{"type": "FeatureCollection", "features": [{"id": "10.5061/dryad.cz8w9gj78", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:13Z", "type": "Dataset", "title": "Soil microbial relative resource limitation exhibited contrasting seasonal patterns along an elevational gradient in Yulong snow mountain", "description": "unspecified", "keywords": ["2. Zero hunger", "mountain ecosystems", "13. Climate action", "microbial metabolic mechanisms", "microbial relative C limitation", "microbial relative P limitation", "C use efficiency", "FOS: Earth and related environmental sciences", "15. Life on land", "elevations"], "contacts": [{"organization": "Zhang, Dandan, Wu, Baoyun, Li, Jinsheng, Cheng, Xiaoli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.cz8w9gj78"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.cz8w9gj78", "name": "item", "description": "10.5061/dryad.cz8w9gj78", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.cz8w9gj78"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.1002/cli2.19", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:00Z", "type": "Journal Article", "created": "2021-10-21", "title": "An alert system for Seasonal Fire probability forecast for South American Protected Areas", "description": "Abstract

Timely spatially explicit warning of areas with high fire occurrence probability is an important component of strategic plans to prevent and monitor fires within South American (SA) Protected Areas (PAs). In this study, we present a five\uffe2\uff80\uff90level alert system, which combines both climatological and anthropogenic factors, the two main drivers of fires in SA. The alert levels are: High Alert, Alert, Attention, Observation and Low Probability. The trend in the number of active fires over the past three years and the accumulated number of active fires over the same period were used as indicators of intensification of human use of fire in that region, possibly associated with ongoing land use/land cover change (LULCC). An ensemble of temperature and precipitation gridded output from the GloSea5 Seasonal Forecast System was used to indicate an enhanced probability of hot and dry weather conditions that combined with LULCC favour fire occurrences. Alerts from this system were first issued in August 2020, for the period ranging from August to October (ASO) 2020. Overall, 50% of all fires observed during the ASO 2017\uffe2\uff80\uff932019 period and 40% of the ASO 2020 fires occurred in only 29 PAs were all categorized in the top two alert levels. In categories mapped as High Alert level, 34% of the PAs experienced an increase in fires compared with the 2017\uffe2\uff80\uff932019 reference period, and 81% of the High Alert false alarm registered fire occurrence above the median. Initial feedback from stakeholders indicates that these alerts were used to inform resource management in some PAs. We expect that these forecasts can provide continuous information aiming at changing societal perceptions of fire use and consequently subsidize strategic planning and mitigatory actions, focusing on timely responses to a disaster risk management strategy. Further research must focus on the model improvement and knowledge translation to stakeholders.

", "keywords": ["0106 biological sciences", "Atmospheric Science", "Land cover", "Flood Risk", "Precipitation", "01 natural sciences", "Environmental science", "Impact of Climate Change on Forest Wildfires", "Global Flood Risk Assessment and Management", "Meteorology", "Engineering", "Machine learning", "False alarm", "Civil engineering", "0105 earth and related environmental sciences", "Climatology", "Global and Planetary Change", "Tropical Cyclone Intensity and Climate Change", "Geography", "Warning system", "Geology", "FOS: Earth and related environmental sciences", "15. Life on land", "Computer science", "Earth and Planetary Sciences", "13. Climate action", "Environmental Science", "Physical Sciences", "Land use", "Telecommunications", "FOS: Civil engineering"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/cli2.19"}, {"href": "https://doi.org/10.1002/cli2.19"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Climate%20Resilience%20and%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/cli2.19", "name": "item", "description": "10.1002/cli2.19", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/cli2.19"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.1002/ecy.2199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:02Z", "type": "Journal Article", "created": "2018-02-27", "title": "Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe", "description": "Abstract

The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.Greenhouse gas (GHGs) emissions from peatlands contribute significantly to ongoing climate change because of human land use. To develop reliable and comprehensive estimates and predictions of GHG emissions from peatlands, it is necessary to have GHG observations, including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), that cover different peatland types globally. We synthesize published peatland studies with field GHG flux measurements to identify gaps in observations and suggest directions for future research. Although GHG flux measurements have been conducted at numerous sites globally, substantial gaps remain in current observations, encompassing various peatland types, regions and GHGs. Generally, there is a pressing need for additional GHG observations in Africa, Latin America and the Caribbean regions. Despite widespread measurements of CO2 and CH4, studies quantifying N2O emissions from peatlands are scarce, particularly in natural ecosystems. To expand the global coverage of peatland data, it is crucial to conduct more eddy covariance observations for long-term monitoring. Automated chambers are preferable for plot-scale observations to produce high temporal resolution data; however, traditional field campaigns with manual chamber measurements remain necessary, particularly in remote areas. To ensure that the data can be further used for modeling purposes, we suggest that chamber campaigns should be conducted at least monthly for a minimum duration of one year with no fewer than three replicates and measure key environmental variables. In addition, further studies are needed in restored peatlands, focusing on identifying the most effective restoration approaches for different ecosystem types, conditions, climates, and land use histories.Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40\uffc2\uffa0years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than \uffc2\uffb18\uffc2\uffa0Pa in the CO2 cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40\uffc2\uffa0year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.

", "keywords": ["Atmospheric sciences", "550", "Astronomy", "Atmosphere (unit)", "FOS: Mechanical engineering", "Library science", "Oceanography", "01 natural sciences", "CO2 ice", "pressure", "Mars Exploration Program", "Engineering", "Surface pressure", "Storm", "Martian Climate", "Space Suit Design and Ergonomics for EVA", "Martian Atmosphere", "Earth and Planetary Astrophysics (astro-ph.EP)", "Climatology", "Global and Planetary Change", "Geography", "Martian Surface", "Physics", "Geology", "Impact crater", "Condensed matter physics", "Anomaly (physics)", "World Wide Web", "Algorithm", "Satellite Observations", "Residual", "Physical Sciences", "Exploration and Study of Mars", "Astrophysics - Instrumentation and Methods for Astrophysics", "Research Article", "FOS: Physical sciences", "Mars", "Aerospace Engineering", "Pressure gradient", "Environmental science", "[SDU] Sciences of the Universe [physics]", "atmospheric mass", "Meteorology", "Orbiter", "0103 physical sciences", "Instrumentation and Methods for Astrophysics (astro-ph.IM)", "Formation and Evolution of the Solar System", "0105 earth and related environmental sciences", "Pressure system", "CO 2 ice", "Astronomy and Astrophysics", "FOS: Earth and related environmental sciences", "Astrobiology", "Computer science", "Physics and Astronomy", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Global Methane Emissions and Impacts", "Environmental Science", "cap sublimation", "Water on Mars", "Astrophysics - Earth and Planetary Astrophysics"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JE007190"}, {"href": "https://doi.org/10.1029/2022je007190"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Planets", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2022je007190", "name": "item", "description": "10.1029/2022je007190", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2022je007190"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "10.1038/s41467-022-31540-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:34Z", "type": "Journal Article", "created": "2022-07-01", "title": "Global stocks and capacity of mineral-associated soil organic carbon", "description": "Abstract

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1\uffe2\uff80\uff89m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world\uffe2\uff80\uff99s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.The tremendous reservoir of soil organic carbon (SOC) in wetlands is being threatened by water-table decline (WTD) globally. However, the SOC response to WTD remains highly uncertain. Here we examine the under-investigated role of iron (Fe) in mediating soil enzyme activity and lignin stabilization in a mesocosm WTD experiment in an alpine wetland. In contrast to the classic \uffe2\uff80\uff98enzyme latch\uffe2\uff80\uff99 theory, phenol oxidative activity is mainly controlled by ferrous iron [Fe(II)] and declines with WTD, leading to an accumulation of dissolvable aromatics and a reduced activity of hydrolytic enzyme. Furthermore, using dithionite to remove Fe oxides, we observe a significant increase of Fe-protected lignin phenols in the air-exposed soils. Fe oxidation hence acts as an \uffe2\uff80\uff98iron gate\uffe2\uff80\uff99 against the \uffe2\uff80\uff98enzyme latch\uffe2\uff80\uff99 in regulating wetland SOC dynamics under oxygen exposure. This newly recognized mechanism may be key to predicting wetland soil carbon storage with intensified WTD in a changing climate.

", "keywords": ["Composite material", "Science", "Soil Science", "Organic chemistry", "Carbon Dynamics in Peatland Ecosystems", "01 natural sciences", "Article", "Environmental science", "Agricultural and Biological Sciences", "Importance of Mangrove Ecosystems in Coastal Protection", "Soil water", "Carbon fibers", "Soil Carbon Sequestration", "Biology", "Groundwater", "Ecosystem", "0105 earth and related environmental sciences", "Soil science", "Ecology", "Q", "Life Sciences", "Composite number", "Geology", "Mesocosm", "FOS: Earth and related environmental sciences", "04 agricultural and veterinary sciences", "15. Life on land", "Soil carbon", "Materials science", "6. Clean water", "Water table", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Wetland", "Environmental chemistry", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Ferrous"]}, "links": [{"href": "https://doi.org/10.1038/ncomms15972"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/ncomms15972", "name": "item", "description": "10.1038/ncomms15972", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/ncomms15972"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-26T00:00:00Z"}}, {"id": "10.1038/s41467-017-00114-5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:33Z", "type": "Journal Article", "created": "2017-07-17", "title": "Recent increases in terrestrial carbon uptake at little cost to the water cycle", "description": "Abstract

Quantifying the responses of the coupled carbon and water cycles to current global warming and rising atmospheric CO2 concentration is crucial for predicting and adapting to climate changes. Here we show that terrestrial carbon uptake (i.e. gross primary production) increased significantly from 1982 to 2011 using a combination of ground-based and remotely sensed land and atmospheric observations. Importantly, we find that the terrestrial carbon uptake increase is not accompanied by a proportional increase in water use (i.e. evapotranspiration) but is largely (about 90%) driven by increased carbon uptake per unit of water use, i.e. water use efficiency. The increased water use efficiency is positively related to rising CO2 concentration and increased canopy leaf area index, and negatively influenced by increased vapour pressure deficits. Our findings suggest that rising atmospheric CO2 concentration has caused a shift in terrestrial water economics of carbon uptake.

", "keywords": ["Atmospheric sciences", "GLOBAL-SCALE", "Climate Change and Variability Research", "02 engineering and technology", "7. Clean energy", "01 natural sciences", "Terrestrial ecosystem", "Carbon fibers", "Climate change", "Terrestrial plant", "Global and Planetary Change", "CLIMATE-CHANGE", "EVAPOTRANSPIRATION", "Evapotranspiration", "Primary production", "Ecology", "Global warming", "Q", "TRANSPIRATION", "Composite number", "Geology", "Carbon cycle", "6. Clean water", "Physical Sciences", "8. Economic growth", "DIOXIDE", "Water-use efficiency", "Composite material", "Atmospheric carbon cycle", "Science", "Carbon dioxide in Earth's atmosphere", "STOMATAL CONDUCTANCE", "0207 environmental engineering", "Article", "Environmental science", "USE EFFICIENCY", "ATMOSPHERIC CO2", "Irrigation", "Biology", "Ecosystem", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Earth and related environmental sciences", "15. Life on land", "TRENDS", "Materials science", "Carbon dioxide", "13. Climate action", "Earth and Environmental Sciences", "FOS: Biological sciences", "Environmental Science", "Global Methane Emissions and Impacts", "VEGETATION", "Water cycle", "Climate Modeling", "Water use"]}, "links": [{"href": "https://www.nature.com/articles/s41467-017-00114-5.pdf"}, {"href": "https://doi.org/10.1038/s41467-017-00114-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41467-017-00114-5", "name": "item", "description": "10.1038/s41467-017-00114-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41467-017-00114-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-24T00:00:00Z"}}, {"id": "10.1038/s41598-019-55251-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:37Z", "type": "Journal Article", "created": "2019-12-16", "title": "Assessing the impact of global climate changes on irrigated wheat yields and water requirements in a semi-arid environment of Morocco", "description": "Abstract

The present work aims to quantify the impact of climate change (CC) on the grain yields of irrigated cereals and their water requirements in the Tensift region of Morocco. The Med-CORDEX (MEDiterranean COordinated Regional Climate Downscaling EXperiment) ensemble runs under scenarios RCP4.5 (Representative Concentration Pathway) and RCP8.5 are first evaluated and disaggregated using the quantile-quantile approach. The impact of CC on the duration of the main wheat phenological stages based on the degree-day approach is then analyzed. The results show that the rise in air temperature causes a shortening of the development cycle of up to 50 days. The impacts of rising temperature and changes in precipitation on wheat yields are next evaluated, based on the AquaCrop model, both with and without taking into account the fertilizing effect of CO2. As expected, optimal wheat yields will decrease on the order of 7 to 30% if CO2 concentration rise is not considered. The fertilizing effect of CO2 can counterbalance yield losses, since optimal yields could increase by 7% and 13% respectively at mid-century for the RCP4.5 and RCP8.5 scenarios. Finally, water requirements are expected to decrease by 13 to 42%, mainly in response to the shortening of the cycle. This decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions.

", "keywords": ["Water resources", "Atmospheric sciences", "Agricultural Irrigation", "environment/Bioclimatology", "550", "Representative Concentration Pathways", "Adaptation to Climate Change in Agriculture", "Arid", "Rain", "[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy", "Climate Change and Variability Research", "Plant Science", "Precipitation", "02 engineering and technology", "01 natural sciences", "Agricultural and Biological Sciences", "Downscaling", "Climate change", "Quantile", "Triticum", "Climatology", "2. Zero hunger", "Global and Planetary Change", "Ecology", "Geography", "Temperature", "Life Sciences", "Geology", "Morocco", "Phenology", "[SDV.EE.BIO]Life Sciences [q-bio]/Ecology", "Seeds", "Physical Sciences", "Metallurgy", "Desert Climate", "Impacts of Elevated CO2 and Ozone on Plant Physiology", "Climate Change", "0207 environmental engineering", "Yield (engineering)", "Climate model", "Article", "Environmental science", "FOS: Economics and business", "Meteorology", "FOS: Mathematics", "Econometrics", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Ecology", " Evolution", " Behavior and Systematics", "0105 earth and related environmental sciences", "[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "Water", "FOS: Earth and related environmental sciences", "Carbon Dioxide", "15. Life on land", "Agronomy", "Materials science", "[SDV.EE.BIO] Life Sciences [q-bio]/Ecology", " environment/Bioclimatology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Crop Yield", "Mediterranean climate", "Mathematics", "Climate Modeling"]}, "links": [{"href": "https://www.nature.com/articles/s41598-019-55251-2.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-55251-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-55251-2", "name": "item", "description": "10.1038/s41598-019-55251-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-55251-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-16T00:00:00Z"}}, {"id": "10.1038/s41598-019-56868-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:37Z", "type": "Journal Article", "created": "2020-01-09", "title": "Modelling photovoltaic soiling losses through optical characterization", "description": "Abstract

The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the \uffc3\uff85ngstr\uffc3\uffb6m turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.

