{"type": "FeatureCollection", "features": [{"id": "10.1038/nature24668", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:36Z", "type": "Journal Article", "created": "2017-12-08", "title": "Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity", "description": "Fire frequency is changing globally and is projected to affect the global carbon cycle and climate. However, uncertainty about how ecosystems respond to decadal changes in fire frequency makes it difficult to predict the effects of altered fire regimes on the carbon cycle; for instance, we do not fully understand the long-term effects of fire on soil carbon and nutrient storage, or whether fire-driven nutrient losses limit plant productivity. Here we analyse data from 48 sites in savanna grasslands, broadleaf forests and needleleaf forests spanning up to 65 years, during which time the frequency of fires was altered at each site. We find that frequently burned plots experienced a decline in surface soil carbon and nitrogen that was non-saturating through time, having 36 per cent (\u00b113 per cent) less carbon and 38 per cent (\u00b116 per cent) less nitrogen after 64 years than plots that were protected from fire. Fire-driven carbon and nitrogen losses were substantial in savanna grasslands and broadleaf forests, but not in temperate and boreal needleleaf forests. We also observe comparable soil carbon and nitrogen losses in an independent field dataset and in dynamic model simulations of global vegetation. The model study predicts that the long-term losses of soil nitrogen that result from more frequent burning may in turn decrease the carbon that is sequestered by net primary productivity by about 20 per cent of the total carbon that is emitted from burning biomass over the same period. Furthermore, we estimate that the effects of changes in fire frequency on ecosystem carbon storage may be 30 per cent too low if they do not include multidecadal changes in soil carbon, especially in drier savanna grasslands. Future changes in fire frequency may shift ecosystem carbon storage by changing soil carbon pools and nitrogen limitations on plant growth, altering the carbon sink capacity of frequently burning savanna grasslands and broadleaf forests.", "keywords": ["2. Zero hunger", "Carbon Sequestration", "Time Factors", "Nitrogen", "carbon", "Geographic Mapping", "Phosphorus", "15. Life on land", "Grassland", "01 natural sciences", "nitrogen", "Carbon", "Wildfires", "Soil", "Spatio-Temporal Analysis", "13. Climate action", "XXXXXX - Unknown", "Potassium", "carbon cycle (biogeochemistry)", "Calcium", "ecosystems", "soils", "fire", "Ecosystem", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1038/nature24668"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/nature24668", "name": "item", "description": "10.1038/nature24668", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/nature24668"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-11T00:00:00Z"}}, {"id": "10.1111/1755-0998.12949", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:32Z", "type": "Journal Article", "created": "2018-09-29", "title": "Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA", "description": "Abstract<p>Site occupancy\uffe2\uff80\uff90detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false\uffe2\uff80\uff90positive and false\uffe2\uff80\uff90negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true\uffe2\uff80\uff90/false\uffe2\uff80\uff90positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true\uffe2\uff80\uff90/false\uffe2\uff80\uff90positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.</p>", "keywords": ["0106 biological sciences", "Genetics", " Population", "Spatio-Temporal Analysis", "330", "DNA", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology", "Biostatistics", "15. Life on land", "Biota", "01 natural sciences", "conditionally autoregressive model; sedimentary DNA; spatial autocorrelation; species occupancy-detection model; temporal autocorrelation; true skill statistics; Biostatistics; DNA; Spatio-Temporal Analysis; Biota; Genetics", " Population; Biotechnology; Ecology", " Evolution", " Behavior and Systematics; Genetics"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/635968/2/Chen_et_al-2019-Molecular_Ecology_Resources.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.12949"}, {"href": "https://doi.org/10.1111/1755-0998.12949"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Molecular%20Ecology%20Resources", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/1755-0998.12949", "name": "item", "description": "10.1111/1755-0998.12949", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/1755-0998.12949"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-11-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatio-Temporal+Analysis&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatio-Temporal+Analysis&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatio-Temporal+Analysis&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatio-Temporal+Analysis&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-05-26T00:10:23.660932Z"}