A Dynamic Energy Budget model for the macroalga Ulva lactuca

Romain Lavaud, Ramón Filgueira, André Nadeau, Laura Steeves and Thomas Guyondet

Ecological Modelling, 2020, vol. 418, issue C

Abstract: Macroalgal blooms in eutrophic coastal waters around the globe constitute a rising issue for ecosystems and economic activities. Sometimes leading to anoxic events, a better understanding of its growth dynamics is necessary to develop mitigation strategies and inform policies on nutrient runoff management. The development of a Dynamic Energy Budget (DEB) model for the sea lettuce, Ulva lactuca, provides a generic mechanistic description of energy and matter fluxes within the macroalgae and between macroalgae and the environment. Forcing variables consist of seawater temperature, light intensity, and seawater concentrations of dissolved carbon and nitrogen. The model includes a self-shading module for light reduction under heavy biomass and simulation outputs consist of (but are not limited to) total algae biomass, nutrient uptake (carbon and nitrogen), photosynthetic rate, and state of nutrient reserve. Model parameters were estimated using data-sets from the literature and laboratory experiments, then validated using an independent field study in two estuaries with contrasting nutrient loads that feed into Malpeque Bay, PEI (Canada). The validation step yielded accurate temporal predictions of sea lettuce biomass in both of these estuaries. These results indicate that the present mechanistic modelling approach for predicting sea lettuce dynamics captures salient patterns along a spectrum of nutrient loading and could therefore be of use for managing across diverse ecological conditions, which is particularly relevant for a widespread species like Ulva lactuca.

Keywords: Sea lettuce; Nutrients; Photosynthesis; Eutrophication; Estuary; Synthesizing units (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:418:y:2020:i:c:s0304380019304302

DOI: 10.1016/j.ecolmodel.2019.108922

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