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Samples
Cores were collected during the RV Investigator voyage IN2018_T02 (19 and 20 May 2018, respectively, Fig. 2) to Tasmania, from sites in the Mercury Passage and Maria Island (Fig. 2). We collected one KC Denmark Multi-Core (MCS3, inner core diameter 10 cm, 36 cm long, estimated to cover the last ~ 145 years based on 210Pb dating at the Australian Nuclear Science and Technology Organisation (ANSTO, Lucas Heights, Sydney) in the Mercury Passage (MP, 42.550 S, 148.014 E; 68 m water depth), and one gravity core (GC2; inner core diameter 10 cm, 3 m long) offshore from Maria Island (42.845 S, 148.240 E; 104 m) composed of 2 sections; GC2A (bottom) and GC2B (top) estimated to cover the last ~ 8950 years based on 210Pb and 14C dating, ANSTO). The untreated cores were immediately sealed with plastic caps and sealed with duct-tape, stored initially on-board at 10 °C, followed by transport to and storage at 4 °C at ANSTO. To minimise contamination during core splitting and subsampling (October, 2018, ANSTO), we wiped working benches, sampling and cutting tools with bleach and 80% EtOH, changed gloves immediately when contaminated with sediment, and wore appropriate PPE at all times (gloves, facemask, hairnet, disposable lab gown). We removed the outer ~ 1 cm of the working core-half (working from bottom to the top of the core), then collected plunge samples by pressing sterile 15 mL centrifuge tubes (Falcon) ~ 2 cm deep into the sediment core centre at 5 cm depth intervals. All sedaDNA samples were immediately frozen at − 20 °C and transported to the Australian Centre for Ancient DNA (ACAD), Adelaide. For this study, a total of 30 samples were selected from both cores, representing ~ 2 cm depth intervals within the upper 36 cm of MCS3 and GC2, and ~ 20 cm depth intervals in GC2 downcore from 36 cm below seafloor (cmbsf).
Figure 2

Map of coring sites, inshore (MCS3) and offshore (GC2) of Maria Island, Tasmania, South-East Australian Coast. Map created in ODV (Schlitzer, R., Ocean Data View, https://odv.awi.de, 2018).

Full size image

SedaDNA extractions
We prepared sedaDNA extracts and sequencing libraries at ACAD’s ultra-clean ancient (GC2) and forensic (MCS3) facilities following ancient DNA decontamination standards24. All sample tubes were wiped with bleach on the outside prior to entering the laboratory for subsampling. Our extraction method followed the optimised (“combined”) approach outlined in detail previously7, with a minor modification in that we stored the final purified DNA in TLE buffer (50 μL Tris HCL (1 M), 10 μL EDTA (0.5 M), 5 mL nuclease-free water) instead of customary Elution Buffer (Qiagen) (see Supplementary Material Methods). To monitor laboratory contamination, we used extraction blank controls (EBCs) by processing 1–2 (depending on the extraction-batch size) empty bead-tubes through the extraction protocol. A total of 30 extracts were generated from sediment samples and 7 extracts from EBCs.
RNA-baits design
We designed two RNA hybridisation bait-sets, one targeting phyto- and zooplankton for a more detailed overview of plankton diversity (hereafter ‘Planktonbaits1’), and one targeting specific plankton organisms and their predators to enable detailed investigation of HABs, especially those caused by dinoflagellates, in coastal marine ecosystems (hereafter, ‘HABbaits1’). Planktonbaits1 was based on 18S-V9 and 16S-V4 sequences of major phyto- and zooplankton groups, whereas we designed HABbaits1 from a collection of LSU, SSU, D1-D2-LSU, COI, rbcL and ITS sequences for specific marine target organisms often associated with HABs in our study region (Table 1).
Table 1 Planktonbaits1 and HABbaits1.
Full size table

Planktonbaits1
To design Planktonbaits1 we downloaded the W2_V9_PR2 database25 (containing 18S-V9 rDNA and rRNA sequences of marine protists and their predators, downloaded on 30 July 2018), deduplicated using Geneious software (Geneious NZ), and filtered the remaining sequences to keep only those from major phyto- and zooplankton groups (Table 1). In collaboration with Arbor Biosciences, USA, we designed RNA baits based on these 15,035 target sequences by masking any repeating Ns (i.e., any consecutive Ns that were  83% overlap, and  > 95% identity). We added five 16S-V4 rRNA sequences (the prokaryotic equivalent of the small subunit ribosomal rRNA gene) of common marine cyanobacteria (one Trichodesmium erythraeum sequence, and two Prochlorococcus marinus and Synechococcus sp. sequences each), acquired from the SILVA database26; Table 1). To check and ensure target-taxon specificity, these five cyanobacterial sequences were mapped against a non-target sequence (Escherichia coli 16S RefSeq sequence NR_114042.1), then reverse-transcribed to DNA, and BLASTed to the same NCBI RefSeq database described above. BLAST hits of  More

