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645 results for “spatial distribution”
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Spatial and temporal distribution and abundance of moths in the Andrews Experimental Forest, 1994 to 2008
The distribution and abundance of macromoth species is strongly influenced by geographical (region-neighboring plots) scale, elevation, aspect, plant community, management regime, and time of year. Noctural macromoths have been observed at a total of 263 sample sites throughout the Andrews Forest watershed since 1994. Only a limited subset of these sites is sampled each year. From 2004 to 2008, 20 sites were sampled consistently using a hierarchical sampling design stratified by elevation and vegetation type. Moths are sampled using blacklight traps deployed for one night every two weeks at each site from April through October. A total of 503 species have been observed, and approximately 300 species may be observed in any given year. The watershed can be divided into 13 distinct zones. The northwest ridge above the Andrews headquarters has the highest number of species (n = 321) and the lowest number of species occurred at upper Lookout Creek (n = 239). Each of 13 zones is missing ca. 200 of the 500 resident species, suggesting that heterogeneity in the landscape is important. A breakdown of the species into functional groups based on larval feeding habits: conifers, hardwood, herb, mix, unknown shows that 43% of Andrews species rely on a hardwoods and 63% rely on hardwoods and herbaceous angiosperms. Conifer-feeders only represent 8% of moth species. However, moths associated with conifer hosts are the most abundant; for instance, in the zone representing the midlevel of Carpenter Mountain 67% of moth individuals are conifer feeders, but only 14% of the species feed on conifers. In contrast, within the zone represented by the Headquarters site, only 32% of the individual moths feed on conifers whereas 56% feed on hardwoods. Moth biogeographic zones correspond to elevation zones and to potential vegetation.
Spatial and temporal distribution and abundance of butterflies in the Andrews Experimental Forest, 1994-1996
This database contains information on species abundance according to date and location within the H.J. Andrews Experimental Forest Lookout Creek watershed. The database provides the information needed to assess patterns in the abundance of butterflies across time and space. The distribution and abundance of butterfly species on the Andrews Forest is strongly influenced by geographical scale, elevation, aspect, plant community, management regime, and time of year. Patterns of distribution and abundance are based on an historical total of 80 species, of which 73 are resident species and about 55 of which may be observed in any given year. Butterflies were surveyed at two- week intervals from late April through early October over a three-year period (1994-6). Approximately one-third of the watershed was covered during each visit, thus each area was sampled at about 6 week intervals within each sample season.
Spatially Distributed Lake Mendota EXO Multi-Parameter Sonde Measurements 2019-2022
This data was collected over 34 sampling trips during four summers (May-October), 2019-2022. 35 grid boxes were generated over Lake Mendota. Before each sampling effort, sample point locations were randomized within each grid box. Surface measurements were taken with an EXO multi-parameter sonde at a subset of the 35 grid boxes throughout Lake Mendota during each sampling trip. Measurements include temperature, conductivity, chlorophyll, phycocyanin, turbidity, dissolved organic material, ODO, pH, and pressure.
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
Data on spatial distribution of tracers for optical sensing of stream surface flow
<p>Here, we present the numerical and field data used in the manuscript entitled <em>Spatial distribution of tracers for optical sensing of stream surface flow</em>. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.</p>
Phylogenetic and Spatial Distribution of Evolutionary Isolation and Threat in Turtles and Crocodilians (Non-Avian Archosauromorphs)
The origin of turtles and crocodiles and their easily recognized body forms dates to the Triassic. Despite their long-term success, extant species diversity is low, and endangerment is extremely high compared to other terrestrial vertebrate groups, with ~ 65% of ~25 crocodilian and ~360 turtle species now threatened by exploitation and habitat loss. Here, we combine available molecular and morphological evidence with machine learning algorithms to present a phylogenetically-informed, comprehensive assessment of diversification, threat status, and evolutionary distinctiveness of all extant species. In contrast to other terrestrial vertebrates and their own diversity in the fossil record, extant turtles and crocodilians have not experienced any mass extinctions or shifts in diversification rate, or any significant jumps in rates of body-size evolution over time. We predict threat for 114 as-yet unassessed or data-deficient species and identify a concentration of threatened crocodile and turtle species in South and Southeast Asia, western Africa, and the eastern Amazon. We find that unlike other terrestrial vertebrate groups, extinction risk increases with evolutionary distinctiveness: a disproportionate amount of phylogenetic diversity is concentrated in evolutionarily isolated, at-risk taxa, particularly those with small geographic ranges. Our findings highlight the important role of geographic determinants of extinction risk, particularly those resulting from anthropogenic habitat-disturbance, which affect species across body sizes and ecologies.
Data from: Complementary strengths of spatially-explicit and multi-species distribution models
<p><span><span><span><span><span><span><span><span><span><span><span> Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (<i>Tsuga canadensis</i>L.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span> Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.</span></span></span></span></span></span></span></span></span></span></p>
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
FIG. 7 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 7. — Dendrogram of floristic similarity of the bryophyte flora of mangroves on the Northern and Southeastern coast of Brazil.
FIG. 5. — A in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 5. — A, mean richness; B, density of bryophytes in the sampled mangroves per light tolerance guilds; C, interaction plot between sampled zones and light tolerance guilds on mean richness of bryophytes; D, interaction plot between sampled zones and light tolerance guilds on mean density of bryophytes.
FIG. 4 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 4. — Violin plot with included boxplot: A, species richness; B, species density. Alpha-diversity indices: C, Shannon Index (H'); D, Pielou's Evenness (J').
FIG. 3 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 3. — Accumulation curves based on the abundance of individuals in the fringe and inland zones of the mangroves of Salvaterra, Pará, Brazil: A, species richness (q = 0); B, Shannon diversity (q = 1). The fringe zone is shown in red color and the inland zone in blue color. Continuous line represents interpolation and dotted line represents extrapolation.
FIG. 2 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 2. — Mangroves on the east coast of the municipality of Salvaterra, Marajó Island, Pará: A, B, mangrove in inland zone; C, D, fringe zone.
FIG. 1 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 1. — Location map of collection points in Marajó Island, Pará, Brazilian Amazon:A, localization of Marajó Island in Pará, Brazil, South America (red rectangle); B, localization of Salvaterra in Marajó Island; C, localization of sampling points on the east coast of the Salvaterra, with 1 km between the fringe zone and the inland zone in each area (map prepared by P.W.P. Gomes).
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a precision level of 10 and 25%.
Figure 2 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 2. Sample sizes required to achieve a given precision level of 10 and 25% at different mean densities of T. urticae per leaf.
Figure 1 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 1. Relationship between variance and mean density (all stages combined per leaf) of T. urticae samples collected from bean fields near Varamin vicinity, Tehran province, Iran. The red lines are the best-fitting lines of Taylor's power law.
FIG. 5. Correlation between the spatial distribution index and population density. A in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 5. Correlation between the spatial distribution index and population density. A. For Brephulopsis cylindrica and Xeropicta derbentina at 160 plots for three years. B. For Harmozica ravergiensis in nine sites×20 plots for two years. РИС. 5. Корреляция меЖду индексом пространственного распределения и плотностью популяции. А. Для Brephulopsis cylindrica и Xeropicta derbentina на 160 плоЩадках За три года. В. Для Harmozica ravergiensis на девяти участках по 20 плоЩадок За два года.
ScienceDex guides
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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.