{"type": "FeatureCollection", "features": [{"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=conditionally+autoregressive+model&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=conditionally+autoregressive+model&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=conditionally+autoregressive+model&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=conditionally+autoregressive+model&offset=1", "hreflang": "en-US"}], "numberMatched": 1, "numberReturned": 1, "distributedFeatures": [], "timeStamp": "2026-05-26T00:11:30.791954Z"}