Ecology

Field collectionEndophytic fungi and insects were assessed from dormant twig samples from 155 tree species at 51 locations in 32 countries. Sampled tree species belonged to genera that are native to, and occur widely across, either the northern or southern hemisphere, since very few tree genera occur naturally in both hemispheres (e.g., in our study only Podocarpus appears in both hemispheres but has a limited distribution in the northern hemisphere). We sampled largely in botanical gardens and arboreta, which allowed us to sample native and non-native, congeneric and confamiliar, tree species at each location. At each location, one native and one to three non-native congeneric or confamiliar tree species were sampled.At each location, twenty 50-cm long asymptomatic twigs were collected from 1–5 individual trees per species, from different branches and different parts of the crown (Fig. 1). The number of individual trees per species depended on the number of trees available in the specific botanical garden or arboretum, which was often low (Table 1). All twigs per tree species and location were pooled and analysed as a single sample. On some occasions two samples of the same tree species at the same location are considered. Sampling was conducted in the month with the shortest day-length in the year (end of December 2017 in the Northern hemisphere, end of June 2018 in the Southern hemisphere). Samples originating from a tropical region (eleven samples from Tanzania) were collected in June 2018. Trees were sampled in winter to align with the timing of trade, i.e. most woody plants are traded in winter or early spring, as plants will be planted in the following spring, and to reduce the risk of introducing foliar pests in deciduous trees. Evergreen gymnosperm and angiosperm tree species, which were also considered, do not lose foliage during winter, and are thus sold with leaves/needles.Table 1 Site information for sampling locations included in this study.Full size tableFungal endophytesTo assess fungal communities, a total of 352 samples from 145 native and non-native tree species, belonging to nine families of angiosperms and gymnosperms, were collected. Sampling was done at 44 locations in 28 countries on five continents (Fig. 1, Table 1).From each twig in a sample, one bud, one needle/leaf and one 1 cm long twig segment were taken (Fig. 1). Needles from gymnosperms, and leaves from evergreen angiosperms were sampled to accurately assess the risk of trading these species. Twig segments were cut from the twig bases. The selected plant parts were surface sterilized by immersion in 75% ethanol for 1 min, 4% NaOCl for 5 min, and 75% ethanol for 30 s26. After air drying on a sterile bench, the following material from each of 20 twigs per sample was pooled: half of one bud, a 0.5 cm long piece of a needle (from gymnosperms) or a 0.25 cm2 leaf (for evergreen angiosperms) and a 0.5 cm long piece of twig.DNA extraction, PCR amplification and Illumina sequencingTotal genomic DNA was extracted from 50 mg of pooled, surface sterilized, and ground tissue (Fig. 1) using DNeasy PowerPlant Pro Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. For a total of 31 out of 352 samples, DNA was extracted from different tissues separately, and DNA extracts were then pooled. DNA concentrations were quantified using the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, USA) on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific) and DNA was diluted to 5 ng/μl. Samples that yielded less than 5 ng/μl were not diluted. The ITS2 region was amplified with the 5.8S-Fung and ITS4-Fung primers27. PCR amplifications were carried out in 20 μl reaction volumes containing 25 ng of DNA template, 1 mg/ml BSA, 1 mM of MgCl2, 0.4 μM of each primer, and 0.76 × JumpStart REDTaq ReadyMix Reaction Mix (Sigma-Aldrich, Steinheim, Germany). PCR was performed using Veriti 96-Well Thermal Cycler (Applied Biosystems, Foster City, CA, USA) as described in Franić et al. (2019). Each sample was amplified in triplicates and successful PCR amplification confirmed by visualization of the PCR products, before and after pooling the triplicates, on 1.5% (w/v) agarose gel with ethidium bromide staining. Pooled amplicons were sent to the Génome Québec Innovation Center at McGill University (Montréal, Quebec, Canada) for barcoding using Fluidigm Access Array technology (Fluidigm, South San Francisco, CA, USA) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Raw sequences obtained in this study are deposited at the NCBI Sequence Read Archive under BioProject accession number PRJNA70814822.Bioinformatics and taxonomical classification of ASVsQuality filtering and delineation into ASVs were done with a customized pipeline28 largely based on VSEARCH29, as described by Herzog et al.30. The output data available on Figshare show the abundances of fungal ASVs in the samples24. Taxonomic classification of ASVs was conducted using Sintax31 implemented in VSEARCH against the UNITE v.7.2 database32 with a bootstrap support of 80%. The data on the taxonomic classification of fungal ASVs is deposited in Figshare24.Quality filtering, delineation into ASVs, and taxonomical assignments were done on a larger data set (total of 474 samples), which increased the confidence in the selected centroid sequences. This data set consisted of (1) sequences obtained from 352 samples of pooled tree tissues that are presented here22, (2) sequences