", "keywords": ["Photovoltaic Arrays", "Cleanliness", "Particle", "PV", "02 engineering and technology", "Oceanography", "7. Clean energy", "soiling; experimental; transmittance; spectrum", "Turbidity", "Size", "Materials Science and Engineering", "\u00c5ngstr\u00f6m turbidity equation", "Transmittance", "0202 electrical engineering", " electronic engineering", " information engineering", "Photovoltaic system", "Ultraviolet", "Microscopy", "Soiling", "Energy", "Ecology", "Physics", "Q", "R", "Imaging and sensing", "Geology", "Particle size", "6. Clean water", "Photovoltaic Efficiency", "Chemistry", "Physical chemistry", "Particle (ecology)", "Physical Sciences", "Sunlight", "Medicine", "Infrared", "570", "Particle-size distribution", "PV System", "Energy science and technology", "Science", "Optical spectroscopy", "Partial Shading", "530", "Modelling", "Article", "Environmental science", "Techniques and instrumentation", "Optical physics", "Meteorology", "Artificial Intelligence", "Machine Learning Methods for Solar Radiation Forecasting", "Optical techniques", "Optoelectronics", "Aerosol", "Biology", "Renewable Energy", " Sustainability and the Environment", "Electronics", " photonics and device physics", "Building Integrated Photovoltaics", "Optics", "Photovoltaic Maximum Power Point Tracking Techniques", "FOS: Earth and related environmental sciences", "Materials science", "Photovoltaics", "Optics and photonics", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Solar Thermal Energy Technologies"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625670/2/Smestad_Modelling_2020.pdf"}, {"href": "https://www.nature.com/articles/s41598-019-56868-z.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56868-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-56868-z", "name": "item", "description": "10.1038/s41598-019-56868-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56868-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.1080/09064710.2022.2136583", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:03Z", "type": "Journal Article", "created": "2022-10-26", "title": "Exploring structural sediment connectivity via surface runoff in agricultural lands of Finland", "description": "Spatial information on the distribution of erosion areas and sediment transport pathways within agricultural landscapes is limited. Thus, we assess structural sediment connectivity via surface runoff by using a digital elevation model (2 \u00d7 2 m2) and RUSLE-based erosion estimates to compute index of connectivity (IC) and sediment delivery estimates. The variables were analyzed within and between two topographically contrasting subcatchments. We found greater spatial variability of IC within a subcatchment than between the subcatchments. The majority of field parcel areas (65%\u201397%) were structurally connected to adjacent open ditches and streams. Areas with high erosion estimates also tended to be structurally well-connected, both at the pixel (Pearson r = 0.58\u20130.63) and parcel scale (r = 0.49\u20130.67). The IC model was not highly sensitive to parameter variations. In contrast, the magnitude of sediment delivery estimates was highly sensitive to parameter variations. However, based on the high rank correlation (Spearman rs > 0.95) between computed sediment delivery estimates, the tool provided consistent information on potentially high sediment delivery areas. More empirical data and dynamic model applications could be applied to improve the accuracy of the estimates. The method provides a feasible tool to generate open data on connectivity.", "keywords": ["550", "ta1172", "rusle", "SB1-1110", "Inorganic Chemistry", "Sociology", "FOS: Chemical sciences", "FOS: Mathematics", "RUSLE", "ta218", "Connectivity", "Ecology", "connectivity index", "Plant culture", "lowlands", "FOS: Earth and related environmental sciences", "04 agricultural and veterinary sciences", "ta4111", "15. Life on land", "erosion", "59999 Environmental Sciences not elsewhere classified", "FOS: Sociology", "FOS: Biological sciences", "connectivity", "Medicine", "19999 Mathematical Sciences not elsewhere classified", "0401 agriculture", " forestry", " and fisheries", "69999 Biological Sciences not elsewhere classified", "Biotechnology"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/09064710.2022.2136583"}, {"href": "https://doi.org/10.1080/09064710.2022.2136583"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Agriculturae%20Scandinavica%2C%20Section%20B%20%E2%80%94%20Soil%20%26amp%3B%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/09064710.2022.2136583", "name": "item", "description": "10.1080/09064710.2022.2136583", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/09064710.2022.2136583"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-26T00:00:00Z"}}, {"id": "10.5061/dryad.0cfxpnw4m", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:07Z", "type": "Dataset", "title": "Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning", "description": "unspecifiedPlease see the ReadMe file.", "keywords": ["2. Zero hunger", "Driving Forces", "13. Climate action", "Machine learning", "cross validation", "FOS: Earth and related environmental sciences", "SOC", "spatiotemporal dynamics", "15. Life on land", "random forest"], "contacts": [{"organization": "Li, Huiwen, Wu, Yiping, Liu, Shuguang, Xiao, Jingfeng, Zhao, Wenzhi, Chen, Ji, Alexandrov, Georgii, Cao, Yue,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.0cfxpnw4m"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.0cfxpnw4m", "name": "item", "description": "10.5061/dryad.0cfxpnw4m", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.0cfxpnw4m"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-23T00:00:00Z"}}, {"id": "10.1093/ismejo/wrae025", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:11Z", "type": "Journal Article", "created": "2024-02-12", "title": "Stronger compensatory thermal adaptation of soil microbial respiration with higher substrate availability", "description": "Abstract

Ongoing global warming is expected to augment soil respiration by increasing the microbial activity, driving self-reinforcing feedback to climate change. However, the compensatory thermal adaptation of soil microorganisms and substrate depletion may weaken the effects of rising temperature on soil respiration. To test this hypothesis, we collected soils along a large-scale forest transect in eastern China spanning a natural temperature gradient, and we incubated the soils at different temperatures with or without substrate addition. We combined the exponential thermal response function and a data-driven model to study the interaction effect of thermal adaptation and substrate availability on microbial respiration and compared our results to those from two additional continental and global independent datasets. Modeled results suggested that the effect of thermal adaptation on microbial respiration was greater in areas with higher mean annual temperatures, which is consistent with the compensatory response to warming. In addition, the effect of thermal adaptation on microbial respiration was greater under substrate addition than under substrate depletion, which was also true for the independent datasets reanalyzed using our approach. Our results indicate that thermal adaptation in warmer regions could exert a more pronounced negative impact on microbial respiration when the substrate availability is abundant. These findings improve the body of knowledge on how substrate availability influences the soil microbial community\uffe2\uff80\uff93temperature interactions, which could improve estimates of projected soil carbon losses to the atmosphere through respiration.CSH, Shimadzu, Japan). The dissolved organic nitrogen (DON) concentrations were calculated by subtracting dissolved inorganic nitrogen (sum of NH4+ and NO3-) from TDN concentrations (DON = TDN - NH4+ - NO3-) to obtain DOC/DON ratios in soil solution. The DOC flux at the depth of 0 cm (the bottom of organic layers) and the bottom of B horizon (the bottom of rooting zone) was estimated by multiplying DOC concentrations in soil solution and water fluxes at each depth. Soil water fluxes were estimated by hydrological models or precipitation-evapotranspiration water budgets. Annual root production was measured by ingrowth core method, net sheet method, or sequential sampling method and estimated to be equal to annual root litter inputs. Proportion of DOC flux from the O horizon relative to C input via both throughfall and litterfall was calculated by dividing DOC flux from the O horizon by C input via both throughfall and litterfall. DOC retention in the mineral soil was calculated as the percentage of net decrease in DOC flux between O and B horizons relative to DOC flux from the O horizon. The apparent turnover time (yr) of soil C was estimated by dividing soil C stocks (Mg C ha\u20131) by C inputs (net DOC inputs and root litter inputs into the mineral soil) (Mg C ha\u20131 yr\u20131).", "keywords": ["tropical forest", "FOS: Earth and related environmental sciences", "Soil pH", "dissolved organic carbon", "dissolved organic nitrogen"], "contacts": [{"organization": "Fujii, Kazumichi", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.k6djh9wdx"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.k6djh9wdx", "name": "item", "description": "10.5061/dryad.k6djh9wdx", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.k6djh9wdx"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-19T00:00:00Z"}}, {"id": "10.5061/dryad.6m905qg4x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:10Z", "type": "Dataset", "title": "Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe", "description": "Open AccessWe used data on natural enemy communities in 66 paired winter wheat fields in four Northwestern European countries (Germany, the Netherlands, Sweden and United Kingdom) to investigate the response of natural enemy communities to landscape complexity, local land-use intensity and soil organic matter content, and specifically examined whether and how responses differ between dominant and non-dominant species. We focused on carabid beetles and spiders as they represent the two groups of natural enemies in arable fields in Northwestern European and widely used as bioindicators (Lang et al., 1999; Borchard et al., 2014). We used pitfall traps to collect carabids and spiders in field pairs that covered a gradient in land-use intensity and landscape complexity, with fields within pairs having contrasting soil organic carbon content.\u00a0 Pitfall traps (polypropylene beakers 155 mm high and 95 mm across) were used to survey ground-dwelling arthropods during the wheat flowering season (late May to early June). We placed one pitfall trap in the center of each treatment subplot at least 10 m from the field edge and filled it with 200 mL of a mixed solution of 2/3 water and 1/3 glycol and a drop of detergent to lower surface tension. A square aluminum plate was placed approximately 10 cm above each pitfall trap to prevent flooding by rain. Pitfall traps were opened for 10 days. All of the collected arthropods were stored in 70% ethanol solution for later identification. For the purpose of our study, the two most abundant species groups, carabid beetles (Carabidae) and adult spiders (Araneae), were selected as our bioindicators and they were counted and identified to species level using standard keys (Hackston, 2020; Nentwig et al., 2021). We determined the diet preference of each carabid beetle species based on Larochelle (1990) and the hunting strategy of all observed spider species based on Cardoso et al. (2011) following Gall\u00e9 et al. (2019). Furthermore, because the arthropod communities will inevitably differ in composition between countries, we classified the carabids or spiders as nationally dominant and non-dominant species based on whether species made up respectively more or less than 5% of the total number of individuals caught of each species group in a country following Kleijn et al. (2015).", "keywords": ["2. Zero hunger", "soil organic carbon", "ecological intensification", "Earth and related environmental sciences", "pest control service", "evenness", "dominant species", "14. Life underwater", "FOS: Earth and related environmental sciences", "15. Life on land", "natural enemies"], "contacts": [{"organization": "Mei, Zulin, Scheper, Jeroen, Bommarco, Riccardo, de Groot, Gerard Arjen, Garratt, Michael P. D., Hedlund, Katarina, Potts, Simon G., Redlich, Sarah, Smith, Henrik G., Steffan-Dewenter, Ingolf, van der Putten, Wim H., van Gils, Stijn, Kleijn, David,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.6m905qg4x"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.6m905qg4x", "name": "item", "description": "10.5061/dryad.6m905qg4x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.6m905qg4x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.3389/fsoil.2023.1240930", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:34Z", "type": "Journal Article", "created": "2023-07-11", "title": "Editorial: Greenhouse gas measurements in underrepresented areas of the world", "description": "Open Access\u0645\u0642\u0627\u0644 \u062a\u062d\u0631\u064a\u0631\u064a Front. Soil Sci., 11 July 2023Sec. \u0627\u0644\u0643\u064a\u0645\u064a\u0627\u0621 \u0627\u0644\u062d\u064a\u0648\u064a\u0629 \u0644\u0644\u062a\u0631\u0628\u0629 \u0648\u0631\u0643\u0648\u0628 \u0627\u0644\u062f\u0631\u0627\u062c\u0627\u062a \u0627\u0644\u063a\u0630\u0627\u0626\u064a\u0629 \u0627\u0644\u0645\u062c\u0644\u062f 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930", "keywords": ["Soil nutrients", "Mechanics and Transport in Unsaturated Soils", "representativeness", "Oceanography", "Greenhouse gas", "Environmental science", "climate change mitigation", "12. Responsible consumption", "Impact of Climate Change on Forest Wildfires", "Engineering", "greenhouse gases", "Soil water", "11. Sustainability", "TA703-712", "QD1-999", "Biology", "Civil and Structural Engineering", "Soil science", "2. Zero hunger", "Global and Planetary Change", "nitrous oxide", "Geography", "Ecology", "greenhouse gas emissions", "Global Forest Drought Response and Climate Change", "methane", "carbon dioxide", "Cycling", "Geology", "Forestry", "Engineering geology. Rock mechanics. Soil mechanics. Underground construction", "FOS: Earth and related environmental sciences", "Biogeochemistry", "15. Life on land", "6. Clean water", "livestock", "Chemistry", "climate change", "Global Emissions", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Nutrient"]}, "links": [{"href": "https://doi.org/10.3389/fsoil.2023.1240930"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsoil.2023.1240930", "name": "item", "description": "10.3389/fsoil.2023.1240930", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsoil.2023.1240930"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-11T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\uffcf\uff83\uffc2\uffb0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \uffcf\uff83\uffc2\uffb0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \uffcf\uff83\uffc2\uffb0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \uffcf\uff83\uffc2\uffb0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\uffe2\uff88\uff923).

", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.3390/rs12244018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:49Z", "type": "Journal Article", "created": "2020-12-08", "title": "Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco", "description": "

