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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
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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 ].

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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.

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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.

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Introduction.

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Modelling and the monitoring of mesocosm experiments: two case studies

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Matthew C. Watts, Grant R. Bigg, Modelling and the monitoring of mesocosm experiments: two case studies, Journal of Plankton Research , Volume 23, Issue 10, October 2001, Pages 1081–1093, https://doi.org/10.1093/plankt/23.10.1081

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Mesocosm experiments have played an important role over the last decade in increasing our understanding of marine ecosystems. Many studies use these controlled environments to examine ecosystem responses to factors such as nutrient addition and light limitation. A few studies have been able to successfully model mesocosm experiments using carefully designed and comprehensive models and experimental design. Nevertheless, it is rare for such models to be compared with oceanic studies, and to consider the sulphur cycle as well as the carbon and nitrogen budgets. Here we take an ecosystem model, including a dimethylsulphide loop, that has been successfully used in modelling the evolution of the planktonic system in a Lagrangian field experiment in the North Atlantic—the PRIME cruise of 1996—and apply it to two mesocosm experiments. These were operated by two different groups of scientists, but in the same field station in a Norwegian fjord. The experiments were not explicitly designed with the data requirements of any model in mind. We show that the model is best able to simulate mesocosm data using model parameters taken from the real ocean. However, most sensitivity tests are significantly less successful in the mesocosm environment than the real ocean, especially if the model parameters are allowed to vary in a best fit sense. The limitations of the model–mesocosm match highlight the importance of comprehensive monitoring of mesocosms if they are to be useful for validating models suitable for the open ocean.

The use of mesocosms to study both the marine and fresh water planktonic environment has been a major trend of the last decade. These have usually been employed to examine the effect of a controlled change to the environment, such as pH ( Chen and Durbin, 1994 ), light ( Demers et al. 1998 ; Wangberg et al. 1999 ), temperature ( Keller et al. 1999 ), mesozooplankton ( Levasseur et al. , 1996 ) or, most commonly, nutrients ( Aksnes et al. 1994 ; Jacobsen et al. 1995 ; Escaravage et al. 1996 ; Harada et al. 1996 ; Schluter, 1998 ; Williams and Egge, 1998 ; Baretta-Becker et al. 1998 ; Escaravage et al. 1999 ). Other studies take states resembling natural environments, but possibly with some initial artificial nutrient enrichment, and follow their evolution within a mesocosm ( Kuiper, 1977 ; Oviatt et al. 1986 ; Sörensson et al. 1989 ; Riemann et al. 1990 ).

Despite this extensive literature there has been recognition that mesocosms have a number of drawbacks in representing conditions in the open ocean. The physical size of mesocosm enclosures can vary from 1 m in depth ( Petersen et al. 1997 ) to 18 m ( Harada et al. 1996 ), or roughly 1–400 m 3 in volume. Smaller mesocosms will have distinct problems with scaling up to oceanic conditions ( Petersen et al. 1997 ), while the vertical mixing environment in larger enclosures can be made to be very similar to the surrounding waters ( Harada et al. 1996 ). Most mesocosm experiments are conducted in less than a few tens of cubic metres of water (as was the case in the experiments used in this paper). In this scale range, mixing regimes need to balance the creation of natural water column stratification with minimizing artificial sedimentation but preventing break-up of diatom chains ( Demers et al. 1998 ). Deposition of material on the container walls can be a problem. For example, it has been estimated that some 45% of the nutrient additions to the PRIME mesocosm bags ( Williams and Egge, 1998 ) ended up being lost to wall growth (Skidmore and Williams, personal communication).

Some mesocosm experiments are carried out in coastal waters ( Williams and Egge, 1998 ; Baretta-Becker et al. 1998 ) while others are land-based ( Demers et al. 1998 ; Keller et al. 1999 ; Suzuki et al. 2000 ). The former can perhaps better examine natural forcings, such as wind or rainfall events, but their results are therefore also harder to interpret as a consequence. The containers need to allow sufficient light into and out of the system for photosynthesis to be similar to that in the natural environment being studied, but be robust enough to withstand the necessary mixing regimes imposed. Light availability therefore varies widely, from 90% transparency to photosynthetically active radiation ( Williams and Egge, 1998 ) to 30% ( Suzuki et al. 2000 ). There is also always the question of whether the ecosystem succession found in a mesocosm experiment actually represents what occurs in natural conditions or is an artefact of the controlled environment of the mesocosm. Duarte et al . ( Duarte et al . 1997 ) strongly advocated field experiments as the way to new insights in microbial ecology because of these various mesocosm limitations. Vallino gives a recent review of some of the disadvantages discussed above ( Vallino, 2000 ).

Nevertheless, using ecosystem models to study the internal dynamics of mesocosm experiments has been successful in a number of instances. Baretta-Bekker et al. simulated a shallow coastal mesocosm in Knebel Vig, Denmark (Baretta-Bekker et al. , 1994; 1998). The broad trends of both the evolution of the ecosystem over 10 days, and nutrient variation, were reproduced. The later model (Baretta-Bekker et al. , 1998) was considerably better at replicating the evolution of the nutrient factors. This model included: (1) picoalgae and mixotrophs explicitly; (2) the ability of bacteria to take up both dissolved in-organic and organic nutrients; (3) parameterization of luxury uptake, where cells do not have to obey the Redfield ratio; and (4) decoupling of the carbon and nitrogen dynamics. Applying the latter model to the different environment of a Norwegian fjord mesocosm showed broad agreement with observations in enclosures with no phosphate limitation, although the magnitude of change tended to be under-estimated for nitrate and microzooplankton levels (Baretta-Bekker et al. , 1998).

Aksnes et al. also found that simulation of mesocosms in a Norwegian fjord were improved by allowing both inorganic and organic forms of phosphate to be available, this time to the flagellate Emiliania huxleyi ( Aksnes et al. 1994 ). In inorganic phosphate-limited bags their model showed better fits to nitrate and E. huxleyi over the month-long campaign. Nevertheless, while their model gave moderately good fits for the evolution of populations of diatoms, flagellates and the separate E. huxleyi variable it nearly always significantly over-estimated the nitrate, and to a lesser extent, phosphate supply. Aksnes et al. ascribed this to light limitation and thus poor nitrate utilization ( Aksnes et al. 1994 ). However, the good fit of the model with the end-users of the nutrients suggests that loss to container wall growth may also have contributed to the discrepancy.

Suzuki et al. ( Suzuki et al ., 2000 ), studying a freshwater system, were able to model the broad trends of ecosystem development over a 2 month period. Their mesocosms, however, did not generally possess sharp blooms, and those experiments with more rapid variations (in the presence of planktivorous fish) were not well modelled, despite attempts to adjust the system's parameters. Vallino ( Vallino, 2000 ) used parameter optimization, as we will do, and data assimilation to better constrain his model to mesocosm data. Although reasonable fits with his food web model were found it lacked the robustness to be applicable across trophic gradients, such as those found in coastal environments. In particular the model was extremely sensitive to parameter changes, which are inherently non-stationary in the natural environment.

In this paper we will use an ecosystem model known to be fairly robust in simulating oceanic experiments ( Watts and Bigg, 2001 ) and apply it to two mesocosm data sets, both from the same experimental station in a Norwegian fjord. The predominant planktonic species for which the model has been tested in the open ocean was the same as the dominant species in the mesocosm experiments, namely E. huxleyi . Taking a successful, relatively simple, oceanic ecosystem model and applying it to a mesocosm gives us an opportunity to assess the applicability of mesocosm experiments to the oceanic environment.

The model used to simulate the biological processes in the mesocosms is an adaptation of that described previously ( Gabric et al. 1993 , 1995 , 1998 ). It is a network flow model of the ocean plankton community and bacteria, including both nitrogen and sulphur, the nitrogen part being based on a model of Moloney et al. ( Moloney et al ., 1986 ). The model of Gabric et al . contains eight state variables, as shown in Figure 1 . These variables are: phytoplankton ( P ); bacteria ( B ); zooflagellates ( ZF ); large protozoa ( LP ); zooplankton ( Z ); dissolved inorganic nitrogen ( DIN ); dimethylsulphoniopropionate ( DMSP ); and dimethylsulphide ( DMS ). It should be noted that it is rare to obtain field information for all of the grazers ( P , LP and Z ); the model tends to conserve the relative proportions of these variables so it could be thought of as effectively a six variable model.

While most of the state variables are calculated as mg N m –3 , DMS and DMSP are, of necessity, in mg S m –3 . Factors converting S, as DMS and DMSP, to N are taken to be 0.15 and 0.37, respectively, while the factor converting a flow of S as DMSP to a flow of S as DMS is 2.16 ( Gabric et al. 1993 ; Watts, 1999 ). A remaining factor, γ, is required to convert a flow of N to a flow of S (as DMSP). This parameter can vary by nearly two orders of magnitude, depending on the dominant species making up P , LP and Z . A typical value for the major constituent of P in our mesocosms, E. huxleyi , is 0.14 ( Gabric et al. 1993 ). However, we will sometimes allow this to be a parameter fitted to the data, as described below.

The basic system to be solved is a set of eight coupled ordinary differential equations of the form:

where X i represents the ith state variable, t is time, F ij is the rate of flow of nitrogen or sulphur from variable i to variable j , a ij is mostly zero or ±1 but otherwise one of the factors converting N and S as described above and S i is a loss term to the system, through photo-oxidation ( DMS ), ventilation to the atmosphere ( DMS ) or detrital export out of the photic zone ( P and Z ). F ij and S i are functions of one or both of the state variables X i or X j and in addition contain 32 parameters describing aspects of the various nitrogen and sulphur pathways. A full description of the system given in equation (1) was given previously ( Gabric et al. 1993 ; Watts, 1999 ), and the 32 parameters are listed in these references and in Watts and Bigg ( Watts and Bigg, 2001 ) and Table I .

Equation (1) was solved in idealized situations, with initial values of the state variables chosen, and fixed, to simulate a bloom ( Gabric et al. 1993 ) or the annual cycle ( Gabric et al. 1995 , 1998 ). In our calculations we have solved equation (1) using a semi-explicit extrapolation method described by Press et al. ( Press et al ., 1992 ) and appropriate for stiff sets of differential equations. It has, however, the added feature of a minimization element to find the values of the 33 (32 + γ) rate parameters that give the best fit to experimental data. This minimization technique has been fully described ( Watts, 1999 ; Watts and Bigg, 2001 ), and bears some similarity to that developed by Fasham and Evans (1995), but it is appropriate to briefly outline its form here.

To obtain a set of parameters which gives a best fit, defined as the minimum root mean square difference error, to experimental data we used Powell's Direction Set Method in Multi-dimensions ( Press et al. 1992 ), but with a simpler bracketing sub-routine. Each parameter is constrained by a penalty function to remain within a factor (usually 5) of its initial guess, although within this range it is almost completely unconstrained. There are thus two (multiplied) components to the minimization: one represents the root mean square difference (rmsd) between prediction and experiment while the other is the value of the penalty function.

This solution technique has been shown to be robust and work well in Lagrangian field experiments in the open ocean. Watts and Bigg ( Watts and Bigg, 2001 ) applied it to both the BOFS cruise of April–June 1990 near (20°W, 47°N) ( Savidge et al. 1992 ) and the PRIME cruise of June–July 1996 near (20.3°W, 59.4°N) ( Savidge and Williams, 2001 ). Both were Lagrangian experiments over an active, rapidly changing, bloom period. Emiliania huxleyi was a major planktonic species in each experiment. In both cases measurements of only sub-sets of the state variables were available at the time of analysis: P , Z and DIN for the BOFS cruise and P , B , DIN and DMS for the PRIME cruise. It will be seen below that a similar paucity of data was also the case for the two mesocosm experiments, allowing perhaps more realistic comparison of the two experimental situations. Nevertheless, good fits with a rmsd of around 20% were achievable in both open ocean experiments. The system was quite sensitive to parameter values. Despite most of the 33 parameters varying by less than 50% between the initial choice and optimal solution (see Table I ) merely using the initial guess of the Gabric et al. ( Gabric et al. 1993 ) parameters produced simulations that did not even represent the general trends in the data ( Watts, 1999 ; Watts and Bigg, 2001 ). Similarly, changes between the BOFS and PRIME best-fit parameters are of this same order. Clearly, growth and decay parameters vary with the environment ( Vallino, 2000 ). Nevertheless, this model has shown that it can permit appropriate variation to describe ecosystems successfully over at least a wide range of the northeast Atlantic.

