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385 results for “Environmental factors”
Figure 2 in Interactions between environmental factors and the mesozooplankton community from the Romanian Black Sea waters
Figure 2. Matrix of mesozooplankton abundance and biomass in 2013–2020, by seasons.
Figure 4 in Interactions between environmental factors and the mesozooplankton community from the Romanian Black Sea waters
Figure 4. Black Sea nutrients box plot by sector and season, 2013–2020.
Fig. 1 in Predicting the risk of Alaria alata infestation in wild boar on the basis of environmental factors
Fig. 1. The trend in prevalence of A. alata in provinces with WETLANDS.
Spatial and local environmental factors outperform geo-climatic gradients in structuring taxonomically- and traits-based β‐diversity of benthic algae
<p><b>Aim</b>: Understanding the variation in biodiversity and its underlying drivers and mechanisms is a core task in biogeography and ecology. In this study, we examined: i) the relative contributions of species replacement (i.e. turnover) and richness difference (i.e. nestedness) to taxonomically- and traits-based β-diversity of stream benthic algae; ii) whether these two facets of β-diversity are correlated with each other; and iii) the relative contributions of local environmental (e.g. water chemistry, flow velocity, habitat quality), geo-climatic (e.g. land use types, elevation, precipitation), and spatial factors (e.g. using principal coordinates of neighborhood matrices) to the two facets of β-diversity and their components (i.e. total β-diversity, turnover, and nestedness).</p> <p><b>Location</b>: Hun-Tai River Basin, northeastern China</p> <p><b>Taxon</b>: Stream benthic algae</p> <p><b>Methods</b>: A total of 157 sites were sampled. Mantel tests were used to examine the complementarities between the two facets of β-diversity and their components. Distance-based redundancy analysis and variation partitioning were utilized to investigate the relative contributions of local environmental, geo-climatic, and spatial factors to each facet of β-diversity and their components.</p> <p><b>Results</b>: Weak correlations between taxonomically- and traits-based β‐diversity and their components were detected, which indicated complementarity of ecological information. Taxonomically-based total β‐diversity was largely driven by turnover, whereas traits-based total β-diversity was more driven by nestedness. Variation partitioning results indicated that local environmental and spatial factors contributed more than geo-climatic variables to the total explained variation in taxonomically- and traits-based β‐diversity.</p> <p><b>Main conclusions</b>: Our findings highlighted the importance of the different facets of β‐diversity and their decomposition for understanding diversity patterns of benthic algae relative to abiotic factors. A high level of traits-based convergence among benthic algae communities, despite high taxonomic divergence, demonstrated turnover of species with similar biological traits across our study region. Our study provides a traits-based insight into stream benthic algae communities, which was less documented by previous freshwater studies that focused on regions undergoing recovery following human disturbances.</p>
Fig. 2 in Environmental and geographic factors driving dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) diversity in the dipterocarp forests of Peninsular Malaysia
Fig. 2. All dung beetle capture data for the eight sampling sites rarefied by trap.
Fig. 1 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 1. The Iguatemi River basin, showing the sampling sites in the Jogui and Iguatemi rivers.
Fig. 4 in The effect of various environmental factors on the distribution of terrestric slugs (Gastropoda: Pulmonata: Arionidae) - An exemplary study
Fig. 4: Distribution maps of four environmental factors recorded in the study area.
