microcosm experiment examples

How Microcosms Help Us Understand Ecology

Is it possible to witness evolution in action? Researchers at University of California, San Diego, aiming to do just that, cultured a harmless virus in a flask and changed its environmental circumstances. Evolution happened all right, way ahead of schedule. Before long, the virus had been replaced by two new viruses that were well-adapted to the new environment in the flask. Meanwhile, the original virus went extinct.

These systems-in-a-jar, used to understand broader processes, are called microcosms. Microcosms can also easily be observed over multiple generations since the microorganisms within don’t live very long. Microcosms are particularly helpful to ecologists and evolutionary biologists, since the system can be controlled experimentally in a way that the actual world cannot.

Take, for example, climate change . Predicting the impacts of climate change on an organism can be very tough since it depends on so many factors—what is happening where the organism lives, the interactions between different organisms, and so on. Understanding the impact on entire communities of organisms is even tougher.

To that end, researchers at Rutgers University created 240 microcosms, each containing three different species of bacteria and one or both of two different microorganism predators (another advantage of microcosms—you can easily build a lot of them). Sets of microcosms were kept at five different temperatures to simulate a range of possible temperatures.

Roughly six weeks later, the results were in. When they were alone, each microorganism predator easily survived to the end of the experiment. When they were together competing for food in the same jar, one of them, paramecium, was quickly outcompeted by the other. As the temperature increased, paramecium went extinct in the flask at an increasingly rapid pace.

The importance of the study is not in the details of the results, or its direct applicability to natural ecosystem. Clearly this system is much simpler than a real food web, and climate change itself is more complex than just a direct temperature increase. But it does show that interactions between organisms can impact how a species responds to temperature change and that these interactions between species are very difficult to predict.

Besides modeling the future, microcosms can be equally valuable for understanding the present. Consider a thorny ecological problem—how does biodiversity affect an ecosystem? Looking at decomposer bacteria , another Rutgers study found that decomposition increased when more species of bacteria were present, in comparison to when higher abundance of one bacteria species was present. This kind of global-scale question is very difficult to test under controlled circumstances without a microcosm.

Microcosm results don’t always translate directly to the real world, but these tiny universes are a great place to start.

JSTOR is a digital library for scholars, researchers, and students. JSTOR Daily readers can access the original research behind our articles for free on JSTOR.

Get Our Newsletter

Get your fix of JSTOR Daily’s best stories in your inbox each Thursday.

Privacy Policy   Contact Us You may unsubscribe at any time by clicking on the provided link on any marketing message.

More Stories

• The Scientists, the Engineers, and the Water Wheel

Biofuels: Feeding the Earth or Feeding the Engine?

The Case of the Volcano on the Moon

Meteorites from Mars

Recent posts.

• Reclaiming a Coal Town
• Printing Anarchy
• Gonna Make You a ( Bangsawan ) Star
• The Complex History of American Dating

Support JSTOR Daily

Sign up for our weekly newsletter.

Main advantages and disadvantages of microcosm and mesocosm experiments.

Context in source publication

Similar publications.

• Helena M. Galvão

• BIOGEOCHEMISTRY

• Svein Norland

• Anupama Baral

• Daniel Koske

• ADV MAR BIOL

• Lisandro Benedetti-Cecchi
• Antonio Terlizzi
• Bopaiah Biddanda

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Loading metrics

Open Access

Primers provide a concise introduction into an important aspect of biology highlighted by a current PLOS Biology research article.

See all article types »

Mesocosms Reveal Ecological Surprises from Climate Change

* E-mail: [email protected]

Affiliation The Environment Institute and School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia

• Damien A. Fordham

Published: December 17, 2015

• https://doi.org/10.1371/journal.pbio.1002323
• Reader Comments

Understanding, predicting, and mitigating the impacts of climate change on biodiversity poses one of the most crucial challenges this century. Currently, we know more about how future climates are likely to shift across the globe than about how species will respond to these changes. Two recent studies show how mesocosm experiments can hasten understanding of the ecological consequences of climate change on species’ extinction risk, community structure, and ecosystem functions. Using a large-scale terrestrial warming experiment, Bestion et al. provide the first direct evidence that future global warming can increase extinction risk for temperate ectotherms. Using aquatic mesocosms, Yvon-Durocher et al. show that human-induced climate change could, in some cases, actually enhance the diversity of local communities, increasing productivity. Blending these theoretical and empirical results with computational models will improve forecasts of biodiversity loss and altered ecosystem processes due to climate change.

Citation: Fordham DA (2015) Mesocosms Reveal Ecological Surprises from Climate Change. PLoS Biol 13(12): e1002323. https://doi.org/10.1371/journal.pbio.1002323

Copyright: © 2015 Damien A. Fordham. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Funding: The author received no specific funding for this work.

Competing interests: The author has declared that no competing interests exist.

Introduction

Models forecast that human-induced climate change is likely to cause extinctions and alter diversity patterns, directly and in synergy with other drivers of global change (habitat destruction, overexploitation, and introduced species), but the range of estimates for its total impact remains worryingly large [ 1 ]. A more evidence-focused approach to climate impacts research is required to gain deeper insights into the likely effects of shifts in climate on biodiversity over the coming decades to centuries—and, through these insights, to design effective adaptation strategies that mitigate climate-driven biodiversity loss [ 2 ].

Data from natural history collections, repeated surveys, and other monitoring activities continue to be used to study biotic responses to 20th century climate change [ 3 ]. Although these studies have increased our knowledge of how species can vary their phenologies, distributions, abundances, and phenotypes in response to climate change, linking these observations to long-term effects on species’ persistence, community structure, and ecosystem function has proven difficult [ 4 ]. This is partly because resurvey and monitoring studies inevitably focus on near-term outcomes, meaning that they are typically unable to consider species responses to large shifts in climate—those similar in magnitude to those predicted for the 21st century and beyond [ 5 ]. Another problem is the lack of field-based experimental approaches (e.g., translocation experiments) in climate ecological research, which can directly attribute ecological mechanisms to biotic responses to different climatic conditions using cause-effect relationships [ 6 ].

In contrast, laboratory microcosm (or small-scale field experiments) and larger scale mesocosm experiments allow rigorous testing of climate impacts on populations and communities, improving our theoretical understanding of ecological responses to likely climate shifts [ 7 ]. They do this by providing tractable yet ecologically realistic bridges between simplified experimental conditions and the real world [ 8 ]. For example, warming experiments have provided important stimulus for further research on trait plasticity and resilience to climate change [ 9 ], the importance of synergies among drivers of endangerment [ 10 ], the role of temperature and habitat isolation on community composition [ 11 ], and the impact of global change on ecosystem function [ 12 ]. As ecological climate change research moves to increasingly more mechanistic approaches, experiments are today being constructed at ever larger scales with higher biocomplexity, with the ultimate aim being to parameterize, test, and refine models that accurately predict the effects of climate change on biodiversity ( Box 1 ) [ 13 ]. Two papers recently published in PLOS Biology highlight why mesocosm experiments provide such powerful tools for identifying the ecological processes that drive population- and community-level responses to climate change and for testing fundamental principles of ecology.

Box 1. Integrating Mesocosms with Ecological Models to Improve Predictions of the Ecological Consequences of Climate Change

Mesocosms have a central role to play in predicting the impact of climate on different ecological levels, ranging from individual species to whole communities (and potentially to entire ecosystems). At the species level, they enable the effect of global warming on demographic traits (fecundity, mortality, density dependent population growth rate, etc.) to be directly estimated. This information can be integrated into population models to determine risk of extinction in the absence of immigration and emigration ( Fig 1 ) [ 14 ]. Data on species’ physiological tolerance from mesocosm experiments can also be coupled with spatial geographic information system (GIS) layers of present-day and likely future climatic conditions to predict the potential range of a species [ 15 ]. Using this information in metapopulation models to define dynamic patch structures improves estimates of extinction risk from climate change, by accounting for important spatial and demographic processes and their interaction [ 16 ]. If natal dispersal is not estimated in the mesocosm experiment, field-based or allometric estimates can be used in the metapopulation model. Mesocosm experiments can also be used to directly improve our understanding of key principles of population ecology, including the importance of plasticity in life history traits and predator–prey dynamics on persistence ( Fig 1 ). Furthermore, metapopulation and demographic models, coupled to mesocosm experimental data, can be used to test and improve theoretical expectations. Together this will lead to better forecasts of extinction risk and range dynamics [ 17 ], especially if the sensitivity of evolutionary adaptation to environmental and demographic conditions can be quantified and incorporated in models of population persistence [ 18 ].

At the community level, mesocosms provide an important opportunity to explore and disentangle mechanisms of community assembly and, thus, better establish how climate shifts are likely to affect biodiversity, community structure, and the ecosystem processes that they maintain. Mesocosms can be used to quantify the effect of global warming on species composition and turnover, the strength of biotic interactions, and the distribution of functional traits (e.g., body size), among other ecological processes. This information can be used to parameterize models of local (α) and regional (ϒ) diversity ( Fig 1 ). For example, metacommunity models can potentially be used to explore the likely influence of climate change on connected local community assemblages (i.e., communities linked by dispersal and multiple interacting species) and to improve key theoretical paradigms on how spatial dynamics and local interactions shape community structure [ 19 ]. Furthermore, estimates of ecological mechanisms driving temperature-related shifts in species assemblages can be used to test key theories underpinning spatial community ecology, such as temperature-driven body-size reduction at the community level [ 20 ], the effect of trophic interaction strengths on food-web structure, and the role of community composition on stability and persistence [ 7 ]. Together this will improve forecasts of biodiversity loss and provide crucial information on how to maintain ecosystem processes and services in the face of species loss ( Fig 1 ). Forecasts and theoretical evidence of ecological responses to climate change will be strongest if mesocosms account for a wide range of future climate change scenarios (including variation in extreme events) [ 13 ] and potential synergies of drivers of global change (e.g., habitat fragmentation and exploitation) [ 11 ].