Mesocosm experiments

Data from two mesocosm experiments were used in this modelling study. Both were experiments conducted at Espegrend field station, in a fjord near Bergen, Norway and both studied blooms of E. huxleyi . The first study( Levasseur et al. , 1996 ) was carried out between May 10 and May 31, 1993 as part of the Norwegian EHUX programme; the second ( Williams and Egge, 1998 ) was carried out between June 6 and July 5, 1995 as part of the UK's PRIME programme. Both experiments used enclosures of the same design, each with a volume of 11 m 3 (2 m diameter and 4.25 m depth), open to the air, and with 10% of the volume renewed daily at a depth of 1 m. Samples were taken daily in both experiments, although not all quantities required by the model were measured, and those that were measured were not necessarily determined on all samples or added volumes. Neither experiment was explicitly designed with the requirements of modellers in mind, unlike, for example, the mesocosm experiments of Baretta-Becker et al. ( Baretta-Becker et al. 1994 , 1998 ). Their sampling strategies were, instead, not dissimilar to those carried out in open ocean experiments. In this paper we will show that while such strategies may be sufficient for testing models in open ocean conditions the more artificial environment of a mesocosm requires a fuller sampling programme if it is to allow a non-case-specific model to have any chance of describing the experiment's evolution.

In the EHUX experiment concentrations of chlorophyll a and DIN were routinely measured, but only two tanks, C 1 and H 1 , measured bacteria, while DMS was only measured bi-daily and towards the end of the experiment. Thus only these two tanks were modelled, with only P , DIN and B being optimized below to the observed data. Tank C 1 was a fjord water control, while H 1 was pre-treated to increase the nitrate level by a factor of several hundred (although with a N : P ratio of 80 : 1 rather than the Redfield ratio). There was heavy precipitation on May 17 (Day 7) which had a marked effect on planktonic growth (Figure 2 ). Apart from this period light levels were high and the sea temperature was 9–12°C.

During the PRIME experiment DMS was routinely measured as well as DIN, bacteria and chlorophyll a . Of the eight bags, three were modelled, numbers 1, 5 and 8. The first was an unfertilized control bag (similar to C 1 of the EHUX experiment), while tank 5 was pre-fertilized with nitrate and phosphate in the Redfield ratio(15 μM : 1 μM) and then 10% additions daily. Tank 8 was supplied with nutrients in a high N : P ratio (15 : 0.2) until Day 21 (in the same way as in Tank 5), and then additional phosphate was added (1 μM) with an additional 0.1 μM daily for the rest of the experiment. There were two stormy periods during the experiment responsible for enhanced nitrate input to the upper fjord waters (Figure 2 ).

To convert chlorophyll a to a model phytoplankton unit of mg N m –3 we used a conversion factor of 20 ( Ducklow et al. 1993 ). An initial estimate of zooplankton for the EHUX experiment was derived from Levasseur et al. ( Levasseur et al. , 1996 ) for another tank, where on May 22 (Day 12) the biomass of zooplankton was ~40 mg m –3 . Assuming the N : P ratio of the biomass roughly obeys the Redfield ratio then an approximate initial zooplanton concentration is 5 mg N m –3 , although several other values were also tested. These were also used as a guide for the PRIME model runs, as no initial zooplankton levels were recorded. To convert bacterial counts of cells l –1 to mg N m –3 we converted first to μg C l –1 , assuming the average volume of a cell to be 0.04 μm 3 (Belviso et al. , 1990) and the carbon content to be 220 fg C μm –3 ( Priddle et al. 1995 ), and then took a typical C : N ratio of 45 : 10 ( Zweifel et al ., 1993 ). The zooflagellates and large protozoa, being unmeasured in both studies, were initialized to 0.05 and 0.1 of the total zooplankton value respectively, as done by others ( Gabric et al. 1993 ; Watts and Bigg, 2001 ). Where DMS was unmeasured it was initially set to zero.

EHUX experiment

The first model runs to simulate the ecosystem's evolution within the two studied bags of this mesocosm followed the optimization procedure outlined above, with P , DIN and B being optimized to best fit observed values and other variables allowed free movement according to the system's set of equation (1) . This maximized the constraints that were possible with the data available. The best fit curves are very poor, no matter what reasonable initial values are chosen for zooplankton (recall, from the last section, that only one, mid-experiment, value for Z was available from an unmodelled tank in the EHUX experiment; no values were available from the PRIME experiment). In all cases the rmsd values were ~0.8, meaning that the discrepancy between model and experiment was in the region of a factor of two for most data points (Figure 3 ). One problem with this procedure is evident in the nitrate variation in the tanks (Figure 2 ). While nitrite concentrations remained extremely low throughout the experiment, nitrate levels in both tanks showed increases of ~2 mg N m –3 following the storm event on Day 7. The nitrate evolution, therefore, may not have been determined purely by the ecosystem dynamics but there may have been artificial enhancement, not modellable just by equation (1) , through the daily addition of fjord water, or material from the flexible walls being dislodged (not observed). If upper-ocean levels of nitrate in the fjord had been enhanced by either vertical mixing or run-off, then this would later feed into the mesocosm bags.

To attempt to overcome this possible problem DIN was next set in the model to equal observed values each day, forcing the model to have the correct nutrient supply. The best fits to P and B were then calculated using three different initial zooplankton concentrations (2.5, 5 and 10 mg N m –3 ). Setting DIN effectively meant abandoning the optimization procedure for a significant number of rate parameters, so we set all parameters to either (a) the values used by Gabric et al. ( Gabric et al. 1993 ) or (b) the best fit parameters for the PRIME cruise study [( Watts and Bigg, 2001 ) Table I ]. The resulting behaviour of P, B, Z and DMS is compared to observations from tank C 1 in Figures 4 and 5 for parameter sets (a) and (b) respectively, while all rmsd fits for both tanks are shown in Table II . Only one model run shows significant improvement on the optimized rmsd fits shown in Figure 3 , namely using the PRIME cruise parameters with an initial zooplankton concentration of 2.5 mg N m –3 (Figure 5 ). Even here, the DMS predictions are an order of magnitude smaller than observed and zooplankton numbers by Day 12 are about double what was expected from the very limited observational evidence. No runs for tank H 1 are anywhere near as good as this one C 1 run (Table II ), possibly because the large N : P ratio of the fertilization would have made much of the DIN unavailable to the phytoplankton. An additional run was performed, allowing the initial Z to be an optimizable parameter, but no further improvement was made on the result shown in Figure 5 .

PRIME experiment

The initial, optimizing, model run for the PRIME experiment was just as unsuccessful as for the EHUX data (Figure 6 ). Values of rmsd were again around 0.8. As in the EHUX experiment, Figure 2 shows significant nutrient injection during several periods of stormy weather during the experiment. Thus, we once again set the model DIN to observed values, and tested the model with three different initial zooplankton concentrations. The resulting behaviour of P , B , Z and DMS is compared to observations from tank 1 in Figures 7 and 8 for the same two parameter sets (a) and (b) respectively, while all rmsd fits for both tanks are shown in Table III . Two model runs show significant improvement in the rmsd values, namely those using the PRIME cruise parameters with initial zooplankton concentrations of either 2.5 or 5 mg N m –3 (Figure 8 ). The moderate fits arise because B and DMS are well predicted over the first half of the experiment, but significant underestimation of P occurs throughout the experiment time. Some runs for tank 5, where the N : P addition was according to the Redfield ratio, do a better job of predicting an early peak in P , but these still have much poorer fits overall because of their poor description of B and DMS . The poor performance of the model for tanks 5 and 8 (Table III ) suggests that any form of nutrient addition, whether obeying the Redfield ratio or not, moved the bag ecosystem away from a natural one having some chance of prediction using a model applicable to open oceanconditions.

This study has illustrated both positive and negative aspects of mesocosm experiments as exemplars for open ocean ecosystems. A positive result has been that a model previously shown to be applicable to Lagrangian motion of an E. huxleyi bloom in the open ocean ( Watts and Bigg, 2001 ) is also capable of describing the first order behaviour of a similar bloom in relatively small mesocosms. It is also heartening that this was achieved using the same rate parameters as in the open ocean.

However, the study has also highlighted several drawbacks. Firstly, it is vital to take sufficient observations of enough state variables to enable even relatively simple open ocean models such as the one used here to be given a chance to produce a successful simulation. In some instances (for Z and DMS for example) the absence of initial, or any, values for the experiments limited what the model could be expected to achieve, given the sensitivity of these type of PNZ models to initial conditions (Figures 4, 5, 7, 8 ). While some studies have benefited from a sound, data-rich, modelling–observation strategy ( Aksnes et al. 1994 ; Baretta-Bekker et al. , 1998) closer links between mesocosm designers and modellers are needed at an early stage in any experimental plan so that data are useful to both communities.

Second, and paradoxically, mesocosms, even though limited in size, tend to be more sensitive to external environmental influences than the open ocean. Containers may be too small to readily scale up to the ocean [particularly true for the Z : P ratio ( Petersen et al ., 1997 )], poor at reproducing natural light conditions ( Suzuki et al. 2000 ), subject to inappropriate mixing ( Demers et al., 1998 ), or to lose nutrients to wall growth (Skidmore and Williams, personal communication) or artificial sedimentation. For our two mesocosm examples, storm events added nutrients to the fjord waters, either by enhanced vertical mixing or run-off. This altered the nutrient budget of each bag in ways that were not measured, partly because of the 10% daily fjordal exchange, and necessitated an unnatural control on DIN .

Mesocosms are often used to examine the response of ecosystems to nutrient additions. Most models rely on single-valued parameters, even if these are allowed to adjust to best fit the data over the course of the experiment. However, parameters may vary with environmental conditions ( Vallino, 2000 ). Therefore most models are poorly conditioned for step responses driven by external inputs. In our examples, bags with nutrient additions were modelled significantly less well than the control, fjord-like bags. Even if a model can be tuned to accommodate this sort of response it is then unlikely to be very representative for different circumstances. For example, Baretta-Bekker et al. (Baretta-Bekker et al. , 1998) were able to obtain good model fits to nutrient-enriched mesocosms in one location, but were less successful in using their model at another location.

Mesocosms are open to careful modelling as a way to explain ecological behaviour observed within them. Nevertheless, they are not ideal environments in which to test models intended for open ocean situations. We therefore follow Duarte et al. ( Duarte et al. 1997 ) in suggesting that well-measured field experiments are most likely to advance modelling of the marine biosphere.

