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Study wetlands and datasets
Four salt marshes located in Waquoit Bay and adjacent estuaries at Cape Cod, MA, USA were used as the case study sites: (1) Sage Lot Pond (SL), (2) Eel Pond (EP), (3) Great Pond (GP), and (4) Hamblin Pond (HP) (Fig. 1). The marshes represent a moderate gradient in nitrogen loading and a wide range of human population density31, 32. On the basis of nitrogen loading influx, SL is in relatively pristine condition (~ 5 kg/ha/year), whereas HP (~ 29 kg/ha/year), EP (~ 63 kg/ha/year), and GP (~ 126 kg/ha/year) represent a medium to high nitrogen loading31, 33. The vegetation community of the marshes is mostly dominated by Spartina alterniflora (a C4 plant) in the low marsh zone.
Figure 1

Locations of the case study salt marshes along the southern shore of Cape Cod in the Waquoit Bay and adjacent estuaries, MA. Nitrogen loading rates of the Sage Lot Pond, Hamblin Pond, Eel Pond, and Great Pond were 5, 29, 63, and 126 kg/ha/year, respectively.

Full size image

A comprehensive detail on the collections and processing of gas fluxes and environmental variables for the four salt marshes were presented in Abdul-Aziz et al.7. Closed chamber-based measurements of the net ecosystem exchange (NEE) of CO2 were made using a cavity ring-down spectrometer (CRDS) gas-analyzer (Model G2301, Picarro, Inc., Santa Clara, CA; frequency: 1 Hz; precision: 0.4 ppm) for different days during the extended growing season (May to October) in 2013 at the low marsh zones of the four salt marshes. The spectrometer analyzer was connected to the transparent, closed acrylic chamber (60 cm × 60 cm × 60 cm) through tubes. We calculated the molar concentrations of CO2 in the chamber using the ideal gas law. The instantaneous molar concentrations of CO2 were then linearly regressed with time (s). The regression slopes (i.e., rates of changes in CO2 concentrations) were normalized by the chamber area (60 cm × 60 cm = 3,600 cm2 = 0.36 m2) to compute the corresponding fluxes of CO2 (i.e., changes in CO2 concentrations per unit area and per unit time in μmol/m2/s) between the wetland soil and the atmosphere inside the chamber for each sampling period (typically ~ 5 min)6, 7. To avoid impacts of any experimental error, a coefficient of determination (R2) of 0.90 was set as the minimum threshold for the regression to qualify the computed CO2 fluxes as accurate and acceptable for analyses6, 7.
The employed enclosed chamber-based technique of measuring CO2 fluxes is a widely-used method in the carbon research domain34,35,36,37. The technique provides an effective way to measure surface-atmospheric gas fluxes. As demonstrated above, the method first involves the calculation of the gradient of molar concentrations of CO2 in time, which is then divided by the chamber area to compute the vertical CO2 fluxes. Since the measurement chamber is small (e.g., 60 cm × 60 cm × 60 cm for our equipment) and enclosed, the vertical fluxes of CO2 between soil and atmosphere (and not the divergence of CO2 fluxes) drives the changes in CO2 concentrations with time inside the chamber. Therefore, the chamber area-normalized rates of changes in CO2 molar concentrations represent the vertical CO2 gas fluxes between the soil and atmosphere inside the chamber.
The associated instantaneous environmental variables such as the photosynthetically active radiation (PAR), air temperature (AT), soil temperature (ST), and porewater salinity (SS) were concurrently measured7. The corresponding observations of atmospheric pressure (Pa) were collected from the nearby NOAA National Estuarine Research Reserve System (NOAA-NERRS) monitoring station located at Carriage House, MA38. The filtered daytime net uptake fluxes of CO2 (NEECO2,uptake) represented the measurements made between 8 a.m. and 4.30 p.m. (Eastern Standard Time, EST), with the corresponding PAR higher than 1.5 µmole/m2/s. AT was used to calculate the fluxes of NEECO2,uptake using the ideal gas law7; AT was, therefore, excluded as an environmental driver from further analyses. Instead, soil temperature (ST) was considered to represent the impact of temperature on NEECO2,uptake. The dataset included 137 observational panels from the four study wetlands for 25 sampling days (Table S1, Figure S1 and S2 in Supplemental notes).
Theoretical formulation of dimensionless numbers through parametric reductions
Dimensional analysis using Buckingham pi ((Pi)) theorem were applied to formulate wetland ecological similitudes and derive dimensionless functional groups or (Pi) numbers18, 20. According to the pi theorem, a combination of ({text{n}}) dimensional variables would lead to (({text{n}} – {text{r}})) dimensionless Π numbers (({text{r}} =) number of relevant fundamental dimensions). NEECO2,uptake, PAR, ST, SS, Pa, and the time-scale of measurement or estimation (t) were used for the dimensional analysis. PAR, ST, SS were the most dominant drivers of NEECO2,uptake, as identified in the study of Abdul-Aziz et al.7. Furthermore, Pa negatively correlates with net photosynthesis as stomatal conductance increases with decreasing pressure39. The selected variables for the dimensional analysis included four fundamental dimensions (mass: M; length: L; temperature: K; time: T) (Table 1). Since the variables were in different unitary systems, they were converted to the SI units by using appropriate conversion factors (Table 1). As the temperature dimension (K) was only represented by ST, specific heat of wet soil (cp = 1.48 kJ/kg/K) was further incorporated in the dimensional analysis to normalize ST. Following the pi theorem, a functional relationship ((f)) among the response (NEECO2,uptake) and the potential predictors was expressed as follows:

$$fleft( {NEE_{CO2,uptake} , PAR, ST, SS,{ }P_{a} ,{ }c_{p} , t} right) = 0$$
(1)

where the total number of variables, (n = 7); number of fundamental dimensions, (r = 4). Therefore, the total number of possible ({Pi }) numbers (= {text{n}} – {text{r}} = 3). The functional relation of Eq. (1) was then represented with (Phi) in terms of dimensionless numbers as follows:

$$Phi left( {Pi _{1} ,Pi _{2} , Pi _{3} } right) = 0$$
(2)

Table 1 List of variables, units and dimensions used for the dimensional analysis.
Full size table

Based on the pi theorem, four variables ((r = 4)) could be considered as “repeating variables” in each iteration to formulate a dimensionless number by involving any of the remaining variables. Although the repeating variables should include all relevant fundamental dimensions (M, L, K, and T in this study), they should not form a dimensionless number among themselves. For example, considering PAR, ST, SS, and t as the “repeating variables”, the first pi number (left( {Pi _{1} } right)) was expressed as follows:

$$Pi _{1} = PAR^{a} cdot ST^{b} cdot SS^{c} cdot t^{d} cdot NEE_{CO2,uptake}$$
(3)

where (a), (b), (c), and (d) were exponents. For (Pi _{1 }) to be dimensionless, the following equation was obtained using the principle of dimensional homogeneity (i.e., equal dimensions on both sides):

$$M^{0} cdot L^{0} cdot T^{0} cdot K^{0} = left( {frac{M}{{L^{2} T}}} right)^{{text{a}}} cdot left( K right)^{b} cdot left( {frac{M}{{L^{3} }}} right)^{c} cdot left( T right)^{d} cdot frac{M}{{L^{2} T}}$$
(4)

Therefore,

$$M^{0} cdot L^{0} cdot T^{0} cdot K^{0} = M^{{{text{a}} + {text{c}} + 1}} cdot L^{{ – 2{text{a}} – 3c – 2}} cdot T^{ – a + d – 1} cdot K^{b}$$
(5)

Equating the exponents of M, L, K, and T on both sides, we obtained the following matrix–vector form:

$$left[ {begin{array}{*{20}c} 1 & 0 & 1 & 0 \ { – 2} & 0 & { – 3} & 0 \ { – 1} & 0 & 0 & 1 \ 0 & 1 & 0 & 0 \ end{array} } right] left[ {begin{array}{*{20}c} a \ b \ c \ d \ end{array} } right] = left[ {begin{array}{*{20}c} { – 1} \ 2 \ 1 \ 0 \ end{array} } right]$$
(6)

