microcosm experiment definition

How Microcosms Help Us Understand Ecology

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 Before long, the virus had been replaced by two new viruses that were well-adapted to the new environment in the flask.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

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

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

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

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

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

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

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

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• Published: 07 October 2019

A microcosm approach highlights the response of soil mineral weathering bacterial communities to an increase of K and Mg availability

• O. Nicolitch   ORCID: orcid.org/0000-0003-0225-7208 1 , 2 ,
• M. Feucherolles   ORCID: orcid.org/0000-0002-2052-8975 1 ,
• J.-L. Churin 1 ,
• L. Fauchery   ORCID: orcid.org/0000-0002-2571-0274 1 ,
• M.-P. Turpault 2 &
• S. Uroz 1 , 2  

Scientific Reports volume  9 , Article number:  14403 ( 2019 ) Cite this article

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• Element cycles
• Microbial ecology

The access and recycling of the base cations are essential processes for the long-lasting functioning of forest ecosystems. While the role of soil bacterial communities has been demonstrated in mineral weathering and tree nutrition, our understanding of the link between the availability of base cations and the functioning of these communities remains limited. To fill this gap, we developed a microcosm approach to investigate how an increase in key base cations (potassium or magnesium) impacted the taxonomic and functional structures of the bacterial communities. During a 2-month period after fertilization with available potassium or magnesium, soil properties, global functions (metabolic potentials and respiration) as well as mineral weathering bioassays and 16S rRNA amplicon pyrosequencing were monitored. Our analyses showed no or small variations in the taxonomic structure, total densities and global functions between the treatments. In contrast, a decrease in the frequency and effectiveness of mineral weathering bacteria was observed in the fertilized treatments. Notably, quantitative PCR targeting specific genera known for their mineral weathering ability (i.e., Burkholderia and Collimonas ) confirmed this decrease. These new results suggest that K and Mg cation availability drives the distribution of the mineral weathering bacterial communities in forest soil.

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Introduction.

Understanding how microorganisms adapt to variations in resource availability is a central question in ecology and evolutionary biology. For plants, different behaviours related to the competition for resources have been described, such as competitors, which adapt to rapidly consume available resources, stress tolerators, which persist in nutrient-poor environments, and ruderals, which adapt to disturbances 1 . These different competitive strategies as well as the related theories have been applied to the analysis of environmental microbial communities 2 , 3 , 4 . However, most of the studies dealing with this question have focused on organic nutrients (C, N, S) or iron. The important role of the composition, quality and recalcitrance of organic carbon compounds in the regulation of the composition of microbial communities has been demonstrated in several aquatic and terrestrial environments 5 , 6 , 7 . Similarly, the form of nitrogen (i.e., ammonium, nitrate, nitrite, amino acids) is known to strongly impact the composition and functioning of microbial communities 8 , 9 . As an example, N application is known to modify the abundance of nitrifiers and denitrifiers 10 or methanotrophs 11 . For most of these substrates, the presence and increasing concentrations of compounds directly select microorganisms capable of consuming them or having a better affinity. In contrast, the absence or decreasing concentrations of certain substrates, such as iron, are known to allow the selection of competitive taxa capable of mobilizing those nutrients through the production of siderophores and the regulation of their production 11 , 12 , 13 . Comparatively, the relative impact of base cations on the soil bacterial communities at both the taxonomic and functional levels has been poorly investigated.

Base cations correspond to inorganic compounds, also termed mineral nutrients or inorganic nutrients, such as potassium (K), magnesium (Mg) or calcium (Ca), which act as nutrients, co-factors, or structural components of the cell for plants and the living biosphere 14 , 15 , 16 . These inorganic nutrients are particularly important for plant nutrition, and their depletion can cause plant nutrient deficiencies 14 , 15 . In both forest and agricultural environments, the concentrations of available K or Mg are low compared to other nutrients ( e . g ., C, N) but for different reasons 17 . In agricultural soils, those low concentrations are due to the intensive removal of the crop biomass, but the pool of base cations is usually restored by regular NPK or manure fertilizations. In temperate forest ecosystems, the low concentrations of base cations are due to the nutrient-poor soils in which forests are usually developed, the absence of amendments, and the slow replenishment of the soil fertility 18 . In addition, this phenomenon is accentuated in harvested forests, where trees and the cations accumulated in their biomass are exported 19 . In these low-input and nutrient-poor ecosystems, the available base cations are mainly derived from the dissolution of soil minerals and rocks. In this context, many of the soil microorganisms living in such conditions are likely facing nutritive stress, which probably determines their distribution and functioning.

The potential relationships between the nutrient availability and the taxonomic and functional structures of the soil bacterial communities have been reported in several studies considering soil chronosequences 20 , 21 , soil gradients 22 , 23 , 24 , soil profiles 25 , 26 , or the effect of inorganic fertilization with Ca or Mg in forest sites 27 , 28 . However, in most of these studies, the main soil factors considered were the pH and the availability of C, P or N forms and rarely the availability of base cations by themselves. Among the soil bacterial communities that may be the most impacted by variations in the availability of base cations are those involved in the dissolution of soil minerals ( e . g ., mineral weathering bacteria). Indeed, mineral weathering bacteria have been found in various nutrient-poor ecosystems such as deserts or forests 29 , 30 and in different microbial habitats such as in the (mycor)-rhizosphere 29 , 31 , bulk soil 26 or on the surface of minerals 20 , 32 , 33 , 34 , 35 , 36 , 37 . The frequency and effectiveness of mineral weathering bacteria were notably shown to vary following a mineral amendment 27 , 28 or according to the physico-chemical properties of the mineral substrates 34 , 37 . In both cases, the conditions presenting the highest base cation availability (i.e., amended soil, highly weatherable minerals) were characterized by the lowest frequency of effective mineral weathering bacteria, suggesting a potential link between this functional group and nutrient availability. The ability to weather minerals is a functional trait observed in a wide range of bacterial genera 38 . It was proposed to be a functional adaptation to nutrient limiting environments 39 , making effective mineral weathering bacteria potentially more competitive in such conditions or stress tolerators according to the Grime definition 1 . However, a common feature of all these studies was the co-variation of the nutrient availability and pH, due to the in situ variations of the soil parameters 40 , 41 and/or to the effect of the mineral amendment ( i . e ., liming 28 ), limiting the conclusions. Indeed, pH is known as one of the main soil parameters determining the distribution of the soil bacterial communities 24 and liming is used to rectify the acidity and fertility of the soil 42 , 43 .

In this context, our study aimed to understand how the availability of important base cations, such as potassium or magnesium, drives the taxonomic and functional distribution of the soil bacterial communities and especially the mineral weathering bacteria in forest soil. Such questioning is important in forestry, as liming is considered as an effective management practice to recover soil fertility and its use may be intensified to increase wood productivity. Consequently, it is important to know how such amendment will impact soil bacterial communities and their functioning. To do so, we considered a nutrient-poor soil (classified as Hyperdystric Cambisol) from the Montiers forest experimental site (France) characterized by a low concentration of base cations, limiting for the growth of trees for K and Mg 17 and a high proportion of effective mineral weathering bacterial communities 41 . Calcium was not considered in our study, as it was not limiting in this soil 17 . Notably, the soil chemical analyses collected from this site and from other studies evidenced an enrichment of effective mineral weathering bacteria in soil conditions limited in potassium and magnesium compared to nutrient-rich conditions 41 . To disentangle the relative effect of the base cation availability from the effect of the pH on the soil bacterial communities, we developed a soil microcosm approach based on a fertilization with K or Mg. These nutrients were added to the soil in aqueous solution presenting the same pH as the pH of the soil considered. This experimental design allowed us to test the hypothesis that under nutrient-poor conditions ( i . e ., limiting in K and Mg), mineral weathering bacteria are competitive, while in nutrient-rich conditions ( i . e ., fertilized with K or Mg), they are not. Consequently, an increase in base cation availability is expected to directly impact mineral weathering bacteria and to reduce their frequency and effectiveness. As we did not know if the effect of the K or Mg fertilization on the soil bacterial communities was immediate, analyses were performed at different time points. During a 2-month period following K- or Mg-fertilization, the functional response of microbial communities through basal respiration (MicroResp) and carbon-substrate metabolization (Biolog EcoPlate) measurements was evaluated each 15 day-period. The soil chemical properties were measured to demonstrate the increase of available K or Mg in the soil as well as the stability of the pH. The mineral weathering potential of the bacterial communities was studied through a culture-dependent method. In addition, the taxonomic structure of bacterial communities was analysed by 16S rRNA amplicon-pyrosequencing. The same soil samples were also used for quantitative PCR analyses to quantify the total bacterial abundance as well as specific bacterial genera such as Burkholderia , Collimonas and Pseudomonas .

Soil chemical analyses

Soil chemical analyses confirmed that the chemical parameters of the soil were modified according to the treatments applied (i.e., water only (Ct), water + Mg (Mg) or water + K (K) input) at each sampling time (Table  1 ). When considered overall, the K-fertilized treatment exhibited a significantly higher exchangeable K content (K: 0.23 ± 0.02 cmol+ kg −1 ) than the control and the Mg-fertilized treatments (control (Ct) and treatment fertilized with Mg: 0.12 ± 0.004 cmol+ kg −1 ). Similarly, the Mg-fertilized treatment exhibited a significantly higher exchangeable Mg content (Mg: 0.32 ± 0.05 cmol+ kg −1 ) compared to the control and the K-fertilized treatments (control (Ct): 0.07 ± 0.008 cmol+ kg −1 and treatment fertilized with K: 0.05 ± 0.007 cmol+ kg −1 ). Notably, the cationic exchange capacity (CEC) was not significantly modified by the fertilization treatments (P > 0.05). The other parameters remained unchanged compared to those in the control treatment, except for the exchangeable Al 3+ or slight modifications at the T 3 (i.e., 45 days) sampling time (Na, Mn). No significant time effect was observed for the other soil chemical characteristics (P > 0.05).

