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10 results for “Multiple environmental drivers”
Net effect of environmental fluctuations in multiple global-change drivers across the tree of life
<p>This dataset contains the raw data presented in the article and supplementary material by M. J. Cabrerizo and E. Marañón entitled: "<strong>Net effect of environmental fluctuations in multiple global-change drivers across the tree of life"</strong></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>
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>
Data from: Phenological responses to multiple environmental drivers under climate change: insights from a long-term observational study and a manipulative field experiment
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Data from: Disentangling the effects of multiple environmental drivers on population changes within communities
1. The effects of different environmental drivers on the changes in species' population abundances can be difficult to disentangle since they often act simultaneously. Researchers have built statistical models that include environmental variables (such as annual temperature), or species attributes (such as a species' temperature preference), which are assumed to detect the impacts of specific drivers (such as climate change). However, these approaches are often applied separately or, if combined, not explicitly compared. 2. We show the complementary insights gained by applying both these approaches to a community dataset on Danish terrestrial birds. We use our analysis to compare the relative importance of climate change and agricultural land-use change for the abundance changes within the community between 1983 and 2013. 3. Population models were fitted to the community data of species' annual abundances with predictors comprising: species attributes (species' temperature and habitat preferences), environmental variables (climatic and agricultural land-use change variables), or both. Relationships between species' abundances and environmental variables were used to identify the drivers associated with average abundance changes of species in the community. Relationships between species' abundances and their attributes were used to understand the drivers causing interspecific variation in abundance changes.4. Warmer winters were positively associated with community-level abundances, and warm-adapted species had more positive abundance changes than cold-adapted ones. Agricultural land-use area was negatively associated with community-level abundances, and birds using a high proportion of meadow and habitat specialists had more negative abundance changes than birds using other habitats and habitat generalists. Effect sizes of environmental variables were larger for agricultural land-use change while those for species attributes were larger for climate change. 5. The environmental-data approach suggested that agricultural land-use change has decreased the average abundances of species in the community, affecting total community size while the species-attribute based approach suggested that climate change has caused variation in abundance among species, affecting community composition. Environmental variables and species attributes that are hypothesized to link to specific drivers can be used together to provide complementary information on the impacts of different drivers on communities.
Responses of marine diatom-dinoflagellate competition to multiple environmental drivers: abundance, elemental and biochemical aspects
<p>A key challenge in ecology and biogeochemistry is to quantitatively determine the effects of multiple environmental factors on the diatom-dinoflagellate community and the related changes in elemental and biochemical composition. Here, laboratory experiments were conducted to investigate the responses of a diatom-dinoflagellate community to the interactions between temperature, N and P concentrations and their ratios. In particular, we focus on quantitatively assessing the changes in elemental stoichiometry and lipid biomarkers associated with the shifts of community composition. The phytoplankton species used in the experiments were the diatom<em> Phaeodactylum tricornutum</em> and the dinoflagellate <em>Prorocentrum minimum</em>. Both monocultures and bicultures of the two species were exposed to a fully factorial combination of three temperatures (12, 18 and 24°C), three N:P supply ratios (molar ratios 10:1, 24:1 and 63:1) and three nutrient concentration levels in temperature-controlled cabinets. Algal cells were harvested at steady state in semi-continuous cultures. The following parameters were analyzed: Cell density, POC, PON, POP, FAs and sterols.</p>
Data from: Disentangling the effects of multiple environmental drivers on population changes within communities
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Data from: Multiple environmental drivers structure plant traits at the community level in a pyrogenic ecosystem
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Data from: Growth responses of a green alga to multiple environmental drivers
One feature of global change is that biota must respond not to single, but to multiple environmental drivers. By growing a model photosynthetic microbe in environments containing between one and eight different drivers, including changes in CO2, temperature, and pH, in different combinations, we show that the number as well as the identities of drivers explain shifts in population growth rates. This is because the biotic response to multiple environmental drivers depends on the response to the single dominant driver, and the chance of a driver of large effect being present increases with the number of drivers. Interactions between drivers slightly counteract the expected drop in growth. Our results demonstrate that population growth declines in a predictable way with the number of environmental drivers, and provide an empirically supported model for scaling up from studies on organismal responses to single drivers to predict responses to large numbers of environmental drivers.
Data from: Growth responses of a green alga to multiple environmental drivers
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International Brain Laboratory public data
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OpenNeuro
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