The system of linear equations was algebraically solved to compute the exponents as: (a = – 1), (b = 0), (c = 0), and (d = 0) (see Text S1 in Supplemental notes for detailed algebraic equations and solutions). Therefore, from Eq. (3), we obtained the first pi number as

$$Pi _{1} = frac{{NEE_{CO2,uptake} }}{PAR}$$
(7)

Similarly, the other two ({Pi }) numbers were formulated as (see Text S1 in Supplemental notes)

$$Pi_{2} = frac{{SS cdot P_{a} }}{{PAR^{2} }}$$
(8)

$$Pi_{3} = frac{{ST cdot c_{p} cdot SS^{2} }}{{PAR^{2} }}$$
(9)

The pi theorem also allowed the derivation of new (Pi) numbers by combining any two (or more) original (Pi) numbers through multiplication or division as follows:

$$Pi _{4} =Pi _{2} timesPi _{3} = frac{{ST cdot c_{p } cdot SS^{3} cdot P_{a} }}{{PAR^{4} }}$$
(10)

$$Pi _{5} = frac{{Pi _{3} }}{{Pi _{2} }} = frac{{ST cdot c_{p} cdot SS}}{{P_{a} }}$$
(11)

Thus, the functional relationship of Eq. (2) could be represented in any of the following forms:

$$Phi left( {Pi _{1} ,Pi _{4} } right) = 0$$
(12)

$$Phi left( {Pi _{1} ,Pi _{5} } right) = 0$$
(13)

Therefore, dimensional analysis reduced the 7 original variables to 2–3 dimensionless numbers. Recalling the definition of similitude from the physical domain18, 20, such parametric reductions for the daytime net uptake fluxes of CO2 and the associated environmental drivers were termed as “wetland ecological similitudes” in this research. As apparent, (Pi _{1}) represented the dimensionless CO2 flux number (i.e., response), whereas (Pi _{2}) to (Pi _{5}) represented the environmental driver numbers (i.e., predictors).
Various sets of dimensionless numbers were obtained by iteratively changing the “repeating variables” (Table S2; see Text S1 in Supplemental notes for full derivations). However, only the unique ({Pi }) numbers were considered for further analysis with empirical data. For example, (frac{{ SS^{2} cdot ST cdot c_{p} }}{{PAR^{2} }}) (iteration-1 or 4 in Table S2) and (frac{{SS cdot sqrt {ST cdot c_{p} } }}{PAR}) (iteration-3) were considered non-unique ({Pi }) numbers, because the latter could be obtained as a square root function of the former. Similarly, (frac{{P_{a} }}{{PAR cdot sqrt {ST cdot c_{p} } }}) (iteration-3) could be obtained from a square root and inversion of (frac{{PAR^{2} cdot ST cdot c_{p} }}{{P_{a}^{2} }}) (iteration-2 or 5), and were considered the same number. Based on the pi theorem, the response ({Pi }) number (i.e., dimensionless CO2 flux number) were expressed as a general function ((psi)) of all unique dimensionless environmental numbers as follows:

$$frac{{NEE_{CO2,uptake} }}{PAR} = psi left[ {left( {frac{{SS cdot P_{a} }}{{PAR^{2} }}} right),left( {frac{{ST cdot c_{p} cdot SS^{2} }}{{PAR^{2} }}} right),left( {frac{{ST cdot c_{p } cdot SS^{3} cdot P_{a} }}{{PAR^{4} }}} right),left( {frac{{ST cdot c_{p} cdot SS}}{{P_{a} }}} right),left( {frac{{PAR^{2} cdot ST cdot c_{p} }}{{P_{a}^{2} }}} right),left( {frac{{SS cdot P_{a}^{3} }}{{PAR^{4} cdot ST cdot c_{p} }}} right)} right]$$
(14)

