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36 results for “MaxEnt model”
Modelling the potential distribution of African Wormwood (Artemisia afra) using machine learning algorithm-based approach (MaxEnt) in Sekhukhune District, South Africa
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New England and eastern cottontail data for Maxent and niche overlap modeling
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Data from: Prediction of Platycodon grandiflorus distribution in China using MaxEnt model concerning current and future climate change
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Maxent species distribution modelling of 10 cetacean species in the northeastern Pacific from citizen science occurrence records
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Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species
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Methodology based on the MaxEnt algorithm for modelling habitat distribution
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Figure 8. MAXENT distribution models produced for Tropidurus madeiramamore. A, present. B, Holocene. C in A highly polymorphic South American collared lizard (Tropiduridae: Tropidurus) reveals that open-dry refugia from South-western Amazonia staged allopatric speciation
Figure 8. MAXENT distribution models produced for Tropidurus madeiramamore. A, present. B, Holocene. C, Last Glacial Maximum (LGM). D, Last Interglacial (LIG).
Prediction of potential distribution of seven plant species of <em>Aster</em> (Asteraceae) based on MaxEnt model
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The impact of data quality filtering of opportunistic citizen science data on species distribution model performance: dataset used for Maxent modelling.
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Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters
<p><span>Seagrass meadows are generally diverse in China and have the same essential ecosystem services as elsewhere. However, an evaluation of seagrass distribution across China is still lacking, and the magnitude and direction of changes in seagrass meadows remains unclear. Our primary objective was to provide a nationwide seagrass distribution map, and to explore the dynamic changes of seagrass population under global climate change. We use simulation studies within the modelling software MaxEnt with 58961 occurrence records and 27 marine environmental variables, to simulate the potential distribution of seagrasses and calculate the area. 7 environmental variables were deleted before the modelling processes based on a correlation analysis to ensure predicted suitability. The predicted area was 790.09 km<sup>2</sup>, which is much larger than the known seagrass distribution in China, and would be increased to 923.62 km<sup>2</sup> by the year 2100. However, the suitable habitat of almost all seagrass will shift northwest in the future. The sum of individual family will under-predict the national distribution of seagrass, showed a downward trend consistently in the future. Out of all environmental variables, the physical ones (e.g. depth, land distance and sea surface temperature) had the greatest contribution in predicting seagrass distributions, and nutrients (e.g. nitrate, phosphate) ranked among the key influential predictors for habitat suitability in our focal area. As this is a first effort to fill a gap in our understanding of the distribution of seagrass in China, further studies are necessary using both modeling and biological/ecological approaches. </span></p>
Figure 2 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 2. Jackknifing test of variable importance in the development of the uncorrelated model at 250-m resolution. Blue bars indicate the gain achieved when including that predictor only. Green gray bars show how much the total gain is diminished without the given predictor. Red bar indicate the gain achieved when including all predictors.
Table ¹: Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis. in Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)
<p><b>Table ¹:</b> Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis.</p><table><tbody><tr><th><b>Prediction set</b></th><th><b>FC</b></th><th><b>RM</b></th><th><b>P</b> <b>ROC</b></th><th><b>OR</b></th><th><b>AICc</b></th><th><b>ΔAICc</b></th><th><b>WAICc</b></th><th><b>Number of parameters**</b></th></tr></tbody><tbody><tr><th>313</th><td>lqp</td><td>0.7</td><td>0</td><td>0</td><td>752.875</td><td>1.226</td><td>0.0337</td><td>10</td></tr><tr><th>314*</th><td>lqp</td><td>1</td><td>0</td><td>0</td><td>751.649</td><td>0.000</td><td>0.057</td><td>7</td></tr></tbody></table><p>The selected model set (*) met the statistical significance and omission rate criteria during evaluation with train and test data.**Number of environmental variables. FC, feature class; RM, regularization multiplier; OR = omission rate; ΔAICc, Akaike information criterion corrected for small simple sizes; WAICc, weighted AICc.</p>
Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters
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Carbon Pools across CONUS using the MaxEnt Model, 2005, 2010, 2015, 2016, and 2017
This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Figure 7 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 7. Location of the protected area containing goitered gazelle in central Iran.
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International Brain Laboratory public data
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OpenNeuro
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