Carbon substrate metabolization ability

When all the time points were combined and regardless the treatment, overall higher metabolization potentials were observed at T 1 (i.e., 15 days after the fertilization) for carboxylic acids (T1 > T2 = T3 = T4; P = 0.003) and on the contrary lower metabolization potentials at T 1 for polymers and miscellaneous substrates (T1 < T2 = T3 = T4; P polymers  = and P miscellaneous  = 0.0004). When the treatments were considered, a significant effect was observed for the Mg fertilization compared to the control for the polymers (P = 0.003; Mg ≥ K ≥ Ct) and the carboxylic acids (P = 0.0002; Ct ≥ K ≥ Mg).

When each time point (i.e. T1 to T4) was considered independently, our analyses revealed a significant effect of Mg fertilization on the absorbance measures (P < 0.05; Table  S1 ). For the carboxylic acids, a significant decrease was observed in the Mg and K fertilized treatments compared to the control treatment at T2 (and a trend at T3 and T4; Table  S1 ). For polymers, a significant increase was observed in the Mg fertilized treatment compared to the control treatment at T2, T3 (and a trend at T4; Table  S1 ).

Respiration profiles

The MicroResp analyses revealed a decreasing respiration rate in the control treatment between T 0 and the other sampling times (Fig.  S1 ; Resp T0 : 2.5 µg CO2/gr of soil (Dw)/hr; Resp T1 : 1.77; Resp T2 : 1.74; Resp T3 : 1.64; Resp T4 :1.64; P < 0.0001). The same MicrResp analyses also showed both treatment and time effects according to a 2-factor (time, treatment) ANOVA (P = 0.02). When analysed globally, the addition of K and Mg induced a decrease in microbial respiration compared to that of the control treatment (Resp K : 1.51 ± 0.03; Resp Mg : 1.40 ± 0.01 vs Resp Conrtol : 1.70 ± 0.03, P < 0.05), but this effect decreased with time. At T 1 (i.e., 15 days), the addition of Mg caused a significant decrease compared to the control treatment (Resp K : 1.70; Resp Mg : 1.43; Resp Control : 1.77; P = 0.005), but the addition of K did not. At T 2 (30 days), both K and Mg treatments allowed a significant decrease compared to the control treatment (Resp K : 1.35; Resp Mg : 1.34; Resp Control : 1.74; P < 0.0005). No significant effect of K and Mg addition was observed at T 3 (i.e., 45 days) and T 4 (i.e., 60 days) sampling times compared to the control, although a decreasing respiration rate was observed.

Bacterial densities

Dilutions and plate counting revealed relatively similar culturable bacterial densities between treatments and sampling times, ranging from 1.15 × 10 6 to 3.47 × 10 6 (expressed as CFU /gram of soil; Table  2 ).

Determination of the total bacterial densities by qPCR did not reveal any treatment or time effects, with densities ranging from 3.89 × 10 7 to 8.71 × 10 7 (expressed as 16S rRNA gene copies/gram of soil]. Quantification of Burkholderia revealed a decrease between the control and fertilized (K and Mg) treatments at T 1 and T 3 ; however, this was only significant at the T 1 sampling time (Ct-T1: 1.15 × 10 6 vs 3.09 × 10 6 and 3.31 × 10 6 for K and Mg, respectively; P < 0.0005). Quantification of Collimonas revealed a decrease between the control and fertilized (K and Mg) treatments at T 1 and T 3 ; however, this was only significant at the T 1 sampling time (Ct-T1: 1.07 × 10 6 vs 2.95 × 10 5 and 2.04 × 10 5 for K and Mg, respectively; P < 0.0001). Quantification of Pseudomonas revealed a decrease between the control and Mg-fertilized treatment only at the T 1 sampling time (Ct-T 1 : 1.07 × 10 6 vs 2.4 × 10 5 for Mg; P < 0.05). The relative decrease of the abundance of Burkholderia and Collimonas according to the K and Mg fertilizations was confirmed by a global analysis of the ratios (total bacteria/ Burkholderia , P = 0.003 ; total bacteria/ Collimonas , P = 0.008), but no significant effect was observed for Pseudomonas (Table  2 ).

Bacterial diversity and community profiles

Analyses of the total 16S rRNA sequence dataset revealed globally high coverage according to the coverage index, ranging from 84 ± 0.01% in the control treatment at T 1 (Ct_T 1 ) to 89 ± 0% in the control and K-fertilized treatments at T 2 and T 4 (Table  S2 ). Analysis of the α diversity revealed no difference between the treatments and sampling times for the Shannon indices (average: 3.82 ± 0.07) and the number of observed OTUs (average S obs : 110 ± 3). A sampling time effect was observed for the Chao and Simpson indices with a higher diversity in the T 1 (Simpson: 36.80 ± 3.72; Chao: 226.40 ± 13.06) than in the T 3 (Simpson: 16.66 ± 1.91; Chao: 176.66 ± 9.30) sampling time (P = 0.0009 and P = 0.0144, respectively). Concerning the community structure, no treatment and sampling time effect was observed according to PermANOVA analyses performed on the OTU table (P > 0.05).

Taxonomic composition of bacterial communities

A total of 13 phyla (55% of the sequences), 25 classes (53%), 41 orders (52%), 49 families (46%) and 53 genera (14%) were identified in our 16S rRNA sequence dataset. The first three phyla of the dataset represented more than 50% of the total sequences and were affiliated with Acidobacteria (30%), Proteobacteria (17%; Alphaproteobacteria: 9%, Gammaproteobacteria: 4%, Betaproteobacteria: 3% and Deltaproteobacteria: 1%; Fig.  S2 ) and Actinobacteria (3.5%). The other phyla, Saccharibacteria, Chlamydiae, Bacteroidetes, Gemmatimonadetes, Verrucomicrobia, Nitrospirae, Elusimicrobia, Firmicutes and Armatimonadetes, represented less than 1% of the total dataset each. The top 10 most represented genera in the 16S rRNA sequences were Burkholderia (1.4%), Acidothermus (1.3%), Collimonas (0.3%), Rhizomicrobium (0.3%), Variibacter (0.2%), Streptacidiphilus (0.1%), Cupriavidus (0.1%), Dokdonella (0.1%), Nitrospira (0.08%) and Roseiarcus (0.07%).

When each sampling time was considered independently, significant differences were observed between the different treatments (Table  S3 ). Betaproteobacteria were significantly more abundant in the control treatment at T 2 (i.e., 30 days) than in the K-fertilized treatment. This difference was partly explained by a higher proportion of sequences assigned to Burkholderiales in the control treatment at T 1 and T 2 compared to the K-fertilized treatment. However, this effect was not observed at the genus level. At the OTU level, significant differences were observed among treatments with i) the OTU0001 enriched in K-fertilized treatment compared to the control treatment (unclassified bacteria) and ii) the OTU0002 enriched in the control treatment compared to the K-fertilized treatment (unclassified bacteria).

Mineral weathering potential of bacterial strains

The functional screenings of the bacterial strains isolated from the different treatments highlighted a global negative impact of fertilization (Mg or K) on the relative distribution and effectiveness of the mineral weathering (MW) bacteria (Figs  1 and 2 ; P < 0.002).

Relative mineral weathering effectiveness of the bacteria according to the treatment (control, K fertilized and Mg fertilized) and the sampling time (T0, T1 and T3). ( A ) Relative effectiveness at mobilizing iron and ( B ) Relative effectiveness at solubilizing inorganic phosphorus. For each bar, the data presented are mean +/− Standard deviation. The error bars indicate standard deviations. Lowercase letters (a or b) indicate significant differences between treatments in one sampling time. Statistics were obtained according to a one- factor ANOVA followed by a Tukey test (P < 0.05). A total of 412 and 357 bacterial strains have been tested on the TCP and CAS media, respectively. Each strain was tested with 3 replicates.

Distribution in classes of mineral weathering effectiveness of the bacteria depending on the treatment (control, K fertilized and Mg fertilized) and on the sampling time (T0, T1 and T3). ( A ) Iron mobilizing ability and ( B ) Inorganic phosphorus solubilizing ability. For each bioassay, determination of the class of effectiveness was done depending on the median (M) of the discoloration zones of the positive strains (black: strong activity, discoloration zone > M; grey: moderate activity, discoloration zone < M; white: no degradation activity). Capital letters ( A , B or C ) indicate significant differences between the sampling times, within one treatment. Lowercase letters (a,b of c) indicate significant differences between the treatments, within one sampling time. Statistics were obtained according to a Chi2 test (P < 0.05). A total of 412 and 357 bacterial strains have been tested on the TCP and CAS media, respectively. Each strain was tested with 3 replicates.

Notably, the Mg-fertilization treatment had the strongest effect on the ability to mobilize iron (CAS; Fig.  1A ) and to solubilize inorganic phosphorus (TCP; Fig.  1B ). Indeed, in this treatment, the average MW effectiveness of the bacteria passed from T 0 (T 0 _TCP = 1.29 ± 0.10 and T0_CAS = 0.76 ± 0.07 cm of haloes) to a significantly lower average MW effectiveness at T 1 (T 1 _TCP = 0.56 ± 0.06 and T 1 _CAS = 0.17 ± 0.05 cm of haloes) (P < 0.0001). The same trend was observed at T 3 but was only significant for the ability to solubilize inorganic phosphorus. A significant effect of Mg fertilization was also observed on the relative distribution of the effective MW bacteria. This frequency significantly decreased from T 0 (T 0 _TCP: T 0  = 35% and T 0 _CAS = 23%) to T 1 (TCP_T 1+Mg  = 10% and CAS_T 1+Mg  = 3%; both P < 0.0001, Fig. 2 ). The same trend was observed at T3.

