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This is mainly because fish species distribution often covaries with other biotic and abiotic factors, complicating the study design. We tackled this issue in the northern part of the Jordan River system, in which a few fish species geographically overlap, across steep gradients of water temperature and salinity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using 16S rRNA metabarcoding, we studied the water properties that shape the skin bacterial communities, and their interaction with fish taxonomy. To better characterise the indigenous skin community, we excluded bacteria that were equally abundant in the skin samples and in the water samples, from our analysis of the skin samples. With this in mind, we found alpha diversity of the skin communities to be stable across sites, but higher in benthic loaches, compared to other fish. Beta diversity was found to be different among sites and to weakly covary with the dissolved oxygen, when treated skin communities were considered. In contrast, water temperature and conductivity were strong factors explaining beta diversity in the untreated skin communities. Beta diversity differences between co-occurring fish species emerged only for the treated skin communities. Metagenomics predictions highlighted the microbiome functional implications of excluding the water community contamination from the fish skin communities. Finally, we found that human-induced eutrophication promotes dysbiosis of the fish skin community, with signatures relating to fish health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Consideration of the background water microbiome when studying fish skin microbiomes, across varying fish species and water properties, exposes patterns otherwise undetected and highlight among-fish-species differences. We suggest that sporadic nutrient pollution events, otherwise undetected, drive fish skin communities to dysbiosis. This finding is in line with a recent study, showing that biofilms capture sporadic pollution events, undetectable by interspersed water monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-020-0784-5","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T12:03:05Z","timestamp":1580472185000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Dissecting the factors shaping fish skin microbiomes in a heterogeneous inland water system"],"prefix":"10.1186","volume":"8","author":[{"given":"Yaron","family":"Krotman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timur M.","family":"Yergaliyev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rivka","family":"Alexander Shani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yosef","family":"Avrahami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7309-2845","authenticated-orcid":false,"given":"Amir","family":"Szitenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"784_CR1","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/1297-9716-42-92","volume":"42","author":"VS Raj","year":"2011","unstructured":"Raj VS, Fournier G, Rakus K, Ronsmans M, Ouyang P, Michel B, et al. 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