Diazotrophic Macroalgal Associations With Living and Decomposing Sargassum

Abstract

Despite several studies reporting diazotrophic macroalgal associations (DMAs), biological nitrogen fixation (BNF) is still largely overlooked as a potential source of nitrogen (N) for macroalgal productivity. We investigated the role of BNF, via the acetylene reduction method, throughout different life stages of the invasive macroalga, Sargassum horneri, in its non-native Southern California coastal ecosystem. Throughout most of its life cycle, BNF rates were not detectable or yielded insignificant amounts of fixed N to support S. horneri productivity. However, during late summer when nutrient concentrations are usually at their minimum, BNF associated with juvenile S. horneri contributed ∼3–36% of its required N, potentially providing it with a competitive advantage. As DMAs remain poorly understood within macroalgal detrital systems, long term (15–28 days) laboratory decomposition time series were carried out to investigate the role of BNF throughout decomposition of the endemic macroalga, S. palmeri, and the invasive S. horneri. Nitrogenase activity increased drastically during the second phase of decomposition, when increasing microbial populations are typically thought to drive macroalgal degradation, with BNF rates associated with S. palmeri and S. horneri reaching up to 65 and 247 nmol N × g-1(dw) × h-1, respectively. Stimulation of BNF rates by glucose and mannitol additions, up to 42× higher rates observed with S. palmeri, suggest that labile carbon may be limiting at varying degrees in these detrital systems. Comparable, if not higher, dark BNF rates relative to light incubations during S. horneri decomposition suggest an important contribution from heterotrophic N fixers. Inhibition of nitrogenase activity, up to 98%, by sodium molybdate additions also suggest that sulfate reducers may be an important constituent of the detrital diazotrophic community. As labile N can become limiting to the microbial community during macroalgal decomposition, our results suggest that BNF may provide a source of new N, alleviating this limitation. Additionally, while BNF is rarely considered as a source for N enrichment with aging macroalgal detritus, we found it to account for ∼1–11% of N immobilized with decaying S. horneri. Our investigations suggest that DMAs may be globally important with Sargassum and potentially occur within other macroalgal detrital systems.

A NEMO-based model of <i>Sargassum</i> distribution in the tropical Atlantic: description of the model and sensitivity analysis (NEMO-Sarg1.0)

“…The Sargassum model relies on the strategy used to represent the distribution of other macroalgae species and their transport by 2-D advection-diffusion equations (e.g., Martins and Marques, 2002;Solidoro et al, 1997;Perrot et al, 2014;Ren et al, 2014;Ménesguen et al, 2006;Bergamasco and Zago, 1999). Here, we also consider sink due to stranding at the coast.…”

“…As the eco-phycological features are species dependent, the actual knowledge on the three morphotypes of holopelagic Sargassum does allow us to discriminate between them. Following the formalism given in Ren et al (2014), the physiological behavior is described from three state variables: the content in carbon (C), nitrogen (N), and phosphorus (P ), with local variations reflecting the difference between uptake and loss rates.…”

“…The mortality term depends on m the mortality rate, λ m a mortality coefficient, and temperature T . We thus represent thallus senescence and bacterial activity as a growing function of temperature (Bendoricchio et al, 1994;Ren et al, 2014).…”

“…Once a set of values minimizing L was found, one-at-a-time sensitivity experiments were also performed, where only one single parameter is varied by ±10 % in two model runs while the others are fixed (e.g., Ren et al, 2014;Perrot et al, 2014). This allows us to capture the direct contribution from each parameter to the output variance, with parameters varying within an acceptable range.…”

“…This allows us to capture the direct contribution from each parameter to the output variance, with parameters varying within an acceptable range. The set of values for the fixed parameters is given in Table 1 and were taken from the simulation with higher L. Following Ren et al (2014), deviation from the baseline simulation (D) is quantified as…”