{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:58:04Z","timestamp":1771235884139,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,11,25]],"date-time":"2017-11-25T00:00:00Z","timestamp":1511568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The characterization and quantification of ecological interactions and the construction of species\u2019 distributions and their associated ecological niches are of fundamental theoretical and practical importance. In this paper, we discuss a Bayesian inference framework, which, using spatial data, offers a general formalism within which ecological interactions may be characterized and quantified. Interactions are identified through deviations of the spatial distribution of co-occurrences of spatial variables relative to a benchmark for the non-interacting system and based on a statistical ensemble of spatial cells. The formalism allows for the integration of both biotic and abiotic factors of arbitrary resolution. We concentrate on the conceptual and mathematical underpinnings of the formalism, showing how, using the naive Bayes approximation, it can be used to not only compare and contrast the relative contribution from each variable, but also to construct species\u2019 distributions and ecological niches based on an arbitrary variable type. We also show how non-linear interactions between distinct niche variables can be identified and the degree of confounding between variables accounted for.<\/jats:p>","DOI":"10.3390\/e19120547","type":"journal-article","created":{"date-parts":[[2017,11,27]],"date-time":"2017-11-27T11:07:08Z","timestamp":1511780828000},"page":"547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Bayesian Inference of Ecological Interactions from Spatial Data"],"prefix":"10.3390","volume":"19","author":[{"given":"Christopher","family":"Stephens","sequence":"first","affiliation":[{"name":"C3\u2014Centro de Ciencias de la Complejidad and Instituto de Ciencias Nucleares, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Circuito Exterior, A. Postal 70-543, Ciudad de M\u00e9xico 04510, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"S\u00e1nchez-Cordero","sequence":"additional","affiliation":[{"name":"Instituto de Biolog\u00eda, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Circuito Exterior, A. Postal 70-543, Ciudad de M\u00e9xico 04510, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7347-714X","authenticated-orcid":false,"given":"Constantino","family":"Gonz\u00e1lez Salazar","sequence":"additional","affiliation":[{"name":"C3\u2014Centro de Ciencias de la Complejidad and Instituto de Ciencias Nucleares, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Circuito Exterior, A. Postal 70-543, Ciudad de M\u00e9xico 04510, Mexico"},{"name":"Departamento de Ciencias Ambientales, CBS Universidad Aut\u00f3noma Metropolitana, Unidad Lerma, Estado de M\u00e9xico 52006, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1080\/13658816.2010.508043","article-title":"Space, time and visual analytics","volume":"24","author":"Andrienko","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/TVCG.2009.143","article-title":"Flow mapping and multivariate visualization of large spatial interaction data","volume":"15","author":"Guo","year":"2009","journal-title":"IEEE Trans. Vis. Comput. 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