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27 results for “Bioclimatic variables”
2005-2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea.
<p><em><span>This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005-2099 under the RCP8.5 scenario, with a spatial resolution of 1/24 degree (~4km²). Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. We downscaled these to 1/24 degree and applied Quantile Delta Mapping bias correction using CMEMS reanalysis products for 2005-2020. The dataset is provided in a user-friendly format, making it accessible for various ecological and environmental modelling applications.</span></em></p>
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Global Soil Bioclimatic variables at 30 arc second resolution
<p>Soil temperature layers were calculated by adding monthly soil temperature offsets to monthly air-temperature maps from CHELSA (date range 1979-2013) (Karger et al. 2017, Sci Data). These soil temperature layers were then used to calculate annual means, temperature ranges, standard deviation, warmest and coldest months and quarters. Wettest and driest quarters were identified for each pixel based on CHELSA monthly values. A quarter is a period of three months (1/4 of the year).</p> <p>When using any of these layers, please cite: Lembrechts et al., Global maps of soil temperature (2021). Global Change Biology. DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16060">10.1111/gcb.16060</a></p> <p>For each Soil Bioclim layer, two depth intervals are available: 0 - 5 cm and 5 - 15 cm.</p> <p>We have followed the generally accepted definitions of BIO 1 - BIO 11: </p> <ul> <li>SBIO1 = Annual Mean Temperature</li> <li>SBIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))</li> <li>SBIO3 = Isothermality (BIO2/BIO7) (×100)</li> <li>SBIO4 = Temperature Seasonality (standard deviation ×100)</li> <li>SBIO5 = Max Temperature of Warmest Month</li> <li>SBIO6 = Min Temperature of Coldest Month</li> <li>SBIO7 = Temperature Annual Range (BIO5-BIO6)</li> <li>SBIO8 = Mean Temperature of Wettest Quarter</li> <li>SBIO9 = Mean Temperature of Driest Quarter</li> <li>SBIO10 = Mean Temperature of Warmest Quarter</li> <li>SBIO11 = Mean Temperature of Coldest Quarter</li> </ul> <p>These layers are also publicly available as Google Earth Engine assets. These are acessible via:</p> <ul> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_0_5cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm</a></li> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_5_15cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm</a></li> </ul> <p>Also available are monthly maps of soil temperature, for two depth intervals (0-5 cm and 5-15 cm). For example, the soil temperature map for month 1 (January) at 0-5 cm is named soilT_1_0_5cm.tif'. </p> <p>To mask pixels by the proportion of extrapolation, the files 'PCA_int_ext_0_5cm.tif' and 'PCA_int_ext_5_15cm.tif' can be used. </p>
Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
ClimateAnalyzer: set of scripts to delimit regions based on bioclimatic variables
<p>Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the LGM and the present, in different tropical alpine regions. Finally, we discuss the link between habitat stability and tropical alpine plant diversity. Our climate-based delimitation approach can be easily applied to other ecosystems using our developed code, facilitating macro-comparative studies of habitat dynamics through time.</p>
Fig. 5. The marginal response curve for the explanatory variable Bio14 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 5. The marginal response curve for the explanatory variable Bio14 (Precipitation of driest week). (HS — habitat suitability).
Fig. 4. The marginal response curve for the explanatory variable Bio09 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 4. The marginal response curve for the explanatory variable Bio09 (Mean temperature of driest quarter). (HS — habitat suitability).
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
KGClim historical: A 1-km global dataset of historical (1979-2013) Köppen-Geiger climate classification and bioclimatic variables
<p>We presented a new dataset of 1-km Köppen-Geiger climate classification maps and 12 bioclimatic variables for the historical periods (1979-2008, 1980-2009, 1981-2010, 1982-2011, 1983-2012, 1984-2013).</p>
ClimateAnalyzer: set of scripts to delimit regions based on bioclimatic variables
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Downscaled climate grids of California at 90m for a variety of bioclimatic variables from 1971-2000, derived from historical climate grids
This dataset is comprised of 90 Geotiff images of selected bioclimatic variables for the state of California (extended past state lines to river basin boundaries). Originally created to model plant species distributions in California (Franklin et al. 2013. Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology 19: 473-483).
