ALBERT

Description: Chlorophyll (chl a) concentration in coastal seas exhibits variability on various spatial and temporal scales. Resuspension of particulate matter can somewhat limit algal growth, but can also enhance productivity because of the intrusion of nutrient-rich pore water from sediments or bottom water layers into the whole water column. This study investigates whether characteristic changes in net phytoplankton growth can be directly linked to resuspension events within the German Bight. Satellite-derived chl a were used to derive spatial patterns of net rates of chl a increase/decrease (NR) in 2003 and 2004. Spatial correlations between NR and mean water column irradiance were analysed. High correlations in space and time were found in most areas of the German Bight (R2 〉 0.4), suggesting a tight coupling between light availability and algal growth during spring. These correlations were reduced within a distinct zone in the transition between shallow coastal areas and deeper offshore waters. In summer and autumn, a mismatch was found between phytoplankton blooms (chl a 〉 6 mg m−3) and spring-tidal induced resuspension events as indicated by bottom velocity, suggesting that there is no phytoplankton resuspension during spring tides. It is instead proposed here that frequent and recurrent spring-tidal resuspension events enhance algal growth by supplying remineralized nutrients. This hypothesis is corroborated by a lag correlation analysis between resuspension events and in-situ measured nutrient concentrations. This study outlines seasonally different patterns in phytoplankton productivity in response to variations in resuspension, which can serve as a reference for modelling coastal ecosystem dynamics.

Description: Photoacclimation models are a prerequisite for accurate estimates of primary production in aquatic environments under typically variable light conditions. They generally start from empirical functions of the internal chlorophyll a (chl a) or nutrient quota (e.g. the Droop model). We propose that physiological variations in phytoplankton reflect phenotypic adaptation which maximizes the growth rate. Growth maximization has to account for indirect effects of the enhancement of carbon (C) acquisition by acclimation, primarily through concomitant changes in the intracellular nitrogen (N) budget. Our model expresses, for the first time, the indirect effect of alterations in N uptake on C assimilation by a parameter-free trade-off between the 2 uptake functions. The model explicitly prescribes optimal protein partitioning between N and C uptake and sub-partitioning into carboxylation (1,5-bisphosphate carboxylase/oxygenase, Rubisco) and light harvesting. Applications to various published experimental data for different phytoplankton species support the validity of the optimality hypothesis and point to different flexibility in the re-organization of chloroplasts between taxa as well as to different time-scales on which photoacclimation operates. Simulations of a batch culture with the haptophyte Isochrysis galbana show that a decoupling in pigment N:C from cellular N:C may explain observed lag phases in chl a:C regulation. For diatoms, seemingly stronger constraints in intra-cellular stoichiometry determine the photoacclimative response to variable light regimes, as simulated and reported for Skeletonema costatum. N and chl a quotas correlate well in nutrient-limited chemostats of Thalassiosira fluviatilis, but in part decouple under light limitation. In N limited growth, non-linearity in N:C as expressed by the Droop function results from a combination of a linear quota dependency, down-regulation of relative carboxylation capacity, and increasing N costs of chl a synthesis at elevated growth rates. Our optimality assumption that includes indirect feed-backs through the concept of protein partitioning generates an accurate model for adaptation in physiological traits.

Description: Autotrophic organisms reveal an astounding flexibility in their elemental stoichiometry, with potentially major implications on biogeochemical cycles and ecological functioning. Notwithstanding, stoichiometric regulation, and co-limitation by multiple resources in autotrophs were in the past often described by heuristic formulations. In this study, we present a mechanistic model of autotroph growth, which features two major improvements over the existing schemes. First, we introduce the concept of metabolic network independence that defines the degree of phase-locking between accessory machines. Network independence is in particular suggested to be proportional to protein synthesis capability as quantified by variable intracellular N:C. Consequently, the degree of co-limitation becomes variable, contrasting with the dichotomous debate on the use of Liebig's law or the product rule, standing for constantly low and high co-limitation, respectively. Second, we resolve dynamic protein partitioning to light harvesting, carboxylation processes, and to an arbitrary number of nutrient acquisition machineries, as well as instantaneous activity regulation of nutrient uptake. For all regulatory processes we assume growth rate optimality, here extended by an explicit consideration of indirect feed-back effects. The combination of network independence and optimal regulation displays unprecedented skill in reproducing rich stoichiometric patterns collected from a large number of published chemostat experiments. This high skill indicates (1) that the current paradigm of fixed co-limitation is a critical short-coming of conventional models, and (2) that stoichiometric flexibility in autotrophs possibly reflects an optimality strategy. Numerical experiments furthermore show that regulatory mechanisms homogenize the effect of multiple stressors. Extended optimality alleviates the effect of the most limiting resource(s) while down-regulating machineries for the less limiting ones, which induces an ubiquitous response surface of growth rate over ambient resource levels. Our approach constitutes a basis for improved mechanistic understanding and modeling of acclimative processes in autotrophic organisms. It hence may serve future experimental and theoretical investigations on the role of those processes in aquatic and terrestrial ecosystems.

Description: The value of mechanistic ecosystem modelling has long been appreciated, and in connection with trait-based approaches it has recently stimulated a more process-based understanding of adaptive capacities and trade-offs. Notwithstanding recent advances, even sophisticated state-of-the-art models of plankton ecosystems, some of which include hundreds of idealized species, do not accurately represent the great biodiversity of plankton, or the associated flexible adaptive response of plankton communities. We build on previous reviews to suggest that it may be necessary to discard some common assumptions and try new approaches in order to construct models that can make new and testable predictions about the ``adaptive capacity'' of plankton ecosystems. Major challenges remain unresolved for modelling interacting communities of producers and consumers. Rather than the common approach of mixing and matching existing model components, each laden with its own legacy assumptions, we suggest that a judicious combination of innovative, mechanistic approaches that combine traits and trade-offs will likely better address such challenges.