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# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Reference | Source for the data. | ## Sharing/Access information Data was derived from the following sources: **Appendix 1. Full list of references providing the data used in impact and recovery analyses supporting Table S1** Arceo, H. O., Quibilan, M. C., Aliño, P. M., Lim, G., & Licuanan, W. Y. (2001). Coral bleaching in Philippine reefs: Coincident evidences with mesoscale thermal anomalies. Bulletin of Marine Science, 69(2), 579-593. Aronson, R. B., Precht, W. F., Toscano, M. A., & Koltes, K. H. (2002). The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology, 141(3), 435-447. Aronson, R. B., Sebens, K. P., & Ebersole, J. P. (1994). Hurricane Hugo's impact on Salt River submarine canyon, St. Croix, US Virgin Islands. Proceedings of the colloquium on global aspects of coral reefs, Miami, 1993, 189-195. Bahr, K. D., Rodgers, K. S., & Jokiel, P. L. (2017). Impact of three bleaching events on the reef resiliency of Kāne'ohe Bay, Hawai'i. Frontiers in Marine Science, 4(DEC). Baird, A. H., Álvarez-Noriega, M., Cumbo, V. R., Connolly, S. R., Dornelas, M., & Madin, J. S. (2018). Effects of tropical storms on the demography of reef corals. Marine Ecology Progress Series, 606, 29-38. Barranco, L. M., Carriquiry, J. D., Rodríguez-Zaragoza, F. A., Cupul-Magaña, A. L., Villaescusa, J. A., & Calderón-Aguilera, L. E. (2016). Spatiotemporal variations of live coral cover in the Northern Mesoamerican reef system, Yucatan Peninsula, Mexico. Scientia Marina, 80(2), 143-150. Bastidas, C., Bone, D., Croquer, A., Debrot, D., Garcia, E., Humanes, A., . . . Rodríguez, S. (2012). Massive hard coral loss after a severe bleaching event in 2010 at Los Roques, Venezuela. Revista de Biologia Tropical, 60(SUPPL. 1), 29-37. Booth, D. J., & Beretta, G. A. (2002). Changes in a fish assemblage after a coral bleaching event. Marine Ecology Progress Series, 245, 205-212. Brandl, S. J., Emslie, M. J., & Ceccarelli, D. M. (2016). Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. Ecosphere, 7(11). Brown, D., & Edmunds, P. J. (2013). Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. Journal of Experimental Marine Biology and Ecology, 441, 62-70. Brown, V. B., Davies, S. A., & Synnot, R. N. (1990). Long-term Monitoring of the Effects of Treated Sewage Effluent on the Intertidal Macroalgal Community Near Cape Schanck, Victoria, Australia. Botanica Marina, 33(1), 85-98. Bruckner, A. W., Coward, G., Bimson, K., & Rattanawongwan, T. (2017). Predation by feeding aggregations of Drupella spp. inhibits the recovery of reefs damaged by a mass bleaching event. Coral Reefs, 36(4), 1181-1187. Burt, J. 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Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE, 12(9). Crabbe, M. J. C. (2014). Evidence of initial coral community recovery at Discovery Bay on Jamaica’s north coast. Revista de Biologia Tropical, 62, 137-140. Crosbie, A. J., Bridge, T. C., Jones, G., & Baird, A. H. (2019). Response of reef corals and fish at Osprey Reef to a thermal anomaly across a 30 m depth gradient. Marine Ecology Progress Series, 622, 93-102. Darling, E. S., McClanahan, T. R., & Côté, I. M. (2010). Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conservation Letters, 3(2), 122-130. De Bakker, D. M., Meesters, E. H., Bak, R. P. M., Nieuwland, G., & Van Duyl, F. C. (2016). Long-term Shifts in Coral Communities On Shallow to Deep Reef Slopes of Curaçao and Bonaire: Are There Any Winners? Frontiers in Marine Science, 3(247). Depczynski, M., Gilmour, J. 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(2016). 27 years of benthic and coral community dynamics on turbid, highly urbanised reefs off Singapore. Scientific Reports, 6. Guillemot, N., Chabanet, P., & Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). Coral Reefs, 29(2), 445-453. Guzmán, H. M., & Cortés, J. (2001). Changes in reef community structure after fifteen years of natural disturbances in the Eastern Pacific (Costa Rica). Bulletin of Marine Science, 69(1), 133-149. Guzman, H. M., Cortes, J., Richmond, R. H., & Glynn, P. W. (1987). Effects of "El Nino - Southern oscillation' 1982/83 in the coral reefs at Isla del Cano, Costa Rica. Revista de Biologia Tropical, 35(2), 325-332. Haapkylä, J., Melbourne-Thomas, J., Flavell, M., & Willis, B. L. (2013). Disease outbreaks, bleaching and a cyclone drive changes in coral assemblages on an inshore reef of the Great Barrier Reef. Coral Reefs, 32(3), 815-824. Hagan, A., & Spencer, T. (2008). Reef resilience and change 1998–2007, Alphonse Atoll, Seychelles. Paper presented at the Proc 11th Int Coral Reef Symp. Harii, S., Hongo, C., Ishihara, M., Ide, Y., & Kayanne, H. (2014). Impacts of multiple disturbances on coral communities at Ishigaki Island, Okinawa, Japan, during a 15 year survey. Marine Ecology Progress Series, 509, 171-180. Harrison, H. B., Álvarez-Noriega, M., Baird, A. H., Heron, S. F., MacDonald, C., & Hughes, T. P. (2018). Back-to-back coral bleaching events on isolated atolls in the Coral Sea. Coral Reefs. Holbrook, S. J., Adam, T. C., Edmunds, P. J., Schmitt, R. J., Carpenter, R. C., Brooks, A. J., . . . Briggs, C. J. (2018). Recruitment Drives Spatial Variation in Recovery Rates of Resilient Coral Reefs. Scientific Reports, 8(1). Hongo, C., & Yamano, H. (2013). Species-Specific Responses of Corals to Bleaching Events on Anthropogenically Turbid Reefs on Okinawa Island, Japan, over a 15-year Period (1995-2009). PLoS ONE, 8(4). 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Three decades of coral reef community dynamics in St. John, USVI: A contrast of scleractinians and octocorals. Ecosphere, 8(1). Van Woesik, R., De Vantier, L. M., & Glazebrook, J. S. (1995). Effects of Cyclone "Joy' on nearshore coral communities of the Great Barrier Reef. Marine Ecology Progress Series, 128(1-3), 261-270. Van Woesik, R., Sakai, K., Ganase, A., & Loya, Y. (2011). Revisiting the winners and the losers a decade after coral bleaching. Marine Ecology Progress Series, 434, 67-76. Vercelloni, J., Kayal, M., Chancerelle, Y., & Planes, S. (2019). Exposure, vulnerability, and resiliency of French Polynesian coral reefs to environmental disturbances. Scientific Reports, 9(1). Walsh, W. J. (1983). Stability of a coral reef fish community following a catastrophic storm. Coral Reefs, 2(1), 49-63. Wilkinson, C. (2004). Status of coral reefs of the world: 2004 (Vol. 2). Queensland, Australia: Global Coral Reef Monitoring Network. Wilkinson, C. R., & Souter, D. (2008). Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.

