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133 results for “conservation models”
Embeddings from protein language models predict conservation and variant effects
<p>For this work, we used protein language model representations (embeddings) to predict sequence conservation without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthew Correlation Coefficient – MCC - for ProtT5 embeddings of 0.596±0.006 vs. 0.608±0.006 for ConSeq).</p> <p><strong><em>ConSurf10k</em>- Dataset for the development of ProtT5cons:</strong> The method (ProtT5cons) predicting residue conservation used <em>ConSurf-DB </em>(Ben Chorin et al. 2020). This resource provided sequences and conservation for 89,673 proteins. For all, experimental high-resolution three-dimensional (3D) structures were available in the Protein Data Bank (PDB) (Berman et al. 2000). As standard-of-truth for the conservation prediction, we used the values from ConSurf-DB generated using HMMER (Mistry et al. 2013), CD-HIT (Fu et al. 2012), and MAFFT-LINSi (Katoh and Standley 2013) to align proteins in the PDB (Burley et al. 2019). For proteins from families with over 50 proteins in the resulting MSA, an evolutionary rate at each residue position is computed and used along with the MSA to reconstruct a phylogenetic tree. The ConSurf-DB conservation scores ranged from 1 (most variable) to 9 (most conserved). The PISCES server (Wang and Dunbrack 2003) was used to redundancy reduce the data set such that no pair of proteins had more than 25% pairwise sequence identity. We removed proteins with resolutions >2.5Å, those shorter than 40 residues, and those longer than 10,000 residues. The resulting data set (ConSurf10k) with 10,507 proteins (or domains) was randomly partitioned into training (9,392 sequences), cross-training/validation (555) and test (519) sets.</p> <p>Uploaded data:</p> <ul> <li>ConSuf10k_PDBid_seq_cons.fasta: fasta file with PDBid, sequence and conservation annotation</li> <li>consurf10k_test_ids.txt: txt file with id's of test set</li> <li>consurf10k_train_ids.txt: txt file with id's of train set</li> <li>consurf10k_val_ids.txt: txt file with id's of cross-validation set</li> </ul>
Throw trap and Electrofishing Data from Water Conservation Area 3B, Florida, USA, 2019-2022 for the Decompartmentalization Physical Model Project
This dataset includes densities and biomass of fishes and macroinvertebrates collected using throw traps or an airboat-mounted electrofisher in the study region of the Decompartmentalization Physical Model (DPM) located in Water Conservation Area (WCA) 3B. Some sites in this region experienced seasonal increases in water flow due to the operations of the S-152 structure. The sites sampled for this dataset were either located along a gradient of water flow (downstream the S-152) or were in a reference area that had ambient flow conditions. The purpose of this dataset was to quantify community responses of consumers groups to flowing water and how it may interact with local nutrient conditions at the site level. Hydrological, floc nutrient and periphyton volume data used in the analyses are included. This data package includes the R script that was used to run the statistical models for the manuscript titled "Discharge and nutrients interact to determine trophic structure in a wetland: evidence from a landscape-scale manipulation". The data collection for this data package is complete.
Conservation outcomes of dietary transitions across different values of nature - model outputs
<p>Archive of output reports produced for the MAgPIE v4.8.2 paper <strong>Conservation outcomes of dietary transitions across different values of nature</strong>.</p> <p><strong>Data</strong></p> <p>The data contain the MAgPIE model and post-processing outputs for all assessed scenarios, including the sensitivity test across the SSP1 and SSP3 scenarios.</p> <ul> <li>The file <em>rev9_healthyLscps_AOH_full_report.Rds </em>contains the outputs of our Area of Habitat (AOH) assessment for all assessed species and across all scenarios. The summary statistics shown in Figure 2 and Figure 3 have been derived from this data set. The data has also been used to create Extended Data Figures<span lang="EN-GB"> 5-</span>7<span lang="EN-GB"> and is shown in the Supplementary Information. </span>However, for Extended Data Figure<span lang="EN-GB"> 7</span> the data was <span lang="EN-GB">used in combination</span> with <span lang="EN-GB">rasterized </span>range polygons obtained from the <span lang="EN-GB">IUCN Red List Database (IUCN 2020).</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_pollSuff_report_all.Rds </em>reports the global and regional cropland area (Mha) that is subject to insufficient and sufficient pollination supply. This is specified in the column ‘poll_class’, where 1 is insufficient and 2 is sufficient pollination supply. The data is shown in Figure 4, Extended Data Figure 8 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_RUSLE_report_all.Rds</em> reports estimated global and regional soil loss in Pg per year across all modelled scenarios. The data is displayed in Figure 4, Extended Data Figure 9 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_allSSP_report.rds </em>contains regional and global output variables from the MAgPIE model used in this study. The data is shown in Figure 1 and 5, as well as in Extended Data Figures 2-4 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_validation.rds </em>contains validation data for the MAgPIE model output variables.</span></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Model code</span></strong></p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><em>MAgPIE model code</em><em>:</em></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.13833444">https://doi.org/10.5281/zenodo.13833444</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></li> </ul> <p><em>MAgPIE model documentation</em><em>:</em></p> <ul> <li><a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.8.2/</a></li> </ul> <p><em>SEALS model code</em><em>:</em></p> <ul> <li><a href="https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0">https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0</a></li> </ul> <p><em>SEALS mode documentation:</em></p> <ul> <li><a href="https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html">https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html</a></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Cited references</span></strong></p> <p><span>IUCN. (2020). <em>The IUCN Red List of Threatened Species. Version 2020-2</em>. https://www.iucnredlist.org.</span><em><span> </span></em><span>Downloaded on 25 November 2020</span></p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 4. Areas (polygons) in Western Podillya (Ukraine), where there is a predicted probability for the accommodation 9, 8 or 7 reptile species (gradient from dark gray — 9 species to light — 7 species). Districts numbered as in fig. 3.
