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AbstractPrincipal Component Analysis (PCA) was used to evaluate groundwater quality data acquired in the pre-injection and injection periods for the Illinois Basin – Decatur Project (IBDP), a large-scale carbon capture and storage (CCS) project located in Decatur, Illinois, USA. For the pre-injection and injection periods three principal components explained 76.6% and 80.0% of the total data variance, respectively. Analysis of the pre-injection data set determined that highly positive loadings for total dissolved solids, chloride, bromide, sodium, magnesium, potassium, and electrical conductance designated the first component (PC1) as the salinity factor. High loadings for calcium, iron, and sulfate in component two (PC2) represents an oxidation-reduction component. The third component (PC3) represents groundwater acidity because of highly positive loading of pH. For the injection data set the variables contributed to the first component are bromide, sodium, total dissolved solids, chloride, electrical conductance, potassium, sulfate, iron, and calcium. Sulfate, magnesium, and calcium contribute to the second component and pH to the third component and represent salinity, dissolution, and acidity of groundwater. The results of the PC analysis indicate that water-rock interactions are the primary mechanism governing groundwater quality during both periods. The results of this analysis indicate that CO2 injection activities have not impacted the quality of the shallow groundwater in the project area.

Daily rainfall is measured as the total (mm) over the 24-hour period 0900 to 0900 GMT. It includes all precipitation - snow, rain, mist and fog. Rainfall was first recorded at Rothamsted in March 1853, using a copper funnel rain gauge (5 inch / 12.7 cm diameter) and measured using a graduated cylinder. Since 2004 it has been measured using an electronic tipping bucket rain gauge (10 inch / 25.4cm diameter), ARG100, calibrated to tip at 0.2mm (which has since become the minimum amount of rain that can be recorded). The rain gauge is placed within a 30cm deep 1.5m radius turf wall, retained by brick, to reduce wind eddies that may potentially blow rain out of the gauges. Data were collected daily manually until 2004 and since then by Automatic Weather Station using a standard protocol. There are differences in the capture rate between the two gauges, see Rainfall for further information. The monthly summary data contained in this spreadsheet are derived from daily data measured at Rothamsted Meteorological Station, Harpenden. Total monthly data is determined from daily data using Genstat 19th Edition. Verification includes checks for instrument errors, for missing data and outliers. The original raw daily data is available, after registering, from the e-RA database. Please contact the e-RA Curators for an access password and further details. This dataset represents the mean monthly rainfall recorded at Rothamsted from October 1985 - September 2017 and is derived from continuous daily records measured at the site. Location: Rothamsted Meteorological Station, Harpenden, Hertfordshire, England 51.82 N 0.37 W 128 m asl.

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

1. While climate change is altering ecosystems on a global scale, not all ecosystems are responding in the same way. The resilience of ecological communities may depend on whether food webs are producer- or detritus-based (i.e. “green” or “brown” food webs, respectively), or both (i.e. “multi-channel” food web). 2. Food web theory suggests that the presence of multiple energy pathways can enhance community stability and resilience and may modulate the responses of ecological communities to disturbances such as climate change. Despite important advances in food web theory, few studies have empirically investigated the resilience of ecological communities to climate change stressors in ecosystems with different primary energy channels. 3. We conducted a factorial experiment using outdoor stream mesocosms to investigate the independent and interactive effects of warming and drought on invertebrate communities in food webs with different energy channel configurations. Warming had little effect on invertebrates, but stream drying negatively impacted total invertebrate abundance, biomass, richness, and diversity. 4. Although resistance to drying did not differ among energy channel treatments, recovery and overall resilience were higher in green mesocosms than in mixed and brown mesocosms. Resilience to drying also varied widely among taxa, with larger predatory taxa exhibiting lower resilience. 5. Our results suggest that the effects of drought on stream communities may vary regionally and depend on whether food webs are fueled by autochthonous or allochthonous basal resources. Communities inhabiting streams with large amounts of organic matter and more complex substrates that provide refugia may be more resilient to the loss of surface water than communities inhabiting streams with simpler, more homogeneous substrates.

AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

Identifying climate analogs We followed the methods of Abatzoglou et al. (2020) and Parks et al. (2022) to characterize climate and identify backward and forward climate analogs. The specific climate variables we used were average minimum temperature of the coldest month (Tmin), average maximum temperature of the warmest month (Tmax), annual actual evapotranspiration (AET), and annual climate water deficit (CWD). AET and CWD concurrently account for evaporative demand and availability of water (N. L. Stephenson, 1990). These four variables provide complementary information pertinent to ecological systems and collectively capture the major climatic constraints on species distributions and ecological processes across a range of taxa (Dobrowski et al., 2021; Lutz et al., 2010; Parker & Abatzoglou, 2016; N. Stephenson, 1998; C. M. Williams et al., 2015). Monthly data acquired from TerraClimate (Abatzoglou et al., 2018) were used to produce these annual summaries from 1961-1990 (resolution = ~4km), which were then averaged over the same time period to represent reference period climate normals. The reference time period (1961–1990) is meant to represent climate conditions and climate niches prior to the bulk of recent warming. Future climate conditions were also computed from TerraClimate (available from www.climatologylab.org/terraclimate.html) and correspond to a 2°C increase above pre-industrial levels that are likely to manifest by mid-21st century without immediate and massive changes in global climate policies (Friedlingstein et al., 2014). As with the reference period climate, we summarized the four +2°C climate metrics annually and over a 30-year time period to represent future climate normals. All analyses in this study were conducted in the R statistical platform (R Core Team, 2020). We identified backwards and forwards analogs by estimating the climatic dissimilarity between each protected focal pixel (resolution = ~4km to match gridded climate data) and all protected pixels within a 500-km radius using a standardized Mahalanobis distance (Mahony et al., 2017). We chose the 500-km search radius as it encompasses an upper range of dispersal for some terrestrial animals and plants (Chen et al., 2011) when assuming 2°C warming by the mid-21st century; this search radius has also been used in previous studies (Bellard et al., 2014; Parks et al., 2022; J. W. Williams et al., 2007). The Mahalanobis distance metric synthesized the four climate variables (i.e. Tmin, Tmax, AET, and CWD; fig. 2a) by measuring distance in multivariate space away from a centroid using principal components analysis of standardized anomalies. Mahalanobis distance scales multivariate mean climate conditions between a pixel and those within the search radius by the focal pixel’s covariance and magnitude of interannual climate variability (ICV) across the four metrics. For backwards analogs, we characterized +2°C ICV and reference period climate normals to calculate climatic dissimilarity; for forward analogs, we used reference period ICV and +2°C climatic normals to calculate climatic dissimilarity. We standardized Mahalanobis distance to account for data dimensionality by calculating a multivariate z-score (σd) based on a Chi distribution (Mahony et al., 2017). σd represents the climate similarity between each focal pixel and its candidate backward and forward analogs (i.e. all other protected terrestrial pixels within 500 km), and we considered any protected pixels with σd ≤ 0.5 as climate analogs (fig. 2b) (following Parks et al., 2022). We were unable to calculate Mahalanobis distance when there was no ICV for any one of the four variables, and as a consequence, these areas are omitted from all analyses; this affects, for example, a relatively small tropical area in South America (CWD=0 each year) and areas perennially covered by snow (CWD=0 each year; e.g. most of Greenland). We focused our analyses on protected areas as defined by the World Database on Protected Areas (WDPA) (IUCN & UNEP-WCMC, 2019) and included protected areas classified as IUCN (International Union of Conservation for Nature) Management Categories I-VI, except those identified as ‘proposed’, ‘marine’, or otherwise aquatic (e.g. wetland, riverine, endorheic). A large number of protected areas, however, were not assigned an IUCN category in the WDPA (identified as ‘Not Reported’, ‘Not Assigned’, or ‘Not Applicable’) but are likely to have reasonably high levels of protection (e.g. Kruger National Park in South Africa). We included these additional protected areas if the level of human modification was similar or less than that observed within IUCN category I-VI protected areas. To do so, we measured mean land-use intensity within each IUCN category I-VI protected area using the Human Modification Gradient (HMG) raster dataset (Kennedy et al., 2019) and calculated the 80th percentile of the resulting distribution. Any unassigned protected areas with a mean HMG less than or equal to this identified threshold were included in our study (following Dobrowski et al., 2021). We then converted this vector-based polygon dataset to raster format (resolution = ~4km to match gridded climate data; n=1,063,748 pixels). It is well-recognized that the WDPA contains a large number of duplicate and overlapping polygons (Palfrey et al., 2022; Vimal