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180 results for “Environmental Drivers”
Data from: Drivers of coastal benthic communities in a complex environmental setting
<p>Analyzing the environmental factors affecting benthic communities in coastal areas is crucial for uncovering key factors that require conservation action. Here, we collected benthic and environmental (physical-chemical-historical and land-based) data for 433 transects in Taiwan. Using a k-means approach, five communities dominated by crustose coralline algae, turfs, stony corals, digitate, or bushy octocorals were first delineated. Conditional random forest models then identified physical, chemical, and land-based factors (e.g., light intensity, nitrite, and population density) relevant to community delineation and occurrence. Historical factors, including typhoons and temperature anomalies, had only little effect. The prevalent turf community correlated positively with chemical and land-based drivers, which suggests that anthropogenic impacts are causing a benthic homogenization. This mechanism may mask the effects of climate disturbances and regional differentiation of benthic assemblages. Consequently, management of nutrient enrichment and terrestrial runoff is urgently needed to improve community resilience in Taiwan amidst increasing challenges of climate change.</p>
Fig. 5 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 5: Spatial and temporal distribution of Gobius niger in the Marchica Lagoon.
Pre-analysis and figure data for Lomax et al. (2024), Untangling the environmental drivers of gross primary productivity in African rangelands
<p>Data required to reproduce main analyses and main text figures for the following publication:</p> <p>Lomax, G. A., Powell, T. W. R., Lenton, T. M., Economou, T., and Cunliffe, A. M. (in press), Untangling the environmental drivers of gross primary productivity in African rangelands.</p> <p> </p> <p>File details:</p> <ol> <li>df_annual.csv - the full 19-year dataset of model variables for reproducing the main analysis (for Figures 2-3).</li> <li>df_multi_annual.csv - a smaller dataset of multi-annual mean and variability variables for reproducing the analysis behind Figure 4.</li> <li>Fig1_data.tif - raster dataset containing the four variables shown in Figure 1.</li> <li>Fig2_data.csv - model results for the main analysis underlying Figure 2.</li> <li>Fig3_data.csv - model results for the binned analysis underlying Figure 3.</li> <li>Fig4_data.csv - model results for the multi-annual analysis underlying Figure 4.</li> </ol>
Medicinal Plant Utilisation and Environmental and Management Drivers Influencing Forest Medicinal Plants in the Czech Republic
<p>This data on medicinal plant utilization and the influence of environmental drivers on medicinal plant availability is part of a broader survey on forest ecosystem services and health conducted in the Czech Republic under the project "Excellent research as a support for the adaptation of forestry and timber industry to global change and the 4th industrial revolution"<em> (EVA 4.0) (</em>CZ.02.1.01/0.0/0.0/16_019/0000803).</p>
Related data to article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest"
<p>Data related to the article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest", currently (2022-12-05) under review for publication in JGR:Biogeosciences.</p>
Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration
<p># GPS locations of Alpine ibex</p> <p>This dataset contains migratory tracks of Alpine ibex identified using the application Migration Mapper (https://migrationinitiative.org/content/migration-mapper) and used in the work <strong>Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (<em>Capra ibex</em>) as tools for conserving migration</strong></p> <p># Dataset structure</p> <p>Each row of the dataset represents a GPS location with its coordinates contained in the x (longitude) and y(latitude) columns. Coordinates are given in wgs84 (epsg 4326).<br> The column t1_ informs on the date and time the location was recorded.<br> The id and pop columns provide information about the identity of the animal and the population to which it belongs.</p> <p> </p>
Distribution and environmental drivers of fungal denitrifiers in global soils
<p>Meta- and source data as well as newick phylogeny associated to the article 'Distribution and Environmental Drivers of Fungal Denitrifiers in Global Soils '.</p>
Data from: Hidden demographic impacts of fishing and environmental drivers of fecundity in a sea turtle population
<p><span>Fisheries bycatch is a critical threat to sea turtle populations worldwide, particularly because turtles are vulnerable to multiple gear types. The Canary Current is an intensely fished region, yet there has been no demographic assessment integrating bycatch and population management information of the globally significant Cabo Verde loggerhead turtle (<em>Caretta</em> <em>caretta</em>) population. Using Boa Vista island (Eastern Cabo Verde) subpopulation data from capture-recapture and nest monitoring (2013–2019), we evaluated population viability and estimated regional bycatch rates (2016–2020) in longline, trawl, purse-seine, and artisanal fisheries. We further evaluated current nesting trends in the context of bycatch estimates, existing hatchery conservation measures, and environmental (net primary productivity) variability in turtle foraging grounds. We projected that current bycatch mortality rates would lead to the near extinction of the Boa Vista subpopulation. Bycatch reduction in longline fisheries and all fisheries combined would increase finite population growth rate by 1.76% and 1.95%, respectively. Hatchery conservation increased hatchling production and reduced extinction risk, but alone it could not achieve population growth. Short-term increases in nest counts (2013–2021), putatively driven by temporary increases in net primary productivity, may be masking ongoing long-term population declines. When fecundity was linked to net primary productivity, our hindcast models simultaneously predicted these opposing long-term and short-term trends. Consequently, our results showed conservation management must diversify from land-based management. The masking effect we found has broad-reaching implications for monitoring sea turtle populations worldwide, demonstrating the importance of directly estimating adult survival and that nest counts might inadequately reflect underlying population trends.</span></p>