Positive forced aeration is widely used in industrial composting plants to supply sufficient oxygen, accelerating compost maturity. However, this technology results in significant gaseous emission, especially NH3 and GHGs emissions. To reduce gaseous emissions and investigate aeration efficiency, negative pressure aeration was used during cattle manure þ corn stalk composting in 50 L-scale reactors. Composting with negative pressure aeration at three different flow rates (0.25, 0.50 and 0.75 L/min/kg dry weight, named Negative-L, Negative-M and Negative-H treatments) were conducted. Treatment with positive pressure aeration was set as a control (Positive-M, with flow rate at 0.50 L/min/kg dry weight). The results showed that negative pressure aeration changed the temporal distribution of oxygen and temperature. With the same flow rate, the Negative-M treatment maintained a longer thermophilic period, accelerating organic matter degradation (47.6% in treatment Negative-M and 41.4% in Positive-M) and the maturity of feedstock (germination index was 105.9% in Negative-M and 58.5% in Positive-M). Ammonia emissions were significantly reduced by composting with negative pressure aeration. During composting, 36.7%, 15.8%, 16.8% and 16.0% of the initial total nitrogen was lost via NH3 volatilizations in the Positive-M, Negative-L, Negative-M and Negative-H treatments, respectively, indicating NH3 emissions were reduced by ~55% compared to the positive pressure aeration treatment. Even though both CH4 and N2O emission were greater from the negative pressure aeration treatments, the global warming potential was significantly reduced in treatments with negative pressure aeration because of the lower NH3 emission (an indirect N2O source). This indicates the benefit of NH3 emission mitigation was larger than the increase in CH4 and N2O emissions. Positive forced aeration is widely used in industrial composting plants to supply sufficient oxygen, accelerating compost maturity. However, this technology results in significant gaseous emission, especially NH3 and GHGs emissions. To reduce gaseous emissions and investigate aeration efficiency, negative pressure aeration was used during cattle manure þ corn stalk composting in 50 L-scale reactors. Composting with negative pressure aeration at three different flow rates (0.25, 0.50 and 0.75 L/min/kg dry weight, named Negative-L, Negative-M and Negative-H treatments) were conducted. Treatment with positive pressure aeration was set as a control (Positive-M, with flow rate at 0.50 L/min/kg dry weight). The results showed that negative pressure aeration changed the temporal distribution of oxygen and temperature. With the same flow rate, the Negative-M treatment maintained a longer thermophilic period, accelerating organic matter degradation (47.6% in treatment Negative-M and 41.4% in Positive-M) and the maturity of feedstock (germination index was 105.9% in Negative-M and 58.5% in Positive-M). Ammonia emissions were significantly reduced by composting with negative pressure aeration. During composting, 36.7%, 15.8%, 16.8% and 16.0% of the initial total nitrogen was lost via NH3 volatilizations in the Positive-M, Negative-L, Negative-M and Negative-H treatments, respectively, indicating NH3 emissions were reduced by ~55% compared to the positive pressure aeration treatment. Even though both CH4 and N2O emission were greater from the negative pressure aeration treatments, the global warming potential was significantly reduced in treatments with negative pressure aeration because of the lower NH3 emission (an indirect N2O source). This indicates the benefit of NH3 emission mitigation was larger than the increase in CH4 and N2O emissions.

Livestock grazing is among the most intensive land-use activities in grasslands and can affect plant communities directly or indirectly via grazing-induced soil legacies. Under climate change, grasslands are threatened globally by recurrent drought. However, the extent to which drought influences grazing-induced soil legacy effects on plant biomass production and community composition remains largely unexplored. We grew five naturally co-occurring plant species (three dominants and two subordinates) in mixed communities in a glasshouse experiment in live and sterilized soil that had or had not been subjected to 19 years of grazing; these plant communities were then exposed to a subsequent drought. We tested the treatment effects on plant community biomass, proportional aboveground biomass of individual species, arbuscular mycorrhizal (AM) fungal root colonization, and soil nutrient availability. Under drought-free conditions, soils from grazed plots produced significantly higher plant aboveground and total community biomass compared to soils from ungrazed plots. In contrast, plant aboveground and total community biomass were similar between grazed and ungrazed soils under drought conditions. Similarly, soils from grazed plots increased the proportional biomass of dominant species but decreased the proportion of subordinate species; however, the proportional biomass of dominant and subordinate species was similar between grazed and ungrazed soils under drought conditions. Soil NO3--N in grazed soil was significantly higher compared to ungrazed soil. Drought dramatically increased soil NO3--N in sterilized soil and had a more pronounced increase in grazed soil than in ungrazed soil. Arbuscular mycorrhizal fungal root colonization from grazed soil was lower compared to ungrazed soil. Drought significantly increased the soil available phosphorus concentration, as well as plant community AM fungal root colonization. Synthesis. Our study suggests that drought can neutralize positive grazing effects on plant community biomass production via altered plant-soil interactions. Also, we found that drought can alleviate the negative effects of grazing legacies on subordinate species by reducing the competitiveness of dominant species. Our study provides new insights for understanding the underlying mechanisms of grazing effects on grassland productivity under climate change. Please see the README document and the accompanying published article: Duan, DD., Tian, Z., Wu, NN., Feng, XX., Hou, FJ., Nan, ZB., Kardol, P., and Chen, T. 2023. Drought neutralizes positive effects of long-term grazing on grassland productivity through altering plant-soil interactions. Functional Ecology. 

