pH gradients in the diffusive boundary layer of subarctic macrophytes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-06-20

AUTHORS

Iris E. Hendriks, Carlos M. Duarte, Núria Marbà, Dorte Krause-Jensen

ABSTRACT

Highly productive macrophytes produce diurnal and seasonal cycles in CO2 concentrations modulated by metabolic activity, which cause discrepancies between pH in the bulk water and near seaweed blades, especially when entering the diffusion boundary layer (DBL). Calcifying epiphytic organisms living in this environment are therefore exposed to a different pH environment than that of the water column. To evaluate the actual pH environment on blade surfaces, we measured the thickness of the DBL and pH gradients within it for six subarctic macrophytes: Fucus vesiculosus, Ascophyllum nodosum, Ulva lactuca, Zostera marina, Saccharina longicruris, and Agarum clathratum. We measured pH under laboratory conditions at ambient temperatures (2–3 °C) and slow, stable flow over the blade surface at five light intensities (dark, 30, 50, 100 and 200 µmol photons m−2 s−1). Boundary layer thickness ranged between 511 and 1632 µm, while the maximum difference in pH (∆pH) between the blade surface and the water column ranged between 0.4 ± 0.14 (average ± SE; Zostera) and 1.2 ± 0.13 (average ± SE; Ulva) pH units. These differences in pH are larger than predictions for pH changes in the bulk water by the end of the century. A simple quadratic model best described the relationship between light intensity and maximum ∆pH, pointing at relatively low optimum PAR of between 28 and 139 µmol photons m−2 s−1 to reach maximum ∆pH. Elevated pH at the blade surface may provide chemical “refugia” for calcifying epiphytic organisms, especially during summer at higher latitudes where photoperiods are long. More... »

PAGES

2343-2348

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00300-017-2143-y

DOI

http://dx.doi.org/10.1007/s00300-017-2143-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1086099225


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