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C-STABILITY description
Organic matter representation
The description of SOM in C-STABILITY consists of several subdivisions. First, organic matter is separated in two main pools, one for living microbes (noted Cmic) and one for the substrate (noted Csub) (Fig. 1a). Several groups of living microbes can be considered simultaneously (e.g., bacteria, fungi, etc.) and C-STABILITY classes them into functional communities. Second, SOM is also separated between several biochemical classes, e.g., cellulose (or plant sugar), lignin, lipid, protein, and microbial sugar in this study (Fig. 1a). Third for each biochemical class, substrate accessible to its enzymes (noted ac) is separated from substrate which is inaccessible (noted in) due to specific physicochemical conditions, e.g., interaction between different molecules, inclusion in aggregates, sorption on mineral surfaces, etc.
Polymerization is a driver of interactions between substrate and living microbes and a continuous description of the degree of organic matter polymerization (noted p) is provided for each of these pools, as a distribution (Fig. 1b). The polymerization axis is oriented from the lowest to the highest degree of polymerization. A right-sided distribution corresponds to a highly polymerized substrate whereas a left-sided distribution corresponds to monomer or small oligomer forms. For each biochemical class ∗ (∗ = cellulose, lignin, lipid, etc.), the polymerization range is identical for both accessible and inaccessible pools. The total amount of C (in gC) in the accessible and the inaccessible pools of any biochemical class is as follows:

$${C}_{* }^{rm{ac}}=int_{{p}_{* }^{rm{min}}}^{{p}_{* }^{rm{max}}}{chi }_{* }^{rm{ac}}(p)dp,$$
(1)

$${C}_{* }^{rm{in}}=int_{{p}_{* }^{rm{min}}}^{{p}_{* }^{rm{max}}}{chi }_{* }^{rm{in}}(p)dp,$$
(2)

where ({p}_{* }^{rm{min}}) and ({p}_{* }^{rm{max}}) are the minimum and maximum degrees of polymerization of the biochemical class ∗ and ({chi }_{* }^{rm{ac}}), ({chi }_{* }^{rm{in}}) (gC.p−1) are the polymerization distributions. Finally, the total substrate C pool is defined as the sum of all biochemical pools,

$${C}_{rm{sub}}=sum _{* }left({C}_{* }^{rm{in}}+{C}_{* }^{rm{ac}}right).$$
(3)

Accessibility to microbe uptake is described by the interval (also called domain) ({{mathcal{D}}}_{u}), which corresponds to small substrate compounds, monomers, dimers or trimers smaller than 600 Daltons1, that microbes are able to take up (in red in Fig. 1b). Besides, accessibility to enzymes occurs in the ({{mathcal{D}}}_{rm{enz}}) domain (in blue in Fig. 1b). Over time the substrate accessible to enzymes is depolymerized and its distribution shifts toward ({{mathcal{D}}}_{u}) where it eventually becomes accessible to microbe uptake.
The numerical rules chosen to represent polymerization are as simple as possible in the context of theoretical simulations. Each pool is associated with a polymerization interval ([{p}_{* }^{rm{min}},{p}_{* }^{rm{max}}]) of length two. Initial substrate distributions are represented by Gaussian distributions centered at a relative distance of 25% from ({p}_{* }^{rm{max}}) (here 0.5), with the standard deviation set at 5% of polymerization interval length (here 0.1). In the accessible pool, the microbial uptake domains ({{mathcal{D}}}_{u}) are positioned at the left of the interval with a relative length of 20% (here 0.4), and enzymatic domains ({{mathcal{D}}}_{rm{enz}}) overlap the entire polymerization intervals.
Organic matter dynamics
As described in Fig. 1, three processes drive OM dynamics: (i) enzymatic activity, (ii) microbial uptake, biotransformation, and mortality, and (iii) changes in local physicochemical conditions. First, enzymes have a depolymerization role, which enables the transformation of highly polymerized substrate into fragments accessible to microbes. Second, microbial uptake of substrate is only possible for molecules having a very small degree of polymerization. When C is taken up, a fraction is respired and the remaining is metabolized, and biotransformed into microbial molecules that return to the substrate upon microbe death. Each microbial group has a specific signature that describes its composition in terms of biochemistry and polymerization. Third, changes in local substrate conditions drive exchanges between substrate accessible and inaccessible to enzymes (e.g., aggregate formation and break). All of these processes are considered with a daily time step (noted d).
Enzymatic activity Enzymes are specific to biochemical classes. They are not individually reported, but rather as a family of enzymes contributing to the depolymerization of a biochemical substrate (e.g., combined action of endoglucanase, exoglucanase, betaglucosidase, etc., on cellulose will be reported as cellulolytic action). Figure 2 describes how substrate polymerization distributions are impacted by enzymes. The overall functioning of each enzyme family (noted enz) is described by two parameters: a depolymerization rate ({tau }_{rm{enz}}^{0}) providing the number of broken bonds per time unit and a factor accounting for the type of substrate cleavage αenz. The term ({F}_{rm{enz}}^{rm{act}}) (gC.p−1.d−1) represents the change in polymerization of ({chi }_{* }^{rm{ac}}) due to enzyme activity for all (pin {{mathcal{D}}}_{rm{enz}}),

$${F}_{rm{enz}}^{rm{act}}({chi }_{* }^{rm{ac}},p,t)= -{tau }_{rm{enz}}(t){chi }_{* }^{rm{ac}}(p,t)\ +int_{{{mathcal{D}}}_{rm{enz}}}{{mathcal{K}}}_{rm{enz}}(p,p^{prime} ){tau }_{rm{enz}}(t){chi }_{* }^{rm{ac}}(p^{prime} ,t)dp^{prime}.$$
(4)