For the K-fertilized treatment, a decrease in MW effectiveness was also observed, but later than the effect observed in the Mg-fertilized treatment (Fig.  1 ). Indeed, a significant reduction of the MW effectiveness occurred only between T 1 and T 3 [(T 1 _CAS = 0.67 ± 0.07 cm and T 3 _CAS = 0.47 ± 0.09 cm (Fig.  1A ); P = 0.037; T 1 _TCP = 1.17 ± 0.11 cm and T 3 _TCP = 0.67 ± 0.07 cm of haloes (Fig.  1B ); P < 0.0001)]. A significant effect of K fertilization was also observed on the relative distribution of the effective MW bacteria. This effect was observed between each sampling time for the ability to solubilize inorganic phosphorous (T 0 _TCP = 35%, T 1 _TCP = 31% and T 3 _TCP = 18%) and to mobilize iron (T 0 _CAS = 23%, T 1 _CAS = 13% and T 3 _CAS = 10%) (P < 0.0001; Fig. 2 ). Notably, such a time effect was not observed on the MW effectiveness and the frequency of mineral weathering bacteria in the control treatment (P > 0.05).

Taxonomic affiliation of the bacterial strains and link with function

The taxonomic identification of the bacterial strains by the partial sequencing of the 16S rRNA gene showed that they belonged to the Proteobacteria [45%; Betaproteobacteria (28%), Gammaproteobacteria (5%) and Alphaproteobacteria (1%)], Firmicutes (34%) and Actinobacteria (21%)] phyla. The dominant genera identified were Bacillus (25%), Collimonas (18%) and Arthrobacter (18%), Paenibacillus (16%) and Burkholderia (8%).

Regarding the distribution of these genera according to the treatments applied in our study, our analyses revealed a significant effect of both fertilization treatment and sampling time on the taxonomic structure of the culturable representatives. Notably, the control treatment was dominated by bacteria belonging to Proteobacteria regardless of the sampling time considered and particularly by the Collimonas genus (T 0 : 28% of the collection). In contrast, the proportions of Proteobacteria and Collimonas strongly decreased with time in both fertilized treatments (after 45 days (T 3 ): Collimonas  = 3%; Chi 2 : P < 0.0001). In parallel, the proportion of bacterial strains affiliated with Firmicutes, particularly to the Bacillus genus, increased in the fertilized treatments at T 3 (37%; P = 0.004), while it remained lower in the control treatment regardless of the sampling time (15%).

Analysis of the potential relationship between taxonomy and functional ability revealed that the members of the genera Burkholderia and Collimona s exhibited the strongest MW effectiveness regardless of the bioassay considered (i.e., mobilization of iron and solubilization of inorganic phosphorus) compared to the other genera (Fig.  3 ). The members of the genus Arthrobacter were relatively effective at mobilizing iron. Notably, ca. 80% of the representatives of these two genera belonged to the bacterial groups that were very effective at mobilizing iron. For the ability to solubilize inorganic phosphorous, 80% of the Collimonas strains belonged to the most effective class, while Burkholderia (80%), Arthrobacter (96%) and the remaining Collimonas strains exhibited intermediate effectiveness. In contrast, members of the Bacillus and Paenibacillus genera exhibited very low mineral weathering effectiveness in both bioassays. Notably, our analyses also revealed no significant difference in effectiveness within a genus between treatments or sampling times.

Relationship between mineral weathering effectiveness and taxonomic affiliation of the bacteria. For each bacterial genus represented by a minimum of 10 isolates, the relative effectiveness to: ( A ) mobilize iron and ( B ) solubilize inorganic phosphorus is presented, whatever the origin of the bacterial isolates. For each bar, the data presented are mean +/− Standard deviation. The error bars indicate standard deviations. Lowercase letters (a–c or d) indicate significant differences between genus effectiveness. Statistics were obtained according to a one-way ANOVA followed by a Tukey test (P < 0.05). The number of bacterial isolates tested per genus is presented into the bars.

Comparison of the overlapping portion of the 16S gene sequences between the 454-pyrosequences and Sanger sequences of the bacterial strains revealed a relatively good overlap between the two datasets (Table  S4 ). A total of 87% of the bacterial strains from the collection exhibited more than 97% sequence similarity with the pyrosequences, while only 3.5% of the pyrosequences exhibited high similarity with the Sanger sequences from the bacterial collection. Notably, many Sanger sequences of our bacterial collection presented more than 97% sequence similarity with highly represented OTUs (i.e., OTU14, Burkholderia ; OTU19, Rhodanobacter ; OTU55, Collimonas ) or poorly represented OTUs (OTU776 and OTU829, Paenibacillus ; OTU866, Rhizobium ).

Using a combination of culture-dependent and -independent methods associated with a microcosm approach, we tested the hypothesis that an increase in the concentration of a single base cation in a soil naturally depleted in this element can modify the taxonomic and functional structures of the soil bacterial communities. The resource manipulation we applied in our study to nutrient-poor soil was determined to mimic the nutrient-rich conditions occurring in the nutrient-rich soil of the Montiers toposequence 41 . In this sense, our fertilization reached the quantity of available K (i.e., 0.28 cmol+ kg −1 of soil) and half of the quantity of available Mg (i.e., 0.81 cmol+ kg −1 of soil) measured in the nutrient-rich soil of the Montiers toposequence 41 . Consequently, the fertilization applied was low and corresponded to an increase of 2 and 5 times the available K and Mg in the manipulated nutrient-poor soil. This approach allowed us to highlight how the diversity, composition and function of the bacterial communities were impacted by an increase in the availability of base cations. To our knowledge, this study represents the first experimental demonstration of the effect of the availability of base cations and of its short-term effect on the taxonomic and functional structure of soil bacterial communities. Notably, our culture-dependent approach coupled with qPCR highlighted that the modification of the functional structure observed, which corresponded to a decrease of the frequency of effective mineral weathering bacteria, was related to a decrease in specific taxa.

Compared to other edaphic parameters, such as pH or variations of the concentration of organic compounds, few studies have demonstrated a potential impact of the availability of base cations, such as Mg, K or Ca, on the soil bacterial communities 40 , 44 , 45 . The role of these nutrients has mainly been investigated in the context of fertilization (i.e., NPK or dolomite amendments 45 , 46 ) or as potential toxic elements 47 . Consequently, little is known about their relative effects on soil microorganisms. This discrepancy is partly because most of the environmental studies mainly focused on the C, N or P cycles, as many soil parameters are linked, co-vary and have low concentrations of the base cations in comparison with the amounts of C and N. Indeed, base cations such as Mg, K or Ca usually present concentrations 100 to 1000 times lower than C. However, these cations are essential nutritive elements whose depletion strongly affects plant growth, such as in forest ecosystems developed on nutrient-poor soils 16 , 48 , 49 , 50 . These cations originated from the recycling of the cations contained in dead organic matter or from the weathering of soil minerals. In the soil considered in our study, which was sampled from the organic-mineral horizon, the base cations were mainly derived from the weathering of soil minerals 17 . In both organic matter decomposition and mineral weathering processes, microorganisms have been identified as key actors 51 . Consequently, an increase or depletion of the concentrations of base cations should directly impact their distribution and function.

The functional analyses performed during the 2-month incubation period in our study showed the impact of resource manipulation on the functional structure of the soil microbial communities. Notably, global respiration and catabolic activities appeared to be affected, but only at a low level. Indeed, both activities appeared slightly decreased in the fertilized treatments with a higher effect of Mg, suggesting a functional switch or a potential toxic effect of the cations added to the microbial communities involved in OM decomposition 47 . However, no effect was observable on the catabolic diversity (i.e., Biolog, Shannon index values) nor on the total bacterial quantifications performed both by dilution/plating and qPCR. These findings indicate that fertilization with K or Mg has a negligible effect on the global development of the soil bacterial communities compared to those in the control treatment, excluding the toxicity hypothesis. The few differences observed between the treatments may be related to the two functions measured (i.e., respiration and catabolic potentials). In contrast, the mineral weathering ability of the soil bacterial communities measured using bioassays highlighted a strong decrease of this functional group in the amended treatments, with a higher effect of Mg than K compared to the control treatment. Together, these results suggest an adaptation of the soil microbial communities to an increase in nutritive resources. While the mineral weathering ability seems to be advantageous for the bacteria with this functional trait in nutrient-poor conditions, it is not advantageous in nutrient-rich conditions, and this group is outcompeted.

The global analysis performed on the structure of the soil bacterial communities based on 16S rRNA amplicon pyrosequencing data did not reveal significant variations between the different treatments. Noticeably, the community composition and structure obtained in our microcosm-based study ( i . e ., dominance of Acidobacteria and Proteobacteria and high proportion of unclassified bacteria) appeared very similar to those obtained from the same soil, but from a field experiment in a previous study 40 . A BLASTN analysis revealed that most of the OTUs generated in our study, including the unclassified_bacteria, presented a high sequence homology (99 to 100% identity) with OTU sequences generated by Colin et al . 40 (SRA based comparison). The absence of significant variations according to the treatments applied in our study suggests a low effect of the cation input on the taxonomic structure and composition of the soil microbiome. However, a detailed analysis revealed an increase in the relative abundance of pyrosequences assigned to Actinobacteria (corresponding mainly to Arthrobacter ) and a decrease in the relative abundance of pyrosequences assigned to Betaproteobacteria (Burkholderiales) in the fertilized treatments compared to the control. Notably, the relative abundance of 16S rRNA pyrosequences assigned to Burkholderia ( de novo Paraburkholderia and Caballeronia ) and Collimonas , two genera known for their mineral weathering effectiveness 27 , 52 , 53 decreased in the fertilized treatments (+K, +Mg) compared to that in the control. Effective mineral weathering bacteria affiliated to other genera, such as Arthrobacter , have been previously described 20 , 26 but presented a low mineral weathering potential in our study. The decrease of Burkholderiales was confirmed by the genus-specific qPCR analyses, which showed a significant decrease of Burkholderia and Collimonas in the fertilized samples only. This effect was not observed for the qPCR analyses targeting Pseudomonas . Together, our new results highlight that K or Mg fertilization minimally affects the total density and structure of the soil bacterial communities, while specific bacterial genera such as Arthrobacter , Burkholderia and Collimonas are strongly affected.