Data for effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems
<p><strong><em>Okuhata, B.K., Delevaux, J.M.S., Richards Donà, A., Smith, C.M., Gibson, V.L., Dulai, H., El-Kadi, A.I., Stamoulis, K., Burnett, K.M., Wada, C.A., Bremer, L.L., Effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems</em></strong></p> <p>Environmental change scenarios, with a spatial extent of the Keauhou basal aquifer (Hawai‘i), were produced using a recharge coverage from Engott (2011), land use coverages from the State of Hawai‘i (2022) and National Oceanic and Atmospheric Administration (2006); climate change calculations based on Elison Timm et al. (2015), and native forest conversion calculations from Bremer et al. (2021). Scenarios were developed based on the following assumptions:</p> <p>Scenario 0 (Baseline) assumes current land use, groundwater recharge, and groundwater withdrawal rates (National Oceanic and Atmospheric Administration, 2006; State of Hawai‘i, 2022; Engott, 2011; Commission on Water Resource Management, unpublished data, 2018). Please see Okuhata et al. (2021) for more details regarding the scenario assumptions for the baseline groundwater model.</p> <p>Scenario 1 (Climate Change) assumes current land use, but with Representative Concentration Pathway (RCP) 8.5 mid-century rainfall projections (Elison Timm et al., 2015), where rainfall and recharge calculations were based on estimates from Giambelluca et al. (2013) and Engott (2011). </p> <p>Scenario 2 (Urban Development) assumes RCP 8.5 mid-century rainfall conditions along with future permitted development, which includes an increase in water demand (Fukunaga & Associates, Inc., 2017). </p> <p>Scenario 3 (Native Forest Conversion + Urban Development) assumes RCP 8.5 mid-century rainfall conditions and future permitted development, along with the assumption that native forests are not protected and converted to non-native forests (Bremer et al., 2021), therefore altering recharge estimates (Wada et al., 2017; Engott, 2011).</p> <p>Please note that scenario numbers listed in the groundwater model and marine water quality model shapefiles may differ from the manuscript scenario numbers. The following table assigns the scenario numbers to their respective scenarios in the manuscript, groundwater model, and marine water quality model.</p> <table> <tbody> <tr> <td> <p><strong>Scenario Name</strong></p> </td> <td> <p><strong>Manuscript #</strong></p> </td> <td> <p><strong>Groundwater Model #</strong></p> </td> <td> <p><strong>Marine Water Quality Model #</strong></p> </td> </tr> <tr> <td> <p>Baseline</p> </td> <td> <p>Scenario 0</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 0</p> </td> </tr> <tr> <td> <p>Climate Change</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 1</p> </td> </tr> <tr> <td> <p>Urban Development</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 7</p> </td> <td> <p>Scenario 6</p> </td> </tr> <tr> <td> <p>Native Forest Conversion + Urban Development</p> </td> <td> <p>Scenario 3</p> </td> <td> <p>Scenario 5</p> </td> <td> <p>Scenario 4</p> </td> </tr> </tbody> </table> <p>The groundwater model results are in shapefile format and were produced using the program SEAWAT (Langevin et al., 2008). The spatial extent is the Keauhou basal aquifer, and the projection is NAD 1983 UTM Zone 4N. The two polygon shapefiles represent the first and second layers of the groundwater model, and include groundwater level (meters relative to mean sea level), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (milligrams per liter), and phosphorus (milligrams per liter) results under the assumptions of each scenario. The point shapefile represents the simulated discharge at SGD plumes under the assumptions of each scenario.</p> <p>The marine water quality model results are in floating point TIFF format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the groundwater discharge (cubic meters per month), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (kilograms per month), and phosphorus (kilograms per month) results under the assumptions of each scenario.</p> <p>The limu model results are in shapefile format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the increase and decrease in area (hectares) for <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>under the assumptions of each scenario.</p> <p>The limu experiment results are derived from a csv file, which reports the <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>measured weights (initial and final) for each growth run. These were used to calculate the weight difference. Included also in the dataset are the fixed and random effects used in the R script to run the model.</p> <p>Contact Leah Bremer (<a href="mailto:lbremer@hawaii.edu">lbremer@hawaii.edu</a>) or Brytne Okuhata (bokuhata@hawaii.edu) of the University of Hawaiʻi for more information on these files.</p>
Supplementary material: for Using anticipation to unveil drivers of local livelihoods in Transfrontier Conservation Areas: a call for more environmental justice
