{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:19:23Z","timestamp":1790785163990,"version":"4.1.0"},"reference-count":75,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100001309","name":"Research Corporation for Science Advancement","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001309","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015313","name":"National Wildlife Research Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015313","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ecology and Evolution"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Environmentally mediated transmission is a critical pathway for disease spread, particularly for pathogens with environmental persistence. However, distinguishing which overlapping host movements result in transmission remains difficult, as most mobility models assume homogeneous exposure and overlook key spatial and behavioral factors. We extend previous models of indirect contact to account for variation in host movement behavior that could affect the probability of indirect transmission and examine these effects on transmission dynamics. We considered four models with different assumptions about the influence of movement behavior on indirect contact and transmission probability. The pathogen decay model includes all overlaps occurring within the pathogen's viability window. The high\u2010use area model restricts transmission to frequently visited locations, assuming greater pathogen deposition where hosts repeatedly congregate. The behavioral model filters contacts based on host behaviors relevant to deposition or acquisition. The integrated model incorporates both spatial and behavioral constraints. We applied these models to GPS telemetry data from wild pigs in Florida and simulated disease dynamics using SEIR models with environmental transmission for Influenza A and\n                    <jats:styled-content style=\"fixed-case\">\n                      <jats:italic>Brucella suis<\/jats:italic>\n                    <\/jats:styled-content>\n                    , representing pathogens with short and long environmental persistence, respectively. Compared to pathogen decay model, total contact numbers decreased by 0.3% in high\u2010use model, 70.3% in behavioral model, and 70.4% in integrated model under the short\u2010lived condition. Edge density declined from 0.60 to 0.50, transitivity from 0.81 to 0.77, and assortativity from 0.43 to 0.17.\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>0<\/jats:italic>\n                    <\/jats:sub>\n                    declined from 2.42\u2009\u00b1\u20090.47 to 2.06\u2009\u00b1\u20090.39 (high\u2010use), 1.56\u2009\u00b1\u20090.42 (behavioral), and 1.35\u2009\u00b1\u20090.40 (integrated), with a lag of approximately 21\u2009days in peak incidence. The long\u2010lived scenarios, were quantitively similar to those of short\u2010lived scenarios. These results underscore that assumptions about how indirect contact from movement data maps to pathogen transmission significantly influence network configuration and epidemic projections. Our findings highlight the importance of mechanistically defining indirect transmission in disease modeling.\n                  <\/jats:p>","DOI":"10.1002\/ece3.72571","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T02:54:54Z","timestamp":1764557694000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating Environmental Transmission Risk From Host Movement Data"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0600-1560","authenticated-orcid":false,"given":"Himel","family":"Talukder","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Sustainability University of Oklahoma  Norman Oklahoma USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3892-0251","authenticated-orcid":false,"given":"Ryan 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USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1640-5355","authenticated-orcid":false,"given":"George","family":"Wittemyer","sequence":"additional","affiliation":[{"name":"Department of Fish, Wildlife and Conservation Biology Colorado State University  Fort Collins Colorado USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9931-8312","authenticated-orcid":false,"given":"Kim M.","family":"Pepin","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture Animal and Plant Health Inspection Service, National Wildlife Research Center  Fort Collins Colorado USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9535-2193","authenticated-orcid":false,"given":"Anni","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Sustainability University of Oklahoma  Norman 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