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Mesocosm experiments can be used to improve predictions of the impact of climate change on individual species and whole communities by parameterizing metapopulation and metacommunity models and by testing and refining population and community ecology theory. The figure is described in detail in Box 1 . Photos in panel A show the Metatron infrastructure used to study demographic responses to warming among common lizards ( Zootoca vivipara ) [ 14 ]. Panel B shows the outdoor mesocosm experiment used to determine the impact of warming on the metacommunity dynamics of phytoplankton [ 20 ].

https://doi.org/10.1371/journal.pbio.1002323.g001

Theory predicts that climate change will predominantly threaten tropical ectotherms, which are currently living very close to their optimal temperature, while temperate ectotherms, which are living in climates that are currently cooler than their physiological optima, are expected to resist or even benefit from warming [ 21 ]. However, Bestion et al. [ 14 ] show, using a large-scale outdoor mesocosm experiment, that this generality is by no means universal. Experimental warming of ambient temperature (+ 2°C) increased the juvenile growth rate and reduced the reproduction age of common lizards ( Zootoca vivipara ). However, these temperature-driven enhancements to juvenile and reproductive fitness came at a harmful cost to adult survival. By integrating experimental estimates of survival, growth, and reproduction into population models, Bestion et al. [ 14 ] found that even moderate (and very likely) temperature increases for Europe (+ 2°C) will result in regional extinctions of Z . vivipara at the southern range of their distribution. These results are a far cry from showing that Z . vivipara will resist or even benefit from climate change, as has been suggested for temperate ectotherms more generally. Even more alarming is the fact that Z . vivipara is not a physiological specialist with respect to temperature (having a wide-range across Europe and Asia) and therefore not an obvious “at risk” species from climate change [ 22 ]. Nevertheless, 21st century climate change is likely to have a strong deleterious effect on its range dynamics, causing regional extinctions that will lead to wide-scale range contraction.

Recent studies have linked human-induced climate change to reduced body size at the population or community level, leading to the suggestion that body-size reduction is a universal response to global warming alongside changes in the phenology and distributions of species [ 23 ]. Using a 5-year outdoor mesocosm warming experiment that allowed for natural dispersal, Yvon-Durocher et al. [ 20 ] show the exact opposite pattern for phytoplankton communities, tiny organisms that form the basis of food chains in aquatic ecosystems. The researchers warmed artificial ponds containing plankton by 4°C, replicating likely temperature shifts for many of the world’s lakes and rivers in the near future [ 24 ]. Warming resulted in more species-rich phytoplankton communities, dominated by larger species. The ecological mechanisms responsible for this somewhat unexpected finding appears to be an increase in top-down regulation of community structure, in which warming systematically shifted the taxonomic composition of phytoplankton towards large-bodied species that are resistant to grazing by zooplankton. Increased biodiversity, due to greater species coexistence, is likely to have resulted from a reduction in competitive exclusion between large (and inedible) phytoplankton, which are inferior competitors for nutrients. Furthermore, warmed mesocosms had higher gross primary productivity due to increases in the biodiversity and biomass of the phytoplankton communities. Together, these findings show that in ecosystems where local extinctions can be counterbalanced by immigration, warming can lead to increases in biodiversity and function and to an increase in mean body size at the community level.

Both studies promise to strongly influence future climate-change ecology research. For example, we now have a stronger understanding of the importance of (1) establishing the impact of climate change on the entire life cycle of a species and using this detailed information to identify populations at risk of extirpation from future global warming and (2) taking a “whole community” multispecies-type approach to predicting the impacts of climate change on biodiversity. More generally, these studies are prime illustrations of how mesocosms can deepen our understanding of the ecological consequences of climate change, often providing surprising yet vital results along the way.

Today's scientists are faced with the task of forecasting how climate change will affect species distributions and species assemblages. A pressing challenge is to develop integrated modelling frameworks that account for all aspects of vulnerability: exposure, sensitivity, and adaptive capacity [ 4 ]. Directly accounting for climate-driven changes in survival, persistence, and fitness (sensitivity) can provide improved forecasts of extinction risk [ 16 ], yet model predictions rarely account for the demographic and physiological sensitivities of species to prevailing climates. Biological processes underlying adaptation of a species to its environment remain poorly understood. Rare attempts to include evolutionary responses directly in climate-biodiversity models have shown that predictions of vulnerability can be affected by adaptive capacity [ 15 ]. Mesocosm experiments are key to meeting this shortfall, providing valuable information on aspects of climate change ecology (e.g., the impact of extreme events on species survival, climate as a driver of phenotypic changes) that cannot be readily assembled from other approaches [ 13 ]. Establishing multigenerational mesocosm experiments systematically, using taxa representing a diversity of ecological and evolutionary milieu, and integrating observed demographic and physiological responses into simulation models is likely to strengthen confidence in climate-impact science and improve vulnerability assessments ( Fig 1 ) [ 17 ]. This will be particularly so for short-lived taxa that are passively dispersed or with short active dispersal requirements. Developing mesocosm experiments for long-lived, wide-ranging species will be much less feasible.

At the community level, species will not respond equally to climate change. Some may adapt better, and some may track changing climates faster than others. This will affect the structure and dynamics of species interaction networks both by breaking already established interactions and by the appearance of novel interactions [ 25 ]. By developing and testing theoretical expectations of climate-driven changes in ecological network structures of communities, mesocosms can be used to improve knowledge of how functional traits can predispose species to range expansion or contraction under shifting climates and their associated effects on community structure and stability, and food web organization and dynamics [ 13 , 25 ]. Mesocosms can also be used to better identify and understand ecological mechanisms that enable spatial habitat structure to buffer communities from the effects of climate change [ 11 ]. These types of information are essential if we are to move beyond extrapolating biodiversity loss from species-level models to parameterizing and refining more ecologically realistic multispecies predictive models ( Fig 1 ) [ 26 ].

Deriving the full benefits of coupling mesocosm experiments with theory and real-world observations to better predict and mitigate the worst effects of climate change on biodiversity will require an immediate movement away from short-sighted funding strategies. This is because ecological responses to climate change can take multiple generations to be expressed [ 20 ]. Furthermore, there needs to a be a more unified approach to the use of mesocosms in climate change research, whereby investigators and funding bodies alike see the benefit of simultaneously replicating experiments across different systems, to establish the generality of results and theory [ 7 ]. Doing this will avoid extrapolating from isolated, uncoordinated, and contingent case studies [ 13 ]. Lastly, predictions of biodiversity loss from climate change will be improved by adopting a wider range of future climate change scenarios in mesocosm experiments. Future scenarios should include changes in the frequency, duration, and magnitude of extreme events, as well as gradual shifts in average conditions.

• View Article
• PubMed/NCBI
• Google Scholar
• 13. Stewart RIA, Dossena M, Bohan DA, Jeppesen E, Kordas RL, et al. (2013) Mesocosm Experiments as a Tool for Ecological Climate-Change Research. In: Woodward G, Ogorman EJ, editors. Advances in Ecological Research, Vol 48: Global Change in Multispecies Systems, Pt 3. pp. 71–181.

Application of Microcosm and Mesocosm Experiments to Pollutant Effects in Biofilms

Series: Springer Protocols Handbooks > Book: Hydrocarbon and Lipid Microbiology Protocols

Protocol | DOI: 10.1007/8623_2015_170

• Institute of Aquatic Ecology, University of Girona, Girona, Spain
• Catalan Institute for Water Research -ICRA, Scientific and Technological Park of the University of Girona, Girona, Spain

The search for causal relationships of the effects of pollutants on biofilms requires experimental alternatives that allow careful hypothesis testing. Mesocosms are designed to replicate river ecosystems, and their manipulation translates to similar

The search for causal relationships of the effects of pollutants on biofilms requires experimental alternatives that allow careful hypothesis testing. Mesocosms are designed to replicate river ecosystems, and their manipulation translates to similar effects to be expected in real ecosystems. Microcosms allow even simple experimental conditions and much higher replication than the ones in mesocosms, though the scale is far smaller than the one existing in a real ecosystem. Observations from microcosm and mesocosm experiments are complementary to field observations, and results may shed light to patterns described in natural ecosystems.

Figures ( 0 ) & Videos ( 0 )

Experimental specifications, other keywords.

• Sabater S, Borrego CM (2015) Field and in-situ studies. Springer Protocols Handb. doi: 10.1007/8623_2015_171
• Proia L, Morin S, Peipoch M, Romaní AM, Sabater S (2011) Resistance and recovery of river biofilms receiving short pulses of Triclosan and Diuron. Sci Total Environ 409:3129–3137
• Navarro E, Guasch H, Muñoz I, Real M, Sabater S (2000) Aplicación de un sistema de canales artificiales en el estudio ecotoxicológico de comunidades microbentónicas. Limnetica 18:1–14
• Ramos C, Licht TR, Sternberg C, Krogfelt KA, Molin S (2001) Monitoring bacterial growth activity in biofilms from laboratory flow chambers, plant rhizosphere, and animal intestine. Methods Enzymol 337:21–42
• Rychert K, Neu TR (2011) Protozoan impact on bacterial biofilm formation. Biol Lett 47:3–10
• Zippel B, Rijstenbil J, Neu TR (2007) A flow-lane incubator for studying freshwater and marine phototrophic biofilms. J Microbiol Methods 70:336–345
• Guasch H, Serra A (2009) Uso de ríos artificiales en ecología fluvial. In: Elosegi A, Sabater S (eds) Conceptos y técnicas en ecología fluvial. FBBVA Editions, Madrid
• Rodgers JH, Crossland NO, Kline ER, Gillespie WB, Figueroa RA, Dorn PB (1996) Design and construction of model stream ecosystems. Ecotoxicol Environ Saf 33:30–37
• Ricart M, Barceló D, Geiszinger A, Guasch H, López de Alda M, Romaní AM, Vidal G, Villagrasa M, Sabater S (2009) Effects of low concentrations of the phenylurea herbicide diuron on biofilm algae and bacteria. Chemosphere 76(10):1392–1401
• Proia L, Vilches C, Boninneau C, Kantiani L, Farrè M, Romaní A, Sabater S, Guasch H (2013) Drought episode modulate biofilm response to pulses of Triclosan. Aquat Toxicol 127:36–45
• Noble RT, Fuhrman JA (1998) Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat Microb Ecol 14:113–118
• Porter KG, Feig YS (1980) The use of DAPI for counting and identifying aquatic microflora. Limnol Oceanogr 25:943–948
• Weinbauer MG, Beckmann C, Höfle MG (1998) Utility of green fluorescent nucleic acid dyes and aluminium oxide membrane filters for rapid epifluorescence enumeration of soil and sediment bacteria. Appl Environ Microbiol 64:5000–5003
• Cangelosi GA, Meschke JS (2014) Dead or alive: molecular assessment of microbial viability. Appl Environ Microbiol 80:5884–5891
• Davey HM (2011) Life, death, and in-between: meanings and methods in microbiology. Appl Environ Microbiol 77:5571–5576
• Freese HM, Karsten U, Schumann R (2006) Bacterial abundance, activity, and viability in the eutrophic River Warnow, northeast Germany. Microb Ecol 51:117–127
• Ricart M, Guasch H, Alberch M, Barceló D, Bonnineau C, Geiszinger A, Farré M, Ferrer J, Ricciardi F, Romaní AM, Morin S, Proia L, Sala L, Sureda D, Sabater S (2010) Triclosan persistence through wastewater treatment plants and its potential: toxic effects on river biofilms. Aquat Toxicol 100:346–353
• Savichtcheva O, Okayama N, Ito T, Okabe S (2005) Application of a direct fluorescence-based live/dead staining combined with fluorescence in situ hybridization for assessment of survival rate of Bacteroides spp. in drinking water. Biotechnol Bioeng 92:356–363
• Netuschil L, Auschill TM, Sculean A, Arweiler NB (2014) Confusion over live/dead stainings for the detection of vital microorganisms in oral biofilms-which stain is suitable? BMC Oral Health 14:2–12
• Sträuber H, Müller S (2010) Viability states of bacteria – specific mechanisms of selected probes. Cytometry A 77:623–634
• Dupont CL, Chappell D, Logares R, Vila-costa M (2010) A hitchhiker’s guide to the new molecular toolbox for ecologists. Eco-DAS VIII Symp Proc 2:17–29
• Logares R, Haverkamp TH, Kumar S, Lanzén A, Nederbragt AJ, Quince C, Kauserud H (2012) Environmental microbiology through the lens of high-throughput DNA sequencing: synopsis of current platforms and bioinformatics approaches. J Microbiol Methods 91:106–113
• Sinclair L, Osman OA, Bertilsson S, Eiler A (2015) Microbial community composition and diversity via 16S rRNA gene amplicons: evaluating the illumina platform. PLoS One 10, e0116955
• Porra RJ (2006) Spectrometric assays for plant, algal and bacterial chlorophylls. In: Grimm B, Porra R, Rüdiger W, Scheer H (eds) Chlorophylls and bacteriochlorophylls: biochemistry, biophysics, functions and applications, vol 25, Advances in photosynthesis and respiration. Springer, Dordrecht, pp 95–107
• Garrido JL, Zapata M (2006) Chlorophyll analysis by new high performance liquid chromatography methods. In: Grimm B, Porra R, Rüdiger W, Scheer H (eds) Chlorophylls and bacteriochlorophylls: biochemistry, biophysics, functions and applications, vol 25, Advances in photosynthesis and respiration. Springer, Dordrecht, pp 109–121
• Smith JHC, Benitez A (1955) Chlorophylls: analysis in plant materials. In: Paech K, Tracey MV (eds) Modern methods of plant analysis, vol IV. Springer, Berlin, pp 142–196
• Schreiber U, Schliwaand U, Bilger W (1986) Continuous recording of photochemical and non-photochemical chlorophyll fluorescence quenching with a new type of modulation fluorometer. Photosynth Res 10:51–62
• Bilger W, Bjorkman O (1990) Role of the xanthophyll cycle in photoprotection elucidated by measurements of light-induced absorbance changes, fluorescence and photosynthesis in leaves of Hedera canariensis. Photosynth Res 25:173–185
• Corcoll N, Bonet B, Leira M, Montuelle B, Tlili A, Guasch H (2012) Light history influences the response of fluvial biofilms to Zn exposure. J Phycol 48:1–13
• Hoppe H-G, Kim S-J, Gocke K (1988) Microbial decomposition in aquatic environments: combined process of extracellular enzyme activity and substrate uptake. Appl Environ Microbiol 54:784–790
• Kemp PF, Cole JJ, Sherr BF (1993) Handbook of methods in aquatic microbial ecology. Lewis Publishers, Boca Raton
• Ylla I, Borrego CM, Romaní AM, Sabater S (2009) Availability of glucose and light modulates the structure and function of a microbial biofilm. FEMS Microbiol Ecol 69:27–42