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "0207 environmental engineering", "Agricultural drought", "02 engineering and technology", "01 natural sciences", "630", "Environmental science", "remote sensing", "Land data assimilation systems", "Pathology", "assimilation systems", "Biology", "land data assimilation systems", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Water content", "Ecology", "Drought", "Global Forest Drought Response and Climate Change", "Q", "Hydrology (agriculture)", "Geology", "cereal yield", "Remote Sensing in Vegetation Monitoring and Phenology", "FOS: Earth and related environmental sciences", "Remote sensing", "semiarid region", "15. Life on land", "agricultural drought", "Agronomy", "6. Clean water", "Cereal yield", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "[SDE]Environmental Sciences", "Global Drought Monitoring and Assessment", "Environmental Science", "Physical Sciences", "Leaf area index", "Medicine", "Semiarid region", "land data", "Vegetation (pathology)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://doi.org/10.3390/rs12244018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12244018", "name": "item", "description": "10.3390/rs12244018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12244018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-08T00:00:00Z"}}, {"id": "10.5061/dryad.x95x69psf", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-05-24T16:21:03Z", "type": "Dataset", "created": "2024-03-11", "title": "Effects of biochar soil amendments on soil properties and plant recruitment in coastal climate change adaptation projects", "description": "unspecified# Effects of biochar soil amendments on soil properties and restoration success in coastal climate change adaptation projects [https://doi.org/10.5061/dryad.x95x69psf](https://doi.org/10.5061/dryad.x95x69psf) ### There are five files uploaded as part of this data release: 1. landscape_vegetation.csv - this file details the plant cover derived from drone imagery analysis in 2018-2023 in the landscape scale plots constructed at the Elkhorn Slough National Estuarine Research Reserve. 2. landscape_soil.csv - this file details soil analysis for soils collected in summer 2022 from landscape scale plots. 3. plot_vegetation.csv - this file details the plant cover derived from point-intercept field surveys conducted at Elkhorn Slough National Estuarine Research Reserve, Waquoit Bay National Estuarine Research Reserve, and Prudence Island National Estuarine Research Reserve in November of 2017, September of 2018, March of 2019, August of 2019, and August of 2020. 4. plot_soil.csv - this file details soil analysis for soils collected in March 2019 from small (0.7m x 0.7m) sediment addition plots. 5. particle_size.csv - this file details outputs of grain size analysis for the biochar amended plots for the landscape scale experiment. ## Description of the data and file structure **landscape_vegetation.csv** - This file contains seven fields: site, code, soil, treatment, amendment, date, plant_cover_fraction. The field site refers to which plot was sampled, 1, 2, or 3, where 1 refers to the northernmost series of plots, 2 refers to the middle series of plots, and 3 is the southernmost series of plots. The field code refers to A, B, or C, where A is the series of plots on the left facing south, B refers to the center series of plot facing south, and C is the right series plot facing south. The field soil refers to one of four soil types: hester_soil, which refers to the type of the sediment used in the whole 50-ha restoration, 50_50_mix, a 50:50 mix of granite fines with the restoration sediment, capped_fines a mixture of granite fines capped with restoration soil, or granite_fines alone, which is just granite fines. The field treatment refers to one of four treatments: reference, biochar, fines, or mix, where reference is the same sediment as the rest of the restoration, biochar refers to restoration soil mixed with biochar, fines refers to one of types of granite fine amended soils (see field soil), and mix refers to a mix of granite fine amended soils and biochar. The field amendment refers one of two values, biochar or none, representing biochar amendments or no amendments. The date refers to the date of the drone flight in YYYY-MM-DD format. The plant_cover_fraction refers to the area of the drone image that had plant cover. **landscape_soil.csv** - This file contains 24 fields, including analysis, site, code, replicate, old-Bag-code, new-Bag-code, plant, amendment, plant_cover, LOI, bulk_density, water_fraction, salinity, pH, redox, KCl_NH4, KCl_NO3, D50, sand_frct, mud_frct, silt_frct, clay_frct, sand_fines, CH4_flux_s, CH4_flux_h, CO2_flux. The field analysis refers to one of two codes: GHG or soil, where GHG refers to greenhouse gas flux measures, and soil refers to soil analysis measures. These measures were not taken at the same exact locations and had different numbers of replicates per plot. The field site refers to which plot was sampled, 1, 2, or 3, where 1 refers to the northernmost series of plots, 2 refers to the middle series of plots, and 3 is the southernmost series of plots. The field code refers to A, B, or C, where A is the series of plots on the left facing south, B refers to the center series of plot facing south, and C is the right series plot facing south. The field replicate refers to, where multiple measures are taken in one plot, the replicate number (1 or 2). The field old-Bag-code refers to the code written on the bag. The field new-Bag-code refers to the code which should have been written on the bag. The field plant, may be of two values, 0 or 1, where the value is 1 if the soil was collected beneath a plant or bare soil. The field amendment refers one of two values, biochar or none, representing biochar amendments or no amendments. The field plant_cover refers to field estimated plant cover in the plot, with values from 0-100. The field LOI refers to the organic content of the sediment, as a fraction (0-1). The field bulk_density refers to the bulk density of the soil sample, in g/cc. The field water_fraction is the fraction of the field moist sample that is water. The field salinity is the salinity of the sample, in ppt. The field ORP is redox of the soil sample in mV. The field pH is the pH of the sample on a 1:1 soil to water mix. The field KCl_NH4 is ammonium concentration of KCL extraction (uM / g dry sed). The field KCl_NO3 refers to nitrate concentrations of KCL extraction (uM / g dry sed). The field D50 refers to the median particle size diameter of the sample in micrometers. The field sand_frct refers to the fraction of the sample that is sand (0-1). The field mud_frct refers to the fraction of the sample that is mud (silt and clay) (0-1). The field silt_frct refers to the fraction of the sample that is silt (0-1). The field clay_frct refers to the fraction of the sample that is clay (0-1). The field sand_fines refers to the ratio of sand to mud (silt and clay). The field CH4_flux_s refers to methane emissions (CH4 flux) (in dark flux chambers) in uM/m^2/second. The field CH4_flux_h refers to methane emissions (CH4 flux) (in dark flux chambers) in uM/m2/hour. The field CO2 is soil respiration (CO2 flux) (in dark flux chambers) in uM/m^2/s. Missing data is coded -999. **plot_vegetation.csv** - This file contains eight fields: NERR_code, elevation, plot, treatment, name, cover, date, and time stamp. The NERR_code refers to which site the data was collected at: one of three codes, ELK for Elkhorn Slough, NAR, for Prudence Island, and WQB for Sage Lot Pond, Waquoit Bay. The field elevation is either high or low, as there were five high elevation plots per treatment and five low elevation plots per treatment. The field plot refers to the code of the plot (A, B, C, D, E), or which replicate it is. The field treatment lists one of four treatments: control (a paired plot that received no sediment), reference (a paired plot with high plant cover; the restoration target), 14 (14cm of sediment added) and biochar (14cm of sediment added with 10% biochar admixture). The field name is the plot name that includes the plot, elevation and treatment, H or L for high or low, A, B, C, D, or E for plot, and a code for treatment: 14 cm (14), 14 cm of sediment with biochar (b) reference (R), control (C). The field cover is the percent of the plot that had vegetation cover, with values 0-100. The field date is the date the measure was taken in MM/DD/YEAR. The field timestamp refers to when the measure was taken, before the sediment was added (pre_sediment), during the first year (year1_fall), in the second year during spring (year2_spring), during the second year during fall (year2_fall), and during the third year during fall (year3_fall). Missing data for reference plots is coded -999. **plot_soil.csv** - This file contains 14 fields: NERR_code, date,\u00a0 elevation, \u00a0plot, treatment, name, bulk_density, water_fraction, salinity, ORP, pH, NH4, CO2, vegetation_cover. The NERR_code refers to which site the data was collected at: one of three codes, ELK for Elkhorn Slough, NAR, for Prudence Island, and WQB for Sage Lot Pond, Waquoit Bay. The field date is the date the measure was taken in MM/DD/YEAR. The field elevation is either high or low, as there were five high elevation plots per treatment and five low elevation plots per treatment. The field plot refers to the code of the plot (A, B, C, D, E), or which replicate it is. The field treatment lists one of four treatments: control (a paired plot that received no sediment), reference (a paired plot with high plant cover; the restoration target), 14 (14cm of sediment added) and biochar (14cm of sediment added with 10% biochar admixture). The field name is the plot name that includes the plot, elevation and treatment, H or L for high or low, A, B, C, D, or E for plot, and a code for treatment: 14 cm (14), 14 cm of sediment with biochar (b) reference (R), control (C). The field bulk_density is the bulk density of the soil sample, in g/cc. The field water_fraction is the fraction of the field moist sample that is water. The field salinity is the salinity of the sample, in ppt. The field ORP is redox of the soil sample in mV. The field pH is the pH of the sample on a 1:1 soil to water mix. The field NH4 is ammonium concentration of KCL extraction (uM / g dry sed). The field CO2 is soil respiration (CO2 flux) (in dark flux chambers) in uM/m^2/s. The field vegetation_cover is the year 3 vegetation cover on plots, on a scale of 0-100. The missing or uncollected data is coded -999. **particle_size.csv** - This file has 125 fields, including project, site, code, replicate, old-Bag-code, new-Bag-code, amendment, and 117 codes that reflect particle size bins. The field project has two potential values, landscape or plot. The field site refers to which plot was sampled, 1, 2, or 3, where 1 refers to the northernmost series of plots, 2 refers to the middle series of plots, and 3 is the southernmost series of plots. The field code refers to A, B, or C, where A is the series of plots on the left facing south, B refers to the center series of plot facing south, and C is the right series plot facing south. The field replicate refers to, where multiple measures are taken in one plot, the replicate number (1 or 2). The field old-Bag-code refers to the code written on the bag. The field new-Bag-code refers to the code which should have been written on the bag. The field soil amendment refers one of two values, biochar or none, representing biochar amendments or no amendments. These bins include the following: 0.040\u00a0 0.044\u00a0\u00a0 0.048\u00a0\u00a0 0.053\u00a0\u00a0 0.058\u00a0\u00a0 0.064\u00a0\u00a0 0.070\u00a0\u00a0 0.077\u00a0\u00a0 0.084\u00a0\u00a0 0.093\u00a0\u00a0 0.102\u00a0\u00a0 0.112\u00a0\u00a0 0.122\u00a0\u00a0 0.134\u00a0\u00a0 0.148\u00a0\u00a0 0.162\u00a0\u00a0 0.178\u00a0\u00a0 0.195\u00a0\u00a0 0.214 0.235\u00a0\u00a0 0.258\u00a0\u00a0 0.284\u00a0\u00a0 0.311\u00a0\u00a0 0.342\u00a0\u00a0 0.375\u00a0\u00a0 0.412\u00a0\u00a0 0.452\u00a0\u00a0 0.496\u00a0\u00a0 0.545\u00a0\u00a0 0.598\u00a0\u00a0 0.657\u00a0\u00a0 0.721\u00a0\u00a0 0.791\u00a0\u00a0 0.869\u00a0\u00a0 0.953\u00a0\u00a0 1.047\u00a0\u00a0 1.149\u00a0\u00a01.261\u00a0\u00a0 1.385\u00a0\u00a0 1.520\u00a0\u00a0 1.669\u00a0\u00a0 1.832\u00a0\u00a0 2.010\u00a0\u00a0 2.207\u00a0\u00a0 2.423\u00a0\u00a0 2.660\u00a0\u00a0 2.920\u00a0\u00a0 3.206\u00a0\u00a0 3.519\u00a0\u00a0 3.862\u00a0\u00a0 4.241\u00a0\u00a0 4.656\u00a0\u00a0 5.111\u00a0\u00a0 5.611\u00a0\u00a0 6.158\u00a0\u00a0\u00a0\u00a0 6.761\u00a0\u00a0 7.421\u00a0\u00a0 8.147\u00a0\u00a0 8.944\u00a0\u00a0 9.819\u00a0\u00a0 10.78\u00a0\u00a0 11.83\u00a0\u00a0 12.99\u00a0\u00a0 14.26\u00a0\u00a0 15.65\u00a0\u00a0 17.17\u00a0\u00a0 18.86\u00a0\u00a0 20.70\u00a0\u00a0 22.73\u00a0\u00a0 24.95\u00a0\u00a0 27.38\u00a0\u00a0 30.07\u00a0\u00a0 33.00\u00a0\u00a036.24\u00a0\u00a0 39.77\u00a0\u00a0 43.66\u00a0\u00a0 47.93\u00a0\u00a0 52.63\u00a0\u00a0 57.77\u00a0\u00a0 63.41\u00a0\u00a0 69.62\u00a0\u00a0 76.43\u00a0\u00a0 83.90\u00a0\u00a0 92.09\u00a0\u00a0 101.1\u00a0\u00a0 111.0\u00a0\u00a0 121.8\u00a0\u00a0 133.7\u00a0\u00a0 146.8\u00a0\u00a0 161.2\u00a0\u00a0 176.8\u00a0\u00a0194.2\u00a0\u00a0 213.2\u00a0\u00a0 234.1\u00a0\u00a0 256.8\u00a0\u00a0 282.1\u00a0\u00a0 309.6\u00a0\u00a0 339.8\u00a0\u00a0 373.1\u00a0\u00a0 409.6\u00a0\u00a0 449.7\u00a0\u00a0 493.6\u00a0\u00a0 541.9\u00a0\u00a0 594.9\u00a0\u00a0 653.0\u00a0\u00a0 716.9\u00a0\u00a0 786.9\u00a0\u00a0 863.9\u00a0\u00a0 948.2\u00a0\u00a0 1041\u00a01143\u00a0\u00a0\u00a0 1255\u00a0\u00a0\u00a0 1377\u00a0\u00a0\u00a0 1512\u00a0\u00a0\u00a0 1660\u00a0\u00a0\u00a0 1822\u00a0\u00a0\u00a0 2000. Each of these particle size bins is the percent weight of sediment that falls between the bin labeled (e.g., 2000uM diameter), and the next lowest bin (e.g., 1822 uM). The sum of all the rows of data in the bins equals 100. ## Sharing/Access information There are no other publicly accessible data locations. ## Code/Software No code or software are provided.", "keywords": ["restoration", "Wetlands", "biochar", "FOS: Earth and related environmental sciences"], "contacts": [{"organization": "Barufaldi, Joshua, Fountain, Monique, Raposa, Kenneth, Tyrell, Megan, Ikeh, Rupert, Gray, Andrew, Watson, Elizabeth,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.x95x69psf"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.x95x69psf", "name": "item", "description": "10.5061/dryad.x95x69psf", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.x95x69psf"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-19T00:00:00Z"}}, {"id": "10.5061/dryad.xsj3tx9nx", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:19Z", "type": "Dataset", "created": "2023-12-26", "title": "Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange", "description": "unspecifiedThin layer sediment placement (TLP) is a method to mitigate factors resulting in loss of elevation and severe alteration of hydrology, such as sea level rise and anthropogenic modifications, and prolong the lifespan of drowning salt marshes. However, TLP success may vary due to plant stress associated with reductions in nutrient availability and hydrologic flushing or through the creation of acid sulfate soils. This study examined the influence of sediment grain size and soil amendments on plant growth, soil and porewater characteristics, and greenhouse gas exchange for three key US salt marsh plants: Spartina alterniflora, Spartina patens, and Salicornia pacifica. We found that bioavailable nitrogen concentrations (measured as extractable NH4+-N) and porewater pH and salinity were found to have an inverse relationship with grain size, while soil redox was more reducing in finer sediments. This suggests that utilizing finer sediments in TLP projects will result in a more reduced environment with higher nutrient availability, while larger grain-sized sediments will be better flushed and oxidized. We further found that grain size had a significant effect on vegetation biomass allocation and rates of gas exchange, although these effects were species-specific. We found that soil amendments (biochar and compost) did not subsidize plant growth but were associated with increases in soil respiration and methane emissions. Biochar amendments were additionally ineffective in ameliorating acid sulfate conditions. This study uncovers complex interactions between sediment type and vegetation, emphasizing limitations of soil amendments. The findings aid restoration project managers in making informed decisions regarding sediment type, target vegetation, and soil amendments for successful TLP projects.", "keywords": ["Salt marsh", "Greenhouse gases", "restoration", "soil amendment", "biochar", "FOS: Earth and related environmental sciences", "Particle size distribution", "Sea level rise", "Ecosystems"]}, "links": [{"href": "https://doi.org/10.5061/dryad.xsj3tx9nx"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.xsj3tx9nx", "name": "item", "description": "10.5061/dryad.xsj3tx9nx", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.xsj3tx9nx"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-09T00:00:00Z"}}, {"id": "10.3535/2p4-8vy-qaf", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:40Z", "type": "Dataset", "title": "rocks", "description": "Digital Specimen for the physical specimen hosted at Tallinn University of Technology.", "keywords": ["Geology", "FOS: Earth and related environmental sciences", "Earth System"], "contacts": [{"organization": "Tallinn University of Technology", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3535/2p4-8vy-qaf"}, {"rel": "self", "type": "application/geo+json", "title": "10.3535/2p4-8vy-qaf", "name": "item", "description": "10.3535/2p4-8vy-qaf", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3535/2p4-8vy-qaf"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-31T00:00:00Z"}}, {"id": "10.3535/5td-xfj-g5x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:40Z", "type": "Dataset", "title": "igneous rocks", "description": "Digital Specimen for the physical specimen hosted at Tallinn University of Technology.", "keywords": ["Geology", "FOS: Earth and related environmental sciences", "Earth System"], "contacts": [{"organization": "Tallinn University of Technology", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3535/5td-xfj-g5x"}, {"rel": "self", "type": "application/geo+json", "title": "10.3535/5td-xfj-g5x", "name": "item", "description": "10.3535/5td-xfj-g5x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3535/5td-xfj-g5x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-31T00:00:00Z"}}, {"id": "10.5061/dryad.0k6djhb5k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:07Z", "type": "Dataset", "created": "2023-08-29", "title": "Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest", "description": "unspecifiedSite description Soils were sampled from the Bisley Experimental Watershed of the LEF, Puerto Rico (18.3157 deg. N, 65.7487 deg W), a Long-Term Ecological Research and Critical Zone Observatory and Network site (https://luq.lter.network). The mean maximum daily temperature at Bisley was 27 \u00baC between 1993 and 2010 (Gonzales, 2020), with little seasonality. The mean annual precipitation at Bisley was 3883 (\u00b1 864 s.d.) mm y-1 from 1988 through 2014 (Gonz\u00e1lez, 2017; Murphy et al., 2017). Rainfall occurs all year, though January through April experience slightly less precipitation than other months (Heartsill-Scalley et al., 2007). The site is a humid tropical forest with a diverse tree community of approximately 170 species > 4 cm diameter at breast height (Weaver & Murphy, 1990), and dominated by tabonuco (Dacryodes excelsa Vahl). Elevation of Bisley spans from 261 m a.s.l. at the base to 450 m a.s.l. on the ridges (Scatena, 1989). Soils in Bisley are derived from volcaniclastic sediments of andesitic parent material (Scatena, 1989).\u00a0 Ridge soils are classified as Ultisols (Typic Haplohumults), while slope soils are classified as Oxisols (inceptic and Aquic Hapludox), and valley soils are classified as Inceptisols (Typic Epiaquaepts) (Hall et al., 2015; McDowell et al., 2012; Scatena, 1989). Detailed site descriptions can be found in Scatena (1989), Heartsill-Scalley et al (2010), and McDowell et al (2012). Here we refer to soil organic C (SOC) and soil C interchangeably because there is no detectable inorganic C in these soils. Hurricane occurrence\u00a0 Figure 1: Timeline of major hurricanes that have affected Luquillo Experimental Forest between sampling dates. Nine major hurricanes (category 3 or higher) have impacted Puerto Rico between 1851 and 2019 (L\u00f3pez-Marrero et al., 2019), and five of these hurricanes have impacted the LEF. Until 1998, hurricanes had historically directly impacted the LEF approximately every 60 years (Scatena & Larsen, 1991). Before the initial sampling campaign of this study, Hurricane San Cipri\u00e1n in 1932 was the most recent storm to cause major disturbance to the LEF (Scatena & Larsen, 1991).\u00a0 However, since sampling in 1988, four major hurricanes have impacted the forest (Figure 1). Hurricane Hugo (Category 3-4) in 1989, Hurricane Georges (Category 3) in 1998, and Hurricanes Irma and Maria (Categories 5 and 4, respectively) within two weeks in 2017. The trajectory and windspeeds of all these hurricanes caused widespread defoliation. Litterfall historically takes over five years to return to pre-hurricane levels (Scatena et al., 1996).