Representatives of the Burkholderiales order are known to represent an important proportion of the taxa assigned from culture-dependent and culture-independent analyses in acidic and nutrient-poor forest soils. Burkholderia and Collimonas strains have been isolated, especially in the (mycor-)rhizosphere 54 and in the mineralosphere 34 , 37 . Notably, in the mineralosphere, these taxa appeared significantly enriched on the surfaces of the less-weatherable minerals 34 , suggesting a link between these taxa and the mineral chemistry and/or the nutrient availability. Interestingly, Kelly et al . 55 , analysing the active part of the microbiome, also revealed a variation of the proportion of Burkholderiales in the presence of minerals and according to the physico-chemical properties of the mineral. In our study, the relative abundance of representatives of the Burkholderia and Collimonas genera decreased in the fertilized treatments compared to those in the control, suggesting that these genera are non-adapted or less competitive to an increase in nutrient availability. Culturable representatives of these genera are heterotrophs and have been proposed to be classified as copiotrophs 56 , 57 . Copiotrophs are organisms adapted to live in nutrient-rich environments, particularly rich in carbon. Their physiological requirements are usually superior to oligotrophs organisms which on the contrary live in nutrient-poor environments and require limited resources to grow 56 , 57 . However, our findings highlight that some of these Burkholderiales-related taxa (i.e., Burkholderia and Collimonas ) have oligotrophic behaviour based on their ability to inhabit nutrient-poor environments and their effectiveness in mineral weathering 37 , 53 , 58 , 59 . Moreover, Lepleux et al . 37 demonstrated that phylogenetically related Burkholderia strains isolated from the same soil but from different habitats (bulk soil vs mineralosphere) harboured different metabolic potentials and mineral weathering potentials. Indeed, the Burkholderia strains isolated from the mineral surfaces consumed few carbon substrates with a low effectiveness compared to those of the bulk soil, while they belonged to the same species. Representatives of the Collimonas genus have been mainly reported in nutrient-poor environments (soil, dune, tundra) 53 , 58 , 60 . Consequently, those results fit very well with Ho et al . 2 , who proposed that different functional strategies may exist in the same bacterial order, family or even genus.

Through the resource manipulation performed, this work demonstrated for the first time that an increase of base cation ( i . e ., K and Mg) concentration in the soil more strongly affects the functional structure of the soil bacterial communities than the taxonomic structure. This difference between the total taxonomic structure and the functional structure ( i . e ., mineral weathering ability) of the bacterial communities may be explained by the fact that an important proportion of the soil microbiome is driven by the availability of organic nutrients ( i . e ., C and N), while a smaller proportion is driven by the availability of inorganic nutrients ( i . e ., Ca, K and Mg). An important point in our study is that the soil pH was not modified by the resource manipulation, making a real analysis of the effect of cation availability on the soil microbiome possible. Notably, both the culture-dependent and qPCR analyses revealed no effect of fertilization on the total densities of the bacterial communities during our experiment but a significant decrease in the densities of the Burkholderia and Collimonas populations. Interestingly, the Burkholderia and Collimonas strains isolated in our study harboured a greater effectiveness to weather minerals, confirming previous findings obtained for these two taxa. Together, our results show that under nutrient-poor conditions ( i . e ., non-fertilized soil), these two taxa were dominant, while they were less competitive in nutrient-rich (+K or +Mg) conditions. This finding fits very well with the definition by Grime, which considers organisms capable of developing in limiting conditions as stress tolerators. Based on this definition, our results and previous findings obtained for Burkholderia and Collimonas strains isolated in different geographical locations in forest soils, we proposed to consider them as oligotrophic and effective mineral weathering taxa. These results also bring new perspectives to soil microbial ecology and land use, as the effect of fertilization with base cations on the functional diversity has been rarely considered.

Materials and Methods

Site description and soil sampling.

In January 2017, soil samples were collected from the Montiers forest experimental site located in the Meuse department (northeastern France). This long-term observatory (LTO) is part of AnaEE-France (Analyses et Expérimentations sur les Ecosystèmes - France). It is co-managed by the ANDRA [French national radioactive waste management agency; Permanent Environment Observatory (OPE)] and the INRA (UMR1138 Unit; http://www.nancy.inra.fr/en/Outils-et-Ressources/montiers-ecosystem-research ). The site is dominated by a homogeneous land cover of European beech trees of the even-aged stand ( Fagus sylvatica L . ; 50 years old in 2010) and is characterized by a Jurassic limestone (Tithonian) layer overlaid by acid detrital sediments from the lower Cretaceous (Valanginian). The site is characterized by a soil toposequence covering different soil types: from high-nutritive Cambisol Calcaric to nutrient-poor Cambisol Hyperdystric, with an intermediate Eutric Cambisol 17 , 41 , 44 , 61 . In this study, sampling was performed in Cambisol Hyperdystric soil, which is the most nutrient-poor soil type of the toposequence. The soil was sampled from the same plot after removing leaf litter. The 0–5 cm soil layer was separated, and only the 5–20-cm soil horizon was transported back to the lab. This soil horizon corresponds to the organo-mineral horizon of the soil profile and to the horizon where most of the fine roots of the trees are located. Soil samples were collected using sterile tools to avoid contamination and were returned to the laboratory immediately. All the soil samples were then mixed to homogenize and sieved by a 4 mm mesh. Then, they were stored at 4 °C overnight before the beginning of the experiment.

Microcosm experimental design

A microcosm experiment was conducted to test the relative impact of an increase in nutrient availability on the functional and taxonomic structures of the soil bacterial communities. To do so, we considered two important base cations (magnesium and potassium). These nutrients were selected as previous experiments performed on the Montiers site noted the potential role of magnesium and potassium as limiting base cations for the growth of trees in the Cambisol Hyperdystric soil compared to the Cambisol Calcaric soil 17 and because these two nutrients are important cations for tree growth in nutrient-poor and acidic forests 41 . Moreover, according to the fertility norms established for forest soils 62 , the fertility in K is low in Cambisol Hyperdystric soil, and the fertility in Mg is moderate and at the limit of the norm 17 . The calcium was not considered in our study, as it is not limiting at the site considered as it is developed on limestone rich in calcium. The microcosms were prepared the day following the field sampling so that soil was fresh. The soil was separated into three soil subsamples to allow the application of the different treatments in the soil: (i) control (water), (ii) water + magnesium, and (iii) water + potassium. The concentrations of magnesium and potassium used to fertilize the Cambisol Hyperdystric soil were chosen to reach the same concentrations of available Mg and K in the microcosms as the concentrations of these elements measured in the most nutrient-rich soil type of the toposequence of Montiers ( i . e ., the Calcaric Cambisol; ref. 41 [Table  1 ]). The different treatments considered in our microcosm experiments were (i) K fertilization (KCl, 158.7 mg kg −1 dw (dry weight) soil), (ii) Mg fertilization (MgCl 2 , 701.6 mg kg −1 dw soil), and (iii) distilled water (control). Distilled water was used to prepare the different solutions. Each solution was adjusted to the same pH (4.6), which was the pH of the Cambisol Hyperdystric soil sampled, to avoid any effects due to the pH of the solutions added to the microcosms. Treatments were applied in one pulse at the beginning of the experiment. The moisture of the microcosms was adjusted according to the field soil moisture (35% of the water holding capacity (WHC)), and the soil water content was adjusted throughout the experiment with sterile distilled water. After the addition of the control (water), K and Mg solutions, each of the soil subsamples was mixed and divided into 20 microcosms for the control treatment (including 4 T0 microcosms), 16 microcosms for the K treatment and 16 microcosms for the Mg treatment, giving a total of 52 independent microcosms. The microcosms used consisted of small pots (40 mL, sterile) filled with 27 g of Cambisol Hyperdystric soil (fresh weight) fertilized with the different solutions described above. The microcosms were incubated in a dark experimental chamber at 21 °C for 60 days.

Sample collection

The experiments were run over 60 days, and soil samples were collected at different time points: i) just after the application of the treatment (T 0 ), ii) after 15 days (T 1 ), iii) after 30 days (T 2 ), iv) after 45 (T 3 ) and v) after 60 days (T 4 ). At each sampling time, four microcosms from each treatment were selected to perform the different analyses described below. For each microcosm, the soil collected (27 g) was homogenized before any analysis. A total of 1.5 g of the soil was used to determine the global respiration using the MicroResp system. A total of 5 g of the soil was suspended in 25 mL of sterile distilled water and vortexed twice for 1 min. This soil suspension was diluted at 1/20 to determine the global metabolic potential on Biolog EcoPlates. In parallel, a volume of 0.5 mL of the undiluted soil suspension was used to perform culture-dependent bacterial analyses. A total of 1 g of the soil was conserved at -20 °C for DNA-based analyses. Finally, 5 g of the soil was dried at 35 °C and sieved at 2 mm for chemical analyses.

The cation exchange capacity (CEC) was determined using the cobaltihexamine method 63 , which is based on the titration of the cobaltihexamine chloride soil extract at 472 nm compared to a reference 0.05 N cobaltihexamine chloride extract. The pH was determined by the water method using a soil/water ratio of 1:5 (w/v). Exchangeable cations (Ca, Mg, Na, K, Fe, Mn and Al) and H + were extracted using cobaltihexamine and determined by inductively coupled plasma spectrometry-atomic emission spectrometry (ICP-AES) for cations and by potentiometric measurement using 0.05 M KOH for protons and aluminium 64 . The chemical analyses were performed on the four replicates of each treatment (potassium, magnesium or water) at the T 0 , T 1 and T 3 sampling times.