Empirical analysis to determine the linkages among the derived numbers
The multivariate method of principal component analysis (PCA) was applied to the observational dataset from the salt marshes of Waquoit Bay to identify the important environmental driver number(s) that had dominant linkage(s) with the response pi number40. PCA can resolve multicollinearity (mutual correlations) among the environmental driver numbers in a multivariate space, identifying the relatively unbiased information on their individual linkages with the response40, 41. To incorporate any non-linearity in the data matrix, observed (i.e., calculated) values of all pi numbers were log10-transformed, which were further standardized (centralized and scaled) as follows: (Z = left( {X – overline{X}} right)/s_{X}); (X) = log10-transformed pi number, (overline{X}) = mean of (X), and (s_{X}) = standard deviation of (X). More

NEWS
25 September 2020

Infernos in South America’s Pantanal region have burnt twice the area of California’s fires this year. Researchers fear the rare ecosystem will never recover.

Emiliano Rodríguez Mega

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Firefighters and volunteers in the Pantanal, Brazil, have been scrambling to rescue jaguars from extreme fires.Credit: Andre Penner/AP/Shutterstock

When Luciana Leite arrived in the Pantanal on 2 September, she thought she would be celebrating her wedding anniversary. Instead, the biologist and her husband spent their eight-day planned holiday aiding volunteers and firefighters struggling to extinguish the burning landscape.
A common destination for ecotourists, the Pantanal is the world’s largest tropical wetland, home to Indigenous peoples and a high concentration of rare or endangered species, such as jaguars and giant armadillos. Small fires occur every year in the region, which sprawls over parts of western Brazil and extends into Bolivia and Paraguay.

But 2020’s fires have been unprecedented in extent and duration, researchers say. So far, 22% of the vast floodplain — around 3.2 million hectares (see ‘Biodiversity Hotspot Under Threat’) — has succumbed to the flames, according to Renata Libonati, a remote-sensing specialist at the Federal University of Rio de Janeiro, Brazil, whose data are being used by firefighters to plan containment. That’s more than twice the area that has burnt in the record-breaking fires in California this year.
Scientists worry that the extreme blazes will profoundly alter the already fragile ecosystem of the Pantanal, and that research programmes investigating the region’s ecology and biodiversity will never recover.
“It’s apocalyptic,” says Leite, who studies humanity’s relationship with nature at the Federal University of Bahia in Salvador, Brazil. “It is a tragedy of colossal proportions.”
Scorched earth
Unlike in the nearby Amazon Rainforest, vegetation in the Pantanal has evolved to coexist with fire — many plant species there require the heat from fires to germinate. Often caused by lightning strikes, those natural fires tend to spring up at the end of the dry season, in September. They run out of fuel quickly, and the surrounding floodplains prevent them from spreading.
What’s different this season is that the region is facing its worst drought in 47 years, says Luisa Diele-Vegas, a Brazilian ecologist at the University of Maryland in College Park. And 2019’s fires were also intense, contributing even further to the unusually dry conditions and exacerbating the fire risk this year.

Source: Laboratory for Environmental Satellite Applications, Federal University of Rio de Janeiro

The desiccated vegetation was perfect tinder for fires intentionally set by ranchers clearing land for their cattle. But some of those fires got out of control, adding to the wildfire damage, says Diele-Vegas.
In July, Brazilian President Jair Bolsonaro announced a 120-day moratorium on setting fires in the Amazon and the Pantanal. However, those regulations were not strictly enforced, says José Marengo, a climatologist at the National Center for Monitoring and Early Warning of Natural Disasters in São Paulo. According to news reports, the Bolsonaro government, which has a reputation for being unfriendly towards environmental regulations, reduced the number of environmental inspectors and blocked funding for fire prevention this year.