The depolymerization rate, τenz (d−1), is expressed as a linear function of microbial C biomass Cmic (gC),

$${tau }_{rm{enz}}(t)={tau }_{rm{enz}}^{0}{C}_{rm{mic}}(t),$$
(5)

where ({tau }_{rm{enz}}^{0}) (g({,}_{C}^{-1}).d−1) is the action rate of a given enzyme per amount of microbial C. If several microbial communities are associated with the same enzyme family, we replace the Cmic term by a weighted sum of the C mass of all communities involved in Eq. (5). The ({{mathcal{K}}}_{rm{enz}}) (p−1) kernel provides the polymerization change from (p^{prime}) to p,

$${{mathcal{K}}}_{rm{enz}}(p,p^{prime} )={{mathbb{1}}}_{ple p^{prime} }({alpha }_{rm{enz}}+1)frac{{(p-{p}_{* }^{rm{min}})}^{{alpha }_{rm{enz}}}}{{(p^{prime} -{p}_{* }^{rm{min}})}^{{alpha }_{rm{enz}}+1}},$$
(6)

where ({{mathbb{1}}}_{ple p^{prime} }) equals 1 if (ple p^{prime}) and 0 otherwise. The αenz cleavage factor denotes the enzyme efficiency to generate a large amount of small fragments and to shift the substrate polymerization distribution toward the microbe uptake domain ({{mathcal{D}}}_{u}) (Fig. 2). αenz = 1 is typical of the action of endo-cleaving enzymes, which randomly disrupts any bond of its polymeric substrate and generates oligomers. The shift toward ({{mathcal{D}}}_{u}) is slower if αenz increases. This is characteristic of exo-cleaving enzymes, which attack the end-members of their polymeric substrate, generate small fragments, and preserve highly polymerized compounds. To satisfy the mass balance, the kernel verifies (int {{mathcal{K}}}_{rm{enz}}(p,p^{prime} )dp=1). Then ({F}_{rm{enz}}^{rm{act}}) does not change the total C mass but only the polymerization distribution (i.e., (int {F}_{rm{enz}}^{rm{act}}(chi ,p,t)dp=0)).
Microbial biotransformation Each microbial group (denoted mic) produces new organic compounds from the assimilated C. After death, the composition of the necromass returning to each biochemical pool ∗ of SOM is assumed to be constant, accessible and is depicted with a set of distributions smic,∗, named signature. Each distribution smic,* (p–1) describes the polymerization of the dead microbial compounds returning to the pool ∗. The signature is normalized and unitless to ensure mass conservation, i.e., if we note that,

$${S}_{rm{mic},* }=mathop{int}nolimits_{{p}_{* }^{rm{min}}}^{{p}_{* }^{rm{max}}}{s}_{rm{mic},* }(p)dp,$$
(7)

then we have ∑*Smic,* = 1.
For each accessible pool of substrate, the term ({F}_{rm{mic},* }^{rm{upt}}) describes how the microbes utilize the substrate available in the microbial uptake ({{mathcal{D}}}_{u}) domain (Fig. 1b). For all (pin {{mathcal{D}}}_{u}),

$${F}_{rm{mic},* }^{rm{upt}}({chi }^{rm{ac}},p,t)={u}_{rm{mic},* }^{0}{C}_{rm{mic}}(t){chi }_{* }^{rm{ac}}(p,t),$$
(8)

where ({u}_{rm{mic},* }^{0}) (g({,}_{C}^{-1}).d−1) is the uptake rate per amount of microbe C. The substrate uptake rate linearly depends on the microbial C quantity.
Depending on a carbon use efficiency parameter ({e}_{rm{mic},* }^{0}) (ratio between microbe assimilated C and taken up C), taken up C is respired or assimilated and biotransformed into microbial metabolites. This induces a change in the biochemistry and polymerization (Fig. 1a).
Finally, microbial necromass returns to the substrate pools with a specific mortality, which linearly depends on the microbial C quantity,

$${F}_{rm{mic},* }^{rm{nec}}(p,t)={m}_{rm{mic}}^{0}{C}_{rm{mic}}(t){s}_{rm{mic},* }(p),$$
(9)

where ({m}_{rm{mic}}^{0}) (d−1) is the mortality rate of the microbe (Fig. 1a).
Change in local physicochemical conditions The polymerization of a substrate inaccessible to its enzymes remains unchanged over time. A specific event changing the accessibility to enzymes (e.g., aggregate disruption or desorption from mineral surfaces) is modeled with a flux from the inaccessible to the accessible pool. Transfer between these pools is described by the ({F}_{rm{ac},* }^{rm{loc}}) term for each biochemistry ∗,

$${F}_{rm{ac},* }^{rm{loc}}(p,t)={tau }_{rm{tr}}^{rm{ac}}{chi }_{* }^{rm{in}}(p,t)$$
(10)

where ({tau }_{rm{tr},* }^{rm{ac}}) (d−1) is rate of local condition change toward accessibility.
Transfer in the opposite way (e.g., aggregate formation, association with mineral surfaces) is described by the ({F}_{rm{in},* }^{rm{loc}}) term,

$${F}_{rm{in},* }^{rm{loc}}(p,t)={tau }_{rm{tr}}^{rm{in}}{chi }_{* }^{rm{ac}}(p,t)$$
(11)

where ({tau }_{rm{tr},* }^{rm{in}}) (d−1) is rate of local condition change toward inaccessibility.
Organic matter input We defined time dependent distributions for carbon input fluxes. There are denoted ({i}_{* }^{rm{ac}}) and ({i}_{* }^{rm{in}}) (gC.p−1.d−1) for both accessible and inaccessible pools of biochemical classes ∗. The total carbon input flux, expressed in gC.d−1, is:

$$I(t)=sum _{* }int_{{p}_{* }^{rm{min}}}^{{p}_{* }^{rm{max}}}left({i}_{* }^{rm{in}}(p,t)+{i}_{* }^{rm{ac}}(p,t)right)dp.$$
(12)