obtained from 33 samples of pooled tree tissues which were not included in this manuscript due to violation of the common protocol, (3) sequences from 21 contaminated samples (positive DNA extraction controls), including sequences from the two control samples (not presented here), and (4) sequences obtained from 66 samples of non-pooled tree tissues of Pinus sylvestris and Quercus robur that were collected from the subset of locations considered in this study, but for a different study, and are thus not presented here.Herbivorous insectsInsects were assessed from 227 samples of 109 tree species, collected at 31 locations and in 18 countries (Fig. 1, Table 1).The collected twigs (twenty 50 cm twigs per species per location) were brought to a laboratory close to each sampling location and inspected for the presence of insects that overwinter as adults. Twigs were kept at room temperature with the cut ends immersed in water to induce budding and to allow the development of insects that overwinter as larvae, pupae or eggs. Twigs from each sample were protected with gauze bags to prevent insects moving between samples (Fig. 1). Twigs were inspected for the presence of insects daily for 4 weeks and all collected insects were stored in 95% ethanol for further examination.Morphological and molecular identificationInsects were inspected using a stereo microscope and sorted to taxonomic orders and feeding guilds (i.e. herbivores, predators, parasitoids and other). The abundance of the different feeding guilds and taxonomic orders in the samples is presented in a file deposited on Figshare24. Herbivorous insects were further sorted into morphospecies and at least one specimen per morphospecies was stored at −20 °C for molecular analysis. The abundance of the different morphospecies in each sample is presented in a file deposited on Figshare24. Specimens for molecular analysis were photographed with a Leica DVM6 digital microscope and the Leica Application Suite X (LAS X). Depending on the size of the insects, the whole individual or parts (e.g. legs, head) were used for molecular analysis. Genomic DNA was extracted with a KingFisher (Thermo Fisher Scientific) extraction protocol suitable for insects (35 min incubation at RT, 30 min wash at RT with 3 different washing buffers, 13 min elution at 60 °C) in a 96-well plate. PCR for the COI was carried out in 25 µl reaction volume with 2 µl diluted DNA (1:10), 0.5 µM of each of the primers LCO1490 and HCO219833 and 1 x REDTaq ReadyMix Reaction Mix (Sigma-Aldrich) using a Veriti 96-Well Thermal Cycler (Applied Biosystems) with the following setting: 2 min at 94 °C, five cycles of 30 s at 94 °C, 40 s at 45 °C, and 1 min at 72 °C, 35 cycles of 30 s at 94 °C, 50 s at 51 °C, and 1 min at 72 °C, and a final extension step at 72 °C for 10 min. The success of amplification was verified by electrophoresis of the PCR products in 1.5% (w/v) agarose gel at 90 V for 30 min with ethidium bromide staining. A standard Sanger sequencing of the PCR products in both directions with the same primers was done at Macrogen Europe, Amsterdam, Netherlands. Sequences were assembled and edited with CLC Workbench (Version 7.6.2, Quiagen) and compared to reference sequences in BOLD34. If no conclusive results were found, sequences were compared to reference sequences in the National Centre for Biotechnology Information (NCBI) GenBank databases35. Specimens were assigned to species if the query sequence showed less than 1% divergence from the reference sequence. If two or more taxa matched within the same range, the assignment was ranked down to the next taxonomic level (i.e., genus). When no species match was obtained based on the above criteria, a genus was assigned with a divergence of less than 3%. For lower taxonomic groups the 100 nearest sequences were inspected on the Blast Tree (Fast Minimum Evolution Method) and the taxonomic relationship was evaluated based on that tree. If none of the approaches above revealed a conclusive taxonomic assignment, the morphological identification was taken as reference. The results of morphological and molecular identification of insect specimens are presented in a file deposited on Figshare24. Insect sequences are deposited in GenBank database under accession numbers MW441337-MW44176725.Sample metadataPairwise geographic distances (Euclidean distances) between sampling locations were calculated based on the geographic coordinates of the locations, with function “dist” in the R statistical programme36.Climate data, including mean annual temperature, mean annual precipitation, and temperature seasonality were obtained from the WorldClim database37, at a resolution of 2.5 min, and represent averages between 1970 and 2000.A host-tree phylogeny was constructed with the phylomatic function from the package brranching38 in R using the “zanne2014” reference tree39. One Eucalyptus sample collected in Argentina and two Eucalyptus samples collected in Tunisia were not identified to species. To place them in the phylogeny, we assigned them to different congeneric species that were not sampled in this study and that we considered as representative samples of phylogenetic diversity from across Eucalyptus genus (E. viminalis, E. robusta and E. radiata). Pairwise phylogenetic distances between study tree species were calculated using the “cophenetic” function in R36.The described sample metadata are available in a file on Figshare24. More