What worries scientists further is that this year’s fire season might not be an isolated incident. Climate modelling suggests that the Pantanal could become hotter and drier, with a rise in temperature of up to 7 ºC by the end of the century1. Unpublished data from Diele-Vegas project an even grimmer outlook: by 2050, if climate-change trends continue, annual mean temperatures in the Pantanal could increase by 10.5%, and the annual volume of rain could decrease by 3%.
According to Marengo, these changes could lead to a collapse of the Pantanal’s current vegetation, making it even more susceptible to fires, and could push the region to transform into a different type of ecosystem.
A race against the flames
One of the biggest losses in this year’s fires is the region’s wildlife, says Douglas Morton, a remote-sensing specialist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, who has studied fires and deforestation across Brazil for two decades. Many creatures thrive in the mosaic landscape of the Pantanal, which includes flooded areas, grasslands, lakes and forests. Scientists have so far documented more than 580 species of bird, 271 of fish, 174 mammals, 131 reptiles and 57 amphibians in the region2. “My lasting memory from being in the Pantanal is the cacophony of life,” Morton says. “To me, that’s what’s so heart wrenching about seeing the extent of fires.”

Luciana Leite surveys the burnt landscape of the Pantanal during her trip on 2 September.Credit: Ben Phalan

The flames have also breached five territories in the Pantanal where Indigenous communities live. More than 80% of the land in each of the three most affected — Baía dos Guató, Perigara and Tereza Cristina — has been consumed by fire.
A number of locals have jumped in to rescue as many animals as possible from the flames and smoke. Eduarda Fernandes Amaral, who works as a guide in the Encontro das Águas State Park, is among them. As of 20 September, more than 83% of the park, which is home to a large number of jaguars, capybaras and alligators, had been destroyed.
In the past month, a team including Fernandes Amaral has rescued more than 20 animals, although some had to be euthanized. To deal with the situation, Fernandes Amaral and her colleagues have adopted a mantra. “When we see an animal dying, we have to look at it, be sad for two minutes and understand that there is another in need of help,” she says.

As the blazes advance, animal research in the Pantanal might also suffer. Two years ago, Diele-Vegas started a project to study the distribution of frogs, tree frogs and toads across the Pantanal, and how it might shift owing to land-use change and climate variations. But she doesn’t know whether the amphibian populations she’s monitoring will even survive the blazes.
“We are seeing our fauna and flora burning. And there’s a lot of this fauna and flora that we haven’t had time to study yet,” she says. “We are trying to race against time, but the fire is coming and taking everything down.”
After her initial trip to the Pantanal, Leite couldn’t leave it behind. She returned a few days ago to keep helping the locals. What she’s seen has convinced her that the wetlands will be forever changed.
“If climate trends, land-management trends and the current anti-environment politics persist,” says Leite, “the Pantanal as we know it will cease to exist.”

doi: 10.1038/d41586-020-02716-4

References

1.
Marengo, J. A., Oliveira, G. S. & Alves, L. M. in Dynamics of the Pantanal Wetland in South America (eds Bergier, I. & Assine, M) https://doi.org/10.1007/698_2015_357 (Springer, 2015).

2.
Tomas, W. M. et al. Trop. Conserv. Sci. https://doi.org/10.1177/1940082919872634 (2019).

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Estimation of a generalized wastewater treatment plant microbial community
In order to perform a deep taxonomic survey of microbial communities associated to wastewater treatment, we initially surveyed the EBI MGnify database11, collecting abundance profiles obtained by 16S amplicon based surveys of wastewater treatment communities.
We were able to roughly identify 1465 prokaryotic genera in 3433 samples from 49 studies (see Supplementary Data S1.1), with members of the archaea kingdom in about 22.5% of samples. When we restricted the analysis on sludges the number of studies was reduced to 33 with 1363 samples, however we identified 1379 genera and the number of samples showing archaea was about 40% (see supplementary data S1.2). Such observation underlined the relevance of archaea in wastewater environments. Interestingly, in the wastewater biome we found 128 samples (about 3.7%) from 24 studies (about 50%) showing evidence of anammox species, a percent that grew to about 5% in sludge samples from 10 studies (30%). This result manifests the need of properly taking into account anammox communities when estimating microbial abundance profiles in such environments.
Evaluation of existing primers
We then sought to verify whether existing primer pairs with established high performances and good coverage over the widest range of microbial species were able to appropriately cover wastewater associated communities, especially for the anammox components, using the most updated 16S RDP collection.
All Takahashi et al.10 and Albertsen et al.11 primers pairs were tested in silico using RDP ProbeMatch against updated 16S rRNA sequences from all genera available in the current RDP database. As shown in Fig. 1, we found that all Albertsen et al. primer pairs targeting the V1-V3 and V3-V4 and V4 only 16S region showed good performances for bacteria, but had relatively poor performances for archaeal species, that we have shown above to be relevant for wastewater associated communities12. On the contrary, Takahashi Pro pair (Pro341F and Pro805R) effectively showed high coverage for both bacteria and archaea, despite a surprisingly low performance for microbes highly relevant for the denitrification cycle, namely anammox bacteria especially of the Brocadiaceae family, Candidatus brocadia genus. Accordingly, our further efforts were focused on improving the Takahashi et al. primer pair.
Figure 1

