Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Phosphorus (P) is one of the most important elements for soil biology and biogeochemistry worldwide. Yet, despite decades of research, important uncertainties persist about the drivers and changes in soil P forms during long-term soil formation. Here, we analyzed topsoils from nine globally distributed retrogressive soil chronosequences aiming to evaluate the relative contribution of key environmental factors (that is, soil age, substrate origin, climate, soil attributes, and vegetation) in explaining the long-term dynamics of primary, occluded, non-occluded, organic, and total P across different terrestrial ecosystems. We found that, rather than soil age, substrate origin was the main driver controlling the fate of different P fractions across contrasting environmental conditions. Moreover, our findings suggest that temporal patterns governing the long-term dynamics of different P forms as soils develop are not consistent among soil chronosequences, which is a result of contrasting environmental conditions, especially substrate origin. We further showed that topsoil total P was the greatest at intermediate soil development stage across the globe. Lastly, our results showed that P fractions were highly correlated with multiple surrogates of ecosystem services, such as carbon sequestration, plant productivity, and biodiversity. Together, our work provides new insights into the natural history of P availability, and further highlights that substrate origin, rather than soil age, is essential to predict changes in P availability in response to physical perturbation and climate change. Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Little is known about the global distribution and environmental drivers of key microbial functional traits such as antibiotic resistance genes (ARGs). Soils are one of Earth\uffe2\uff80\uff99s largest reservoirs of ARGs, which are integral for soil microbial competition, and have potential implications for plant and human health. Yet, their diversity and global patterns remain poorly described. Here, we analyzed 285 ARGs in soils from 1012 sites across all continents and created the first global atlas with the distributions of topsoil ARGs.
ResultsWe show that ARGs peaked in high latitude cold and boreal forests. Climatic seasonality and mobile genetic elements, associated with the transmission of antibiotic resistance, were also key drivers of their global distribution. Dominant ARGs were mainly related to multidrug resistance genes and efflux pump machineries. We further pinpointed the global hotspots of the diversity and proportions of soil ARGs.
ConclusionsTogether, our work provides the foundation for a better understanding of the ecology and global distribution of the environmental soil antibiotic resistome.
", "keywords": ["Ecolog\u00eda (Biolog\u00eda)", "0301 basic medicine", "SDG-03: Good health and well-being", "550", "Antibiotic resistance", "Edafolog\u00eda (Biolog\u00eda)", "Antibiotic resistance genes (ARGs)", "910", "http://metadata.un.org/sdg/3", "631.4", "Microbial ecology", "2417.14 Gen\u00e9tica Vegetal", "Soil", "03 medical and health sciences", "XXXXXX - Unknown", "Global scale", "Humans", "Global change", "SCALE", "Ensure healthy lives and promote well-being for all at all ages", "2. Zero hunger", "0303 health sciences", "Ecology", "Research", "QR100-130", "Human health", "15. Life on land", "Gen\u00e9tica", "Anti-Bacterial Agents", "3. Good health", "Phenotype", "Mobile genetic elements", "13. Climate action", "BACTERIA", "2511.02 Biolog\u00eda de Suelos", "RESISTANCE GENES"]}, "links": [{"href": "https://doi.org/10.1186/s40168-022-01405-w"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microbiome", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s40168-022-01405-w", "name": "item", "description": "10.1186/s40168-022-01405-w", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s40168-022-01405-w"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-11T00:00:00Z"}}, {"id": "10.3390/rs13234893", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:34Z", "type": "Journal Article", "created": "2021-12-06", "title": "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model", "description": "The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\uffe2\uff88\uff923 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\uffe2\uff88\uff923 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.