Advertisement

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 01 March 2022

An integrated multiple driver mesocosm experiment reveals the effect of global change on planktonic food web structure

• Hugo Duarte Moreno   ORCID: orcid.org/0000-0001-7836-3985 1 ,
• Martin Köring 1 ,
• Julien Di Pane   ORCID: orcid.org/0000-0003-0973-2702 1 ,
• Nelly Tremblay   ORCID: orcid.org/0000-0002-8221-4680 1 ,
• Karen H. Wiltshire 1 , 2 ,
• Maarten Boersma   ORCID: orcid.org/0000-0003-1010-026X 1 , 3 &
• Cédric L. Meunier   ORCID: orcid.org/0000-0002-4070-4286 1  

Communications Biology volume  5 , Article number:  179 ( 2022 ) Cite this article

4081 Accesses

12 Citations

43 Altmetric

Metrics details

• Climate-change ecology
• Ecosystem ecology

Global change puts coastal marine systems under pressure, affecting community structure and functioning. Here, we conducted a mesocosm experiment with an integrated multiple driver design to assess the impact of future global change scenarios on plankton, a key component of marine food webs. The experimental treatments were based on the RCP 6.0 and 8.5 scenarios developed by the IPCC, which were Extended (ERCP) to integrate the future predicted changing nutrient inputs into coastal waters. We show that simultaneous influence of warming, acidification, and increased N:P ratios alter plankton dynamics, favours smaller phytoplankton species, benefits microzooplankton, and impairs mesozooplankton. We observed that future environmental conditions may lead to the rise of Emiliania huxleyi and demise of Noctiluca scintillans , key species for coastal planktonic food webs. In this study, we identified a tipping point between ERCP 6.0 and ERCP 8.5 scenarios, beyond which alterations of food web structure and dynamics are substantial.

Similar content being viewed by others

On the robustness of an eastern boundary upwelling ecosystem exposed to multiple stressors

Upper environmental pCO2 drives sensitivity to ocean acidification in marine invertebrates

Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments

Introduction.

Human activities and associated increasing greenhouse gas emissions have caused simultaneous changes in a range of marine abiotic parameters. The Intergovernmental Panel on Climate Change (IPCC) established different scenarios projecting that, depending on humanity’s effort to reduce greenhouse gas emissions, by 2100, the temperature may increase by 1–6 °C and pH may decrease by 0.1–0.4 units in the ocean’s upper layers 1 . In addition, urban, agricultural and industrial development will continue to alter biogeochemical cycles through nutrient runoffs, increasing phosphorus limitations in European coastal marine systems 2 . Consequently, marine organisms are currently, and will continue to be, exposed to the simultaneous effects of multiple anthropogenic drivers. The pressure exerted by these changes on coastal marine systems threatens biological community structure and food web functioning 3 , 4 . Planktonic organisms are particularly sensitive to ecosystem change, and, given their central role in marine food webs, these organisms are of vital importance for ecosystem health 5 . Despite the urgent need to understand and predict how global change will influence planktonic food webs, there is still a striking paucity of information on the integrated impact of multiple drivers, especially in a community context. The few studies addressing the combined effects on plankton communities showed, for example, negative effects on copepod abundance, as well as shifts in phytoplankton organismal size 6 , 7 , 8 , 9 .

Among the different methods that can be employed to address community responses to multiple global change drivers experimentally and mechanically, mesocosm approaches provide the highest level of ecological relevance while still being conducive to experimental manipulations 10 . By incorporating natural assemblages and by addressing mechanistic relationships across trophic levels that take place in complex natural systems, mesocosms go beyond small, tightly controlled experiments which suffer from limited realism 11 . The main limitation of the mesocosm approach is the difficulty of replication, due to the high costs of acquiring and maintaining such systems 10 . For this reason, full-factorial mesocosm experiments are scarce. Although understanding the individual effect of global change drivers, such as temperature, pH or dissolved nutrient concentrations, on the functioning of planktonic communities can inform specific mitigation strategies, it is important to consider that these drivers are simultaneously changing in natural environments. Hence, we applied an integrated multiple driver design to assess the potential impact of global change on natural coastal plankton communities. We tested the influence of two future scenarios against current environmental conditions in triplicates: the Ambient condition (ambient temperature and pH) and the Representative Concentration Pathway 6.0 (RCP 6.0, +1.5 °C, −0.2 pH) and RCP 8.5 (+3.0 °C, −0.3 pH), proposed by the IPCC for 2100 1 . In addition, as nutrient inputs are also predicted to change towards considerably higher nitrogen to phosphorus ratios (N:P) in coastal seas, especially those in Europe 2 , we extended the RCP scenarios (ERCP) to simulate changing nutrient regimes, with a N:P ratio (molar) of 16 (Redfield ratio) for the Ambient scenario and 25 for both future scenarios (ERCP 6.0 and 8.5). It is currently of utmost importance to make accurate and reliable predictions of the fate of planktonic communities in future conditions. Although our experimental design does not enable to draw conclusions about individual drivers effect, we believe that our work provides a more realistic assessment of these drivers’ impact than an experiment addressing drivers singly would.

The mesocosm experiment was conducted over three weeks in late summer (August–September) 2018. Seawater containing a natural plankton community was collected from the coastal North Sea. At the onset of the experiment, CO 2 saturated seawater was added to the ERCP scenario mesocosms to adjust p CO 2 and pH levels for each scenario. To create a realistic environment, we also manipulated the atmospheric p CO 2 in the enclosed mesocosm tanks throughout the experiment. Seawater temperature was adjusted daily according to the current North Sea temperature measured at the Helgoland Roads for the Ambient, and 1.5 °C and 3.0 °C warmer for the ERCP 6.0 and ERCP 8.5 scenarios, respectively. Dissolved nutrient concentrations were determined at the onset of the experiment and adjusted to reach the desired N:P ratios. Samples were taken in an interval of 1–3 days depending on the phytoplankton bloom development, and community composition, except for the large mesozooplankton, was monitored throughout the experiment period. Across scenarios, no significant difference was found in biomass of phytoplankton, microzooplankton and bacterioplankton on the first day of the experiment (Kruskal–Wallis test, df = 2, P  > 0.05). The effects of the ERCP scenarios on plankton community biomass were statistically assessed through the likelihood ratio test (LRT), and principal response curve (PRC) analysis was applied to identify the influence of the ERCP scenarios on community composition.

Results and discussion

In all treatments, we observed a first phytoplankton bloom, which lasted roughly 10 days, followed by a second bloom of different magnitude and composition between the treatments. We observed that, throughout the experiment, the planktonic food web was relatively similar in the Ambient treatment and in the ERCP 6.0 scenario, whereas the ERCP 8.5 scenario substantially altered the biomass, structure, and dynamics of multiple trophic levels (Fig.  1 ). The ERCP 8.5 scenario benefited the emergence of nanophytoplankton, specifically coccolithophores, at the expense of larger diatoms, especially in the second bloom. This has implications for the marine carbon pump due to the calcification capacity of coccolithophores 12 . Mesozooplankton biomass was largely reduced in the ERCP 8.5 scenario, whilst the biomass of microzooplankton was higher in this treatment than in the other two. The increase of micrograzers and lower mesozooplankton biomass are indicative of a microbial loop dominance in this future scenario, and of a potential diminution of energy transfer to higher trophic levels. We wish to note that, due to the relatively short duration of the experiment, this study does not consider the potential adaptation of planktonic communities that may take place over longer periods of time.

Colours represent the Ambient treatment and Extended Representative Concentration Pathway (ERCP) scenarios (blue = Ambient, orange = ERCP 6.0, grey = ERCP 8.5), box size represents the total biomass of each compartment, and the number of individuals represents the relative abundance of taxonomic groups within a scenario. Phytoplankton biomass is divided between microphytoplankton (>20 µm) and nanophytoplankton (<20 µm). Plankton biomass and relative abundance are displayed to scale.

