Plankton Functional Type Models Research Articles

On the benefits of using cell quotas in addition to intracellular elemental ratios in flexible-stoichiometry Plankton functional type models. Application to the Mediterranean Sea

In the last decades, among the marine biogeochemical models, there have been an increasing number of marine Plankton Functional Type (PFT) models. Since the precursor NPZD models, the general trend has been towards the development of models that are more and more complex, either by the number of variables they include and/or by the level of detail they consider. In this respect, flexible-stoichiometry models have been an important step in the history of this class of models. Since then, additional developments have been described in the literature, and the present paper focuses on one of them, namely the introduction of abundance in addition to biomass to describe PFTs. This new feature offers an opportunity for model assessment with the increasing number of studies and datasets obtained by flow cytometry, and it provides the means to derive cell quotas (here expressed as an amount of a given biogenic element per unit cell) for each PFT of the model. Cell quotas are used, along with intracellular ratios, for the regulation of the kinetics of the different biogeochemical processes embedded in the model, and it is suggested here that their role may be decisive for the successful representation of some processes, such as, for example, mineralization by heterotrophic bacteria. Several other benefits provided by cell quotas are presented in this paper, among them the in-depth knowledge they provide on the functioning of the trophic web and on the nutritional status of organisms. Finally, the seasonal variations of the cell quotas in the PFT representing large phytoplankton can be used to explain the occurrence of the NW Mediterranean spring bloom from the internal status point of view.

Evaluation of the complexity and performance of marine planktonic trophic models.

Planktonic models represent a powerful tool for creating hypotheses and making predictions about the functioning of marine ecosystems. Their complexity varies according to the number of state variables and the choice of functional forms. We evaluated plankton models during the last 15 years (n =145) with the aims of understanding why they differ in complexity, evaluating model robustness, and describing studies of plankton modelling around the globe. We classified models into four groups: Nutrient-Phytoplankton-Zooplankton (NPZ), Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD), Size-Structured (SS) and Plankton-Functional-Type (PFT). Our results revealed that the number of state variables varied according to the question being addressed: NPZ models were more frequently applied in physical-biological studies, while PFT models were more applied for investigating biogeochemical cycles. Most models were based on simple functional forms which neglect important feedback related to control of plankton dynamics. Modelling studies sometimes failed to describe sensitivity analysis, calibration and validation. The importance of testing different functional forms was commonly overlooked, and the lack of empirical data affected the verification of model robustness. Lastly, we highlight the need to develop modelling studies in the Southern Hemisphere, including Brazil, in order to provide predictions that assist the management of marine ecosystems.

Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling

We propose definitions in terminology to enhance ongoing collaborations between biologists and modellers on plankton ecology. Organism “functional type” should refer to commonality in ecology not biogeochemistry; the latter is largely an emergent property of the former, while alignment with ecology is also consistent with usage in terrestrial science. Adaptation should be confined, as in genetics, to consideration of species inter-generational change; most so-called “adaptive” plankton models are thus acclimative, modifying vital rates in response to stimuli. Trait trade-off approaches should ideally only be considered for describing intra-generational interactions; in applications between generations, and certainly between unrelated species, such concepts should be avoided. We suggest that systems biology approaches, through to complex adaptive/acclimative systems modelling, with explicit modelling of feedback processes (which we suggest should define “mechanistic” models), would provide realistic and flexible bases upon which to develop descriptions of functional type models.

Trait‐based representation of diatom functional diversity in a plankton functional type model of the eutrophied southern North Sea

We introduce a trait‐based description of diatom functional diversity to an existing plankton functional type (PFT) model, implemented for the eutrophied coastal ecosystem in the Southern Bight of the North Sea. The trait‐based description represents a continuum of diatom species, each characterized by a distinct cell volume, and includes size dependence of four diatom traits: the maximum growth rate, the half‐saturation constants for nutrient uptake, the photosynthetic efficiency, and the relative affinity of copepods for diatoms. Through competition under seasonally varying forcing, the fitness of each diatom varies throughout time, and the outcome of competition results in a changing community structure. The predicted seasonal change in mean cell volume of the community is supported by field observations: smaller diatoms, which are more competitive in terms of resource acquisition, prevail during the first spring bloom, whereas the summer bloom is dominated by larger species which better resist grazing. The size‐based model is used to determine the ecological niche of diatoms in the area and identifies a range of viable sizes that matches observations. The general trade‐off between small, competitive diatoms and large, grazing‐resistant species is a convenient framework to study patterns in diatom functional diversity. PFT models and trait‐based approaches constitute promising complementary tools to study community structure in marine ecosystems.