et al., 2021). Although this does not affect summaries across the globe or for individual countries (described below), it provides a challenge when trying to summarize by individual protected areas (due to double-counting). Consequently, we ‘cleaned’ the WDPA prior to summarizing the climate connectivity metrics for individual protected areas by removing polygons that exhibited ≥ 90% overlap with another; this resulted in 29,752 individual protected areas (available in the Electronic Supplemental Material). Least-cost path modelling Following Dobrowski and Parks (2016) and Carroll et al. (2018), we used least-cost path modelling (Adriaensen et al. 2003) to build potential climate-induced movement routes between each protected focal pixel and its backward and forward analogs. The least-cost models were parameterized with resistance surfaces based on climate dissimilarity and the human modification gradient (HMG) (Kennedy et al., 2019). For backward analog modelling, we characterized climatic dissimilarity (i.e. climatic resistance) using two intermediate surfaces, the first being the Mahalanobis distance between each focal pixel (using +2°C ICV) and all other pixels using reference period climate normals (fig. 2c) and the second being the Mahalanobis distance (using +2°C ICV) and all other pixels using +2°C climate normals (fig. 2d). These two surfaces provide a proxy for climate similarity designed to capture transient changes between the reference period and +2°C climate; these were then averaged to characterize the overall climatic resistance across time and space (fig. 2d). For forward analog modelling, the process is similar except we used reference period ICV when characterizing climatic resistance (fig. 2a-2d). We then multiplied the climatic resistance (fig. 2d) by HMG (fig. 2e) to create the final resistance surface for least-cost path modeling (cf. Parks et al., 2020). Prior to this step, we rescaled HMG from its native range (0–1) to 1–25 to correspond with the range of Mahalanobis distance values and thereby grant comparable weights to climatic resistance and HMG resistance (~95% of all Mahalanobis distance values are below 25 within a 500km radius). Open water was given a resistance=25 so that paths would avoid water when possible. Least-cost path modelling was achieved using the gdistance package (van Etten, 2017); paths represent the least accumulated cost across the final resistance surface (fig. 2f) between each focal pixel and analog (fig. 2g). Because paths were rarely straight lines, some were longer than the 500km that we established as a search radius. We removed these longer paths to abide by the biologically informed upper dispersal constraint. Calculating climate connectivity metrics and climate connectivity failure We calculated the length (i.e. dispersal exposure), land-use modification (i.e. human exposure), and climatic resistance (i.e. climate exposure) for each path, remembering that each focal pixel may have many analogs and resultant paths. Human exposure represents cumulative HMG (fig. 2e) across all pixels in a path and climate exposure represents cumulative climate resistance (fig. 2d) along a path. Human exposure and climate exposure were calculated by multiplying the mean HMG (unscaled; fig. 2f) and mean climate resistance (fig. 2d) along each path by the length of each path, respectively. Each path’s climate connectivity metric (dispersal, human, and climate exposure) was converted to a percentile (range = 0–100) to facilitate easier interpretation and comparison among metrics; relative to other protected pixels, small percentiles represent low exposure and large percentiles represent elevated exposure. We summarized (i.e. averaged the percentiles) dispersal exposure, human exposure, and climate exposure across each protected focal pixel (again, remembering that each pixel may have multiple analogs and resultant paths). Our fourth climate connectivity metric, analog exposure, can’t be summarized on a per-path basis, because by definition, there is no least-cost path when there are no protected climate analogs. Instead, protected pixels either do or do not have protected climate analogs. Focal pixels were identified as exhibiting climate connectivity failure when they exceeded the 75th percentile for dispersal or climate exposure, exceeded the 90th percentile for human exposure, or had no protected climate analog. We assumed that focal pixels exceeding these percentiles are located in landscapes that hinder successful range shifts among protected areas (i.e. climate connectivity failure) for a non-negligible proportion of extant species, considering that the biodiversity at a given site comprises mammals, birds, insects, mollusks, amphibians, reptiles, fish, crustaceans, annelids, vascular plants (e.g. trees grasses, shrubs), and non-vascular plants (e.g. fungi, mosses, lichens). The numerous and diverse species at a given site have a wide range of dispersal abilities, sensitivities to human land uses, and climatic tolerances. We used a higher threshold (90th percentile) for describing climate connectivity failure due to human exposure because large, remote protected areas in the network skew human exposure towards lower values from a global perspective. These percentile thresholds are likely conservative when considering the large number and diversity of species at a given site. In terms of