Values of initial parameters [role defined in ( Gabric et al. 1993 )]

Parameter number .	Pathway .	Gabric et al. (1993) parameters .	Best fit parameters for PRIME cruise (Watts and Bigg, 2001)* .
*These numbers multiply those in column 3 to give the dimensional value.
1	P→B	4.5 d	1.68
2	P→B	0.046	0.54
3	P→LP	0.26	2.18
4	P→ZP	0.012	0.76
5	P→DMSP	0.01 d	1.47
6	P→DMS	0.0085 d	1.53
7	P→sinking	0.15 d	1.65
8	B→ZF	17 d	0.38
9	B→ZF	0.138 mg N m	0.47
10	B→N	0.07 d	1.86
11	B→N	0.63	1.16
12	ZF→LP	1.56	1.52
13	ZFN	0.05 d	1.53
14	ZF→N	0.65	1.16
15	LP→ZP	0.12	0.89
16	LP→N	0.05 d	0.84
17	LP→N	0.65	0.97
18	LP→DMSP	0.01 d	1.14
19	ZP→N	0.05 d	0.57
20	ZP→N	0.40	0.88
21	ZP→DMSP	0.01 d	2.83
22	ZP→sinking	0.15	0.74
23	N→P	0.9 d	1.28
24	N→P	0.05	1.41
25	N→B	0.90 d	0.67
26	N→B	0.924	1.00
27	DMSP→DMS	0.5 d	2.09
28	DMS→B	0.95 d	0.42
29	DMS→oxidation	0.5 d	0.48
30	DMS→ventilation	0.13 d	0.60
31	DMSP→B	1.0 d	0.49
32	ZP→export	0.05 d	0.59
γ	N to S conversion	0.14	2.94

Parameter number .	Pathway .	Gabric et al. (1993) parameters .	Best fit parameters for PRIME cruise (Watts and Bigg, 2001)* .
*These numbers multiply those in column 3 to give the dimensional value.
1	P→B	4.5 d	1.68
2	P→B	0.046	0.54
3	P→LP	0.26	2.18
4	P→ZP	0.012	0.76
5	P→DMSP	0.01 d	1.47
6	P→DMS	0.0085 d	1.53
7	P→sinking	0.15 d	1.65
8	B→ZF	17 d	0.38
9	B→ZF	0.138 mg N m	0.47
10	B→N	0.07 d	1.86
11	B→N	0.63	1.16
12	ZF→LP	1.56	1.52
13	ZFN	0.05 d	1.53
14	ZF→N	0.65	1.16
15	LP→ZP	0.12	0.89
16	LP→N	0.05 d	0.84
17	LP→N	0.65	0.97
18	LP→DMSP	0.01 d	1.14
19	ZP→N	0.05 d	0.57
20	ZP→N	0.40	0.88
21	ZP→DMSP	0.01 d	2.83
22	ZP→sinking	0.15	0.74
23	N→P	0.9 d	1.28
24	N→P	0.05	1.41
25	N→B	0.90 d	0.67
26	N→B	0.924	1.00
27	DMSP→DMS	0.5 d	2.09
28	DMS→B	0.95 d	0.42
29	DMS→oxidation	0.5 d	0.48
30	DMS→ventilation	0.13 d	0.60
31	DMSP→B	1.0 d	0.49
32	ZP→export	0.05 d	0.59
γ	N to S conversion	0.14	2.94

Root mean square difference (rmsd) values for the fits between model and EHUX mesocosm experimental data

Tank .	Rate parameters .	Initial Z (mg N m ) .	rmsd .
C	Best fit to data	5	0.85
H	Best fit to data	5	0.91
C	From Gabric (1993)	2.5	0.73
C	From Gabric (1993)	5	0.72
C	From Gabric (1993)	10	0.63
C	From PRIME cruise	2.5	0.27
C	From PRIME cruise	5	0.70
C	From PRIME cruise	10	0.74
H	From Gabric (1993)	2.5	0.75
H	From Gabric (1993)	5	0.66
H	From Gabric (1993)	10	0.84
H	From PRIME cruise	2.5	0.76
H	From PRIME cruise	5	0.76
H	From PRIME cruise	10	0.70

Tank .	Rate parameters .	Initial Z (mg N m ) .	rmsd .
C	Best fit to data	5	0.85
H	Best fit to data	5	0.91
C	From Gabric (1993)	2.5	0.73
C	From Gabric (1993)	5	0.72
C	From Gabric (1993)	10	0.63
C	From PRIME cruise	2.5	0.27
C	From PRIME cruise	5	0.70
C	From PRIME cruise	10	0.74
H	From Gabric (1993)	2.5	0.75
H	From Gabric (1993)	5	0.66
H	From Gabric (1993)	10	0.84
H	From PRIME cruise	2.5	0.76
H	From PRIME cruise	5	0.76
H	From PRIME cruise	10	0.70

Root mean square difference (rmsd) values for the fits between model and PRIME mesocosm experimental data

Tank .	Rate parameters .	Initial Z (mg N m ) .	rmsd .
1	Best fit to data	5	0.81
5	Best fit to data	5	0.84
8	Best fit to data	5	0.78
1	From Gabric (1993)	2.5	0.76
1	From Gabric (1993)	5	0.75
1	From Gabric (1993)	10	0.78
1	From PRIME cruise	2.5	0.31
1	From PRIME cruise	5	0.34
1	From PRIME cruise	10	0.72
5	From Gabric (1993)	2.5	0.77
5	From Gabric (1993)	5	0.71
5	From Gabric (1993)	10	0.69
5	From PRIME cruise	2.5	0.71
5	From PRIME cruise	5	0.71
5	From PRIME cruise	10	0.74
8	From Gabric	2.5	0.76
8	From Gabric (1993)	5	0.72
8	From Gabric (1993)	10	0.70
8	From PRIME cruise	2.5	0.67
8	From PRIME cruise	5	0.66
8	From PRIME cruise	10	0.68

Tank .	Rate parameters .	Initial Z (mg N m ) .	rmsd .
1	Best fit to data	5	0.81
5	Best fit to data	5	0.84
8	Best fit to data	5	0.78
1	From Gabric (1993)	2.5	0.76
1	From Gabric (1993)	5	0.75
1	From Gabric (1993)	10	0.78
1	From PRIME cruise	2.5	0.31
1	From PRIME cruise	5	0.34
1	From PRIME cruise	10	0.72
5	From Gabric (1993)	2.5	0.77
5	From Gabric (1993)	5	0.71
5	From Gabric (1993)	10	0.69
5	From PRIME cruise	2.5	0.71
5	From PRIME cruise	5	0.71
5	From PRIME cruise	10	0.74
8	From Gabric	2.5	0.76
8	From Gabric (1993)	5	0.72
8	From Gabric (1993)	10	0.70
8	From PRIME cruise	2.5	0.67
8	From PRIME cruise	5	0.66
8	From PRIME cruise	10	0.68

Network flow model, showing eight state variables and the various links between these that are represented in the model.

Measured DIN through the EHUX and PRIME mesocosm experiments. ( a ) Tank C 1 (EHUX), ( b ) Tank H 1 (EHUX), ( c ) Tank 1 (PRIME) and ( d ) Tank 5 (PRIME). Tank 8 is not shown as it closely resembles ( d ).

Modelling EHUX mesocosm data in Tank C 1 using the optimization procedure to find the parameters that best fit the observations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are denoted by the dashed curve. j.rmsd = 0.85

Modelling EHUX mesocosm data in Tank C 1 using the Gabric et al. parameters ( Gabric et al. 1993 ) in the model equations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are for initial Z of 2.5 mg N m –3 (bold dashed line), 5 mg N m –3 (dotted line) and 10 mg N m –3 (lightly dashed line).

Modelling EHUX mesocosm data in Tank C 1 using the PRIME cruise best fit parameters in the model equations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are for initial Z of 2.5 mg N m –3 (bold dashed line), 5 mg N m –3 (dotted line) and 10 mg N m –3 (lightly dashed line).

Modelling PRIME mesocosm data in Tank 1 using the optimization procedure to find the parameters that best fit the observations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are denoted by the dashed curve.

Modelling PRIME mesocosm data in Tank 1 using the Gabric et al. parameters ( Gabric et al. 1993 ) in the model equations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are for initial Z of 2.5 mg N m –3 (bold dashed line), 5 mg N m –3 (dotted line) and 10 mg N m –3 (lightly dashed line).

Modelling PRIME mesocosm data in Tank 1 using the PRIME cruise best fit parameters in the model equations. ( a ) phytoplankton, ( b ) bacteria, ( c ) DMS and ( d ) zooplankton. Inverted triangles represent the observations, the model results are for initial Z of 2.5 mg N m –3 (bold dashed line), 5 mg N m –3 (dotted line) and 10 mg N m –3 (lightly dashed line).

To Whom Correspondence Should Be Addressed

We would like to thank the EHUX and PRIME programmes for making their mesocosm data available to us. This work formed part of the NERC-funded PhD of M.C.W. Sue Turner made useful comments on the work. This is PRIME contribution 127.

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v.13(12); 2015 Dec

Mesocosms Reveal Ecological Surprises from Climate Change

Damien a. fordham.

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

This Primer uses two recent studies to examine how mesocosm experiments are improving our understanding of the ecological consequences of climate change on species’ extinction risk, community structure, and ecosystem function.

Introduction

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

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Funding Statement

The author received no specific funding for this work.

Microcosms and Mesocosms: A Way to Test the Resilience of Microbial Communities in Cuatro Ciénegas

First Online: 03 October 2018

Cite this chapter

Nguyen E. López-Lozano 6 ,
Silvia Pajares 7 ,
Ana E. Escalante 8 ,
Luis E. Eguiarte 9 ,
Valeria Souza 9 &
Gabriela Olmedo-Álvarez 10

Part of the book series: Cuatro Ciénegas Basin: An Endangered Hyperdiverse Oasis ((CUCIBA))

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Given the fragility of the Cuatro Cienegas Basin and the danger of its loss, we started to study the variables affecting the resilience of the microbial community using different experimental approaches. How do microbial communities react to different kinds of perturbations and global change scenarios? We analyzed a series of experimental models that represent different ecosystem compartments: bulk soil, soil crusts, water, and sediment. The experiments were performed in mesocosm or microcosm model systems, which we call in general “cosm” experiments. Different questions were addressed. How does water availability affect the recovery of microbial communities in disturbed soil patches? How do changes in temperature affect microbial crusts? How do bacterioplankton and bacterial mat communities respond to changes in temperature and UV radiation? What would happen to an oligotrophic environment if there was a high nutrient input? Our results suggested perturbations that influenced community structure and community cohesion were stronger in less fluctuating environments. Although it had been suggested that there was a functional (ecological) equivalence between microbial communities, our results on N 2 -fixing microorganisms of two arid ecosystems showed functional differences, even though similar species occur in both systems. Currently, new experiments are being carried out in “cosms” with replicates of the sediment and water interphase. Sadly, in one of the sites we have studied better, the Churince ecosystem, its conditions were dire the last time that sediment and water were sampled there. This ecosystem has now nearly disappeared. Nevertheless, the sediment in these fish tanks seems to be recovering its original structure. While seeming like a white rhino in a zoo, it might be the only ecosystem of this kind left to learn about what we lost.