Substrate metabolization assay

The ability of microbial communities to metabolize various carbon substrates was assessed at each sampling time and for each treatment through the EcoPlates (Biolog®; Hayward, CA95545 U.S.A), which contain 31 different carbon substrates (pyruvic acid methyl ester;tween 40; tween 80; alpha-cyclodextrin; glycogen; D-cellubiose; alpha-D-Lactose; beta-methyl-D-glucoside; D-xylose; i-Erythritol; D-Mannitol; N-Acetyl-D-Glucosamine; D-Glucosaminidic acid; Glucose 1 phosphate; Glycerol phosphate; D-galactonique acid gamma-lactone; D-galacturonic acid; 2-Hydroxy-benzoic acid; 4-Hydroxy-benzoic acid; Hydroxybutiric acid; Itaconic acid; ketobutyric acid; malic acid; arginine; asparagine; phenylalanine; serine; threonine; glutamic acid; phenylethylamine; putrescine). A volume of 150 µL of the 1/20 diluted soil suspension described above was loaded in each well of a Biolog microplate and incubated at 25 °C for 48 h. For the soil considered 48 h of incubation was enough, saturation was obtained after this period. Colour development was measured at 595 nm with an automatic microplate reader (Bio-Rad, model iMark). The absorbance data were corrected by subtracting the blank, and negative absorbance values were set to zero. The data were expressed as average well color development (AWCD) 65 . The measures were performed on three replicates biological.

The basal soil microbial respiration was measured by the MicroResp method 66 at each sampling time and for each treatment. The measurements were performed on the four replicates. For each replicate, 4 deep-wells of the MicroResp microplate were filled with 0.3 g of soil. Simultaneously, a CO 2 -detection microplate was filled with a pH indicator gel (3% agar, 2.5 mM NaHCO 3 , 150 mM KCl, and 12.5 µg mL −1 of cresol red). The deep-well microplate was placed face-to-face with the CO 2 -detection microplate, and both were firmly sealed together with a silicone rubber gasket following the manufacturer’s instructions 64 . After 6 h of incubation in the dark at 25 °C, the pH change was visible through a colour shift of the indicator gel and was measured at 570 nm with an automatic microplate reader (BioRad, model iMark). The rate of CO 2 (µg CO 2 /gr of dw soil/hr) produced was calculated according to Campbell et al . 66 .

Bacterial collection

For each treatment, bacterial collections were performed considering three replicates. For each replicate, 5 grams of soil was sampled and suspended in 25 mL of sterile distilled water. A volume of 0.2 mL of the pure soil suspension was serially diluted in sterile distilled water and spread onto a 1/10 diluted tryptic soy agar (TSA) medium (tryptic soy broth from Difco, 3 g L −1 and agar, 15 g L −1 ) supplemented with 100 µg L −1 cycloheximide (Sigma; final concentration). The Petri dishes were incubated at 25 °C for 5 days. Colonies were counted after the incubation period and expressed as colony forming units (CFUs) g −1 of soil (dry weight). For each of the three biological replicates and considering the same dilution for all the treatments, all the colonies were collected. In our study, it corresponded to ca 20 colonies per replicate for a total of 60 bacterial strains per treatment and sampling time. The bacterial isolates were purified by three successive platings on 1/10 diluted TSA and then cryopreserved at −80 °C in 20% glycerol. A total of 420 bacterial strains were isolated. All the bacterial isolates used in this study were then cultivated on 1/10 diluted TSA at 25 °C for 72 h.

Mineral weathering potential of bacterial isolates

The mineral weathering potential, meaning the ability to mobilize inorganic nutrients of each bacterial isolate, was assessed through two commonly used bioassays measuring the ability: i) to mobilize iron and ii) to solubilize inorganic phosphorus 28 , 38 , 59 . The ability to mobilize iron was tested through the detection of siderophore production on the chrome azurol S (CAS) medium, following the protocol of Frey-Klett et al . 67 . This medium is composed of one litre of 800 mL of Solution 1 (34.36 g of Pipe Na 2 Buffer (Sigma), 0.3 g KH 2 PO 4 , 0.5 g NaCl, 1 g NH 4 Cl, 0.246 g MgSO 4 ∙7H 2 O, 0.0147 g CaCl 2 ∙2H 2 O and 15 g agar in 800 mL H 2 O; pH 6.8), 100 mL of Solution 2 (4 g glucose in 100 mL H 2 O) and 100 mL of Solution 3 (Mix of 0.0905 g chrome azurol S in 75 mL, 0.0024 g FeCl 3 in 15 mL, 0.1640 g hexadecyltrimethylammonium bromide in 60 mL H 2 O). Solutions 2 and 3 were added after autoclaving at a temperature of ca. 70 °C. The ability to solubilize inorganic phosphorous was tested using a 1/10 diluted tri-calcium phosphate (TCP) bioassay, according to Lepleux et al . 28 . This medium is composed of one litre of distilled water of 0.5 g NH 4 Cl, 0.1 g NaCl, 0.1 g MgSO 4 ∙7H 2 O, 1 g glucose, 0.4 g Ca 3 (PO 4 ) 2 and 15 g agar (pH 6.5).

For each bioassay, bacterial isolates were grown for 72 h in 10 mL of liquid lysogeny broth (LB) medium at 25 °C. Two millilitres of each bacterial culture were then collected, washed three times in sterile water and suspended in 2 mL sterile water to obtain a calibrated inoculum suspension at λ 595 nm = 0.50 (ca. 10 7 cell mL −1 ). After vortexing, 10 µL of this suspension was then used in triplicate for the CAS and TCP assays. After incubation at 25 °C for 7 days, the diameters of colonies and the diameter of the discoloration zone were measured to determine the ability of each bacterial isolate to mobilize iron (CAS) and to solubilize inorganic phosphorous (TCP). The diameter of the discoloration zones was used to determine the relative efficacy of each bacterial isolate in each bioassay and their distribution in the three classes of efficacy based on the size of the discoloration zone on the different media (no mobilization activity, low activity and strong activity). For each medium, the determination of the class of efficacy was performed depending on the median of the discoloration zones of the positive strains (TCP = 1.4 cm; CAS = 1.8 cm) as follows: positive bacterial isolates with discoloration zones lower than the median were classified as bacterial isolates of low activity, whereas bacterial isolates with discoloration zones higher than the median were classified as bacterial isolates of strong activity. A total of 412 and 357 bacterial strains grew on TCP and CAS media, respectively.

Molecular identification of bacterial isolates

The 16S rRNA gene amplification of the bacterial isolates was performed using the universal set of primers pA [(5′-AGAGTTTGATCCTGGCTCAG-3′) and 907r (5′-CCGTCAATTCMTTTGAGTTT- 3′) 68 , 69 ]. Polymerase chain reactions were performed in a total reaction volume of 50 µL containing 20 µL PCR Master mix (5-PRIME), 2 µL primers (10 µM) and 4 µL of inoculum suspension. The same inoculum suspension used for the functional assays described above was used for molecular characterization. The following temperature cycle was used: initial denaturation for 4 min at 94 °C, followed by 30 cycles of 30 s denaturation at 94 °C, 1 min annealing at 53 °C and 1 min 30 s extension at 72 °C, and a final extension for 10 min at 72 °C. PCR controls with no template or extraction control as a template were negative. PCR products were purified using the QIAquick purification kit (Qiagen, Valencia, CA) as recommended by the manufacturer. The sequencing was performed by MWG Eurofin Operon using the primers described above. The partial 16S rRNA gene sequences generated were compared with those of the GenBank databases using the BLAST programme 70 . They were aligned with reference 16S rRNA gene sequences using SeaView (version 4.5.4 71 ). Bootstrap values were generated using 1,000 replicates. Trees were generated using Dendroscope 3 software (version 3.4.4 72 ).

DNA extraction and quantitative PCR

For each treatment (K, Mg fertilized and control) and sampling time, total DNA was extracted from 250 mg of soil. Extractions were performed in three replicates using the ‘PowerSoilTM DNA Isolation Kit’ (MoBio Laboratories, Carlsbad, CA, USA) as recommended by the manufacturer. Concentrations of DNA were measured using a Nanodrop-1000 spectrometer (NanoDrop Technologies, Wilmington, DE, USA). The same quantity of total DNA was used to quantify the total bacterial 16S as well as the bacterial genera ( Burkholderia, Collimonas and Pseudomonas ) using the 16S rRNA gene-specific primers [10 μM each; 968 F/1401 R (total bacteria 73 ), Burk3/BurkR ( Burkholderia 74 ), Eddy3For/Eddy3Rev/Sophie ( Collimonas 58 ) and PSf/Psr ( Pseudomonas 75 ), respectively]. For T0, T1 and T3, absolute quantifications were performed using serial dilutions of standard plasmids containing total or genus-specific bacterial 16S rDNA inserts (from 10 9 to 10 2 gene copies/μl) and the SsoAdvanced Universal SYBR Probes Supermix (classical qPCR for quantification of total bacteria, Burkholderia and Pseudomonas ) and Probes Supermix (TaqMan qPCR for quantification of Collimonas ) from Bio-Rad. PCR reaction were performed in a final volume of 20 µl containing 10 µl of the mix (SsoAdvanced Universal SYBR Probes Supermix or Probes Supermix), 1 µl of the total DNA, 2 µl of the primers (and of the Taqman probe when necessary) and milliQ water. The different bacterial genera and total bacterial quantifications were performed using the following cycle parameters: 1 cycle of 98 °C for 3 min followed by 40 cycles of 98 °C for 15 s, AT (annealing temperature) °C for 30 s (AT: 56 °C for total bacterial; 64 °C for Burkholderia ; 63 °C for Pseudomonas ; 66 °C for Collimonas ). For each qPCR run using SYBR technology, a melting curve was performed at the end. The population sizes of total bacteria, Burkholderia , Collimonas and Pseudomonas , are expressed as log[number of 16S rRNA gene copies per gram dry weight soil].

