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487 results for “species distribution modeling”
Supplementary material 7 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
The short description of invasive range of IAS in Russia
Supplementary material 3 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Species native range, introduction year, occurrence records
Supplementary material 2 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
General description and conceptual structure of the database (FDB)
Supplementary material 8 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Species richness of IAS in Northern Eurasia
Supplementary material 5 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Moran's I indexes of residual spatial autocorrelation for MaxEnt models
Supplementary material 6 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Moran's I correlograms of residual spatial autocorrelation for MaxEnt models
Data from: Model parameterization of four species distribution models
<p>Species Distribution Models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on a recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict non-sampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.</p>
Supplementary material 1 from: Rojas-Arias L, Gómez-Morales D, Stiegel S, Ospina-Torres R (2023) Niche modeling of bumble bee species (Hymenoptera, Apidae, Bombus) in Colombia reveals highly fragmented potential distribution for some species. Journal of Hymenoptera Research 95: 231-244. https://doi.org/10.3897/jhr.95.87752
Points used for the Niche modeling of Bumblebee species (Hymenoptera, Apidae, Bombus) in Colombia
Supplementary material 1 from: McCulloch-Jones EJ, Kraaij T, Crouch N, Faulkner KT (2023) Assessing the invasion risk of traded alien ferns using species distribution models. NeoBiota 87: 161-189. https://doi.org/10.3897/neobiota.87.101104
Supplementary information
Predictors for "Integrating High-Temporal-Resolution Climate Projections into Species Distribution Models"
<p>Predictors to train and predict SDMs</p>
Data from: The ‘golden kelp’ Laminaria ochroleuca under global change: integrating multiple eco-physiological responses with species distribution models
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Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
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Data from: The critical role of local refugia in postglacial colonization of Chinese pine: joint inferences from DNA analyses, pollen records, and species distribution modeling
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Data from: Minimum required number of specimen records to develop accurate species distribution models
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Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
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Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat
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Spatial sampling bias and model complexity in stream-based species distribution models: a case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, U.S.A.
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Data from: Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex
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Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
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Data from: Model parameterization of four species distribution models
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Allen Brain Atlas
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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.