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CONACyT-División de Ciencias Ambientales, Instituto Potosino de Investigación Científica y Tecnológica (IPICYT), San Luis Potosí, Mexico

Nguyen E. López-Lozano

Unidad de Ecología y Biodiversidad Acuática, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

Silvia Pajares

Laboratorio Nacional de Ciencias de la Sostenibilidad (LANCIS), Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

Ana E. Escalante

Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

Luis E. Eguiarte & Valeria Souza

Departamento de Ingeniería Genética, Cinvestav Unidad Irapuato, Irapuato, Gto, Mexico

Gabriela Olmedo-Álvarez

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López-Lozano, N.E., Pajares, S., Escalante, A.E., Eguiarte, L.E., Souza, V., Olmedo-Álvarez, G. (2018). Microcosms and Mesocosms: A Way to Test the Resilience of Microbial Communities in Cuatro Ciénegas. In: Souza, V., Olmedo-Álvarez, G., Eguiarte, L. (eds) Cuatro Ciénegas Ecology, Natural History and Microbiology. Cuatro Ciénegas Basin: An Endangered Hyperdiverse Oasis. Springer, Cham. https://doi.org/10.1007/978-3-319-93423-5_7

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Open access
Published: 22 March 2023

Diversity and function of methyl-coenzyme M reductase-encoding archaea in Yellowstone hot springs revealed by metagenomics and mesocosm experiments

Mackenzie M. Lynes ORCID: orcid.org/0000-0002-9410-0285 1 na1 ,
Viola Krukenberg ORCID: orcid.org/0000-0001-8369-8114 1 na1 ,
Zackary J. Jay ORCID: orcid.org/0000-0003-3062-4933 1 ,
Anthony J. Kohtz ORCID: orcid.org/0000-0002-0561-8710 1 ,
Christine A. Gobrogge 2 ,
Rachel L. Spietz ORCID: orcid.org/0000-0001-5277-0734 1 &
Roland Hatzenpichler ORCID: orcid.org/0000-0002-5489-3444 1 , 3

ISME Communications volume 3 , Article number: 22 ( 2023 ) Cite this article

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Environmental microbiology

Metagenomic studies on geothermal environments have been central in recent discoveries on the diversity of archaeal methane and alkane metabolism. Here, we investigated methanogenic populations inhabiting terrestrial geothermal features in Yellowstone National Park (YNP) by combining amplicon sequencing with metagenomics and mesocosm experiments. Detection of methyl-coenzyme M reductase subunit A ( mcrA ) gene amplicons demonstrated a wide diversity of Mcr-encoding archaea inhabit geothermal features with differing physicochemical regimes across YNP. From three selected hot springs we recovered twelve Mcr - encoding metagenome assembled genomes (MAGs) affiliated with lineages of cultured methanogens as well as Candidatus ( Ca .) Methanomethylicia, Ca . Hadesarchaeia, and Archaeoglobi. These MAGs encoded the potential for hydrogenotrophic, aceticlastic, hydrogen-dependent methylotrophic methanogenesis, or anaerobic short-chain alkane oxidation. While Mcr-encoding archaea represent minor fractions of the microbial community of hot springs, mesocosm experiments with methanogenic precursors resulted in the stimulation of methanogenic activity and the enrichment of lineages affiliated with Methanosaeta and Methanothermobacter as well as with uncultured Mcr-encoding archaea including Ca . Korarchaeia, Ca . Nezhaarchaeia, and Archaeoglobi. We revealed that diverse Mcr-encoding archaea with the metabolic potential to produce methane from different precursors persist in the geothermal environments of YNP and can be enriched under methanogenic conditions. This study highlights the importance of combining environmental metagenomics with laboratory-based experiments to expand our understanding of uncultured Mcr-encoding archaea and their potential impact on microbial carbon transformations in geothermal environments and beyond.

Active lithoautotrophic and methane-oxidizing microbial community in an anoxic, sub-zero, and hypersaline High Arctic spring

Metabolic potentials of archaeal lineages resolved from metagenomes of deep Costa Rica sediments

Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea

Introduction.

Methane (CH 4 ) is a climate active gas and an integral component in the global carbon cycle. The majority of biogenic methane is generated in anoxic environments by methanogenic archaea [ 1 , 2 , 3 ] that conserve energy by reducing low molecular weight substrates such as CO 2 , acetate, or methylated compounds to CH 4 [ 4 , 5 , 6 , 7 , 8 ]. The final step in methanogenesis, the conversion of methyl-coenzyme M and coenzyme B into CH 4 , is catalyzed by the methyl-coenzyme M reductase (MCR) complex. This enzyme also catalyzes the reversible reaction, the activation of CH 4 in anaerobic methane-oxidizing archaea [ 9 ] that, together with methanogens, control methane fluxes from anoxic environments, impacting global methane emissions to the atmosphere [ 1 , 2 ]. Currently, all cultured methanogens belong to lineages within the Euryarchaeota, and their physiology and biochemistry has been studied for decades [ 10 , 11 , 12 , 13 , 14 , 15 ]. However, recent metagenomic studies discovered genes encoding the MCR complex in metagenome-assembled genomes (MAGs) from a variety of archaeal groups including Candidatus ( Ca .) Methanofastidiosales, Ca . Nuwarchaeales, some members of the Archaeoglobi [ 16 , 17 , 18 ], as well as members of the TACK superphylum Ca . Methanomethylicia ( Ca . Verstraetearchaeota), Ca . Korarchaeia, Ca . Bathyarchaeia, and Ca . Nezhaarchaeia [ 15 , 19 , 20 , 21 , 22 ]. Some of these mcrA genes have been shown to be transcribed in situ but the function of the respective MCR complex has not been demonstrated [ 23 , 24 , 25 , 26 ]. Additionally, some MAGs contain highly divergent genes homologous to mcr [ 21 , 22 , 27 , 28 , 29 , 30 , 31 , 32 ]. These mcr -like genes encode an alkyl-coenzyme M reductase complex, which was experimentally shown to activate short-chain alkanes (i.e., ethane, propane, butane) in Ca . Synthrophoarchaeum, Ca . Argoarchaeum, and Ca . Ethanoperedens [ 27 , 29 , 31 ]. However, as most newly discovered Mcr-encoding archaea are yet uncultured, their proposed methanogenesis and anaerobic methane/alkane metabolism awaits further experimental evaluation. MAGs representing these archaea have frequently been recovered from anoxic and often high temperature geothermal environments, such as deep-sea hydrothermal vents and terrestrial hot springs [ 18 , 19 , 20 , 21 , 27 , 30 , 33 ]. Geothermal environments have been used as model systems in microbial ecology for many decades. Their extreme nature and, as a consequence, reduced microbial complexity make them ideal for testing new technologies [ 34 , 35 , 36 ] and discovering new microbial lineages [ 37 , 38 , 39 ]. Methanogenesis in the geothermal system of Yellowstone National Park (YNP, Wyoming, USA), was initially studied in 1980 [ 40 ] and to date, three strains of the hydrogenotrophic methanogen Methanothermobacter thermoautotrophicus represent the only methanogens isolated from YNP [ 40 , 41 ]. However, mcrA and 16S rRNA genes affiliated with methanogenic archaea and mcr -containing MAGs have been repeatedly recovered from geothermal features across YNP [ 20 , 21 , 24 , 30 , 33 , 42 ], demonstrating the potential for a microbial methane cycle involving diverse archaeal lineages (Fig. 1A ).

A Map of Yellowstone National Park highlighting areas with potential for methanogenesis. Red stars mark areas investigated in this study. Symbols indicate where mcrA genes (triangle), 16S rRNA genes of methanogens (diamond), Mcr-encoding MAGs (square), or methanogen isolates (circle) have been recovered. Map was generated with data from the Wyoming State Geological Survey ( https://www.wsgs.wyo.gov/pubs-maps/gis ). Photographs of main study sites. LCB003, 2019-07-24 ( B ), LCB019, 2019-07-25 ( C ), and LCB024, 2019-07-23 ( D ). Scale bar indicates 30 cm.

In this study, we further explored the potential for methanogenesis in YNP by [ 1 ] combining mcrA gene amplicon sequencing with aqueous geochemical measurements to identify methanogenic populations across 100 previously uncharacterized geothermal features, and [ 2 ] employing metagenomics and methanogenic mesocosm experiments to investigate the metabolic potential and activity of methanogenic populations in three selected hot springs. We describe the taxonomic diversity of mcrA genes detected across contrasting physicochemical conditions, detail the metabolic potential of mcr -containing MAGs, and reveal the responses of Mcr-encoding archaea to methanogenic precursor substrates.

Materials and methods

Selection of geothermal features and sample collection.

A survey of mcrA gene diversity and aqueous geochemistry was conducted on 100 geothermal features including hot springs and mud pots with temperature between 18 and 94 °C and pH between 1.7 and 9.4 distributed across four geothermal regions in YNP (Fig. 1A ). Because many of these geothermal features are not included in the YNP Research Coordination Network ( http://rcn.montana.edu/ ), unique sample identifiers are used in this study, which indicate the area, feature, and DNA sample (e.g., LCB003.1 denotes DNA sample 1 from feature 003 in the Lower Culex Basin). Sediments and microbial mats for mcrA gene amplicon sequencing and shotgun metagenomics were collected in 2017 and 2018 using a stainless-steel cup, homogenized, and frozen immediately until DNA extraction. A slurry of sediment and water (1:9) for mesocosm experiments was obtained in 2019 (SI Tables 1 – 3 ) using a stainless-steel cup, transferred into a glass bottle, and homogenized. A 10 mL subsample was frozen immediately to preserve material for DNA extraction (environmental sample) before the bottle was sealed headspace-free. The slurry was transported in a heated container (~50 °C), placed at in situ temperature within 4 h and used to set up mesocosm experiments within 12 h of retrieval.

Physicochemical measurements, aqueous geochemistry, and elemental analysis

Temperature and pH were recorded in the water column of geothermal features using a thermocouple and portable pH meter. Water samples for aqueous geochemistry were collected and analyzed for dissolved iron, sulfide, and gases (O 2 , CH 4 , CO 2 ) as previously described [ 43 , 44 , 45 ]. Water samples for elemental analysis, anions, total carbon, inorganic carbon, non-purgeable organic carbon, and total nitrogen were processed by the Environmental Analytical Lab (Montana State University). Details in SI Materials and Methods .

Mesocosm experiments

Mesocosm experiments with material from hot springs LCB003, LCB019, and LCB024 were prepared in an anoxic glove box (N 2 /CO 2 /H 2 ; 90/5/5%). Under constant stirring, 10 mL aliquots of sediment slurry were distributed into 25 mL serum bottles using serological plastic pipettes. Mesocosms were set up with the following treatments: acetate, formate, H 2 , H 2 plus CO 2 and bicarbonate, methanol, monomethylamine, methanol plus H 2 , monomethylamine plus H 2 , paraformaldehyde (killed control), bromoethanesulfonate (methanogenesis inhibitor), and no amendment. Two sets of triplicates per treatment were performed: (1) with bacterial antibiotics streptomycin (inhibitor of protein synthesis) and vancomycin (inhibitor of peptidoglycan synthesis) and (2) without antibiotics. Liquid amendments were added to a final concentration of 5 mM except for paraformaldehyde (5% v/v) and antibiotics (50 mg/L). Serum bottles were sealed with butyl rubber stoppers and aluminum crimps before the headspace was exchanged with N 2 (99.999%) at 100 kPa for 5 min and set to 200 kPa N 2 . H 2 and CO 2 were added by exchanging an equal volume of N 2 for a final concentration of 50% H 2 and/or 20% CO 2 . Mesocosms were incubated at in situ temperatures: 74 °C (LCB003), 55 °C (LCB019), 72 °C (LCB024). Mesocosm headspace gas composition was monitored by manually subsampling 2 mL at close to in situ temperature using a gastight syringe for analysis with a Varian gas chromatograph (GC; model CP2900) equipped with a dual-channel thermal conductivity detector system with Ar and He as carrier gases. Methane standards generated from 99.99% methane were measured at each timepoint. Triplicate mesocosms were terminated simultaneously when the first replicate reached a CH 4 plateau or exhausted supplied H 2 . At this time, a 0.5 mL slurry subsample was pelleted (12,000 g for 5 min) and frozen for DNA extraction. Mesocosms with low or no methane production including controls were terminated after 43 (LCB003), 35 (LCB019), or 55 (LCB024) days.

DNA extraction and gene amplification

DNA was extracted from environmental samples (1 mL) and mesocosm samples (pellet from 0.5 mL) using the FastDNA Spin Kit for Soil (MP Biomedicals, Irvine, CA) following the manufacturer’s guidelines. mcrA genes were amplified with primer set mlas-mod-F/mcrA-rev-R [ 46 , 47 ] from environmental DNA extracts. Archaeal and bacterial 16S rRNA genes were amplified with the updated Earth Microbiome Project primer set 515F and 806R [ 48 , 49 , 50 ] from DNA extracts of the mesocosm experiment. Amplicon libraries were prepared as previously described [ 36 ] and sequenced by Laragen Inc. (Culver City, CA) or the Molecular Research Core Facility at Idaho State University (Pocatello, ID) using an Illumina MiSeq platform with 2 × 300 bp ( mcrA amplicon library) and 2 × 250 bp (16S rRNA amplicon library) paired end read chemistry. Details in SI Materials and Methods .

