References
Aguinaga OE., McMahon A., White KN., Dean AP., Pittman JK. 2018.
Microbial community shifts in response to acid mine drainage pollution
within a natural wetland ecosystem. Frontiers in Microbiology9:1–14. DOI: 10.3389/fmicb.2018.01445.
Anderson MJ. 2005. PERMANOVA: A FORTRAN computer program for
permutational multivariate analysis of variance. Department of
Statistics .
Arfken A., Song B., Bowman JS., Piehler M. 2017. Denitrification
potential of the eastern oyster microbiome using a 16S rRNA gene based
metabolic inference approach. PLOS ONE 12:e0185071. DOI:
10.1371/journal.pone.0185071.
Barberan A., Fernandez-Guerra A., Bohannan BJM., Casamayor EO. 2012.
Exploration of community traits as ecological markers in microbial
metagenomes. Molecular Ecology 21:1909–1917. DOI:
10.1111/j.1365-294X.2011.05383.x.
Bojanowski M., Edwards R. 2016. alluvial: R Package for Creating
Alluvial Diagrams.
Bolger AM., Lohse M., Usadel B. 2014. Trimmomatic: A flexible trimmer
for Illumina sequence data. Bioinformatics 30:2114–2120. DOI:
10.1093/bioinformatics/btu170.
van den Boogaart KG., Tolosana-Delgado R., Bren M. 2020. compositions:
Compositional Data Analysis.
Bourlat SJ., Borja A., Gilbert J., Taylor MI., Davies N., Weisberg SB.,
Griffith JF., Lettieri T., Field D., Benzie J., Glöckner FO.,
Rodríguez-Ezpeleta N., Faith DP., Bean TP., Obst M. 2013. Genomics in
marine monitoring: New opportunities for assessing marine health status.Marine Pollution Bulletin 74:19–31. DOI:
10.1016/j.marpolbul.2013.05.042.
Bowman JS., Ducklow HW. 2015. Microbial communities can be described by
metabolic structure: A general framework and application to a seasonally
variable, depth-stratified microbial community from the coastal West
Antarctic Peninsula. PLoS ONE 10:1–18. DOI:
10.1371/journal.pone.0135868.
Breitwieser FP., Lu J., Salzberg SL. 2018. A review of methods and
databases for metagenomic classification and assembly. Briefings
in Bioinformatics 20:1125–1139. DOI: 10.1093/bib/bbx120.
Brooks KM., Mahnken CVW. 2003. Interactions of Atlantic salmon in
the Pacific northwest environment. II. Organic wastes . DOI:
10.1016/S0165-7836(03)00064-X.
Buchfink B., Xie C., Huson DH. 2015. Fast and sensitive protein
alignment using DIAMOND. Nature Methods 12:59–60. DOI:
10.1038/nmeth.3176.
Buschmann AH., Riquelme VA., Hernández-González MC., Varela D., Jiménez
JE., Henríquez LA., Vergara PA., Guíñez R., Filún L. 2006. A review of
the impacts of salmonid farming on marine coastal ecosystems in the
southeast Pacific. ICES Journal of Marine Science 63:1338–1345.
DOI: 10.1016/j.icesjms.2006.04.021.
Callahan BJ., McMurdie PJ., Rosen MJ., Han AW., Johnson AJA., Holmes SP.
2016. DADA2: High-resolution sample inference from Illumina amplicon
data. Nature Methods 13:581–583. DOI: 10.1038/nmeth.3869.
Cordier T. 2020. Bacterial communities’ taxonomic and functional
turnovers both accurately predict marine benthic ecological quality
status. Environmental DNA 2:175–183. DOI: 10.1002/edn3.55.
Cordier T., Alonso‐Sáez L., Apothéloz‐Perret‐Gentil L., Aylagas E.,
Bohan DA., Bouchez A., Chariton A., Creer S., Frühe L., Keck F., Keeley
N., Laroche O., Leese F., Pochon X., Stoeck T., Pawlowski J., Lanzén A.
2020. Ecosystems monitoring powered by environmental genomics: A review
of current strategies with an implementation roadmap. Molecular
Ecology :mec.15472. DOI: 10.1111/mec.15472.