Pure coordination gameIn this section we study the Pure Coordination Game (PCG) (also known as doorway game, or driving game) in which (R=1), (S=0), (T=0), and (P=1), resulting in a symmetric payoff matrix with respect to the two strategies:$$begin{gathered} begin{array}{*{20}c} {} & {quad ; {text{A}}} &; {text{B}} \ end{array}hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & 0 \ 0 & 1 \ end{array} } right) hfill \ end{gathered}$$
(2)
There are two equivalent equilibria for both players coordinating at the strategy A or B (a third Nash equilibrium exists for players using a mix strategy of 50% A and 50% B). As the absolute values of the payoff matrix are irrelevant and the dynamics is defined by ratios between payoffs from different strategies, the payoff matrix (2) represents all games for which the relation (R=P >S=T) is fulfilled.In the PCG the dilemma of choosing between safety and benefit does not exist, because there is no distinction between risk-dominant and payoff-dominant equilibrium. Both strategies yield equal payoffs when players coordinate on them and both have the same punishment (no payoff) when players fail to coordinate. Therefore, the PCG is the simplest framework to test when coordination is possible and which factors influence it and how. It is in every player’s interest to use the same strategy as others. Two strategies, however, are present in the system at the beginning of the simulation in equal amounts. From the symmetry of the game we can expect no difference in frequency of each strategy being played, when averaged over many realisations. Still, the problem of when the system reaches full coordination in one of the strategies is not trivial. We address this question here.Figure 1Time evolution of the coordination rate (alpha) (in MC steps) in individual realisations for different values of the degree k in a random regular network of (N=1000) nodes, using (a) the replicator dynamics, (b) the best response, and (c) the unconditional imitation update rule.Full size imageFigure 2Coordination rate (alpha) and interface density (rho) vs degree k of a random regular network for (N=1000) using (a) the replicator dynamics, (b) the best response, and (c) the unconditional imitation update rule. Each green circle represents one of 500 realisations for each value of the degree k and the average value is plotted with a solid line, separately for (alpha >0.5) and (alpha le 0.5). Results are compared to the ER random network ((alpha _{ER})) with the same average degree.Full size imageFirst, we look at single trajectories as presented in Fig. 1. Some of them quickly reach (alpha =0) or 1, or stop in a frozen state without obtaining global coordination. Other trajectories take much longer and extend beyond the time scale showed in the figure. What we can already tell is that the process of reaching coordination is slower in the replicator dynamics where it usually takes more time than in the best response and unconditional imitation to reach a frozen configuration. For all update rules the qualitative effect of the connectivity is similar—for bigger degree it is more likely to obtain full coordination and it happens faster. For the UI, however, larger values of degree than for the RD and BR are required to observe coordination. For example, in the case of (k=10) or 20 the system stops in a frozen disorder when using UI, while for the RD and BR it quickly reaches a coordinated state of (alpha =0) or 1.To confirm the conclusions from observation of trajectories, we present the average outcome of the system’s evolution in the Fig. 2. The first thing to notice is that all plots are symmetrical with respect to the horizontal line of (alpha = 0.5). It indicates that the strategies are indeed equivalent as expected. In all cases there is a minimal connectivity required to obtain global coordination. For the RD and BR update rules this minimum value is (k=4), although in the case of BR the system fails to coordinate for small odd values of k due to regular character of the graph. This oscillating behaviour does not exist in Erdős–Rényi random networks. When nodes choose their strategies following the UI rule much larger values of k are required to obtain full coordination. Single realisations can result in (alpha = 0), or 1 already for (k=15). However, even for (k=60) there is still a possibility of reaching a frozen uncoordinated configuration.The important conclusion is that there is no coordination without a sufficient level of connectivity. In order to confirm that this is not a mere artefact of the random regular graphs we compare our results with those obtained for Erdős–Rényi (ER) random networks76,77 (black dashed line in Fig. 2). The level of coordination starts to increase earlier for the three update rules, but the general trend is the same. The only qualitative difference can be found in the BR. The oscillating level of coordination disappears and it doesn’t matter if the degree is odd or even. This shows that different behaviour for odd values of k is due to topological traps in random regular graphs78. Our results for the UI update