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487 results for “species distribution modeling”
FIGURE 9. Maximum entropy model developed for D in A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus
FIGURE 9. Maximum entropy model developed for D. andreae sp. n. in Cuba. Values range from high (red areas) to low environmental suitability (blue areas).
Fig. 8 in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 8. Map of central western Argentina showing the known distribution of Brachistosternus diaguita n. sp. (black circles), Brachistosternus montanus (black stars), and Brachistosternus intermedius (black triangles). The "Diaguita" district of the "Altoandina" biogeographical region is depicted in red, the "Cuyano" district of this region is depicted in blue, the "Puna" biogeographical region is depicted in orange, the "Prepuna" district of the "Monte" biogeographical region is depicted in green, and the Septentrional district of this region is depicted in turquoise.
Fig. 7. Brachistosternus diaguita n in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 7. Brachistosternus diaguita n. sp., A‒C. Telson. a. male, dorsoexternal aspect; B. male, lateral aspect; C. female, lateral aspect; D‒F. Hemipermatophore. D. left hemispermatophore, external aspect; E. left hemispermatophore, internal aspect; F. right hemispermatophore, internal aspect. Scale bars: 1 mm.
Fig. 5. A in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 5. A. Brachistosternus intermedius, pedipalp chela, male, internal aspect. B–H. Brachistosternus diaguita n. sp. B‒E. Pedipalp chela, male. B. internal aspect, C. dorsal aspect, D. external aspect, E. ventral aspect; F. Pedipalp chela, female, ventro-internal aspect. G. pedipalp patela, male, external aspect; H. pedipalp femur, male, dorsal aspect. Scale bars: 1 mm.
Fig. 6. A‒F. Metasomal segment V. A‒C. Brachistosternus diaguita n in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 6. A‒F. Metasomal segment V. A‒C. Brachistosternus diaguita n. sp., male. A. ventral aspect; B. dorso-external aspect, C. dorsal aspect; D. Brachistostenus intermedius, male, ventral aspect; E‒F. Brachistosternus montanus, male, E. dorsoexternal aspect, F. dorsal aspect. Scale bars: 1 mm.
Fig. 3 in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 3. Potential distribution of Brachistosternus species studied in this contributions A. Brachistosternus diaguita n. sp. B. Brachistosternus montanus. C. Brachistosternus intermedius. Warmer colors show areas with better predicted conditions. Black dots show the presence locations.
Fig. 2. A in On the southernmost high Andean scorpion species, with the identification of a cryptic new species of Brachistosternus (Bothriuridae) through morphology, molecular data and species distribution models
Fig. 2. A. Bayesian phylogeny of selected Brachistosternus species inferred using MrBayes. The values at the nodes are Bayesian posterior probabilities. The scale bar represents the branch lengths in substitutions per site. B. Bayesian species tree with nodeage estimates inferred using *BEAST. The values at the nodes are Bayesian posterior probabilities and the node bars represent the 95% highest posterior densities of the node age estimates. The scale axis is set to show Million years before present.
