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1,042 results for “model species”
Fig. 9 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products
Fig. 9. Response of Gl. domesticus to aridityIndexThornthwaite.
Fig. 2 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 2. Potential distribution of the East European vole (Microtus levis). Captions as in fig.1.
Fig. 2 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 2. Temporal distribution of numbers of ignitions in ATO zone in 2010–2014.
Data for: Size spectrum model reveals importance of considering species interactions in a freshwater fisheries management context
<p>Inland fisheries have significant cultural and economic value around the globe, providing dietary protein, income, and recreation. Consequently, methods for monitoring and managing these important fisheries are continually being refined. In marine systems, multi-species size spectrum models have been increasingly used to explore management scenarios of important fish stocks within an ecosystem-based fisheries management framework; however, these models have not been applied in freshwater systems. In this study, we developed a multi-species size spectrum model for the fish community of Lake Nipissing, a large, productive lake in Ontario, Canada. To the best of our knowledge, this is the first fully calibrated multi-species size spectrum model for an inland fishery. Using this model, we explored the impacts of different management scenarios on fish community dynamics while taking species interactions into account. Specifically, we examined how changes in fishing mortality affect: (1) species biomass; (2) community size structure; and (3) stock recovery times. We found that community dynamics following changes in fishing mortality were driven by complex interactions among species, including competition and predation. The greatest changes in biomass and community size structure were observed following changes in fishing mortality to top predators, with community size structure most strongly influenced by changes in mortality to the largest species in the community. Counter to predictions based on generation time, the smallest species in our model exhibited the longest time to recovery due to strong competition and predation. Our results demonstrate the importance of taking an ecosystem-based approach and considering species interactions in the management of inland fisheries and highlight the potential of size spectrum model use in freshwater systems.</p>
Publication release: How well do species distribution models predict occurrences in exotic ranges?
<div class="record-description"> <p>Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examined the spatial transferability of SDMs for 647 non-indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. We performed a large-scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability.</p> <p>Here, we provide the code and data for publication in Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How well do species distribution models predict occurrences in exotic ranges?". Provided are the files and scripts necessary to fit and validate the SDMs using distirbutional data from their native and exotic ranges, respectively, formulated as generalized additive models (GAMs) or MaxEnt models. Additionally, provided is a script to validate the SDMs on their native fitting range using 10-fold cross-validation, and to fit the transferability model, as a linear mixed model (LMM), with a provided cleaned data.frame. The dataset provided includes a full species list with GBIF occurrence records, target-group background (TGB) records to use with model fitting and validation, as well as environmental data associated with the sightings.</p> </div>
Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
<p>1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.</p> <p>2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.</p> <p>3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km<sup>2</sup> sampling sites across a <em>P. montanus</em> hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.</p> <p>4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.</p>
Soil chemical variables improve models of understory plant species distributions
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"><strong>Aim</strong></div> <div class="column">To determine the importance of soil variables relative to more commonly used topo-climatic or remotely sensed variables in species distribution models (SDMs) for understory plants.</div> <div class="column"> </div> <div class="column"><strong>Location</strong></div> <div class="column">White Mountain National Forest, New Hampshire, U.S.A.</div> <div class="column"> </div> <div class="column"><strong>Methods</strong></div> <div class="column">We fit models for presence of 41 forest understory plant species across 158 plots using soil, topographic, and spectral predictors to determine the relative contribution of different predictor types. We determined (a) if the potential importance of soil variables is greater than generally described in SDM literature, (b) which predictors are most important, and (c) if a standard subset of predictors can be used to effectively model all species.</div> <div class="column"> </div> <div class="column"><strong>Results</strong></div> <div class="column">Models containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index, elevation, and B horizon exchangeable calcium (B Ca). No standard subset effectively modeled all species.</div> <div class="column"> </div> <div class="column"><strong>Main conclusions</strong></div> <div class="column"> Our results and those of other SDMs that include in-situ soil geochemical data suggest that soil variables are increasingly important with more detailed descriptions of soils. Soil fertility data, such as B C:N and B Ca, are particularly important in acidic, forest soils where pH is a poor indicator of fertility. Commonly used topo-climatic variables provide meaningful predictions but are limited by their use of indirect predictor variables, inhibiting transferability and interpretability. The poor performance of models created using standard subsets of variables highlights the uniqueness of each species' niche and the need to combine flexible model building techniques with a variety of predictor variables.</div> </div> </div> </div>
Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data
<p>As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should strive to incorporate multi-species perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships. We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe. Our findings revealed substantial variation in the factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale. Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores. Anthropogenic pressures also had an important influence on habitat use, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas. Policy implications: This study highlights the importance of adopting a multi-scale, multi-species approach for critical decision-making processes that depend on understanding wildlife distributions and habitat associations, such as protected area, corridor, and buffer zone prioritisation. We use our findings to identify changing rainfall patterns and increasing livestock numbers as two emerging trends that may impact wildlife distributions, both within sub-Saharan Africa and on a global scale.</p>
Quantifying research interests in 7,521 mammalian species with h-index: a case study (model output)
<p>Model results for Quantifying research interests in 7,521 mammalian species with h-index: a case study</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Demographic modelling helps tracking the rapid and recent divergence of a conifer species pair from central Mexico
<p>Secondary contact of recently diverged species may have several outcomes, ranging from rampant hybridization to reinforced reproductive isolation. In plants, selfing tolerance and disjunct reproductive phenology may lead to reproductive isolation at contact zones. However, they can evolve under both allopatric or parapatric frameworks and originate from adaptive and/or neutral forces. Inferring the historical demography of diverging taxa is thus a crucial step to identify those factors that may lead to putative reproductive isolation. We explored various competing hypotheses to account for the rapid divergence of a fir species complex (<em>Abies flinckii - A. religiosa</em>) distributed in 'sky-islands' across central Mexico (i.e., along the Trans-Mexican Volcanic Belt; TMVB). Despite co-occurring in two independent sympatric regions (west and center), these taxa rarely interbreed because of disjunct reproductive phenologies. We genotyped 1,147 SNPs, generated by GBS, across 23 populations, and compared multiple demographic scenarios based on the geological history of the TMVB. The best-fitting model revealed one of the most rapid and complete speciation cases for a conifer species-pair, dating back to ~1.2 Ma. Coupled with the lack of support for stepwise colonization, our coalescent inferences point to an early cessation of interspecific gene flow under parapatric speciation; ancestral gene flow during divergence was asymmetrical (mostly from western firs into A. religiosa) and exclusive to the most ancient (i.e., central) contact zone. Factors promoting rapid reproductive isolation should be explored in other slowly-evolving species complexes as they may account for the large tropical and subtropical diversity.</p>
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
<p>The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.</p> <p>Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude derived from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three common modelling techniques (MaxEnt, BRT and GLM) and four discrimination metrics to evaluate model performance (Sørensen index, overprediction and underprediction rate, AUC and TSS).</p> <p>We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.</p> <p>We recommend fitting models with the finest possible analysis grain (i.e., depending on data availablity) even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrence data, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences.</p>
Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling
<p>The advent of citizen-science databases in conjunction with museum specimen locality information has exponentially increased the power and accuracy of ecological niche modeling (ENM). Increased occurrence data has provided colossal potential to understand the distributions of lesser known or endangered species, including arthropods. Although niche modeling of termites has been conducted in the context of invasive and pest species, few studies have been performed to understand the distribution of basal termite genera. Using specimen records from the American Museum of Natural History (AMNH) as well as locality databases, we generated ecological niche models for 12 basal termite species belonging to six genera and three families. We extracted environmental data from the Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and generated models using MaxEnt. We chose Optimal models based on partial Receiving Operating characteristic (pROC) and omission rate criterion and determined variable importance using permutation analysis. We also calculated response curves to understand changes in suitability with changes in environmental variables. Optimal models for our 12 termite species ranged in complexity, but no discernible pattern was noted among genera, families, or geographic range. Permutation analysis revealed that habitat suitability is affected predominantly by seasonal or monthly temperature and precipitation variation. Our findings not only highlight the efficacy of largely citizen-science and museum-based datasets, but our models provide a baseline for predictions of future abundance of lesser-known arthropod species in the face of habitat destruction and climate change.</p>
Data: Detecting preservation and reintroduction sites for endangered plant species using a two-step modelling and field approach
<p><span>To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two-step approach to identify appropriate (re)introduction sites. The first step involves modelling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analysing stationary ecological parameters, such as soil conditions, and relating them to population-level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (<em>Cypripedium calceolus</em> L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid-to-sunny light conditions and specific soil physico-chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide-scale bioclimatic modelling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions.</span></p>