Water resources index W quantifies the potential dependence of the world's lowland areas on water resources originating in mountain areas upstream. The data cover the timeframe from the 1960s (1961–1970) to the 2040s (2041–2050) in decadal steps. Data for projections from the 2010s onwards are available for three scenario pathways (SSP1-RCP4.5, SSP2-RCP6.0, SSP3-RCP6.0) and show median results from 5 CMIP5 GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1‑M). The files are GeoTIFF formatted and in a regular raster of 5’×5’ (arc minutes in WGS 1984 coordinate system) The values of W can be classified using the following ranges: W ≤ -2 → Essential but vastly insufficient -2 < W < -1 → Essential but insufficient -1 ≤ W < 0 → Essential and sufficient W = 0 → No surplus from mountains 0 < W ≤ 1 → Supportive 1 < W < 2 → Minor W ≥ 2 → Negligible The values of W are rounded to four decimal places and limited to a range of -1110 to 9998. Values falling outside of that range are set to the nearest limit. he following flag values apply to W: -5555 indicates that there is no water balance surplus from the mountain area upstream, but a lowland water balance surplus; -6666 indicates that there is no water balance surplus from the mountain area upstream, and a lowland water balance deficit. Mountain areas and oceans are NODATA, large ice shields are omitted (Greenland: NODATA, Antarctica: not covered in extent). Mountain areas provide disproportionally high runoff in many parts of the world, and here we quantify for the first time their importance for water resources and food production from the viewpoint of the lowland areas downstream. The dataset maps the degree to which lowland areas potentially depend on runoff contributions from mountain areas (39% of land mass) between the 1960s and the 2040s.

The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress to freshwater resources. Ensuring that increases in irrigated production only occur in places where water is relatively abundant is a key objective of sustainable agriculture, and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet a spatially detailed understanding of the evolution of global area equipped for irrigation (AEI) is missing. Here we utilize the latest sub-national irrigation statistics (covering 17298 administrative units) from various official sources to develop a gridded (5 arc-min resolution) global product of AEI for the years 2000, 2005, 2010, and 2015. We find that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha) with locations of both substantial expansion (e.g., northwest India, northeast China) and decline (e.g., Russia). Combining these outputs with information on green (i.e., rainfall) and blue (i.e., surface and ground) water stress, we also examine to what extent irrigation has expanded unsustainably (i.e., in places already experiencing water stress). We find that more than half (52%) of irrigation expansion has taken place in regions that were already water stressed, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security. Recommended citation: Mehta, P., Siebert, S., Kummu, M. et al. Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nat Water (2024). https://doi.org/10.1038/s44221-024-00206-9 Open-access peer reviewed publication available at https://www.nature.com/articles/s44221-024-00206-9 Files G_AEI_*.ASC were produced using the GMIA dataset[https://data.apps.fao.org/catalog/iso/f79213a0-88fd-11da-a88f-000d939bc5d8]. Files MEIER_G_AEI_*.ASC were produced using Meier et al. (2018) dataset [https://doi.pangaea.de/10.1594/PANGAEA.884744].