rule are also consistent with previous work reporting coordination for a complete graph but failure of global coordination in sparse networks40.Figure 3Examples of frozen configuration reached under the UI update rule for small values of the average degree k in random regular networks (top row) and Erdős–Rényi networks (bottom row) with 150 nodes. Red colour indicates a player choosing the strategy A, blue colour the strategy B. Note the topological differences between random regular and ER networks when they are sparse. For (k=1) a random regular graph consists of pairs of connected nodes, while an ER network has some slightly larger components and many loose nodes. For (k=2) a random regular graph is a chain (sometimes 2–4 separate chains), while an ER network has one large component and many disconnected nodes. For (k=3) and (k=4) a random regular graph is always composed of one component, while an ER network has still a few disconnected nodes.Full size imageSince agents using the RD and BR update rule do not achieve coordination for small values of degree, one might suspect that the network is just not sufficiently connected for these values of the degree, i.e. there are separate components. This is only partially true. In Fig. 3, we can see the structures generated by random regular graph and by ER random graph algorithms. Indeed, for (k=1) and 2 the topology is trivial and a large (infinite for (k=1)) average path length23 can be the underlying feature stopping the system to reach coordination. For (k=3), however, the network is well connected with one giant component and the system still does not reach the global coordination when using RD or BR. For the UI update rule coordination arrives even for larger values of k. Looking at the strategies used by players in Fig. 3 we can see how frozen configuration without coordination can be achieved. There are various types of topological traps where nodes with different strategies are connected, but none of them is willing to change the strategy in the given update rule.We next consider the question of how the two strategies are distributed in the situations in which full coordination is not reached. Looking at the trajectories in Fig. 1 we can see that there are only few successful strategy updates in such scenario and the value of (alpha) remains close to 0.5 until arriving at a frozen state for (k=2) (also (k=7) for UI). This suggests that there is not enough time, in the sense of the number of updates, to cluster the different strategies in the network. Therefore, one might expect that they are well mixed as at the end of each simulation. However, an analysis of the density of active links in the final state of the dynamics, presented in Fig. 2, shows a slightly more complex behaviour. When the two strategies are randomly distributed (i.e. well mixed) in a network, the interface density takes the value (rho =0.5). When the two strategies are spatially clustered in the network there are only few links connecting them and therefore the interface density takes small values. Looking at the dependence of (rho) on k, we find that for the replicator dynamics the active link density starts at 0.5 for (k=1), then drops below 0.2 for (k=2) and 3 indicating good clustering between strategies, to fall to zero for (k=4) where full coordination is already obtained. When using the best response update rule the situation is quite different. For (k=1) there are no active links, (rho =0), and hardly any for (k=2). There is a slight increase of the active link density for (k=3), to drop to zero again for (k=4) due to full coordination. Because of the oscillatory level of coordination there are still active links for odd values of (kP) (otherwise we can rename the strategies and shuffle the columns and rows). What defines the outcome of a game are the greater than and smaller than relations among the payoffs. Therefore we can add/subtract any value from all payoffs, or multiply them by a factor grater than zero, without changing the game. Thus, the payoff matrix (1) can be rewritten as:$$begin{gathered} begin{array}{*{20}c} {} & {qquad {text{A}}} & {quad quad {text{B}}} \ end{array} ;; hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & {frac{{S – P}}{{R – P}}} \ {frac{{T – P}}{{R – P}}} & 0 \ end{array} } right) hfill \ end{gathered}$$
(3)
which, after substituting (S’=frac{S-P}{R-P}) and (T’=frac{T-P}{R-P}), is equivalent to the matrix: $$begin{gathered} begin{array}{*{20}c} {} &quad ;;{text{A}} &; {text{B}} \ end{array} ;quad quad quad quad quad quad begin{array}{*{20}c} {} & quad; {text{A}} & ;{text{B}} \ end{array} hfill \ begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & {S^{prime}} \ {T^{prime}} & 0 \ end{array} } right)xrightarrow[{{text{apostrophes}}}]{{{text{skipping}}}}begin{array}{*{20}c} {text{A}} \ {text{B}} \ end{array} left( {begin{array}{*{20}c} 1 & S \ T & 0 \ end{array} } right) hfill \ end{gathered}$$
(4)
From now on we omit the apostrophes and simply refer to parameters S and T. This payoff matrix can represent many games, including e.g. the prisoner’s dilemma14,46 (for (T >1) and (S More