PCR and pyrosequencing analyses

The 16S rRNA gene amplicon libraries were generated in one step using the primers 799 f and 1115r 76 , 77 containing the specific Roche 454-pyrosequencing adaptors and 5 bases barcodes, according to Colin et al . 34 . PCRs contained 1X PCR Master mix (5 PRIME®), 500 nM 799 f primer, 500 nM 1115r primer and 8 ng of DNA in a final volume of 50 µL. Amplifications were performed using the following cycle parameters: 95 °C for 5 min (initial denaturation), followed by 30 cycles of 95 °C for 30 s, 57 °C for 35 s, and 72 °C for 30 s with a final extension step at 72 °C for 10 min. Triplicate PCR products were generated for each replicate and were then checked by gel electrophoresis, pooled and purified using the QIAquick purification kit (Qiagen, Valencia, CA, USA) as recommended by the manufacturer. The concentration of each purified PCR product was then measured using a Nanodrop-1000 spectrometer (NanoDrop Technologies, Wilmington, DE, USA), and an equimolar mix of the 16S rRNA gene amplicons was used for pyrosequencing on a 454 GS Junior system (Roche; Ecogenomic platform). All the treatments were sequenced, except for the Mg amended condition at T2 and T4. A total of 42,425 raw reads were generated. These 16S rRNA reads were then filtered for length (>300 bp), quality score (mean, ≥25), number of ambiguous bases (=0), and length of homopolymer runs (<8) using the trim.seqs script in Mothur v.1.30.2. These treatments generated a total of 31,216 quality sequences 78 . Chimeric sequences were detected using the chimera.uchime command and were removed from further analysis. To avoid any biases associated with different numbers of sequences in each of the samples, a random subsample of 448 sequences (corresponding to the smaller set of sequences after Mothur processing) from each sample was used for all downstream analyses, leading to a total of 14,784 sequences. High-quality sequences were aligned and clustered into 873 operational taxonomic units (OTUs), defined with 3% dissimilarity level and including 373 singletons. Taxonomy was assigned to each OTU by aligning sequences against the SILVA alignment database (version 128) with a bootstrap value of 80 for taxonomic assignment. The 16S rRNA gene sequences assigned as ‘unclassified bacteria’ with Silva were also verified using the BLAST programme 70 against the NCBI database. Based on the OTU assignment, library richness and diversity estimates (Chao1, Shannon and inverse Simpson) were calculated in Mothur. In addition, a comparison of the 16S rRNA pyrosequences and Sanger sequences (generated from the culturable representative strains) was performed using the BLAST programme 70 . Alignment was performed with NCBI-BLAST + an e-value cut-off of 1 e-5 and a 100% overlap on the common portion (120 bases). Similar analyses have been performed with the 16S rRNA pyrosequences generated in a previous study on the same soil 40 .

Statistical analyses

All statistical analyses were performed using R 3.2.3 software 79 . The effect of the treatment and sampling time on the Biolog-substrate utilization profiles and on the respiration rates was determined by analysis of variance (one-factor ANOVA, P < 0.05, followed by a Tukey test). Similarly, ANOVA was conducted to determine the relative efficacy of the bacterial isolates to weather minerals (P < 0.05, followed by a Tukey test). Finally, the hypothesis of an equal distribution of the bacterial isolates according to their efficacy class in the functional assays was tested by a Chi 2 test (P < 0.05) using the NCStats package 80 . Differences in the mineral weathering efficacy of the bacterial strains affiliated with a specific genus were assessed following the same procedure, but only for bacterial genera represented by more than 10 bacterial isolates. Estimation of the taxonomic diversity and richness based on culturable-independent data was carried out with Mothur, followed by statistical analysis (ANOVA followed by a Tukey test, P < 0.05). Permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distances with 999 permutations were performed on the 16S rRNA pyrosequence data using the vegan package 81 . To assess structural differences between bacterial communities of treatments and/or sampling times, relative abundance values at all taxonomic levels (phylum, class, order, family, genus, OTU) were compared for taxa or OTUs presenting a minimum of 100 sequences. These values were transformed using the arcsine square root to achieve a normal distribution. A one-factor ANOVA was then conducted on those data, followed by a Tukey test with a threshold level of P < 0.05. A p adjustment was done using the false-Discovery-Procedure of Benjamini and Hochberg 82 to determine the p value threshold.

Nucleotide sequence accession numbers

The sequences determined in this study have been deposited in the GenBank database for the Sanger sequences (MH918162-MH918556) and in the SRA trace archive for the pyrosequences (SRP013944; SRR7812734- SRR7812719).

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Acknowledgements

This work was funded by the Laboratory of Excellence Arbre (ANR-11-LABX-0002-01) in the frame of the multidisciplinary project ‘INABACT’. O. Nicolitch is a PhD student, and M. Feucherolles is a Master 2 student supported by a fellowship from the Laboratory of Excellence Arbre (ANR-11-LABX- 0002-01). The UMR1136 and UR1138 are supported by the French Agency through the Laboratory of Excellence Arbre (ANR-11-LABX-0002-01). The authors thank ANDRA and ONF. The English language was reviewed by American Journal Experts (ref. 874E-0EB1-A18F-F1C7-994A). The authors also thank Dr. P. Frey-Klett, P. Oger, A. Cébron, Y. Colin, L. Auer and members of the INABACT consortium for helpful discussions during the project.

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The present study was coordinated by S.U. and M.-P.T. The experiments were performed by O.N. and M.F., with the help of L.F. and J.-L.C. The data processing was done by O.N., S.U. and M.-P.T. The manuscript was written by S.U. and O.N. All authors reviewed the manuscript.

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Nicolitch, O., Feucherolles, M., Churin, JL. et al. A microcosm approach highlights the response of soil mineral weathering bacterial communities to an increase of K and Mg availability. Sci Rep 9 , 14403 (2019). https://doi.org/10.1038/s41598-019-50730-y

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The use of microcosms as an experimental approach to understanding terrestrial ecosystem functioning

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Since 1986, a series of microcosm experiments has been conducted at the Unit of Comparative Plant Ecology (UCPE) in an attempt to test our understanding of the principles controlling the structure and dynamics of plant communities and ecosystems. In each experiment microcosms have been seeded with a common pool of organisms, and systems have been allowed to assemble under replicated controlled conditions. Experiment variables have included mineral nutrient supply, temperature, moisture supply, soil depth, carbon dioxide concentration, mycorrhizas, rhizobia, herbivores and carnivores. Results from these experiments are presented to illustrate the value of synthesised ecosystems in ecological research.

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What are the type, direction, and strength of species, community, and ecosystem responses to warming in aquatic mesocosm studies and their dependency on experimental characteristics? A systematic review protocol

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Mesocosm experiments have become increasingly popular in climate change research as they bridge the gap between small-scale, less realistic, microcosm experiments, and large-scale, more complex, natural systems. Characteristics of aquatic mesocosm designs (e.g., mesocosm volume, study duration, and replication) vary widely, potentially affecting the magnitude and direction of effect sizes measured in experiments. In this global systematic review we aim to identify the type, direction and strength of climate warming effects on aquatic species, communities and ecosystems in mesocosm experiments. Furthermore, we will investigate the context-dependency of the observed effects on several a priori determined effect moderators (ecological and methodological). Our conclusions will provide recommendations for aquatic scientists designing mesocosm experiments, as well as guidelines for interpretation of experimental results by scientists, policy-makers and the general public.

We will conduct a systematic search using multiple online databases to gather evidence from the scientific literature on the effects of warming experimentally tested in aquatic mesocosms. Data from relevant studies will be extracted and used in a random effects meta-analysis to estimate the overall effect sizes of warming experiments on species performance, biodiversity and ecosystem functions. Experimental characteristics (e.g., mesocosm size and shape, replication-level, experimental duration and design, biogeographic region, community type, crossed manipulation) will be further analysed using subgroup analyses.

Since the beginning of the industrial era, anthropogenic activities have led to increased atmospheric concentrations of greenhouse gases, which are currently reaching their highest levels in the last 800,000 years [ 1 ]. This has resulted in a measureable increase in both global air and water temperatures—a trend that is projected to intensify through the end of the twenty first century [ 2 ]. Global warming is poised to be one of the most serious threats to aquatic ecosystems, both marine [ 1 ] and freshwater [ 3 ]. Various ecological responses to warming have been documented and demonstrated experimentally at all levels of biological organization, including changes in species distribution, phenology, growth, and metabolism, as well as in community structure, biodiversity and ecosystem functions [ 1 , 3 ].

Understanding and predicting the consequences of climate change on species, communities, and ecosystems is challenging. It can be difficult to disentangle climate-driven signals from natural variability, especially when combined with other environmental stressors such as eutrophication and pollution [ 1 ]. Despite these difficulties, major scientific efforts (i.e., substantial research investments in the form of experiments) have been made to elucidate the ecological effects of climate change using a range of methods: extrapolation, experiments, game-theory population models, phenomenological models, expert opinion, and outcome-driven modelling and scenarios [ 4 ]. Among experimental approaches, mesocosms have become progressively more popular as they narrow the gap between smaller-scale, less realistic, microcosm experiments, and larger-scale, more complex, natural systems, in which mechanistic relationships are often difficult to identify [ 5 ].

Eugene P. Odum first coined the term ‘mesocosm’ to describe replicated experimental setups of a moderate size, where ‘parts (populations) and wholes (ecosystems) can be investigated simultaneously by a team of researchers’ [ 6 ]. Over the years, the term ‘mesocosm’ has been arbitrarily used to specify experimental enclosures of varied shape and volume, from one to thousands of litres [ 7 ]. Today, aquatic mesocosms are used in marine, estuarine, and freshwater systems. Enclosures of pelagic waters, in the laboratory (e.g., [ 8 ]), outdoors, or in situ (e.g., [ 9 ]) have been used to test the effects of warming on plankton communities. Benthic mesocosms have long been used in shallow freshwater ecosystems to establish mechanistic relationships between various stressors and population, community, or ecosystem dynamics [ 10 ]. More recently, benthic mesocosms have also been developed for marine environments to test the effect of warming, acidification, eutrophication and hypoxia on shallow coastal ecosystems while allowing for natural fluctuations, thus increasing realism [ 11 ].

Despite their widespread use in ecological studies, mesocosm experiments have been criticized as unrealistic and simplistic representations of ecological processes, producing results with limited relevance and applicability to natural ecosystems [ 12 ]. Mesocosm dimensions (volume), shape, settings, experimental duration, replication-level, and other design characteristics serve as confounding factors that can strongly influence the measured experimental effect [ 13 ]. To date, the most comprehensive review of mesocosm experiments as a tool for ecological climate change research was conducted by Stewart et al. in 2013 [ 5 ]. This extensive review covers terrestrial, marine and freshwater experiments, describes advantages and caveats of mesocosm methodologies, and illustrates the number of mesocosm studies in different categories. However, it does not provide quantitative measures of how ecological and methodological characteristics may influence the effect sizes measured in these studies, both in magnitude and direction. Therefore, a new quantitative evaluation of the contribution of experimental mesocosms to climate change research is both timely and vital to better understanding the limitations and caveats associated with such an approach.