General dynamics equations The distribution dynamics for each biochemical class * is obtained from Eqs. (4)–(6) and (8)–(11),

$$frac{partial {chi }_{* }^{rm{ac}}}{partial t}(p,t)= , {F}_{rm{ac},* }^{loc}(p,t)-{F}_{rm{in},* }^{rm{loc}}(p,t)\ +{F}_{rm{enz}}^{rm{act}}({chi }_{* }^{rm{ac}},p,t) +sum _{{rm{mic}}}left({F}_{rm{mic},* }^{rm{nec}}(p,t)-{F}_{rm{mic},* }^{rm{upt}}({chi }^{rm{ac}},p,t)right) +{i}_{* }^{rm{ac}}(p,t),$$
(13)

$$frac{partial {chi }_{* }^{rm{in}}}{partial t}(p,t)={F}_{rm{in},* }^{rm{loc}}(p,t)-{F}_{rm{ac},* }^{rm{loc}}(p,t) +{i}_{* }^{rm{in}}(p,t).$$
(14)

Then, the expended equations are,

$$frac{partial {chi }_{* }^{rm{ac}}}{partial t}(p,t)= , {tau }_{rm{tr},* }^{rm{ac}}{chi }_{* }^{rm{in}}(p,t)-{tau }_{rm{tr},* }^{rm{in}}{chi }_{* }^{rm{ac}}(p,t)\ -{tau }_{rm{enz}}^{0}sum _{,text{mic},}{C}_{rm{mic}}(t){chi }_{* }^{rm{ac}}(p,t)\ +{tau }_{rm{enz}}^{0}sum _{,text{mic},}{C}_{rm{mic}}(t)({alpha }_{rm{enz}}+1)\ int_{p}^{{p}_{* }^{rm{max}}}frac{{(p-{p}_{* }^{rm{min}})}^{{alpha }_{rm{enz}}}}{{(p^{prime} -{p}_{* }^{rm{min}})}^{{alpha }_{rm{enz}}+1}}{chi }_{* }^{rm{ac}}(p^{prime} ,t)dp^{prime} \ +sum _{,text{mic},}{C}_{rm{mic}}(t){m}_{rm{mic}}^{0}{s}_{rm{mic},* }(p)\ -sum _{,text{mic},}{C}_{rm{mic}}(t){{mathbb{1}}}_{{{mathcal{D}}}_{u}}(p){u}_{rm{mic},* }^{0}{chi }_{* }^{rm{ac}}(p,t)\ +{i}_{* }^{rm{ac}}(p,t),$$
(15)

$$frac{partial {chi }_{* }^{rm{in}}}{partial t}(p,t)={tau }_{rm{tr},* }^{rm{in}}{chi }_{* }^{rm{ac}}(p,t)-{tau }_{rm{tr},* }^{rm{ac}}{chi }_{* }^{rm{in}}(p,t)+{i}_{* }^{rm{in}}(p,t).$$
(16)

where ({{mathbb{1}}}_{{{mathcal{D}}}_{u}}(p)) equals 1 if (pin {{mathcal{D}}}_{u}) and 0 otherwise.
The dynamics of the total substrate is ruled by,

$$frac{d{C}_{rm{sub}}(t)}{dt}=sum _{* }int_{{p}_{* }^{rm{min}}}^{{p}_{* }^{rm{max}}}left(frac{partial {chi }_{* }^{rm{ac}}}{partial t}(p,t)+frac{partial {chi }_{* }^{rm{in}}}{partial t}(p,t)right)dp.$$
(17)

The dynamics of microbial Cmic is obtained by,

$$frac{d{C}_{rm{mic}}}{dt}(t)=-{m}_{rm{mic}}^{0}{C}_{rm{mic}}(t) +sum _{* }{u}_{rm{mic},* }^{0}{e}_{rm{mic},* }^{0}{C}_{rm{mic}}(t)int_{{{mathcal{D}}}_{u}}{chi }_{* }^{rm{ac}}(p,t)dp,$$
(18)

and the CO2 flux (gC.d−1) produced by the microbes is given by,

$${F}_{rm{CO}_{2}}(t)={C}_{rm{mic}}(t)sum _{* }{u}_{rm{mic},* }^{0}left(1-{e}_{rm{mic},* }^{0}right){int}_{{{mathcal{D}}}_{u}}{chi }_{* }^{rm{ac}}(p,t)dp.$$
(19)

Model implementation
The model was implemented in the Julia© language63,64. An explicit finite difference scheme approximates the solutions of integro-differential equations with a Δt = 0.1d time step and a Δp = 0.01p polymerization step. Differential equations were solved with a Runge–Kutta method.
Scenarios
Scenario 1: cellulose decomposition kinetics and model sensitivity
A first simulation was run to depict cellulose depolymerization and uptake by a decomposer community over one year (see parameters in Table 1). A global sensitivity analysis focusing on the residual cellulose variable was made to determine (i) the relative influence of parameters, and (ii) how parameters influence varies over time (Fig. 3c). A specific attention was given on enzymatic parameters (especially α) to verify the pertinence of their introduction in the model.
We considered a specific method defined by Sobol for calculating sensitivity indices65. It provides the relative contribution of the model parameters to the total model variance, here at different times of the simulation. The method relies on the same principle as the analysis of variance. It was designed to decompose the variance of a model output according to the various degrees of interaction between the n uncertain parameters ({({x}_{i})}_{iin {1,n}}). Formally, by assuming that the parameter uncertainties are independent, the model output, denoted y, could be expressed as a sum of functions that take parameter interactions into account,

$$y={f}_{0}+mathop{sum }limits_{i=1}^{n}{f}_{i}({x}_{i})+mathop{sum }limits_{{i,j=1}atop {ine j}}^{n}{f}_{i,j}({x}_{i},{x}_{j})+…+{f}_{1, , …, , n}({x}_{1},…,{x}_{n}).$$
(20)

