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213 results for “multiple models”
Phylogeographic and demographic modelling analyses of the multiple origins of the rheophytic goldenrod Solidago yokusaiana
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Estimating wildlife populations and their dynamics using multiple data sources and a hierarchical integrated model: the case of California's black bears
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Benefits of modelling abundance for rare species conservation: a case study with multiple birds across one million hectares
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Multiple-model stock assessment frameworks for precautionary management and conservation on fishery-targeted coastal dolphin populations off Japan
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Evaluating multiple historical climate products in ecological models under current and projected temperatures
<p>Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on the roles of GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time has received relatively less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, U.S.A. We first compare fit and support of models employing raw observed or GHCP temperature values versus those with an elevation adjustment, finding (1) greater support for, and better fit using elevation-adjusted versus raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperature from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2 °C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~ 1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models—particularly in mid-elevation areas where the position of treeline may be changing—suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Data from: Spatial heterogeneity in soil nutrient supply modulates nutrient and biomass responses to multiple global change drivers in model grassland communities
Changes in the atmospheric concentration of carbon dioxide ([CO2]), nutrient availability and biotic diversity are three major drivers of the ongoing global change impacting terrestrial ecosystems worldwide. While it is well established that soil nutrient heterogeneity exerts a strong influence on the development of plant individuals and communities, it is virtually unknown how nutrient heterogeneity and global change drivers interact to affect plant performance and ecosystem functioning. We conducted a microcosm experiment to evaluate the effect of simultaneous changes in [CO2], nutrient heterogeneity (NH), nutrient availability (NA) and species evenness on the biomass and nutrient uptake patterns of assemblages formed by Lolium perenne, Plantago lanceolata and Holcus lanatus. When the nutrients were heterogeneously supplied,assemblages exhibited precise root foraging patterns, and had higher above- and belowground biomass (average increases of 32% and 29% for above- and belowground biomass, respectively). Nutrient heterogeneity also modulated the effects of NA on biomass production, complementarity in nitrogen uptake and below: aboveground ratio, as well as those of [CO2] on the nutrient use efficiency at the assemblage level. Our results show that nutrient heterogeneity has the potential to influence the response of plant assemblages to simultaneous changes in [CO2], nutrient availability and biotic diversity, and suggest that it is an important environmental factor to interpret and assess plant assemblage responses to global change.
Data from: Phylogeography and niche modelling of the relict plant Amborella trichopoda (Amborellaceae) reveal multiple Pleistocene refugia in New Caledonia
Amborella trichopoda Baill. (Amborellaceae, Amborellales), the sole living member of the sister group to all other extant Angiosperms, is endemic to New Caledonia. We addressed the intraspecific phylogeography of Amborella by investigating whether its present population genetic structure could be related to its current and past habitats. We found moderate range-wide genetic diversity based on nuclear microsatellite data, and detected four well-differentiated, geographically distinct genetic groups using Bayesian clustering analyses. We modeled the ecological niche of Amborella based on current climatic and environmental conditions. The predictive ability of the model was very good throughout the Central East mainland zone, but Amborella was predicted in the northern part of the island where this plant has not been reported. Furthermore, no significant barrier was detected based on habitat suitability that could explain the genetic differentiation across the area. Conversely, we found that the main genetic clusters could be related to the distribution of suitable habitat at the last glacial maximum (LGM, ca. 21 000 years BP), when Amborella experienced a dramatic 96.5% reduction in suitable area. At least two lineages survived in distinct putative refugia located in the Massif des Lèvres and in the vicinity of Mount Aoupinié. Our findings finally confirmed the importance of LGM rainforest refugia in shaping the current intra- and inter-specific diversity in New Caledonian plants, and revealed the possibility of an as-yet unreported refugium. The combination of niche modelling and population genetics thereby offered novel insight into the biogeographic history of an emblematic taxon.
Data from: Environmental filtering improves ecological niche models across multiple scales
1. A clear challenge for ecological niche modeling is determining how to best mitigate the effects of sampling bias from commonly collected biodiversity data. Recent approaches have focused on filtering occurrences in overrepresented regions based on geographic or environmental proximity. 2. We tested the efficacy of filtering in geographic and environmental space using occurrence data from four species. Our evaluation strategies examined 14 distance measures in geographic and environmental spaces and eight combinations of environmental variables and their ordinations. This resulted in 78 datasets for each species, which we evaluated using area under the curve (AUC), the difference between training and testing AUC, omission rate, the true skill statistic, and Schoener's D to examine the effects of different filtering schemes. 3. The degree of change produced by filtering on predicted suitability and evaluation statistics increased with increasing range size. Environmental filtering resulted in higher model fit at larger extents and retained more occurrences than geographic filtering. 4. Our results indicate that models should be evaluated using multiple evaluation statistics at multiple thresholds. The use of bin sizes when filtering in environmental space allows for simple comparison between species and filter types and makes for an easily reportable and repeatable distance metric. We specifically recommend that ecological niche models using natural history collection data filter in environmental space with variables derived from permutation importance or the first few axes of a principal components ordination.