The rise of nanophytoplankton

Total cumulative phytoplankton biomass was not affected by the experimental treatment (GLM, df 86, ERCP 6.0 P  = 0.90, ERCP 8.5 P  = 0.17, n  = 3, Fig.  2a ). It appeared that the timing in phytoplankton biomass was also not statistically different among treatments (LRT; df 86, P  = 0.46). Phytoplankton biomass increased exponentially from the beginning of the experiment in all treatments to reach a stationary phase on day 4. During this first phytoplankton bloom, we observed a gradient in the relative abundance of the large diatom Guinardia flaccida (GLM, df 86, ERCP 6.0 P  = 0.01, ERCP 8.5 P  < 0.0001, n  = 3) from high in Ambient, to lower in ERCP 6.0 and ERCP 8.5, and the opposite in the contribution of nanophytoplankton (<20 µm) to the total phytoplankton biomass (GLM, df 86, ERCP 6.0 P  = 0.88, ERCP 8.5 P  = 0.04, n  = 3, Figs.  1 and   2b ). During this first bloom, both ERCP scenarios yielded lower phytoplankton biomass and were largely favourable towards nanophytoplankton at the expense of larger microalgal species (Supplementary Fig.  1 ). This result is similar to previous studies showing a negative effect of warming and acidification on the mean cell size of phytoplankton communities 13 , 14 , which can be exacerbated when nutrient availability is low 15 , 16 . For instance 15 , used a semi-continuous microcosm approach to disentangle the direct temperature-mediated effects from indirect nutrient-limitation effects on phytoplankton size, and identified that nutrient effects largely dominate over direct temperature effects. While nutrient limitation has been associated with a reduction in light absorption leading to a reduction in cell size 17 , small cells have low surface:volume ratios, which facilitates nutrient uptake efficiency and is therefore an advantageous feature in low nutrient waters 18 . In contrast to the two future scenarios, DIN was depleted before DIP in the Ambient scenario (Supplementary Fig.  2 ). These results are associated with our manipulation of N:P ratios, which are expected to increase in coastal seas 2 , and support predictions that human-induced nitrogen enrichment is altering the balance with P 19 . Since the phytoplankton bloom rapidly depleted DIP in the ERCP 6.0 and 8.5 scenarios (Supplementary Fig.  2 ), we pose that the above-described phytoplankton biomass responses were mostly driven by DIP availability. The ERCP scenarios-induced smaller phytoplankton cell sizes are favourable for microzooplankton and as a consequence direct the flow of energy to the microbial food web, rather than efficiently fuelling higher trophic levels 20 , 21 .

a Phytoplankton biomass; x axis represents the days of the experiment, different colours represent the Ambient treatment and extended representative concentration pathway (ERCP) scenarios (blue = Ambient, orange = ERCP 6.0, grey = ERCP 8.5), mean ±  standard deviation. Cumulative phytoplankton biomass was not affected by the scenarios (LRT, df 86, P  = 0.46, n  = 3). b Relative abundances of different taxa of the phytoplankton communities.

Following the bloom decay phase, abundances of the small coccolithophore Emiliania huxleyi increased in all treatments, but E. huxleyi only remained dominant in the ERCP 8.5 scenario until the end of the experiment, forming, together with the diatom Leptocylindrus danicus , a second phytoplankton bloom (Fig.  2a, b ). The coccolithophore E. huxleyi has been the “canary” for ocean acidification research for a long time, as lower pH values are predicted to be detrimental to calcification processes present in this species 22 . Recent studies, however, challenge this view, showing the strain-specific response of this species to higher p CO 2 22 . In fact, it has been suggested that this phytoplankton species may become more competitive at higher CO 2 concentrations due to increased carbon fixing enzymatic activity 23 , 24 . Coccolithophore blooms, which are common during summer or early autumn in temperate regions 25 , 26 , have increased in intensity over the past decades in the North Atlantic 27 . Furthermore, E. huxleyi has been reported to outcompete diatom blooms when nutrients, such as silica and phosphorus, become depleted 28 , 29 , 30 . While the calcification process in E. huxleyi under high p CO 2 is modulated by temperature 31 , positive effects of warming coupled with high p CO 2 on calcification of this coccolithophore have been reported 32 . This fact along with lower P availability may have created favourable growth conditions in the ERCP 8.5 scenario. Hence, we suggest that simultaneous p CO 2 and temperature increases, and lower dissolved nutrient concentrations, may promote intense E. huxleyi blooms in the future, which would significantly influence the role of this calcifying species in the marine carbon pump 12 , 33 .

The fate of larger grazers

We observed a significant difference in the abundance of large grazers, from high in Ambient, to lower in ERCP 6.0, and even lower in ERCP 8.5 (Fig.  1 , GLM, df 86, ERCP 6.0 P  = 0.36, ERCP 8.5 P  = 0.0003 and LRT, df 86, P  < 0.0001, n  = 3). The mesozooplankton community was largely dominated by the sea sparkle Noctiluca scintillans . Its abundance continuously increased from the beginning to the end of the experiment in the Ambient treatment (Fig.  3a ), whereas this species died out on days 13 and 21 in the ERCP 8.5 and ERCP 6.0 scenarios, respectively (Fig.  3a ). The abundance of copepods decreased during the experiment and was lower in both ERCP scenarios compared to the Ambient treatment (ANOVA, F 3,9 276.1, P  < 0.0001, n  = 3, Fig.  3b ). The second most numerous mesozooplankton species, the cladoceran Penilia avirostris , was more numerous on day 15 compared to initial values and was present in higher abundances in the ERCP 6.0 scenario and in lower abundances in the ERCP 8.5 scenario and Ambient treatment (ANOVA, F 3,9 26.62, P  = 0.0003, n  = 3 Fig.  3b ). This difference might result from an interaction between food availability, and nutritional requirements at elevated temperature and p CO 2 . While temperatures during our experiment were well within the tolerance range of N. scintillans 34 and P. avirostris 35 , this driver generally increases metabolic processes and energetic demands 36 , and may intensify the sensitivity of consumers to low food availability. The scarcity of prey in the ERCP 8.5 might also have been the reason for the hump-shaped response of P. avirostris to the ERCP scenarios, as this species is not expected to be negatively affected by the temperature ranges used in our experiment. Given the correlation between temperature and metabolic rates, global warming could modify the metabolic demands of consumers, which, together with resource quality shifts, creates the potential for nutritional mismatches 37 . Recent work shows that the nutritional requirements of zooplankton, and the resource quality which maximises the growth of these ectotherms, is not constant but rather varies with temperature 38 , 39 . However, as seston C:N:P stoichiometry did not vary across treatments (Supplementary Fig.  3 ), bottom-up effects were likely driven by resource availability rather than by elemental stoichiometric quality. Noctiluca scintillans and Penilia avirostris can feed on a broad range of prey sizes 40 , 41 , 42 , and may have been little affected by the shift in size from micro- to nanophytoplankton. Rather, we suggest that the lower phytoplankton biomass, and hence food availability, in the ERCP 6.0 and 8.5 scenarios, during the first bloom and its decay phase, were responsible for the differences observed. However, as there was no top-down control on mesozooplankton during the experiment, it is important to note that the effects seen here could differ from communities in which their predators are present. In functional and numerical response experiments in which different phytoplankton taxa were fed to N. scintillans 43 , identified, in addition to the importance of nutrient availability that this large heterotrophic dinoflagellate grew fast when fed with diatoms. Hence, the collapse of N. scintillans may be driven by a marginally non-significant increase from Ambient, to ERCP 6.0, to ERCP 8.5, in the proportion of diatoms within the phytoplankton community (LRT, df 86, P  = 0.05, n  = 3). Altogether, we suggest that multiple global change stressors may act synergistically and reduce the abundance of mesozooplankton in the future via altered food availability and demand, with potential consequences for higher trophic levels 44 , 45 , 46 , 47 . In parallel to bottom-up effects and to a lesser extent, we expect that the lower grazing pressure from meszooplankton might also have contributed to the increase of Emiliania huxleyi in the ERCP 8.5 scenario 48 .

a Abundance of the dominant mesozooplankton species, Noctiluca scintillans , throughout the experiment period; x axis represents the days of the experiment, different colours represent the Ambient treatment and Extended Representative Concentration Pathway (ERCP) scenarios (blue = Ambient, orange = ERCP 6.0, grey = ERCP 8.5), mean ± standard deviation. Noctiluca scintillans abundance was significantly different across scenarios (LRT, df 86, P  < 0.0001, n  = 3). b Abundance of copepods. Copepods abundance is lower in all scenarios compared to Initial, but it is significantly higher in the Ambient compared to the ERCP scenarios (ANOVA, F 3,9 276.1, P  < 0.0001, n  = 3). c Abundance of Penilia avirostris . The abundance of the cladoceran Penilia avirostris is higher in all scenarios compared to Initial (ANOVA, F 3,9 26.62, P  = 0.0003, n  = 3). d Abundance of Hydrozoa. e Abundance of other mesozooplankton. Initial corresponds to values in situ when seawater for the experiment was collected. Ambient treatment and ERCP scenarios represent samples from day 15. Data are displayed as mean ± standard deviation.

