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736 results for “habitat distribution”
Fig.3 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.3. Neckera pennata distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzemes, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.1 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.1. Anomodon longifolius distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical reģions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumnkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J-upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N-upland Latgale, O-Austrumlatgale.
Fig.4 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.4. Lejeunea cavifolia distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.2 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.2. Homalia trichomanoides distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Distribution of the Natura 2000 habitat type 7220 (Cratoneurion) in Flanders and Brussels Capital Region, Belgium (version 2025)
<p>The dataset is a geospatial collection of points that correspond with the presence or absence of the Natura 2000 habitat type <code>7220</code> (Petrifying springs with tufa formation (<em>Cratoneurion</em>)) in springs and streaming water segments in the Flemish and Brussels Capital Region, Belgium. The dataset also contains a number of locations that were visited during mapping projects but where <code>7220</code> was found absent, or where additional survey is needed to decide on presence/absence of the habittype. The file is a GeoJSON format RFC7946 (WGS84).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).</p> <p>Headers are: </p> <ul> <li><code>id</code>; </li> <li><code>source</code>: original data source;</li> <li><code>validity status</code>: field inventory carried out (<code>gecontroleerd</code>) or not (<code>niet gecontroleerd</code>);</li> <li><code>name</code>: unique name of the site;</li> <li><code>system_type</code>: stream type (<code>rivulet</code>), mire type (<code>mire</code>), unknown (<code>unknown</code>) or na (<code>NA</code>);</li> <li><code>habitattype</code>: <code>7220</code>, no Natura 2000 type <code>(gh)</code>, unconfirmed <code>7220</code> <code>(7220, gh)</code>, alkaline fen<code>(7230)</code>;</li> <li><code>unit_id</code>: spatially related sites are identified by a common identifier</li> <li><code>area_m2</code>: area in square meters;</li> <li><code>year</code>: year of field inventory;</li> <li><code>sbz</code>: inside (1) or outside (0) special area of conservation;</li> <li><code>geometry</code>: latitude, longitude in decimal degrees.</li> </ul>
Acaulescence promotes speciation and shapes the distribution patterns of palms in Neotropical seasonally dry habitats
<p>Rainforests have been a source of lineages to open and seasonally dry habitats throughout Angiosperm evolution, especially in the Neotropics. However, the underlying mechanisms that allow such shifts remain poorly understood at large spatial scales. Here, we test whether acaulescence (an underground stem or a very short stem concealed in the ground) has affected the colonization and speciation in Neotropical seasonally dry habitats by <span>cocosoid palms</span> (Cocoseae). Acaulescent species maintain their growth underground, which increases their chances of survival from prolonged seasonal dry season and frequent fires. We use an integrative approach based on trait‐dependent diversification models, phylogenetic comparative methods, and ecological niche models. We found that shifts towards acaulescent growth form were accompanied by evolutionary transitions to seasonally dry habitats. Acaulescent lineages had higher speciation rates than non-acaulescent ones.<i> </i>However, the interaction between acaulescence and seasonally dry habitats had no significant effect on Cocoseae speciation rates. Acaulescent palms are primarily distributed in Neotropical seasonally dry habitats and non-acaulescent palms are concentrated in Amazonian rainforests. Our results suggest that an underground stem, with high carbohydrate and water storage capacity, is a preadaptation by which rainforest lineages were able to colonize and diversify in new fire-prone, increasingly seasonal and drier adaptive zones. The projected global expansion of dry seasonal habitats requires an understanding of how drought-avoidance functional traits evolve and how they are linked to seasonally dry habitats. Our results are, thus, a step forward in determining plant response mechanisms to drier and seasonal conditions.</p>
Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.
Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.
FIGURE 7 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 7. Photographs of habitat for Actenonyx aotearoa sp. nov. A. Glaciated valley at east end of Homer Tunnel, Fiordlands National Park, South Island, view looking north; B. Snowmelt streams at east end of Homer Tunnel, just south of tunnel entrance.
FIGURE 2 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 2. Male genitalia of Actenonyx species. A and B. A. bembidioides White (South Island, Southland, Te Anau Downs, Mistletoe Creek, 216 m); C and D. A. aotearoa sp. nov. (South Island, Fiordlands National Park, east end of Homer Tunnel, 915m). A and C. Dorsal aspect; B and D. Left lateral aspect. Scale line = 0.5 mm.
FIGURE 6 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 6. Photograph of habitat for Actenonyx bembidioides White. Arrow River at Arrowtown, Otago, South Island.
FIGURE 5 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 5. Map showing distribution records for specimens of Actenonyx aotearoa sp. nov. examined for this study (red circle = type locality; blue circles = localities where specimens have pronotal lateral setae; black circles = other localities). Scale line = 200 km.
FIGURE 1 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 1. Dorsal habitus of Actenonyx species. A. A. bembidioides White (South Island, Southland, Te Anau Downs, Mistletoe Creek, 216 m); B. A. aotearoa sp. nov. (South Island, Fiordlands National Park, east end of Homer Tunnel, 915m). Scale lines = 1.0 mm.
FIGURE 4 in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 4. Map showing distribution records for specimens of Actenonyx bembidioides White examined for this study (red circle = type locality; black circles = other localities). Scale line = 200 km.
FIGURE 3. Female genitalia and reproductive tract. A and C. A in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 3. Female genitalia and reproductive tract. A and C. A. bembidioides White (South Island, Southland, Te Anau Downs, Mistletoe Creek, 216 m); B and D. A. aotearoa sp. nov. (South Island, Fiordlands National Park, east end of Homer Tunnel, 915m); A and B. Tergite IX, dorsal aspect; C and D. Genital capsule and reproductive tract; bc = bursa copulatrix; co = common oviduct; gc1 = gonocoxite 1; gc2 = gonocoxite 2; sbb = spermathecal basal bulb; sg = spermathecal gland; sgd =spermathecal gland duct. Scale lines = 0.5 mm.
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
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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.