{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:09:21Z","timestamp":1776085761366,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Joint species distribution models are popular in ecology for modelling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in each sample, along with sample-specific covariates. A key problem is that current models implicitly assume that the list of species under consideration is predefined and finite, while for highly diverse groups of organisms, it is impossible to anticipate which species will be observed in a study, and discovery of unknown species is common. This article proposes a new modelling paradigm for statistical ecology, which generalizes traditional multivariate probit models to accommodate large numbers of rare species and new species discovery. We discuss theoretical properties of the proposed modelling paradigm and implement efficient algorithms for posterior computation. Simulation studies and applications to fungal biodiversity data provide compelling support for the new modelling class.<\/jats:p>","DOI":"10.1093\/biomet\/asaf055","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T17:54:41Z","timestamp":1753206881000},"source":"Crossref","is-referenced-by-count":1,"title":["Infinite joint species distribution models"],"prefix":"10.1093","volume":"112","author":[{"given":"F","family":"Stolf","sequence":"first","affiliation":[{"name":"Department of Statistical Science, Duke University , Box 90251 , Durham, North Carolina 27708,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D B","family":"Dunson","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University , Box 90251 , Durham, North Carolina 27708,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"2026032607095790200_asaf055-B2","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1038\/s41586-024-07658-9","article-title":"Airborne DNA reveals predictable spatial and seasonal dynamics of fungi","volume":"631","author":"Abrego","year":"2024","journal-title":"Nature"},{"key":"2026032607095790200_asaf055-B1","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/1755-0998.12755","article-title":"Give me a sample of air and I will tell which species are found from your region: molecular identification of fungi from airborne spore samples","volume":"18","author":"Abrego","year":"2018","journal-title":"Molec. Ecol. Res"},{"key":"2026032607095790200_asaf055-B3","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1093\/jrsssb\/qkad062","article-title":"Normalised latent measure factor models","volume":"85","author":"Beraha","year":"2023","journal-title":"J. R. Statist. Soc. B"},{"key":"2026032607095790200_asaf055-B4","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1214\/13-BA823","article-title":"Feature allocations, probability functions, and paintboxes","volume":"8","author":"Broderick","year":"2013","journal-title":"Bayesian Anal."},{"key":"2026032607095790200_asaf055-B5","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1080\/01621459.2022.2115918","article-title":"Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation","volume":"119","author":"Camerlenghi","year":"2024","journal-title":"J. Am. Statist. Assoc"},{"key":"2026032607095790200_asaf055-B6","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1080\/01621459.2023.2260053","article-title":"Bayesian inference on high-dimensional multivariate binary responses","volume":"119","author":"Chakraborty","year":"2024","journal-title":"J. Am. Statist. Assoc"},{"key":"2026032607095790200_asaf055-B7","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1093\/biomet\/85.2.347","article-title":"Analysis of multivariate probit models","volume":"85","author":"Chib","year":"1998","journal-title":"Biometrika"},{"key":"2026032607095790200_asaf055-B8","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1515\/9781400833023.257","article-title":"Biodiversity: concepts, patterns, and measurement","volume":"663","author":"Colwell","year":"2009","journal-title":"Princeton Guide Ecol"},{"key":"2026032607095790200_asaf055-B9","doi-asserted-by":"crossref","first-page":"581835","DOI":"10.3389\/fevo.2020.581835","article-title":"Metabarcoding from microbes to mammals: comprehensive bioassessment on a global scale","volume":"8","author":"Compson","year":"2020","journal-title":"Front. Ecol. Evol."