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
Data from: Global migration is driven by the complex interplay between environmental and social factors
<p><strong>The datasets were produced in the following article. When using the data, please use the following citation:</strong></p> <p>Niva V, Kallio M, Muttarak R, Taka M, Varis O, Kummu M. 2021. Global migration is driven by the complex interplay between environmental and social factors. Environmental Research Letters. <a href="https://doi.org/10.1088/1748-9326/ac2e86">https://doi.org/10.1088/1748-9326/ac2e86</a></p> <p>The data include the following files:</p> <p><strong>AC.tif </strong></p> <p> Composite index computed based on the four AC variables by taking a mean over the respective variables:</p> <p> <strong>economy.tif</strong></p> <p> Downscaled and min-max normalized income data.</p> <p> <strong>education.tif</strong></p> <p> Min-max normalized education data.</p> <p> <strong>governance.tif</strong></p> <p> Min-max normalized governance data.</p> <p> <strong>health.tif</strong></p> <p> Min-max normalized health data.</p> <p>For all of the above data, 0 and 1 represent the lowest and highest <strong>capacity</strong>, respectively.</p> <p><strong>ES.tif</strong></p> <p> Composite index computed based on the four ES variables by taking a mean over the respective variables:</p> <p> <strong>foodProdScarcityScaled.tif</strong></p> <p> Food production scarcity data based on food production data.</p> <p> <strong>droughtRiskScaled.tif</strong></p> <p> Computed and scaled drought risk based on SPEI index.</p> <p> <strong>waterRiskScaled.tif</strong></p> <p> Computed and scaled water risk data based on three water stress indices.</p> <p>For all of the above data 0 and 1 represent the lowest and highest <strong>stress</strong>, respectively. Kindly note that data for natural hazards is available at its source (please see the list below). </p> <p><strong>class_raster.tif</strong></p> <p> Spatial representation of the classification matrix.</p> <p><strong>cntryID.gpkg</strong></p> <p> Country polygons with country IDs.</p> <p><strong>cntry_raster_masked.tif</strong></p> <p> Country raster with country IDs.</p> <p><strong>countriesRegionsZones.csv</strong></p> <p> Country groups and countries.</p> <p>Dataset specifications:</p> <p>spatial extent: -180, 180, -90, 90</p> <p>spatial resolution: 5 arc-min (0.083333333 degrees)</p> <p>projection: long/lat WGS84</p> <p>no data value: NA</p> <p> </p> <p><strong>Original data to produce the above indicators and to replicate the full analysis is available at the following sources:</strong></p> <p>Net-migration data (30 arc-sec resolution): https://doi.org/10.7927/H4319SVC</p> <p>Natural hazards: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Governance effectiveness: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Human Development Indicators (income, education, health): <a href="https://doi.org/10.1038/sdata.2019.38">https://doi.org/10.1038/sdata.2019.38</a></p> <p>Water risk indicators: <a href="https://doi.org/10.46830/writn.18.00146">https://doi.org/10.46830/writn.18.00146</a></p> <p>Drought (SPEI index): <a href="https://doi.org/10.1175/2009JCLI2909.1">https://doi.org/10.1175/2009JCLI2909.1</a></p> <p>Food production: <a href="https://doi.org/10.1038/nature11420">https://doi.org/10.1038/nature11420</a></p> <p>Population data: <a href="https://doi.org/10.1177%2F0959683609356587">https://doi.org/10.1177/0959683609356587</a></p>
Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China
<p>Understanding the distribution of biodiversity and its determinants, particularly that of ecologically sensitive ones, has long been intriguing to the science community and will help formulate conservation strategies under future climate changes. To this end, we conducted extensive field surveys on the distribution of orchid flora in the Beipan River Basin in Guizhou Province, which is one of the biodiversity conservation priorities in China. The data we acquired, together with those published previously, were converted into orchid species richness for each of the 3km × 3km grid cells covering the study region. Redundancy analysis (RDA) and Geographically Weighted Regression (GWR) were then applied to determine which of the 30 environmental factors are potentially critical for the spatial distribution of orchid flora we have observed. Despite a moderate spatial extent, we found that the Beipan River Basin harbors about 249 native orchid species belonging to 74 genera, equivalent to 14.5% of orchid flora of China. Orchid species richness in this area follows a descending gradient from the southeast to the northwest, 70.41% of its variation among grid cells can be explained by environmental factors and spatial variables, and spatial variables accounted for 63.90% of the spatial variation of orchid distribution, indicating that spatial variables played a dominant role in the distribution of wild orchidaceae species richness. In addition, the main environmental driver is the mean temperature of the wettest quarter. Our study provides a good example for revealing the main drivers of orchid distribution characteristics, and has a certain reference value for the development of orchid conservation strategies.</p>
