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101 results for “Range Maps”
Imputed Forest Composition Map for New England Screened by Species Range Boundaries 2001-2006
Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.
Map of ecological sites and ecological states for the USDA Jornada Experimental Range
This data package includes an ArcMap geodatabase: a polygon feature class, associated attribute table and metadata. The spatial data, JERStateMap_v1.gdb.zip, represents the ecological sites and states on the Jornada Experimental Range. The attribute table for the spatial data, JERStateMap.csv, and a summary of the spatial metadata, JERStateMapMetadata.pdf, are also included.
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Geographic range maps for Mammal Diversity Database v1.3 taxonomy
<p>Update of mammal maps based on the taxonomy of the Mammal diversity database. These maps are different from the original, have been downscaled and are distributed under the R package mdd (github.com/alrobles/mdd).</p>
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village in A new record of the northern birch mouse Sicista betulina (Pallas, 1779) in the north of the Arkhangelsk Region (Rodentia: Sminthidae)
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village
FIGURE 1. Location map. A, A in The Pannonian Basin System northern margin paleogeography, climate, and depositional environments in the time range during MMCT (Central Paratethys, Novohrad-Nógrád Basin, Slovakia)
FIGURE 1. Location map. A, A) Position of Novohrad-Nógrád Basin within the Pannonian Basin System. Modified from Rybár et al., 2016. B) Location map of studied sections. C) Lithostratigraphic scheme. Explanatory notes: FPB- Fiľakovo-Pétervására Basin; DB- Danube Basin; NNB-Novohrad-Nógrád Basin. Modified from Gradstein et al., 2012.
Рис. 1. МестопоΛожение НационаΛьного парка «СмоΛьный» в МорΑовии (A) (на основе карты в https://old.bigenc.ru/geography/text/5746181) и схема НационаΛьного парка «СмоΛьный», показывающая номера квартаΛов в Λесничествах (Б) (оригинаΛ). a, корΑон Обрезки; b, корΑон Мокров; c, санаторий «АΛатырь»; d, урочище «СеΛищинская чащоба» Fig. 1. The location of the Smolny National Park in Mordovia; Fig. 1А. (based on the map at https://old. bigenc.ru/geography/text/5746181) and the map of the Smolny National Park showing sector numbers in forest ranges; Fig. 1Б. (original). a, Obrezki ranger station; b, Mokrov ranger station; c, Alatyr health centre; d, the natural boundary of Selishchinskaya Chashchoba in Neuroptera and Raphidioptera of the Smolny National Park, Republic of Mordovia, Russia
Рис. 1. МестопоΛожение НационаΛьного парка «СмоΛьный» в МорΑовии (A) (на основе карты в https://old.bigenc.ru/geography/text/5746181) и схема НационаΛьного парка «СмоΛьный», показывающая номера квартаΛов в Λесничествах (Б) (оригинаΛ). a, корΑон Обрезки; b, корΑон Мокров; c, санаторий «АΛатырь»; d, урочище «СеΛищинская чащоба» Fig. 1. The location of the Smolny National Park in Mordovia; Fig. 1А. (based on the map at https://old. bigenc.ru/geography/text/5746181) and the map of the Smolny National Park showing sector numbers in forest ranges; Fig. 1Б. (original). a, Obrezki ranger station; b, Mokrov ranger station; c, Alatyr health centre; d, the natural boundary of Selishchinskaya Chashchoba
Text-fig. 4. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAP. For legend see Text-fig. 3. in Palaeoenvironmental Analysis Of The Messinian Macrofossil Floras Of Tossignano And Monte Tondo (Vena Del Gesso Basin, Romagna Apennines, Northern Italy)
Text-fig. 4. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAP. For legend see Text-fig. 3.
Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
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Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
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Co-seismic and post-seismic differential Interferograms and displacement maps for the 2020 M6.5 Monte Cristo Range, Nevada earthquake
<p>Differential interferograms for the Mw 6.5 Monte Cristo, Nevada earthquake, processed with SNAP and CNR-IREA P-SBAS in the <a href="https://geohazards-tep.eu">Geohazards Exploitation Platform</a>.</p> <p>USGS event page for the Monte Cristo earthquake:</p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/nn00725272/">https://earthquake.usgs.gov/earthquakes/eventpage/nn00725272/</a></p> <p>Co-seismic and post-seismic (May 16 - May 23) interferograms are included. For each interferometric pair, three products are included: coherence, phase interferogram and unwrapped interferogram (LOS displacement). Decomposition.zip files includes the processed East-West and Vertical deformation maps from combining ascending and descending interferograms.</p> <p> </p>
Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data
<p><span>We</span> have developed a tree-based approach to create spatially continuous forest stand mean height maps across China through integrating high-<span>point</span> density, high-precision close-range LiDAR data and multisource remote sensing data. The accuracy analysis of the arithmetic mean height (Ha) and the weighted mean height (Hw) demonstrates the feasibility of the proposed method. A practical framework for forestry investigation based on close-range LiDAR was proposed. The mean values of Ha and Hw are 13.3 ± 3.3 m 11.3 ± 2.9 m on pixel level, respectively. Validation based on LiDAR and field sample data shows that the RMSE values, range from 2.6 to 4.1 m for Ha and 2.9 to 4.3 m for Hw, respectively, indicating that our approach outperforms existing forest canopy height maps derived from area-based approaches. Hopefully, our methods and maps will serve as a foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. </p>
Map 1 in Range extension of Ictinogomphus decoratus (Selys, 1854) (Insecta: Odonata: Gomphidae) to India
Map 1. Global and Indian distribution of Ictinogomphus decoratus (Selys, 1854).