", "keywords": ["feature importance", "Science", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "antecedent precipitation index", "SDG 13 - Climate Action", "Global scale", "Antecedent precipitation index; Feature importance; Global scale; In situ constrained; Random forest; Soil moisture", "soil moisture; random forest; global scale; in situ constrained; feature importance; antecedent precipitation index", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "Antecedent precipitation index", "Q", "In situ constrained", "15. Life on land", "Feature importance", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Soil moisture", "soil moisture", "ITC-GOLD", "in situ constrained", "random forest", "Random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/938135/1/2021_Ljie_Zeng_et_al_remotesensing.pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://doi.org/10.3390/rs13234893"}, {"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/rs13234893", "name": "item", "description": "10.3390/rs13234893", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13234893"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:19Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving Earth system science for over a decade", "description": "Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.
Phosphorus (P) is one of the most important elements for soil biology and biogeochemistry worldwide. Yet, despite decades of research, important uncertainties persist about the drivers and changes in soil P forms during long-term soil formation. Here, we analyzed topsoils from nine globally distributed retrogressive soil chronosequences aiming to evaluate the relative contribution of key environmental factors (that is, soil age, substrate origin, climate, soil attributes, and vegetation) in explaining the long-term dynamics of primary, occluded, non-occluded, organic, and total P across different terrestrial ecosystems. We found that, rather than soil age, substrate origin was the main driver controlling the fate of different P fractions across contrasting environmental conditions. Moreover, our findings suggest that temporal patterns governing the long-term dynamics of different P forms as soils develop are not consistent among soil chronosequences, which is a result of contrasting environmental conditions, especially substrate origin. We further showed that topsoil total P was the greatest at intermediate soil development stage across the globe. Lastly, our results showed that P fractions were highly correlated with multiple surrogates of ecosystem services, such as carbon sequestration, plant productivity, and biodiversity. Together, our work provides new insights into the natural history of P availability, and further highlights that substrate origin, rather than soil age, is essential to predict changes in P availability in response to physical perturbation and climate change. Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository. The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\u22123 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\u22123 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally. Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.
Little is known about the global distribution and environmental drivers of key microbial functional traits such as antibiotic resistance genes (ARGs). Soils are one of Earth\uffe2\uff80\uff99s largest reservoirs of ARGs, which are integral for soil microbial competition, and have potential implications for plant and human health. Yet, their diversity and global patterns remain poorly described. Here, we analyzed 285 ARGs in soils from 1012 sites across all continents and created the first global atlas with the distributions of topsoil ARGs.
ResultsWe show that ARGs peaked in high latitude cold and boreal forests. Climatic seasonality and mobile genetic elements, associated with the transmission of antibiotic resistance, were also key drivers of their global distribution. Dominant ARGs were mainly related to multidrug resistance genes and efflux pump machineries. We further pinpointed the global hotspots of the diversity and proportions of soil ARGs.
ConclusionsTogether, our work provides the foundation for a better understanding of the ecology and global distribution of the environmental soil antibiotic resistome.
", "keywords": ["Ecolog\u00eda (Biolog\u00eda)", "0301 basic medicine", "SDG-03: Good health and well-being", "550", "Antibiotic resistance", "Edafolog\u00eda (Biolog\u00eda)", "Antibiotic resistance genes (ARGs)", "910", "631.4", "Microbial ecology", "2417.14 Gen\u00e9tica Vegetal", "Soil", "03 medical and health sciences", "XXXXXX - Unknown", "Global scale", "Humans", "Global change", "SCALE", "2. Zero hunger", "0303 health sciences", "Ecology", "Research", "QR100-130", "Human health", "15. Life on land", "Gen\u00e9tica", "Anti-Bacterial Agents", "3. Good health", "Phenotype", "Mobile genetic elements", "13. Climate action", "BACTERIA", "2511.02 Biolog\u00eda de Suelos", "RESISTANCE GENES"]}, "links": [{"href": "https://doi.org/1959.7/uws:76535"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microbiome", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:76535", "name": "item", "description": "1959.7/uws:76535", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:76535"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-11T00:00:00Z"}}, {"id": "3216678516", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:19Z", "type": "Journal Article", "created": "2021-12-06", "title": "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model", "description": "The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\u22123 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\u22123 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.