Danovaro R., Carugati L., Berzano M., Cahill AE., Carvalho S., Chenuil
A., Corinaldesi C., Cristina S., David R., Dell’Anno A., Dzhembekova N.,
Garcés E., Gasol JM., Goela P., Féral J-P., Ferrera I., Forster RM.,
Kurekin AA., Rastelli E., Marinova V., Miller PI., Moncheva S., Newton
A., Pearman JK., Pitois SG., Reñé A., Rodríguez-Ezpeleta N., Saggiomo
V., Simis SGH., Stefanova K., Wilson C., Lo Martire M., Greco S.,
Cochrane SKJ., Mangoni O., Borja A. 2016. Implementing and innovating
marine monitoring approaches for assessing marine environmental status.Frontiers in Marine Science 3:213. DOI: 10.3389/fmars.2016.00213.
Deiner K., Bik HM., Mächler E., Seymour M., Lacoursière‐Roussel A.,
Altermatt F., Creer S., Bista I., Lodge DM., Vere N., Pfrender ME.,
Bernatchez L. 2017. Environmental DNA metabarcoding: Transforming how we
survey animal and plant communities. Molecular Ecology26:5872–5895. DOI: 10.1111/mec.14350.
Douglas GM., Maffei VJ., Zaneveld J., Yurgel SN., Brown JR., Taylor CM.,
Huttenhower C., Langille MGI. 2019. PICRUSt2: An improved and extensible
approach for metagenome inference. bioRxiv :672295. DOI:
10.1101/672295.
Ernst S., Langer R., Cooney CL., Sasisekharan R. 1995. Enzymatic
Degradation of GlycosaminogIycans. Critical Reviews in
Biochemistry and Molecular Biology 30:387–444. DOI:
10.3109/10409239509083490.
Escalas A., Hale L., Voordeckers JW., Yang Y., Firestone MK.,
Alvarez-Cohen L., Zhou J. 2019. Microbial Functional Diversity: From
Concepts to Applications. Ecology and Evolution 9:1–17. DOI:
10.1002/ece3.5670.
Franzosa EA., McIver LJ., Rahnavard G., Thompson LR., Schirmer M.,
Weingart G., Lipson KS., Knight R., Caporaso JG., Segata N., Huttenhower
C. 2018. Species-level functional profiling of metagenomes and
metatranscriptomes. Nature Methods 15:962–968. DOI:
10.1038/s41592-018-0176-y.
Gloor GB., Macklaim JM., Pawlowsky-Glahn V., Egozcue JJ. 2017.
Microbiome datasets are compositional: And this is not optional.Frontiers in Microbiology 8:1–6. DOI: 10.3389/fmicb.2017.02224.
Grossart H., Massana R., McMahon KD., Walsh DA. 2020. Linking
metagenomics to aquatic microbial ecology and biogeochemical cycles.Limnology and Oceanography 65. DOI: 10.1002/lno.11382.
Hong Y., Wu J., Wilson S., Song B. 2019. Vertical stratification of
sediment microbial communities along geochemical gradients of a
subterranean estuary located at the gloucester beach of Virginia, United
States. Frontiers in Microbiology 10:1–11. DOI:
10.3389/fmicb.2018.03343.
Hornick KM., Buschmann AH. 2018. Insights into the diversity and
metabolic function of bacterial communities in sediments from Chilean
salmon aquaculture sites. Annals of Microbiology 68:63–77. DOI:
10.1007/s13213-017-1317-8.
Iwai S., Weinmaier T., Schmidt BL., Albertson DG., Poloso NJ., Dabbagh
K., DeSantis TZ. 2016. Piphillin: Improved prediction of metagenomic
content by direct inference from human microbiomes. PLoS ONE11:1–18. DOI: 10.1371/journal.pone.0166104.
Jacobsen Á., Shi X., Shao C., Eysturskarδ J., Mikalsen S-O., Zaia J.
2019. Characterization of Glycosaminoglycans in Gaping and Intact
Connective Tissues of Farmed Atlantic Salmon ( Salmo salar ) Fillets by
Mass Spectrometry. ACS Omega 4:15337–15347. DOI:
10.1021/acsomega.9b01136.
Kaul A., Mandal S., Davidov O., Peddada SD. 2017. Analysis of Microbiome
Data in the Presence of Excess Zeros. Frontiers in Microbiology8:1–10. DOI: 10.3389/fmicb.2017.02114.
Keeley N., Valdemarsen T., Woodcock S., Holmer M., Husa V., Bannister R.
