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307 results for “Model combination”
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Combining conservation status and species distribution models for planning assisted colonisation under climate change
<p>Effects of climate change are particularly important in the Mediterranean Biodiversity hotspot where rising temperatures and drought are negatively affecting several plant taxa, including endemic species. Assisted Colonisation (AC) represents a useful tool for reducing the effect of climate change on endemic plant species threatened by climate change.</p> <p>We combined SDMs for 188 taxa endemic to Italy with the IUCN red listing range loss threshold under criterion A (30%) to define: a) the number of AC (measured as 2×2 km grid cells that should be occupied by new populations, that is grid cells = new populations) required to fully compensate for predicted range loss and to halt the decline below the 30% of range loss; b) The number of cells necessary to compensate for range loss was calculated as the number of currently occupied cells lost under future climate due to unsuitable conditions. We used two Representative Concentration Pathways, +2.6 and +8.5 W/m2, optimistic and pessimistic scenarios, respectively. Availability of suitable areas for AC was also assessed within the current species distribution and within protected areas.</p> <p>Under the optimistic scenario, no taxa would lose more than 30% of their range and AC would not be required. Under the pessimistic scenario, roughly 90% of taxa showed a cell loss higher than 30%. Eight taxa were predicted to lose >95% of their range. For these species, AC was required from 13 to 16 new populations (= 13 to 16 grid cells) per taxon to cap the range loss at 30%. For currently VU or EN species, an average number of 32 to 35 AC attempts would be necessary to fully compensate for their range loss under a pessimistic scenario. Suitable recipient sites within protected areas falling in their projected range were identified, allowing for short-distance AC.</p> <p>Synthesis. Combining SDMs and red listing thresholds under Criterion A has enabled the strategic planning of multiple-species AC minimising the effort in terms of new populations to be created and maximising the conservation benefit in terms of range loss compensation.</p>
Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.
Data from: Gene trees, species trees and Earth history combine to shed light on the evolution of migration in a model avian system
The evolution of migration in birds has fascinated biologists for centuries. In this study, we performed phylogenetic-based analyses of Catharus thrushes, a model genus in the study of avian migration, and their close relatives. For these analyses, we used both mitochondrial and nuclear genes, and the resulting phylogenies were used to trace migratory traits and biogeographic patterns. Our results provide the first robust assessment of relationships within Catharus and relatives and indicate that both mitochondrial and autosomal genes contribute to overall support of the phylogeny. Measures of phylogenetic informativeness indicated that mitochondrial genes provided more signal within Catharus than did nuclear genes, whereas nuclear loci provided more signal for relationships between Catharus and close relatives than did mitochondrial genes. Insertion and deletion events also contributed important support across the phylogeny. Across all taxa included in the study, and for Catharus, possession of long-distance migration is reconstructed as the ancestral condition, and a North American (north of Mexico) ancestral area is inferred. Within Catharus, sedentary behaviour evolved after the first speciation event in the genus and is geographically and temporally correlated with Central American distributions and the final closure of the Central American Seaway. Migratory behaviour subsequently evolved twice in Catharus and is geographically and temporally correlated with a recolonization of North America in the late Pleistocene. By temporally linking speciation events with changes in migratory condition and events in Earth history, we are able to show support for several competing hypotheses relating to the geographic origin of migration.
Data from: Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous Ugandan cattle to East Coast fever
East Coast fever (ECF) is a fatal sickness affecting cattle populations of eastern, central, and southern Africa. The disease is transmitted by the tick Rhipicephalus appendiculatus, and caused by the protozoan Theileria parva parva, which invades host lymphocytes and promotes their clonal expansion. Importantly, indigenous cattle show tolerance to infection in ECF-endemically stable areas. Here, the putative genetic bases underlying ECF-tolerance were investigated using molecular data and epidemiological information from 823 indigenous cattle from Uganda. Vector distribution and host infection risk were estimated over the study area and subsequently tested as triggers of local adaptation by means of landscape genomics analysis. We identified 41 and seven candidate adaptive loci for tick resistance and infection tolerance, respectively. Among the genes associated with the candidate adaptive loci are PRKG1 and SLA2. PRKG1 was already described as associated with tick resistance in indigenous South African cattle, due to its role into inflammatory response. SLA2 is part of the regulatory pathways involved into lymphocytes' proliferation. Additionally, local ancestry analysis suggested the zebuine origin of the genomic region candidate for tick resistance.
