The metabolism of lake plankton does not support the metabolic theory of ecology

We tested if the metabolic theory of ecology (MTE) correctly predicts plankton metabolism in a temperate lake, based on a long-term (about 15 years), high-frequency dataset of body size, abundance and production, using two different techniques: least squares regression and maximum likelihood. For phytoplankton, the general fit was relatively poor (r 2 0 0.53). The assumption of the MTE on temperature dependence of metabolism was not supported, and the assumed value of 3 ⁄ 4 of the allometric exponent was barely within 95% confidence limits. For some of the models, the value of b was significantly higher than 3 ⁄ 4 . When radiation was included as an additional predictor, it improved the model considerably (r 2 00.67). Including grazing by zooplankton reduced the model residuals during the summer period, when grazing is a dominant factor. The allometric exponent had virtually no effect for phytoplankton, due to little variability in average individual size. Zooplankton production, on the other hand, was better predicted by MTE, showing stronger effects of temperature and body size, the average of which varied by a factor of more than a hundred. However, the best-fitting value of the allometric exponent for zooplankton was 0.85, and significantly higher than the 3 ⁄ 4 predicted by the theory. The ratio of observed production to biomass for the entire plankton community declined linearly with the body size (in log-log) with a slope corresponding to a value of b 00.85. We conclude that the MTE has little predictive power for the metabolism of lacustrine plankton, in particular for phytoplankton, and especially at the scale of variability of this study, and that this could be improved by incorporating radiation into the model

The first decade of oligotrophication of Lake Constance

In Lake Constance, after several decades of cutrophication, a decrease in phosphorus loading over the last decade has lead to a partial recovery from eutrophication. Here we analyse the shift in the taxonomic composition of phytoplankton during the first decade of oligotrophication in Lake Constance. During the 1980s, spring total P concentrations decreased from ca. 130 to less than 50 mgr·l–1. This decrease was reflected by an approximately proportional decrease in summer phytoplankton biomass while spring phytoplankton biomass seemed unresponsive. Major taxonomic changes occured during both growth seasons. In spring, the proportion of diatoms, green algae and Chrysophyta increased while the proportion of Cryptophyta decreased. The summer trend was very different: the relative importance of diatoms decreased and Cryptophyta and Chrysophyta increased, while Chlorophyta reached their peak around 1985. These trends are also analysed at the genus level. Comparison with taxonomic trends during the eutrophication period shows the expected reversals in most cases. Comparison with other lakes shows general similarities, with the notable exception that Planktothrix rubescens has never been important in Lake Constance. The increase of diatoms during spring is attributed to their improved competitive performance with increasing Si:P ratios. Their decrease during summer is explained by the increasing silicate removal from the epilimnion by increasing spring populations

Steeper size spectra with decreasing phytoplankton biomass indicate strong trophic amplification and future fish declines

Under climate change, model ensembles suggest that declines in phyto�plankton biomass amplify into greater reductions at higher trophic levels, with serious implications for fisheries and carbon storage. However, the extent and mechanisms of this trophic amplification vary greatly among models, and validation is problematic. In situ size spectra offer a novel alternative, com�paring biomass of small and larger organisms to quantify the net efficiency of energy transfer through natural food webs that are already challenged with multiple climate change stressors. Our global compilation of pelagic size spectrum slopes supports trophic amplification empirically, independently from model simulations. Thus, even a modest (16%) decline in phytoplankton this century would magnify into a 38% decline in supportable biomass of fish within the intensively-fished mid-latitude ocean. We also show that this amplification stems not from thermal controls on consumers, but mainly from temperature or nutrient controls that structure the phytoplankton baseline of the food web. The lack of evidence for direct thermal effects on size structure contrasts with most current thinking, based often on more acute stress experiments or shorter-timescale responses. Our synthesis of size spectra integrates these short-term dynamics, revealing the net efficiency of food webs acclimating and adapting to climatic stressor

Steeper size spectra with decreasing phytoplankton biomass indicate strong trophic amplification and future fish declines