IPCC Climate Change 2014: Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) 151 (IPCC, Geneva, Switzerland, 2014).Grizzetti, B., Bouraoui, F. & Aloe, A. Changes of nitrogen and phosphorus loads to European seas. Glob. Change Biol. 18, 769–782 (2012).
Google Scholar 
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).CAS 
PubMed 

Google Scholar 
Duarte, C. M. Global change and the future ocean: a grand challenge for marine sciences. Front. Mar. Sci. 1, 1–16 (2014).
Google Scholar 
Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305, 1609–1612 (2004).CAS 
PubMed 

Google Scholar 
Rose, J. M. et al. Effects of increased pCO2 and temperature on the North Atlantic spring bloom. II. Microzooplankton abundance and grazing. Mar. Ecol. Prog. Ser. 388, 27–40 (2009).CAS 

Google Scholar 
Sommer, U., Paul, C. & Moustaka-Gouni, M. Warming and ocean acidification effects on phytoplankton—from species shifts to size shifts within species in a mesocosm experiment. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Garzke, J., Hansen, T., Ismar, S. M. H. & Sommer, U. Combined effects of ocean warming and acidification on copepod abundance, body size and fatty acid content. PLoS ONE 11, 1–22 (2016).
Google Scholar 
Horn, H. G., Boersma, M., Garzke, J., Sommer, U. & Aberle, N. High CO2 and warming affect microzooplankton food web dynamics in a Baltic Sea summer plankton community. Mar. Biol. 167, 1–17 (2020).
Google Scholar 
Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Change Biol. 24, 2239–2261 (2018).
Google Scholar 
Stewart, R. I. A. et al. Mesocosm experiments as a tool for ecological provided for ecological climate-change research. In Advances in Ecological Research/Guy Woodward (ed. O’Gorman, E. J.) 71–181 (Academic Press, 2013).Rost, B. & Riebesell, U. Coccolithophores and the biological pump: responses to environmental changes. In Coccolithophores: From Molecular Processes to Global Impact (eds Thierstein, H. R. & Young, J. R.) 99–125 (Springer, 2004).Peter, K. H. & Sommer, U. Phytoplankton cell size reduction in response to warming mediated by nutrient limitation. PLoS ONE 8, 1–6 (2013).
Google Scholar 
Bermúdez, J. R., Riebesell, U., Larsen, A. & Winder, M. Ocean acidification reduces transfer of essential biomolecules in a natural plankton community. Sci. Rep. 6, 1–8 (2016).
Google Scholar 
Peter, K. H. & Sommer, U. Interactive effect of warming, nitrogen and phosphorus limitation on phytoplankton cell size. Ecol. Evolution 5, 1011–1024 (2015).
Google Scholar 
Alvarez-Fernandez, S. et al. Plankton responses to ocean acidification: the role of nutrient limitation. Prog. Oceanogr. 165, 11–18 (2018).
Google Scholar 
Stramski, D., Sciandra, A. & Claustre, H. Effects of temperature, nitrogen, and light limitation on the optical properties of the marine diatom Thalassiosira pseudonana. Limnol. Oceanogr. 47, 392–403 (2002).CAS 

Google Scholar 
Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).
Google Scholar 
Peñuelas, J., Sardans, J., Rivas‐Ubach, A. & Janssens, I. A. The human-induced imbalance between C, N and P in Earth’s life system. Glob. Change Biol. 18, 3–6 (2011).
Google Scholar 
Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–63. (1983).
Google Scholar 
Legendre, L. & Le Fèvre, J. Microbial food webs and the export of biogenic carbon in oceans. Aquat. Microb. Ecol. 9, 69–77 (1995).
Google Scholar 
Beaufort, L. et al. Sensitivity of coccolithophores to carbonate chemistry and ocean acidification. Nature 476, 80–83 (2011).CAS 
PubMed 

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

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

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

Google Scholar 
León, P. et al. Seasonal variability of the carbonate system and coccolithophore Emiliania huxleyi at a Scottish Coastal Observatory monitoring site. Estuar., Coast. Shelf Sci. 202, 302–314 (2018).
Google Scholar 
Rivero-Calle, S., Gnanadesikan, A., Del Castillo, C. E., Balch, W. M. & Guikema, S. D. Multidecadal increase in North Atlantic coccolithophores and the potential role of rising CO2. Science 350, 1533–1537 (2015).CAS 
PubMed 