Effects of environmental factors on the ecology and survival of a widespread, endemic Cerrado frog
<p><span>Understanding the mechanisms that affect habitat use by vertebrates is critical for understanding how species are distributed across landscapes and how they cope with habitat change. The Brazilian Savanna (the Cerrado) has vegetation ranging from grassland to woodland savannas and harbors a rich and diverse amphibian fauna impacted by accelerated habitat loss. Here, we test the influence of vegetation type (from grassy scrubland to woodland) and distance from breeding sites (ephemeral water bodies) on body size, abundance, and survival of the frog <em>Physalaemus nattereri</em> in a natural metapopulation system of south-central Brazil. We also test whether body size is a significant predictor of population abundance. We found that the abundance of <em>P. nattereri</em> varies according to the mean snout-vent length of each metapopulation (sampling unit), as well as a higher estimated mortality rate in woodlands compared to typical Cerrado. Furthermore, we found no difference in estimated mortality among sampling units located far or close to ephemeral water bodies. Thus, our results highlight variable responses of <em>P. nattereri</em> metapopulations to environmental factors, despite the observed high heterogeneity among sampled habitats and the importance of ephemeral water bodies for reproduction. These findings highlight that land</span> <span>cover and availability of breeding sites might not always interact to explain population persistence of Cerrado frogs. </span></p>
The ginseng transcriptome, ginsenoside and environmental factors dataset
<p>Ginseng is a world-renowned and precious Chinese herbal medicine. Its practical components have apparent effects on alleviating sub-health and rehabilitation. In our study, we conducted WGCNA bioinformatics analysis and verified it using transcriptome expression, saponin phenotype, and environmental factors data. We found the basic rule of typical saponins accumulation mediated by transcriptome expression profiles through the effect of some typical environmental factors and built a prediction model that makes biological sense. Using relevant data, we can further analyze the relationship between the change in environmental factors and the accumulation of effective components of ginseng, which lays a foundation for the establishment of a digital ginseng model more in line with the growth and development characteristics of ginseng.</p> <p>This data set involves the transcriptome expression of 42 ginseng samples, the content of saponins, the expression of 11 key enzyme genes associated with saponin Rb1 and typical ecological factors at the same time.</p>
Dataset for manuscript "Host-related and environmental factors influence long-term ectoparasite infestation dynamics of mouse lemurs in northwestern Madagascar" to be published in the American Journal of Primatology
<p>This Excel-file contains three datasets, corresponding to the initial raw dataset resulting from all ectoparasite inspections (n = 2,241), the merged dataset used for host-related and temporal modeling (n = 1940), and the even more condensed dataset with one datapoint per individual used for climatic modeling (n = 583), respectively.</p>
Data for: The impact of wildlife and environmental factors on hantavirus infection in host and its translation into human risk
<p><span>Identifying factors that drive infection dynamics in reservoir host populations is essential in understanding human risk from wildlife-originated zoonoses. We studied zoonotic <em>Puumala</em> <em>orthohantavirus</em> (PUUV) in the host, the bank vole (<em>Myodes</em> <em>glareolus</em>), populations in relation to the host population, rodent and predator community and environment-related factors and whether these processes are translated into human infection incidence. We used 5-year rodent trapping and bank vole PUUV serology data collected from 30 sites located in 24 municipalities in Finland. We found that PUUV seroprevalence was negatively associated with the abundance of red foxes, but this process did not translate into human disease incidence, which showed no association with PUUV seroprevalence. The abundance of weasels, the proportion of juvenile bank voles in the host populations and rodent species diversity were negatively associated with the abundance index of PUUV-positive bank voles, which, in turn, showed a positive association with human disease incidence. Our results suggest certain predators, high proportion of young bank vole individuals and a diverse rodent community, may reduce PUUV risk for humans through their negative impacts on the abundance of infected bank voles.</span></p>