2019. Resilience of dynamic coastal benthic ecosystems in response to
large-scale finfish farming. Aquaculture Environment Interactions11:161–179. DOI: 10.3354/aei00301.
Klindworth A., Pruesse E., Schweer T., Peplies J., Quast C., Horn M.,
Glöckner FO. 2013. Evaluation of general 16S ribosomal RNA gene PCR
primers for classical and next-generation sequencing-based diversity
studies. Nucleic Acids Research 41:1–11. DOI:
10.1093/nar/gks808.
Kuhn M. 2020. caret: Classification and Regression Training.
Langille MGI., Zaneveld J., Caporaso JG., McDonald D., Knights D., Reyes
JA., Clemente JC., Burkepile DE., Vega Thurber RL., Knight R., Beiko
RG., Huttenhower C. 2013. Predictive functional profiling of microbial
communities using 16S rRNA marker gene sequences. Nature
Biotechnology 31:814–821. DOI: 10.1038/nbt.2676.
Langmead B., Salzberg SL. 2012. Fast gapped-read alignment with Bowtie
2. Nature Methods 9:357–359. DOI: 10.1038/nmeth.1923.
Laroche O., Pochon X., Tremblay LA., Ellis JI., Lear G., Wood SA. 2018.
Incorporating molecular-based functional and co-occurrence network
properties into benthic marine impact assessments. FEMS
Microbiology Ecology 94:1–12. DOI: 10.1093/femsec/atx167.
Leese F., Altermatt F., Bouchez A., Ekrem T., Hering D., Mergen P.,
Pawlowski J., Piggott J., Abarenkov K., Beja P., Bervoets L., Boets P.,
Bones A., Borja Á., Bruce K., Carlsson J., Coissac E., Costa F.,
Costache M., Creer S., Csabai Z., Deiner K., DelValls Á., Duarte S.,
Fazi S., Graf W., Hershkovitz Y., Japoshvili B., Jones J., Kahlert M.,
Kalamujic Stroil B., Kelly-Quinn M., Keskin E., Mächler E., Mahon A.,
Marečková M., Mejdandzic M., Montagna M., Moritz C., Mulk V., Navodaru
I., Pálsson S., Panksep K., Penev L., Petrusek A., Pfannkuchen M.,
Rinkevich B., Schmidt-Kloiber A., Segurado P., Strand M., Šulčius S.,
Traugott M., Turon X., Valentini A., van der Hoorn B., Vasquez Hadjilyra
M., Viguri J., Vogler A., Zegura B. 2016. DNAqua-Net: Developing new
genetic tools for bioassessment and monitoring of aquatic ecosystems in
Europe. Research Ideas and Outcomes 2:e11321. DOI:
10.3897/rio.2.e11321.
Lobo J., Shokralla S., Costa MH., Hajibabaei M., Costa FO. 2017. DNA
metabarcoding for high-throughput monitoring of estuarine macrobenthic
communities. Scientific Reports 7:15618. DOI:
10.1038/s41598-017-15823-6.
Louca S., Parfrey LW., Doebeli M. 2016. Decoupling function and taxonomy
in the global ocean microbiome. Science 353:1272–1277. DOI:
10.1126/science.aaf4507.
Louca S., Polz MF., Mazel F., Albright MBN., Huber JA., O’Connor MI.,
Ackermann M., Hahn AS., Srivastava DS., Crowe SA., Doebeli M., Parfrey
LW. 2018. Function and functional redundancy in microbial systems.Nature Ecology & Evolution 2:936–943. DOI:
10.1038/s41559-018-0519-1.
Martin M. 2011. Cutadapt removes adapter sequences from high-throughput
sequencing reads. EMBnet.journal 17:10. DOI:
10.14806/ej.17.1.200.
Martiny JBH., Jones SE., Lennon JT., Martiny AC. 2015. Microbiomes in
light of traits: A phylogenetic perspective. Science 350. DOI:
10.1126/science.aac9323.
Millares L., Pérez-Brocal V., Ferrari R., Gallego M., Pomares X.,
García-Núñez M., Montón C., Capilla S., Monsó E., Moya A. 2015.
Functional Metagenomics of the Bronchial Microbiome in COPD. PLOS
ONE 10:e0144448. DOI: 10.1371/journal.pone.0144448.