Google Scholar 
Purdie, D. A. & Finch, M. S. Impact of a coccolithophorid bloom on dissolved carbon dioxide in sea water enclosures in a Norwegian fjord. Sarsia 79, 379–387 (1994).
Google Scholar 
Nejstgaard, J. C., Gismervik, I. & Solberg, P. T. Feeding and reproduction by Calanus finmarchicus, and microzooplankton grazing during mesocosm blooms of diatoms and the coccolithophore Emiliania huxleyi. Mar. Ecol. Prog. Ser. 147, 197–217 (1997).
Google Scholar 
Leblanc, K. et al. Distribution of calcifying and silicifying phytoplankton in relation to environmental and biogeochemical parameters during the late stages of the 2005 North East Atlantic Spring Bloom. Biogeosciences 6, 2155–2179 (2009).CAS 

Google Scholar 
Sett, S. et al. Temperature modulates coccolithophorid sensitivity of growth, photosynthesis and calcification to increasing seawater pCO2. PLoS ONE 9, e88308 (2014).PubMed 
PubMed Central 

Google Scholar 
Benner, I. et al. Emiliania huxleyi increases calcification but not expression of calcification-related genes in long-term exposure to elevated temperature and pCO2. Philos. Trans. R. Soc. B 368, 20130049 (2013).
Google Scholar 
Borchard, C., Borges, A. V., Händel, N. & Engel, A. Biogeochemical response of Emiliania huxleyi (PML B92/11) to elevated CO2 and temperature under phosphorous limitation: a chemostat study. J. Exp. Mar. Biol. Ecol. 410, 61–71 (2011).CAS 

Google Scholar 
Harrison, P. J. et al. Geographical distribution of red and green Noctiluca scintillans. Chin. J. Oceanol. Limnol. 29, 807–831 (2011).
Google Scholar 
Johns, D. G., Edwards, M., Greve, W. & SJohn, A. W. G. Increasing prevelance of the marine cladoceran Penilia avirostris (Dana, 1852) in the North Sea. Helgol. Mar. Res. 59, 215–218 (2005).
Google Scholar 
O’Connor, M. I. O., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, 1–6 (2009).
Google Scholar 
Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. change Biol. 21, 1025–1040 (2015).
Google Scholar 
Boersma, M. et al. Temperature driven changes in the diet preference of omnivorous copepods: no more meat when it’s hot? Ecol. Lett. 19, 45–53 (2016).PubMed 

Google Scholar 
Anderson, T. R., Hessen, D. O., Boersma, M., Urabe, J. & Mayor, D. J. Will invertebrates require increasingly carbon-rich food in a warming world? Am. Naturalist 190, 725–742 (2017).
Google Scholar 
Kirchner, M., Sahling, G., Uhlig, G., Gunkel, W. & Klings, K.-W. Does the red tide-forming dinoflagellate Noctiluca scintillans feed on bacteria? Sarsia 81, 45–55 (2015).
Google Scholar 
Elbrächter, M. & Qi, Y. Aspects of Noctiluca (Dinophyceae) population dynamics. In Physiological Ecology of Harmful Algal Blooms (ed. Anderson, M. D.) 315–335 (Springer-Verlag, 1998).Atienza, D., Saiz, E. & Calbet, A. Feeding ecology of the marine cladoceran Penilia avirostris: natural diet, prey selectivity and daily ration. Mar. Ecol. Prog. Ser. 315, 211–220 (2006).
Google Scholar 
Zhang, S., Liu, H., Chen, B. & Chih-Jung, W. Effects of diet nutritional quality on the growth and grazing of Noctiluca scintillans. Sci. Rep. 527, 73–85 (2015).CAS 

Google Scholar 
Reid, P. C., Borges, M. F. & Svendsen, E. A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery. Fish. Res. 50, 163–171 (2001).
Google Scholar 
Beaugrand, G., Brander, K. M., Lindley, J. A., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).CAS 
PubMed 

Google Scholar 
Payne, M. R. et al. Recruitment in a changing environment: the 2000s North Sea herring recruitment failure. ICES J. Mar. Sci. 66, 272–277 (2009).
Google Scholar 
Perälä, T., Olsen, E. M. & Hutchings, J. A. Disentangling conditional effects of multiple regime shifts on Atlantic cod productivity. PLoS ONE 15, e0237414 (2020).PubMed 
PubMed Central 