Under independence assumptions between models parameters variability, model variance is:

$${{mathbb{V}}ar}(y)= , mathop{sum }limits_{i=1}^{n}{{mathbb{V}}ar}({f}_{i}({x}_{i}))\ +mathop{sum }limits_{{i,j=1}atop {ine j}}^{n}{{mathbb{V}}ar}({f}_{i,j}({x}_{i},{x}_{j}))\ +…+{{mathbb{V}}ar}({f}_{1, , …, , n}({x}_{1},…,{x}_{n})).$$
(21)

This variance decomposition leads to the definition of several sensitivity indices. The first-order Sobol’s index of each parameter is,

$${S}_{i}=frac{{{mathbb{V}}ar}({f}_{i}({x}_{i}))}{{{mathbb{V}}ar}(y)},$$
(22)

and higher order indices are defined by:

$${S}_{i,j}=frac{{{mathbb{V}}ar}({f}_{i,j}({x}_{i},{x}_{j}))}{{{mathbb{V}}ar}(y)},$$
(23)

and so on. These indices are unique, with a value of 0–1 and their sum equals 1. Here we focused on Sobol’s first-order indices as they are usually sufficient to give a straightforward interpretation of the actual influence of different parameters66,67. We computed the sensitivity of the model outputs at several times of the simulation to highlight the role of model’s parameters at different phases. Figure 3c shows the normalized Sobol’s first-order indices to illustrate the relative influence of the model parameters on residual cellulose-C amount. Sobol’s indices were estimated using a Monte Carlo estimator of the variance68. This was performed for a small variation in parameter values (±5% uniform variability), by running 12,000 model simulations for the Monte Carlo sampling.
Scenario 2: effect of substrate inaccessibility to enzyme on litter decomposition kinetics
A simulation of lignocellulose (76% cellulose, 24% lignin) degradation was performed by taking into account peroxidases, which deconstruct the lignin polymer, and cellulases, which hydrolyze cellulose. The cellulose was initially embedded in lignin and inaccessible to cellulase. The lignolytic activity (peroxidases) induces a disentanglement of the cellulose from the lignocellulosic complex. Therefore, the action of peroxidases was seen as a change of cellulose physicochemical local conditions resulting in a progressive transfer to the accessible pool. This transfer was assumed to be linearly related to the activity of lignolytic enzymes in Eq. (10),

$${tau }_{rm{tr,cell.}}^{rm{ac}}={tau }_{rm{tr,cell.}}^{rm{ac},0}int_{{{mathcal{D}}}_{rm{lig.}}}{tau }_{rm{lig.}}^{0}{C}_{rm{mic}}(t){chi }_{rm{lig.}}^{rm{ac}}(p,t)dp,$$
(24)

where ({{mathcal{D}}}_{rm{lig.}}) is the domain of lignolytic activity and where the ({tau }_{rm{tr,cell.}}^{ac,0}) coefficient is set at 13 g({,}_{C}^{-1}) for the current illustration.
The simulation was performed over one year. Enzymatic and microbial parameters given in Table 1 were chosen to be closely in line with the litter decomposition and enzyme action observation16,37,69.
Scenario 3: effect of community succession on C fluxes and substrate biochemistry
We simulated the succession of two microbial functional communities, on the same previous lignocellulose, considering microbial residue recycling. The parameters (Table 1) were chosen according to the microbial community succession observations43,45,70. The first microbial community was specialized in plant substrate degradation, the second was specialized in the degradation of microbial residues. We referred to them as plant decomposers and microbial residue decomposers. Microbial residue decomposers were more competitive than plant decomposers because of their higher carbon use efficiency and lower mortality rate (Table 1). Both communities had the same biochemical signature, i.e., 50% polysaccharides, 30% lipids, and 20% proteins. We tested the impact of cheating as follows. Either uptake was impossible, i.e., u0 equaled 0 for the community not involved in enzyme production, or uptake was possible but at a lower rate than the enzyme producers because the substrate fragments were released in the vicinity of the enzyme producers (Table 1).
Scenario 4: soil organic matter composition at steady state
We resolved the analytic formulation of the C stock and chemistry at steady state under several assumptions. We only considered one microbial community, a continuous constant plant input I at a rate of 2.74.10−4 gC.cm−2.d−1 and microbial recycling47. To be able to explicitly calculate the steady state, we only considered accessible pools, then cellulose substrate was not embedded in lignin but directly accessible to cellulolytic enzymes (Fig. 6). Finally, we considered that C use efficiency (({e}_{rm{mic}}^{0})) and uptake (({u}_{rm{mic}}^{0})) parameters were identical for all biochemical classes. A full mathematical proof is given in Supplementary Note 3.
At steady state, the amount of microbial carbon is,

$${C}_{rm{mic}}=frac{{e}_{rm{mic}}^{0}I}{(1-{e}_{rm{mic}}^{0}){m}_{rm{mic}}^{0}}.$$
(25)

For each biochemical class ∗, we define ({p}_{* }^{u}) which verifies ({p}_{* }^{rm{min}} More