Study site This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 soil plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots, measuring approximately 1 m2, evenly span a temperature gradient of 10-35°C. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015 (see Robinson et al. 2021 for plot details and sampling regime). Soil properties Soil temperature was measured at 5 cm depth at each plot on 15th, 18th, and 22nd August, and a mean plot temperature calculated. Soil physiochemical properties were analysed from 3 soil cores of 3 cm in diameter, taken from the upper 10 cm soil stratum at each plot; one quarter of each subsample was pooled to obtain an estimate per plot. Aboveground plant matter, excluding roots, were removed from each core. Percentage soil moisture was calculated by measuring the weight of one pooled soil sample before and after drying for 24 h in a 70°C drying oven. Soil pH was obtained from 20 g of the dry soil by adding 100 ml distilled water, shaking for 5 min on 150 rpm, letting the sample stand for 2 h, and measuring soil pH from the water layer using an InoLab pH 720 (WTW) probe. Soil PO4, NH4, and NO3 concentrations were analysed from a second pooled soil; 60 g of fresh soil was extracted in 100 ml distilled water, filtered through a GF/C (1.2μm) glass microfiber filter (Whatman, GE Healthcare Europe GmbH), and analysed using a Lachat QuikChem 8000 analyser (Zallweger Analytics, Inc., Lachat Instruments Division, USA). Total mineral N was calculated as the sum of NH4 and NO3. Soil organic matter content (excluding dry root biomass) was calculated as the weight lost from an oven dried (105°C for 24 hours) soil sample after heating at 550 °C for 5 h. Decomposition rate of soil organic matter was measured using the Cotton-strip Assay method (Tiegs et al. 2013) by placing a 2.5 cm x 8 cm strip of Fredrix-brand unprimed 12-oz. heavyweight cotton fabric (Style #548) 5 cm belowground at 60 plots, concurrently with a Maxim Integrated DS1921G Thermocron iButton temperature logger, on 13th May 2015. The strips were collected on 3rd July, rinsed with stream water to remove residual soil, soaked in 96% ethanol for 30 seconds to kill bacteria and halt decomposition, and dried at 60 °C for 12 h. Using a universal testing machine (Instron 5866 with 500 kN tensile holding clamps), maximum tensile strench of each cotton strip was measured. % tensile loss (proxy for decomposition) was calculated as (C-T) / C x 100, where T is the maximum tensile strength for each strip collected from the field, and C is the mean tensile strength of seven control strips, which had not been placed in the ground. See Robinson et al. 2021 for detailed description of plots sampled in 2015. Microbial abundance Bacterial and fungal abundance was estimated from additional soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including the litter layer) at each plot. DNA was extracted using the PowerSoil DNA Isolation Kit (Qiagen, Germany). DNA was quantified using the high-sensitivity Qubit assay (Thermo Fisher Scientific, Switzerland). Relative abundances of bacterial and fungal communities were determined by quantitative PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). PCR amplification of partial bacterial small-subunit ribosomal RNA genes (region V1–V3 of 16S; primers 27F and 512R) and fungal ribosomal internal transcribed spacers (region ITS2; primers IT3 and ITS4) was performed as described previously (Frey et al. 2020, Frey et al. 2021). For qPCR analyses, 2.5 ng DNA in a total volume of 6.6 µL and 8.4 µL GoTaq qPCRMaster Mix (Promega, Switzerland), containing 1.8 mM of each primer and 0.2 mg mL-1 of BSA, were used. The PCR conditions consisted of an initial denaturation at 95 ºC for 10 min, 40 cycles of denaturation at 95 ºC for 40 s, annealing at 58 ºC for 40 s and elongation at 72 ºC for 60 s followed by the final data acquisition step at 80 ºC for 60 s. The specificity of the amplification products was confirmed by melting-curve analysis. Three standard curves per target region (correlations ≥0.997) were obtained using tenfold serial dilutions (10-1 to 10-9 copies) of plasmids generated from cloned targets (Frey et al. 2020). Data were converted to represent the average copy number of targets per μg DNA and per g soil. Vegetation properties Vascular plant biomass was measured from a randomly placed 30 x 30 cm quadrat at each plot. To measure aboveground biomass (AGB) of plants, the aboveground layer of vegetation was cut and removed, dried at 70 °C for 24 h and weighed to obtain biomass per unit area. AGB was estimated as the biomass of graminoids plus forbs; total biomass of mosses was also estimated. Graminoid