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ABOVEGROUND NITROGEN USE EFFICIENCY AND GROWTH DYNAMICS IN SPARTINA ALTERNIFLORA AND DISTICHLIS SPICATA
Citation:
Kanaskie, C., N. Moore, T. Hill, AND A. Oczkowski. ABOVEGROUND NITROGEN USE EFFICIENCY AND GROWTH DYNAMICS IN SPARTINA ALTERNIFLORA AND DISTICHLIS SPICATA. New England Estuarine Research Society (NEERS) Fall Meeting, Block Island, RI, October 20 - 22, 2016.
Impact/Purpose:
An improved understanding of nitrogen dynamics under ambient conditions will improve our ability to predict wetland community response to environmental perturbations.
Description:
Long-term nitrogen (N) fertilization studies suggest shifting dominance from Spartina alterniflora to Distichlis spicata, although the underlying mechanism is unclear. A limitation on our ability to predict changes is a poor understanding of resource use under ambient conditions. The present project compares growth rates and N use dynamics between two emerging salt marsh dominants, S. alterniflora and D. spicata. We hypothesize that under ambient Narragansett Bay nutrient conditions, S. alterniflora is a more efficient user of N than D. spicata. Spartina alterniflora and D. spicata cores were collected from the field and raised in a greenhouse. Heights of all stems were measured weekly to determine growth rates. To understand N movement, a pulse of 15N was added and three cores were sacrificed each subsequent week. Live aboveground biomass was separated into stems and leaves, with leaves categorized based on their position from the top of the stem. Samples were analyzed by isotope ratio mass spectrometry to trace N accumulation in different pools over time. One week after the 15N pulse, most of the aboveground 15N was bound in the stems and the youngest leaves. Efficient nutrient transfer in photosynthetic material likely provides a stronger competitive advantage for taller plants, which are able to compete better for light. Growth rates of S. alterniflora proved to be more variable over time than that of D. spicata. A better understanding of N dynamics under ambient conditions will improve our ability to predict response to environmental perturbations.