Many precautions should be taken to limit the modification of the commensal communities studied and the increase of interindividual variation not attributable to the experimental variables. The following factors can influence the human microbiota and should be considered when designing studies targeting the lung microbiota: the administration of antibiotics or neoadjuvant25,26,27,28, the size of the lesion, the type of surgical procedure, the type of pulmonary pathology under study, and living habits of patients (e.g., smoking status, physical exercise, buccal hygiene, alcohol consumption)29,30,31,32,33,34.
A more exhaustive list of concomitant factors was pointed out by Carney et al.35. However, as the different fields of microbiota studies expand, it is likely that additional variables that can alter its composition will be uncovered. The molecular tools currently used to analyze the human microbiota do not have the power to discriminate the impact of that many factors over the microbial profiles. Whenever possible, patients selected for lung microbiota studies should be extensively screened so that they can be as similar as possible. Longitudinal studies could also minimize the impact of those variables, as the same patient, with similar concomitant factors through the study, would be compared to himself overtime.
Tissue management steps should consider the contamination possibilities. In addition to the selection of a less contamination-prone procedure, such as thoracoscopic lobectomy, the manipulations and the instrument used in subsampling the excised organ should be taken into account. A combination of bleach and humid heat was chosen to sterilize the instruments used to sample the cancerous and healthy tissue as it was considered the most easily accessible method. The use of humid heat itself (autoclave) lacks the power to completely neutralize bacterial genomic DNA in solutions and on surfaces36. On the other hand, the utilization of bleach, or a chlorinated detergent, leads to the complete degradation of contaminating DNA on surfaces, such as benches and instruments37,38, but requires rinsing to avoid corrosion. Hence, combining both methods, soaking the instruments in bleach 1.6% for 10 min before rinsing with distilled water and autoclaving in a sterilization pouches, ensures a minimal amount of DNA has to be degraded by moist heat. The rest of the single-use equipment used was commercially sterilized with ionizing radiation.
Healthy lung tissue was subsampled from the pulmonary lobe containing the tumor to ensure that the developed method could be used on a variety of lung tissue samples. It could also act as a control of non-pathologic microbiota to allow comparisons of cancerous and non-cancerous samples within the same subject, hence minimizing the impact on inter-individual microbiota variations. In fact, Riquelme et al. found that the gut microbiota has the capacity to specifically colonize pancreatic tissue8. Correspondingly, the use of adjacent pulmonary tissue to the tumor could help get better insights at a specific colonization of the tumor by lung bacteria. A 5 cm distance between the tumor and the healthy sample was ensured to minimize the potential effect of increased inflammation surrounding the tumor. Furthermore, the lung microbiota composition seems to vary dependently on the position and depth of the respiratory tract, even inside a same lobe39. The healthy tissue was collected in the same tierce of pulmonary depth (Supplementary Fig. 4) in an attempt to sample a microbial community that it would be as representative of non-pathologic microbiota in the tumoral region as possible.
The homogenization of frozen and thawed pulmonary tissues was attempted and was unsuccessful, both with the use of only a 2.8 mm tungsten bead in the Retsch – MM301 mixer mill (30 beats/s, 20 min) or of the Fisherbrand 150 homogenizer with plastic probes (Fisher scientific, Pittsburg). The elasticity of the tissue or its frozen state make the mass nearly unbreakable. The use of the Liberase™ TM enzymatic cocktail (collagenase I & II, thermolysin) prior to the mechanical homogenization proved successful and a homogeneous suspension was obtained using the two-step homogenization protocol (Supplementary Fig. 3). Multiple ratios of liquid to mass of tissue were tested and 3 mL/g was found optimal, as it facilitates the homogenization without overly diluting to sample. A similar ratio of liquid to tissue was used in breast tissue microbiota study40. The samples were first thawed at 4 °C to reduce potential growth or degradation of microorganisms. The digestion was performed directly in the 50 mL collection tube to limit the tissue manipulation and ensure possible contaminant tracking.
Our team was also unable to replicate the results obtain by Yu et al. on larger tissue samples using 0.2 mg/mL of Proteinase K for 24 h13. The samples remained firm and turned brown. Using the Liberase™ cocktail enabled a much faster digestion (75 min) and broke down specifically the lung component responsible for its elasticity, the collagen.
Three commercially available DNA extraction kits were tested. They were selected for their previous successful use in the study of pulmonary or gut microbiota and their intended application as described by the manufacturer. The extraction kits were first tested on homogenized lung tissue spiked with whole-cell bacterial community to assess the efficiency of DNA extraction and recuperation of the commercial kits. The three kits were able to recover more than 88% of the genera added to the samples. All the genera that were not detected by the Microbial and Powersoil (Cutibacterium acnes, Bacteroides vulgatus, Bifidobacterium adolescentis, D. radiodurans, Clostridium beijerinckii, L. gasseri), with the exception of H. pylori, were Gram-positive bacteria. This type of bacteria has been reported to require more aggressive extraction methods to break their tougher cell walls19. However, the bacterial community did not go through the enzymatic and physical homogenization that usually takes place before DNA extraction since we needed to obtain a homogenous tissue sample that could be processed with or without spiked bacteria. These hard to lyse Gram-positive bacteria could have been fragilized by these processes, rendering them easier to break down during the extraction protocol. Furthermore, the detection of the artificially incorporated bacteria does not account for the natural physical association that may occur between the human tissue and microbial cells. Nonetheless, these high percentages of recovery were promising and lead us to continue with the characterization of the extraction kits in a real-life context, meaning the analysis of the base-level microbiota in pulmonary samples collected and processed through the entire pipeline.