},{"key":"2026032607095790200_asaf055-B10","author":"Di Benedetto","year":"2020"},{"key":"2026032607095790200_asaf055-B11","author":"Doshi","year":"2009"},{"key":"2026032607095790200_asaf055-B12","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TPAMI.2014.2321387","article-title":"Distance dependent infinite latent feature models","volume":"37","author":"Gershman","year":"2014","journal-title":"IEEE Trans. Pat. Anal. Mach. Intel"},{"key":"2026032607095790200_asaf055-B13","first-page":"1185","article-title":"The Indian buffet process: an introduction and review","volume":"12","author":"Griffiths","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2026032607095790200_asaf055-B14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1198\/016214501750332758","article-title":"Gibbs sampling methods for stick-breaking priors","volume":"96","author":"Ishwaran","year":"2001","journal-title":"J. Am. Statist. Assoc"},{"key":"2026032607095790200_asaf055-B15","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1080\/01621459.2018.1505626","article-title":"MCMC for imbalanced categorical data","volume":"114","author":"Johndrow","year":"2019","journal-title":"J. Am. Statist. Assoc"},{"key":"2026032607095790200_asaf055-B16","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1016\/j.jmva.2009.04.008","article-title":"Generating random correlation matrices based on vines and extended onion method","volume":"100","author":"Lewandowski","year":"2009","journal-title":"J. Mult. Anal"},{"key":"2026032607095790200_asaf055-B17","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1639\/0007-2745-119.4.361","article-title":"The 2016 classification of lichenized fungi in the Ascomycota and Basidiomycota - approaching one thousand genera","volume":"119","author":"L\u00fccking","year":"2017","journal-title":"Bryologist"},{"key":"2026032607095790200_asaf055-B18","doi-asserted-by":"crossref","DOI":"10.1093\/biomet\/asaf056","article-title":"Factor pre-training in Bayesian multivariate logistic models","volume":"112","author":"Mauri","year":"2025","journal-title":"Biometrika"},{"key":"2026032607095790200_asaf055-B19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-385","article-title":"Interactive metagenomic visualization in a web browser","volume":"12","author":"Ondov","year":"2011","journal-title":"BMC Bioinform"},{"key":"2026032607095790200_asaf055-B20","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/2041-210X.12501","article-title":"Using latent variable models to identify large networks of species-to-species associations at different spatial scales","volume":"7","author":"Ovaskainen","year":"2016","journal-title":"Meth. Ecol. Evol"},{"key":"2026032607095790200_asaf055-B21","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.pld.2020.07.005","article-title":"Similar mycorrhizal fungal communities associated with epiphytic and lithophytic orchids of Coelogyne corymbosa","volume":"42","author":"Qin","year":"2020","journal-title":"Plant Divers"},{"key":"2026032607095790200_asaf055-B22","volume-title":"R: A Language and Environment for Statistical Computing","author":"R Development Core Team","year":"2025"},{"key":"2026032607095790200_asaf055-B23","doi-asserted-by":"crossref","first-page":"2920","DOI":"10.1093\/bioinformatics\/btw346","article-title":"Unbiased probabilistic taxonomic classification for DNA barcoding","volume":"32","author":"Somervuo","year":"2016","journal-title":"Bioinformatics"},{"key":"2026032607095790200_asaf055-B24","first-page":"1","volume-title":"Adv. Neural Info. Proces. Syst.","author":"Teh","year":"2009"},{"key":"2026032607095790200_asaf055-B25","author":"Thibaux","year":"2007"},{"key":"2026032607095790200_asaf055-B26","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1080\/01621459.1986.10478240","article-title":"Accurate approximations for posterior moments and marginal densities","volume":"81","author":"Tierney","year":"1986","journal-title":"J. Am. Statist. Assoc"},{"key":"2026032607095790200_asaf055-B27","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1111\/2041-210X.13345","article-title":"Joint species distribution modelling with the R-package HMSC","volume":"11","author":"Tikhonov","year":"2020","journal-title":"Meth. Ecol. Evol"},{"key":"2026032607095790200_asaf055-B28","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1214\/21-BA1279","article-title":"The attraction Indian buffet distribution","volume":"17","author":"Warr","year":"2022","journal-title":"Bayesian Anal."},{"key":"2026032607095790200_asaf055-B29","author":"Williamson","year":"2010"}],"container-title":["Biometrika"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asaf055\/63823946\/asaf055.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/112\/4\/asaf055\/63823946\/asaf055.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/112\/4\/asaf055\/63823946\/asaf055.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:10:08Z","timestamp":1774523408000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomet\/article\/doi\/10.1093\/biomet\/asaf055\/8210536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10,21]]}},"URL":"https:\/\/doi.org\/10.1093\/biomet\/asaf055","relation":{},"ISSN":["1464-3510"],"issn-type":[{"value":"1464-3510","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"asaf055"}}