Abstract
Abstract. The recycling of organic material through bacteria and microzooplankton to higher trophic levels, known as the "microbial loop", is an important process in aquatic ecosystems. Here the significance of the microbial loop in influencing nutrient supply to phytoplankton has been investigated in Lake Kinneret (Israel) using a coupled hydrodynamic–ecosystem model. The model was designed to simulate the dynamic cycling of carbon, nitrogen and phosphorus through bacteria, phytoplankton and zooplankton functional groups, with each pool having unique C : N : P dynamics. Three microbial loop sub-model configurations were used to isolate mechanisms by which the microbial loop could influence phytoplankton biomass, considering (i) the role of bacterial mineralisation, (ii) the effect of micrograzer excretion, and (iii) bacterial ability to compete for dissolved inorganic nutrients. The nutrient flux pathways between the abiotic pools and biotic groups and the patterns of biomass and nutrient limitation of the different phytoplankton groups were quantified for the different model configurations. Considerable variation in phytoplankton biomass and dissolved organic matter demonstrated the sensitivity of predictions to assumptions about microbial loop operation and the specific mechanisms by which phytoplankton growth was affected. Comparison of the simulations identified that the microbial loop most significantly altered phytoplankton growth by periodically amplifying internal phosphorus limitation due to bacterial competition for phosphate to satisfy their own stoichiometric requirements. Importantly, each configuration led to a unique prediction of the overall community composition, and we conclude that the microbial loop plays an important role in nutrient recycling by regulating not only the quantity, but also the stoichiometry of available N and P that is available to primary producers. The results demonstrate how commonly employed simplifying assumptions about model structure can lead to large uncertainty in phytoplankton community predictions and highlight the need for aquatic ecosystem models to carefully resolve the variable stoichiometry dynamics of microbial interactions.
Highlights
One of the principal objectives for water quality management of freshwater bodies is to reduce the magnitude and frequency of nuisance algal blooms
Each configuration led to a unique prediction of the overall community composition, and we conclude that the microbial loop plays an important role in nutrient recycling by regulating the quantity, and the stoichiometry of available N and P that is available to primary producers
The results demonstrate how commonly employed simplifying assumptions about model structure can lead to large uncertainty in phytoplankton community predictions and highlight the need for aquatic ecosystem models to carefully resolve the variable stoichiometry dynamics of microbial interactions
Summary
One of the principal objectives for water quality management of freshwater bodies is to reduce the magnitude and frequency of nuisance algal blooms. Li et al.: Microbial loop effects on lake stoichiometry paradigm, which assumes a relatively simple flow of nutrients to autotrophic and heterotrophic pools It is well-documented both in oceanographic and, to a lesser extent, in limnological applications, that higher order predators such as crustacean zooplankton or fish can be supported by two paths: the so-called “green” (algal-based) and “brown” (detrital-based) food web components (Moore et al, 2004). The latter refers to the dynamics of the heterotrophic bacteria and the microzooplankton grazers (defined here as size less than 125 μm that account for rotifers, ciliates and juvenile macrograzers; Thatcher et al, 1993) – often termed the “microbial loop”. This has been shown to play an important role in shaping carbon fluxes in lakes and in enhancing nutrient cycling at the base of food webs (Gaedke et al, 2002), including in Lake Kinneret which is the focus in this study (Stone et al, 1993; Hart et al, 2000; Hambright et al, 2007; Berman et al, 2010)
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