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This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

Flight trajectories of fruit flies and mosquitoes in a wind tunnel.This data file is a MySQL database file which must be uploaded to a MySQL database management system (DBMS) (e.g., via the MAMP installation: http://localhost:8888/MAMP/?language=English, as was used in the associated manuscript). Once you have installed a MySQL DBMS on your machine, make a new database called “wind_tunnel_db”. To populate this database using the data file, first download all of the data files and join them together using: cat wind_tunnel_db_* > wind_tunnel_db.sql Then run the following command to populate the wind_tunnel_db MySQL database with the result. /path/to/mysql -uroot -proot wind_tunnel_db < /path/to/wind_tunnel_db.sql replacing the paths and username/passwords as appropriate. It will take several minutes since it is a large file. The database contains several tables, which are mostly self explanatory. The key tables of interest are the “experiment” table, which lists the 4 experiments contained in this data set, the “timepoint” table, which contains the position, velocity, etc., of every fly/mosquito at every measured time point, and the “trajectory” table, which indicates which set of time points correspond to which individual trajectories. Other useful tables that have been pre-populated are the “crossing” table, which specifies trajectory segments corresponding to each plume crossing, and the “crossing_group” table, which groups sets of crossings together according to experiment and crossing identification criteria. The code that interacts with this database and recreates the figures in the associated manuscript is contained at https://github.com/rkp8000/wind_tunnel.wind_tunnel_db_aaPart 2wind_tunnel_db_abPart 3wind_tunnel_db_acPart 4wind_tunnel_db_adPart 5wind_tunnel_db_aePart 6wind_tunnel_db_afPart 7wind_tunnel_db_agPart 8wind_tunnel_db_ahPart 9wind_tunnel_db_aiInfotaxis databaseBase database for running infotaxis simulations. To see how to prepare and populate this database with simulated trajectory data, see the file _paper_auxiliary_code in the GitHub repository http://github.com/rkp8000/wind_tunnel.infotaxis_db.sql Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

We monitored golden-cheeked warbler territories in 10 plots within an urban preserve to determine abundance, delineate territories, and document breeding success. We determined environmental conditions across the study period to examine temporal and landscape effects. We then used these data to estimate adult survival and productivity and relate these vital rates to environmental conditions experienced during our study period. We used supported covariates to predict potential effects on this population 25 years into the future. These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future. We assembled and conducted the analysis in R.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Conservation of thermally sensitive species depends on monitoring organismal and population-level responses to environmental change in real time. Epigenetic processes are increasingly recognized as key integrators of environmental conditions into developmentally plastic responses, and attendant epigenomic datasets hold potential for revealing cryptic phenotypes relevant to conservation efforts. Here, we demonstrate the utility of genome-wide DNA methylation (DNAm) patterns in the face of climate change for a group of especially vulnerable species, those with temperature-dependent sex determination (TSD). Due to their reliance on thermal cues during development to determine sexual fate, contemporary shifts in temperature are predicted to skew offspring sex ratios and ultimately destabilize sensitive populations. Using reduced-representation bisulfite sequencing, we profiled the DNA methylome in blood cells of hatchling American alligator (Alligator mississippiensis), a TSD species lacking reliable markers of sexual dimorphism in early life-stages. We identified 120 sex-associated differentially methylated cytosines (DMCs; FDR < 0.1) in hatchlings incubated under a range of temperatures, as well as 707 unique temperature-associated DMCs. We further developed DNAm-based models capable of predicting hatchling sex with 100% accuracy (in 20 training samples and 4 test samples) and past incubation temperature with a mean absolute error of 1.2˚C (in 4 test samples) based on the methylation status of 20 and 24 loci, respectively. Though largely independent of epigenomic patterning occurring in the embryonic gonad during TSD, DNAm patterns in blood cells may serve as non-lethal markers of hatchling sex and past incubation conditions in conservation applications. These findings also raise intriguing questions regarding tissue-specific epigenomic patterning in the context of developmental plasticity. 

The ecological impacts of multiple stressors are hard to predict but important to understand. When multiple stressors influence foundation species, the effects can cascade throughout the ecosystem. Gulf of Mexico seagrass ecosystems are currently experiencing a suite of novel stressors, including warmer water temperatures and increased herbivory due to tropicalization and conservation efforts. We investigated the impact of warming temperatures and grazing history on plant performance, morphology, and palatability by integrating a mesocosm study using the seagrass Thalassia testudinum with feeding trials using the sea urchin Lytechinus variegatus. Warming temperatures negatively impacted T. testudinum tolerance traits, reducing belowground biomass by 34%, productivity by 74%, shoot density by 10%, and the number of leaves per plant by 24%, and negatively impacted resistance traits through 13% lower toughness of young leaves and a trend for reduced leaf carbon:nitrogen. Lytechinus variegatus individuals preferred to consume plants grown under heated conditions, which supports findings of enhanced palatability. Simulated turtle grazing impacted more plant traits than grazing by other herbivores, potentially diminishing plant resilience to future disturbances through reduced rhizome non-structural carbohydrate concentrations and increasing palatability through reduced fiber content and 23% lower leaf carbon:phosphorus. Simulated turtle, simulated parrotfish, and urchin grazing reduced leaf carbon:nitrogen by 11%, also potentially increasing nutritive value. Interactions between warming temperatures and grazers on plant traits were additive for 16 out of 19 response variables. However, the stressors non-additively impacted the number of leaves per plant, fiber content, and epiphyte load. We suggest that the impacts of grazers on leaf turnover rate and leaf age may vary based on water temperature, potentially driving these interactions. Overall, increased temperatures and grazing pressure will likely reduce seagrass resilience, structure, and biomass, potentially impacting feedback systems and producing negative consequences for seagrass cover, associated species, and ecosystem services.