Microzooplankton and the microbial loop

The scenarios we tested had the opposite effect on microzooplankton than on mesozooplankton. We observed a gradual increase in the biomass of microzooplankton from Ambient, to ERCP 6.0, to ERCP 8.5 scenarios (Fig.  1 ). Microzooplankton biomass increased along the first phytoplankton bloom and decreased after the phytoplankton bloom had decayed (Fig.  4a ). Whereas the microzooplankton biomass was not statistically different and continuously decreased until the end of the experiment in the Ambient and ERCP 6.0 treatments (GLM, df 86, P  = 0.16, n  = 3), the bloom of small coccolithophores in the ERCP 8.5 scenario coincided with an increase in microzooplankton biomass towards the end of the experiment (GLM, df 86, P  = 0.0004, n  = 3). Interestingly, coccoliths have been suggested as an effective defence mechanism against grazing from zooplankton 49 , but a recent meta-analysis of data collected during mesocosm studies demonstrated that calcification of E. huxleyi , fails to deter microzooplankton grazing, thereby indicating that the possession of calcium carbonate scales does not provide E. huxleyi effective protection from microzooplankton grazing 50 . Moreover, bacterial biomass fluctuated during the experiment, it was higher at the end than at the beginning of the experiment in all treatments, and it reached higher levels in the ERCP 8.5 scenario than in the other two treatments during the decay phase of the first bloom (LRT, df 86, P  = 0.02, n  = 3, Supplementary Fig.  4 ). The increase in microzooplankton biomass at the end of the experiment might also be related to the increasing bacterioplankton biomass during this time, as picoplankton also provides an important source of food for these small grazers 51 . Together with the collapse of mesozooplankton in the ERCP 8.5 scenario, these results indicate that marine coastal planktonic food webs may shift from being mesozooplankton-dominated towards a dominant role of the microbial loop in response to global change in (Fig.  1 ). In support of this hypothesis, previous studies indicated that microzooplankton communities are rather unaffected by high p CO 2 52 , 53 , and that the combination of warming and ocean acidification may in fact increase the interaction strength between microzooplankton and their phytoplanktonic as well as bacterial prey 54 , 55 , 56 , 57 . Such shifts in bottom-up and top-down processes are not ecologically insignificant 58 , 59 . While microzooplankton are a natural trophic link between phytoplankton and bacteria, on the one hand, and mesozooplankton on the other hand 60 , intensified trophic pathways through microzooplankton may diminish energy transfer efficiency to higher trophic levels. Strengthened energy flow through an additional trophic level leads to additional loss of organic carbon and, therefore, less efficient energy transfer to larger grazers 61 , 62 . The gain in prominence of microzooplankton over mesozooplankton we report here is supported by 63 and 62 who predicted lower energy transfer to higher trophic levels when the direct link from phytoplankton to mesozooplankton is shunted through an intermediary trophic level comprised of microzooplankton. Indeed, microzooplankton can directly compete with mesozooplankton for phytoplankton prey 64 , and the addition of a trophic step between phytoplankton and mesozooplankton could reduce food web trophic efficiency, thereby creating a ‘trophic sink’ for production in the food web 65 , 66 , 67 .

a Microzooplankton biomass; x axis represents the days of the experiment, different colours represent the Ambient treatment and extended representative concentration pathway (ERCP) scenarios (blue = Ambient, orange = ERCP 6.0, grey = ERCP 8.5), mean ± standard deviation. Microzooplankton biomass was significantly higher in the ERCP 8.5 (LRT, df 86, P  < 0.0001, n  = 3), compared to Ambient and ERCP 6.0 scenario. b Relative abundances of different taxa of the microzooplankton communities.

Conclusions

Here, we applied an integrated multiple driver design in a mesocosm experiment, to test the short-term effect of different global change scenarios on natural coastal plankton communities. This study identifies an ecological tipping point between the ERCP 6.0 and the ERCP 8.5 scenarios (Fig.  1 ). By promoting the growth of microzooplankton and nanophytoplankton, and by negatively impacting mesozooplankton, environmental conditions in the ERCP 8.5 scenario have the potential to considerably alter the structure and functioning of planktonic food webs in temperate coastal systems. In addition to these large structural shifts, we also observed that global change scenarios can cause the rise and demise of key species, such as Emiliania huxleyi and Noctiluca scintillans . The fact that planktonic food webs were relatively similar under Ambient and ERCP 6.0 conditions reinforces the goals of the 'Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels' 68 to substantially reduce environmental risks and impacts of climate change.

Experimental design

With an integrated multiple driver approach, we tested the influence of two global change scenarios on the structure and dynamics of plankton food webs based on predictions by the Intergovernmental Panel on Climate Change for the end of the 21st century 1 . Temperature and p CO 2 levels were chosen to represent (1) ambient conditions, (2) a moderate global change scenario based on RCP 6.0 (+1.5 °C and −0.2 pH) and (3) a more severe global change scenario based on RCP 8.5 (+3 °C and −0.3 pH). As nutrient inputs are also predicted to change towards considerably higher nitrogen to phosphorus ratio (N:P) in coastal European seas 2 , we extended the RCP scenarios (ERCP) to include the predicted changing nutrient regime, with the Ambient and the ERCP scenarios having an N:P ratio (molar) of 16 (Redfield ratio) and 25, respectively, at the onset of the experiment.

Mesocosm system

The experiment was conducted in the mesocosm facility located at the Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI) Wadden Sea Station on the Island of Sylt 69 . The outdoor facility consists of 12 double-hulled, insulated, cylindrical tanks, made of UV stabilised high-density polyethylene (HDPE; Spranger Kunststoffe, Plauen, Germany). Each tank has a height of 85 cm, an inner diameter of 170 cm, and a net volume of 1800 L. To avoid the introduction of unwanted material, each mesocosm tank is covered with a translucent lid made of HDPE, which allows penetration of 90% of the photosynthetically active radiation. An adjustable flow-through system from the AWI Wadden Sea Station constantly supplies the tanks with fresh, unfiltered seawater. The temperature is regulated every 30 min by a Labview-based computer software (4H-Jena engineering, Jena, Germany), which periodically receives temperature data from Hydrolab DS5X Probes (OTT Messtechnik GmbH, Kempten, Germany) and controls external cooling units (Titan 2000 or Titan 4000 Aqua Medic, Bissendorf, Germany) and heaters (Titanium heater 500 W, Aqua Medic, Bissendorf, Germany).

We installed 450 L low-density polyethylene (LDPE) bags in each mesocosm tank (Supplementary Fig.  5 ). The LDPE was chosen as the material for the bags as it should not represent a risk for planktonic organisms either, since it also is used in food industry for packaging. The LDPE bags were filled with seawater collected from the open North Sea with natural plankton communities (see details about filling procedures below). The bags were fixed in the centre of the tanks. By regulating the temperature and aerating the surrounding flow-through water as described above, we indirectly regulated temperature and p CO 2 in the LDPE bags. We replicated each treatment four times, for a total of 12 mesocosms. Due to damage to the bags and potential contamination of the plankton communities by the surrounding water, we excluded one replicate from each treatment, leaving triplicates for each of the three treatments. Despite the low number of replicates, the consistent response across scenarios and strong statistical results still reinforce the reliability of our results. The temperature in the Ambient conditions mesocosms was adjusted daily to the seawater temperature measured at the Helgoland Roads station (54°11.3’N, 7°54.0’E) and was increased by 1.5 and 3.0 °C for the ERCP 6.0 and ERCP 8.5 scenarios, respectively. We mounted small mortar mixer engines (TC-MX 1400-2 E, Einhell Germany AG, Landau/Isar, Germany) on top of each mesocosm tank, which were connected to a custom-made HDPE propeller (AWI, Helgoland, Germany). To avoid sedimentation of the planktonic organisms and mimic the relatively well-mixed water column condition found in the southern part of the North Sea 70 , the submerged propellers gently homogenised the water column of the LDPE bags at 50 rpm in a 1-min-mixing/30-min-pause interval. To reach the desired p CO 2 in the different ERCP scenarios, streaming pipes aerated each tank with the desired gas mixture in the water outside the LDPE bags (Supplementary Fig.  5 ). The aeration outside the mesocosm bag was intended to prevent damage to fragile planktonic organisms that are sensitive to bubbling. The Ambient conditions mesocosms were bubbled with pressured air, ERCP 6.0 scenario with 800 µatm p CO 2 and the ERCP 8.5 with 1000 µatm p CO 2 , which were determined by a central CO 2 -mixing facility (GMZ 750, HTK, Hamburg, Germany). The mesocosm cover trapped the p CO 2 -controlled atmosphere above the mesocosm water column, hence realistically mimicking future environmental conditions.

Seawater collection and filling of the mesocosm bags

On August 14, 2018, we collected water from the open North Sea 45 km west of the island of Sylt (55°01'20.0“N 7°38'41.0“E), during a cruise with the AWI research vessel Uthörn. During the water collection and filling procedure of the mesocosm bags, we did not use any pumps, but transferred seawater via gravity flow to prevent any damage to fragile organisms within the planktonic community. To sample seawater onboard, we submerged a 500 L tub attached to a crane to fill it with seawater from the upper 5 m sea surface. The tub was subsequently lifted up to let the water flow through a hose connected to the tub into 1000 L polyethylene Intermediate Bulk Containers (IBC, AUER Packaging GmbH, Amerang, Germany). We attached a 1000-µm mesh to the end of the hose, to exclude larger organisms, such as jellyfish and fish larvae. This procedure prevented any disproportionally large impact which larger consumers can have on the rest of the plankton community in a 450 L enclosed water volume. Furthermore, this approach enabled us to focus on bottom-up processes since there was no top-down control on mesozooplankton. The procedure was repeated until eight IBC tanks were filled (8000 L), which took about 3 h.

Before filling the mesocosm bags, we first gently homogenised the water in the IBC tanks. Then, we attached a four-way distributor to one IBC tank, and the tank was lifted by a wheel-loader to allow gravity flow of the seawater into the mesocosm bags. At the end of each connected hose, a flowmeter measured the exact volume of water that was released into each mesocosm bag. We filled 80 L of seawater simultaneously to four bags, and then filled the next quadruplet of mesocosms. This enabled an equal distribution of the water contained in each IBC tank among the twelve mesocosms. This procedure was repeated until all mesocosm bags were filled with 450 L of North Sea seawater. This procedure enabled us to successfully tackle a major challenge when conducting mesocosm experiments, the difficulty of achieving homogenous replicates at the onset of the experiment 10 . Across scenarios, no significant difference was found in biomass of phytoplankton, microzooplankton and bacterioplankton on the first day of the experiment (Kruskal–Wallis test, df = 2, P  > 0.05). Once the filling procedure was completed, we directly measured the dissolved N and P concentrations in each mesocosm bag according to the method described in ref. 71 , and subsequently adjusted the dissolved N:P ratios to 16 (Ambient conditions) and 25 (ERCP scenarios). We added DIN to reach 5 µmol L −1 in all scenarios, DIP to reach 0.31 µmol L −1 in the Ambient scenario and 0.2 µmol DIP L −1 in the ERCP 6.0 and 8.5 scenarios. These values correspond to mean values for that period of the year according to data from the Helgoland roads time series. At the onset of the experiment, we bubbled a small volume of seawater with pure CO 2 , which lowered its pH to 4.8 at saturation. Using a 50 mL plastic syringe connected to a 1-m hose, we injected 400 mL (ERCP 6.0) and 760 mL (ERCP 8.5) of the saturated CO 2 seawater at the bottom of the mesocosm bags to reduce the initial pH values by −0.2 and −0.3 for the ERCP 6.0 and ERCP 8.5 scenarios, respectively. During the rest of the experiment, the pH was influenced by the planktonic communities through photosynthesis and respiration, and by the atmospheric p CO 2 (see above).

Physical-chemical conditions in the mesocosm bags

Temperature, pH, light irradiance and salinity were measured every day at 9:00 (Supplementary Fig.  6 ). Light intensity was measured just below the water surface with a Li-cor Li-250 Light metre (Bad Homburg, Germany). Temperature measurements were done directly inside the mesocosm bags using a Testo 110—temperature metre (Lenzkirch, Germany). Total alkalinity (TA) samples were taken by plunging, filling, and closing an air-tighten 100 mL transparent glass bottle inside the mesocosm to avoid air bubbles. The samples were stored at 4 °C before being analysed within 36 h through linear Gran-titration 72 using a TitroLine alpha plus (Schott, Mainz, Germany). Samples for dissolved inorganic nutrients and TA were taken at an interval of 1–3 days depending on the phytoplankton bloom development.

