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129 results for “ecological niche models”
Figure 3 in Natural history and ecological niche modelling of coastal Atyphella Olliff Larvae (Lampyridae: Luciolinae) in Vanuatu
Figure 3. Distributions of pronotal widths and resulting predicted instars in two species of coastal Atyphella.
Figure 2 in Natural history and ecological niche modelling of coastal Atyphella Olliff Larvae (Lampyridae: Luciolinae) in Vanuatu
Figure 2. (a) Collection site of coastal Atyphella. Yellow dots indicate locations specimens were collected in 2018. (b) Predictive model for possible localities of coastal Atyphella in Vanuatu. White squares indicate locations Atyphella was collected in 2018.
Figure 1 in Natural history and ecological niche modelling of coastal Atyphella Olliff Larvae (Lampyridae: Luciolinae) in Vanuatu
Figure 1. (a) Typical habitat of coastal Atyphella (Efate, Vanuatu). (b) Typical habitat of coastal Atyphella (Malekula, Vanuatu). (c) Experimental setup of submersion experiment. (d) Captive coastal Atyphella feeding on snail.
Reassessing the taxonomy of Libidibia ferrea complex, the iconic Brazilian tree "pau-ferro" using morphometrics and ecological niche modeling
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Ecological niche modelling to project past, current and future distributional shift of black ebony tree (Diospyros melanoxylon Roxb.) in India
<p>The present study utilized an ensemble modelling approach to predict the distribution of <em>D. melanoxylon</em> under present, past (Last Glacial Maximum, ~22,000 cal yr BP, Middle Holocene ~6000 cal yr BP) and future climate change scenarios (RCP 2.6 and 8.5 for 2050s and 2070s). The annual mean temperature, mean temperature of the wettest quarter and annual precipitations were the most critical parameters that chiefly influence the distribution of <em>D. melanoxylon</em>. The ensemble model rendered high accuracy with AUC=0.93, TSS=0.74, and Kappa=0.71. Past projections of <em>D. melanoxylon</em> indicated a widespread distribution during the Last Glacial Maximum and Middle Holocene suggesting its adaptability to semi-dry as well as warm and humid climates, respectively. The presence of fossil pollen evidence of <em>D. melanoxylon</em> in the suitable habitats derived through past projections in this study complements the model results and marks occurrences of the species during the Last Glacial Maximum and Middle Holocene. By 2050s and 2070s (RCP 8.5), there would be a decline in the distribution by only 0.4% (13622 km2) and 0.2% (6842 km2) of the extremely habitat suitable, respectively. The main factor leading to reduced habitat suitability is the anticipated rise in temperature and variations in seasonal precipitation patterns. Our findings, help in identifying the parts of the country which would be severely affected by future climate change scenarios and plan conservation strategies for this commercially important species to facilitate its growth in suitable habitats which are likely to sustain under future climatic conditions.</p>
FIGURE 6 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 6. MaxEnt model outputs for Ampedus samedovi. Minimum Training Presence threshold is applied to outputs.
FIGURE 5 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 5. MaxEnt model outputs for Ampedus platiai. Minimum Training Presence threshold is applied to outputs.
FIGURE 2 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 2. Distributions and collecting localities of Ampedus platiai and Ampedus samedovi. Red: Distribution of only A. platiai in the provinces, Blue: Distribution of only A. samedovi in the provinces, Yellow: Distribution of A. platiai and A. samedovi in the provinces (The map is designed in ArcGis 10.2).
FIGURE 1 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 1. Habitus and aedeagi photos of examined species. A–B. Ampedus platiai, C–D. A. samedovi, E–F. A. pomonae (Aedeagi of A. platiai and A. samedovi are redrawn from Kabalak 2010 and aedeagus of A. pomonae is redrawn from Platia 1994.). BML: Basal struts of median lobe, BP: Basal piece, ML: Median Lobe, PDT: Paramere distal tooth, PR: Paramere.