A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA
<p>Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application. In preparation for a Species Status Assessment of clay‐loving wild buckwheat (<em>Eriogonum pelinophilum</em>), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1‐meter resolution digital elevation model derived from LiDAR and a high‐resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and the discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We then further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes to the model marginally improved performance (AUC‐PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence in utilizing predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.</p>
Using species distribution modeling to generate relative abundance information in unstable territories: conservation of Felidae in Mexico
<p>Raw data used in the abovementioned manuscript</p>
Data from: Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography
<p>This dataset contains GPS coordinates of occurrences from 22 species from Sulawesi, Indonesia. It was used in the manuscript "Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography" to demonstrate the utility of the fauxcurrence R package (<a href="https://github.com/ogosborne/fauxcurrence">https://github.com/ogosborne/fauxcurrence)</a>.</p>
DNA barcode analyses improve accuracy in fungal species distribution models
<p class="western"><span>Species distribution models based on environmental predictors are useful to explain a species geographic range. For many groups of organisms, including fungi, the increase of occurrence data sets has generalized their use. However, fungal species are not always easy to distinguish, and taxonomy of many groups is not completely settled. This study explores the effect of taxonomic uncertainty in databases used for modeling fungal distributions. We analyze distribution models for three morphospecies from the corticioid genus <i>Xylodon</i> (Hymenochaetales, Basidiomycota), comparing models based on species names on vouchers specimens with models derived from species identified by DNA barcode. Differences in the contribution of predictors driving the distribution of each modeled taxon and the extent of their ranges were studied. Records under <i>X</i><i>ylodon</i><i> paradoxus</i>, <i>X</i>.<i> flaviporus</i> and <i>X</i>. <i>raduloides</i> were obtained from fungarium collections and GenBank repository. Two grouping criteria were used: (1) specimens were grouped by their collection or sequence voucher names and (2) specimens were grouped following molecular identification using ITS sequences through barcoding gap species recognition (BGSR). Climatic, geographic and biotic variables were used to predict the potential distribution of each taxon through MaxEnt algorithm. From the three morphospecies selected according to voucher names, up to 19 species candidates were detected using BGSR. Climatic variables were the most important predictors in distribution models made from names on voucher specimens, but their importance decreased when BGSR was applied. In general, the extent of species distributions was more restricted for taxa under BGSR. Our results show that taxonomic uncertainty has a strong effect in <i>Xylodon</i> species distribution models. Misleading results can be obtained when cryptic species or identification errors mask the actual diversity of the presence records. Preserved specimens in natural history collections offer the possibility to assess if the species name on labels matches the current species recognition criteria.</span></p>
Data from: Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail
Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution or movement patterns can ameliorate this limitation, but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. Synthesis and applications. In this analysis, Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas and population monitoring and also highlight critical knowledge gaps.
FIGURE 32. Potential natural distribution models, Proekoides species. A, P. cedarbergensis Stiller, 1986. B, P. koebergis Stiller, 1986. C in Leafhoppers of the Fynbos Biome of South Africa: Colistra, Proekes, Proekoides and a new genus (Insecta, Hemiptera, Cicadellidae, Deltocephalinae, Bonaspeiini)
FIGURE 32. Potential natural distribution models, Proekoides species. A, P. cedarbergensis Stiller, 1986. B, P. koebergis Stiller, 1986. C, Proekoides species merged in one model.
FIGURE 31. Potential natural distribution models, Colistra species. A in Leafhoppers of the Fynbos Biome of South Africa: Colistra, Proekes, Proekoides and a new genus (Insecta, Hemiptera, Cicadellidae, Deltocephalinae, Bonaspeiini)
FIGURE 31. Potential natural distribution models, Colistra species. A, Colistra parvulus (Linnavuori, 1961). B, C. semialius sp. n. C, C. bucapitatus sp. n. D, C. acapitatus sp. n.
FIGURE 33. Potential natural distribution models, Proekes species. A, P in Leafhoppers of the Fynbos Biome of South Africa: Colistra, Proekes, Proekoides and a new genus (Insecta, Hemiptera, Cicadellidae, Deltocephalinae, Bonaspeiini)
FIGURE 33. Potential natural distribution models, Proekes species. A, P. cephaleus (Naudé, 1926). B, Proekes species merged in one model.