Comparison of the overall theoretical performance in coverage (percent of members of the given rank mapped) of the different primer pairs used in this study.

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Predicted improvement of coverage on RDP database
When specifically matched against Brocadiales sequences, we found the possibility of improving the coverage of the Takahashi PRO primer pair by introducing a purine degeneration in the forward primer Pro341F, so that most member of our community of interest was matched. To design this, we extracted from the RDP global dataset all high quality ( > 1200 bp) 16S classified as Brocadiaceae at the family rank. On this dataset we simulated amplicons formation with RDP probeMatch, systematically imposing degenerations that could accommodate members of this family in the most complete as well as parsimonious way. We ended up with a modified primer, Pro341FB, that was paired with the original reverse primer Pro805R and tested in silico using a mismatch 0 approach and considering the taxonomic coverage as a selection metric. As shown in Fig. 1, the primer pair Pro341FB + Pro805R (TAKB_v3v4) proved a very modest 0.007% coverage increase for archaea with respect to primer Pro341F + Pro805R (TAK_v3v4), while we found a noticeable 1% coverage increase for bacteria. Primer Pro341FB was theoretically able to amplify a total of 59% of the approximately 3.2 million sequences present in the bacteria data bank. In particular, primer Pro341FB was found to target phyla that were completely ignored by the primer Pro341F. As shown in Fig. 2, phyla which received an increase in coverage of more than 25% were found to be Chlamydiae (41%), Lentisphaerae (76%), Omnitrophica (63%), Parcubacteria (44%), candidate division WPS-1 (46%) and, importantly for this study, Planctomycetes (46%). Descending the taxonomic tree from phylum Planctomycetes to genera involved in anaerobic ammonium oxidation (anammox) we systematically observed an increase in coverage (class Planctomycetia 45%, order Candidatus Brocadiales 28%, family Candidatus Brocadiaceae 28%, genus Candidatus Brocadia 75%). As shown in Fig. 3, all anammox bacteria (genera Candidatus Brocadia, Candidatus Kuenenia, Candidatus Anammoxoglobus, Candidatus Jettenia and Candidatus Scalindua), that were almost neglected by the original Pro341F primer (red bars, secondary y-axis), resulted, as expected, markedly more covered when the Pro341FB primer was used. Major numerical details on the results of this comparison are available in supplementary materials (Supplementary data S2).
Figure 2

Improvement of taxonomic coverage by the newly optimized primer Pro341FB. The coverage percent value refers to the proportion between the total RDP database sequences annotated with the specific taxonomic rank and those that proved to generate an amplicon using the currently optimized Pro341FB primer and the original Pro341F (white bars), paired with the common reverse primer Pro805R. Only taxonomies with a difference in coverage higher than 25% are shown. The suffixes P, C, O, F and G refers to the ranks phylum, class, order, family and genus, respectively. Black columns mark taxonomic ranks associated with anammox bacteria.

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Figure 3

Comparison of the theoretical coverage performance between the new Pro341FB (black, left axis) and the original Pro341F (red, right axis). The Brocadiaceae family consists of 5 genera, 4 of which are represented in the figure. A further genus named Candidatus jettenia is not present since no high-quality sequence (i.e.  > 1200 bp) was present in RDP database.