\u00a0 Sampling Sample collection occurred in 1988 and again in 2018. In both years, samples were collected from three depths: 0\u201310 cm (the A horizon), 10\u201335 cm (all of the B1 horizon and part of B2), and 35\u201360 cm (B2 to C) using an 8 cm diameter soil auger. Soils in this study were sampled at three separate sites at least 40 m from one another for each of three topographic locations, ridge, slope, and upland valley. Two separate cores were taken from a fourth topographic location in the riparian valley, that characterized a smaller proportion of the area of these watersheds (Scatena & Lugo, 1995). Riparian valley sites were ephemeral streambeds with a high boulder presence that limited sampling to less than 25 cm depth in one case. Sampling sites from 1988 were marked with flags, and samples from 2018 were collected from within 15 m of the same locations as the replicates from 1988, for consistency. Samples collected in 1988 were analyzed for bulk density, pH, soil moisture, and a suite of soil chemical properties (see Silver et al. 1994). Samples were then air-dried and stored in closed Ziploc bags within paper bags in a storage facility in Richmond, CA, USA before density fractionation in 2018. Fresh samples collected in 2018 were also characterized for pH, soil moisture, and soil chemistry. Approximately 3 g subsamples from each fresh sample in 2018 were immediately extracted with 45 mL of 0.2 M sodium citrate/0.5 M ascorbate solution, shaken for 16 hours, then centrifuged and the supernatant decanted to measure concentrations of poorly crystalline iron (Fe) oxides. Within two days of being double-bagged in Ziploc bags, fresh samples were further subsampled and analyzed for pH in a 1:1 soil-to-water slurry (Thomas, 1996) and for gravimetric soil moisture by oven-drying ~10 g subsamples at 105 \u00baC until a constant weight. Soil samples were air-dried before further processing and analysis. Air-dried soils from both sampling years were sieved to 2 mm and large roots were sorted out. Soil Density fractionation Soil was fractionated by density following the method of Swanston et al. (2005), as modified by Marin-Spiotta et al., (2009). Approximately 20 g of air-dried soil was added to centrifuge tubes. Sodium polytungstate (SPT, Na6 [H2W12O40] TC-Tungsten Compounds, Bavaria, Germany) in solution of density 1.85 g cm-3 was added to centrifuge tubes and agitated before centrifuging. The density of the SPT followed previous studies from this and nearby sites to allow direct comparison (Guti\u00e9rrez del Arroyo & Silver, 2018; Hall et al., 2015). Particulate organic matter floating at the surface after centrifugation, the free light fraction (FLF), was aspirated and then rinsed with 100 ml of deionized water 5 times on a 0.8 \u00b5m pore polycarbonate filter (Whatman Nuclepore Track Etch Membrane, Darmstadt, Germany). Rinsed FLF was oven-dried at 65 \u00baC until weight had stabilized. The remainder of the sample was combined with 70 ml of additional SPT and mixed using an electric benchtop mixer (G3U05R, Lightning, New York, NY, USA) at 1700 rpm for 1 min and sonicated in an ice bath for 3 min at 70% pulse (Branson 450 Sonifier, Danbury, CT, USA). Sonication is intended to disrupt soil structure and liberate organic matter that has been occluded in aggregates. The sonicated slurry was centrifuged again, and the light fraction at the surface, the occluded light fraction (OLF), was aspirated, rinsed, and dried using the same method as for the FLF. The remaining soil pellet was considered the heavy fraction (HF), or mineral-associated organic matter fraction. The HF was rinsed by thoroughly mixing with 150 ml of deionized water in the centrifuge tube, centrifuging, and removing the supernatant repeatedly until the fraction had been rinsed 5 times. The rinsed HF was oven-dried at 105 \u00baC until weight stabilized. The average mass recovery was 98%. Soil C and N and \u03b413C Dried bulk and HF soils were homogenized separately using a Spex Ball mill (SPEX Sample Prep Mixer Mill 8000D, Metuchen, NJ). The FLF and OLF were homogenized separately by hand using a mortar and pestle. All homogenized samples were then analyzed at U. C. Berkeley for C and N concentrations on the CE Elantech elemental analyzer (Lakewood, NJ) and for \u03b413C in the Stable Isotope Laboratory at UC Berkeley, using a CHNOS Elemental Analyzer interfaced to an IsoPrime 100 mass spectrometer (Cheadle Hulme, UK), with a long-term external precision of 0.10 %. \u00a0Soil C stocks were calculated by multiplying the C concentrations (%) by the oven-dry mass of bulk soil (< 2 mm) and dividing by depth and the bulk density as measured in 1988 (Silver et al., 1994; Throop et al., 2012). Radiocarbon Homogenized soil samples were combusted to CO2 in sealed glass tubes along with silver (Ag) and copper oxide (CuO) at the Center for Accelerator Mass Spectrometry at Lawrence Livermore National Lab. The CO2 was then graphitized on Fe powder under pressurized hydrogen gas (Vogel et al., 1984). Graphite was pressed into aluminum targets and run on the Compact Accelerator Mass Spectrometer for radiocarbon analysis (Broek et al., 2021). Radiocarbon is reported in \u039414C, following Stuiver & Polach (1977), and calculated based on the fraction of modern isotope composition, corrected for the year of sampling, and corrected for mass-dependent fractionation with observed \u03b413C values of the sample. The compact AMS had an average \u039414C precision of 3.2 %. We report the corrected \u039414C value and \u0394\u039414C, which is calculated as \u039414C of the sample minus \u039414C of the atmosphere, to account for rapidly changing atmospheric \u039414C during the study period. Atmospheric radiocarbon has been decaying nonlinearly since the peak of weapons testing in the 1950s. Radiocarbon signatures in the soil are strongly influenced by the atmospheric D14C signature, making them useful for modeling soil C age and transit time, especially since the 1950s. To compare the contribution of modern C between 1988 and 2018, it is useful to take the difference between soil and atmospheric D14C values, or DD14C, because atmospheric D14C declined between 1988 (98 %) and 2018 (4.4 %) in Northern Hemisphere Zone 2 (Hua et al., 2013). We note that the decline in atmospheric D14C is nonlinear, and thus the DD14C in 2018 soil will be less sensitive to short-term shifts in D14C inputs than the samples from 1988. Carbon age and transit time modeling Transit times and ages of C were modeled with the package \u201cSoilR\u201d (Sierra et al., 2012, 2014) in R, version 4.0.2. The change in C density fractions over time, termed C flow, was modeled using a 3-pool structure with a series flow matrix, under the simplifying assumption that C flows from the litter pool to the FLF, where it is sequentially transferred into the OLF and HF pools (Figure 2). The model structure is depicted in basic form in equation 1, \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (1)\u00a0 dC(t)/dt = Inputs - k*C \u00a0in matrix form with explicit pools in equation 2, \u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (2)\u00a0 dC(t)/dt = [Litter Inputs; 0; 0] + [-kFLF, 0, 0 ; a21,\u00a0-kOLF, 0; 0, a32, -kHRF] * [CFLF; COLF; CHF] where k is the first-order decay constant for each pool, a is the C transfer rate between pools (i.e. a21 is the transfer from FLF (pool 1) to OLF (pool 2) and a32 is the transfer from OLF (pool 2) to HF (pool 3)), and C is the C stock of each pool. The transitTime and systemAge functions within the \u201csoilR\u201d package use this model structure to solve for the distribution of ages (time since entry) of each pool, and the distribution of transit times (times between entry and exit from the bulk soil) (Sierra et al 2016). Distributions of age and transit time were time-independent and did not assume a specific distribution (Sierra et al., 2014, 2017). Figure 2: Hypothesized flow of C in soils. Free light fraction (FLF) C (pink) is either decomposed (at cycling rate -kFLF * FLF) or transferred to the occluded light fraction pool (OLF, blue) with the transfer proportion defined by a21. Carbon transfer between the OLF and heavy fraction (HF, purple) is defined by transfer coefficient a32, and is respired from these pools at cycling rates -kOLF* OLF and -kHF* HF, respectively. Figure adapted from Sierra et al. (2012). Soil D14C and C stock mean and standard deviations from each time point, depth, and fraction were used to constrain the matrix model describing the movement of C through three soil pools and losses of C from each pool. Topography was not a strong predictor of patterns in D14C, C stocks, or C fractions, so samples from all topographies were aggregated for model simulations. The model used mean observed C content in each pool for each depth in 1988 as initial conditions for SOC stocks. Above and belowground litter inputs at 0\u201310 cm were assumed to be 900 g C m-2 in non-hurricane or hurricane recovery years, based on observations from the same site (Liu et al., 2018; Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Inputs to the 10\u201335 cm and 35\u201360 cm depths were estimated using observations of live fine roots on the surface and typical root distribution in the forest (Silver & Vogt, 1993). Total root input is approximately threefold the input of fine roots alone (McCormack et al., 2015; Yaffar & Norby, 2020), and live fine roots in the 0\u201310 cm depth had a mean biomass of 80 - 250 g C m-2 \u00a0(Hall et al., 2015), suggesting that total root C inputs of approximately 450 g C m-2 to the surface would be well within the expected range. Root inputs below 0\u201310 cm were estimated assuming that inputs follow the typical distribution of root biomass in Puerto Rican tropical forests, with 60\u201370% of root biomass in 0\u201310 cm, an additional 20-30% of biomass in 10\u201335 cm (~135 g C m-2\u00ad), and 5\u20138% of biomass is in the 35\u201360 cm depth (~40 g C m-2\u00ad) (Silver & Vogt, 1993; Yaffar & Norby, 2020). The model was parameterized under two scenarios for each depth: 1) constant inputs, assuming a steady-state undisturbed forest, and 2) hurricane inputs, which simulated the input fluxes from defoliation during the three major hurricanes, followed by a subsequent reduction in litter inputs and then litterfall increasing linearly to pre-hurricane inputs over 6 years (Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Hurricane inputs were imposed as an additional pulse of litter inputs to each depth interval, declining with depth. \u00a0The 0\u201310 cm interval received 100% of the surface input pulse, the 10\u201335 cm depth received a pulse of root inputs equivalent to 30% of the surface input pulse, and the 35\u201360 cm depth received root inputs equal to 10% of the surface input pulse. Surface litter pulses under hurricanes were specified according to measured litterfall values and were 42.5 g C m-2\u00ad to the surface in 1989 (Hurricane Hugo) and 1998 (Hurricane Georges) (Scatena et al., 1993; Silver et al., 1996) and 1611 g C m-2 \u00a0in 2017 (Hurricanes Irma and Maria) (Liu et al. 2018a). The same soil D14C and C stock observations were used to constrain the model under each scenario, with only the input regime varying. Parameters of the transfer matrix (-k\u00ad\u00adFLF, -k\u00ad\u00adOLF, -k\u00ad\u00adHF, a21, a32) were constrained using a cost function to accept or reject potential parameter sets over 1000 iterations, based on observed D14C and C stock means and standard errors from both time points (1988 and 2018). A Markov chain Monte Carlo (MCMC) simulation initialized with cost-optimized parameters was run to assimilate observed data and optimize parameter choices to the observations using function modMCMC() from R package \u201cFME\u201d (Sierra et al., 2014; Soetaert & Petzoldt, 2010). The MCMC was iterated over at least 20,000 simulations or until parameter solutions converged according to the trace, which was over 100,000 iterations at the 35\u201360 cm depth. The first half of the iterations was considered the burn-in period before the chain started to converge near an equilibrium, and these iterations were discarded in calculations of optimal parameters. The model output for the surface soils of the HF pool was validated using published radiocarbon values from the mineral-associated fraction (the only fraction analyzed) of samples from the site taken in 2012 (Hall et al., 2015).\u00a0 Bulk and pool soil C age and transit time density distributions and mean values were calculated using the systemAge() and transitTime() functions from the \u201cSoilR\u201d package. Mean density distributions were calculated using the mean parameter set given from the MCMC analysis. Standard deviation from the mean was calculated using the systemAge() and transitTime() functions on 200 sets of five parameters selected randomly within one standard deviation of the mean of each parameter given as output from the MCMC. Lower and upper limits of SOC ages and transit times were calculated using the upper and lower ranges of these iterations. Statistics Statistics were run in R, version 4.0.2 (R Core Team, 2020). The statistical model selection followed the recommendations of Zuur et al (2009). Statistical models were chosen using a linear mixed effects model in package \u201clme4\u201d, with random slopes accounting for the influence each core, or sampling site, had on the response variable values as they varied with depth. This random effect of the core site on the depth effect was evaluated using a restricted maximum likelihood approach and was included in the initial evaluation of all model comparisons. Linear mixed effect models included year, topographic position, depth, and interactions as fixed factors, and the depth effect of each core as a random factor for each of the response variables: C concentration, N concentration, d13C, DD14C. In evaluations of some response variables with AIC and BIC criteria, the random effect no longer enhanced the model, and model comparison proceeded using ANOVAs of linear models without random effects. Topographic effects on C concentrations are discussed in the supplemental information. Model assumptions were evaluated using the check_model function in R package \u201cperformance\u201d, to check for multicollinearity, normality of residuals, homoscedasticity, homogeneity of variance, influential observations, and normality of random effects. In the cases when random effects were significant (bulk soil d13C and DD14C, FLF DD14C and HF C and N concentrations), fixed effects were chosen using ANOVA of subsequent models using maximum likelihood estimation, with the random effects held constant. Once fixed effects were established, the model was re-fitted using a restricted maximum likelihood approach to report model estimates, and an ANOVA was run to determine the significance of the response variable. In all cases, P-values were estimated using Tukey\u2019s honest significant post-hoc test to assess significant differences between variables, in the package \u201cagricolae\u201d in R, and contrasts and standard errors of contrasts were estimated using lsmeans() function in package \u201clsmeans\u201d in R. Values of\u00a0P < 0.10 were reported as significant unless otherwise specified. The topographic position was not a significant predictor for most variables, so results are reported as means aggregated across positions.", "keywords": ["soil organic carbon", "Transit time", "Tropical forest soil", "FOS: Earth and related environmental sciences", "Soil R", "density fractions", "Radiocarbon"], "contacts": [{"organization": "Mayer, Allegra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.0k6djhb5k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.0k6djhb5k", "name": "item", "description": "10.5061/dryad.0k6djhb5k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.0k6djhb5k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-01T00:00:00Z"}}, {"id": "10.5061/dryad.8cz8w9gv6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:11Z", "type": "Dataset", "title": "Climate mitigation potential and soil microbial response of cyanobacteria-fertilized bioenergy crops in a cool semi-arid cropland", "description": "unspecifiedBioenergy carbon capture and storage (BECCS) systems can serve as decarbonization pathways for climate mitigation. Perennial grasses are a promising second-generation lignocellulosic bioenergy feedstock, but optimizing their sustainability, productivity, and climate mitigation potential requires an evaluation of how nitrogen (N) fertilizer strategies interact with greenhouse gas (GHG) and soil organic carbon (SOC) dynamics. Further, crop and fertilizer choice can affect the soil microbiome which is critical to soil organic matter turnover, nutrient cycling, and sustaining crop productivity\u00a0but these feedbacks are poorly understood due to the paucity of data from agroecosystems. Here, we examine the climate mitigation potential and soil microbiome response to establishing two functionally different perennial grasses, switchgrass (Panicum virgatum, C4), and tall wheatgrass (Thinopyrum ponticum, C3), in a cool semi-arid agroecosystem under two fertilizer applications, a novel cyanobacterial biofertilizer (CBF) and urea. Finally, we examine shifts in soil microbial composition resulting from crop establishment and fertilizer regime. We find that in contrast to the C4 crop, the C3 crop achieved 98% greater productivity and had a higher N use efficiency when fertilized and the CBF produced the same biomass enhancement as urea. Non-CO2 greenhouse gas fluxes across all treatments were low and we observed a three-year net loss of SOC under the C4 crop and a net increase under the C3 crop at a 0-30 cm soil depth regardless of fertilization. Further, we detected crop-specific changes in the soil microbiome, including an increased relative abundance of arbuscular mycorrhizal fungi under the C3, and potentially pathogenic fungi in the C4 grass. Taken together, these findings highlight the potential of CBF-fertilized C3 crops as a second-generation bioenergy feedstock in semiarid regions as a part of a climate mitigation strategy.", "keywords": ["2. Zero hunger", "root chemistry", "13. Climate action", "soil nitrogen", "plant tissue chemistry", "FOS: Earth and related environmental sciences", "Greenhouse Gas Flux", "15. Life on land", "aboveground biomass", "7. Clean energy", "Soil carbon", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Gay, Justin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.8cz8w9gv6"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.8cz8w9gv6", "name": "item", "description": "10.5061/dryad.8cz8w9gv6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.8cz8w9gv6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-22T00:00:00Z"}}, {"id": "10.5061/dryad.18931zd1m", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:08Z", "type": "Dataset", "title": "Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale", "description": "unspecifiedThis is the input dataset to a Python script (https://github.com/oskeng/MF-bio-grass) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. For more information about the dataset and the study, see the original article: Englund, O., Mola-Yudego, B., B\u00f6rjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy", "keywords": ["2. Zero hunger", "spatial modelling", "climate mitigation", "grass", "Agriculture", "FOS: Earth and related environmental sciences", "15. Life on land", "Environmental impacts", "Soil carbon", "Europe", "13. Climate action", "environmental benefits", "Land-use", "perennial crops"], "contacts": [{"organization": "Englund, Oskar", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.18931zd1m"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.18931zd1m", "name": "item", "description": "10.5061/dryad.18931zd1m", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.18931zd1m"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-17T00:00:00Z"}}, {"id": "10.5061/dryad.3bk3j9kt3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:09Z", "type": "Dataset", "created": "2024-03-31", "title": "Data from: Burrowing crab effects on the properties and functions of coastal soft sediments", "description": "unspecified# Data from: Burrowing crab effects on the properties and functions of coastal soft sediments [https://doi.org/10.5061/dryad.3bk3j9kt3](https://doi.org/10.5061/dryad.3bk3j9kt3) Effect size calculations (including means, sample sizes, and standard deviation) of crab burrowing effects (i.e., high density vs low density) on the properties, nutrient stocks, and functions of coastal sediments. Data comes from studies conducted across Africa, Asia, Australia, North America, and South America. ## Description of the data and file structure **File list:** 1. Rinehart_et_al.202X_Effectsizes CSV file containing the Hedges d effect size calculations (including the raw means, sample sizes, and standard deviations) for each extracted comparison/study from all 59 manuscripts. Additional extracted data (e.g., crab taxa, experimental conditions, habitat, burrow density) are also included for each comparison/study. 