Nagpal S., Haque MM., Mande SS. 2016. Vikodak - A modular framework for
inferring functional potential of microbial communities from 16S
metagenomic datasets. PLoS ONE 11:1–19. DOI:
10.1371/journal.pone.0148347.
Oksanen J., Blanchet FG., Friendly M., Kindt R., Legendre P., McGlinn
D., Minchin PR., O’Hara RB., Simpson GL., Solymos P., Stevens MHH.,
Szoecs E., Wagner H. 2019. vegan: Community Ecology Package.
Pacheco-Sandoval A., Schramm Y., Heckel G., Brassea-Pérez E.,
Martínez-Porchas M., Lago-Lestón A. 2019. The Pacific harbor seal gut
microbiota in Mexico: Its relationship with diet and functional
inferences. PLoS ONE 14:1–21. DOI: 10.1371/journal.pone.0221770.
Pawlowski J., Kelly-Quinn M., Altermatt F., Apothéloz-Perret-Gentil L.,
Beja P., Boggero A., Borja A., Bouchez A., Cordier T., Domaizon I., Feio
MJ., Filipe AF., Fornaroli R., Graf W., Herder J., van der Hoorn B.,
Iwan Jones J., Sagova-Mareckova M., Moritz C., Barquín J., Piggott JJ.,
Pinna M., Rimet F., Rinkevich B., Sousa-Santos C., Specchia V., Trobajo
R., Vasselon V., Vitecek S., Zimmerman J., Weigand A., Leese F., Kahlert
M. 2018. The future of biotic indices in the ecogenomic era: Integrating
(e)DNA metabarcoding in biological assessment of aquatic ecosystems.Science of the Total Environment 637–638:1295–1310. DOI:
10.1016/j.scitotenv.2018.05.002.
Pearman JK., Aylagas E., Voolstra CR., Anlauf H., Villalobos R.,
Carvalho S. 2019. Disentangling the complex microbial community of coral
reefs using standardized Autonomous Reef Monitoring Structures (ARMS).Molecular Ecology 28:3496–3507. DOI: 10.1111/mec.15167.
Pilliod DS., Laramie MB., MacCoy D., Maclean S. 2019. Integration of
eDNA-Based Biological Monitoring within the U.S. Geological Survey’s
National Streamgage Network. Journal of the American Water
Resources Association :1–14. DOI: 10.1111/1752-1688.12800.
Pollock J., Glendinning L., Wisedchanwet T., Watson M. 2018. The Madness
of Microbiome: Attempting To Find Consensus “Best Practice” for 16S
Microbiome Studies. Applied and Environmental Microbiology84:1–12. DOI: 10.1128/AEM.02627-17.
R Core Team. 2017. R: A Language and Environment for Statistical
Computing.
Ren Y., Niu J., Huang W., Peng D., Xiao Y., Zhang X., Liang Y., Liu X.,
Yin H. 2016. Comparison of microbial taxonomic and functional shift
pattern along contamination gradient. BMC Microbiology 16:110.
DOI: 10.1186/s12866-016-0731-6.
Reverter M., Tapissier-Bontemps N., Lecchini D., Banaigs B., Sasal P.
2018. Biological and ecological roles of external fish mucus: A review.Fishes 3:1–19. DOI: 10.3390/fishes3040041.
Ruppert KM., Kline RJ., Rahman MS. 2019. Past, present, and future
perspectives of environmental DNA (eDNA) metabarcoding: A systematic
review in methods, monitoring, and applications of global eDNA.Global Ecology and Conservation 17:e00547. DOI:
10.1016/j.gecco.2019.e00547.
Salerno JL., Little B., Lee J., Hamdan LJ. 2018. Exposure to crude oil
and chemical dispersant may impact marine microbial biofilm composition
and steel corrosion. Frontiers in Marine Science 5:1–14. DOI:
10.3389/fmars.2018.00196.
Starke R., Capek P., Morais D., Callister SJ., Jehmlich N. 2020. The
total microbiome functions in bacteria and fungi. Journal of
Proteomics 213:103623. DOI: 10.1016/j.jprot.2019.103623.
Sun S., Jones RB., Fodor AA. 2020. Inference-based accuracy of
metagenome prediction tools varies across sample types and functional
categories. Microbiome 8:1–9. DOI: 10.1186/s40168-020-00815-y.