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 3. Areas (downward diagonal filled polygons) in Western Podillya (Ukraine), where the average predicted habitat suitability for reptile species exceeds 0.5 (Districts: 1 — Terebovlianskyi, 2 —Husiatynskyi, 3 — Buchatskyi, 4 — Chortkivskyi, 5 — Chemerovetskyi, 6 — Horodenkivskyi, 7 — Zalishchytskyi, 8 — Borshchivskyi, 9 — Kamianets-Podilskyi, 10 — Zastavnivskyi, 11 — Khotynskyi).
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 4. Two upper categories ("Moderate" and "High") collapsed to identify areas of predicted presence (dark gray shading) for B. variegata in the study area.
Fig. 1 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 1. Response of Bombina variegata to Bio 11: x-axis — mean temperature of coldest quarter (°C x 10); y- axis— logistic output (probability of presence).
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 3. Response of Triturus cristatus to the Human Footprint: x-axis — Human Footprint; y-axis — logistic output (probability of presence).
Fig. 2 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 2. Response of Pelobates fuscus to Bio 3: x-axis — isothermality; y-axis — logistic output (probability of presence).
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</p>
Fig. 9 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 9. Areas within the potential niche of the Ocellated Lizard (in orange) for which there are no observation data (in red), based on Model 6 with its presence probability threshold of 0.70 combined with presence data from observations including a buffer zone with a 5-km radius.
Fig. 5 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 5. Change in vegetation cover from 2000 to 2016: standard deviation of the yearly NDVI values relative to the NDVI mean value for the month of July.
Fig. 3 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 3. Localization of the total presence data for the Ocellated Lizard Timon lepidus collected in France during the period 1970 to 2016.
Fig. 8 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 8. Location of populations that have disappeared in relation to the predicted presence map generated by Model 6 (blue dots).
Fig. 7 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 7. The predicted distribution map generated by Model 6 (in pink) with specific locations of presence data (from observations) from the dataset (black dots).
Fig. 6 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Fig. 6. Predictive modelling maps showing presence probability of the Ocellated Lizard (Timon lepidus) in the study area.
Figure 10 in Modelling the distribution of the Ocellated Lizard in France: implications for conservation
Figure 10: Contribution of protected nature areas to the conservation of Timon lepidus. Predicted presence within protected areas (dark purple) and predicted distribution range (light purple).
Fig. 4 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 4. The predicted distribution of the black-shanked douc (P. nigripes) generated by the MaxEnt software under the RCP8.5 scenario. BGM = Bu Gia Map National Park; CYS = Chu Yang Sin National Park; CT = Cat Tien National Park; HB = Hon Ba Nature Reserve; KL-SM = Kalon-Song Mao Nature Reserve; KT = Krong Trai Nature Reserve; NK = Nam Ka Nature Reserve; NN = Nam Nung Nature Reserve; NC = Nui Chua National Park; NO = Nui Ong Nature Reserve; TK = Takou Nature Reserve; VC = Vinh Cuu Nature Reserve.
Fig. 2 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 2. The predicted distribution of the black-shanked douc (P. nigripes) generated by the MaxEnt software under the RCP4.5 scenario. BGM = Bu Gia Map National Park; CYS = Chu Yang Sin National Park; CT = Cat Tien National Park; HB = Hon Ba Nature Reserve; KL-SM = Kalon-Song Mao Nature Reserve; KT = Krong Trai Nature Reserve; NK = Nam Ka Nature Reserve; NN = Nam Nung Nature Reserve; NC = Nui Chua National Park; NO = Nui Ong Nature Reserve; TK = Takou Nature Reserve; VC = Vinh Cuu Nature Reserve.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.