TY - JOUR

T1 - Environmental metabarcoding reveals heterogeneous drivers of microbial eukaryote diversity in contrasting estuarine ecosystems

AU - Lallias, D.S.

AU - Lallias, D.

AU - Hiddink, J.G.

AU - Fonseca, V.G.

AU - Gaspar, J.M.

AU - Sung, W.

AU - Neill, S.P.

AU - Barnes, N.

AU - Ferrero, T.

AU - Hall, N.

AU - Lambshead, P.J.

AU - Packer, M.

AU - Thomas, W.K.

AU - Creer, S.

PY - 2014/11/25

Y1 - 2014/11/25

N2 - Assessing how natural environmental drivers affect biodiversity underpins our understanding of the relationships between complex biotic and ecological factors in natural ecosystems. Of all ecosystems, anthropogenically important estuaries represent a ‘melting pot’ of environmental stressors, typified by extreme salinity variations and associated biological complexity. Although existing models attempt to predict macroorganismal diversity over estuarine salinity gradients, attempts to model microbial biodiversity are limited for eukaryotes. Although diatoms commonly feature as bioindicator species, additional microbial eukaryotes represent a huge resource for assessing ecosystem health. Of these, meiofaunal communities may represent the optimal compromise between functional diversity that can be assessed using morphology and phenotype–environment interactions as compared with smaller life fractions. Here, using 454 Roche sequencing of the 18S nSSU barcode we investigate which of the local natural drivers are most strongly associated with microbial metazoan and sampled protist diversity across the full salinity gradient of the estuarine ecosystem. In order to investigate potential variation at the ecosystem scale, we compare two geographically proximate estuaries (Thames and Mersey, UK) with contrasting histories of anthropogenic stress. The data show that although community turnover is likely to be predictable, taxa are likely to respond to different environmental drivers and, in particular, hydrodynamics, salinity range and granulometry, according to varied life-history characteristics. At the ecosystem level, communities exhibited patterns of estuary-specific similarity within different salinity range habitats, highlighting the environmental sequencing biomonitoring potential of meiofauna, dispersal effects or both.

AB - Assessing how natural environmental drivers affect biodiversity underpins our understanding of the relationships between complex biotic and ecological factors in natural ecosystems. Of all ecosystems, anthropogenically important estuaries represent a ‘melting pot’ of environmental stressors, typified by extreme salinity variations and associated biological complexity. Although existing models attempt to predict macroorganismal diversity over estuarine salinity gradients, attempts to model microbial biodiversity are limited for eukaryotes. Although diatoms commonly feature as bioindicator species, additional microbial eukaryotes represent a huge resource for assessing ecosystem health. Of these, meiofaunal communities may represent the optimal compromise between functional diversity that can be assessed using morphology and phenotype–environment interactions as compared with smaller life fractions. Here, using 454 Roche sequencing of the 18S nSSU barcode we investigate which of the local natural drivers are most strongly associated with microbial metazoan and sampled protist diversity across the full salinity gradient of the estuarine ecosystem. In order to investigate potential variation at the ecosystem scale, we compare two geographically proximate estuaries (Thames and Mersey, UK) with contrasting histories of anthropogenic stress. The data show that although community turnover is likely to be predictable, taxa are likely to respond to different environmental drivers and, in particular, hydrodynamics, salinity range and granulometry, according to varied life-history characteristics. At the ecosystem level, communities exhibited patterns of estuary-specific similarity within different salinity range habitats, highlighting the environmental sequencing biomonitoring potential of meiofauna, dispersal effects or both.

UR - https://static-content.springer.com/esm/art%3A10.1038%2Fismej.2014.213/MediaObjects/41396_2015_BFismej2014213_MOESM64_ESM.doc

U2 - 10.1038/ismej.2014.213

DO - 10.1038/ismej.2014.213

M3 - Article

VL - 9

SP - 1208

EP - 1221

JO - The ISME Journal

JF - The ISME Journal

SN - 1751-7362

ER -