Phytoplankton competition during the spring bloom in four plankton functional type models

Abstract. We investigated the mechanisms of phytoplankton competition during the spring bloom, one of the most dramatic seasonal events in lower-trophic-level ecosystems, in four state-of-the-art plankton functional type (PFT) models: PISCES, NEMURO, PlankTOM5 and CCSM-BEC. In particular, we investigated the relative importance of different ecophysiological processes on the determination of the community structure, focusing both on the bottom-up and the top-down controls. The models reasonably reproduced the observed global distribution and seasonal variation of phytoplankton biomass. The fraction of diatoms with respect to the total phytoplankton biomass increases with the magnitude of the spring bloom in all models. However, the governing mechanisms differ between models, despite the fact that current PFT models represent ecophysiological processes using the same types of parameterizations. The increasing trend in the percentage of diatoms with increasing bloom magnitude is mainly caused by a stronger nutrient dependence of diatom growth compared to nanophytoplankton (bottom-up control). The difference in the maximum growth rate plays an important role in NEMURO and PlankTOM5 and determines the absolute values of the percentage of diatoms during the bloom. In CCSM-BEC, the light dependency of growth plays an important role in the North Atlantic and the Southern Ocean. The grazing pressure by zooplankton (top-down control), however, strongly contributes to the dominance of diatoms in PISCES and CCSM-BEC. The regional differences in the percentage of diatoms in PlankTOM5 are mainly determined by top-down control. These differences in the mechanisms suggest that the response of marine ecosystems to climate change could significantly differ among models, even if the present-day ecosystem is reproduced to a similar degree of confidence. For further understanding of plankton competition and for the prediction of future change in marine ecosystems, it is important to understand the relative differences in each physiological rate and life history rate in the bottom-up and the top-down controls between PFTs.

Distribution of known macrozooplankton abundance and biomass in the global ocean

Abstract. Macrozooplankton are an important link between higher and lower trophic levels in the oceans. They serve as the primary food for fish, reptiles, birds and mammals in some regions, and play a role in the export of carbon from the surface to the intermediate and deep ocean. Little, however, is known of their global distribution and biomass. Here we compiled a dataset of macrozooplankton abundance and biomass observations for the global ocean from a collection of four datasets. We harmonise the data to common units, calculate additional carbon biomass where possible, and bin the dataset in a global 1 × 1 degree grid. This dataset is part of a wider effort to provide a global picture of carbon biomass data for key plankton functional types, in particular to support the development of marine ecosystem models. Over 387 700 abundance data and 1330 carbon biomass data have been collected from pre-existing datasets. A further 34 938 abundance data were converted to carbon biomass data using species-specific length frequencies or using species-specific abundance to carbon biomass data. Depth-integrated values are used to calculate known epipelagic macrozooplankton biomass concentrations and global biomass. Global macrozooplankton biomass, to a depth of 350 m, has a mean of 8.4 μg C L−1, median of 0.2 μg C L−1 and a standard deviation of 63.5 μg C L−1. The global annual average estimate of macrozooplankton biomass in the top 350 m, based on the median value, is 0.02 Pg C. There are, however, limitations on the dataset; abundance observations have good coverage except in the South Pacific mid-latitudes, but biomass observation coverage is only good at high latitudes. Biomass is restricted to data that is originally given in carbon or to data that can be converted from abundance to carbon. Carbon conversions from abundance are restricted by the lack of information on the size of the organism and/or the absence of taxonomic information. Distribution patterns of global macrozooplankton biomass and statistical information about biomass concentrations may be used to validate biogeochemical and plankton functional type models. Macrozooplankton abundance and biomass dataset doi:10.1594/PANGAEA.777398.

Optimal foraging in marine ecosystem models: selectivity, profitability and switching

One of the most troubling aspects of ecosystem models is the use of rather arbitrary feeding and preference functions. The predictions of plankton functional type models have been shown to be highly sensitive to the choice of foraging model, particularly in multiple prey situa- tions. Here we propose ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items, it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting their diets towards the most profitable prey items. We present a simple algorithm for plankton feeding on a size-spectrum of prey with particular energetic con- tent, handling times and capture probabilities. We show that the optimal diet breadth changes with relative densities, but in a different way to the preference functions commonly used in mod- els today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the 'switching functions' often applied in marine ecosystem models. Dietary inclusion is dictated by 2 optimality choices: (1) the diet breadth and (2) the actual feeding mode. The optimality model does not generate 'safety in low densities' for prey, as the 'switching function' does in ecosystem models, unless predators are shifting feeding mode adap- tively. The actual diet, feeding rate and energy flux in ecosystem models can be determined by letting predators maximise energy intake or more properly, some measure of fitness where preda- tion risk and cost are also included. An optimal foraging or fitness-maximising approach will give marine ecosystem models a sound principle to determine trophic interactions.

Parameterising competing zooplankton for survival in plankton functional type models

Marine plankton ecosystems are an important component of biogeochemical cycling in the oceans. Operational plankton functional type (PFT) models, that group plankton according to their biogeochemical properties, are currently being developed to resolve biogenic gas exchange between the ocean and atmosphere, and to model the lowest trophic levels in fisheries models. A fundamental problem with these models is that PFTs often go extinct in computer simulations, effectively removing the biogeochemical processes from the models. Cropp and Norbury [Cropp, R., Norbury, J., 2009a. Parameterizing plankton functional type models: insights from a dynamical systems perspective. J. Plankton Res. 31, 939–963] demonstrated that parameter combinations that allowed all PFTs to stay extant for all time in stable, homogeneous environments were rare in a PFT model with two competing phytoplankton and one zooplankton ( NP 1 P 2 Z). In this paper, we examine the dynamical properties of a generic predator–predator–prey PFT model, and apply the analysis techniques developed by Cropp and Norbury to a simple example PFT model with one phytoplankton and two zooplankton ( NPZ 1 Z 2) in order to explore its properties and parameter space. We find that the properties of predator–predator–prey PFT systems are fundamentally different from those of predator–prey–prey PFT systems. The likelihood of parameter combinations for which all PFTs stay extant for all time in predator–predator–prey PFT systems depends critically on the process formulations used, and the properties of co-existing zooplankton (as defined by their parameter values) are quite different to those of co-existing phytoplankton.

