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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.

Forest aboveground carbon density (ACD) for the main eight Hawaiian Islands in 2015-2016. The data are in 30 meter resolution format with the units of Mg C per hectare. The file is a standard GeoTIFF. Use of these data requires citation of this dataset plus citation of the source study as follows: Asner, G.P., S. Sousan, D.E. Knapp, P.C. Selmants, R.E. Martin, R.F. Hughes, and C.P. Giardina. 2016. Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago. Carbon Balance and Management 11, doi:10.1186/s13021-015-0043-4

Two maps are provided from a study of the Republic of Panama. The maps are based on airborne light detection and ranging (lidar) data, combined with satellite-based maps of forest cover and properties, acquired in 2012. The resulting maps are: (1) top of canopy height or TCH; and (2) aboveground carbon density or ACD. Units for TCH are meters above ground. Units for ACD are Mg C per hectare. Maps are provided at 1.0 ha spatial resolution. File format is GeoTIFF. Use of these data require citation of this dataset and the original journal paper that delivered the mapping method. These citations are as follows: Asner, G.P., J. Mascaro, C. Anderson, D.E. Knapp, R.E. Martin, T. Kennedy-Bowdoin, M. van Breugel, S. Davies, J.S. Hall, H.C. Muller-Landau, C. Potvin, W. Sousa, J. Wright and E. Bermingham. 2013. High-fidelity national carbon mapping for resource management and REDD+. Carbon Balance and Management 8:7 (doi:10.1186/1750-0680-8-7) Asner, G.P., J. Mascaro, C. Anderson, D.E. Knapp, and R.E. Martin. 2021. Global Airborne Observatory: Forest canopy height and carbon stocks of Panama (Version 1.0) [Data set]. Zenodo http://doi.org/10.5281/zenodo.4624240

The purpose of this study was to investigate the influence of increasing sulfate concentrations on chromium removal, to evaluate the effect of the presence of Cr(VI) on sulfate removal by Streptomyces sp. MC1 and to analyze the differential protein expression profile in the presence of this metal for the identification of proteins repressed or overexpressed. In the presence of Cr(VI) but in the absence of sulfate ions, bacterial growth was negligible, showing the Cr(VI) toxicity for this bacterium. However, the sulfate presence stimulated bacterium growth and Cr(VI) removal, regardless of its concentrations. Streptomyces sp. MC1 showed ability to remove chromium and sulfate simultaneously. Also, the sulfate presence favored the decrease of total chromium concentration from supernatants reaching a decrease of 50% at 48 h. In presence of chromium, seven proteins were down‐expressed and showed homology to proteins involved in protein biosynthesis, energy production and free radicals detoxification while two proteins involved in oxidation‐reduction processes identified as dihydrolipoamide dehydrogenase and S‐adenosyl‐l‐methionine synthase were overexpressed.

Since long ago, pelagic Sargassum mats have been known to be abundant in the Sargasso Sea, where they provide habitat to diverse organisms. However, over the last few years, massive amounts of pelagic Sargassum have reached the coast of several countries in the Caribbean and West Africa, causing economic and environmental problems. Aiming for lessening the impacts of the blooms, governments and private companies remove the seaweeds from the shore, but this process results expensive. The valorization of this abundant biomass can render Sargassum tides into an economic opportunity and concurrently solve their associated environmental problems. Despite the diverse fields where algae have found applications and the relevance of this recurrent situation, Sargassum biomass remains without large scale applications. Therefore, this review aims to present the potential uses of these algae, identifying the limitations that must be assessed to effectively valorize this bioresource. Due to the constraints identified for each of the presented applications, it is concluded that a biorefinery approach should be developed to effectively valorize this abundant biomass. However, there is an urgent need for investigations focusing on holopelagic Sargassum to be able to truly valorize this seaweed.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

Forest soil acidification caused by acid deposition is a serious threat to the forest ecosystem. To investigate the liming effects of biomass ash (BA) and alkaline slag (AS) on the acidic topsoil and subsoil, a three-year field experiment under artificial Masson pine was conducted at Langxi, Anhui province in Southern China. The surface application of BA and AS significantly increased the soil pH, and thus decreased exchangeable acidity and active Al in the topsoil. Soil exchangeable Ca2+ and Mg2+ in topsoil were significantly increased by the surface application of BA and AS, while an increase in soil exchangeable K+ was only observed in BA treatments. The soil acidity and active Al in subsoil were decreased by the surface application of AS. Compared with the control, soluble monomeric and exchangeable Al in the subsoil was decreased by 38.0% and 29.4% after 3 years of AS surface application. There was a minimal effect on soluble monomeric and exchangeable Al after the application of BA. The soil exchangeable Ca2+ and Mg2+ in the subsoil increased respectively by 54% and 141% after surface application of 10 t ha-1 AS. The decrease of soil active Al and increase of base cations in subsoil were mainly attributed to the high migration capacity of base cations in AS. In conclusion, the effect of surface application of AS was superior to BA in ameliorating soil acidity and alleviating soil Al toxicity in the subsoil of this Ultisol.

AbstractBio-syngas from gasification of sugarcane bagasse is one of the most promising sources for renewable energy. As an agriculture-based biomass, sugarcane bagasse has a high content of moisture (46-52%), fibrous (43-52%) and low bulk density (80-120kg/m3). This quality of bagasse will tend to initiate agglomeration and cause de-fluidization. It will disturb the gasification process and finally will decrease yield and quality of syn-gas. Its chracteristics in low quality can be improved by pretreatment, i.e., torrefaction process, addressed by slow heating of biomass on wet or dry conditions on atmosphere pressure for 1hour before it is used as feedstock gasification.This preliminary work features an experimental investigation of torrefaction process of Indonesian sugarcane cane bagasse. Temperature of torrefaction varies from 150, 175, 200, 225, 250 and 300°C. For bagasse gasification process, the optimum temperature of dry torrefaction is 150°C. At this temperature, yield of syngas will higher than other torrefaction temperature. Temperature of dry torrefaction will give energy saving opportunities than that's of wet torrefaction (180°C, 1 hr).Analysis ultimate and proximate also indicate that sugarcane bagasse with temperature torrefaction 150°C give better result than other torrefaction's temperature in high content of hydrogen and low content of carbon.