For further analyses, water was collected from each mesocosm bag with clean plastic beakers and brought to the lab for processing. The first parameter measured was pH using a WTW pH 330i equipped with a SenTix 81 pH electrode (Letchworth, England). Salinity was measured with a WTW CellOx 325 (probe Oxi 197-S, Letchworth, England). Dissolved inorganic nutrient samples were collected with a sterile plastic syringe and filtered through a 0.45 µm PTFE filter (Minisart, Sartorius, Goettingen, Germany) fitted to the syringe. For this step, the first 2 mL of the sample were used to rinse the filter and directly discarded. Samples for dissolved inorganic nitrogen (DIN) and phosphorus (DIP) were stored at −20 °C, and the samples for dissolved silica (DSi) were stored at 4 °C, until photometric analyses 71 (Supplementary Fig.  2 ). Results of TA, pH, temperature, salinity, atmospheric pressure, DIP and DSi were computed to determine the carbonate system using the CO2Sys Excel Macro 73 with a set of constants defined by 74 (Supplementary Data  1 ). Although p CO 2 in the mesocosms were below levels projected by the RCP scenarios during the experiment, CO 2 concentrations were always different across scenarios within the expected gradient (Supplementary Fig.  6b and Supplementary Data  1 ), where Ambient is lower than ERCP 6.0 that is lower than ERCP 8.5. Given the extreme complexity of keeping p CO 2 constant in mesocosm experiments, even with an appropriate CO 2 atmosphere, and especially throughout a phytoplankton bloom event that is able to change dissolved CO 2 even in the open sea 75 . Therefore, our approach yields the most realistic of CO 2 time courses in a future ocean. The remaining water was used for analyses of the planktonic community.

Planktonic community

To determine plankton species composition and biomass, 100 mL of mesocosm seawater were stored in amber glass bottles and immediately fixed with neutral Lugol’s iodine solution (1% final concentration) to preserve calcifying phytoplankton. Another 250 mL were fixed with acid Lugol’s iodine solution (2% final concentration) to preserve other phytoplankton and microzooplankton species. Phytoplankton were identified using an inverted microscope Zeiss Anxiovert 135 (Jena, Germany) and microzooplankton using a Zeiss Axio Observer 7 A1 (New York, USA) following the method described in 76 . Due to the high biomass of the mesozooplankton Noctiluca scintillans during the experiment, this species was quantified and identified by the Utermöhl method as well, using chamber volumes ranging between 50 and 100 mL. Planktonic organisms were identified to species level, or pooled into size-shape dependent groups when species identification was not possible. Scanning electron microscopy (Philips XL30 SEM, Massachusetts, USA) was applied to identify coccolithophore species by the morphology of the coccoliths. For this procedure, prior to microscopy, 5 mL of the neutral lugol fixed sample were filtered through a 0.2 µm pore size polycarbonate membrane filter (Merck Millipore, Burlington, USA), dried in a drying oven at 40 °C for 12 h, placed on a metal stub using an adherent carbon disc with increased conductivity, and then sputter-coated with a 10-nm gold layer.

Mesozooplankton, with the exception of Noctiluca scintillans , was sampled with a plankton net (200 µm) in situ (Initial) when seawater was collected, and on the day 15 of the experiment by sieving 5 L of seawater from the mesocosm through a 200-µm nylon mesh. The organisms caught on the mesh were flushed back into a 50 mL transparent Kautex container with sterile filtered seawater (0.2 µm), and immediately fixed with formaldehyde. The mesozooplankton community composition was determined by counting the whole sample or three subsamples when splitting was necessary with a Folsom splitter 77 , 78 . The counting took place using a Bogorov chamber under stereomicroscope (Leica M205), and taxonomic identification was conducted as in ref. 79 . Samples for bacterioplankton biomass were taken as 5 mL of seawater, sieved through 20-µm nylon mesh, fixed with glutaraldehyde (0.1% final concentration) and frozen at −80 °C until analysis. The samples were thawed in water bath (20 °C) and stained with SYBR Green (Invitrogen) following the method described by Marie et al. 80 . Bacteria cells were enumerated by flow cytometry (BD AccuriTM C6 Plus, BD Biosciences) with a flow rate of 12 µL min −1 for 1–2 min and diluted in sterile filtered seawater (0.2 µm) when bacterial cell number was higher than 400 events s −1 . As SYBR Green stains DNA molecules without distinguishing taxonomical groups, our results of bacterioplankton include any organisms within the range of picoplankton cell size (~0.2–2 µm), including picocyanobacteria.

Biovolume of each phytoplankton and microzooplankton species was calculated from the measurement of cell dimensions using geometric formulae according to ref. 81 . Cell volume was converted into carbon following the equations of 82 for diatoms (pg C cell −1  = 0.288 × V 0.811 ), dinoflagellates (pg C cell −1  = 0.760 × V 0.819 ) and other protist plankton with the exception of ciliates (pg C cell −1  = 0.216 × V 0.939 ), where V is the cell volume in µm 3 . Ciliate carbon content was calculated as 0.19 pg C µm −3 according to ref. 83 . Noctiluca scintillans C content was determined as 0.138 µg C cell −1 84 . Bacteria cell counts were converted into carbon using the 20 fg C cell −1 factor defined by Lee and Fuhrman 85 . The box size on the infographic of biomass (Fig.  1 ) was determined by the integral area under the curve of the plankton biomass over time (Figs.  2 a, 3 a, 4a and Supplementary Fig.  4 ) and dominant taxa followed the values of the relative abundance of the most abundant taxa (Figs.  2 b, 3 b and 4a, b ). Elemental composition (CNP) of seston was determined by filtering 200 mL of seawater through precombusted GF/F filters. Carbon and nitrogen content were measured with a Vario Micro Cube elemental analyser (Elementar, Hanau, Germany). Phosphorus content was quantified as orthophosphate after oxidation by molybdate-antimony 70 . Functional groups were determined as Phytoplankton, Bacterioplankton, Microzooplankton and Mesozooplankton. The phytoplankton group included diatoms, phytoflagellates and autotrophic dinoflagellates, according to the descriptions of trophic mode for each species 86 . The microzooplankton group comprised heterotrophic and mixotrophic dinoflagellates and ciliates, including nanociliates (< 20 µm). Mesozooplankton species were all the heterotrophic organisms larger than 200 µm.

Statistics and reproducibility

Statistical analyses were performed using R 3.4.3 software 87 . For all analyses, the threshold of significance was set at 0.05. All statistical analyses were applied considering the three individual replicates per scenario. Each replicate was determined as one tank of the mesocosm system. The effect of the ERCP scenario on planktonic biomass was assessed by a generalised linear model (GLM). We first fitted a model of total biomass (either phytoplankton or zooplankton) depending on treatments. It allowed us to check for general treatment effect on planktonic biomass. Then, we created a second model including treatment and time. By comparing the constrained model (time and scenario) against the unconstrained one (only scenario) by Likelihood ratio test (LRT), we could test whether timing in planktonic biomasses were similar among treatments. Effects of the ERCP scenarios on the phyto- and microzooplankton species composition and affinity of species to the scenarios were analysed through the principal response curve (PRC) using the ‘vegan’ R package. This test shows the degree of difference over time of the community composition in the ERCP scenarios in comparison to the Ambient condition, which is set as a control (effect ‘0’). Species weights are analysed as means of their regression coefficient against the control. When the curve of difference of the ERCP scenario has a positive slope, positive values for species weights represent affinity of this species to the scenario, whereas negative values would represent the negative effect of the scenario on such species and vice versa. Differences of mesozooplankton abundance were analysed through Analysis of Variance (ANOVA) followed by a post hoc test (Tukey test). Data were log-transformed when normality and homoscedasticity of residuals were not met for ANOVA and LRT.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and analysed during this study are available in the Pangaea repository: https://doi.pangaea.de/10.1594/PANGAEA.940529 .

IPCC Climate Change 2014 : Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) 151 (IPCC, Geneva, Switzerland, 2014).

Grizzetti, B., Bouraoui, F. & Aloe, A. Changes of nitrogen and phosphorus loads to European seas. Glob. Change Biol. 18 , 769–782 (2012).

Article   Google Scholar  

Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328 , 1523–1528 (2010).

Article   CAS   PubMed   Google Scholar  

Duarte, C. M. Global change and the future ocean: a grand challenge for marine sciences. Front. Mar. Sci. 1 , 1–16 (2014).

Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305 , 1609–1612 (2004).

Rose, J. M. et al. Effects of increased p CO 2 and temperature on the North Atlantic spring bloom. II. Microzooplankton abundance and grazing. Mar. Ecol. Prog. Ser. 388 , 27–40 (2009).

Article   CAS   Google Scholar  

Sommer, U., Paul, C. & Moustaka-Gouni, M. Warming and ocean acidification effects on phytoplankton—from species shifts to size shifts within species in a mesocosm experiment. PLoS ONE 10 , 1–17 (2015).

Garzke, J., Hansen, T., Ismar, S. M. H. & Sommer, U. Combined effects of ocean warming and acidification on copepod abundance, body size and fatty acid content. PLoS ONE 11 , 1–22 (2016).

Horn, H. G., Boersma, M., Garzke, J., Sommer, U. & Aberle, N. High CO 2 and warming affect microzooplankton food web dynamics in a Baltic Sea summer plankton community. Mar. Biol. 167 , 1–17 (2020).

Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Change Biol. 24 , 2239–2261 (2018).

Stewart, R. I. A. et al. Mesocosm experiments as a tool for ecological provided for ecological climate-change research. In Advances in Ecological Research/Guy Woodward (ed. O’Gorman, E. J.) 71–181 (Academic Press, 2013).

Rost, B. & Riebesell, U. Coccolithophores and the biological pump: responses to environmental changes. In Coccolithophores: From Molecular Processes to Global Impact (eds Thierstein, H. R. & Young, J. R.) 99–125 (Springer, 2004).

Peter, K. H. & Sommer, U. Phytoplankton cell size reduction in response to warming mediated by nutrient limitation. PLoS ONE 8 , 1–6 (2013).

Bermúdez, J. R., Riebesell, U., Larsen, A. & Winder, M. Ocean acidification reduces transfer of essential biomolecules in a natural plankton community. Sci. Rep. 6 , 1–8 (2016).

Peter, K. H. & Sommer, U. Interactive effect of warming, nitrogen and phosphorus limitation on phytoplankton cell size. Ecol. Evolution 5 , 1011–1024 (2015).

Alvarez-Fernandez, S. et al. Plankton responses to ocean acidification: the role of nutrient limitation. Prog. Oceanogr. 165 , 11–18 (2018).

