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330 results for “predicted distribution”
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Fig. 3 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 3. The Jackknife test for evaluating the relative importance of environmental variables for Betula ermanii in South Korea.
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Crop & Progenitor Results Predicted Distributions
<p>Results obtained from using the sdmdl package on the dataset obtained for the 'Crop & Progenitor' case study.</p> <p>For all 52 species in the case study it contains:</p> <p>1) Fully trained model. Serialized to a .h5 file.</p> <p>2) Feature impact graph, showing the 20 most important variables sorted by their impact on the model output (in descending order).</p> <p>3) Raster file, containing the final predicticted distribution. Values are on a scale from 0 to 1, representing the probability of a species presence.</p> <p>4) Illustration map, showing the global predicted distribution of a species.</p>
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Figure 6 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 6. Total cross-validation AUC (CV-AUC) and spatial congruence AUC (SC- AUC) for a range of grain sizes.
Figure 4 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 4. Divergence between the uncorrelated and pruned models estimated through Parolo divergence index at 250-m resolution. As is shown, there was little divergence (0– 0.2) between models in most of the study area.
Figure 1 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 1. The location of the study area on a map of western Asia (right). Inset shows DEM of study area with polygons indicating the protected areas where populations of goitered gazelle occur.
Figure 5 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 5. The change in performance index (AUC, left) and habitat suitability area (right) of the output model with increasing extent size (open circles) and grain size (filled circles) from 250 to 3000 m.
Figure 3 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 3. Goitered gazelle distribution maps based on the uncorrelated model (left) and the pruned model (right) for the 250-m grid size.
Fig. 2 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 2. Diagram showing the conceptual framework used to model the distribution of the rufous-necked hornbill in the Western Forest Complex (WEFCOM), Thailand.
Fig. 1 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 1. Map of research locations showing the study areas where the point counts were conducted along14 line-transects, using a hand-held GPS to accurately measure the count locations or extent and position of evergreen forest where the RNH lives, in order to predict RNH distribution.
Fig. 3 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 3. Presence-absence binary models for RNH distributions after introducing equal sensitivity-specific threshold values to the continuous MaxEnt model based on the smallest home range size of the male RNH #15, as derived from Tifong (2007), during: a, the breeding season; b, the non-breeding season; and c, showing the combined habitat classification.
Fig. 5 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 5. Plots showing the correlation between the predicted distribution of Lucilia cuprina (A & C) and Lucilia sericata (B & D) to grease mass (kg) and human population density values (log values).
Fig. 6 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 6. Mean predicted distribution maps for Lucilia sericata (A) and Lucilia cuprina (B), produced using museum records, survey data and personal contact localities. The colour range indicates the likelihood of species distribution from dark blue (least likely) to red (most likely).
Fig. 3 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 3. Wool production of magisterial districts, estimated by total grease mass produced in 2011/2012 in South Africa.
Fig. 8 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 8. Mean distribution maps for Lucilia sericata (A & B) and Lucilia cuprina (C & D) from museum (A & C) and survey (B & D) data only.
Fig. 1 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 1. Map of South Africa, showing the occurrence records and collection trip routes for (A) Lucilia sericata and (B) Lucilia cuprina.
ScienceDex guides
Understand access before you commit
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.