A digital GIS update to the classic Ulbrich 1930 map of muskrat (Undatra zibethica) population range expansion in Central Europe
<p>This dataset is a digital version of a seminal dispersal data set of muskrat spread in Central Europe. We obtained and scanned an original copy of Ulbrich (1930), creating a digital image file, which we then geo-referenced in Arc Gis 10.2. The data herein contains metadata explaining the conversion process, necessary images to geo-reference the map, reference points used to "fix" the dispersal map to a map of present-day Europe, intermediate files (points and polygons produced when tracing the population ranges), and the final spatial dataset.</p> <p> </p> <p>Reference to the original data: Ulbrich, Johannes. 1930. Die Bisamratte; lebenweise, gang ihrer ausbreitung in Europa, wirtschaftliche bedeutung und bekampfung. Heinrich, Dresden.</p>
How to select appropriate hue ranges for sequential color schemes on choropleth maps? A quantitative evaluation using map reading experiments
<p>We propose map reading experiments to quantitatively evaluate the selection of hue ranges for sequential color schemes on choropleth maps. In these experiments, 60 sequential color schemes with six base hues and ten hue ranges were employed as experimental color schemes, and a total of 414 college students were invited to complete identification, comparison, and ranking tasks. Both controlled and real-map experiments were performed, each involving a web-based survey and an eye-tracking experiment. In the controlled experiments, the shapes of the map objects were relatively regular, and attribute data were randomized. In contrast, the shapes were complex in real-map experiments, and real data were employed. Our findings show that widely used color schemes with a hue range of 0º yield poor performance in all tasks; 15º hue ranges yield good performance in the comparison and ranking tasks but poor performance in the identification task. For large hue ranges of 120-360º, participants showed good performance in the identification task but poor performance in the comparison and ranking tasks. For 30-60º hue ranges, participants achieved excellent performance in the comparison and ranking tasks and acceptable performance in the identification task. We also found that the ratings of 0-60º ranges were high.</p>
The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models
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How to select appropriate hue ranges for sequential color schemes on choropleth maps? A quantitative evaluation using map reading experiments
Open the record for dataset details and reuse information.
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE4.Map of selected records of Siphamia species recorded in Queensland and Papua New Guinea waters in 2003–2005, including the type locality of Siphamia guttulata. Some symbols represent more than one specimen.
Data from: Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae
The genetic architecture of adaptive traits can reflect the evolutionary history of populations and also shape divergence among populations. Despite this central role in evolution, relatively little is known regarding the genetic architecture of adaptive traits in nature, particularly for traits subject to known selection intensities. Here we quantitatively describe the genetic architecture of traits that are subject to known intensities of differential selection between host plant species in Timema cristinae stick insects. Specifically, we used phenotypic measurements of 10 traits and 211,004 single-nucleotide polymorphisms (SNPs) to conduct multilocus genome-wide association mapping. We identified a modest number of SNPs that were associated with traits and sometimes explained a large proportion of trait variation. These SNPs varied in their strength of association with traits, and both major and minor effect loci were discovered. However, we found no relationship between variation in levels of divergence among traits in nature and variation in parameters describing the genetic architecture of those same traits. Our results provide a first step toward identifying loci underlying adaptation in T. cristinae. Future studies will examine the genomic location, population differentiation, and response to selection of the trait-associated SNPs described here.
Data from: QTL mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower
The wild North American sunflowers Helianthus annuus and H. debilis are participants in one of the earliest identified examples of adaptive trait introgression, and the exchange is hypothesized to have triggered a range expansion in H. annuus. However, the genetic basis of the adaptive exchange has not been examined. Here, we combine quantitative trait locus (QTL) mapping with field measurements of fitness to identify candidate H. debilis QTL alleles likely to have introgressed into H. annuus to form the natural hybrid lineage H. a. texanus. Two 500-individual BC1 mapping populations were grown in central Texas, genotyped for 384 single nucleotide polymorphism (SNP) markers and then phenotyped in the field for two fitness and 22 herbivore resistance, ecophysiological, phenological and architectural traits. We identified a total of 110 QTL, including at least one QTL for 22 of the 24 traits. Over 75% of traits exhibited at least one H. debilis QTL allele that would shift the trait in the direction of the wild hybrid H. a. texanus. We identified three chromosomal regions where H. debilis alleles increased both female and male components of fitness; these regions are expected to be strongly favoured in the wild. QTL for a number of other ecophysiological, phenological and architectural traits colocalized with these three regions and are candidates for the actual traits driving adaptive shifts. G × E interactions played a modest role, with 17% of the QTL showing potentially divergent phenotypic effects between the two field sites. The candidate adaptive chromosomal regions identified here serve as explicit hypotheses for how the genetic architecture of the hybrid lineage came into existence.
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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.