2. Rinehart_et_al.202X_Publicationbias CSV file containing the pooled standard deviation and the Hedges d effect size calculation for each comparison/study. This datafile was used to conduct analyses of publication bias for a resulting systematic meta-analysis. **Data-specific information for:** (1) Rinehart_et_al.202X_Effectsizes **Number of variables:** 47 **Number of cases/rows:** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. continent: the continent where the data was collected. 6. country: the country where the data was collected. 7. state: the state (united states only) where the data was collected. 8. estuary: the name of the estuary where the data was collected. 9. latitude_dd: the latitude associated with the data collected in decimal degrees (dd). 10. longitude_dd: the longitude associated with the data collected in decimal degrees (dd). 11. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 12. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 13. ecosystem_type: categorical variable noting if the ecosystem was restored, created, or natural. 14. relative_salinity: categorical variable noting the relative salinity in the ecosystem where the data was collected. 15. tidal_amplitude_m: the tidal amplitude (in meters) in the ecosystem where the data was collected. 16. tidal_cycle: categorical variable noting the type of tidal cycle (e.g., diurnal) in the ecosystem where the data was collected. 17. soil_type: categorical variable noting the soil type (e.g., sand) in the ecosystem where the data was collected. 18. elevation_m: the elevation (in meters) of the ecosystem where the data was collected. 19. study_duration_d: the length of time (in days) that the study ran (applies mainly to manipulative studies). 20. study_timing: the seasons or months during which the study was run. 21. dominant_plant_genus: the genus of the dominant plant present in the ecosystem where the data was collected. 22. dominant_plant_species: the species of the dominant plant present in the ecosystem where the data was collected. 23. dominant_plant_functional_group: categorical variable noting the functional group (e.g., grass) of the dominant plant species in the ecosystem where the data was collected. 24. crab_genus: the genus of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 25. crab_species: the species of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 26. crab_diet: categorical variable noting the main feeding strategy (e.g., herbivore, detritivore) used by the dominant crab species. 27. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 28. mean_burrow_diameter_high_crab_treatment_mm: the mean burrow diameter in the study's high crab treatment in mm. 29. mean_burrow_diameter_low_crab_treatment_mm: the mean burrow diameter in the study's low crab treatment in mm. 30. mean_burrow_depth_cm: the mean burrow depth in cm reported by the study. 31. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 32. burrow_density_low_crab_m^2: the mean crab burrow density per meter-squared reported in the study's low crab treatment. 33. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 34. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 35. field_location: categorical variable noting where studies conducted in the field placed their study relative to the shoreline. Specifically, we noted if studied sampled in the ecosystem interior (far from shoreline) or at the ecosystem edge (adjacent to the shoreline). 36. soil_depth_cm: the depth, in cm, within the soil profile from which the sediment samples were collected. 37. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 38. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 39. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 40. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 41. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 42. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 43. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 44. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 45. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 46. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 47. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na Data-specific information for: (2) Rinehart_et_al.202X_Publicationbias ***Number of variables:*** 22 ***Number of cases/rows:*** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 6. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 7. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 8. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 9. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 10. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 11. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 12. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 13. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 14. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 15. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 16. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 17. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 18. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 19. pooled_sd: the pooled standard deviation of the high and low crab treatments for each study. 20. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 21. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 22. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na ## Sharing/Access information All data are included in the provided datafiles. ## Code/Software Hedges\u2019 *d* (hereafter, *d*) effect sizes were calculated using meta-analysis using OpenMEE software (Build date: 26 July 2016; Wallace et al. 2017). Wallace, B. C., M. J. Lajeunesse, G. Dietz, I. J. Dahabreh, T. A. Trikalinos, C. H. Schmid, and J. Gurevitch. 2017. OpenMEE: Intuitive, open-source software for meta-analysis in ecology and evolutionary biology. Methods in Ecology and Evolution 8:941\u2013947.", "keywords": ["coastal wetlands", "density-dependance", "bioturbation", "animal effects", "Burrowing", "functional traits", "FOS: Earth and related environmental sciences", "habitat effects", "zoogeochemistry"], "contacts": [{"organization": "Rinehart, Shelby", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3bk3j9kt3"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3bk3j9kt3", "name": "item", "description": "10.5061/dryad.3bk3j9kt3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3bk3j9kt3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-29T00:00:00Z"}}, {"id": "10.5061/dryad.41ns1rnjs", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:09Z", "type": "Dataset", "title": "Plant growth strategy determines the magnitude and direction of drought-induced changes in root exudates in subtropical forests", "description": "Root exudates are an important pathway for plant-microbial interactions and are highly sensitive to climate change. However, how extreme drought affects root exudates and the main components, as well as species-specific differences in response magnitude and direction, are poorly understood. In this study, root exudation rates of total carbon (C) and its components (e.g., sugar, organic acid, and amino acid) were measured under the control and extreme drought treatments (i.e., 70% throughfall reduction) by in situ collection of four tree species with different growth rates in a subtropical forest. We also quantified soil properties, root morphological traits, and mycorrhizal infection rates to examine the driving factors underlying variations in root exudation. Our results showed that extreme drought significantly decreased root exudation rates of total C, sugar, and amino acid by 17.8%, 30.8%, and 35.0%, respectively, but increased root exudation rate of organic acid by 38.6%, which were largely associated with drought-induced changes in tree growth rates, root morphological traits, and mycorrhizal infection rates. Specifically, trees with relatively high growth rates were more responsive to drought for root exudation rates compared to those with relatively low growth rates, which were closely related to root morphological traits and mycorrhizal infection rates. These findings highlight the importance of plant growth strategy in mediating drought-induced changes in root exudation rates. The co-ordinations among root exudation rates, root morphological traits, and mycorrhizal symbioses in response to drought could be incorporated into land surface models to improve the prediction of climate change impacts on rhizosphere C dynamics in forest ecosystems.", "keywords": ["2. Zero hunger", "root morphological traits", "Drought", "subtropical forsts", "13. Climate action", "Root exudation", "tree growth", "FOS: Earth and related environmental sciences", "15. Life on land", "Organic acid", "6. Clean water", "amino acid", "mycorrhizal infection"], "contacts": [{"organization": "Jiang, Zheng, Fu, Yuling, Zhou, Lingyan, He, Yanghui, Zhou, Guiyao, Dietrich, Peter, Long, Jilan, Wang, Xinxin, Jia, Shuxian, Ji, Yuhuang, Jia, Zhen, Song, Bingqian, Liu, Ruiqiang, Zhou, Xuhui,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.41ns1rnjs"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.41ns1rnjs", "name": "item", "description": "10.5061/dryad.41ns1rnjs", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.41ns1rnjs"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-20T00:00:00Z"}}, {"id": "10.5061/dryad.41ns1rnjd", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:09Z", "type": "Dataset", "title": "Does long-term soil warming affect microbial element limitation? A test by short-term assays of microbial growth responses to labile C, N and P additions", "description": "Open AccessPlease refer to the accompanying README file and the published paper: Shi, C., Malo, C., Tian, Y., Heinzle, J., Kengdo, S. K., Inselsbacher, E., Borken, W., Schindlbacher, A., & Wanek, W. (2023). Does long-term soil warming affect microbial element limitation? A test by short-term assays of microbial growth responses to labile C, N and P additions. Global Change Biology. Accepted.", "keywords": ["2. Zero hunger", "Temperate forest ecosystem", "13. Climate action", "FOS: Agriculture", " forestry and fisheries", "Microbial element limitation", "Soil Sciences", "18O incorporation into microbial DNA", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Shi, Chupei, Urbina-Malo, Carolina, Tian, Ye, Heinzle, Jakob, Kwatcho Kengdo, Steve, Inselsbacher, Erich, Borken, Werner, Schindlbacher, Andreas, Wanek, Wolfgang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.41ns1rnjd"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.41ns1rnjd", "name": "item", "description": "10.5061/dryad.41ns1rnjd", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.41ns1rnjd"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-09T00:00:00Z"}}, {"id": "10.5061/dryad.t4b8gtj8d", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:18Z", "type": "Dataset", "created": "2024-02-07", "title": "Data for: Male, female and mixed-sex poplar plantations support divergent soil microbial communities", "description": "unspecifiedMixed-species forests are often more productive than monocultures because of a lower niche overlap and higher taxonomic and functional diversity of soil microbial communities. Males and females of dioecious plants have sex-specific adaptations to diverse habitats. The potential of using sexual differences in establishing more diverse poplar plantations has not been explored in degraded areas. We conducted a series of greenhouse and field experiments to investigate how belowground competition, soil microbial communities and seasonal variation nitrogen content differ among female, male and mixed-sex Populus cathayana plantations. In the greenhouse experiment, female neighbors suppressed the growth of males under optimal nitrogen conditions. However, male neighbors enhanced \u03b415N of females under inter-sexual competition. In the field, the root length density, root area density and biomass of fine roots were lower in female plantations than in male or mixed-sex plantations. Bacterial networks of female, male and mixed-sex plantations were characterized by different composition of hub nodes, including connectors, module and network hubs. The sex composition of plantations altered bacterial and fungal community structures according to Bray-Curtis distances, with 44% and 65% of variance explained by the root biomass, respectively. The total soil nitrogen content of mixed-sex plantation was higher than that in female plantation in spring and summer. The mixed-sex plantation also had a higher \u03b2-1,4-N-acetyl-glucosaminidase activity in summer and a higher nitrification rate in autumn than the other two plantations. The seasonal soil N content, nitrification rate and root distribution traits demonstrated spatiotemporal niche separation in the mixed-sex plantation. We argue that a strong female-female competition and limited nitrogen content could strongly impede plant growth and reduce the resistance of monosex plantations to climate change and the mixed-sex plantations constitutes a promising way to restore degraded land.", "keywords": ["belowground competition", "plant-microbe interactions", "neighbor sexual identity", "FOS: Earth and related environmental sciences", "microbiota assembly", "dioecious species"], "contacts": [{"organization": "Guo, Qingxue", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.t4b8gtj8d"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.t4b8gtj8d", "name": "item", "description": "10.5061/dryad.t4b8gtj8d", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.t4b8gtj8d"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-23T00:00:00Z"}}, {"id": "10.5061/dryad.51c59zw8j", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:09Z", "type": "Dataset", "title": "Scale dependence in functional equivalence and difference in the soil microbiome", "description": "unspecifiedFull methods can be found in the associated manuscript. Briefly, twenty-seven 1 m2 plots within each of two forest sites (SCBI: Smithsonian Conservation Biological Institute, Front Royal, VA and HARV: Harvard Forest, Petersham, MA) were set up in fall 2019. Soil measurements (temperature, moisture, pH, etc) were taken over a 10-month period from December 2019 to September 2020. Soils from these plots were used to inoculate Quercus rubra leaf litter in a lab microcosm experiment across different moisture treatments. Carbon mineralization was measured over 202 days by measuring CO2 production in each microcosm over 24 h at 17 time points (day 1, 6, 9, 13, 20, 27, 34, 43, 50, 64, 78, 92, 105, 120, 141, 168, 202) with the frequency of measurement decreasing over the course of the experiment. Cumulative C mineralization rates were calculated. To estimate CO2 evolved from Q. rubra litter, cumulative C mineralization from litter-soil microcosms were subtracted from soil-only controls for the corresponding microsite soil sample.", "keywords": ["13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Polussa, Alexander", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.51c59zw8j"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.51c59zw8j", "name": "item", "description": "10.5061/dryad.51c59zw8j", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.51c59zw8j"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-21T00:00:00Z"}}, {"id": "10.5061/dryad.547d7wmf3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:09Z", "type": "Dataset", "created": "2023-08-15", "title": "Data from: Long-term changes in soil carbon and nitrogen fractions in switchgrass, native grasses, and no-till corn bioenergy production systems", "description": "unspecified# Data from: Long-term changes in soil carbon and nitrogen fractions in switchgrass, native grasses, and no-till corn bioenergy production systems These files contain data from soil and root samples use in this publication. The R script uses this data to perform the statistical analysis used in the publication. ## Description of the data and file structure The soil and root data contain measured variables within each experimental unit across multiple years during the study period. The variable in the R script called 'top_level_directory' can be changed to the path of the download files' directory to run the analysis. Note that NA = not available. ## Code/Software There is an R script provided that conducts the statistical analysis used in this study. The necessary packages are listed at the top of the script. The variable in the script called 'top_level_directory' can be changed to the path of the download files' directory to run the analysis.", "keywords": ["2. Zero hunger", "native grasses", "Biofuel feedstocks", "Biofuel Cropping System Experiment", "soil nitrogen", "Bioenergy feedstock", "FOS: Earth and related environmental sciences", "15. Life on land", "7. Clean energy", "Soil carbon", "Zea mays", "mineral-assoicated organic matter", "Panicum virgatum", "13. Climate action", "Particulate organic matter", "root productivity", "soil aggregate"], "contacts": [{"organization": "Perry, Sophie, Falvo, Grant, Mosier, Samantha, Robertson, G. Philip,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.547d7wmf3"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.547d7wmf3", "name": "item", "description": "10.5061/dryad.547d7wmf3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.547d7wmf3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-25T00:00:00Z"}}, {"id": "10.5061/dryad.63xsj3v5k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:10Z", "type": "Dataset", "title": "Dryland soil restoration meta-analysis data", "description": "unspecifiedMicrosoft Excel.", "keywords": ["meta-analysis", "drylands", "restoration", "FOS: Earth and related environmental sciences", "15. Life on land", "soil"], "contacts": [{"organization": "Kimmell, Louisa, Fagan, Jessica, Havrilla, Caroline,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.63xsj3v5k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.63xsj3v5k", "name": "item", "description": "10.5061/dryad.63xsj3v5k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.63xsj3v5k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-20T00:00:00Z"}}, {"id": "10.5061/dryad.7pvmcvdzg", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:11Z", "type": "Dataset", "title": "Data from: Global variations and controlling factors of anammox rates", "description": "Soil anammox is an environmentally-friendly way to eliminate reactive nitrogen (N) without generating nitrous oxide. Nevertheless, the current earth system models have not incorporated the anammox due to the lack of parameters in anammox rates on a global scale, limiting the accurate projection for N cycling. A global synthesis with 1212 observations from 89 peer-reviewed papers showed that the average anammox rate was 1.60 \u00b1 0.17 nmol N g-1 h-1 in terrestrial ecosystems, with significant variations across different ecosystems. Wetlands exhibited the highest rate (2.17 \u00b1 0.31 nmol N g-1 h-1), followed by croplands at 1.02 \u00b1 0.09 nmol N g-1 h-1. The lowest anammox rates were observed in forests and grasslands. The anammox rates were positively correlated with the mean annual temperature, mean annual precipitation, soil moisture, organic carbon (C), total N, as well as nitrite and ammonium concentrations, but negatively with the soil C:N ratio. Structural equation models revealed that the geographical variations in anammox rates were primarily influenced by the N contents (such as nitrite and ammonium) and abundance of anammox bacteria, which collectively accounted for 42% of the observed variance. Furthermore, the abundance of anammox bacteria was well simulated by the mean annual precipitation, soil moisture, and ammonium concentrations, and 51% variance of the anammox bacteria was accounted for. The key controlling factors for soil anammox rates di\ufb00ered from ecosystem type, e.g., organic C, total N, and ammonium contents in croplands, versus soil C:N ratio and nitrite concentrations in wetlands. The controlling factors in soil anammox rate identified by this study are useful to construct an accurate anammox module for N cycling in earth system models.", "keywords": ["2. Zero hunger", "13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Yao, Yanzhong, Han, Bingbing, Liu, Bin, Wang, Yini, Su, Xiaoxuan, Ma, Lihua, Zhang, Tong, Niu, Shuli, Chen, Xinping, Li, Zhaolei,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.7pvmcvdzg"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.7pvmcvdzg", "name": "item", "description": "10.5061/dryad.7pvmcvdzg", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.7pvmcvdzg"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-20T00:00:00Z"}}, {"id": "10.5061/dryad.7sqv9s4vn", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:11Z", "type": "Dataset", "title": "Afforestation can lower microbial diversity and functionality in deep soil layers in a semiarid region", "description": "Afforestation is an effective approach to rehabilitate degraded ecosystems, but often depletes deep soil moisture. Presently, it is not known how an afforestation-induced decrease in moisture affects soil microbial community and functionality, hindering our ability to understand the sustainability of the rehabilitated ecosystems. To address this issue, we examined the impacts of 20 years of afforestation on soil bacterial community, co-occurrence pattern and functionalities along vertical profile (0-500 cm depth) in a semiarid region of China\u2019s Loess Plateau. We showed that the effects of afforestation with a