A species distribution model of the giant kelp Macrocystis pyrifera: Worldwide changes and a focus on the Southeast Pacific
<p>Worldwide climate-driven shifts in the distribution of species is of special concern when it involves habitat-forming species. In the coastal environment, large Laminarian algae—kelps—form key coastal ecosystems that support complex and diverse food webs. Among kelps, <em>Macrocystis pyrifera</em> is the most widely distributed habitat-formingspecies and provides essential ecosystem services. This study aimed to establish the main drivers of future distributional changes on a global scale and use them to predict future habitat suitability. Using species distribution models (SDM), we examined the changes in global distribution of <em>M. pyrifera</em> under different emission scenarios with a focus on the Southeast Pacific shores. To constrain the drivers of our simulations to the most important factors controlling kelp forest distribution across spatial scales, we explored a suite of environmental variables and validated the predictions derived from the SDMs. Minimum sea surface temperature was the single most important variable explaining the global distribution of suitable habitat for <em>M. pyrifera</em>. Under different climate change scenarios, we always observed a decrease of suitable habitat at low latitudes, while an increase was detected in other regions, mostly at high latitudes. Along the Southeast Pacific, we observed an upper range contraction of −17.08° S of latitude for 2090–2100 under the RCP8.5 scenario, implying a loss of habitat suitability throughout the coast of Peru and poleward to −27.83° S in Chile. Along the area of Northern Chile where a complete habitat loss is predicted by our model, natural stands are under heavy exploitation. The loss of habitat suitability will take place worldwide: Significant impacts on marine biodiversity and ecosystem functioning are likely. Furthermore, changes in habitat suitability are a harbinger of massive impacts in the socio-ecological systems of the Southeast Pacific.</p>
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains data, scripts, and code. It is organized into two main directories: <em>Materials and Methods,</em> and <em>Results</em>.</p> <h2>Materials and Methods</h2> <p>The raw data can be accessed in the <em>Materials_and_Methods/Data directory</em>. The processed data used for modeling is available in <em>Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData</em>. All scripts for data processing are located in <em>Materials_and_Methods/Processing_scripts</em>. The <em>Plots_and_Figures </em>directory<em> </em>includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is <em>Main_script.R</em>.</p> <h2>Results</h2> <p>The <em>Results</em> directory contains three subdirectories and the script <em>Models_Outputs.R</em>. The <em>Results/Fitted_models</em> directory includes all .RData files with fitted models for each species. The script <em>Models_Outputs.R </em>generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the <em>Results/Plots </em>folder, while <em>Results/RData</em> contains intermediate .RData files created by <em>Models_Outputs.R</em> to manage computational costs.</p>
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c. Localities used to test and train the model are indicated by
Data from: A mistletoe tale: postglacial invasion of Psittacanthus schiedeanus (Loranthaceae) to Mesoamerican cloud forests revealed by molecular data and species distribution modeling
Background: Ecological adaptation to host taxa is thought to result in mistletoe speciation via race formation. However, historical and ecological factors could also contribute to explain genetic structuring particularly when mistletoe host races are distributed allopatrically. Using sequence data from nuclear (ITS) and chloroplast (trnL-F) DNA, we investigate the genetic differentiation of 31 Psittacanthus schiedeanus (Loranthaceae) populations across the Mesoamerican species range. We conducted phylogenetic, population and spatial genetic analyses on 274 individuals of P. schiedeanus to gain insight of the evolutionary history of these populations. Species distribution modeling, isolation with migration and Bayesian inference methods were used to infer the evolutionary transition of mistletoe invasion, in which evolutionary scenarios were compared through posterior probabilities. Results: Our analyses revealed shallow levels of population structure with three genetic groups present across the sample area. Nine haplotypes were identified after sequencing the trnL-F intergenic spacer. These haplotypes showed phylogeographic structure, with three groups with restricted gene flow corresponding to the distribution of individuals/populations separated by habitat (cloud forest localities from San Luis Potosí to northwestern Oaxaca and Chiapas, localities with xeric vegetation in central Oaxaca, and localities with tropical deciduous forests in Chiapas), with post-glacial population expansions and potentially corresponding to post-glacial invasion types. Similarly, 44 ITS ribotypes suggest phylogeographic structure, despite the fact that most frequent ribotypes are widespread indicating effective nuclear gene flow via pollen. Gene flow estimates, a significant genetic signal of demographic expansion, and range shifts under past climatic conditions predicted by species distribution modeling suggest post-glacial invasion of P. schiedeanus mistletoes to cloud forests. However, Approximate Bayesian Computation (ABC) analyses strongly supported a scenario of simultaneous divergence among the three groups isolated recently. Conclusions: Our results provide support for the predominant role of isolation and environmental factors in driving genetic differentiation of Mesoamerican parrot-flower mistletoes. The ABC results are consistent with a scenario of post-glacial mistletoe invasion, independent of host identity, and that habitat types recently isolated P. schiedeanus populations, accumulating slight phenotypic differences among genetic groups due to recent migration across habitats. Under this scenario, climatic fluctuations throughout the Pleistocene would have altered the distribution of suitable habitat for mistletoes throughout Mesoamerica leading to variation in population continuity and isolation. Our findings add to an understanding of the role of recent isolation and colonization in shaping cloud forest communities in the region.
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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.