Every measurement of the efficiency of extraction, including DNA yield (Supplementary Fig. 5), DNA purity (Supplementary Figs. 6 and 7), and alpha diversity (Fig. 1), pointed in the same direction. In fact, they all showed that the Blood extraction kit was the best option out of the three kits. Therefore, using the Blood kit is recommended as one of the pieces of a complete study design. Additionally, the presence of a high concentration of host DNA in tissue samples might tend to saturate the purification column, which could reduce to amount of bacterial DNA recovered. The superior DNA binding capacity of the affinity column of the Blood kit compared to the two others could explain its better performance and its higher yield in most cases. The samples extracted with the Blood kit were also associated with higher alpha diversity (Shannon index). Therefore, this extraction method was able to recover a higher number of different bacterial organisms (richness) and proportionality in the different OTUs (evenness). The absence of PCR inhibitors and a higher recuperation rate of bacterial DNA in the Blood extracted samples could have led to a more proper amplification in the sequencing process and to the recuperation of very low abundance bacterial DNA in the extraction eluate. For further research, it is advised to take the additional precaution of working under a biosafety cabinet or in the sterile field when analyzing the microbiota of lung tissues to reduce the risk of incorporation of airborne contaminants.
The Illumina Miseq sequencing platform with the use of dual-index strategy has become the dominant technology used in microbial ecology studies for its cost efficiency, low error rate, and user-friendliness41,42,43. Most studies interested in the pulmonary microbiota have also used this technology11,13,14. The sequencing of the 16S rRNA gene amplicon was favored over a shotgun sequencing method because of the overwhelming quantity of human DNA joining bacterial genomes in the pulmonary tissue. The 16S rRNA gene is the most used marker of bacterial identification. No consensus has been reached on the selection of the 16S rRNA gene variable region (V) to sequence for human microbiota18,44. However, it should be kept consistent across studies to allow comparisons. Targeting the V3–V4 regions was suggested using the universal primers developed by Klindworth et al.45. Several microbiota studies, including lung microbiota, have also used these regions7,13,46,47,48.
In the context of this study, genomic mock-community was spiked in DNA extracted from the pulmonary tissue at a biological meaningful concentration. Every genus added to the samples was successfully detected. Consequently, the high ratio of human DNA to bacterial DNA did not interfere with the amplification and detection steps of the sequencing procedure. The sequencing method in place seems adequate for its application in the characterization of pulmonary microbiota.
Contaminating bacteria or DNA can have an important impact of the microbial profile observed in very low biomass samples such as pulmonary tissue23. Consequentially, in addition to proper protocol selection, methodological design that attempts to follow, detect, and account for contamination was proposed. Its main features include the incorporation of a single negative control that monitors the incorporation of contaminants at every step of the experimental method (Supplementary Fig. 3). Since every step of the protocol prior to the extraction is meant to be executed in a single tube and only by the addition of reagents, it is possible to carry and detect the contaminants introduced throughout the procedure. On the contrary, microbiota study methodologies usually dictate for the incorporation of multiple controls at every step of the procedure (e.g. DNA extraction kit, PCR controls, etc.)18. Although more informative as to which step leads to contamination, it makes data analysis harder since the presence of contamination in the multiple controls cannot by added.
No bioinformatics standard operating procedure is available and what should be done with controls sequencing data is still under debate18. Some research groups tried to use a neutral community model49, additional qPCR data50, amplicon DNA yield, or prevalence algorithms51 to assess the influence of methodological contaminants. The removal of every bacterial OTU found in controls from the samples is often not appropriate as these OTUs might also be naturally present in the samples22. We propose using relative abundance ratio between samples and controls to remove contaminating OTUs. Since controls have much lower richness than extracted lung samples and that the total number of reads (sequencing depth) is distributed across every OTU, the relative abundance of reads for each OTU tend to be much higher in the control than the same OTU in samples. Therefore, if the relative abundance of an OTU is greatly superior in the sample than in the control, it is reasonable to think that the same OTU was also in the sample in a substantial quantity. To ensure that OTUs that were present in very low absolute abundance (e.g., from only 1–2 reads) do not lead to the removal of the highly abundant corresponding OTU in samples, only the OTUs with a ratio of 1000 (relative abundance of sample/relative abundance of sample) were kept. The rest of the OTUs found in controls were completely removed from the related samples, since the influence of contaminating DNA could not be differentiated from the pulmonary microbiota. This method would theoretically tolerate no more than 20 reads (0.1%) before removing the entire OTU from the sample if only one OTU was present in the samples (20,000 reads, 100%). The use of relative abundance helps reduce the absolute abundance bias induced by the divergence in sequencing depth. The OTUs were removed from both tissues at the same time or not at all to avoid adding artificial intraindividual variation. The authors acknowledge that the proposed contaminant management method does not have the in-dept validation of other methods, such as described by Davis et al. with the decontam package51. However, it does not share its limitations regarding the lack of consideration for OTU abundance and need of high number of controls to ensure sensitivity while using prevalence-based detection. Further research focused on the development of statistical methods to detect contaminant OTUs in the cases of lung microbiota is needed. This work is to be a starting point toward methodological standardization and its modular nature makes the bioinformatic contaminant management method proposed here interchangeable once a more robust one is uncovered.
Pearson’s correlation tests were performed on the number of reads per OTU between the samples and their respective controls. Although these values were not normally distributed (Shapiro-Wilk, p  More