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Testing primer variations by NGS on selected communities
In order to verify the increase in performances for anammox communities by our modified forward primer Pro341FB, we collected the microbial community samples from 5 different origins, namely activated sludges from a domestic WWTP plant (SCS), activated sludges from a tannery WWTP (CDS), aerobic granular sludge (AGS), and partial nitrification anammox granular sludge (PNA) from pilot scale reactors fed with domestic wastewater. For the two former plants, samples from their anaerobic digestion reactors were also collected (SCD and CDD, respectively). The samples were collected from bioreactors operated in widely different conditions (suspended vs biofilm and aerobic/anoxic vs anaerobic) and fed with various substrates, in order to allow the validation of the protocol in most of the selective conditions typical for microbial communities in wastewater treatment. The total DNA of all communities was extracted and amplicons were generated using the primer pairs Pro341F + Pro805R or Pro341FB + Pro805R. As shown in Fig. 4, NGS revealed that the percentage of identified phyla was almost the same in all samples but in the PNA, where anammox communities were largely underestimated by Pro341F with respect to Pro341FB. As a confirmation it has been recently reported that anammox species largely dominate the granule population1, underlining the underestimation by the original Pro341F primer.
Figure 4

NGS verification of the improvement of coverage percent for members of the Brocadiaceae family coverage by the optimized Pro341FB primer. The tested samples (CDS, CDD, SCS, SCD, AGS, PNA, see text for a description) were amplified with Pro341F (suffix 1) or Pro341FB (suffix 2). Samples marked with the suffix 2 are systematically higher in Brocadia associated ranks.

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Physicochemical properties
In all sampled environments, Typha angustifolia was the main emergent macrophyte and formed almost monospecific communities covering large surface areas. Moreover, sampled environments were located in a relatively small geographical area, thus minimizing weather effects (temperature, average rain, and solar radiation). Nevertheless, physicochemical conditions of the chosen environments were essentially different in terms of nutrient concentrations, salinity and redox potential. Unfortunately, nutrient concentrations in water were not recorded at the moment of sampling. Nitrate and ammonia concentrations in the two studied systems are relatively variable (ranging from undetectable to above 10 mg/L for NO3− and NH4+, NO2− is not detected above 0.2 mg/L) and highly influenced by the performance of the WWTP in the Empuriabrava area, or nutrient discharges due to agricultural activities in the Baix Ter area10, 35, 36. Water temperatures were around 26 °C, being slightly lower in Bassa de les Tortugues and higher in the Daró river mouth (Table 1). When samples were grouped according to the location (i.e. FWS-CW and Baix Ter), no significant differences of temperature were observed (U Mann–Whitney test, p  > 0.05). Sampled environments spanned along a salinity gradient, ranging from slightly saline (conductivity values of 11.95 mS/cm2, estimated salinity 6.84 ppt), such as Bassa de les Tortugues, to low salinity fresh water, such as Rec Coll (0.823 mS/cm2, salinity 0.40 ppt). Water in the Empuriabrava FWS-CW showed typical conductivity values for the system in summer37.
Table 1 Main water physicochemical parameters in the studied sites.
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Water oxygen concentration, pH and Redox values were significantly different between the two geographical locations (U Mann–Whitney test, p  More

