National Oceanic and
Atmospheric Administration
United States Department of Commerce


 

FY 2016

Estimating stable carbon isotope values of microphytobenthos in the Arctic for application to food web studies

Oxtoby, L.E., J.T. Mathis, L.W. Juranek, and M.J. Wooller

Polar Biol., 39(3), 473-483, doi: 10.1007/s00300-015-1800-2 (2016)


Most studies on Arctic food webs have neglected microphytobenthos as a potential food source because we currently lack robust measurements of δ13C values for microphytobenthos from this environment. As a result, the role of microphytobenthos in high latitude marine food webs is not well understood. We combined field measurements of the concentration of aqueous carbon dioxide and the stable carbon isotopic composition of dissolved inorganic carbon (δ13CDIC) from bottom water in the Beaufort and Chukchi seas with a set of stable carbon isotopic fractionation factors reflecting differences in algal taxonomy and physiology to estimate the stable carbon isotope composition of microphytobenthos-derived total organic carbon (δ13Cp). The δ13Cp for Phaeodactylum tricornutum, a pennate diatom likely to be a dominant microphytobenthos taxon, was estimated to be -23.9 ± 0.4 % as compared to a centric diatom (Porosira glacialis, δ13Cp = -20.0 ± 1.6 %) and a marine haptophyte (Emiliana huxleyi, δ13Cp = -22.7 ± 0.5 %) at a growth rate (μ) of 0.1 divisions per day (d–1). δ13Cp values increased by *2.5 % when l increased from 0.1 to a maximum growth rate of 1.4 d–1. We compared our estimates of δ13Cp values for microphytobenthos with published measurements for other carbon sources in the Arctic and sub-Arctic. We found that microphytobenthos values overlapped with pelagic sources, yet differed from riverine and ice-derived carbon sources. These model results provide valuable insight into the range of possible isotopic values for microphytobenthos from this region, but we remain cautious in regard to the conclusiveness of these findings given the paucity of field measurements currently available for model validation.



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