Data from: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models
Genomic selection have been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies of how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee — production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
SITE OCCUPANCY AND ABUNDANCE MODELS FOR ANALYZING MULTIPLE-VISIT DETECTION/NONDETECTION DATA
<p>Data and R codes for the paper: Site occupancy and abundance models for analyzing multiple-visit detection/nondetection data.</p>
Modeling Data from "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. Insights from Radiative Transfer Modeling"
<p>This dataset includes the results from the radiative transfer modeling done in the paper "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars: Insights from Radiative Transfer Modeling" by Sheehan et al. Included are the full posteriors from the model fitting for each source as a Python pickle file that contains a dictionary with keys given by the source names, e.g. "HOPS-2", that point to numpy arrays containing the posterior distributions. The information can be loaded like so:</p> <pre><code class="language-python">import pickle data, keys = pickle.load(open("posteriors.p","rb"))</code></pre> <p>Here "keys" is a list containing the names of the parameters from the model fit that are a part of the posterior distribution for each source.</p> <p>Also included are the configuration files and datasets used in the modeling for each source, as well as the results from the fit so that anyone can work with these models for their own purposes. An example script that shows how to use these files is included, and further information can be found at <a href="http://pdspy.readthedocs.io">http://pdspy.readthedocs.io</a>.</p>
Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"
<p><strong>Abstract</strong></p> <p><strong>Aim:</strong> In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends.<br> <br> <strong>Location:</strong> Switzerland (Europe)<br> <br> <strong>Taxon:</strong> Birds</p> <p><strong>Methods: </strong>We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time.</p> <p><strong>Results: </strong>Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation.<br> Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.</p> <p> </p> <p> </p>
Raw data to "Macrophage Plasticity and Polarization Are Altered in the Experimental Model of Multiple Sclerosis"
<p>Background: Macrophages have been identified as one of the major effectors of inflammation and demyelination in both MS and its animal model, experimental autoimmune encephalomyelitis (EAE). However, the activation and heterogeneity of macrophages in MS is not fully understood.</p> <p>Results: We performed a complete immunophenotyping of M1 and M2 macrophages from EAE and control mice through polychromatic flow cytometry. We found that M1 macrophages possessed a higher proinflammatory profile in EAE compared to control mice, since they expressed higher levels of activation/co-stimulatory markers (iNOS, CD40 and CD80) (Table 1), cytokines/chemokines (IL-6, IL-12, CCL2 and CXCL10) (Table 2) and a higher expression of Toll-like receptors (TLR2) 2 and 5 (Table 3). On the contrary, M2 of EAE mice lost their M2-like phenotype by showing a decreased expression of their signature markers CD206, CD11c, CD44 and CCL22, and a concomitant upregulation of several M1 makers such as iNOS, CD40, CD80/CD86 (Table 1) and proinflammatory CCL22 (Table 2) and TLR4 and 8.</p> <p>Conclusions: Our data account for a phenotypic alteration of M1/M2 balance during MS and this can be of crucial importance not only for a better understanding of the immunopathology of this neurodegenerative disease but also to potentially develop new macrophage-centered therapeutic strategies</p>
Ensembled projection outputs of Lake Sunapee using multiple General Circulation models and Lake models
<p>This dataset contains the ensemble projection output of Lake Sunapee, NH. The conception and methods of the dataset is explained in the manuscript "Uncertainty in projections of future lake thermal dynamics is differentially driven by global climate models and lake models."</p>
Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries
<p>This is the data repository for the paper "Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries"</p>
A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana
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Data from: Modelling habitat distributions for multiple species using phylogenetics
In this paper, we describe an empirical approach to model community structure using phylogenetic signals. That approach combines information about the species (i.e. traits and phylogeny) with information about the habitat (i.e. environmental conditions and spatial distribution of sampling sites) and their interactions to predict the species responses (e.g. the local densities). As an application, we use the approach to model fish densities in rivers. In the model, the different species and size classes were described using a functional trait, body length, and phylogenetic eigenvectors maps whereas the sites were described using water velocity, depth, substrate composition, macrophyte cover, degree-days, total phosphorus, and spatial eigenvector maps. The model (estimated using a regularised Poisson-family Generalised Linear Modelling approach) fitted the data well (likelihood-based R2adj=0.512) and showed fair predictive power (likelihood-based cross-validation R2=0.283) to predict the density of fish pertaining to 48 species totalling 143 combinations of species and size classes in 15 unregulated Canadian rivers. Using the model as a baseline to estimate the effect of flow regulation on community composition, we found that, with few exceptions, the densities of most fish species were lower in regulated than in unregulated rivers. Phylogenetics have been proposed to study community structure, but this is, to our knowledge, the first time phylogenetic information is used explicitly for numerical habitat modelling. We expect that models of that type will be in increasing demand now that development projects are routinely assessed through impact studies.
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6 in Phylogenetic analysis of Micrathena and Chaetacis spiders (Araneae: Araneidae) reveals multiple origins of extreme sexual size dimorphism and long abdominal spines
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6. MkvG model. Posterior probabilities values are indicated below branches.
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
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.