Data collection
We hypothesized that the evolution of underground species affected protein networks in a unique manner in which various types of protein domains served as building blocks of protein evolution. To study the evolution of protein networks, we collected genomic, proteomic, and protein domain classification data, namely, fully sequenced genomes with coding sequences and annotated proteomes, together with protein ortholog assignments, from 32 species living in three broad ecological niches, namely subterranean, fossorial, and aboveground (Table 1, and listed in Materials and Methods). We first sought overall statistics regarding the number of proteins and the number of corresponding orthologous protein families. Overall PPI statistics were calculated, including those predicting PPIs in organisms for which experimentally verified PPI data are missing. We used the KEGG orthologs (KO) group of orthologous proteins in KEGG (Kyoto Encyclopaedia of Genes and Genomes)17 to reproduce gain and loss of protein domains in orthologous proteins. We collected 1,350,898 proteins from the studied organisms that belong to 624,787 KO groups (10,314 are unique ortholog groups). The matching number of interactors and networks for every organism were exhaustively calculated for all these proteins (Fig. 1). We found that 361,615 of the 1,350,898 proteins are distributed among 5,879,879 (predicted and real) PPIs. The mean number of interactors per protein within each habitat, namely, aboveground (A), fossorial (F), and subterranean (S) were 32.07, 32.48, and 32.67, respectively (see details in the supplementary results and in Tables S1–S3). This shows that the number of interactors per protein is similar for organisms from different ecologies.
Table 1 All organisms included in the PASTORAL database, with a complete number of proteins in the corresponding proteome.
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Figure 1

The study overview. Fully sequenced genomes with coding sequences and annotated proteomes were collected from 32 species living in three broad ecological niches: subterranean, fossorial, and aboveground. For collected proteins (1,350,898), protein domains, protein disordered regions, and KEGG orthologous annotation (624,787) were predicted using the Pfam search tool53 along with HMMER60 , IUPred2A44, and the KEGG database17, respectively. Next, 5,879,879 PPIs were evaluated using our previously developed ChiPPI tool15. Briefly, ChiPPI uses a domain-domain co-occurrence table. When a certain domain is missing, ChiPPI evaluates the corresponding missing interactors in the PPI network15, based on real PPI data (363,816) as obtained from BioGrid (release 3.4.163)16. Finally, PPI data are organized in PASTORAL, a dedicated database.

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Additional analysis of PPI features for orthologous proteins (516 KOs) common to all organisms were similar across ecologies. These features included the number of interactors, the number of PPIs, and global/individual clustering coefficients (supplementary results, Figures S1, S2, Table S4). Thus, we studied PPI properties of genes encoding products related to stresses that differ across the ecologies considered, such as hypoxia. Our findings confirm our hypothesis that the design principles of the evolution of underground species involve various types of protein domains serving as building blocks of protein evolution.
Analysis of the PPIs of stress-response proteins cluster organisms according to habitat
To examine how organisms might have adapted to the various stresses in each habitat, we analyzed mutations and changes in the PPIs encoded by stress response genes. Heat-shock, hypoxia, and circadian stresses differ considerably between aboveground and underground environments, and are likely to drive evolutionary selection of proteins that provide optimal function in each niche1,9. We assumed that organisms subject to a shared ecological experience would face similar environmental stresses. PPI networks of stress-related proteins would thus be expected to differ substantially according to ecology.
To test our hypothesis, we performed clustering analysis of all the organisms included in our study, based on mutations and PPI network features, and compared the results for each classification. Such analysis included all orthologous stress-response, hypoxia, heat-shock, and circadian stress proteins (Table 1). In total, 85,173 PPIs related to stress-response proteins were found to be distributed among 1,103 proteins. These comprised of 730 heat shock proteins in 71,940 PPIs, 254 hypoxia-related proteins in 10,256 PPIs, and 119 circadian proteins in 2,977 PPIs (Table 1, Tables S1–S7). All orthologous stress-response genes (KO groups) were obtained by querying the KEGG database with the terms “heat-shock”, “hypoxia”, and “circadian” terms. The results are listed in Table 2, while the corresponding lists of proteins are found in Tables S5, S6 and S7, respectively.
Table 2 KEGG Orthologs: Heat-shock (upper), hypoxia-related (middle) and circadian (bottom) proteins.
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Next, we performed clustering analysis based on sequence mutations and PPI features for the full set of heat-shock, hypoxia, and circadian stress proteins (Table 2). Remarkably, proteins related to hypoxia, heat-shock, and circadian stresses in the 32 organisms studied did not all cluster according to shared ecology based on sequence mutations (Fig. 2A) but significantly did so on the basis of “PPI network clustering coefficient” (Fig. 2B–D; p value (AU)  More