Amplicon sequence analysis

Both 16S rRNA and mcrA gene reads were processed using QIIME 2 version 2020.2 [ 51 ]. In short, primer sequences were removed from demultiplexed reads using cutadapt [ 52 ] with error rate 0.12, reads were truncated (145 bp forward, 145 bp reverse and 260 bp forward, 200 bp reverse for 16S rRNA and mcrA datasets, respectively), filtered, denoised, and merged in DADA2 with default settings [ 53 ]. 16S rRNA gene amplicon sequence variants (ASVs) were taxonomically classified with the sklearn method and the SILVA 132 database [ 54 ]. mcrA gene ASVs were assigned a taxonomy using vsearch with a minimum identity of 70% and no consensus classification against a reference database of representative near-full length mcrA genes encompassing the diversity of publicly available mcrA . Contamination was removed using the R package decontam [ 55 ]. The mcrA gene dataset was curated by removing ASVs ≤400 bp and non- mcrA gene ASVs as identified by evaluating the top hits of a blastx search against the NCBI NR database. Samples with less than 5000 reads or 10,000 reads for the 16S rRNA and mcrA gene dataset, respectively, were excluded from further analyses. Diversity metrics and Bray–Curtis dissimilarity were calculated with the R packages phyloseq [ 56 ] and vegan [ 57 ].

Metagenome sequencing, assembly, and annotation

Metagenomes were generated at the Joint Genome Institute (JGI) from 10 ng (LCB003.1) and 100 ng (LCB019.1 and LCB024.1) DNA, and raw reads were processed according to JGI’s analysis workflow (see SI Materials and Methods). Quality controlled reads were assembled using SPAdes 3.11.1 [ 58 ] with options -m 2000, -k 33,55,77,99,127 -meta. Assembled scaffolds ≥2000 bp were binned with six implementations of four different programs including, Maxbin v2.2.4 [ 59 ], Concoct v1.0.0 [ 60 ], Metabat v2.12.1 [ 61 ], and Autometa v1 [ 62 ]. Bins generated from each program were refined with DAS_Tool [ 63 ] and bin quality statistics were determined with CheckM [ 64 ]. MAGs were assigned alphanumerical identifiers (e.g., LCB003-007 indicates bin 7 from feature 003 in the Lower Culex Basin) and MAGs containing at least one mcrA gene (>300 nt) and a complete or near-complete set of mcrABGCD were considered in this study. For these Mcr-encoding MAGs, annotations provided by the IMG/M-ER pipeline v7 [ 65 ] for genes associated with methanogenesis pathways, coenzyme and cofactor biosynthesis, energy conservation, and beta-oxidation, were manually evaluated using analysis of gene neighborhoods, NCBI BLASTP, NCBI’s Conserved Domain Database, TMHMM, InterPro, and the hydrogenase classifier HydDB [ 66 , 67 , 68 , 69 ]. See SI Data 3 for a complete list of gene annotations relevant to this study. Amino acid identity (AAI) values were computed with compareM using aai_wf and –proteins and taxonomies were assigned with GTDB-Tk v1.2.0 [ 70 ] and GTDB release 207 [ 71 ].

Phylogenetic analyses

A set of 18 single-copy marker proteins [ 72 , 73 ] detected in LCB Mcr-encoding MAGs and selected publicly available archaeal reference genomes (SI Table 4 ) were aligned using MUSCLE [ 74 ], trimmed with trimAL [ 75 ] with 50% gap threshold, and concatenated. A maximum likelihood phylogenetic tree was reconstructed with IQ-tree2 v2.0.6 [ 76 ] using the final concatenated alignment of 3916 positions, LG + F + R10 model, and 1000 ultrafast bootstraps.

McrA from LCB metagenomes (>100 aa), abundant ASVs (140 aa), and publicly available references were aligned with MAFFT-linsi [ 77 ], trimmed with trimAL with 50% gap threshold, and used for maximum likelihood phylogenetic analysis with IQtree2 with LG + C60 + F + G model and 1000 ultrafast bootstraps.

Sequence similarities between selected ASVs and 16S rRNA genes from metagenomes and MAGs were determined by blastn v2.13.0+.

Results and discussion

Survey of mcra genes across physicochemically contrasting geothermal features.

The presence and diversity of Mcr-encoding populations was assessed in 100 geothermal features of YNP via mcrA gene amplicon sequencing. Gene amplicons were recovered from 66 sediment and/or microbial mat samples spanning 39 geothermal features located in the Lower Culex Basin (LCB; 61 samples, 35 features), the Mud Volcano Region (MVR; 4 samples, 3 features), and the White Creek Area (WCA; 1 sample, 1 feature). These features were characterized by a wide range of temperature (22–86.3 °C), pH (2.40–9.77), and dissolved methane (40–1784 nM), oxygen (<13–771 µM), and sulfide (<2–27 µM; Figs. 1 A, 2 , SI Data 1 ).

Relative sequence abundance of mcrA gene amplicons affiliated with abundant lineages (relative sequence abundance >1% in at least one sample). Samples selected for metagenomics are underlined in bold. Samples were collected from geothermal features (identified by numbers) in the Lower Culex Basin (LCB, circle), Mud Volcano Region (MVR, diamond), and White Creek Area (WCA, triangle) and consisted of either sediment (black), microbial mat (white), or a mixture of sediment and mat material (grey). Physicochemical parameters of the geothermal water were recorded at the time of sample collection. X: no data available. Clustering based on Bray-Curtis dissimilarity using relative sequence abundance data of the presented lineages. No correlative trends between taxonomic affiliation of mcrA genes and physicochemistry were observed (SI Fig. 2 ). See SI Data 1 for details.

Generally, the mcrA -containing microbial community in each geothermal feature was composed of a small number [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] of mcrA ASVs with >1% relative sequence abundance. The alpha diversity of mcrA tended to decrease with increasing temperature (SI Fig. 1 , SI Data 1 ), a trend consistent with previous results based on 16S rRNA gene diversity in geothermal environments [ 42 , 78 , 79 ]. The mcrA- containing populations detected across samples included both confirmed methanogens (Methanomassiliicoccales, Methanosarcinales, Methanomicrobiales, Methanocellales, and Methanobacteriales) and lineages with proposed but untested methane/alkane metabolism (Archaeoglobi, Ca . Methanofastidiosales, Ca . Methanomethylicia, and Ca . Korarchaeia) (Fig. 2 , SI Data 1 ). Confirmed methanogens dominated most samples (46/66) and were frequently identified in geothermal features with moderate temperatures (<60 °C). Other Mcr-encoding lineages prevailed at elevated temperatures (>60 °C) and were exclusively detected in LCB024.1, LCB058.1, and LCB063.2 (SI Fig. 2 , SI Data 1 ). Particularly, Archaeoglobi-affiliated mcrA genes dominated at high temperatures (≥70 °C) and elevated concentrations of dissolved sulfide (≥3 µM; SI Fig. 2 ), which are conditions similar to those environments in which Archaeoglobi were previously detected [ 18 , 21 , 23 , 80 ]. Notably, Ca . Korarchaeia were present at high relative sequence abundance (16%) in LCB003.1. Anaerobic methane-oxidizing archaea (ANME-1) as well as Ca . Methanofastidiosales were detected only with low relative sequence abundance (<2% and <4%, respectively; SI Data 1 ).

Overall, this survey of mcrA genes indicated that taxonomically diverse mcrA- containing archaea exist across a wide range of physicochemical regimes in the geothermal environments of YNP, particularly in the LCB geothermal area. As primer-based diversity surveys are inherently biased, we note that the primer set used in this study has historically been widely applied to amplify mcrA genes of Euryarchaeota origin [ 47 ]. While these primers bind to currently known mcrA genes of Ca . Methanomethylicia and Ca . Korarchaeia without mismatches, multiple mismatches to mcrA genes of other lineages exist (e.g., Ca. Nezhaarchaeia). Consequently, our amplicon-based gene survey likely underrepresented certain mcrA genes and underestimated mcrA gene diversity. To further investigate the methanogenic communities in the LCB, metagenomics and mesocosm experiments were conducted with material from hot springs LCB003, LCB019, and LCB024 characterized by elevated temperatures (47–73 °C) and circumneutral pH (3.0–7.8; SI Tables 1 – 3 , Fig. 1B–D ).

mcrA gene diversity and mcr -containing MAGs recovered from the LCB

In the hot springs selected as main study sites, abundant mcrA ASVs were related to confirmed methanogens in LCB019 and LCB003, and archaea with proposed methane/alkane metabolism in LCB024 and LCB003 (SI Table 6 , SI Data 1 ). Environmental metagenomics recovered ten medium and two high quality MAGs [ 81 ] encoding McrA and a complete/near-complete MCR complex, which ranged in size from 0.73–1.78 Mbp and estimated completeness from 72 to 100% (Table 1 ). According to phylogenomic analysis of 18 archaeal single copy genes (SI Table 4 ), four MAGs belonged to lineages of previously cultured methanogens: Methanothermobacter (LCB019-055), Methanomassiliicoccales (LCB019-061), Methanothrix (LCB019-064), and Methanolinea (LCB019-065) while eight MAGs belonged to lineages of proposed methanogens or methane/alkane-oxidizing archaea: Ca . Methanomethylicia (LCB019-004, −019, −026, LCB024-024, −038, LCB003-007), Archaeoglobi (LCB024-003), and Ca . Hadesarchaeia (LCB024-034) (Fig. 3 , SI Fig. 3 ). Interestingly, Ca . Methanomethylicia related MAGs were recovered from all three hot spring metagenomes indicating that members of this lineage can inhabit a wide range of physicochemical conditions. In contrast, MAGs affiliated with lineages of confirmed methanogens were only identified in LCB019, as initially reflected by mcrA amplicons. In total, 12 near-complete (≥500 aa) and 24 partial (100–499 aa) McrA sequences were recovered from the metagenome assemblies, suggesting that the Mcr-encoding MAGs reconstructed here do not reflect the full diversity and metabolic potential of the Mcr-encoding populations present. According to phylogenetic analysis, McrA proteins were categorized into MCR-type and ACR-type [ 21 , 82 ] and affiliated with McrA of confirmed methanogens (group I), proposed methanogens (group II), or with McrA-like proteins of proposed alkane-metabolizing archaea (group III) [ 22 , 27 ] (Fig. 3 ). Overall, the mcrA genes and Mcr-encoding MAGs recovered via metagenome sequencing confirmed the diversity of mcrA- containing archaea detected via amplicon sequencing and extended it by detecting Methanomassiliicoccales, Ca . Nezhaarchaeia, and Ca . Hadesarchaeia (Fig. 3 ).

A Maximum-likelihood tree, inferred with IQtree and the best-fit LG + F + R10 model, using a concatenated set of 18 conserved arCOGs (SI Table 4 ). Squares indicate ultrafast bootstrap values of 100 (black) and 95–99 (gray). Diamonds indicate lineages with Mcr-encoding MAGs detected in this study and shown in detail. B Maximum-likelihood tree, inferred with IQtree and the LG + C60 + F + G model, from the amino acid alignment of McrA. Filled circles: McrA identified in a MAG, open circles: metagenomic McrA (unbinned, >100 aa), open squares: abundant mcrA ASVs (>1% relative sequence abundance). For details see SI Table 6. Dashed line indicates previously proposed McrA/AcrA groups [ 21 , 82 ]: I) McrA from methanogens and ANME (MCR-type), II) McrA from TACK lineages (MCR-type), III) McrA-like from proposed and experimentally confirmed alkane oxidizing archaea (ACR-type). Colors: orange, LCB003, magenta, LCB019, blue, LCB024.