In this study, we used the black garden ant Lasius niger to investigate the benefits and factors of pleometrosis, the transitory association between founding queens. The monitoring of colonies founded by one or two queens showed that pleometrosis increased and accelerated offspring production. Then, the experimental pairing of L. niger founding queens revealed that in pairs of queens of different fecundity but similar size, the most fecund queen was more likely to survive. Our experiment could not detect a similar effect of size when controlling for fecundity. Finally, we found that queens associated preferentially with less fecund queens.
Our findings of pleometrosis benefiting offspring production are in line with the literature for this, and other ant species3,7,9,10,12,22,23. Interestingly, we only detected these benefits at the colony level, as pleometrosis had either no effect or a negative influence on the per capita offspring production9,12,22. However, colony-level measurements are more relevant in the case of pleometrosis, as the queen that survives the association inherits all the offspring produced during colony foundation. In the field, colonies with a faster, more efficient worker production would have a competitive advantage over neighbouring founding colonies3,4. This is especially true for L. niger, which shows high density of founding colonies that compete for limiting resources and raid the brood of other colonies10. Thus, the competitive advantage provided by pleometrosis likely enhances colony growth and survival.
The increased and faster production of workers in colonies with two queens may stem from a nutritional boost for the larvae. L. niger founding queens do not forage, and produce the first cohort of workers from their own metabolic reserves. Larvae have been observed to cannibalize both viable and non-viable (trophic) eggs24. We found that colonies with two queens produced more eggs, but that this did not translate in them having more larvae. However, more of these larvae became pupae—and ultimately workers. In addition, while the time to produce the first egg and larva did not differ between colonies with one and two queens, the first pupa and worker were produced faster when two queens were present, consistent with a shorter larval stage. We propose that larvae in pleometrotic colonies developed faster and were more likely to reach pupation because they had more eggs that provided nutrients, boosting the development rate of the first workers.
These benefits of pleometrosis are only inherited by the queens that survive, it is thus important to understand the factors that determine queen survival in pleometrotic associations. Although this question has been relatively well studied3,16,17,18,19,20,21, it has remained challenging to disentangle the effects of correlated factors. For example, we found that size, which has been reported to predict queen survival16,19, correlated with fecundity, which would itself be confounded with the parentage of workers in the first cohort produced. To address this issue, we disentangled size and fecundity experimentally, and used foreign workers that developed from pupae collected in field colonies to prevent any potential nepotistic behaviour.
We found that fecundity, but not size, determined queen survival. The finding that, despite being of similar size, more fecund queens are more likely to survive indicates that the outcome of pleometrosis is not the mere consequence of physical dominance. The higher fecundity could reflect a better health condition, which may give the advantage to the more fecund queen in direct fights3,15, or if workers initiate the fights. Natural selection may have favoured workers that skew aggression toward the less fecund queen, both because this queen would be less efficient at building a colony, and because the workers would be more likely to be the offspring of the more fecund queen. The latter would not necessarily involve direct nepotistic behaviours (the workers would not behave according to parentage, but to fecundity), which have remained elusive in social insects in general25,26,27, and in pleometrotic associations in particular16,17,20. Despite regular behavioural observations, we did not observe who initiated aggression in our experiments, and it remains unclear whether the queens and/or the workers are responsible for the onset of fights. Consistently with previous studies16,23, we found that a certain proportion of queen death occurred before worker emergence, suggesting that worker presence is not required for queen execution. Finally, we cannot rule out that the least fecund queens were more likely to die because of a weaker health status, possibly combined with the stress of being associated with another, healthier queen.
Although it has not been directly reported before, our finding that fecundity determines queen survival is consistent with previous reports of weight being associated with queen survival17, more fecund queens being more aggressive28, cuticular hydrocarbon profiles differing between surviving and culled queens21, and between more and less fecund queens28. We could not directly support previous reports of size correlating with survival16,19. This could be because in those studies, size could have been confounded with fecundity, and/or because we lacked the statistical power to detect such effect in our experiment.
Pleometrosis provides clear benefits, but these benefits are only inherited by the surviving queens, and the losing queens pay the great cost of dying without contributing to the next generation. Natural selection should thus favour queens that decide whether or not to join a pleometrotic association based on the relative benefits compared to individual foundation—these may differ across ecological contexts29—and the likelihood of surviving the association. As fecundity appears to determine queen survival in L. niger, queens may have evolved the ability to choose among potential partners according to their fecundity. Our results are consistent with this hypothesis, as queens preferentially associated with partners that would later produce fewer eggs, possibly because they were less fecund, and therefore less healthy and easier to eliminate. This suggests that founding queens may assess the fecundity of potential partners, possibly via their cuticular hydrocarbon profile28. This result further supports our finding that fecundity plays an important role in pleometrotic associations. It is important to note that this difference in egg production could have alternative explanations. First, it could stem from more fecund queens having no interest in forming an association because they are able to start a competitive colony alone. Second, it could be a consequence, rather than a cause, of the outcome of the choice experiment. We cannot rule out that entering an association with another queen and/or leaving this association prematurely at the end of the choice experiment may have been stressful for the chosen queens, and affected their later production of eggs. We could not detect any difference between chosen and not chosen queens in the number of larvae and pupae produced, which are likely influenced by factors other than fecundity (e.g., brood care behaviour). Interestingly, we did not find that queens chose according to size, consistent with our finding that size may not affect which queen survives the pleometrotic association.
Our study informs on the benefits and factors of pleometrosis, and highlights the role of fecundity in the decision to associate with another queen, and in determining which queen survives the association. As such, it contributes to a better understanding of the onset and outcome of pleometrosis, a classic case of cooperation between unrelated animals. More