Under climate change, model ensembles suggest that declines in phytoplankton biomass amplify into greater reductions at higher trophic levels, with serious implications for fisheries and carbon storage. However, the extent and mechanisms of this trophic amplification vary greatly among models, and validation is problematic. In situ size spectra offer a novel alternative, comparing biomass of small and larger organisms to quantify the net efficiency of energy transfer through natural food webs that are already challenged with multiple climate change stressors. Our global compilation of pelagic size spectrum slopes supports trophic amplification empirically, independently from model simulations. Thus, even a modest (16%) decline in phytoplankton this century would magnify into a 38% decline in supportable biomass of fish within the intensively-fished mid-latitude ocean. We also show that this amplification stems not from thermal controls on consumers, but mainly from temperature or nutrient controls that structure the phytoplankton baseline of the food web. The lack of evidence for direct thermal effects on size structure contrasts with most current thinking, based often on more acute stress experiments or shorter-timescale responses. Our synthesis of size spectra integrates these short-term dynamics, revealing the net efficiency of food webs acclimating and adapting to climatic stressors

Plankton ecology: The past two decades of progress

This is a selected account of recent developments in plankton ecology. The examples have been chosen for their degree of innovation during the past two decades and for their general ecological importance. They range from plankton autecology over interactions between populations to community ecology. The autecology of plankton is represented by the hydromechanics of plankton (the problem of life in a viscous environment) and by the nutritional ecology of phyto- and zooplankton. Population level studies are represented by competition, herbivory (grazing), and zooplankton responses to predation. Community ecology is represented by the debate about bottom- up vs. top-down control of community organization, by the PEG model of seasonal plankton succession, and by the recent discovery of the microbial food web

Climate change effects on phytoplankton depend on cell size and food web structure

We investigated the effects of warming on a natural phytoplankton community from the Baltic Sea, based on six mesocosm experiments conducted 2005–2009. We focused on differences in the dynamics of three phytoplankton size groups which are grazed to a variable extent by different zooplankton groups. While small-sized algae were mostly grazer-controlled, light and nutrient availability largely determined the growth of medium- and large-sized algae. Thus, the latter groups dominated at increased light levels. Warming increased mesozooplankton grazing on medium-sized algae, reducing their biomass. The biomass of small-sized algae was not affected by temperature, probably due to an interplay between indirect effects spreading through the food web. Thus, under the higher temperature and lower light levels anticipated for the next decades in the southern Baltic Sea, a higher share of smaller phytoplankton is expected. We conclude that considering the size structure of the phytoplankton community strongly improves the reliability of projections of climate change effects

Mechanistic origins of variability in phytoplankton dynamics. Part II: analysis of mesocosm blooms under climate change scenarios

Driving factors of phytoplankton spring blooms have been discussed since long, but rarely analyzed quantitatively. Here, we use a mechanistic size-based ecosystem model to reconstruct observations made during the Kiel mesocosm experiments (2005–2006). The model accurately hindcasts highly variable bloom developments including community shifts in cell size. Under low light, phytoplankton dynamics was mostly controlled by selective mesozooplankton grazing. Selective grazing also explains initial dominance of large diatoms under high light conditions. All blooms were mainly terminated by aggregation and sedimentation. Allometries in nutrient uptake capabilities led to a delayed, post-bloom dominance of small species. In general, biomass and trait dynamics revealed many mutual dependencies, while growth factors decoupled from the respective selective forces. A size shift induced by one factor often changed the growth dependency on other factors. Within climate change scenarios, these indirect effects produced large sensitivities of ecosystem fluxes to the size distribution of winter phytoplankton. These sensitivities exceeded those found for changes in vertical mixing, whereas temperature changes only had minimal impacts

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability – the highest achievable predictability given the degree to which system dynamics are the result of deterministic v. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model‐free, information‐theoretic measure of the complexity of a time series. By means of simulations we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a dataset of 461 empirical ecological time series. We show how deviations from the expected PE‐FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically‐grounded basis for a model‐free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model‐free baseline of forecasting proficiency against which modeling efforts can be evaluated

The response of temperate aquatic ecosystems to global warming: novel insights from a multidisciplinary project

This article serves as an introduction to this special issue of Marine Biology, but also as a review of the key findings of the AQUASHIFT research program which is the source of the articles published in this issue. AQUASHIFT is an interdisciplinary research program targeted to analyze the response of temperate zone aquatic ecosystems (both marine and freshwater) to global warming. The main conclusions of AQUASHIFT relate to (a) shifts in geographic distribution, (b) shifts in seasonality, (c) temporal mismatch in food chains, (d) biomass responses to warming, (e) responses of body size, (f) harmful bloom intensity, (f), changes of biodiversity, and (g) the dependence of shifts to temperature changes during critical seasonal windows