Suzek BE., Wang Y., Huang H., McGarvey PB., Wu CH. 2015. UniRef
clusters: A comprehensive and scalable alternative for improving
sequence similarity searches. Bioinformatics 31:926–932. DOI:
10.1093/bioinformatics/btu739.
Switzer A., Burchell L., McQuail J., Wigneshweraraj S. 2020. The
adaptive response to long-term nitrogen starvation in Escherichia coli
requires the breakdown of allantoin . Journal of
Bacteriology :1–11. DOI: 10.1128/jb.00172-20.
Thomsen PF., Willerslev E. 2015. Environmental DNA – An emerging tool
in conservation for monitoring past and present biodiversity.Biological Conservation 183:4–18. DOI:
10.1016/j.biocon.2014.11.019.
Truong DT., Franzosa EA., Tickle TL., Scholz M., Weingart G., Pasolli
E., Tett A., Huttenhower C., Segata N. 2015. MetaPhlAn2 for enhanced
metagenomic taxonomic profiling. Nature Methods 12:902–903. DOI:
10.1038/nmeth.3589.
Valdemarsen T., Kristensen E., Holmer M. 2009. Metabolic threshold and
sulfide-buffering in diffusion controlled marine sediments impacted by
continuous organic enrichment. Biogeochemistry 95:335–353. DOI:
10.1007/s10533-009-9340-x.
Valentini A., Taberlet P., Miaud C., Civade R., Herder J., Thomsen PF.,
Bellemain EVA., Besnard A., Coissac E., Boyer F., Gaboriaud C., Jean P.,
Poulet N., Roset N., Copp GH., Geniez P., Pont D., Argillier C., Baudoin
J-M., Peroux T., Crivelli AJ., Olivier A., Acqueberge M., Le Brun M.,
Møller PR., Willerslev E., Dejean T., Civade L., Valentini A., Taberlet
P., Miaud C., Rapha E., Besnard L., Thomsen PF., Bellemain EVA., Aur E.,
Argillier C., Baudoin J-M., Peroux T., Alain J. 2016. Next-generation
monitoring of aquatic biodiversity using environmental DNA
metabarcoding. Molecular Ecology 25:929–942. DOI:
10.1111/mec.13428.
Wang X., Olsen L., Reitan K., Olsen Y. 2012. Discharge of nutrient
wastes from salmon farms: environmental effects, and potential for
integrated multi-trophic aquaculture. Aquaculture Environment
Interactions 2:267–283. DOI: 10.3354/aei00044.
Wang P., Yan Z., Yang S., Wang S., Zheng X., Fan J., Zhang T. 2019.
Environmental DNA: An Emerging Tool in Ecological Assessment.Bulletin of Environmental Contamination and Toxicology103:651–656. DOI: 10.1007/s00128-019-02720-z.
Wang L., Zhang J., Li H., Yang H., Peng C., Peng Z., Lu L. 2018. Shift
in the microbial community composition of surface water and sediment
along an urban river. Science of the Total Environment627:600–612. DOI: 10.1016/j.scitotenv.2018.01.203.
Wemheuer F., Taylor JA., Daniel R., Johnston E., Meinicke P., Thomas T.,
Wemheuer B. 2018. Tax4Fun2: a R-based tool for the rapid prediction of
habitat-specific functional profiles and functional redundancy based on
16S rRNA gene marker gene sequences. bioRxiv :490037. DOI:
10.1101/490037.
White CA., Woodcock SH., Bannister RJ., Nichols PD. 2017. Terrestrial
fatty acids as tracers of finfish aquaculture waste in the marine
environment. Reviews in Aquaculture 11:133–148. DOI:
10.1111/raq.12230.
Wickham H. 2016. ggplot2: Elegant Graphics for Data Analysis .
Springer-Verlag New York.
Woodcock SH., Meier S., Keeley NB., Bannister RJ. 2019. Fate and
longevity of terrestrial fatty acids from caged fin-fish aquaculture in
dynamic coastal marine systems. Ecological Indicators 103:43–54.
DOI: 10.1016/j.ecolind.2019.03.057.
Zaneveld JRR., Thurber RL V. 2014. Hidden state prediction: A
modification of classic ancestral state reconstruction algorithms helps
unravel complex symbioses. Frontiers in Microbiology 5:1–8. DOI:
10.3389/fmicb.2014.00431.