Google Scholar 
Behrenfeld, M. J., Boss, E. S. & Halsey, K. H. Phytoplankton community structuring and succession in a competition-neutral resource landscape. ISME COMMUN. 1, 1–8 (2021).Monteiro, F. M. et al. Why marine phytoplankton calcify. Sci. Adv. 2, 1–14 (2016).
Google Scholar 
Mayers, K. M. J. et al. The possession of coccoliths fails to deter microzooplankton grazers. Front. Mar. Sci. 7, 976 (2020).
Google Scholar 
Zhao, Y. et al. Grazing by microzooplankton and copepods on the microbial food web in spring in the southern Yellow Sea, China. Mar. Life Sci. Technol. 2, 442–455 (2020).
Google Scholar 
Aberle, N. et al. High tolerance of microzooplankton to ocean acidification in an Arctic coastal plankton community. Biogeosciences 10, 1471–1481 (2013).
Google Scholar 
Horn, H. G. et al. Low CO2 sensitivity of Microzooplankton communities in the Gullmar Fjord, Skagerrak: evidence from a long-term Mesocosm Study. PLoS ONE 11, 1–24 (2016).
Google Scholar 
Chen, B., Landry, M. R., Huang, B. & Liu, H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57, 519–526 (2012).CAS 

Google Scholar 
Vázquez-Domínguez, E., Vaqué, D. & Gasol, J. M. Temperature effects on the heterotrophic bacteria, heterotrophic nanoflagellates, and microbial top predators of the NW Mediterranean. Aquat. Microb. Ecol. 67, 107–121 (2012).
Google Scholar 
Lara, E. et al. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems. Aquat. Microb. Ecol. 70, 17–32 (2013).
Google Scholar 
Olson, M. B., Solem, K. & Love, B. Microzooplankton grazing responds to simulated ocean acidification indirectly through changes in prey cellular characteristics. Mar. Ecol. Prog. Ser. 604, 83–97 (2018).CAS 

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

Google Scholar 
Brander, K. & Kiørboe, T. Decreasing phytoplankton size adversely affects ocean food chains. Glob. Change Biol. 26, 5356–5357 (2020).
Google Scholar 
Irigoien, X. et al. A high frequency time series at weathership M, Norwegian Sea, during the 1997 spring bloom: feeding of adult female Calanus finmarchicus. Mar. Ecol. Prog. Ser. 172, 127–137 (1998).
Google Scholar 
Fenchel, T. The microbial loop—25 years later. J. Exp. Mar. Biol. Ecol. 366, 99–103 (2008).
Google Scholar 
Aberle, N., Malzahn, A. M., Lewandowska, A. M. & Sommer, U. Some like it hot: the protozooplankton— copepod link in a warming ocean. Mar. Ecol. Prog. Ser. 519, 103–113 (2015).
Google Scholar 
Berglund, J., Müren, U., Båmstedt, U. & Andersson, A. Efficiency of a phytoplankton-based and a bacteria-based food web in a pelagic marine system. Limnol. Oceanogr. 52, 121–131 (2007).CAS 

Google Scholar 
Sherr, E. B. & Sherr, B. F. Heterotrophic dinoflagellates: a significant component of microzooplankton biomass and major grazers of diatoms in the sea. Mar. Ecol. Prog. Ser. 352, 187–197 (2007).
Google Scholar 
Gifford, D. J. The protozoan-metazoan trophic link in pelagic ecosystems. J. Protozool. 38, 81–86 (1991).
Google Scholar 
Rollwagen-Bollens, G. & Gifford, S. The role of protistan microzooplankton in the upper San Francisco estuary planktonic food web: source or sink? Estuaries Coasts 34, 1026–1038 (2011).CAS 

Google Scholar 
Anjusha, A. et al. Trophic efficiency of plankton food webs: observations from the Gulf of Mannar and the Palk Bay, Southeast Coast of India. J. Mar. Syst. 115, 40–61 (2013).
Google Scholar 
IPCC. Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In The Context of Strengthening the Global Response to The Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (Masson-Delmotte, V. et al (eds.) 616 (IPCC, Geneva, Switzerland, 2018).Pansch, A., Winde, V., Asmus, R. & Asmus, H. Tidal benthic mesocosms simulating future climate change scenarios in the field of marine ecology. Limnol. Oceanogr.: Methods 14, 257–267 (2016).
Google Scholar 
van Leeuwen, S., Tett, P., Mills, D. & van der Molen, J. Stratified and nonstratified areas in the North Sea: long-term variability and biological and policy implications. J. Geophys. Res.: Oceans 120, 4670–4686 (2015).
Google Scholar 
Grasshoff, K., Kremling, K. & Ehrhardt, M. (eds). Methods of Seawater Analysis, 3rd edn. (Wiley-VCH, Weinheim, 1999).Dickson, A. G. An exact definition of total alkalinity and a procedure for the estimation of alkalinity and total inorganic carbon from titration data. Deep-Sea Res. 28, 609–623 (1981).CAS 