leaf N concentration was analysed from dried and ground leaf material using a LECO CNS-2000 analyser (LECO Corporation, Saint Joseph, MI, USA). Belowground biomass (BGB) of vascular plants was estimated from a soil core of 3 cm in diameter taken from the 10 cm upper soil stratum (excluding aboveground plant material) at each quadrat. Roots were extracted from the soil cores by rinsing in water using a 250-μm sieve, dried at 70 °C for 24 hours and weighed to obtain biomass per unit area. Root to shoot ratio was calculated as dry weight of BGB per cm2 divided by dry weight of AGB per cm2, and the total vascular plant biomass as the sum of AGB and BGB. Invertebrate community Enchytraied and nematode biomass was estimated from 3 soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including litter layer) at each plot. Enchytraieds were extracted using wet funnels (O'Connor 1962) from a pooled sample of one half of each of the three soil cores, counted live, and classified into size classes (length 0-2, 2.1-4, 4.1-6, 6.1-8, 8.1-10, 10.1-12 or >12 mm) and their biomass was calculated according to Abrahamsen (1973). Nematodes were also extracted using wet funnels (Sohlenius 1979) from a pooled sample of a quarter of each of the three soil cores, counted live and preserved in 70% ethanol. Fifty individuals from each sample were identified and classified by trophic group (bacterivore, fungivoe, herbivore, omnivore, predator; Yeates et al. 1993). Soil micro-arthropods were extracted using a modified high-gradient-extractor (MacFayden 1961) from soil cores of 5.4 cm in diameter, taken from the upper 4 cm soil straum (including litter layer) at each plot. Total micro-arthropod biomass was calculated as the sum of all individual species' biomasses, obtained using length-weight regressions (see Robinson et al. 2021), and abundance of individual trophic groups (microbivore/detritivore, herbivore, omnivore, predator) calculated. Epigeal invertebrates were sampled by deploying five pitfall traps in each plot. White plastic cups of 7 cm in diameter and 8.5 cm in depth were filled with 10 ml of ethylene glycol and 30 ml of stream water, and left for 48 h before collection. Samples from the five traps at each plot were combined into a 250-μm sieve and stored in 70% ethanol. Invertebrate activity density (abundance) was estimate as the total number of individuals in the five traps, and total biomass as the sum of all individual species' biomasses. Invertebrates were identified to species level where possible and split into trophic groups, exluding adult Diptera, Hymenoptera, and Lepidoptera. Further details of sampling and collection of epigeal invertebrates are detailed in Robinson et al. (2018). References: Abrahamsen G. (1973) Studies on body-volume, body-surface area, density, and live weight of enchytraeidae (Oligochaeta). Pedobiologia 13: 6–15. Frey B, Carnol M, Dharmarajah A, Brunner I, Schleppi P. (2020) Only minor changes in the soil microbiome of a sub-alpine forest after 20 years of moderately increased nitrogen loads. Frontiers in Forests and Global Change 3: 77. Frey B, Walthert L, Perez-Mon C, Stierli B, Köchli R, Dharmarajah A, Brunner I (2021) Deep soil layers of drough-exposed forests harbor poorly known bacterial and fungal communities. Frontiers in Microbiology 12: 1061. MacFayden A. (1961) Improved funnel-type extractors for soil arthropods. Journal of Animal Ecology 30: 171–184. O’Connor FB. (1962) The extraction of Enchytraeidae from soil. In: P. W. Murphy (Ed.) Progress in soil zoology. Butterworth, London, UK; 279–285. Robinson SI, McLaughlin ÓB, Marteinsdóttir B, O'Gorman EJ. (2018) Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. Journal of Animal Ecology 87: 634–46. Robinson SI, Mikola J, Ovaskainen O, O’Gorman EJ. (2021) Temperature effects on the temporal dynamics of a subarctic invertebrate community. Journal of Animal Ecology 90: 1217-1227. Sohlenius B. (1979) A carbon budget for nematodes, rotifers and tardigrades in a Swedish coniferous forest soil. Holarctic Ecology 2: 30–40. Tiegs SD, Clapcott JE, Griffiths NA, Boulton AJ. (2013) A standardized cotton-strip assay for measuring organic-matter decomposition in streams. Ecological Indicators 32: 131–139. Yeates GW, Bongers T, De Goede RGM, Freckman DW, Georgieva SS. (1993) Feeding habits in soil nematode families and genera—an outline for soil ecologists. Journal of Nematology 25: 315–331. This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots span a temperature gradient of 10-35 °C over the sampling period, and this temperature gradient is consistent over time. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015. See README_Robinson_Hengill2018.txt