deep-rooted legume tree on cropland were greater in deep than that of in top layers, resulting in decreased bacterial beta diversity, more responsive bacterial taxa and functional groups, increased homogeneous selection, and decreased network robustness in deep soils (120-500 cm). Organic carbon and nitrogen decomposition rates and multifunctionality also significantly decreased by afforestation, and microbial carbon limitation significantly increased in deep soils. Moreover, changes in microbial community and functionality in deep layer was largely related to changes in soil moisture. Such negative impacts on deep soils should be fully considered for assessing afforestation\u2019s eco-environment effects and for the sustainability of ecosystems because deep soils have important influence on forest ecosystems in semiarid and arid climates.", "keywords": ["2. Zero hunger", "13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Wei, Xiaorong", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.7sqv9s4vn"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.7sqv9s4vn", "name": "item", "description": "10.5061/dryad.7sqv9s4vn", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.7sqv9s4vn"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-07T00:00:00Z"}}, {"id": "10.5061/dryad.9ghx3ffkj", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Sacred groves of Central India: Diversity status, carbon storage and conservation strategies", "description": "Sacred groves (SGs) play an important role in the conservation of local biodiversity and provide numerous ecosystem services worldwide. We studied how the ecological status of Central Indian SGs contributes to regional tree diversity and carbon (C) storage. We inventoried the trees in fifty-nine SGs of Madhya Pradesh and recorded a total of 109 tree species (90 genera, 40 families). The most species-rich families were Fabaceae, Combretaceae, Malvaceae and Moraceae. The tree density ranged from 75 to 925 individuals ha-1 (mean: 398 \u00b1 32 individuals ha-1), while the basal area varied from 2.5 to 69.2 m2 ha-1 (mean: 24.2 \u00b1 1.9 m2 ha-1). The total C stock {tree C + soil organic C (SOC; 0-30 cm)} ranged from 44.7 to 455.4 Mg C ha-1 (mean: 153.8 \u00b1 9.6 Mg C ha-1) across the SGs. The studied SGs represented 74.7% of the total tree diversity and contained 33.1% higher total C stock than the forests of the state. Tree C stock was significantly positively correlated with tree basal area (r57 = 0.965, P < 0.0001), distance from the nearest village (r57 = 0.432, P < 0.001) and number of years of existence (r57 = 0.615, P < 0.0001). The present study highlighted the crucial role of the studied SGs in sustaining regional biodiversity and storing significant amounts of C in biomass and soil. Continued conservation efforts and contained anthropogenic interferences are necessary in order to maintain the current role of these SGs as biodiversity and carbon reservoirs of Central India.", "keywords": ["2. Zero hunger", "tree diversity", "carbon density", "Tropical dry deciduous forests", "FOS: Earth and related environmental sciences", "15. Life on land", "Madhya Pradesh", "Protected forests", "Soil carbon"], "contacts": [{"organization": "Khan, Mohammed Latif, Dar, Javid Ahmad, Kothandaraman, Subashree, Khare, Pramod Kumar,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9ghx3ffkj"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9ghx3ffkj", "name": "item", "description": "10.5061/dryad.9ghx3ffkj", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9ghx3ffkj"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-13T00:00:00Z"}}, {"id": "10.5061/dryad.9ghx3ffpz", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "created": "2023-10-24", "title": "The functional significance of tree species diversity in European forests - the FunDivEUROPE dataset", "description": "unspecifiedGeneral design The FunDivEUROPE project, short for 'Functional Significance of Forest Biodiversity in Europe,' aimed at exploring the intricate relationships between forest biodiversity and ecosystem functioning, focusing specifically on European forests (Baeten et al., 2019; Baeten et al., 2013; Ratcliffe et al., 2017; van der Plas et al., 2016a; van der Plas et al., 2016b; van der Plas et al., 2018). In total, 209 mature forest plots measuring 30 x 30 meters were located in six European countries, ranging from boreal to Mediterranean zones, and with each representing a major European forest type: Finland (28 plots, boreal forest), Poland (43 plots, hemiboreal forest), Germany (38 plots, temperate deciduous forest), Romania (28 plots, mountainous deciduous forest), Italy (36 plots, thermophilous deciduous forest), and Spain (36 plots, Mediterranean mixed forest). These plots were primarily established to investigate the role of the richness of regionally common and economically important \u2018target\u2019 species on ecosystem functioning and were hence selected to differ as much as possible in the richness of these. Plot selection was aimed at mimicking the design of a biodiversity experiment, in which variation in environment is minimized and diversity is not confounded with composition, as in most observational studies of diversity. Hence, plots were carefully selected so that correlations between tree species richness and community composition, topography (slope, altitude), and potentially confounding soil factors (texture, depth, pH) were minimized, thus ensuring robust tests of diversity-ecosystem function relationships (comparative study design). Most forest plots were historically used for timber production but are now managed by low-frequency thinning or with minimal intervention. Hence, species compositions and diversity patterns in forests are predominantly management-driven and/or are the result of random species assembly, from the regional species pool. All sites are considered as mature forests. In total, there were 15 target species across all 209 plots, and plots were selected so that almost all possible combinations of these target species were realized. Target species contributed to more than 90% of the tree biomass in the plots and therefore we expected them to be most important for ecosystem functioning. Richness levels of one, two, three, four, and five target species were replicated 56, 67, 54, 29, and 3 times, respectively, across countries, and most possible target species compositions were realized. For the majority of species combinations, we included two or more \u201crealizations\u201d (not strict replicates, because species abundances differ), which allows for comparing the importance of species diversity with that of species composition for this subset of plots. At each richness level, each target tree species was present in at least one plot, allowing us to statistically test for the effects of presence/absence of species on ecosystem functioning. Since species evenness might also affect ecosystem functioning, all plots were selected to have target species with similar abundances (with Pielou\u2019s evenness values above 0.6 in > 91% of the plots). To reach this goal, we a priori decided to exclude locally rare target species (<2 individuals per plot) in richness measures. To describe community composition and to estimate biomass values of each tree in each plot, we identified all stems \u22657.5 cm in diameter to species and permanently marked them (12,939 stems in total). More details about the design of the FunDivEUROPE plot network can be found in Baeten et al. (2013). We determined a high number of basic data for each of the 209 plots, describing geographic and geomorphological, as well as soil and bedrock characteristics, see also Ratcliffe et al (2017). Soil pH was determined in the same samples used for C and N determination (see below) with a 0.01M CaCl2 solution at a ratio of 1:2.5 using a 827 pH labs Metrohm AG, Herisau, Switzerland; see details in Dawud et al. (2017). For each plot, we extracted mean annual temperature, temperature seasonality (standard deviation of mean monthly temperatures), annual precipitation, and precipitation seasonality (standard deviation of mean monthly precipitation) from the WorldClim dataset (interpolated from measurements taken between 1960 and to 1990 and at a spatial resolution of one square kilometer) and the slope from the GTOPO30\u2014digital elevation model with a spatial resolution of one square kilometer (data available from the U.S. Geological Survey); see details in Kambach et al. (2019). We further quantified several measures of tree diversity, based on the initial inventory made in each plot, see Baeten et al. (2013). Short description of all these variables are available in the \u201cMetadata\u201d sheet of the data file. Ecosystem functions methodology A major strength of the FunDivEUROPE project was the general philosophy to measure all ecosystem functions in all plots, following the same protocol by the same observers across the six forest types. Measurements are thus directly comparable across plots and show high coverage. In each of the 209 plots, 27 ecosystem functions were measured. The functions were a priori classified into six groups reflecting basic ecological processes (groups 1 to 5 below), and which have established links to supporting, provisioning, regulating, or cultural ecosystem services. These functions were also used in Chao et al. (in press): Hill-Chao numbers allow decomposing gamma-multifunctionality into alpha and beta components. Ecology Letters. In addition, we quantified timber quality as an additional ecosystem service. \u00a0 In the following, we describe the methodology for each measured ecosystem function/service. (For more details, see also Baeten et al., 2019; Ratcliffe et al., 2017; van der Plas et al., 2016a; van der Plas et al., 2016b; van der Plas et al., 2018), and other FunDivEUROPE publications that focus on specific ecosystem properties and functions. Additional datasets are stored in the FunDivEUROPE data portal (https://data.botanik.uni-halle.de/fundiveurope/, logon required to view most data; all metadata is publicly available). 1. Nutrient and carbon cycling-related drivers (header in the data table in parentheses): a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Earthworm biomass: \u00a0Biomass of all earthworms [g m-2] (earthworm_biomass) Earthworm sampling was carried out in spring 2012 in Italy, Germany, and Finland, and in autumn 2012 in Poland, Romania, and Spain. Plots were divided in nine (10 x 10) m subplots. One sample per plot was taken in the center subplot. Sampling close to tree stems was avoided and whenever possible performed, in between multiple, different tree species. At each sampling point, earthworms were sampled by means of a combined method. First litter was handsorted over an area of (25 x 25) cm2. After litter removal over an enlarged area of 0.5 m\u00b2, ethological extraction using a mustard suspension was applied. Finally, hand sorting of a soil sample of (25 \u00d7 25) cm2 and 20 cm depth was performed in the middle of the 0.5 m\u00b2 area. Earthworms were preserved in ethanol (70%) for two weeks, and transferred to a 5% formaldehyde solution for fixation (until constant weight), after which they were transferred to ethanol (70%) again for further preservation and identification. All worms were individually weighed, including gut content, and identified to species level. \u00a0Results per unit area of the three sampling techniques were summed to determine the total earthworm biomass per m\u00b2. For details on earthworm biomass measurements, we refer to De Wandeler et al. (2018; 2016). b.\u00a0\u00a0\u00a0\u00a0\u00a0 Fine woody debris: Number of snags and standing dead trees shorter than 1.3 m and thinner than 5 cm DBH, and all stumps and other dead wood pieces lying on the forest floor (fine_woody_debris) Fine woody debris (FWD) was measured in two circular subplots (radius of 7 m) located in the opposite corners of each plot. All standing dead trees thinner than 5 cm diameter at breast height and snags shorter than 1.3 m, and all stumps and other dead wood pieces lying on the forest floor, were surveyed. In this study, we used the number of FWD pieces in each plot. c.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Microbial biomass: Mineral soil (0\u20135cm layer) microbial biomass carbon [mg C kg-1] (microbial_biomass_mineral) For soil sampling, each of the 209 plots was divided into nine 10x10m subplots. A soil sample was taken from five of the nine subplots and mixed to obtain one representative composite sample from each plot. Forest floor and mineral soil horizons (0-5 cm) were sampled separately. Soils were sieved fresh (4mm), stored at 4\u00b0C and analyzed within two weeks. Sampling was performed in spring 2012 in Italy, Germany, and Finland, and in autumn 2012 in Poland, Romania, and Spain. No forest floor was collected from the plots in Germany. Soil microbial biomass C was determined by the chloroform fumigation extraction method, of 10g and 15g (organic and mineral soil, respectively) soil, followed by 0.5 M K2SO4 extraction of both fumigated and unfumigated soils (soil:solution ratio, 1:5). Fumigations were carried out for three days in vacuum desiccators with alcohol-free chloroform. Extracts were filtered (Whatman n\u00b0 42), and dissolved organic carbon in fumigated and unfumigated extracts was measured with a Total Organic Carbon analyser (Labtoc, Pollution and Process Monitoring Limited, UK). Soil microbial biomass C was calculated by dividing the difference of total extract between fumigated and unfumigated samples with a kEC (extractable part of microbial biomass C after fumigation) of 0.45 for biomass C (Joergensen and Mueller, 1996). d.\u00a0\u00a0\u00a0\u00a0\u00a0 Soil carbon stocks: \u00a0Total soil carbon stock in forest floor and 0\u201310 cm mineral soil layer combined [Mg ha-1] (soil_c_ff_10) Soil sampling was carried out from May 2012 to October 2012 (i.e. Poland in May 2012, Spain in June 2012, Finland and Germany in August 2012, Romania in September 2012 and Italy in October 2012). Nine forest floor samples and nine cores of mineral soil were collected from each plot and these were subsequently pooled into one sample per plot by each soil layer, i.e. forest floor, 0\u201310cm and 10\u201320cm depths for samples from Germany, Finland, Italy, and Romania. For Poland, the fixed depth was extended to 20\u201330cm and 30\u201340 cm whereas for Spain it was only possible to sample up to the 0\u201310cm layer due to the stoniness of the site. We oven-dried the samples at 55\u00b0C to constant weight, sorted out stones and other materials, ground the forest floor first with a heavy-duty SM 2000-Retsch cutting mill, and we then took subsamples and ground it further into finer particles with a planetary ball mill (PM 400-Retsch) for six minutes at 280rpm. The mineral soil samples were sieved through 2mm diameter mesh. We carried out carbonate removal treatments for those soil samples whose pH value exceeded the threshold point and proved presence of carbonates when tested with a 4N HCl fizz test. We used 6% (w/v) H2SO3 solution and followed the carbonate removal procedure described by\u00a0(Skjemstad and Baldock, 2007). We took subsamples and further ground it into finer particles with a planetary ball mill (PM 400-Retsch) for six minutes at 280 rpm before analyzing soil organic carbon (SOC) with a Thermo Scientific FLASH 2000 soil CN analyzer. Soil organic C stocks were estimated by multiplying the SOC concentrations with soil bulk density, relative root volume and relative stone volume using the formula described in Vesterdal et al., (2008). We also determined the moisture content of the soil samples by oven-dried subsamples at 105\u00b0C and the reported SOC stock is thus on 105\u00b0C dry weight basis.\u00a0 2. Nutrient cycling related processes a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Litter decomposition: Decomposition of leaf litter using the litterbag methodology [% daily rate] (litter_decomp_day) Litter collection and litterbag construction Leaf litter from all target tree species of the cross-region exploratory platform was collected at tree species-specific peak leaf litter fall between October 2011 and November 2012. Except for the Finnish forests, where freshly fallen leaf litter was collected from the forest floor, litter was collected using suspended litter traps, which were regularly harvested at one to two-week intervals. In all cases, litter was collected nearby, but not within the experimental plots. Litter was then air-dried and stored until the preparation of the litterbags. Litterbags (15 x 15 cm) were constructed using polyethylene fabrics of two different mesh sizes. For the bottom side of the litterbags, we used a small mesh width of 0.5 x 0.5 mm in order to minimize losses of litter fragments, while for the upper side, we used a large mesh width of 5 x 8 mm to allow soil macrofauna access to the litter within bags. Litterbags were filled with 10 g of litter. For litter mixtures, litterbags were filled with equivalent proportion of each litter species. Subsamples of all litter species were weighed, dried at 65\u00b0C for 48 h and reweighed to get a 65\u00b0C dry mass correction factor. Litterbag incubation Within each experimental plot, three litterbags with the plot-specific litter type (either single litter species or specific mixtures) were placed on bare soil after the natural litter layer had been removed, and fixed to the soil by placing chicken wire on top of it. The litterbags were removed from the field when 50\u201360% of the initial litter mass of the region\u2019s fastest decomposing species was remaining (evaluated with an extra set of litterbags that were harvested regularly). As a consequence, the duration of litter decomposition varied among regions. This procedure ensured that litter was sampled at similar decomposition stages across all sites, facilitating meaningful comparisons of litter diversity effects. Litter processing Harvested litterbags were sent to Montpellier where they were dried at 65\u00b0C. Litter was cleaned of pieces of wood, stones or other foreign material that occasionally got into the litterbags. Litter was then weighed, ground to a particle size of 1 mm with a Cyclotec Sample Mill (Tecator, H\u00f6gan\u00e4s, Sweden). To correct for potential soil contamination during decomposition in the field, we determined the ash content of initial and final litter material on all samples and expressed litter mass loss on ash-free litter mass.\u00a0 Litter mass loss was expressed as the percentage of mass lost from each litterbag, calculated as followed: Mass Loss = 100 x (Initial (ash free) mass \u2013 Final (ash free) mass)/Initial (ash free) mass. For details on litter decomposition measurements, we refer to Joly et al. (2017; 2023). b.\u00a0\u00a0\u00a0\u00a0\u00a0 Nitrogen resorption efficiency: Difference in N content between green and senescent leaves divided by N content of green leaves [%] (nutrient_resorption_efficiency) In each plot, fresh leaf and needle samples were collected from the south-exposed sun crown of all dominant tree species during the growing season (June to August) of 2012 and 2013. Twigs with leaves and needles were cut down from six trees per species in the monocultures and from three trees per species in the mixtures. Depending on the local conditions, tree loppers, tree climbers, or ruffles were used for this purpose. The selected material was placed in paper bags and was either oven-dried or air-dried, depending on the facilities available. Furthermore, collection of leaves from the litter traps, as representative of senescent leaves, has been conducted at periods of maximum litterfall during 2012 and 2013. For this purpose, five litter traps per plot were established and the collected litter was separated into the different species it originated from (see \u201cLitter production\u201d below). All samples were ground and analysed for nitrogen and calcium content by means of Near Infra Red Spectroscopy (NIRS) as described in detail by Pollastrini et al. (2016a). For the calibration of the NIRS spectra for the Ca analysis, a subset of samples was analysed with an atom absorption spectrometer (AAS, iCE 3000 series, ThermoScientific, China). Nitrogen resorption efficiency was calculated as follows, taking into account the N content of green and senescent leaves: NRE(%) = 100 x ((N green leaves - N senescent leaves)/(N green leaves)) Furthermore, the estimated NRE was corrected in order to take into account the leaf mass loss occurring during senescence. Thus, NRE was corrected based on the Ca foliar concentration, since Ca is rather immobile and is not resorbed during senescence (Van Heerwaarden et al., 2003). To validate the correction of NRE based on Ca concentrations, the Mass Loss Correction Factors (MLCF) suggested by Vergutz et al. (2012) have also been used. c.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil C/N ratio: Soil C/N ratio in forest floor and 0\u201310 cm mineral soil layer combined (soil_cn_ff_10) Soil sampling was carried out between May 2012 and October 2012 in all the regions.\u00a0Nine forest floor samples were collected using a 25 x 25 cm wooden frame, and the mineral soil (0-10 cm layer) was sampled, after forest floor removal, using a cylindrical metal corer. Total soil carbon and nitrogen concentrations were measured with a Thermo Scientific FLASH 2000 soil CN analyser on the forest floor and 0-10 cm layer samples. For full details on soil carbon and nitrogen methodology see Dawud et al. (2017). d.