Potential for methane and alkane metabolism in mcr -containing MAGs

Four MAGs were affiliated with lineages of confirmed hydrogenotrophic, aceticlastic, and hydrogen-dependent methylotrophic methanogens and encoded mcrA genes related to those of cultured methanogens (group I). LCB019-065 and LCB019-055 shared amino acid identity (AAI) values of 80% and 98% with cultured representatives of the hydrogenotrophic methanogens Methanolinea and Methanothermobacter , respectively. Congruently, both MAGs encoded the genes required for generating methane from H 2 and CO 2 , including the complete Wood-Ljungdahl Pathway (WLP), methyl-H 4 M(S)PT:coenzyme M methyltransferase (Mtr) complex, F 420 -reducing hydrogenase (Frh), methyl-viologen-reducing hydrogenase (Mvh) (incomplete in LCB019-065), and energy-converting hydrogenase (Ehb, LCB019-055; Ech, LCB019-065) (Fig. 4 , SI Discussion). Additionally, a complete formate dehydrogenase complex (FdhABC) was encoded in LCB019-65 and while LCB019-055 encoded FdhAB, FdhC was not detected. Consistently, cultured representatives of Methanolinea utilize formate as a substrate for methanogenesis while those of Methanothermobacter do not [ 83 , 84 ]. LCB019-064 showed AAI values of 90% to the aceticlastic methanogen Methanothrix thermoacetophila and encoded all genes necessary for aceticlastic methanogenesis including the Mtr complex and acetyl-CoA decarbonylase/synthase:CO dehydrogenase complex (ACS/CODH) (Fig. 4 , SI Discussion). LCB019-061 shared AAI of 59% with cultured Methanomassiliicoccus sp., suggesting it may represent a novel lineage within the Methanomassiliicoccales. Consistent with a hydrogen-dependent methylotrophic methanogenesis lifestyle of Methanomassiliicoccales isolates, LCB019-061 encodes methyltransferases (SI Fig. 4 ) but lacks the WLP and a complete Mtr complex. A mtrH gene encoded in proximity to methyltransferase corrinoid activation protein ( ramA ) suggests LCB019-061 may reduce unknown methylated substrates to methane [ 19 , 80 ].

Squares indicate gene/gene set detected (filled), gene/gene set not detected (open) or gene set partially detected with the majority of genes present (half filled). * indicates only one gene in a gene set detected. Circles indicate the methane/alkane metabolism predicted for each MAG based on the gene repertoire. Colors: orange, LCB003, magenta, LCB019, blue, LCB024. A complete list of genes described in this figure and their abbreviations is reported in SI Data 3 .

Six MAGs shared high AAI values (>96%) with MAGs of Ca . Methanomethylicia and encoded an McrA affiliated with those of other Ca . Methanomethylicia MAGs (group II). Consistent with Ca . Methanomethylicia MAGs proposed to perform hydrogen-dependent methylotrophic methanogenesis [ 19 ], the six MAGs lack the WLP and a complete Mtr complex but encode a variety of methyltransferases including methanol:coenzyme M methyltransferase ( mtaA) , monomethylamine methyltransferase ( mtmB ), and dimethylamine corrinoid ( mtbC ) and/or trimethylamine corrinoid protein ( mttC ). LCB019-026 additionally encoded a trimethylamine methyltransferase ( mttB ). A methyltransferase subunit H of the Mtr complex, mtrH , encoded near other corrinoid protein and methyltransferase genes (SI Data 3 ) suggests that methane may be formed from unknown methylated substrates [ 19 , 80 ]. Although methylamine-specific cobamide:coenzyme M methyltransferase ( mtbA ) was not identified, MtaA could substitute for the activity of MtbA (SI Fig. 4 ) [ 85 ]. Thus, all six Ca . Methanomethylicia MAGs contain the gene repertoire needed for hydrogen-dependent methylotrophic methanogenesis (Fig. 4 , SI Fig. 4, SI Discussion ). In addition, LCB019-004 encoded a second McrA, that clustered with the McrA-like proteins of ethane-oxidizing archaea Ca . Ethanoperedens and Ca . Argoarchaeum (McrA group III, ACR/ECR type) and a recently recovered MAG of Ca . Methanosuratus [ 25 , 82 ] proposed to perform ethanogenesis or ethane oxidation via an unknown pathway [ 82 ]. This indicates that anaerobic methane/alkane metabolism within the Ca . Methanomethylicia may be more diverse than previously anticipated.

The Archaeoglobi affiliated MAG LCB024-003 showed low AAI values (65%) to Archaeoglobales isolates, which are all non-methanogenic sulfate-reducers. Instead, LCB024-003 shared high AAI values (>98%) to Mcr-encoding Archaeoglobi MAGs of proposed hydrogenotrophic methanogens (WYZ-LMO10, SJ34) or hydrogen-dependent methylotrophic methanogens ( Ca . M. hydrogenotrophicum) [ 21 , 33 , 80 ]. Consistently, its two partial McrA (192 and 193 aa) cluster with McrA of other proposed methanogenic Archaeoglobi (group II) (Fig. 3 ) [ 18 , 21 ]. LCB024-003 encodes genes required for hydrogenotrophic methanogenesis including the WLP pathway, hydrogenase Mvh, and a F 420 H 2 :quinone oxidoreductase complex ( fqo DHIF) which may substitute for Frh to generate reduced F 420 as previously suggested [ 23 , 86 , 87 ]; however, a complete Mtr complex was not detected. In contrast to Ca . M. hydrogenotrophicum, LCB024-003 encodes a truncated 5,10-methylenetetrahydromethanopterin reductase ( mer ) while mtaABC were not identified, suggesting it is unable to use methanol for methanogenesis [ 23 ]. Although LCB024-003 encodes the beta-oxidation pathway, other genes typically associated with short-chain alkane oxidation including an ACR-type MCR, ACS/CODH complex, and methyltransferases were absent. Hence, unlike the MAGs of Ca . Polytropus marinifundus and JdFR-42 [ 18 , 21 ], LCB024-003 may not represent an anaerobic alkane oxidizer [ 88 ] and instead may utilize the beta-oxidation pathway for long chain fatty acid metabolism as has been shown for Archaeoglobus fulgidus [ 89 ]. Further, genes encoding dissimilatory sulfate reduction ( sat, aprAB, dsrABC ) present in some mcr- containing Archaeoglobi MAGs ( Ca . M. dualitatem [ 23 ]) were not detected. Together, the genomic information from LCB024-003 suggests that this Archaeoglobi representative may live as a hydrogenotrophic methanogen (Fig. 4 , SI Discussion).

LCB024-034 shared AAI values of >79% with other Mcr-encoding Hadesarchaeia [ 21 , 80 ] and encoded a partial McrA (216 aa) related to the ACR-type proteins of Hadesarchaeia (group III) [ 27 ]. Congruently with the hypothesis of short-chain alkane metabolism in Ca . Hadesarchaeia, LCB024-034 encoded the beta-oxidation pathway and an ACS/CODH complex. However, most genes encoding the WLP required for oxidizing activated alkanes to CO 2 were missing [ 27 ]. Thus, short-chain alkane metabolism in LCB024-034 remains speculative, awaiting further genomic and experimental data.

Together, the 12 mcr- containing MAGs reconstructed here reflect the potential for archaeal short-chain alkane-oxidation as well as hydrogenotrophic, aceticlastic, and hydrogen-dependent methylotrophic methanogenesis in geothermal environments of YNP. Further, these MAGs extend the genomic data available for future analysis of diversity and evolution of Mcr-encoding archaea and suggest geothermal environments are a promising source for the recovery of these archaea.

Methanogenic activity and enrichment of methanogens in mesocosms

Mesocosm experiments were performed to reveal activity and enrichment of methanogens. Methane accumulation was monitored in the headspace of mesocosms under (1) close to in situ conditions (i.e., no amendment), (2) conditions favoring methanogenesis (i.e., substrate amendment), and (3) conditions inhibiting bacterial metabolism (i.e., antibiotics treatment) (SI Fig. 5 ). Inhibition of bacterial metabolism may have disrupted potential symbiotic partnerships between methanogens and bacteria and/or favored substrate availability for methanogens through the limitation of competition. Mesocosms were also analyzed for enrichment in potential methanogenic populations via 16S rRNA gene amplicon sequencing. Abundant 16S rRNA gene ASVs (>1% relative sequence abundance) related to Mcr-encoding archaea amounted for ~1% in LCB019 and <1% in LCB024 and LCB003, indicating that methanogens represent a minor fraction of the in situ community (SI Figs. 6 , 7 ). However, in mesocosms from all three hot springs, methane production was observed under close to in situ conditions with strongly varying maximum methane yields (17,000, 1900, and 150 ppm for LCB019, LCB024, and LCB003, respectively; Fig. 5 , SI Fig. 5 ). Substrate amendment had considerably different effects on methane production and, except for LCB019, mesocosm triplicates showed strong variation and long response times (20–40 days) likely due to an uneven distribution of initially low abundant methanogen cells across replicates. For LCB024, substrate amendment (particularly H 2 ), appeared to suppress methane production, which may indicate that either hydrogenotrophic methanogens were not present, not active, or were outcompeted by other community members considering the shift in the microbial community (SI Fig. 7 ). Antibiotic amendments resulted, on average across treatments, in increased methane production in mesocosms from LCB024 and LCB003, and a strong decrease in methane production in mesocosms from LCB019, indicative of substrate competition or metabolic interdependencies between methanogens and bacteria, respectively.

A Maximum methane produced in the headspace of mesocosms. Replicates measuring <100 ppm not shown. B Enrichment of 16S rRNA gene ASVs (>3% relative sequence abundance) affiliated with Mcr-encoding archaea across treatments (bars) paired with respective headspace methane yields (circles). Circle size proportional to the log2 fold change in methane yield between treatment and control (i.e., mesocosm under close to in situ condition) for each site. Dashed lines indicate 1% methane. Solid lines indicate average methane concentration in mesocosms under close to in situ conditions (no substrate, no antibiotics) for each site. Open symbols: without antibiotics; filled symbols: with antibiotics. Colors: orange, LCB003, magenta, LCB019, blue, LCB024. Abbreviations: NON, no amendment control; ACE, acetate; MET, methanol; MMA, monomethylamine; FOR, formate; HYD, hydrogen (H 2 ); DIC, dissolved inorganic carbon (HCO 3 − + CO 2 ); BES, bromoethanesulfonate (methanogenesis inhibitor); PFA, paraformaldehyde (killed control). Replicates indicated as A-C and+, with antibiotics or -, without antibiotics. Methane curves and extended relative abundance data for all mesocosm replicates are reported in SI Figs. 5 – 7 and SI Data 4 .

