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487 results for “species distribution model”
Figure 68 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 68: Predicted and recorded distribution of Phanaeus melibaeus.
Figure 21 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 21: Predicted distribution of Phanaeus endymion species group.
Figure 24 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 24: Predicted and recorded distribution of Phanaeus arletteae.
Figure 7 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 7: Predicted distribution of Phanaeus beltianus species group.
Figure 6 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 6: Predicted and recorded distribution of Phanaeus melampus.
Figure 12 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 12: Predicted and recorded distribution of Phanaeus bispinus.
Data from: Integrating diverse data for robust species distribution models in a dynamic ocean
<p><strong>Aim: </strong>Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. While various approaches can be used to integrate different datasets, studies comparing their performance, particularly for highly migratory and mobile species, are scarce. Here, we assess whether a model-based integrative framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types.</p> <p><strong>Location: </strong>North Atlantic Ocean</p> <p><strong>Time Period: </strong>1993 - 2019</p> <p><strong>Major Taxa Studied: </strong>Blue shark (<em>Prionace glauca</em>)</p> <p><strong>Methods: </strong>We trained traditional, correlative SDMs and integrated SDMs (iSDMs) with three distinct data types: fishery-dependent marker tags, fishery observer records, and fishery-independent electronic tag data. We evaluated data pooling and ensemble approaches in a correlative SDM framework and compared performance to an iSDM approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset.</p> <p><strong>Results: </strong>While each integration approach yielded robust models, model performance varied among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model's ability to explain the spatiotemporal dynamics of the training data. iSDMs that explicitly accounted for seasonal variability yielded the most accurate and ecologically realistic estimates. However, such approaches are computationally intensive and warrant identifying model purpose as an important step in the data-integration process.</p> <p><strong>Main Conclusions: </strong> Our findings reveal important trade-offs among the current techniques for integrating data in SDMs, including variability in accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, maximizing our ability to leverage available data with robust analytical approaches will be instrumental in enhancing conservation and management efforts and for understanding current and future species distributions in a dynamic ocean.</p>
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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.