Heterogeneous microgeographic genetic structure of the common cockle (Cerastoderma edule) in the Northeast Atlantic Ocean: biogeographic barriers and environmental factors
<p>Knowledge of genetic structure at the finest level is essential for conservation of genetic resources. Despite no visible barriers limiting gene flow, significant genetic structure has been shown in marine species. The common cockle (<em>Cerastoderma</em> <em>edule</em>) is a bivalve of great commercial and ecological value inhabiting the Northeast Atlantic Ocean. Previous population genomics studies demonstrated significant structure both across the Northeast Atlantic, but also within small geographic areas, highlighting the need to investigate fine-scale structuring. Here, we analysed two geographic areas that could represent opposite models of structure for the species: 1) the SW British Isles region, highly fragmented due to biogeographic barriers, and 2) Galicia (NW Spain), a putative homogeneous region. 9,250 SNPs genotyped by 2b-RAD on 599 individuals from 22 natural beds were used for the analysis. The entire SNP dataset mostly confirmed previous observations related to genetic diversity and differentiation, however, neutral and divergent SNP outlier datasets enabled disentangling physical barriers from abiotic environmental factors structuring both regions. While Galicia showed a homogeneous structure, the SW British Isles region was split into four reliable genetic regions related to oceanographic features and abiotic factors, such as sea surface salinity and temperature. The information gathered supports specific management policies of cockle resources in SW British and Galician regions also considering their particular socio-economic characteristics; further, these new data will be added to those recently reported in the Northeast Atlantic to define sustainable management actions across the whole distribution range of the species.</p>
How environmental factors affect the abundance and distribution of two congeneric species of Amazonian frogs
<p>In this study, we test the hypothesis that, at a fine scale, environmental variables influence differently sister species that live in sympatry and are phylogenetically closely related. We sampled two Amazonian anuran species, <em>Phyzelaphryne</em> <em>miriamae</em> and <em>Phyzelaphryne</em> sp., in 11 permanent sampling modules distributed across ~600 km in the Purus-Madeira Interfluve between 2013 and 2014. Using mixed generalized linear models, we found that the species have distinct environmental associations, which may facilitate their coexistence in sympatry. <em>Phyzelaphryne</em> <em>miriamae</em> was more frequent in environments with low precipitation and low water tables, suggesting this species is better adapted to live in drier places. In contrast, <em>Phyzelaphryne</em> sp. appeared to be a generalist regarding to habitat and resource use. These patterns are in accordance with the hypothesis that environmental variables influence sister species differently on a fine scale. <em>Phyzelaphryne</em> <em>miriamae</em> is larger than <em>Phyzelaphryne</em> sp., which may make it more resistant to dehydration, allowing it to explore drier environments. In conclusion, our results are in concordance with the hypothesis that the evolution of characteristics resulting from selection may have reduced competition for resources between closely related species, thus facilitating coexistence in sympatry.</p>
Datasets for "The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades"
<p>The environmental changes can affect gross primary productivity (GPP) by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapor pressure deficit (VPD), and soil moisture) on global GPP from 1982 to 2020. The three datasets(RF_LAI, RF_GPP, and RF_GPPlai) are derived from LAI random forest model, GPP random forest model and hierarchical nested model respectively.</p>
Data from: Cyanobacterial colonization on epilithic mosses in degraded karst ecosystem: The role of moss traits and environmental factors
Open the record for dataset details and reuse information.
Spatial and local environmental factors outweigh geo-climatic gradients in structuring taxonomically and trait-based β-diversity of benthic algae
Open the record for dataset details and reuse information.
Species matrix of Indonesian litter and soil Collembola with environmental factors
Open the record for dataset details and reuse information.
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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.