{"type": "FeatureCollection", "features": [{"id": "10.1007/s10021-010-9384-8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:45Z", "type": "Journal Article", "created": "2010-09-29", "title": "Biotic And Abiotic Changes In Ecosystem Structure Over A Shrub-Encroachment Gradient In The Southwestern Usa", "description": "In this study, we investigate changes in ecosystem structure that occur over a gradient of land-degradation in the southwestern USA, where shrubs are encroaching into native grassland. We evaluate a conceptual model which posits that the development of biotic and abiotic structural connectivity is due to ecogeomorphic feedbacks. Three hypotheses are evaluated: 1. Over the shrub-encroachment gradient, the difference in soil properties under each surface-cover type will change non-linearly, becoming increasingly different; 2. There will be a reduction in vegetation cover and an increase in vegetation-patch size that is concurrent with an increase in the spatial heterogeneity of soil properties over the shrub-encroachment gradient; and 3. Over the shrub-encroachment gradient, the range at which soil properties are autocorrelated will progressively exceed the range at which vegetation is autocorrelated. Field-based monitoring of vegetation and soil properties was carried out over a shrub-encroachment gradient at the Sevilleta National Wildlife Refuge in New Mexico, USA. Results of this study show that vegetation cover decreases over the shrub-encroachment gradient, but vegetation-patch size increases, with a concurrent increase in the spatial heterogeneity of soil properties. Typically, there are significant differences in soil properties between non-vegetated and vegetated surfaces, but for grass and shrub patches, there are only significant differences for the biotic soil properties. Results suggest that it is the development of larger, well-connected, non-vegetated patches that is most important in driving the overall behavior of shrub-dominated sites. Results of this study support the hypothesis that feedbacks of functional connectivity reinforce the development of structural connectivity, which increases the resilience of the shrub-dominated state, and thus makes it harder for grasses to re-establish and reverse the vegetation change.", "keywords": ["2. Zero hunger", "0106 biological sciences", "570", "Ecohydrology", "Function - Land degradation.", "Structure", "910", "15. Life on land", "Grassland", "01 natural sciences", "Spatial autocorrelation", "Shrubland", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Turnbull, Laura, Brazier, Richard E., Wainwright, John, Bol, Roland,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1007/s10021-010-9384-8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10021-010-9384-8", "name": "item", "description": "10.1007/s10021-010-9384-8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10021-010-9384-8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-09-30T00:00:00Z"}}, {"id": "10.1016/j.compag.2021.106262", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:06Z", "type": "Journal Article", "created": "2021-07-15", "title": "A multifunctional matching algorithm for sample design in agricultural plots", "description": "Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.", "keywords": ["2. Zero hunger", "Partially-observed data", "Agricultural sampling", "Representative sampling given covariates", "0207 environmental engineering", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Two-phase study", "310", "Original Papers", "Spatial autocorrelation"]}, "links": [{"href": "https://doi.org/10.1016/j.compag.2021.106262"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.compag.2021.106262", "name": "item", "description": "10.1016/j.compag.2021.106262", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.compag.2021.106262"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-01T00:00:00Z"}}, {"id": "10.1111/1755-0998.12949", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:19:15Z", "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"}}, {"id": "10.5281/zenodo.6513429", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:42Z", "type": "Dataset", "title": "Data files belonging to the paper \"Dealing with clustered samples for assessing map accuracy by  cross-validation\"", "description": "Open Access{'references': ['de Bruin et al., 2022. Dealing with clustered samples for assessing map accuracy by cross-validation. https://doi.org/10.1016/j.ecoinf.2022.101665']}", "keywords": ["Soil organic carbon", "Above-ground biomass", "Machine learning", "Life Science", "15. Life on land", "Spatial cross-validation", "Spatial autocorrelation"], "contacts": [{"organization": "de Bruin, Sytze, Brus, Dick, Heuvelink, Gerard, van Ebbenhorst Tengbergen, Tom, Wadoux, Alexandre,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6513429"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6513429", "name": "item", "description": "10.5281/zenodo.6513429", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6513429"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10261/252976", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:25:55Z", "type": "Journal Article", "created": "2021-07-15", "title": "A multifunctional matching algorithm for sample design in agricultural plots", "description": "Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.", "keywords": ["2. Zero hunger", "Partially-observed data", "Agricultural sampling", "Representative sampling given covariates", "0207 environmental engineering", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Two-phase study", "310", "Original Papers", "Spatial autocorrelation"]}, "links": [{"href": "https://doi.org/10261/252976"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/252976", "name": "item", "description": "10261/252976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/252976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-01T00:00:00Z"}}, {"id": "3184389424", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:46Z", "type": "Journal Article", "created": "2021-07-15", "title": "A multifunctional matching algorithm for sample design in agricultural plots", "description": "Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.", "keywords": ["2. Zero hunger", "Partially-observed data", "Agricultural sampling", "Representative sampling given covariates", "0207 environmental engineering", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Two-phase study", "310", "Original Papers", "Spatial autocorrelation"]}, "links": [{"href": "https://doi.org/3184389424"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3184389424", "name": "item", "description": "3184389424", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3184389424"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-01T00:00:00Z"}}, {"id": "PMC8329933", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:30:08Z", "type": "Journal Article", "created": "2021-07-15", "title": "A multifunctional matching algorithm for sample design in agricultural plots", "description": "Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.", "keywords": ["2. Zero hunger", "Partially-observed data", "Agricultural sampling", "Representative sampling given covariates", "0207 environmental engineering", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Two-phase study", "310", "Original Papers", "Spatial autocorrelation"]}, "links": [{"href": "https://doi.org/PMC8329933"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC8329933", "name": "item", "description": "PMC8329933", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC8329933"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatial+autocorrelation&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=Spatial+autocorrelation&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=Spatial+autocorrelation&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Spatial+autocorrelation&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-04-16T03:23:07.104866Z"}