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Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

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 

We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.

The impacts of increasing natural climate disasters are threatening food security in the Asia-Pacific region. Rice is Asia’s most important staple food. Climate variability and change directly impact rice production, through changes in rainfall, temperature and CO2 concentrations. The key for sustainable rice crop is water management. Adaptation can occur through shifts of cropping to higher latitudes and can profit from river systems (via irrigation) so far not considered. New opportunities arise to produce more than one crop per year in cooler areas. Asian wheat production in 2005 represents about 43 % of the global total. Changes in agronomic practices, such as earlier plant dates and cultivar substitution will be required. Fisheries play a crucial role in providing food security with the contribution of fish to dietary animal protein being very high in the region – up to 90 % in small island developing states (SIDS). With the warming of the Pacific and Indian Oceans and increased acidification, marine ecosystems are presently under stress. Despite these trends, maintaining or enhancing food production from the sea is critical. However, future sustainability must be maintained whilst also securing biodiversity conservation. Improved fisheries management to address the existing non-climate threats remains paramount in the Indian and Pacific Oceans with sustainable management regimes being established. Climate-related impacts are expected to increase in magnitude over the coming decades, thus preliminary adaptation to climate change is valuable.

Atmospheric concentrations of the greenhouse gas nitrous oxide (N(2)O) have increased significantly since pre-industrial times owing to anthropogenic perturbation of the global nitrogen cycle, with animal production being one of the main contributors. Grasslands cover about 20 per cent of the temperate land surface of the Earth and are widely used as pasture. It has been suggested that high animal stocking rates and the resulting elevated nitrogen input increase N(2)O emissions. Internationally agreed methods to upscale the effect of increased livestock numbers on N(2)O emissions are based directly on per capita nitrogen inputs. However, measurements of grassland N(2)O fluxes are often performed over short time periods, with low time resolution and mostly during the growing season. In consequence, our understanding of the daily and seasonal dynamics of grassland N(2)O fluxes remains limited. Here we report year-round N(2)O flux measurements with high and low temporal resolution at ten steppe grassland sites in Inner Mongolia, China. We show that short-lived pulses of N(2)O emission during spring thaw dominate the annual N(2)O budget at our study sites. The N(2)O emission pulses are highest in ungrazed steppe and decrease with increasing stocking rate, suggesting that grazing decreases rather than increases N(2)O emissions. Our results show that the stimulatory effect of higher stocking rates on nitrogen cycling and, hence, on N(2)O emission is more than offset by the effects of a parallel reduction in microbial biomass, inorganic nitrogen production and wintertime water retention. By neglecting these freeze-thaw interactions, existing approaches may have systematically overestimated N(2)O emissions over the last century for semi-arid, cool temperate grasslands by up to 72 per cent.

SUMMARYThis article discusses how decentralisation policies are enacted in the planning and implementation of natural resource management interventions in rural Ethiopia. A key element of decentralisation policy is the emphasis on greater participation by local communities. Drawing on qualitative research conducted with government staff and farmers, this paper illustrates how different actors perceive and implement national policy and how these actions affect the longer‐term sustainability of land management interventions. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.