To characterize the effect of substrate amendment on methanogenic populations, we analyzed ASVs related to Mcr-encoding archaea with enrichment >3% relative sequence abundance across treatments. H 2 plus DIC (HCO 3 − + CO 2 ) amended mesocosms from LCB019 showed rapid methane production, with highest maximum methane concentrations (>170,000 ppm) reached within 6 days (SI Fig. 5 ). These mesocosms were enriched (26–35%) in ASV_5ea58, identical to the 16S rRNA gene of MAG LCB019-055 as well as Methanothermobacter thermautotrophicus , a thermophilic hydrogenotrophic methanogen isolated from YNP [ 40 , 41 ]. Similarly, for LCB003, H 2 plus DIC or methylated compounds resulted in the strongest stimulation of methanogenesis and most pronounced enrichment (up to 71%) of an ASV affiliated with Methanothermobacter crinale (ASV_520b7, 99.6% sequence identity). Notably, H 2 amendment without DIC supply did not result in a comparable response, suggesting that in closed mesocosm systems hydrogenotrophic methanogens were limited by inorganic carbon, which unlikely occurs in situ where concentrations of aqueous CO 2 were elevated (SI Table 2 ). For LCB019, acetate amendment resulted in elevated methane production and concomitant enrichment (3–5%) of ASV_daa7b, which shared high sequence similarity with the aceticlastic methanogen Methanothrix thermoacetophila (98%) and a 16S rRNA gene recovered from the LCB019 metagenome (100%; SI Data 4 ). MAG LCB019-064, related to Methanothrix thermoacetophila (89% AAI similarity) encoded the potential for methanogenesis from acetate and may represent the enriched Methanothrix sp. population. Thus, our mesocosm experiments complemented findings from metagenomics, confirming the potential for hydrogenotrophic methanogenesis by Methanothermobacter sp. and aceticlastic methanogenesis by Methanothrix sp. in LCB019 and revealing the potential for hydrogenotrophic methanogenesis by Methanothermobacter sp. in LCB003 (SI Table 6 ).

In addition to previously cultured methanogens, uncultured Mcr-encoding lineages were enriched. An ASV identified as Ca . Methanodesulfokores washburnensis (ASV_74dd7, 100% sequence identity) was highly abundant (25–54%) in two mesocosms from LCB003 amended with methanol, hydrogen, and antibiotics. A MAG of this Ca . Korarchaeia representative previously recovered from YNP encodes versatile metabolic capabilities including hydrogen-dependent methylotrophic methanogenesis from methanol [ 20 ]. Methane yields in these mesocosms, while comparably low after 43 days (<2000 ppm), were strongly elevated compared to the no amendment control of LCB003 (log2 fold change (FC) 3–4). mcrA and 16S rRNA genes of Ca . Methanodesulfokores washburnensis were also detected via amplicon and metagenome sequencing, confirming the presence of this lineage in LCB003 (Figs. 2 , 3 , SI Data 4 ). In one mesocosm from LCB024 amended with monomethylamine, stimulation of methanogenesis (log2 FC 4, 310,000 ppm) and enrichment (8%) of Archaeoglobi-affiliated ASV_78ad2 was observed. The Archaeoglobi MAG LCB024-003 recovered from LCB024 encoded the potential for hydrogenotrophic methanogenesis while genes required for methylotrophic methanogenesis were not detected (Fig. 4 ). However, potential for methylotrophic methanogenesis has been described for some Archaeoglobi MAGs and the recovery of several Archaeoglobi related mcrA and 16S rRNA genes from LCB024 suggests that diverse Archaeoglobi populations are present, possibly including methylotrophic methanogens. An enrichment of a Ca . Nezhaarchaeia related ASV (ASV_27aa3) was highest (8%) in methanol amended mesocosms from LCB003 and cooccurred with elevated methane yields (log2 FC 3.5, 85,000 ppm), confirming the persistence of a Ca . Nezhaarchaeia population detected by metagenomic 16S rRNA and mcrA genes (SI Data 2 ). Previously described mcr -containing Ca . Nezhaarchaeia MAGs encode the potential for hydrogenotrophic methanogenesis, and while no enrichment was detected in hydrogen amended mesocosms, microbially produced hydrogen may have facilitated limited methanogenic activity and enrichment of hydrogenotrophic methanogens in other mesocosms. Ca . Methanomethylicia related ASVs were detected in multiple mesocosms, however their enrichment remained low (<3%) (SI Data 4 ).

Overall, minor methanogenic populations, not or hardly detectable in hot springs via 16S rRNA gene or metagenome sequencing, were enriched in mesocosm experiments under selective methanogenic conditions. Specifically, acetate or hydrogen plus DIC enabled the enrichment of Methanothrix or Methanothermobacter , respectively, while methyl compounds favored the enrichment of Ca . Korarchaeia, Ca . Nezhaarchaeia, or Archaeoglobi. Further research is needed to decipher the metabolism of the here enriched populations of uncultured archaea, their proposed methanogenic capacities, and potential metabolic interdependencies with other community members.

Implications for methane cycling in YNP

We explored the potential for methanogenesis in previously uncharacterized geothermal environments of YNP, primarily the LCB, and our results warrant further research into the magnitude of biological methane production in this area. While the methanogenic communities of eight geothermal features in YNP had previously been investigated [ 20 , 21 , 24 , 30 , 33 , 42 ] we detected mcrA genes across an additional 39 geothermal features indicating the wide distribution of diverse populations of Mcr-encoding archaea, including both confirmed methanogens and lineages proposed to engage in anaerobic methane/alkane cycling. The methanogenic pathways encoded across mcr -containing MAGs suggests methanogenesis in LCB hot springs could proceed from different precursors including H 2 /CO 2 , acetate, and methyl compounds plus hydrogen. The genetic potential for hydrogen-dependent methylotrophic methanogenesis was encoded by the majority of MAGs, including Ca . Methanomethylicia and Methanomassiliicoccales, and was detected in all three hot springs, possibly reflecting prevalence of this metabolism in geothermal environments as previously proposed [ 80 ]. While methanogenic populations accounted for minor fractions of the microbial community, methanogenesis may proceed in situ as it was observed in mesocosms under close to in situ conditions. The potential for hydrogenotrophic and aceticlastic methanogenesis revealed by metagenomics was confirmed by the enrichment of Methanothermobacter and Methanothrix in mesocosms under selective substrate amendment. In situ, methanogenesis in hot springs is likely constrained by physicochemical regimes, substrate availability, and metabolic interdependencies. Methanogenic precursors may be supplied from organic matter degradation as metabolic intermediates of syntrophic communities (e.g., H 2 , acetate), products of respiration (e.g., CO 2 ), or through geothermal alteration from the subsurface (e.g., H 2 , CO 2 ) [ 40 , 90 ]. As hot springs often present dynamic systems, methanogens may frequently respond with activity and growth to favorable conditions. This may be exemplified by Methanothermobacter ’s capacity to rapidly respond, resulting in high activity and fast growth upon supply of H 2 /CO 2 , which it may sporadically or consistently encounter in situ (Fig. 5 , SI Table 2 , SI Fig. 5 ).

Although methanogenic activity and isolation of Methanothermobacter thermoautotrophicus have been demonstrated [ 40 , 41 ], the environmental impact of methanogens on methane emissions from YNP’s geothermal environments is not well understood. Methane is an important component of the gas flux in YNP [ 90 , 91 , 92 ] and the isotopic composition of gas emitted from geothermal features across YNP has suggested methane is primarily generated through abiogenic and/or thermogenic processes, while methanogenesis is not a significant source of methane [ 91 ]. Although we detected varying concentrations of aqueous methane in geothermal features in an area of YNP that had not been previously investigated, the source and fate of this methane is currently unknown. In general, methane emissions from terrestrial geothermal environments are not considered in estimates of the global atmospheric methane budget and little is known about their contribution to the global methane flux [ 1 , 3 , 14 ]. YNP contains more than 14,000 geothermal features, the largest concentration in the world, making it a superior candidate for studying CH 4 flux in these environments [ 93 , 94 , 95 ].

Environmental mcrA gene surveys and metagenomics aid in identifying environments in which methanogenesis may occur. Subsequent quantification of in situ metabolic activities, including methane production rates, as well as deciphering the interplay between methanogens and methanotrophs will lead to a better understanding of the impact methanogens have on the local carbon cycle and their contribution to methane emissions from YNP’s geothermal environments.

Uncultured Mcr-encoding lineages are globally distributed across a wide range of ecosystems and could play important roles in the biogeochemical carbon cycle [ 14 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 28 , 30 , 33 ]. In this study, we described a previously unrecognized diversity of Mcr-encoding archaea in geothermal environments of YNP. Environmental metagenomics provided insights into the metabolic potential of these Mcr-encoding archaea and mesocosm experiments revealed, for some lineages for the first time, their activity and enrichment under methanogenic conditions. The ability to enrich these uncultured Mcr-encoding archaea in laboratory settings presents a clear path towards their cultivation. Future work, including experiments under close to in situ conditions and culture-dependent physiology and biochemistry studies, will be essential for advancing our understanding of these still widely enigmatic archaea.

Data availability

16S rRNA gene and mcrA gene amplicon data as well as mcr- containing MAGs are deposited at NCBI under BioProject PRJNA859922 (SI Table 5 ). Metagenomes are available on IMG/M (JGI) under IMG Genome IDs 3300028675 (LCB003), 3300031463 (LCB019), and 3300029977 (LCB024).

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Acknowledgements

This study was funded through an NSF RII Track-2 FEC award DBI-1736255 (to RH) with minor support from NASA Exobiology award 80NSSC19K1633 (to RH). A portion of this research was performed under a Facilities Integrating Collaborations for User Science (FICUS) program award (proposal: 10.46936/fics.proj.2017.49972/6000002 to RH), and used resources at the Joint Genome Institute (JGI; https://ror.org/04xm1d337 ), which is a DOE Office of Science User Facility. JGI is sponsored by the Office of Biological and Environmental Research and operated under Contract No. DE-AC02-05CH11231. MML was supported in part by the Thermal Biology Institute and Montana State University’s Vice President’s Office of Research, Economic Development, and Graduate Education. We appreciate the U.S. National Park Service, in particular Annie Carlson, at the Yellowstone Center for Resources, for permitting work in Yellowstone National Park under permit number YELL-SCI-8010. We thank William Inskeep, Timothy McDermott, and Luke McKay for helpful discussions that informed field sampling efforts.

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These authors contributed equally: Mackenzie M. Lynes, Viola Krukenberg.

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Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, 59717, USA

Mackenzie M. Lynes, Viola Krukenberg, Zackary J. Jay, Anthony J. Kohtz, Rachel L. Spietz & Roland Hatzenpichler

Environmental Analytical Lab, Montana State University, Bozeman, MT, 59717, USA

Christine A. Gobrogge

Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, 59717, USA

Roland Hatzenpichler

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MML, VK, and RH developed the study. MML and RH collected initial survey samples. MML and CAG conducted geochemical analyses. ZJJ processed metagenomes, recovered MAGs, and provided bioinformatics support. AJK and ZJJ performed phylogenetic analysis of MAGs. VK, and ZJJ performed McrA phylogenetic analysis. MML and AJK reconstructed metabolic potential of MAGs. VK and MML prepared amplicon libraries and processed amplicon data. MML, VK, AJK, and RLS designed and conducted mesocosm experiments. MML and VK devised the figures and wrote the initial manuscript with input from RH. The final version was edited and approved by all authors.

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Correspondence to Viola Krukenberg or Roland Hatzenpichler .

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Lynes, M.M., Krukenberg, V., Jay, Z.J. et al. Diversity and function of methyl-coenzyme M reductase-encoding archaea in Yellowstone hot springs revealed by metagenomics and mesocosm experiments. ISME COMMUN. 3 , 22 (2023). https://doi.org/10.1038/s43705-023-00225-9

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The Kiel Off-Shore Mesocosms for Ocean Simulations (KOSMOS) are a seagoing mobile platform for mesocosm experiments which can be deployed from any mid-sized research vessel. It presently comprises 9 free-floating units, each consisting of a floatation frame and a flexible bag 2 m in diameter and 20 m long (Fig. 1). After deployment, the bag unfolds by weights pulling down its lower end to depth, thereby enclosing an undisturbed water column 55 m 3 in volume. The bag is then closed at the bottom by a full-diameter sediment trap. Regular cleaning of the mesocosm bags prevents wall growth and thus enables long-term experiments covering the seasonal plankton succession and allowing for studies on evolutionary adaptation. The enclosed water encompasses the entire plankton community up to the level of fish larvae and small pelagic fish. Sampling of sedimented matter in combination with frequent measurements of dissolved and suspended matter and air-sea gas exchange can be used for budget calculations of major elements (C, N, P, Si). Potential experimental perturbations include the addition of inorganic nutrients or organic compounds, CO 2 enrichment, manipulation of mixed layer depth, simulation of deep-water upwelling, species exclusion and addition of invasive species.