Pollen-based reconstructions of bioclimatic variables at 6000 yr BP
<p>Gridded reconstructions of mean annual precipitation, mean annual temperature, temperature of the coldest month, growing degree days and plant available moisture (alpha) derived from the Bartlein et al. (2011) data set. These data are used to generate Figure 8 in <strong>Harrison, S.P.</strong>, Gaillard, M-J., Stocker, B., Vander Linden, M., Klein Goldewijk, K., Boles, O., Braconnot, P., Dawson, A., Fluet-Chouinard, E., Kaplan, J.O., Kastner, T., Pausata, F.S.R., Robinson, E., Whitehouse, N., Madella, M., Morrison, K.D., 2019. Development and testing of scenarios for implementing Holocene LULC in Earth System Model Experiments. <em>Geoscientific Model Development</em> <em>Discussions, </em><strong><a href="https://doi.org/10.5194/gmd-2019-125%20%20%20%207">https://doi.org/10.5194/gmd-2019-125 7</a></strong></p>
Chclim25 - bioclimatic variables (biovars)
<p>Bioclimatic variables are calculated from CHclim25 minimum (Tmin) and maximum temperature (Tmax) and sum of precipitation (Prec) using function <a href="https://rdrr.io/cran/dismo/man/biovars.html">biovars</a> in the R package dismo. Individual years, current average (1981-2010) and future average (2020-2049, 2045-2074, and 2070-2099)<strong> </strong>layers can be downloaded from separate zip files. </p> <p>Future layers are based on the transient daily time series of gridded climate scenarios of temperature at 0.02°D (~2.2 km) provided by the <a href="https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html">CH2018 initiative</a>. We calculated future climatic layers for 4 GCMs (HADGEM, ECEARTH, MPIESM, and IPSL), 3 time slices (2020-2049, 2045-2074, and 2070-2099) and 3 representative concentration pathways (RCP 2.6, 4.5 and 8.5)</p> <p>The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).</p>
Data from: Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally
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The way bioclimatic variables are calculated has impact on potential distribution models
<p>1. Bioclimatic variables (BCVs) are routinely used in potential distribution models, typically without considering their calculation options in detail. We aimed at studying the impact of a decision, yet unexamined, on the calculation of BCVs, namely whether the identity of specific months/quarters in the calculation of BCVs should be updated for the future periods (temporal context). Effects on the performance of potential distribution models and on their projections were investigated. Additionally, we also aimed at comparing the impact of month/quarter shifts to that of climate model selection and covariate selection.</p> <p>2. Potential natural vegetation models encompassing eight habitat types and the whole territory of Hungary were created using boosted regression trees. We tested multiple initial covariate sets to compare the impact of the temporal context to that of covariate selection. The resulting models were applied to the reference and one future time period (with data from two regional climate models). The effect of the BCV calculation approach was tested by linear mixed-effects models and model goodness-of-fit measures in a comprehensive framework of 192 predictions. Area Under the ROC Curve (AUC) and True Positive Rate (TPR) curves were used to evaluate the models.</p> <p>3. Our results show that (1) temporal context of BCVs in interaction with covariate selection had a strong effect on model structure as well as on projections; (2) no evidence supporting the superiority of the widely applied calculation approach of BCVs was found. However, we found notable differences under the two approaches and examples of projection artefacts when applying the widespread way of calculation.</p> <p>4. We conclude that (1) more attention and more transparent communication is needed when BCVs are used as covariates in distribution models; (2) not only ecophysiology but also the way covariates are calculated should be considered when preselecting covariates for potential distribution models.</p>
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.
Figure 3 in Potential geographic distribution niche modeling based on bioclimatic variables of three species of Temnomastax Rehn and Rehn, 1942 (Orthoptera: Eumastacidae)
Figure 3. Potential geographic distribution predicted by DOMAIN model to Temnomastax ricardoi Descamps, 1973 (blue), and Temnomastax tigris (Burr, 1899) (green). Darkest regions represent higher probabilities of occurrence than clearest regions. (■) Temnomastax ricardoi Descamps, 1973 records; (▲) Temnomastax tigris (Burr, 1899) records.
Figure 2 in Potential geographic distribution niche modeling based on bioclimatic variables of three species of Temnomastax Rehn and Rehn, 1942 (Orthoptera: Eumastacidae)
Figure 2. Potential geographic distribution predicted by DOMAIN model to Temnomastax hamus Rehn and Rehn, 1942 (green). Darkest regions represent higher probabilities of occurrence than clearest regions. On the left is marked the Andes in red, orange and yellow. (●) species records.
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OpenNeuro
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