dispersal, for example, many species have maximum dispersal capabilities on the range of 1 km/year or less (Jenkins et al., 2007; McLachlan et al., 2005; Schwartz et al., 2001). This represents dispersal of 75 km under 2°C warming in the 75 years covering the midpoint of the reference period (1975) to mid-21st century. In our study, the 75th percentile path length, corresponding to dispersal exposure, is ~385 km, well above such dispersal limits, supporting our assertion that the 75th percentile is conservative for estimating climate connectivity failure. Furthermore, the mean HMG value for a 100km path at the 90th percentile threshold is 0.22, which is well above the 0.1 threshold that Brennen et al. (2022) used to identify areas moderately to highly impacted by human land-uses. Lastly, the mean climatic distance for a 100km path at the 75th percentile is well over two standard deviations different, on average, from the focal pixel and analog. We report the percent of protected pixels across the globe and within each country that exhibits climate connectivity failure. We also assessed the potential for each of the 29,752 individual protected areas (e.g. Yellowstone National Park, Serengeti National Park) to undergo climate connectivity failure using a slightly different method. To do so, we calculated the mean percentile among pixels within each protected area for each of dispersal exposure, human exposure, and climate exposure (each metric was averaged across a protected area; the metrics themselves were not averaged with each other). We then calculated the percent of each protected area that did not have a protected climate analog (analog exposure). Although a binary approach (has or does not have an analog) is appropriate when evaluating individual focal pixels, a percent-based valuation is most appropriate and informative when evaluating individual protected areas with up to thousands of pixels. Individual protected areas exhibited climate connectivity failure if the mean dispersal exposure or climate exposure exceeded the 75th percentile, mean human exposure exceeded the 90th percentile, or the analog exposure exceeded 75%. References Abatzoglou, J. T., Dobrowski, S. Z., & Parks, S. A. (2020). Multivariate climate departures have outpaced univariate changes across global lands. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-60270-5 Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), Article 1. https://doi.org/10.1038/sdata.2017.191 Bellard, C., Leclerc, C., Leroy, B., Bakkenes, M., Veloz, S., Thuiller, W., & Courchamp, F. (2014). Vulnerability of biodiversity hotspots to global change. 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Protected-area targets could be undermined by climate change-driven shifts in ecoregions and biomes. Communications Earth & Environment, 2(1), Article 1. https://doi.org/10.1038/s43247-021-00270-z Dobrowski, S. Z., & Parks, S. A. (2016). Climate change velocity underestimates climate change exposure in mountainous regions. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms12349 Friedlingstein, P., Andrew, R. M., Rogelj, J., Peters, G. P., Canadell, J. G., Knutti, R., Luderer, G., Raupach, M. R., Schaeffer, M., van Vuuren, D. P., & Le Quéré, C. (2014). Persistent growth of CO2 emissions and implications for reaching climate targets. Nature Geoscience, 7(10), Article 10. https://doi.org/10.1038/ngeo2248 IUCN & UNEP-WCMC. (2019). Protected Planet: World Database on Protected Areas (WDPA). Accessed September 2019. Available at www.protectedplanet.net. (Accessed September 2019) [Map]. www.protected.planet.net Jenkins, D. G., Brescacin, C. R., Duxbury, C. V., Elliott, J. A., Evans, J. A., Grablow, K. R., Hillegass, M., Lyon, B. N., Metzger, G. A., Olandese, M. L., Pepe, D., Silvers, G. A., Suresch, H. N., Thompson, T. N., Trexler, C. M., Williams, G. E., Williams, N. C., & Williams, S. E. (2007). Does size matter for dispersal distance? Global Ecology and Biogeography, 16(4), 415–425. https://doi.org/10.1111/j.1466-8238.2007.00312.x Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811–826. https://doi.org/10.1111/gcb.14549 Lutz, J. A., van Wagtendonk, J. W., & Franklin, J. F. (2010). Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography, 37(5), 936–950. https://doi.org/10.1111/j.1365-2699.2009.02268.x Mahony, C. R., Cannon, A. J., Wang, T., & Aitken, S. N. (2017). A closer look at novel climates: New methods and insights at continental to landscape scales. Global Change Biology, 23(9), 3934–3955. https://doi.org/10.1111/gcb.13645 McLachlan, J. S., Clark, J. S., & Manos, P. S. (2005). Molecular indicators of tree migration capacity under rapid climate change. Ecology, 86(8), 2088–2098. https://doi.org/10.1890/04-1036 Palfrey, R., Oldekop, J. A., & Holmes, G. (2022). Privately protected areas increase global protected area coverage and connectivity. Nature Ecology & Evolution, 6(6), Article 6. https://doi.org/10.1038/s41559-022-01715-0 Parker, L. E., & Abatzoglou, J. T. (2016). Projected changes in cold hardiness zones and suitable overwinter ranges of perennial crops over the United States. Environmental Research Letters, 11(3), 034001. https://doi.org/10.1088/1748-9326/11/3/034001 Parks, S. A., Carroll, C., Dobrowski, S. Z., & Allred, B. W. (2020). Human land uses reduce climate connectivity across North America. Global Change Biology, 26(5), 2944–2955. https://doi.org/10.1111/gcb.15009 