Fig. 6 a–d. Ecological niche models for H. fumariifolia populations. a in Refugia and geographic barriers of populations of the desert poppy, Hunnemannia fumariifolia (Papaveraceae)
Fig. 6 a–d. Ecological niche models for H. fumariifolia populations. a Prediction of suitable habitat in the current environment. b Prediction projected onto past climatic layers (LGM; CCSM). c Prediction under
Data from: Do ecological niche models accurately identify climatic determinants of species ranges?
Defining species' niches is central to understanding their distributions and is thus fundamental to basic ecology and climate change projections. Ecological niche models (ENMs) are a key component of making accurate projections and include descriptions of the niche in terms of both response curves and rankings of variable importance. In this study, we evaluate Maxent's ranking of environmental variables based on their importance in delimiting species' range boundaries by asking whether these same variables also govern annual recruitment based on long-term demographic studies. We found that Maxent-based assessments of variable importance in setting range boundaries in the California tiger salamander (Ambystoma californiense; CTS) correlate very well with how important those variables are in governing ongoing recruitment of CTS at the population level. This strong correlation suggests that Maxent's ranking of variable importance captures biologically realistic assessments of factors governing population persistence. However, this result holds only when Maxent models are built using best-practice procedures and variables are ranked based on permutation importance. Our study highlights the need for building high-quality niche models and provides encouraging evidence that when such models are built, they can reflect important aspects of a species' ecology.
Figure 1 in Distribution of the meadow lizard in Europe and its realized ecological niche model
Figure 1. Distribution map of the meadow lizard (Darevskia praticola) in south-eastern Europe given on an MGRS UTM 10 × 10 km grid scale. A small overview map shows the study region and the two separate parts of the meadow lizard distribution – separate geographic units and evolutionary lineages of the species (modified from Agasyan et al. 2009). Letters on the distribution map refer to the names of larger (100 × 100 km) MGRS squares. Occurrence records were compiled from a large literature survey and our own data (see Supplemental material 1) and classified on the map according to the time frame of the findings.
Figure 2 in Distribution of the meadow lizard in Europe and its realized ecological niche model
Figure 2. Habitat suitability maps for the meadow lizard (Darevskia praticola) in south-eastern Europe given separately for the low resolution (a) and the high resolution ecological niche model (b). Training points used for fitting the ecological niche models are represented with white dots, while the discarded occurrences are shown as '×' signs and placed for the overall visual representation of the model accuracy.
Figure 3 in Distribution of the meadow lizard in Europe and its realized ecological niche model
Figure 3. Examples showing details from the forest cover (Vegetation Continuous Fields layer) and the Maxent's habitat suitability map for the meadow lizard (Darevskia praticola). The maps show two localities: (a) a part of a fragmented forest area in southern Romania where the species occurs, and (b) an area near the Danube River along the border between Serbia and Romania, which is one of the places of greater habitat suitability for this species. The maps also show the difference between the low (upper images) and high (lower images) resolution of both the VCF layer and the habitat suitability.
Data from: The phylogeographic history of Megistostegium (Malvaceae) in the dry, spiny thickets of southwestern Madagascar using RAD-seq data and ecological niche modeling.
<p class="MsoCommentText">The spiny thicket of southwestern Madagascar represents an extreme and ancient landscape with extraordinary levels of biodiversity and endemism. Few hypotheses exist for explaining speciation in the region and few plant studies have explored hypotheses for species diversification. Here we investigate three species in the endemic genus <i>Megistostegium </i>(Malvaceae) to evaluate phylogeographic structure and explore the roles of climate, soil and paleoclimate oscillations on population divergence and speciation throughout the region. We combine phylogenetic and phylogeographic inference of RADseq data with ecological niche modeling across space and time. Population structure is concurrent with major rivers in the region and we identify a new, potentially important biogeographic break coincident with several landscape features. Our data further suggests that niches occupied by species and populations differ substantially across their distribution. Paleodistribution modelling provide evidence that past climatic change could be responsible for the current distribution, population structure and maintenance of species in <i>Megistostegium.</i></p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Data from: Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania
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Data from: Genomic analysis of demographic history and ecological niche modeling in the endangered Sumatran Rhinoceros Dicerorhinus sumatrensis
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Data from: Using data from related species to overcome spatial sampling bias and associated limitations in ecological niche modeling
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Data from: Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account
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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.