Objective of this review

This systematic review is aimed at identifying the type, direction, and strength of species, community, and ecosystem responses to experimental warming in aquatic mesocosms. We will also investigate the context-dependency of observed effects on several a priori ecological and methodological moderators. This global review will cover existing studies conducted in aquatic ecosystems (i.e., marine, estuarine, and freshwater), across all biogeographical regions, and with all species. Studies will be considered included based upon the criteria described below.

Primary question

What are the type, direction, and strength of species, community, and ecosystem responses to warming in aquatic mesocosm studies?

Secondary question

How do experimental characteristics of aquatic mesocosms change the direction and magnitude of effect sizes in climate change research?

The list of components that will help guide the search and analysis of the extracted data is shown in Table  1 .

Search strategy

We will use a pre-determined list of keywords to search for relevant studies in the academic databases Web of Science, Scopus and Google Scholar, and in non-academic websites using Google Custom International Governmental Organizations (IGO) search ( https://cse.google.com/cse/home?cx=006748068166572874491:55ez0c3j3ey ) and Google Custom Non-Governmental Organizations (NGO) search ( https://cse.google.com/cse/home?cx=012681683249965267634:q4g16p05-ao ). The list of search terms and Boolean operators that will be used to identify relevant aquatic mesocosm studies is provided in Table  2 . Within each category (population, exposure and intervention, outcomes), the search terms will be combined in parentheses and separated using the Boolean operator ‘OR’. These categories will then be combined using the Boolean operator ‘AND’. An asterisk (*) indicates a ‘wildcard’, which allows databases to include multiple words with different prefixes or suffixes; for example, estuar* captures [estuary OR estuaries OR estuarine]. Quotation marks (“”) around two or more words restrict the search to instances where that exact phrase occurs.

While reading the full-text publications we will look for further relevant material (e.g., cited papers) that may include useful data for this systematic review that were missed in our search of publication databases. In the case of papers reporting incomplete information, we will attempt to obtain the relevant information by contacting the authors. The resulting list of publications will be managed using reference management software (Mendeley), which will be used to eliminate redundant publications.

The degree of comprehensiveness of the search strategy and its ability to identify all relevant articles will be assessed using sensitivity analyses [ 14 ].

Article screening and study inclusion criteria

The eligibility of the articles obtained by the aforementioned search for the final analysis will be assessed via a set of inclusion criteria at three successive levels: title, abstract and full-text. First, we will evaluate articles by title to remove citations spuriously returned by our search. Next, we will evaluate the remaining citations based on their abstracts to further remove unrelated citations. At this stage, all participating reviewers will assess an identical subset of the articles (5%), and a Kappa inter-rater agreement statistic [ 15 ] will be calculated based on the assessments. If the statistic indicates that reviewers are inconsistent in their assessment of article relevance, discrepancies will be discussed and the inclusion criteria will be clarified or revised to ensure that consistent methods are utilized by all authors. We will iterate this process until the computed Kappa statistic exceeds 0.6 [ 15 ]. Finally, the full text of the remaining articles will be evaluated for the meta-analysis. If it is unclear whether an article meets the inclusion criteria at an initial level of screening, it will be included for evaluation at the next level of the systematic review. A table listing all articles excluded at full text stage with reasons for exclusion (based on the inclusion criteria) will be provided as a supplementary for the systematic review.

Selected publications must contain the following information: (1) replication level/sample size, (2) averages (arithmetic means) of control and treatment groups, and (3) variance estimates (as standard deviation, standard error or confidence interval). Further evaluations will be based on whether populations, exposures, comparators, outcomes, and study types meet the following criteria:

Relevant populations

Any aquatic species, population or community, including marine, brackish, and freshwater systems.

Relevant exposures

An experiment that manipulates water temperature (warming) and is conducted in a mesocosm setup. We will deem all replicated experimental setups whose volume is equal to or larger than 1 L as mesocosms [ 7 ].

Relevant comparators

(1) Experiments comparing “treated” (warmed) and “control” (ambient temperature) conditions (CI); (2) Experiments comparing “before” (ambient temperature) and “after” (warmed) conditions (BA).

Relevant outcomes

We will search for a broad range of outcomes (i.e., ecological responses): (1) changes in species richness, evenness, and diversity, (2) changes in species and community metabolism (productivity, respiration, calcification), (3) changes in species survival, mortality, size and growth, (4) changes in nutrient flux (carbon, nitrogen, phosphorus, sulfur), and (5) changes in species and communities resilience, stability or resistance.

Study quality assessment

Studies that have passed the inclusion criteria described above will be subject to an evaluation for bias. Susceptibility to bias will be defined by any of the following factors: lack of true replication, lack of methodological information (e.g. sample size), uninterpretable outcomes, and difficulty in interpreting exposure (mesocosm setup) and intervention (warming treatment) data. Based on assessing these criteria, studies will be categorised as having high, medium or low susceptibility to bias. Studies with high susceptibility will be excluded from the review. The list of studies, their level of susceptibility to bias (high, medium, low) and the categorization justification will be provided in the systematic review.

Data extraction and effect size calculation

Means, sample sizes and variance estimators will be extracted directly from the text and tables, or from figures using image analysis software (e.g., ImageJ). All three data components must be reported for a study to be included. Hedges’ g [ 16 ] will be used to calculate the effect size. Hedges’ g is the unbiased mean difference estimator, which estimates the difference in the response variable between the ‘treatment’ (i.e., warmed mesocosm units) and control (ambient temperature) groups. This measure is standardized by the within-group standard deviation, penalizing studies with large variances and/or few observations. In substance, this estimator transforms all effect sizes to a common metric, thus enabling the calculation of summary effects across data that may have been captured on different scales [ 17 ]. All extracted data records will be made available as additional files.

Potential effect modifiers and reasons for heterogeneity

Study-level modifiers may contribute to the variation in effect size and can thus be regarded as potential effect-moderators [ 18 ]. These modifiers can be related to either the characteristics of the studied species/habitats/regions or the methodology used. For each outcome category we will define “characteristic” categorical moderators, e.g., system type (lentic, lotic, marine, estuarine), community type (benthic/pelagic/both), mesocosm size (volume), experimental duration, replication type and level (gradient, repeated treatment), mesocosm settings (indoors/outdoors/in situ), experimental design (closed/open/semi-open-system), water source (natural, artificial), focal taxa, focal taxa size, number of trophic levels, number of species, biogeographic region (marine) or ecoregion (freshwater and estuarine), latitude, longitude, crossed manipulation (none, acidification, nutrient addition/depletion, exposure to invasive species, oxygen depletion, toxins/pollutants, feeding regime, exposure to disease/parasites, salination, flow, precipitation and sea level). Each of these attributes will be identified for each study, as relevant.

Data synthesis and presentation

The effect size estimates from individual studies will be aggregated using the ‘metafor’ package in R [ 19 ], and presented in forest plots. Assuming heterogeneous studies, the summary effect in each category will be calculated using a random-effects model. Funnel plots and the Trim and Fill algorithm [ 20 ] will be used to evaluate publication bias. To assess the relationship between potential effect-moderators and the effect size within each category, we will perform subgroup analyses using a mixed-effects model structure. These subgroup analyses will show which moderators, if any, have the most impact on mesocosm experimental design, thereby informing aquatic scientists wishing to plan mesocosm experiments. Our results will also provide guidelines for interpretation of climate warming experiments by scientists, policy-makers, and the general public.

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Authors’ contributions

The work presented here was carried out in collaboration between all authors. TGH initiated and wrote the manuscript, designed the protocol, and discussed its components. HA, RB, LB, CBA, TB, XD, AF, JG, LG, SH, RH, CJ, DM, AM, MM, AN, AJR, HS and KS contributed to the design of the protocol and text of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to Paul Kemp and Lydia Baker for valuable feedback on this project, and to Gil Rilov, Jacob Silverman, Eyal Rahav and Martin Wahl for their valuable comments on an earlier draft of this manuscript.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Should the manuscript be accepted, the data supporting the results will be archived in an appropriate public repository and the data DOI will be included in the article.

This systematic review protocol originated from discussions that took place at the Ecological Dissertations in Aquatic Sciences (EcoDAS) XII Symposium in October 2016, which was hosted by the Center for Microbial Oceanography (C-MORE) and the University of Hawaii. Funding for EcoDAS XII was provided by the NSF biological oceanography program (Award OCE-1356192) and the Association for the Sciences of Limnology and Oceanography (ASLO).