\u00a0\u00a0\u00a0\u00a0\u00a0 Wood decomposition: Decomposition of flat wooden sticks placed on forest floor [% daily rate] (wood_decomp_day) Flat wooden sticks (wooden tongue depressors made of Betula pendula wood) were placed to decompose at each plot of the exploratory platform. Each wooden stick was initially weighed (average of 2.5 g). As the weighing was done on air-dry sticks, subsamples were weighed, dried at 65\u00b0C for 48 h and reweighed to get a 65\u00b0C dry mass correction factor. Within each plot, three wooden sticks were placed on the bare soil after the natural litter layer had been locally removed, and fixed to the soil by placing chicken wire on top of it. The wooden sticks stayed in the field for different durations among regions depending on the mass loss of the region\u2019s fastest decomposing litter species (target of 50 to 60 % mass remaining), that was placed in the field at the same time as the wooden sticks.\u00a0 After field exposure wooden sticks were harvested, dried at 65\u00b0C, and weighed. Mass loss of wooden sticks was expressed as the percentage of initial mass lost, calculated as followed: Mass Loss = 100 x (Initial mass \u2013 Final mass)/Initial mass. For details on wood decomposition measurements, we refer to Joly et al. (2017; 2023). 3. Primary production a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Fine root biomass: Total biomass of living fine roots in forest floor and 0-10 mineral soil layer combined [g m-2] (root_biomass) On each plot for determining fine root biomass, nine soil samples were taken from a predefined grid. The sampling was done in the six countries during May-October 2012. The forest floor was sampled using a wooden frame of size 25 cm x 25 cm, and thereafter the mineral soil was sampled using a cylindrical metal corer with 36 mm of inside diameter. The mineral soil was sampled down to 20 cm, except for the plots in Poland (down to 40 cm) and in Spain (down to 10 cm). Samples were pooled by layer and plot into one sample. Living fine roots (diameter \u2264 2 mm) were separated from the soil samples by hand to two categories, tree roots and ground vegetation roots. After separation, the roots were washed with water to remove adhering soil. Subsequently, the roots were dried at 40\u00b0C until constant mass and weighed for biomass. The root biomass was corrected with a correction factor for soil stoniness (CFstones= 100-(% stones)/100), where the respective volumetric stoniness was estimated with the metal rod method (Tamminen and Starr, 1994) on each plot. For this study, total tree fine root biomass for each plot was calculated (g m-2) for the sampled soil layer (forest floor + sampled mineral soil). For further details, see also Fin\u00e9r et al. (2017). b.\u00a0\u00a0\u00a0\u00a0\u00a0 Leaf mass: Leaf Area Index (lai) As a proxy for the leaf mass of each plot, we used the Leaf Area Index (LAI), which is the projected leaf area per unit of ground area. Five measurements of LAI in each plot were carried out at two time points, either early in the morning (shortly before sunrise) or late in the evening (shortly after sunset) in order to work in the presence of diffuse solar radiation and thus reduce the effect of scattered blue light in the canopy. LAI measurements were carried out in early September 2012, before the beginning of leaf shedding, using a Plant Canopy Analyzer LAI-2000 (LI-Cor Inc., Nebraska). With the LAI-2000, the incident light above the canopy and the light transmission below the canopy were measured using one sensor with five fisheye light sensors (lenses), with central zenith angle of 7\u00b0,23\u00b0, 38\u00b0, 53\u00b0 and 68\u00b0 (LAI-2000 manual, Li-Cor). The protocol used in each plot consisted of five measurements within the plots (light transmission below the canopy), and five measurements outside the forest (as proxy of the light incidence above the canopy), in an open space that was in close proximity of the sampled plots. LAI data were processed using Li-Cor\u2019s FV2200 software (LI-COR Biogeosciences, Inc. 2010). The light transmittance measurements of the fifth ring were removed to minimise the boundary effects on LAI. The LAI value per plot was the mean value of the five measurements for each plot. \u00a0For full details of the LAI measurement, see Pollastrini et al., (2016a) c.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Litter production: Annual production of foliar litter dry mass [g] (leaf_litter_production) In each of the 209 plots, five geodetic litter traps of 0.5m\u00b2 collection surface were installed in a regular grid. The sampling period covered a whole year and litters were collected several times. Sampling frequency was irregular and depended on working capacity within a region and seasonality of litter production. The litter was pooled per plot, and stored in plastic bags for transportation from the field site to the local laboratories. After air-drying, litter samples were sorted by species and by different fractions for dry weighing and chemical analysis. The following fractions were used: foliar litter (leaves or needles), woody litter (twigs, branches, bark parts), reproductive litter (flowers, cones, fruits, seeds, fruit capsules, etc.), other (e.g. bud scales, indefinable or small parts). Here, only the foliar litter is reported. A subsample of all litter types per species and region was dried at 65\u00b0C to constant weight to determine the conversion factor from air-dried to oven-dried values of litter dry mass (g). d.\u00a0\u00a0\u00a0\u00a0\u00a0 Photosynthetic efficiency: Chlorophyll fluorescence methodology [ChlF] (photo_eff_tot) Photosynthetic efficiency was measured using chlorophyll fluorescence (ChlF). ChlF measurements were replicated on eight randomly chosen leaves per tree from both the top and the bottom of the crown. The measurements were done on the twigs after the dark adaptation (i.e. after a minimum of 4 hours in a black plastic bag, at ambient temperature). In evergreen conifers, chlorophyll fluorescence measurements were taken in the current year\u2019s needles (i.e. needles sprouted in 2012). For full details of the ChlF measurement see Pollastrini et al. (2016b). e.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Tree productivity: Annual aboveground wood production [Mg C ha-1 yr-1] (tree_growth) Wood cores Tree ring data were used to reconstruct the past annually resolved wood production. Between March and October of 2012, bark-to-pith increment cores (5 mm in diameter) were collected for a subset of trees in each plot following a size-stratified random sampling approach (Jucker et al., 2014a). We cored 12 trees per plot in monocultures and six trees per species in mixtures (except in Poland, where only five cores per species were taken in all plots due to restrictions imposed by park authorities), for a total of 3138 cored trees. Short of coring all trees within a stand, this approach has been shown to provide the most reliable estimates of plot-level productivity when using tree ring data, as it ensured that the size distribution of each plot is adequately represented by the subsample. Wood cores were stored in polycarbonate sheeting and allowed to air dry before being mounted on wooden boards and sanded with progressively finer grit sizes. A high-resolution flatbed scanner (2400 dpi optical resolution) was then used to image the cores. \u00a0From tree rings to aboveground wood production We followed a four-step approach (i\u2013iv) to estimate temporal trends in aboveground wood production (AWP, in MgC ha-1 yr-1) from tree ring data (Jucker et al., 2014a). i.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Measuring growth increments from wood cores We measured yearly radial growth increments (mm yr-1) for each cored tree from the scanned images. To minimize measurement errors associated with incorrectly placed ring boundaries, we crossdated each sample against a species-level reference curve obtained by averaging all ring-width chronologies belonging to a given species from a given site. In this process, 188 cores which showed poor agreement with reference curves were excluded from further analysis, giving a final total of 2950 tree ring chronologies. Both radial growth measurements and crossdating were performed using CDendro (Cybis Elektronik & Data, Saltsj\u00f6baden, Sweden). Here we report data from the five-year period between 2007 and 2011. ii. Converting diameter increments into biomass growth We combined radial increments and allometric functions to express the growth rate of individual trees in units of biomass. We calculated the average yearly biomass growth between 2007\u20132011 (G, kgC yr-1) of cored trees as G = (AGBt2 \u2013 AGBt1)/ \u0394t, where AGBt2 is the tree\u2019s biomass, estimated with equations presented in Jucker et al. (2014b) in the most recent time period (i.e., end of 2011) and AGBt1 is its biomass at the previous time step (i.e., end of 2006), \u0394t and is the elapsed time (i.e., five years). AGBt1 was estimated by replacing current diameter and height measurements used to fit biomass equations with past values. Past diameters were reconstructed directly from wood core samples by progressively subtracting each year\u2019s diameter increment. Height growth was estimated by using height-diameter functions to predict the past height of a tree based on its past diameter. iii. Modelling individual tree biomass growth We modelled the biomass growth of each species as a function of tree size, competition for light, species richness, and a random plot effect: log(Gi) = \u03b1j[i] + \u03b21 x log(Di) + \u03b22 x CIi + \u03b23 x SRj + \u03b5i\u00a0 where Gi, Di and CIi are, respectively, the biomass growth, stem diameter and crown illumination index of tree i growing in plot j; SRj is the species richness of plot j; \u03b1j is a species\u2019 intrinsic growth rate for a tree growing in plot j; \u03b21-3 are, respectively, a species\u2019 growth response to size, light availability and species richness; and \u03b5i is the residual error. The structure of the growth model is adapted from Jucker et al. (2014b) and was fitted using the lmer function in R. Model robustness was assessed both visually, by comparing plots of predicted vs observed growth, and through a combination of model selection and goodness-of-fit tests (AIC model comparison and R2). Across all species, individual growth models explained much of the variation in growth among trees (Jucker et al., 2014a). iv. Scaling up to plot-level AWP To quantify AWP at the plot level, we used the fitted growth models to estimate the biomass growth of all trees that had not been cored. For each plot, we then summed the biomass growth of all standing trees to obtain an estimate of AWP. Growth estimates were generated using the predict.lmer function in R. f.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Tree biomass: Aboveground biomass of all trees [Mg C ha-1] (tree_biomass) In each plot, the aboveground biomass (AGB, Mg C ha-1) of all the individual trees was estimated using tree diameter and height measurements in combination with species-specific biomass functions (see above). Biomass estimates of the individual trees were then summed to quantify the plot-level tree biomass. g.\u00a0\u00a0\u00a0\u00a0\u00a0 Understorey biomass: Dry weight of all understorey vegetation in a quadrant [g] (total_understorey_weight) In three subplots in each plot (upper right, central, lower left), a quadrant of 5 m x 5 m was marked for identification and estimation of cover of understorey vascular plant species (both woody and non-woody). Within each quadrant, all understorey vegetation was identified to species and afterwards clipped in a zone of 0.5 m x 0.5 m, where vegetation was relatively abundant and the composition was representative of the whole quadrant. The biomass samples (g) were dried for 48 h at 70\u00b0C before weighing. 4 4.\u00a0Regeneration a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sapling growth: Growth of saplings up to 1.60 m tall [cm] (sapling_growth) Sapling growth measurements (cm) were taken in 2012 on a total of 30 saplings per species wherever possible. Saplings (up to 1.60 m tall) of all tree species in the regional species pool were selected in a subplot of 4x4 m located in the central part of the main plot. Sapling growth was quantified as the distance between the bud scars (internodes) along the main stem of the last five years (i.e. from 2007 to 2011), without considering the shoot of the current growing season. For details on the methodology, see Bastias et al. (2019). b.\u00a0\u00a0\u00a0\u00a0\u00a0 Tree seedling regeneration: Number of saplings up to 1.60 m tall (regeneration_seedlings) Field sampling for tree seedling regeneration was carried out at the same time and in the same subplot as the tree juvenile regeneration (see below). Tree seedling regeneration was quantified as the number of tree seedlings (i.e. less than a year old) of all tree species in the regional species pool. For details on the methodology, see Bastias et al. (2019). c.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Tree juvenile regeneration: Number of tree seedlings less than a year old (regeneration_juveniles) Field sampling to quantify regeneration was carried out in 2012, from April to late August, in a subplot of 4x4m (16m2) delimited in the central part of the main plot. Tree juvenile regeneration was quantified as the number of sapling trees of tree species in the regional species pool over one year old and up to 1.60 m tall. For details on the methodology, see Bastias et al. (2019). 5 5.\u00a0Resistance to disturbance a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Resistance to drought: Difference in carbon isotope composition in wood cores between dry and wet years [\u2030] (wue) For each plot, we randomly selected six trees among the 12 largest ones (i.e. largest diameter at breast height, DBH). For the mixed plots, three trees per species were randomly selected among the six largest trees of each species. This selection was conducted as to only select dominant and/or co-dominant trees in order to avoid confounding factors related to light interception. From each selected tree, a wood core was extracted at breast height during the summers of 2012 and 2013. For each site, we selected two years with contrasting climatic conditions during the growing season (dry vs. wet year) during the 1997-2010 period, see Grossiord et al. (2014) for full details. Latewood samples from these two years were carefully extracted from each wood core. The late wood sections from a given year and a given species in a given plot were bulked and analyzed for their carbon isotope composition (\u03b413C, \u2030) with a mass spectrometer. By only selecting latewood sections, we characterized the functioning of the trees during the second part of the growing season and avoided potential effects related to the remobilization of stored carbohydrates from the previous growing season or to a favorable spring climate. Plot-level \u03b413C was calculated as the basal-area weighted average value of species-level \u03b413C measurements. Soil drought exposure in each forest stand was calculated as the stand-level increase in carbon isotope composition of late wood from the wet to the dry year (\u0394\u03b413CS). For more details on resistance to drought measurements, we refer to Grossiord et al. 2014 (2014). b.\u00a0\u00a0\u00a0\u00a0\u00a0 Resistance to insect damage: Foliage not damaged by insects [%] (resistance_insects) As for fungal pathogens sampling (see below), we estimated insect herbivory on six trees per species in monocultures and three trees per focal species in mixed forests. The herbivory assessment was done once, from late spring to early summer (see periods on fungal pathogens protocol below). The insect herbivory protocol was derived from the ICP Forests manual. It was adapted to better account for total insect damage by observing the whole tree crown, instead of the \u201cassessable crown\u201d only. Damage on the crown exposed to sunlight and in the shade was recorded separately, as foliar loss may be also due to competition for light or natural pruning in the shaded part, particularly in heliophilous tree species. We considered damage as leaf area loss or shoot mortality i.e. defoliation. To estimate herbivore impact, we compared the sampled trees to a \u201creference tree\u201d, i.e. a healthy tree with intact foliage in its vicinity. Using binoculars, we estimated the proportion of defoliation in the living crown (i.e. the crown excluding the dead branches) in both parts of the crown (sunlight-exposed PDL and in the shade PDS) and put the estimates in one out of seven percentage classes: 0%, 0.5-1%, 1-12.5%, 12.5-25%, 25-50%, 50-75% and > 75% damage. The assessment was done from at least two sides of the crown to account for all damage. When a different score was attributed from different sides to a focal tree, the mean of damage class median was used. The total percent of defoliation was calculated as the natural logarithm of the sum of PDL and PDS. For further details on the methodology, see Guyot et al. (2016). c.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Resistance to mammal browsing: Twigs not damaged by browsers [%] (lack_browsing) All plots were sampled using four 5m x 5m subplots located in the same areas of each plot.\u00a0 Within each of the four 5x5m subplots each woody species individual was visually inspected for browsing damage (bitten twigs).\u00a0 When browsing was found, the species was recorded, an estimation of the percentage of twigs browsed (between a height of 0.5\u20132 m) was made (biomass removed), and the stem diameter (at the base) and upper and lower limits of browsing were recorded. With these data, a plot-level average of the percentage of twigs browsed was calculated, and resistance to mammal browsing was defined as 100 - % of twigs browsed. d.\u00a0\u00a0\u00a0\u00a0\u00a0 Resistance to pathogen damage: Foliage not damaged by pathogens [%] (no_pathogen_damage) Fungal pathogen damage was assessed over a two-week period at each plot during the growing period, over two years. Foliage was collected from Italy (June-July 2012), Germany (July 2012), Finland (August 2012), Spain (June 2013), Romania (July 2013), and Poland (July-August 2013). In each plot, the six trees with the largest DBH per species were selected for trees within monoculture plots, and three trees with the largest DBH per species for trees within mixture plots. Foliage (leaves and shoots) samples were collected from branches from two levels of the tree canopy (25-60 leaves and 10 current-year shoots per branch) for each focal tree species. The number of leaves sampled from each focal tree and the number of plots within each tree species richness levels are enumerated in Table S8 in van der Plas et al. (2016a).\u00a0 Visual assessments for fungal pathogen damages were conducted on fresh leaves within one day of sampling. Leaves and shoots were assessed for four classes of fungal damages: oak powdery mildew and leaf spots for the broadleaved tree species, and rust and needle cast for the conifer species. The number of leaves or shoots with the respective damages per tree was recorded, as well as the number of leaves and shoots free from fungal pathogen damage, i.e. healthy foliage. To obtain a value of healthy foliage at the plot level, the sum of all healthy foliage for all trees within the plot was calculated and this was divided by the total number of foliage replicates to acquire a plot-level proportion of healthy foliage. All assessments were conducted by one person to avoid observer bias. For details on the sampling effort, we refer to Nguyen et al.(2016). e.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Tree growth recovery: Ratio between post-drought growth and growth during the respective drought period (tree_growth_recovery) Following Lloret et al. (Lloret et al., 2011), growth recovery was defined as the ability to recover growth rates (see tree productivity section) after a decline in growth experienced during the low-growth period (see growth resistance section). It corresponds to the ratio between the average post-drought growth in the five years after a drought year and the growth during the respective low-growth year. Values less than 1 indicate a decline in growth after the drought year, while values greater than one indicate (partial) recovery. f.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Tree growth resilience: Ratio between growth after and before the drought period (tree_growth_resilience) Following Lloret et al. (Lloret et al., 2011), growth resilience was defined as the capacity of the forest stand to return to pre-drought growth (see tree productivity section) levels after a drought and is estimated as the ratio between average growth in the five years after and before the low-growth period (see growth resistance section). g.