Stramski, D., Sciandra, A. & Claustre, H. Effects of temperature, nitrogen, and light limitation on the optical properties of the marine diatom Thalassiosira pseudonana . Limnol. Oceanogr. 47 , 392–403 (2002).

Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7 , 241–264 (2015).

Peñuelas, J., Sardans, J., Rivas‐Ubach, A. & Janssens, I. A. The human-induced imbalance between C, N and P in Earth’s life system. Glob. Change Biol. 18 , 3–6 (2011).

Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10 , 257–63. (1983).

Legendre, L. & Le Fèvre, J. Microbial food webs and the export of biogenic carbon in oceans. Aquat. Microb. Ecol. 9 , 69–77 (1995).

Beaufort, L. et al. Sensitivity of coccolithophores to carbonate chemistry and ocean acidification. Nature 476 , 80–83 (2011).

Langer, G., Nehrke, G., Probert, I., Ly, J. & Ziveri, P. Strain-specific responses of Emiliania huxleyi to changing seawater carbonate chemistry. Biogeosciences 6 , 2637–2646 (2009).

Winter, A., Henderiks, J., Beaufort, L., Rickaby, R. E. M. & Brown, C. W. Poleward expansion of the coccolithophore Emiliania huxleyi . J. Plankton Res. 36 , 316–325 (2014).

Hopkins, J., Henson, S. A., Painter, S. C., Tyrrell, T. & Poulton, A. J. Phenological characteristics of global coccolithophore blooms. Glob. Biogeochemical Cycles 29 , 239–253 (2015).

León, P. et al. Seasonal variability of the carbonate system and coccolithophore Emiliania huxleyi at a Scottish Coastal Observatory monitoring site. Estuar., Coast. Shelf Sci. 202 , 302–314 (2018).

Rivero-Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M. & Guikema, S. D. Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO 2 . Science 350 , 1533–1537 (2015).

Purdie, D. A. & Finch, M. S. Impact of a coccolithophorid bloom on dissolved carbon dioxide in sea water enclosures in a Norwegian fjord. Sarsia 79 , 379–387 (1994).

Nejstgaard, J. C., Gismervik, I. & Solberg, P. T. Feeding and reproduction by Calanus finmarchicus , and microzooplankton grazing during mesocosm blooms of diatoms and the coccolithophore Emiliania huxleyi . Mar. Ecol. Prog. Ser. 147 , 197–217 (1997).

Leblanc, K. et al. Distribution of calcifying and silicifying phytoplankton in relation to environmental and biogeochemical parameters during the late stages of the 2005 North East Atlantic Spring Bloom. Biogeosciences 6 , 2155–2179 (2009).

Sett, S. et al. Temperature modulates coccolithophorid sensitivity of growth, photosynthesis and calcification to increasing seawater p CO 2 . PLoS ONE 9 , e88308 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Benner, I. et al. Emiliania huxleyi increases calcification but not expression of calcification-related genes in long-term exposure to elevated temperature and p CO 2 . Philos. Trans. R. Soc. B 368 , 20130049 (2013).

Borchard, C., Borges, A. V., Händel, N. & Engel, A. Biogeochemical response of Emiliania huxleyi (PML B92/11) to elevated CO 2 and temperature under phosphorous limitation: a chemostat study. J. Exp. Mar. Biol. Ecol. 410 , 61–71 (2011).

Harrison, P. J. et al. Geographical distribution of red and green Noctiluca scintillans . Chin. J. Oceanol. Limnol. 29 , 807–831 (2011).

Johns, D. G., Edwards, M., Greve, W. & SJohn, A. W. G. Increasing prevelance of the marine cladoceran Penilia avirostris (Dana, 1852) in the North Sea. Helgol. Mar. Res. 59 , 215–218 (2005).

O’Connor, M. I. O., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7 , 1–6 (2009).

Google Scholar  

Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. change Biol. 21 , 1025–1040 (2015).

Boersma, M. et al. Temperature driven changes in the diet preference of omnivorous copepods: no more meat when it’s hot? Ecol. Lett. 19 , 45–53 (2016).

Article   PubMed   Google Scholar  

Anderson, T. R., Hessen, D. O., Boersma, M., Urabe, J. & Mayor, D. J. Will invertebrates require increasingly carbon-rich food in a warming world? Am. Naturalist 190 , 725–742 (2017).

Kirchner, M., Sahling, G., Uhlig, G., Gunkel, W. & Klings, K.-W. Does the red tide-forming dinoflagellate Noctiluca scintillans feed on bacteria? Sarsia 81 , 45–55 (2015).

Elbrächter, M. & Qi, Y. Aspects of Noctiluca ( Dinophyceae ) population dynamics. In Physiological Ecology of Harmful Algal Blooms (ed. Anderson, M. D.) 315–335 (Springer-Verlag, 1998).

Atienza, D., Saiz, E. & Calbet, A. Feeding ecology of the marine cladoceran Penilia avirostris : natural diet, prey selectivity and daily ration. Mar. Ecol. Prog. Ser. 315 , 211–220 (2006).

Zhang, S., Liu, H., Chen, B. & Chih-Jung, W. Effects of diet nutritional quality on the growth and grazing of Noctiluca scintillans . Sci. Rep. 527 , 73–85 (2015).

CAS   Google Scholar  

Reid, P. C., Borges, M. F. & Svendsen, E. A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery. Fish. Res. 50 , 163–171 (2001).

Beaugrand, G., Brander, K. M., Lindley, J. A., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426 , 661–664 (2003).

Payne, M. R. et al. Recruitment in a changing environment: the 2000s North Sea herring recruitment failure. ICES J. Mar. Sci. 66 , 272–277 (2009).

Perälä, T., Olsen, E. M. & Hutchings, J. A. Disentangling conditional effects of multiple regime shifts on Atlantic cod productivity. PLoS ONE 15 , e0237414 (2020).

Behrenfeld, M. J., Boss, E. S. & Halsey, K. H. Phytoplankton community structuring and succession in a competition-neutral resource landscape. ISME COMMUN. 1 , 1–8 (2021).

Monteiro, F. M. et al. Why marine phytoplankton calcify. Sci. Adv. 2 , 1–14 (2016).

Mayers, K. M. J. et al. The possession of coccoliths fails to deter microzooplankton grazers. Front. Mar. Sci. 7 , 976 (2020).

Zhao, Y. et al. Grazing by microzooplankton and copepods on the microbial food web in spring in the southern Yellow Sea, China. Mar. Life Sci. Technol. 2 , 442–455 (2020).

Aberle, N. et al. High tolerance of microzooplankton to ocean acidification in an Arctic coastal plankton community. Biogeosciences 10 , 1471–1481 (2013).

Horn, H. G. et al. Low CO 2 sensitivity of Microzooplankton communities in the Gullmar Fjord, Skagerrak: evidence from a long-term Mesocosm Study. PLoS ONE 11 , 1–24 (2016).

Chen, B., Landry, M. R., Huang, B. & Liu, H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57 , 519–526 (2012).

Vázquez-Domínguez, E., Vaqué, D. & Gasol, J. M. Temperature effects on the heterotrophic bacteria, heterotrophic nanoflagellates, and microbial top predators of the NW Mediterranean. Aquat. Microb. Ecol. 67 , 107–121 (2012).

Lara, E. et al. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems. Aquat. Microb. Ecol. 70 , 17–32 (2013).

Olson, M. B., Solem, K. & Love, B. Microzooplankton grazing responds to simulated ocean acidification indirectly through changes in prey cellular characteristics. Mar. Ecol. Prog. Ser. 604 , 83–97 (2018).

Sherr, E. B. & Sherr, B. F. Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs. Microb. Ecol. 28 , 223–235 (1994).

Brander, K. & Kiørboe, T. Decreasing phytoplankton size adversely affects ocean food chains. Glob. Change Biol. 26 , 5356–5357 (2020).

Irigoien, X. et al. A high frequency time series at weathership M, Norwegian Sea, during the 1997 spring bloom: feeding of adult female Calanus finmarchicus . Mar. Ecol. Prog. Ser. 172 , 127–137 (1998).

Fenchel, T. The microbial loop—25 years later. J. Exp. Mar. Biol. Ecol. 366 , 99–103 (2008).

Aberle, N., Malzahn, A. M., Lewandowska, A. M. & Sommer, U. Some like it hot: the protozooplankton— copepod link in a warming ocean. Mar. Ecol. Prog. Ser. 519 , 103–113 (2015).

Berglund, J., Müren, U., Båmstedt, U. & Andersson, A. Efficiency of a phytoplankton-based and a bacteria-based food web in a pelagic marine system. Limnol. Oceanogr. 52 , 121–131 (2007).

Sherr, E. B. & Sherr, B. F. Heterotrophic dinoflagellates: a significant component of microzooplankton biomass and major grazers of diatoms in the sea. Mar. Ecol. Prog. Ser. 352 , 187–197 (2007).

Gifford, D. J. The protozoan-metazoan trophic link in pelagic ecosystems. J. Protozool. 38 , 81–86 (1991).

Rollwagen-Bollens, G. & Gifford, S. The role of protistan microzooplankton in the upper San Francisco estuary planktonic food web: source or sink? Estuaries Coasts 34 , 1026–1038 (2011).

Anjusha, A. et al. Trophic efficiency of plankton food webs: observations from the Gulf of Mannar and the Palk Bay, Southeast Coast of India. J. Mar. Syst. 115 , 40–61 (2013).

IPCC. Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In The Context of Strengthening the Global Response to The Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (Masson-Delmotte, V. et al (eds.) 616 (IPCC, Geneva, Switzerland, 2018).

Pansch, A., Winde, V., Asmus, R. & Asmus, H. Tidal benthic mesocosms simulating future climate change scenarios in the field of marine ecology. Limnol. Oceanogr.: Methods 14 , 257–267 (2016).

van Leeuwen, S., Tett, P., Mills, D. & van der Molen, J. Stratified and nonstratified areas in the North Sea: long-term variability and biological and policy implications. J. Geophys. Res.: Oceans 120 , 4670–4686 (2015).

Grasshoff, K., Kremling, K. & Ehrhardt, M. (eds). Methods of Seawater Analysis , 3rd edn. (Wiley-VCH, Weinheim, 1999).

Dickson, A. G. An exact definition of total alkalinity and a procedure for the estimation of alkalinity and total inorganic carbon from titration data. Deep-Sea Res. 28 , 609–623 (1981).

Pierrot, D. E., Lewis, E. & Wallace, D. W. R. MS Excel program developed for CO 2 system calculations. ORNL/CDIAC-105a. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee https://doi.org/10.3334/CDIAC/otg.CO2SYS_XLS_CDIAC105a (2006).

Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep-Sea Res. 34 , 1733–1743 (1987).

