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56 results for “Habitat suitability modelling”
Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
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Data from: Mapping Tasmania's cultural landscapes: using habitat suitability modelling of archaeological sites as a landscape history tool
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Support for a relationship between demography and modeled habitat suitability is scale dependent for the Purple Martin, Progne subis
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Data from: Combining human acceptance and habitat suitability in a unified socio-ecological suitability model: a case study of the wolf in Switzerland
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Modelling seasonal habitat suitability for wide-ranging species: Invasive wild pigs in northern Australia
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Data from: Habitat suitability and connectivity modeling reveal priority areas for Indiana bat (Myotis sodalis) conservation in a complex habitat mosaic
Context <p>Conservation for the Indiana bat (<i>Myotis sodalis), </i>a federally endangered species in the United States of America, is typically focused on local maternity sites; however, the species is a regional migrant, interacting with the environment at multiple spatial scales. Hierarchical levels of management may be necessary, but we have limited knowledge of landscape-level ecology, distribution, and connectivity of suitable areas in complex landscapes.</p> Objectives <p>We sought to 1) identify factors influencing <i>M. sodalis </i>maternity colony distribution in a mosaic landscape, 2) map suitable maternity habitat, and 3) quantify connectivity importance of patches.</p> Methods <p>Using 3 decades of occurrence data, we tested <i>a priori</i>,<i> </i>hypothesis-driven<i> </i>habitat suitability models. We mapped suitable areas and quantified connectivity importance of habitat patches with probabilistic habitat availability metrics.</p> Results <p>Factors improving landscape-scale suitability included limited agriculture, more forest cover, forest edge, proximity to medium-sized water bodies, lower elevations, and limited urban development. Areas closer to hibernacula and rivers were suitable. Binary maps showed that thirty percent of the study area was suitable for <i>M. sodalis</i> and 29% was important for connectivity. Most suitable patches were important for intra-patch connectivity and far fewer contributed to inter-patch connectivity.</p> Conclusions <p>While simple models may be effective for small, homogenous landscapes, complex models are needed to explain habitat suitability in large, mixed landscapes. Suitability modeling identified factors that made sites attractive as maternity areas. Connectivity analysis improved our understanding of important areas for bats, identified suitable patches that may be isolated from the habitat network, and prioritized areas to target restoration.</p>
Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat
Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat-specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or "typical" cases. Overlaying the species distribution model output with aquatic habitat-specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species.
Figure 2 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 2. Histograms illustrating descriptive analyses of climatic and environmental characteristics related to the occurrence point of Maghreb Magpies.
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>
Table ²: Percentage of contribution of the selected variables for habitat suitability modeling for the striped hyaena in Batna, Algeria. in Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)
<p><b>Table ²:</b> Percentage of contribution of the selected variables for habitat suitability modeling for the striped hyaena in Batna, Algeria.</p><table><tbody><tr><th>Environmental variables</th><th>Contribution to prediction capacity (%)</th></tr></tbody><tbody><tr><th>Shrubland</th><td>37.5</td></tr><tr><th>Slope</th><td>30.2</td></tr><tr><th>Built-up areas</th><td>12</td></tr><tr><th>Distance to roads</th><td>7.9</td></tr><tr><th>DEM</th><td>5.2</td></tr><tr><th>BIO3 (isothermality)</th><td>4.9</td></tr><tr><th>Distance to waterbodies</th><td>2.3</td></tr></tbody></table>
Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat
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Data from: Habitat suitability and connectivity modeling reveal priority areas for Indiana bat (Myotis sodalis) conservation in a complex habitat mosaic
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Predicting hedgehog mortality risks on British roads using habitat suitability modelling
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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>
Data from: Phylogeography, historical demography, and habitat suitability modelling of freshwater fishes inhabiting seasonally fluctuating Mediterranean river systems: a case study using the Iberian cyprinid Squalius valentinus
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Environmental gradients, covering coastal central California, which are relevant to modeling habitat suitability of Deinandra increscens subsp. villosa
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