<ol> <li> <span>Calling on the concept of environmental justice in its </span><span>distributive, procedural, and recognition</span><span> dimensions, </span><span>w</span><span>e implemented a collaborative scenario-building approach to explore sustainable livelihood pathways in four sites belonging to two Transfrontier Conservation Areas (TFCAs) in southern Africa. </span> </li> <li><span>Grounded on participation and transdisciplinarity, as a foundation for decolonised anticipatory action research, we aimed at stimulating knowledge exchange and providing insights on the future of local livelihoods by engaging experts living within these TFCAs. </span></li> <li><span>Our results show that wildlife and wildlife-related activities are not seen as the primary drivers of local livelihoods, despite the focus and investments of dominant stakeholders in these sectors. Instead, local governance and land use regulations emerged as key drivers in the four study sites. The state of natural resources, including water, and appropriate farming systems also appeared critical to sustain future livelihoods in TFCAs, together with the recognition of indigenous culture, knowledge, and value systems.</span></li> <li><span>Nature conservation, especially in Africa, is rooted in its colonial past and struggles to free or decolonise itself from the habits of this past despite decades of reconsideration. To date, the enduring coloniality of conservation prevents local citizens from truly participating in the planning and designing of the TFCAs they live in, leaving room for limited benefits to local citizens and often limiting indigenous people's capacity to conserve. </span></li> <li> <span>A practical way forward is to consider environmental justice as a cement between the two pillars of the TFCA concept, i.e.</span><span>nature conservation and socioeconomic development of local or neighbouring communities,</span><span> as part of a more broad and urgent need to rethink the relationships between people in, and with, the rest of nature.</span> </li> </ol>
Early detection and environmental drivers of sewage fungus outbreaks in rivers
<p>Sewage effluent is a major ongoing threat to water quality and biodiversity in freshwater environments. It can cause outbreaks of sewage fungus (filamentous bacteria which form macroscopic masses) but, until now, these were only qualitatively recorded from visual inspection, ignoring microscopic forms. Here, we used an innovative method which combines machine learning, microscopy and flow cytometry, to rapidly and efficiently quantify the presence and abundance of sewage fungus in rivers. Our study involved 11 rivers with (n=6) and without (n=5) sewage input in the south-west of England over four sampling occasions. We were able to detect and enumerate the filaments before masses became visible to the naked eye and, as expected, we found a higher number of filaments downstream of sites where treated sewage was offloaded into the river. Therefore, our detection method could be used as a 'canary in the coal mine' for future outbreaks allowing early intervention. Combining our quantitative data on filaments with data on the physical and chemical parameters of the rivers, we found that high conductivity, sulphate, nitrates and TDS were associated with the presence and proliferation of sewage fungus. This information can be extremely useful for regulatory bodies and water companies to develop mitigating strategies and action to prevent future outbreaks.</p>
Environmental drivers and distribution of cold-water corals in the global ocean - Habitat Suitability Models
<p><strong>Publication Abstract</strong></p> <p>Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species <em>Madrepora oculata</em> showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, <em>Oculina varicosa</em>, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species.</p> <p><strong>Data description</strong></p> <p>These datasets (compressed Zip archives) contain the habitat suitability model outputs generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R. </p> <p>AA.zip = <em>Acanella arbuscula</em><br> DP.zip = <em>Desmophyllum pertusum</em> (former and now unaccepted synonym <em>Lophelia pertusa</em>)<br> ER.zip = <em>Enallopsammia rostrata</em><br> GD.zip = <em>Goniocorella dumosa</em><br> MO.zip = <em>Madrepora oculata</em><br> OV.zip = <em>Oculina varicosa</em><br> PA.zip = <em>Paragorgia arborea</em><br> PP.zip = <em>Paramuricea placomus</em><br> PR.zip = <em>Primnoa resedaeformis</em><br> SV.zip = <em>Solenosmilia variabilis</em></p>
Environmental drivers and distribution of cold-water corals in the global ocean - Environmental Data Layers
<p><strong>Publication Abstract</strong></p> <p>Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species <em>Madrepora oculata</em> showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, <em>Oculina varicosa</em>, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species.</p> <p><strong>Data description</strong></p> <p>These datasets (compressed Zip archives) contain the ten global environmental layers that were generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, using a trilinear interpolation approach based on the 500m GEBCO bathymetric data product. These layers are representations of seafloor conditions. Please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R. </p> <p>aoxu.zip = Apparrent Oxygen Utilization<br> diso2.zip = Dissolved Oxygen<br> nit.zip = Nitrate<br> oa.zip = Omega Aragonite<br> oc.zip = Omega Calcite<br> ph.zip = pH<br> phos.zip = Phosphate<br> sal.zip = Salinity<br> sil.zip = Silicate<br> temp.zip = Temperature</p>
Data and code from: Environmental drivers of wild bee reproductive performance across a South American dryland ecoregion
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Environmental drivers of Sphagnum growth in peatlands across the Holarctic region
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Environmental drivers of local abundance-mass scaling in soil animal communities
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Data from: Identifying environmental drivers of greenhouse gas emissions under warming and reduced rainfall in boreal-temperate forests
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Supplementary material: for Using anticipation to unveil drivers of local livelihoods in Transfrontier Conservation Areas: a call for more environmental justice
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Data from: Disentangling evolutionary, environmental and morphological drivers of plant anatomical adaptations to drought and cold in Himalayan graminoids
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Data from: Hidden demographic impacts of fishing and environmental drivers of fecundity in a sea turtle population
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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.