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GEOMAR, Helmholtz Centre of Ocean Research Kiel, Benthosökologie, Düsternbrooker Weg 20, 24105, Kiel, Germany

Tamar Guy-Haim

Israel Oceanographic and Limnological Research, National Institute of Oceanography, P.O. Box 8030, 31080, Haifa, Israel

Department of Population Health and Reproduction, University of California, Davis, Davis, CA, 95616, USA

Harriet Alexander

Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA

Tom W. Bell

Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden

Raven L. Bier

Institute for Wetland and Waterfowl Research, Ducks Unlimited Canada, P.O. Box 1160, Stonewall, MB, R0C 2Z0, Canada

Lauren E. Bortolotti

Oregon State University, Hatfield Marine Science Center, 2030 SE Hatfield Drive, Newport, OR, 97365, USA

Christian Briseño-Avena

Nicholas School of the Environment, Duke University, 450 Research Drive, Durham, NC, 27708, USA

Xiaoli Dong

School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA

Alison M. Flanagan

Biological Oceanography, GEOMAR, Helmholtz Centre of Ocean Research Kiel, Düsternbrooker Weg 20, 24105, Kiel, Germany

Julia Grosse

Biodiversity Department, Centre for Water and Environmental Research, University of Duisburg-Essen, Universitaetstrasse 5, 45239, Essen, Germany

Lars Grossmann

Department of Biology, Queen’s University, 116 Barrie Street, Kingston, ON, KJL 3N6, Canada

Sarah Hasnain

School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, WA, 98195, USA

Rachel Hovel

University of Maryland, College Park, College Park, MD, 20782, USA

Cora A. Johnston

Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900, Ramat Gan, Israel

Dan R. Miller

Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA

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W.M. Keck Science Department, 925N. Mills Ave, Claremont, CA, 91711, USA

Akana E. Noto

Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY, 12545, USA

Alexander J. Reisinger

Center for Biofilm Engineering, Montana State University, Bozeman, MT, 591717, USA

Heidi J. Smith

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Guy-Haim, T., Alexander, H., Bell, T.W. et al. What are the type, direction, and strength of species, community, and ecosystem responses to warming in aquatic mesocosm studies and their dependency on experimental characteristics? A systematic review protocol. Environ Evid 6 , 6 (2017). https://doi.org/10.1186/s13750-017-0084-0

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DOI : https://doi.org/10.1186/s13750-017-0084-0

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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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Small wonder that the oldest meaning of microcosm in our dictionary is “little world”: the word comes ultimately from the Greek phrase mikros kosmos , meaning “little universe.” That meaning can be applied to many a wee realm, as in “the microcosm of the atom,” but microcosm was originally used by medieval scholars specifically to refer to humans as miniature embodiments of the natural universe. Microcosm soon expanded to refer to places (such as neighborhoods or other communities) thought to embody at a small scale characteristics of larger places, and later to anything serving as an apt representation of something bigger—as when Arthur C. Clarke , famed author of much fiction and nonfiction set in the cosmos , noted that “a sunken ship is a microcosm of the civilization that launched it.”

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Middle English, from Medieval Latin microcosmus , modification of Greek mikros kosmos

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Application of Microcosm and Mesocosm Experiments to Pollutant Effects in Biofilms

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

Protocol | DOI: 10.1007/8623_2015_170

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

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

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

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Experimental specifications, other keywords.

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• Proia L, Vilches C, Boninneau C, Kantiani L, Farrè M, Romaní A, Sabater S, Guasch H (2013) Drought episode modulate biofilm response to pulses of Triclosan. Aquat Toxicol 127:36–45
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WHAT IS A MESOCOSM?

Aquatic mesocosms, or experimental water enclosures, are designed to provide a limited body of water with close to natural conditions, in which environmental factors can be realistically manipulated.

Such mesocosms provide a powerful tool to link between in situ but often only correlative field studies on the one side, and small-scale far from natural laboratory experiments including a single or a few species only, on the other side. Thus mesocosm studies have the advantage compared to laboratory approaches that it maintains a natural community under close to natural conditions, taking into account relevant aspects from ‘the real world’ such as indirect effects, biological compensation and recovery, and ecosystem resilience.

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 parameterization in ecosystem and biogeochemical models.

See a short video about mesocosm experiments.

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What does microcosm mean?

Definitions for microcosm ˈmaɪ krəˌkɒz əm; ˌmaɪ krəˈkɒz məs, -moʊs mi·cro·cosm, this dictionary definitions page includes all the possible meanings, example usage and translations of the word microcosm ., princeton's wordnet rate this definition: 0.0 / 0 votes.

• microcosm noun

a miniature model of something

GCIDE Rate this definition: 4.5 / 2 votes

Microcosm noun

A relatively small object or system considered as representative of a larger system of which it is part, exhibiting many features of the complete system.

Wiktionary Rate this definition: 0.0 / 0 votes

Human nature or the human body as representative of the wider universe; man considered as a miniature counterpart of divine or universal nature.

The human body; a person.

A smaller system which is seen as representative of a larger one.

A small natural ecosystem; an artificial ecosystem set up as an experimental model.

Etymology: From microcosme, from microcosmus, from μικρός + κόσμος.

Samuel Johnson's Dictionary Rate this definition: 0.0 / 0 votes

The little world. Man is so called as being imagined, by some fanciful philosophers, to have in him something analogous to the four elements.

Etymology: μίϰρος and ϰόσμος.

You see this in the map of my microcosm. William Shakespeare , Coriolanus.

She to whom this world must itself refer, As suburbs, or the microcosm of her; She, she is dead; she’s dead, when thou know’st this, Thou know’st how lame a creeple this world is. John Donne.

As in this our microcosm, the heart Heat, spirit, motions gives to every part: So Rome’s victorious influence did disperse All her own virtues through the universe. John Denham.

Philosophers say, that man is a microcosm, or little world, resembling in miniature every part of the great; and the body natural may be compared to the body politick. Jonathan Swift.

ChatGPT Rate this definition: 0.0 / 0 votes

A microcosm is a small, representative system or community that encapsulates in miniature the characteristics of something much larger, such as a society, an ecosystem, or the universe. It can also refer to a unit or an individual as a miniature representation of a whole.

Webster Dictionary Rate this definition: 0.0 / 0 votes

a little world; a miniature universe. Hence (so called by Paracelsus), a man, as a supposed epitome of the exterior universe or great world. Opposed to macrocosm

Etymology: [F. microcosme, L. microcosmus, fr. Gr. mikro`s small + ko`smos the world.]

Wikidata Rate this definition: 0.0 / 0 votes

Microcosm is a museum of particle physics located at CERN in the Canton of Geneva, Switzerland, near the town of Meyrin. It covers a broad range of particle physics topics, as well as the entire history of CERN. Exhibits include: ⁕explanations of the purpose of CERN and particle physics research in general. ⁕a mock-up, hands-on version of Rutherford's gold foil experiment ⁕a real-time cosmic ray detector ⁕a mock-up of the Large Hadron Collider tunnel ⁕models and explanations of current and future CERN experiments ⁕equipment from old experiments, including a large part of the UA1 detector, which ran at the Super Proton Synchrotron at CERN from 1981 to 1984, and helped discover the W and Z bosons.

Chambers 20th Century Dictionary Rate this definition: 0.0 / 0 votes

mī′krō-kozm, n. a little universe or world: (often applied to) man, who was regarded by ancient philosophers as a model or epitome of the universe.— adjs. Microcos′mic , -al , pertaining to the microcosm.— n. Microcosmog′raphy . [Fr.,—L.,—Gr.— mikros , little, kosmos , world.]

The Nuttall Encyclopedia Rate this definition: 5.0 / 1 vote

name given by the Middle Age philosophers to man as representing the macrocosm or universe in miniature.

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Usage in printed sources from:  .

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How to pronounce microcosm?

Alex US English David US English Mark US English Daniel British Libby British Mia British Karen Australian Hayley Australian Natasha Australian Veena Indian Priya Indian Neerja Indian Zira US English Oliver British Wendy British Fred US English Tessa South African

How to say microcosm in sign language?

Chaldean Numerology

The numerical value of microcosm in Chaldean Numerology is: 7

Pythagorean Numerology

The numerical value of microcosm in Pythagorean Numerology is: 9

Examples of microcosm in a Sentence

Nyaka NiiLampti :

The NFL is a microcosm of society and what we’ve seen, particularly with the pandemic, is a greater willingness to utilize mental health resources.

Clark Spencer Larsen :

It is a microcosm of what is happening in Italy and all of Europe during this time frame.

James Scott :

A CISO's job is to streamline, harmonize and propagate cybersecurity and cyber hygiene throughout the organizational IoT microcosm and staff

Carol Brody :

Palazzo doesn't want to meet with any of his constituents or his opponents. He thinks he's going to rule from that little microcosm he has, i've gone to town hall meetings where he did not show up. They just put his picture on the chair.

Tara Swart :

A hotel makes for a particularly interesting place to observe differences in mental resilience, and be able to offer suggestions as to how it might be improved, it's a unique microcosm filled with people under differing types and degrees of mental pressure, from jet lag to meetings to poor sleep.

Popularity rank by frequency of use

• ^  Princeton's WordNet http://wordnetweb.princeton.edu/perl/webwn?s=microcosm
• ^  GCIDE https://gcide.gnu.org.ua/?q=microcosm
• ^  Wiktionary https://en.wiktionary.org/wiki/Microcosm
• ^  Samuel Johnson's Dictionary https://johnsonsdictionaryonline.com/views/search.php?term=microcosm
• ^  ChatGPT https://chat.openai.com
• ^  Webster Dictionary https://www.merriam-webster.com/dictionary/microcosm
• ^  Wikidata https://www.wikidata.org/w/index.php?search=microcosm
• ^  Chambers 20th Century Dictionary https://www.gutenberg.org/files/37683/37683-h/37683-h.htm#:~:text=microcosm
• ^  The Nuttall Encyclopedia https://www.gutenberg.org/cache/epub/12342/pg12342-images.html#Microcosm

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• First Online: 08 April 2022

Cite this chapter

• Yolanda F. Wiersma 33  

Part of the book series: Landscape Series ((LAEC,volume 29))

712 Accesses

Mesocosms, also known as “container experiments”, are appealing because they offer precise manipulative control over the factors of interest, but still capture aspects of the natural world. They have been in use in aquatic ecology for many decades and are particularly well suited to address questions in community and population ecology. In this chapter, I illustrate how to apply them to landscape ecology research. All mesocosms represent in situ distributed experiments, which a researcher can place within a landscape to facilitate addressing a spatially explicit question. In this chapter, I also show how, with some creativity, researchers can harness mesocosms to conduct perception experiments, tracer experiments, and manipulations of patch quality and/or connectivity. Mesocosms can be relatively inexpensive to set up, and provide an arena in which meeting the criteria for good experimental design (control, replication, randomization) is often relatively easy. However, it can be challenging to scale inferences drawn from mesocosm experiments to larger scales. I offer suggestions to overcome these challenges and add more of a landscape focus to mesocosm experiments.

Curiouser and curiouser! – Alice in Wonderland

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Wiersma, Y.F. (2022). Mesocosms. In: Experimental Landscape Ecology. Landscape Series, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-95189-4_8

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