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
<p>Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.</p>
Modeling the demography of species providing extended parental care: A capture-recapture approach with a case study on Polar Bears (Ursus maritimus)
<p><span><span><span><span><span><span><span><span><span><span><span>1. In species providing extended parental care, one or both parents care for altricial young over a period including more than one breeding season. We expect large parental investment and long-term dependency within family units to cause high variability in life trajectories among individuals with complex consequences at the population level. So far, models for estimating demographic parameters in free-ranging animal populations mostly ignore extended parental care, thereby limiting our understanding of its consequences on parents and offspring life histories.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. We designed a capture-recapture multi-event model for studying the demography of species providing extended parental care. It handles statistical multiple-year dependency among individual demographic parameters grouped within family units, variable litter size, and uncertainty on the timing at offspring independence. It allows for the evaluation of trade-offs among demographic parameters, the influence of past reproductive history on the caring parent's survival status, breeding probability and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data, and illustrate its use with a long-term dataset collected on the Svalbard polar bears (<i>Ursus maritimus</i>).</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability and litter size probability. Results showed that the outcome of the previous reproduction influenced breeding probability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Overall, our results show the importance of accounting for i) the multiple-year statistical dependency within family units, ii) uncertainty on the timing at offspring independence, and iii) past reproductive history of the caring parent. If ignored, estimates obtained for breeding probability, litter size, and survival can be biased. This is of interest in terms of conservation because species providing extended parental care are often long-living mammals vulnerable or threatened with extinction.</span></span></span></span></span></span></span></span></span></span></span></p>
Supporting data for article comparison and Uncertainty Analysis of Species Distribution Models
<p>Downloaded from Web of Science for the supporting data of article comparison and Uncertainty Analysis of Species Distribution Models.</p>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Dataset: Habitat suitability models to make conservation decisions based on areas of high species richness and endemism
<p>This repository contains the files associated with the following article:</p> <p>Hernández-Quiroz NS, EI Badano, F Barragán-Torres, J Flores & C Pinedo-Álvarez. Habitat suitability models to make conservation decisions based on areas of high species richness and endemism. Biodiversity and Conservation, 27, pp. 3185-3200. <a href="https://doi.org/10.1007/s10531-018-1596-9">https://doi.org/10.1007/s10531-018-1596-9</a></p> <p>The Microsoft Excel file (SM 01-Oak occurrences.xlsx) contains the occurrence points used to calibrate the habitat suitability model of each oak species (59 species in total). This file indicates the name of the species (column A), latitude and longitude of each occurrence point (columns B and C; in geographic coordinates) and the full set of bioclimatic variables (columns D-V) and topographic variables (columns W-Z) associated to each point. These later data are provided as they were gathered from the bioclimatic layers of WorldClim and the topographic layers of the Mexican National Institute of Statistics and Geography. The repository also contains interactive maps indicating the predicted and observed distributions of the 59 Mexican oak species (SM 02-Estimated oak distribution ranges.kmz), and the probability-based and occurrence-based map of oak richness and endemic species (SM 03-Oak richness maps.kmz). These geographic projections are provided in KMZ format to make them easy to visualize in Google Earth (freely available at www.google.com/earth). Details about these KMZ files can be consulted by accessing the file properties after opening them in Google Earth.</p>
Comprehensive comparison of two global multi-species MHD models of Mars
<p>Understanding the interaction between Mars and the solar wind is crucial for comprehending the atmospheric evolution and climate change on Mars. To gain a comprehensive understanding of the Martian plasma environment, global numerical simulations are essential in addition to spacecraft observations. However, there are still discrepancies among different simulation models. This study investigates how these discrepancies stem from the considered physical processes and numerical implementations. We compare two global multispecies MHD models: the "Sun model" based on the BATS-R-US code and the "Sakata model" based on a newly developed multifluid model MAESTRO. By employing the same typical upstream conditions and the same neutral atmosphere for current Mars, along with similar numerical implementations such as inner boundary conditions, we obtain simulation results that exhibit unprecedented agreement between the two models. The dayside results are nearly identical, especially along the subsolar line, indicating the reliability of MHD models to predict dayside interaction under given upstream conditions and ionosphere assumptions. The escape rates of planetary ions are also in good agreement. However, discrepancies remain in the terminator and nightside regions. Detailed numerical implementations, including inner boundary conditions, magnetic field divergence control methods, and radial resolutions, are shown to influence certain aspects of the results greatly, such as magnetotail configuration and ion diffusion.</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.