAG-1 in whole animals
GEO Series GSE144225. Caenorhabditis elegans. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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.