Parks, S. A., Holsinger, L. M., Littlefield, C. E., Dobrowski, S. Z., Zeller, K. A., Abatzoglou, J. T., Besancon, C., Nordgren, B. L., & Lawler, J. J. (2022). Efficacy of the global protected area network is threatened by disappearing climates and potential transboundary range shifts. Environmental Research Letters, 17(5), 054016. https://doi.org/10.1088/1748-9326/ac6436 R Core Team. (2020). R: A language and environment for statistical computing. Schwartz, M. W., Iverson, L. R., & Prasad, A. M. (2001). Predicting the potential future distribution of four tree species in Ohio using current habitat availability and climatic forcing. Ecosystems, 4(6), 568–581. https://doi.org/10.1007/s10021-001-0030-3 Stephenson, N. (1998). Actual evapotranspiration and deficit: Biologically meaningful correlates of vegetation distribution across spatial scales. 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(2007). Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences, 104(14), 5738–5742. https://doi.org/10.1073/pnas.0606292104 Species across the planet are shifting their ranges to track suitable climate conditions in response to climate change. Given that protected areas have higher quality habitat and often harbor higher levels of biodiversity compared to unprotected lands, it is often assumed that protected areas can serve as steppingstones for species undergoing climate-induced range shifts. However, there are several factors that may impede successful range shifts among protected areas, including the distance that must be travelled, unfavorable human land uses and climate conditions along potential movement routes, and lack of analogous climates. Through a species-agnostic lens, we evaluate these factors across the global terrestrial protected area network as measures of climate connectivity, which is defined as the ability of a landscape to facilitate or impede climate-induced movement. We found that over half of protected land areas and two-thirds of the number of protected units across the globe are at risk of climate connectivity failure, casting doubt on whether many species can successfully undergo climate-induced range shifts among protected areas. Consequently, protected areas are unlikely to serve as steppingstones for a large number of species under a warming climate. As species disappear from protected areas without commensurate immigration of species suited to the emerging climate (due to climate connectivity failure), many protected areas may be left with a depauperate suite of species under climate change. Our findings are highly relevant given recent pledges to conserve 30% of the planet by 2030 (30x30), underscore the need for innovative land management strategies that allow for species range shifts, and suggest that assisted colonization may be necessary to promote species that are adapted to the emerging climate. There are three files in this repository: 1) backward.analogs - master.table.xlsx – results for backward analogs: · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by country; percent protected lands in each country that exhibit climate connectivity failure is also indicated. · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by protected area. Values represent the mean pixel-based percentile. Also included is a binary (0, 1) indicator of whether the protected area exhibits climate connectivity failure. 2) forward.analogs - master.table.xlsx – results for forward analogs: · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by country; percent protected lands in each country that exhibit climate connectivity failure is also indicated. · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by protected area. Values represent the mean pixel-based percentile. Also included is a binary (0, 1) indicator of whether the protected area exhibits climate connectivity failure. 3) PA_shapefile - cleaned.zip: This is the ‘cleaned’ (see Methods) protected area shapefile we used as a way to summarize dispersal exposure, human exposure, climate exposure, and analog exposure for each protected area. Note that two of these files are Microsoft Excel; they should be accessible via LibreOffice and R and potentially other open-source alternatives.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under grassland cover, no-tillage, cover crops, residue return and organic amendments were compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include the following key descriptor variables and covariates useful for development of scoring functions: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class, pH, and SOC, 3) depth and timing of soil sampling, 4) analytical methods for SQI quantification, 5) units used in published works (i.e. equivalent mass, concentration), 6) SQI abundances, and 7) statistical significance of difference comparisons. *Note: Blank values in tables are considered unreported data.

Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L-1 selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 �� 17 mgSe L-1 day-1) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L-1 day-1, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856��26 mgSe-1day-1gbiomass-1, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H2Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (���80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 ��m. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.