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162 results for “habitat mapping”
FIGURES 44–47. Maps and habitat. 44 in New and little known Coptotriche and Tischeria species (Lepidoptera: Tischeriidae) from Primorskiy Kray, Russian Far East
FIGURES 44–47. Maps and habitat. 44, map of the region (courtesy of T. Patterson, USA), showing the collecting locality (red dot) in Primorskiy Kray (= Primorskiy Territory) in August 2011 (see Methods and material); 45, currently known distribution of Tischeria sichotensis Ermolaev (red dots); 46, 47, habitat of Coptotriche minuta Diškus & Stonis, sp. nov., Tischeria unca Diškus & Stonis, sp. nov., T. decidua siorkionla Kozlov and T. sichotensis Ermolaev—dense deciduous, predominantly broadleaf forest near Gornotayezhnoe Biological Station, 20 km E Ussuriysk, Primorskiy Kray (Primorskiy Territory, Russian Far East).
Habitat mapping of coastal dunes with deep learning - Scripts & Data
<p><strong>Authors</strong>: Eva M. Lansu, Valérie C. Reijers, Freek Daniëls, Rebecca James, Marjolijn J. A. Christianen, Tjisse van der Heide</p> <p> </p> <p><strong>Abstract</strong></p> <p><span lang="EN-GB">About one-third of the world's shoreline is defined by sandy coasts with developed dune ecosystems</span><span lang="EN-GB">. </span><span lang="EN-GB">These ecosystems </span><span lang="EN-GB">drastically degraded them due to anthropogenic pressures. </span><span lang="EN-GB">To develop strategic management that counteracts this degradation, it is essential to closely monitor ongoing habitat changes.</span><span lang="EN-GB"> Traditionally, coastal dune monitoring is based on field observations, which are labour intensive and costly. While automated analyses of aerial imagery could reduce monitoring efforts and enhance spatial coverage, t</span><span lang="EN-GB">o date, its application </span><span><span lang="EN-GB">has </span></span><span><span lang="EN-GB">remained limited to a single small-scale trial (<2 km</span></span><span><sup><span lang="EN-GB">2</span></sup></span><span><span lang="EN-GB">). </span></span><span><span lang="EN-GB">Here, we trained a Convolutional Neural Network to map the Dutch coastal dunes </span></span><span><span lang="EN-GB">(562 km<sup>2</sup>) </span></span><span><span lang="EN-GB">at 25 cm resolution using six habitat classes: bare sand, shrubs, fresh water, grass, broadleaf trees, and needleleaf trees. </span></span><span lang="EN-GB">Training the network on only RGB imagery resulted in predictions with 92% accuracy, 80% average recall and 70% precision. Model performance increased when the network was trained on all available data - RGB imagery, near-infrared, distance to sea, digital surface model, and canopy height - resulting in 95% accuracy, 88% averaged recall and 80% precision. Finally, we compared the predictions with 499 in-field observations across the Dutch coastal dunes and found 88% accuracy, 74% averaged recall and 62% precision. We used this model to create a map of the entire Dutch coastal dunes, which enables </span><span lang="EN-GB">rapid and precise </span><span lang="EN-GB">assessments of habitat diversity and extent</span><span lang="EN-GB">. As habitat and species diversity are intrinsically linked, our results showcase how automated image analysis can enable biodiversity monitoring on a national scale. </span></p> <p>==============================================</p> <p><strong>Methods</strong></p> <p>The analyses rely on the following datasets:</p> <ul> <li>Orthophoto mosaics including a near-infrared band (from <u><a href="http://geotiles.nl/">http://geotiles.nl/</a></u>)</li> <li>Digital surface model and a digital terrain model (from <a href="https://www.ahn.nl/">https://www.ahn.nl/</a>)</li> <li>A land-use map (from <u><a href="https://lgn.nl/basiskaart">https://lgn.nl/basiskaart)</a></u></li> </ul>
Area of habitat maps for amphibians and reptiles of Italy
<p>Area of Habitat (AOH) maps reveal the distribution of the habitat available to the species within their geographic range. Information on the distribution of species' habitats can help identify sites where viable populations of a species are found. We produced high resolution (100 m), freely accessible global area of habitat maps for 60 species of reptiles and amphibians distributed in Italy, which represent 60% of all Italian amphibian and reptile species. AOH maps can be used as a reference for conservation planning and can help monitoring habitat loss, which is known to be a major threat to many reptile and amphibian species in Europe.</p>
FIGURE 20–21. Distribution map and habitat. 20 in On the taxonomy of genus Teliphasa Moore, 1888 (Lepidoptera: Pyralidae Epipaschiinae) with the description of two new species and two new species records from India
FIGURE 20–21. Distribution map and habitat. 20, distribution map of Teliphasa spp. 21, landscape view: India, Chirbatiya (Uttarakhand).
Metadata for Geospatial Mapping Tools, Indicators and Metrics for Fish Habitat in the Pacific Region
<p>Metadata on geospatial tools, indicators, metrics and scoring benchmarks useful for assessing the status of threats to freshwater fish habitat in British Columbia</p>
Map 1 in Distribution and habitat preferences of Galápagos ants (Hymenoptera: Formicidae)
Map 1. Galápagos archipelago with terrestrial ecological zones. The largest island Isabela is composed of six volcanoes.
FIGURE 9. Location map and Collecting sites. a in Additions to the microfungi in Taiwan: introducing Pseudorobillarda camelliaesinensis sp. nov., (Pseudorobillardaceae) and new host records of pleosporalean taxa in mountainous habitats
FIGURE 9. Location map and Collecting sites. a Location map of collecting sites b Tea plantation of Alishan Mountain. c Mountain areas of Alishan Mountain. d Mountain areas of Fenghuang Mountain e Tea plantation of Fenghuang Mountain. (Captured by A. R. Rathnayaka and D. S. Tennakoon).
Supplementary material 4 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Abundance of invertebrates collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Supplementary material 3 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Results of criteria met for each location pinpointed as potential colluvial Mesovoid Shallow Substratum (MSS)
Supplementary material 1 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Characterization of each of the locations found in situ as colluvial Mesovoid Shallow Substratum (MSS): latitude, longitude and estimated area (m2)
Supplementary material 5 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Total invertebrate abundance, collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Area of Habitat maps for the world's terrestrial birds and mammals
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Area of habitat maps for amphibians and reptiles of Italy
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Data from: Mapping habitats in a marine reserve showed how a 30-year trophic cascade altered ecosystem structure
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High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
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Data from: Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier-targeting of interventions against malaria vectors
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Data from: Habitat mapping of coastal wetlands using expert knowledge and Earth observation data
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Data from: Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada
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Data from: Mapping Tasmania's cultural landscapes: using habitat suitability modelling of archaeological sites as a landscape history tool
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Data from: Ensemble approach for potential habitat mapping of invasive Prosopis in Turkana, Kenya
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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.