Data from: Combining human acceptance and habitat suitability in a unified socio-ecological suitability model: a case study of the wolf in Switzerland
Habitat suitability models (HSMs) are commonly used in conservation practise to assess the potential of an area to be occupied and colonised. A major limitation of these models, however, is the omission of spatially explicit understanding of human acceptance towards the focal species. As wildlife is more and more subject to human-dominated landscapes, ignoring the sociological component will result in misrepresentation of the observed processes and inappropriate management. We distributed 10 000 questionnaires across Switzerland and identified key socio-demographical factors correlated with human acceptance of the wolf. We then created a spatially explicit acceptance model based on geo-referenced socio-demographical, social and geographical information. Finally, we combined our acceptance model with a HSM to obtain a unified socio-ecological suitability model, which included human and ecological components. We showed that the key factors associated with human acceptance were perception of how harmful the wolf is, interest in wolf-related issues, need for livestock protection, and fear of the wolf. Perceived harmfulness was in turn correlated with direct and indirect experience with the wolf, and level of education. Our acceptance map predicted decreasing acceptance with increasing altitude of residency and proximity to locations of confirmed wolf presence. This resulted in the overall opposition to the wolf for the Alpine region, albeit substantial regional differences. We found little spatial overlap (6% of Switzerland) between areas where the wolf was accepted and areas of suitable habitat. These areas of socio-ecological suitability were concentrated in the Jura Mountains and in the eastern and southern Alps, and were absent in the western and central Alps. Particularly in the Jura region, which is yet to be colonised, management of human acceptance will be a crucial conservation target. Synthesis and applications. We developed an integrative, socio-ecological approach that allowed us to accurately reproduce recent wolf recolonisation. We anticipate our framework to be a powerful tool to reliably evaluate overall suitable habitats and predict short to medium-term range expansion for species whose distribution is also dependent on human attitudes. Because our approach is sensitive to both the ecological and human component, it is ideally suited to identify key regions where proactive and targeted socio-ecological management plans are needed.
Crack nucleation using combined crystal plasticity modelling, HR-DIC and HR-EBSD in a superalloy containing non-metallic inclusions under fatigue
<p>The uploaded data are required to reproduce the experimental results in the paper. </p> <p>To replicate figure 4, both GID.mat and thermal_E11.mat should be loaded into matlab. </p> <p>To replicate figure 6, strain_11.mat should be uploaded. Then fDIC_GB.m should be executed. </p> <p>If the reader has further questions, please contact Tiantian Zhang at tiantian.zhang08@imperial.ac.uk or tzhang6@wpi.edu</p>
Dataset for the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"
<p>This dataset contains data, which were used in preparation of the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"</p>
FIGURE 3 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies
FIGURE 3. Potential distribution of Arthroleptis xenodactyloides under current climatic and two proposed Last Glacial Maximum palaeoclimatic scenarios (CCSM, MIROC) showing mean values obtained from 10 models computed with randomly selected 30 % of the 46 records (black dots) for model evaluation and the remaining 70 % for model training and corresponding standard deviations (SD).
FIGURE 2 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies
FIGURE 2. (A) Model performance and (B) presence/absence thresholds obtained from 10 models computed with randomly selected 30 % of the 46 records for model evaluation and the remaining 70 % for model training; as Maxent values the logistic model output is chosen.
FIGURE 1 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies
FIGURE 1. Elevation map of part of coastal eastern Africa showing records of Arthroleptis xenodactyloides (dots) processed in this study; those of Blackburn & Measey (2009) are indicated by name.
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Data for "Simulating and analysing seabird flyways: an approach combining least-cost path modelling and machine learning"
Open the record for dataset details and reuse information.
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.
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
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Chemical and Structural In-Situ Characterization of Model Electrocatalysts by Combined Infrared Spectroscopy and Surface X-Ray Diffraction
<p>Raw and treated data</p>
Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
ScienceDex guides
Understand access before you commit
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.