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Fig. 2: left: Study locations of KOSMOS experiments. right: KOSMOS mesocosms deployed off the coast of Ny Ålesund, Svalbard (Photo: Signe Klavsen, GEOMAR).

These studies, which focussed on the effects of ocean acidification plankton dynamics and biogeochemical cycling, involved researchers from a wide range of scientific fields, including marine and atmospheric chemistry, molecular and evolutionary biology, marine ecology and biological oceanography, aquaculture, fish biology, and biogeochemistry. Results of the KOSMOS experiments have been used in various ecosystem and biogeochemical modelling activities.

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Future KOSMOS campaigns: Planned and funded campaigns in the coming five years include studies on the effects of ocean deoxygenation in oxygen minimum zones of eastern boundary upwelling systems, effects of ocean acidification on oligotrophic pelagic systems, and the potential of artificial upwelling in raising productivity and fishery harvest in low-productive ‘ocean deserts’.

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Mussels with luggage: the influence of artificially attached "backpack" devices on mussel movement behavior

Drainas, Konstantina
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Freshwater mussels are important keystone and indicator species of aquatic ecosystems. Recent advances in sensor technology facilitate applications to individually track mussels and to record and monitor their behavior and physiology. These approaches require the attachment of sensor devices as "backpacks" to the outer shell surface. The interpretation of such data makes it necessary to understand the influence of these attachments on the horizontal and vertical movement behaviors of freshwater mussels. Over a series of mesocosm experiments, this study systematically investigated the effects of three size- and wiring-specific variants of artificially attached backpacks on the horizontal and vertical movement behavior of Anodonta anatina.

Freshwater mussels;
Ecological indicators;
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A diagram with arrows depicting the movement of microbes through tangential flow filtration and sequential filtration steps.

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A synopsis of our method for investigating waterborne coral disease. Some coral diseases, such as stony coral tissue loss disease, are known to be waterborne, meaning that pathogen(s) are shed by infected corals into the surrounding water column and spread to other corals. Our approach uses diseased corals placed into mesocosms containing sterile seawater, which then infuse the water with the unknown pathogen(s). Because the infectious dose needed to infect healthy corals is not known, our method involves concentrating the sample via tangential flow filtration (TFF). Mesocosm water is passed through a mesh screen to remove any large debris, then passed over a series of 100 kDa filter cassettes using a peristaltic pump. Through this process, pure water is filtered out of the sample, while any microbes are retained, gradually concentrating the microbial community present in the mesocosm in a small volume of water. Because different microbial groups (e.g. bacteria, viruses, etc) largely can be grouped by size, this concentrate is then passed through a series of different pore size filters to capture different size classes of the microbial community. These filters can then be investigated using targeted sequencing approaches, or could be used in transmission experiments to identify the size class, and by association the likely microbial group, that is responsible for causing the coral disease.

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Courtesy of Biology Methods and Protocols, " Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases ." Published by Oxford University Press 2022. This work is written by US Government employees and is in the public domain in the U.S.

Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases

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Wetland mesocosm experiment done!
Schematic diagram of mesocosm experiment.
Mesocosm experiment to understand the resilience of zooplankton
Lancaster-mesocosm-335-ceh.jpg
The mesocosm experiment photos. (a) The mesocosm units in the field
Making the Mesocosm

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Mesocosm
Mesocosm. Diagram of a small form closed system mesocosm. A mesocosm ( meso- or 'medium' and -cosm 'world') is any outdoor experimental system that examines the natural environment under controlled conditions. In this way mesocosm studies provide a link between field surveys and highly controlled laboratory experiments. [ 1]
Mesocosm Experiments as a Tool for Ecological Climate-Change Research
Mesocosm experiments in lotic systems (see Ledger et al., 2013a, Ledger et al., 2013b): (A and B) field set-up and (C and D) quantitative food webs for control and the most extreme drought treatment, respectively. Drought reduced the numbers of species, trophic links and biomass flux. The white bars represent basal resources (width is ...
Mesocosms Reveal Ecological Surprises from Climate Change
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 . Furthermore, metapopulation and demographic models, coupled to mesocosm experimental data, can be used to test and ...
Mesocosm Experiments as a Tool for Ecological Climate-Change Research
Whilst we must always bear in mind their obvious limitations (Benton et al., 2007, Cadotte et al., 2005, Fraser and Keddy, 1997), mesocosm experiments will form an integral part of the jigsaw in this field of ecology for the foreseeable future. They will undoubtedly become increasingly crucial elements in the climate-change ecologist's ...
Modelling and the monitoring of mesocosm experiments: two case studies
This article compares an ecosystem model with two mesocosm experiments in a Norwegian fjord, where nutrients were added to the water. The model is able to simulate the mesocosm data better with parameters from the real ocean, but the sensitivity tests are less successful.
Mesocosms
A mesocosm experiment is an experimental design in which the researcher makes use of contained experiments distributed across a study area. These generally consist of experiments within bounded containers, such as pails, bins, or aquaria, usually in an outdoor setting.
An integrated multiple driver mesocosm experiment reveals the ...
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.
A review of mesocosm experiments on heavy metals in marine ...
A mesocosm experiment investigating how copper is increasing in the sediment and affecting the ecosystem functions in a sediment microbial community shows that copper impairs the assimilative ability of benthic microbes (Mayor et al. 2013). A team of researchers in Norway conducted the marine mesocosm experiments to evaluate the short-term ...
Aquatic Mesocosm Strategies for the Environmental Fate and Risk
In the past decade, mesocosms have emerged as a useful tool for the environmental study of engineered nanomaterials (ENMs) as they can mimic the relevant exposure scenario of contamination. Herein, we analyzed the scientific outcomes of aquatic mesocosm experiments, with regard to their designs, the ENMs tested, and the end points investigated. Several mesocosm designs were consistently ...
WHAT IS A MESOCOSM?
The mesocosm approach is therefore often considered to be the experimental ecosystem closest to the real world, without losing the advantage of reliable reference conditions and replication. By integrating over multiple direct and indirect species effects up or down the food web, the responses obtained from mesocosm studies can be used for ...
Mesocosm
Mesocosm studies combined with field studies can enhance not only the statistical power of experiments, but also inference about structure and function of natural systems (Gido et al. 1999, Evans-White et al. 2001, Gido and Matthews 2001).The significance of behavior has been inferred largely from field collections that infer differences in foraging morphology and diet, rather than direct ...
Combining mesocosms with models reveals effects of global warming and
Blending mesocosm experiments with "real-world" ecological models has been questioned on the grounds that they are unlikely to attain realistic projections (Carpenter, 1996). One reason is that experimental outcomes can be swayed by community structure, ecological complexity, and trophic levels considered within mesocosms. Even the most ...
PDF An integrated multiple driver mesocosm experiment reveals the ...
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
Slice of PLOS: Mesocosms and Climate Change
On the other hand, lab-based experiments, while offering better control over those variables, are excessively artificial and may not reflect natural conditions. The Metatron, a mesocosm system used by Bestion and colleagues to see how lizard populations coped with the temperature of 2100 (credit: 10.1371/journal.pbio.1002281.g001)
Temperature increase and fluctuation induce ...
We thus suggest that long-term and multi-seasonal mesocosm experiments are required to discern effects of changes on plankton communities, notably at middle latitudes where seasonal variability is also an important factor for plankton phenology (Sommer, Gliwicz, Lampert, & Duncan, 1986). 4.1 Plankton phenology
Mesocosm Experiments as a Tool for Ecological Climate-Change Research
A mesocosm experiment testing the effect of common lentic predators on the abundance of the lake chironomid Chironomus zealandicus, showed that if invertebrate predators were present in the lake ...
Mesocosms Reveal Ecological Surprises from Climate Change
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 ...
Forest tree growth is linked to mycorrhizal fungal composition and
A long history of micro- and mesocosm experiments has demonstrated that different EMF species vary by orders of magnitude in their effects on seedling development [9, 12, 13, 19,20,21,22,23,24,25 ...
Microcosms and Mesocosms: A Way to Test the Resilience of ...
The term mesocosm was proposed for middle-sized experiments falling between laboratory microcosms and the large, complex, real world macrocosms that include more biological complexity (Grice and Reeve 1982a, b; Odum 1983). Other definitions describe microcosms as generic systems in which species composition and abiotic characteristics are ...
Simulating atmospheric drought: Silica gel packets dehumidify mesocosm
In our outdoor mesocosm experiment, we found that silica packets dried air microclimates by decreasing relative humidity and increasing vapor pressure deficit most effectively when soil moisture was low. The 5% RH reduction capacity we observed was sufficient to increase vapor pressure deficit by up to 0.4 kPa ...
What we can learn from mesocosms
A mesocosm is a more scientifically controlled version of this. It is a simulated outdoor environment that is designed by scientists to determine the effects that different manipulations have on an ecosystem. The people doing the experiment can control what enters the mesocosm environment so that if they see an effect, they know what caused it.
PDF Mesocosms: An idea that became a reality and then a necessity!
mesocosm and restoration experiments. Canadian Journal of Botany 80:617-624. Lindig-Cisneros, R. and J. B. Zedler. 2002. Relationships between canopy complexity and germination microsites for Phalaris arundinacea L. Oecologia 133:159-167. Where is Roberto now? Dr. Lindig-Cisneros is Professor of Ecology at Universidad Nacional Autónoma
Diversity and function of methyl-coenzyme M reductase-encoding ...
Mesocosm experiments. Mesocosm experiments with material from hot springs LCB003, LCB019, and LCB024 were prepared in an anoxic glove box (N 2 /CO 2 /H 2; 90/5/5%). Under constant stirring, 10 mL ...
KOSMOS
KOSMOS - Kiel Off-Shore Mesocosms for Oceanographic Studies The Kiel Off-Shore Mesocosms for Ocean Simulations (KOSMOS) are a seagoing mobile platform for mesocosm experiments which can be deployed from any mid-sized research vessel. It presently comprises 9 free-floating units, each consisting of a floatation frame and a flexible bag 2 m in diameter and 20 m long (Fig. 1).
Mussels with luggage: the influence of artificially attached "backpack
Over a series of mesocosm experiments, this study systematically investigated the effects of three size- and wiring-specific variants of artificially attached backpacks on the horizontal and vertical movement behavior of Anodonta anatina. Publication: Environmental Sciences Europe. Pub Date: August 2024 DOI: 10.1186/s12302-024-00976-9 ...
Diagram of waterborne coral disease investigation method
Mesocosm water is passed through a mesh screen to remove any large debris, then passed over a series of 100 kDa filter cassettes using a peristaltic pump. Through this process, pure water is filtered out of the sample, while any microbes are retained, gradually concentrating the microbial community present in the mesocosm in a small volume of ...

Mesocosms Reveal Ecological Surprises from Climate Change

Introduction

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

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Modelling and the monitoring of mesocosm experiments: two case studies

Mesocosm experiments

EHUX experiment

PRIME experiment

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Mesocosms Reveal Ecological Surprises from Climate Change

Introduction

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

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Microcosms and Mesocosms: A Way to Test the Resilience of Microbial Communities in Cuatro Ciénegas

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Diversity and function of methyl-coenzyme M reductase-encoding archaea in Yellowstone hot springs revealed by metagenomics and mesocosm experiments

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KOSMOS – Kiel Off-Shore Mesocosms for Oceanographic Studies

Mussels with luggage: the influence of artificially attached "backpack" devices on mussel movement behavior

Diagram of waterborne coral disease investigation method

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Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases

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