Arrigo, K. R. et al. Phytoplankton community structure and the drawdown of nutrients and CO 2 in the Southern Ocean. Science 283 , 365–368 (1999).

Utermöhl, H. Zur Vervollkommnung der quantitativen Phytoplankton- Methodik. Int. Ver. für. Theoretische und Angew. Limnologie: Mitteilungen 9 , 1–38 (1958).

McEwen, G. F., Johnson, M. W. & Folsom, T. R. A statistical analysis of the performance of the Folsom plankton sample splitter, based upon test observations. Archiv für. Archiv Meteorologie, Geophysik und Bioklimatologie, Ser. A 7 , 502–527 (1954).

Sell, D. W. & Evans, M. S. A statistical analysis of subsampling and an evaluation of the Folsom plankton splitter. Hydrobiologia 94 , 223–230 (1982).

Boersma, M., Wiltshire, K. H., Kong, S., Greve, W. & Renz, J. Long-term change in the copepod community in the southern German Bight. J. Sea Res. 101 , 41–50 (2015).

Marie, D., Simon, N. & Vaulot, D. Phytoplankton cell counting by flow cytometry. Algal Culturing Tech. 1 , 253–267 (2005).

Hillebrand, H., Dürselen, C., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35 , 403–424 (1999).

Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45 , 569–579 (2000).

Putt, M. & Stoecker, D. K. An experimentally determined carbon: volume ratio for marine “oligotrichous” ciliates from estuarine and coastal waters. Limnol. Oceanogr. 34 , 1097–1103 (1989).

Beran, A. et al. Carbon content and biovolume of the heterotrophic dinoflagellate Noctiluca scintillans from the Northern Adriatic Sea. In Proceedings of the CESUM-BS 2003, Varna . 28 (Book of Abstracts, UNESCO, Paris, 2003).

Lee, S. & Fuhrman, J. A. Relationships between biovolume and biomass of naturally derived marine bacterioplankton. Appl. Environ. Microbiol. 53 , 1298–1303 (1987).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kraberg, A., Baumann, M. & Dürselen, C. Coastal Phytoplankton: Photo Guide for Northern European Seas (Dr. Friedrich Pfeil, München, 2010).

R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).

Download references

Acknowledgements

H.D.M., M.K. and C.L.M. were supported by the German Federal Ministry of Education and Research (BMBF grant no. 01LN1702A). M.B. was funded by the German Science Foundation (DFG) with the Priority Program DynaTrait. We thank the colleagues from Alfred-Wegener-Institut for the technical and scientific support during the experiment, especially Julia Haafke, Petra Kadel, Silvia Peters, Andreas Kornmann, Inga Kirstein, Johannes Rick, Ragnhild Asmus and Harald Asmus. Sincere thanks to the colleagues who supported us in analysing some of the samples, including Ursula Ecker (mesozooplankton), Tatyana Romanova (dissolved inorganic nutrients), Bernhard Fuchs (bacterioplankton), Sebastian Rokitta and Gernot Nehrke (scanning electron microscopy). We thank Herwig Stibor, Helmut Hillebrand and Ulf Riebesell for providing their expert opinion on the experimental design. We thank Herwig Stibor and Maria Stockenreiter for providing the LDPE bags in which the experiment was conducted.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Biologische Anstalt Helgoland, Ostkaje 1118, 27498, Helgoland, Germany

Hugo Duarte Moreno, Martin Köring, Julien Di Pane, Nelly Tremblay, Karen H. Wiltshire, Maarten Boersma & Cédric L. Meunier

Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Wattenmeerstation, Hafenstr. 43, 25992, List auf Sylt, Germany

Karen H. Wiltshire

University of Bremen, FB 2, Bibliothekstr. 1, 28359, Bremen, Germany

Maarten Boersma

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualisation: H.D.M., K.H.W., M.B. and C.L.M. Data acquisition: H.D.M., M.K., N.T. and C.L.M. Data analysis: H.D.M. and J.D.P. Writing—original draft: H.D.M. and C.L.M. Writing—review and editing: H.D.M., M.K., J.D.P., N.T., K.H.W., M.B. and C.L.M.

Corresponding author

Correspondence to Hugo Duarte Moreno .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Communications Biology thanks Eoin O'Gorman and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editors: Linn Hoffmann and Luke R. Grinham. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Peer review file, supplementary information, description of additional supplementary files, supplementary data 1, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Moreno, H.D., Köring, M., Di Pane, J. et al. An integrated multiple driver mesocosm experiment reveals the effect of global change on planktonic food web structure. Commun Biol 5 , 179 (2022). https://doi.org/10.1038/s42003-022-03105-5

Download citation

Received : 07 May 2021

Accepted : 01 February 2022

Published : 01 March 2022

DOI : https://doi.org/10.1038/s42003-022-03105-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Climate change impacts on a sedimentary coast—a regional synthesis from genes to ecosystems.

• Christian Buschbaum
• L. N. S. Shama
• K. H. Wiltshire

Marine Biodiversity (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Reference work entry
• pp 3523–3527
• Cite this reference work entry

• A. Fahy 2 &
• B. McKew 2  

415 Accesses

1 Citations

Microcosms are widely used in microbiological research, and assume many forms. In this section, we evaluate the advantages and limitations of microcosms, and discuss which answers can be drawn from microcosm-based research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Introduction: Mesocosms and Microcosms

Introduction: Field and In Situ Studies

Coefficient of Assessment for the Microcosm System

Bordenave S, Goñi-Urriza MS, Caumette P, Duran R (2007) Effects of heavy fuel oil on the bacterial community structure of a pristine microbial mat. Appl Environ Microbiol 73: 6089–6097.

Article   PubMed   CAS   Google Scholar  

Coulon F, McKew BA, Osborn AM, McGenity TJ, Timmis KN (2007) Effects of temperature and biostimulation on oil-degrading microbial communities in temperate estuarine waters. Environ Microbiol 9: 177–186.

Head IM, Jones DM, Röling WFM (2006) Marine microorganisms make a meal of oil. Nature Rev Microbiol 4: 173–182.

Article   CAS   Google Scholar  

Hendrickx B, Dejonghe W, Boënne W, Brennerova M, Cernik M, Lederer T, Bucheli-Witschel M, Bastiaens L, Verstraete W, Top EM, Diels L, Springael D (2005) Dynamics of an oligotrophic bacterial aquifer community during contact with a groundwater plume contaminated with benzene toluene, ethylbenzene, and xylenes: an in situ mesocosm study. Appl Environ Microbiol 71: 3815–3825.

Hernandez-Raquet G, Budzinski H, Caumette P, Dabert P, Le Ménach K (2006) Molecular diversity studies of bacterial communities of oil polluted microbial mats from the Etang de Berre (France). FEMS Microbiol Ecol 58: 550–562.

Herrmann S, Kleinsteuber S, Neu TR, Richnow HH, Vogt C (2008) Enrichment of anaerobic benzene-degrading microorganisms by in situ microcosms. FEMS Microbiol Ecol 63: 94–106.

Kasai Y, Kishira H, Sasaki T, Syutsubo K, Watanabe K, Harayama S (2002) Predominant growth of Alcanivorax strains in oil-contaminated and nutrient-supplemented sea water. Environ Microbiol 4: 141–147.

Google Scholar  

Liou JS-C, DeRito CM, Madsen EL (2008) Field-based and laboratory stable isotope probing surveys of the identities of both aerobic and anaerobic benzene-metabolizing microorganisms in freshwater sediment. Environ Microbiol 10(8): 1964–1977.

McKew BA, Coulon F, Osborn AM, Timmis KN, McGenity TJ (2007a) Determining the identity and roles of oil-metabolizing marine bacteria from the Thames estuary, UK. Environ Microbiol 9: 165–176.

McKew BA, Coulon F, Yakimov MM, Denaro R, Genovese M, Smith CJ, Osborn AM, Timmis KN, McGenity TJ (2007b) Efficacy of intervention strategies for bioremediation of crude oil in marine systems and effects on indigenous hydrocarbonoclastic bacteria. Environ Microbiol 9: 1562–1571.

Pallud C, Meile C, Laverman AM, Abell J, Van Cappellen P (2007) The use of flow-through sediment reactors in biogeochemical kinetics: Methodology and examples of applications. Mar Chem 106: 256–271.

Pritchard PH, Bourquin AW (1984) The use of microcosms for evaluation of interactions between pollutants and microorganisms. In Advances in Microbial Ecology, KC Marshall (ed.). New York: Plenum Press, pp. 133–215.vol. 7.

Röling WFM, de Brito IRC, Swannell RPJ, Head IM (2004a) Response of archaeal communities in beach sediments to spilled oil and bioremediation. Appl Environ Microbiol 70: 2614–2620.

Article   PubMed   Google Scholar  

Röling WFM, Milner MG, Jones DM, Fratepietro F, Swannell RPJ, Daniel F, Head IM (2004b) Bacterial community dynamics and hydrocarbon degradation during a field-scale evaluation of bioremediation on a mudflat beach contaminated with buried oil. Appl Environ Microbiol 70: 2603–2613.

Röling WFM, Milner MG, Jones DM, Lee K, Daniel F, Swannell RJP, Head IM (2002) Robust hydrocarbon degradation and dynamics of bacterial communities during nutrient-enhanced oil spill bioremediation. Appl Environ Microbiol 68: 5537–5548.

Syutsubo K, Kishira H, Harayama S (2001) Development of specific oligonucleotide probes for the identification and in situ detection of hydrocarbon-degrading Alcanivorax strains. Environ Microbiol 3: 371–379.

Watson IA, Oswald SE, Mayer KU, Wu YX, Banwart SA (2003) Modeling kinetic processes controlling hydrogen and acetate concentrations in an aquifer-derived microcosm. Environ Sci Technol 37: 3910–3919.

Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, Timmis KN, Golyshin PN, Giluliano L (2005) Natural microbial diversity in superficial sediments of Milazzo Harbor (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol 7: 1426–1441.

Download references

Author information

Authors and affiliations.

Department of Biological Sciences, University of Essex, Colchester, UK

A. Fahy & B. McKew

You can also search for this author in PubMed   Google Scholar

Editor information

Editors and affiliations.

Environmental Microbiology Laboratory, Helmholtz Centre for Infection Research, Inhoffenstrasse 7, 38124, Braunschweig, Germany

Kenneth N. Timmis ( Professor ) ( Professor )

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry.

Fahy, A., McKew, B. (2010). Microcosms. In: Timmis, K.N. (eds) Handbook of Hydrocarbon and Lipid Microbiology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77587-4_275

Download citation

DOI : https://doi.org/10.1007/978-3-540-77587-4_275

Publisher Name : Springer, Berlin, Heidelberg

Print ISBN : 978-3-540-77584-3

Online ISBN : 978-3-540-77587-4

eBook Packages : Biomedical and Life Sciences Reference Module Biomedical and Life Sciences

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research