\u00a0\u00a0\u00a0\u00a0\u00a0 Tree growth resistance: Ratio of tree growth during a drought period and growth during the previous five-year high-growth period (tree_growth_resistance) Following Lloret et al. (Lloret et al., 2011), growth resistance was quantified by comparing tree growth in a low-growth year to the mean growth in the preceding five years. The year with the lowest growth across the regions was 2003, with the exception of Germany and Spain, where the lowest growth was in 1998 and 2005, respectively. 1998 and 2003 were known as drought years across Europe, with the exception of Spain where 2005 was even drier. Growth resistance was defined as the reversal of the reduction in growth (methodology described in the tree productivity section) during the drought: as the ratio of growth during the low-growth year and the growth during the previous five-year high-growth period. The larger the value, the greater the resistance of tree growth to drought. h.\u00a0\u00a0\u00a0\u00a0\u00a0 Tree growth stability: Mean annual tree growth divided by standard deviation in annual tree growth between 1992 and 2011 (tree_growth_stability) Using the annual aboveground wood production (AWP, see tree productivity section above), for each plot the growth stability was calculated as: mean(AWP) / sd(AWP) where mean(AWP) is the temporal mean AWP and sd(AWP) is the standard deviation in AWP between 1992 and 2011. See Jucker et al. (2014) for more details. 6 6.\u00a0Timber quality a.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Stem quality: Mean plot silvicultural quality assessment based on stem characteristics (timber_quality) For timber quality measurements, in each plot, dendrometric data and externally visible stem characteristics were recorded. The silvicultural quality assessment was based on stem characteristics that can be measured and evaluated non-destructively and rapidly along with a measurement of potentially influencing factors at the tree- and stand-level. For each tree within a plot, total height, height of the crown base, height of the lowest dead branch (> 1 cm diameter), and type of fork (or steeply angled branch) were measured. In addition, the presence of the following stem quality parameters was recorded: curving, stem lean, epicormic branching, coppicing, pathogenic, and other defects. Due to the multiple factors constituting stem quality and wood quality, a four-class stem quality grading scheme was used to aggregate all stem quality parameters collected for each tree into an appropriate stem quality score, allowing for the analysis of a single response variable across all regions, species diversity levels and compositions; see Table 1 in Benneter et al (2018), with quality class D=1 being the lowest, and class A=4 being the highest quality class. The assessment of stem quality parameters was limited to the butt log of the tree, which represented the lowest 5 meters of the stem for broadleaved tree species and a maximum of 10 meters from the stem base for conifers. Multiples of the 5-meter section were only considered if the second log showed at least quality class C=2, but only if the green crown base was above the section considered. It has been estimated that for most commercial species in Europe, these butt logs comprise up to 50-70 % (softwood) and 80-95 % (hardwood) of the total commercial tree value. Plot-level timber quality was then calculated as the average timber quality of all the individual trees. For further details, see Benneter et al. (2018). We further quantified the diversity of several forest-associated taxonomic groups (bats, birds, spiders, insects, earthworms, fungal pathogens, soil microbes, understorey plants, and their multi-diversity and multi-abundance/-activity indices) and many aspects of habitat quality (tree functional and structural diversity), in each plot; the respective data can be found here: Allan, E. et al. (2019). Tree diversity is key for promoting the diversity and abundance of forest\u2010associated taxa in Europe [Dataset]. Dryad. https://doi.org/10.5061/dryad.sf7m0cg22. See also: Ampoorter, E. et al. (2020) Tree diversity is key for promoting the diversity and abundance of forest-associated taxa in Europe. Oikos 129, 133-146. In addition, detailed measurements on soil fauna, properties, and functions have been quantified within the SoilForEUROPE project, see https://websie.cefe.cnrs.fr/soilforeurope/. References Baeten, L. et al., 2019. Identifying the tree species compositions that maximize ecosystem functioning in European forests. Journal of Applied Ecology, 56(3): 733-744. Baeten, L. et al., 2013. A novel comparative research platform designed to determine the functional significance of tree species diversity in European forests. Perspect Plant Ecol, 15: 281-291. Bastias, C.C., Mor\u00e1n-L\u00f3pez, T., Valladares, F. and Benavides, R., 2019. Seed size underlies the uncoupling in species composition between canopy and recruitment layers in European forests. Forest Ecol Manag, 449: 117471. Benneter, A., Forrester, D.I., Bouriaud, O., Dormann, C.F. and Bauhus, J., 2018. Tree species diversity does not compromise stem quality in major European forest types. Forest Ecol Manag, 422: 323-337. Dawud, S.M. et al., 2017. Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types. Functional Ecology, 31: 1153-1162. De Wandeler, H. et al., 2018. Tree identity rather than tree diversity drives earthworm communities in European forests. Pedobiologia, 67: 16-25. De Wandeler, H. et al., 2016. Drivers of earthworm incidence and abundance across European forests. Soil Biology and Biochemistry, 99: 167-178. Fin\u00e9r, L. et al., 2017. Conifer proportion explains fine root biomass more than tree species diversity and site factors in major European forest types. Forest Ecol Manag, 406(Supplement C): 330-350. Grossiord, C. et al., 2014. Tree diversity does not always improve resistance of forest ecosystems to drought. Proceedings of the National Academy of Sciences, 111(41): 14812-14815. Guyot, V., Castagneyrol, B., Vialatte, A., Deconchat, M. and Jactel, H., 2016. Tree diversity reduces pest damage in mature forests across Europe. Biology Letters, 12(4): 20151037. Joergensen, R.G. and Mueller, T., 1996. The fumigation-extraction method to estimate soil microbial biomass: Calibration of the kEN value. Soil Biology and Biochemistry, 28(1): 33-37. Joly, F.-X. et al., 2017. Tree species diversity affects decomposition through modified micro-environmental conditions across European forests. New Phytologist, 214: 1281-1293. Joly, F.-X., Scherer-Lorenzen, M. and H\u00e4ttenschwiler, S., 2023. Resolving the intricate role of climate in litter decomposition. Nature Ecology & Evolution, 7(2): 214-223. Jucker, T., Bouriaud, O., Avacaritei, D. and Coomes, D.A., 2014a. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol Lett, 17(12): 1560\u20131569. Jucker, T. et al., 2014b. Competition for light and water play contrasting roles in driving diversity\u2013productivity relationships in Iberian forests. J Ecol, 102: 1202\u20131213. Kambach, S. et al., 2019. How do trees respond to species mixing in experimental compared to observational studies? Ecology and Evolution, 9(19): 11254-11265. Lloret, F., Keeling, E.G. and Sala, A., 2011. Components of tree resilience: Effects of successive low-growth episodes in old ponderosa pine forests. Oikos 120: 1909\u20131920. Nguyen, D. et al., 2016. Fungal disease incidence along tree diversity gradients depends on latitude in European forests. Ecology and Evolution, 6(8): 2426-2438. Pollastrini, M. et al., 2016a. Physiological significance of forest tree defoliation: results from a survey in a mixed forest in Tuscany (central Italy). Forest Ecology and Management 361: 170-178. Pollastrini, M. et al., 2016b. Taxonomic and ecological relevance of the chlorophyll a fluorescence signature of tree species in mixed European forests. New Phytologist, 212(1): 51-65. Ratcliffe, S. et al., 2017. Biodiversity and ecosystem functioning relations in European forests depend on environmental context. Ecol Lett, 20: 1414-1426. Skjemstad, J.O. and Baldock, J.A., 2007. Total and organic carbon. Soil sampling and methods of analysis. CRC Press, Boca Raton, FL. Tamminen, P. and Starr, M., 1994. Bulk density of forested mineral soils. Silva Fennica 28 (1): article id 5528. van der Plas, F. et al., 2016a. Jack-of-all-trades effects drive biodiversity-ecosystem multifunctionality relationships in European forests. Nature Communications, 7: 11109. van der Plas, F. et al., 2016b. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proceedings of the National Academy of Sciences, 113(13): 3557-3562. van der Plas, F. et al., 2018. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol Lett, 21(1): 31-42. Van Heerwaarden, L.M., Toet, S. and Aerts, R., 2003. Current measures of nutrient resorption efficiency lead to a substantial underestimation of real resorption efficiency: facts and solutions. Oikos 101: 664-669. Vergutz, L., Manzoni, S., Porporato, A., Novais, R.F. and Jackson, R.B., 2012. Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecological Monographs 82: 205-220. Vesterdal, L., Schmidt, I.K., Callesen, I., Nilsson, L.O. and Gundersen, P., 2008. Carbon and nitrogen in forest floor and mineral soil under six common European tree species. Forest Ecol Manag, 255(1): 35-48.", "keywords": ["Ecology", "FunDivEUROPE", "Biodiversity", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water", "multifunctionality", "13. Climate action", "FOS: Biological sciences", "11. Sustainability", "Ecosystem functioning", "14. Life underwater", "Ecology", " Evolution", " Behavior and Systematics", "Nature and Landscape Conservation"], "contacts": [{"organization": "Scherer-Lorenzen, Michael, Allan, Eric, Ampoorter, Evy, Avacaritiei, Daniel, Baeten, Lander, Barnoaiea, Ionut, Bastias, Cristina C., Bauhus, J\u00fcrgen, Benavides, Raquel, Benneter, Adam, Berger, Sigrid, Bonal, Damien, Bouriaud, Olivier, Bruelheide, Helge, Bussotti, Filippo, Carnol, Monique, Castagneyrol, Bastien, Che\u0107ko, Ewa, Coomes, David, Coppi, Andrea, Cosofret, Cosmin, Danila, Iulian, Dawud, Seid Muhie, De Wandeler, Hans, Domisch, Timo, Duduman, Gabriel, Fin\u00e9r, Leena, Fischer, Markus, Fotelli, Mariangela, Gessler, Arthur, Gimeno, Teresa E., Grossiord, Charlotte, Guyot, Virginie, H\u00e4ttenschwiler, Stephan, Jactel, Herv\u00e9, Jaroszewicz, Bogdan, Joly, Fran\u00e7ois\u2010Xavier, Jucker, Tommaso, Koricheva, Julia, L\u00f3pez-Quiroga, David, Milligan, Harriet, M\u00fcller, Sandra, Muys, Bart, Nguyen, Diem, Pollastrini, Martina, Rabasa, Sonia G., Radoglou, Kalliopi, Ratcliffe, Sophia, Raulund\u2010Rasmussen, Karsten, Ruiz\u2010Benito, Paloma, Seidl, Rupert, Seiferling, Ian, Selvi, Federico, Smerczy\u0144ski, Ireneusz, Stenlid, Jan, Valladares, Fernando, van der Plas, Fons, Verheyen, Kris, Vesterdal, Lars, von Wilpert, Klaus, Wirth, Christian, Zavala, Miguel A.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9ghx3ffpz"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9ghx3ffpz", "name": "item", "description": "10.5061/dryad.9ghx3ffpz", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9ghx3ffpz"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-06T00:00:00Z"}}, {"id": "10.5061/dryad.9kd51c5n4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Species of fast bulk-soil nutrient cycling have lower rhizosphere effects: A nutrient spectrum of rhizosphere effects", "description": "Tree roots not only acquire readily-usable soil nutrients but also affect microbial decomposition and manipulate nutrient availability in their surrounding soils, i.e., rhizosphere effects (REs). Thus, REs challenge the basic understanding how plants adapt to environment and co-exist with other species. Yet, how REs vary among species in response to species-specific bulk soil nutrient cycling is not well-known. Here we studied how plant-controlled microbial decomposition activities in rhizosphere soils respond to those in their corresponding bulk soils and whether these relations depend on species-specific nutrient cycling in the bulk soils. We targeted 55 woody species of different clades and mycorrhizal types in three contrasting biomes, namely a temperate forest, a subtropical forest and a tropical forest. We found that microbial decomposition activities in rhizosphere soils responded linearly to those in their corresponding bulk soils at the species level. Thereafter, we found that REs (parameters in rhizosphere soils minus those in corresponding bulk soils) of microbial decomposition activities had negative linear correlations with microbial decomposition activities in corresponding bulk soils. A multiple factor analysis revealed that soil organic carbon, total nitrogen and soil water content favored bulk soil decomposition activities in all three biomes, showing that the magnitude of REs varied along a fast-slow nutrient cycling spectrum in bulk soils. The species of fast nutrient cycling in their bulk soils tended to have smaller or even negative REs. Therefore, woody plants commonly utilize both positive and negative REs as a nutrient-acquisition strategy. Based on the trade-offs between REs and other nutrient-acquisition strategies, we proposed a push and pull conceptual model which can bring plant nutrient-acquisition cost and plant carbon economics spectrum together in the future. This model will facilitate not only the carbon and nutrient cycling but also the mechanisms of species co-existence in forest ecosystems.", "keywords": ["2. Zero hunger", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Zhu, Biao, Sun, Lijuan, Tsujii, Yuki, Xu, Tianle, Han, Mengguang, Li, Rui, Han, Yunfeng, Gan, Dayong,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9kd51c5n4"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9kd51c5n4", "name": "item", "description": "10.5061/dryad.9kd51c5n4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9kd51c5n4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-09T00:00:00Z"}}, {"id": "10.5061/dryad.9kd51c5nq", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Climate warming alters the relative importance of plant root and microbial community in regulating the accumulation of soil microbial necromass carbon in a Tibetan alpine meadow", "description": "Climate warming is predicted to considerably affect variations in soil organic carbon (SOC), especially in alpine ecosystems. Microbial necromass carbon (MNC) is an important contributor to stable soil organic carbon pools. However, accumulation and persistence of soil MNC across a gradient of warming are still poorly understood. An eight-year field experiment with four levels of warming was conducted in a Tibetan meadow. We found that low-level (+0-1.5 \u2103) warming mostly enhanced bacterial necromass carbon (BNC), fungal necromass carbon (FNC), and total MNC compared with control treatment across soil layers, while no significant effect was caused between high-level (+1.5-2.5 \u2103) treatments and control treatments. The contributions of both MNC and BNC to soil organic carbon were not significantly affected by warming treatments across depths. Structural equation modeling analysis demonstrated that the effect of plant root traits on MNC persistence strengthened with warming intensity, while the influence of microbial community characteristics waned along with strengthened warming. Overall, our study provides novel evidence that the major determinants of MNC production and stabilization may vary with warming magnitude in alpine meadows. This finding is critical for updating our knowledge of soil carbon storage in response to climate warming.", "keywords": ["2. Zero hunger", "13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Cai, Mengke, Zhao, Guang, Zhao, Bo, Cong, Nan, Zheng, Zhoutao, Zhu, Juntao, Duan, Xiaoqing, Zhang, Yangjian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9kd51c5nq"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9kd51c5nq", "name": "item", "description": "10.5061/dryad.9kd51c5nq", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9kd51c5nq"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-24T00:00:00Z"}}, {"id": "10.5061/dryad.b2rbnzsj9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Observation\u2010based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models", "description": "unspecifiedThis dataset provides a global gridded product at 0.5-degree resolution of predicted annual soil heterotrophic respiration (Rh) during 1982\u20132018. A Random Forest (RF) approach was used to derive the predicted Rh trained with 761 observations with 19 predictors (including climate, vegetation, soil biotic and abiotic variables). To improve the RF model accuracy, we developed a stratified 10-fold cross-validation, by grouping our dataset into three climate zone classes (i.e., tropical, temperate and boreal zones) and ensuring each class was approximately equally represented across each fold. The average predicted map across the RF model ensemble was used as the final product.", "keywords": ["13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "He, Yue, Ding, Jinzhi, Dorji, Tsechoe, Wang, Tao, Li, Juan, Smith, Pete,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.b2rbnzsj9"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.b2rbnzsj9", "name": "item", "description": "10.5061/dryad.b2rbnzsj9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.b2rbnzsj9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-12T00:00:00Z"}}, {"id": "10.5061/dryad.bnzs7h4fn", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Landcover map for the central region of the Yukon-Kuskokwim Delta, Alaska", "description": "unspecifiedThis landcover classification was created for the purposes of determining watershed landcover as potential drivers of downstream waterbody CH4\u00a0and CO2\u00a0concentrations. The region of interest is a watershed in the central portion of the Yukon-Kuskokwim Delta, Alaska, where field observations were based. The landcover map has been clipped to the watershed extent, and included as a shapefile. We created a 10-m resolution landcover map for the region of interest to determine the presence and abundance of various terrestrial, wetland, surface waterbodies, and disturbed areas in sample watersheds. We used an unsupervised k-means algorithm (Google Earth Engine, \u201cwekaKMeans\u201d) with the surface reflectance raw bands, derived bands (NDWI, NDVI), slope, and elevation as inputs for the classification. The Alaska Interagency Coordination Center historical wildfire database was used for wildfire delineations. Wildfires in the region of interest included fire scars from the 1970s, 1990s, and early 2000s, collectively designated as \u201cold fires,\u201d and fire scars from the large area burned in 2015. First, the region of interest was divided into unburned, old fire scars, and 2015 fire scars, and the classification algorithm was run separately for each. We used an initial number of classes \u201ck\u201d higher than the number of known landcover types in order to capture the variability in the driving layers, then later grouped similar classes produced by the k-means algorithm. Landcover accuracy was assessed using 350 randomly stratified points from the region of interest. The classifications at these points were compared to higher resolution (Worldview-2) imagery using Google Earth Engine and reclassified using expert assessment. We used a confusion matrix to assess the balanced accuracy of each classification, which ranged from 0.75 to 0.99 (Figure S2 in Supporting Information S1 from Ludwig et al. 2022 (the article associated with this dataset)).", "keywords": ["Arctic", "13. Climate action", "vegetation", "waterbody", "FOS: Earth and related environmental sciences", "15. Life on land", "Tundra", "6. Clean water", "landcover", "Lake"], "contacts": [{"organization": "Ludwig, Sarah, Natali, Susan M., Schade, John D., Powell, Margaret, Fiske, Greg, Commane, Roisin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.bnzs7h4fn"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.bnzs7h4fn", "name": "item", "description": "10.5061/dryad.bnzs7h4fn", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.bnzs7h4fn"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-13T00:00:00Z"}}, {"id": "10.5061/dryad.bvq83bkbg", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:12Z", "type": "Dataset", "title": "Recent photosynthates are the primary carbon source for soil microbial respiration in subtropical forests", "description": "unspecifiedTropical and subtropical forests represent the largest terrestrial carbon pool. Elucidating the carbon sources for soil microbial respiration (Rm) in tropical and subtropical forests is of fundamental importance to the global carbon cycle in a warming world. Based on hourly measurements, we quantified Rm of\u00a0in situ\u00a0forest soil and soil cores from a subtropical forest. We found recent photosynthates, not soil organic carbon (SOC), contributed 88% \u00b1 12% of the carbon source fueling Rm. The control of recent photosynthates on Rm is also supported by the close relationship between Rm and photosynthetically active radiation as well as literature data synthesis results. These results challenge conventional models based on the tenet that Rm is mainly regulated by soil temperature in all forest ecosystems. The results imply that the widely observed warming-induced Rm increases are largely explained by the enhanced input of recent photosynthates in tropical forests, not SOC consumption.", "keywords": ["recent photosynthates", "microbial respiration", "13. 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