Stepwise building of plankton functional type (PFT) models: A feasible route to complex models?

We discuss the strategy of building models of the lower part of the planktonic food web in a stepwise manner: starting with few plankton functional types (PFTs) and adding resolution and complexity while carrying along the insight and results gained from simpler models. A central requirement for PFT models is that they allow sustained coexistence of the PFTs. Here we discuss how this identifies a need to consider predation, parasitism and defence mechanisms together with nutrient acquisition and competition. Although the stepwise addition of complexity is assumed to be useful and feasible, a rapid increase in complexity strongly calls for alternative approaches able to model emergent system-level features without a need for detailed representation of all the underlying biological detail.

Parameterizing plankton functional type models: insights from a dynamical systems perspective

The spectre of anthropogenic global climate change has focused attention on biogeochemical cycling in the oceans as marine plankton ecosystems are involved in the cycling of several compounds thought to have significant implications for climate. To better understand these processes, modellers are developing plankton functional type (PFT) models that group plankton according to their biogeochemical properties. There is some debate as to whether our understanding of plankton ecosystems is sufficiently well developed for PFT models to be reliable and for their predictions to be treated with confidence. In this paper, we examine the dynamical properties of a generic predator-prey-prey PFT model, then apply these analysis techniques to a simple example PFT model with two phytoplankton and one zooplankton in order to explore its parameter space. We find that parameter combinations for which all PFTs stay extant for all time appear rare, but develop a simple heuristic that allows such parameter sets to be identified relatively easily for many PFT models. We observe that such systems often have phytoplankton with similar growth rates, but that differ in other properties such as differing nutrient utilisation strategies or different susceptibilities to grazing. We also note that persistent PFT systems are more likely if neither phytoplankton have a low specific mortality rate or is a highly nutritious food for the grazer.

Reply to Horizons Article ‘Plankton functional type modelling: running before we can walk’ Anderson (2005): II. Putting trophic functionality into plankton functional types

While Anderson (Anderson, 2005) considers the pros and cons of plankton functional type models, I question whether we are missing something far more basic— trophic functionality, the ways in which organisms interact with each other. What is a functional type? Does the grouping accord with physiology (e.g. ‘Si-requiring’; mainly diatoms) or with respect to ecological function (‘dominant primary producer in an immature ecosystem’; diatoms need not always fulfil that role)? There is some level of acknowledgement (e.g. ERSEM; Blackford et al., 2004) that plankton functional types (PFTs) describe ‘ecological functionality’, but their description is still based on simple physiology. Even on a physiological basis, PFT design is fraught with problems; summer diatoms will not have the same physiology as their winter, spring or autumnal counterparts. That is not to say that physiology is unimportant; it is indeed vital because without it we cannot appreciate how the performance of an individual organism or group may be affected by the presence or absence of others. But is such gross physiological detail sufficient? Surely, competitive advantage for phytoplankton must be a function of more than just maximum growth rates, the value of alpha (slope of the Chl-specific photosynthesis–irradiance curve) or substrate affinity constants? Even if it were described in these terms, the simple sad fact is that not for a single clone of a plankton species can we construct a model describing its activity under a realistic range of environmental conditions. This is before we consider the impact of genetic diversity. I suspect that the vast majority of PFT models used in ecological simulations would fail to describe adequately a culture flask experiment in a manner representative of the organisms they purport to describe, even if one could find such a data set for verification. We need data, and lots of them. But we do not need them just for the growth of individual organisms; vitally we also need data for combinations of organisms. What is becoming increasingly clear is that there are a whole host of interactions between members of the plankton that the vast majority of models do not even hint at. Although it may appear that PFT approaches are developing such that ‘complexity in nature is mirrored by complexity in models’ (Anderson, 2005), I fear that the description will have to become more complex yet before we make real progress, even if it is to appreciate better how we must then simplify models back down again. Most models of planktonic systems take a collection of very crude models, at best purporting to have a physiological basis, parameterized using rather few, and sometimes unreliable, values taken from the literature or by tuning between parameter values from the same literature. Complexity is more than using an increasing number of arrayed boxes describing different groups; we must recognize not only that interactions between groups exist (Anderson, 2005) but also how these change during population growth and trophic dynamics. Currently, for example, even in the more comprehensive models, predator–prey interactions are usually described using a fixed set of rules based on JOURNAL OF PLANKTON RESEARCH j VOLUME 28 j NUMBER 9 j PAGES 873–875 j 2006