Google Scholar 
Pierrot, D. E., Lewis, E. & Wallace, D. W. R. MS Excel program developed for CO2 system calculations. ORNL/CDIAC-105a. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee https://doi.org/10.3334/CDIAC/otg.CO2SYS_XLS_CDIAC105a (2006).Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep-Sea Res. 34, 1733–1743 (1987).CAS 

Google Scholar 
Arrigo, K. R. et al. Phytoplankton community structure and the drawdown of nutrients and CO2 in the Southern Ocean. Science 283, 365–368 (1999).CAS 
PubMed 

Google Scholar 
Utermöhl, H. Zur Vervollkommnung der quantitativen Phytoplankton- Methodik. Int. Ver. für. Theoretische und Angew. Limnologie: Mitteilungen 9, 1–38 (1958).
Google Scholar 
McEwen, G. F., Johnson, M. W. & Folsom, T. R. A statistical analysis of the performance of the Folsom plankton sample splitter, based upon test observations. Archiv für. Archiv Meteorologie, Geophysik und Bioklimatologie, Ser. A 7, 502–527 (1954).
Google Scholar 
Sell, D. W. & Evans, M. S. A statistical analysis of subsampling and an evaluation of the Folsom plankton splitter. Hydrobiologia 94, 223–230 (1982).
Google Scholar 
Boersma, M., Wiltshire, K. H., Kong, S., Greve, W. & Renz, J. Long-term change in the copepod community in the southern German Bight. J. Sea Res. 101, 41–50 (2015).
Google Scholar 
Marie, D., Simon, N. & Vaulot, D. Phytoplankton cell counting by flow cytometry. Algal Culturing Tech. 1, 253–267 (2005).
Google Scholar 
Hillebrand, H., Dürselen, C., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Google Scholar 
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).CAS 

Google Scholar 
Putt, M. & Stoecker, D. K. An experimentally determined carbon: volume ratio for marine “oligotrichous” ciliates from estuarine and coastal waters. Limnol. Oceanogr. 34, 1097–1103 (1989).
Google Scholar 
Beran, A. et al. Carbon content and biovolume of the heterotrophic dinoflagellate Noctiluca scintillans from the Northern Adriatic Sea. In Proceedings of the CESUM-BS 2003, Varna. 28 (Book of Abstracts, UNESCO, Paris, 2003).Lee, S. & Fuhrman, J. A. Relationships between biovolume and biomass of naturally derived marine bacterioplankton. Appl. Environ. Microbiol. 53, 1298–1303 (1987).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kraberg, A., Baumann, M. & Dürselen, C. Coastal Phytoplankton: Photo Guide for Northern European Seas (Dr. Friedrich Pfeil, München, 2010).R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021). More

Rutz, C. Predator fitness increases with selectivity for odd prey. Curr. Biol. 22, 820–824 (2012).CAS 
PubMed 

Google Scholar 
Santos, C. D. et al. Personality and morphological traits affect pigeon survival from raptor attacks. Sci. Rep. 5, 1–8 (2015).
Google Scholar 
Brown, M. B. & Wells, E. Skeletal dysplasia-like syndromes in wild giraffe. BMC Res. Notes 13, 569 (2020).PubMed 
PubMed Central 

Google Scholar 
van Grouw, H. What colour is that bird? The causes and recognition of common colour aberrations in birds. Br. Birds 106, 17–29 (2013).
Google Scholar 
Slagsvold, T., Rofstad, G. & Sandvik, J. Partial albinism and natural selection in the hooded crow Corvus corone cornix. J. Zool. 214, 157–166 (1988).
Google Scholar 
Stevens, M. et al. Revealed by conspicuousness: distractive markings reduce camouflage. Behav. Ecol. 24, 213–222 (2013).
Google Scholar 
van Grouw, H. What’s in a name? Nomenclature for colour aberrations in birds reviewed. Bull. Br. Ornithol. Club 141, 276–299 (2021).
Google Scholar 
Parsons, G. J. & Bonderup-Nielsen, S. Partial albinism in an island population of Meadow Voles, Microtus pennsylvanicus, from Nova Scotia. Can. Field-Nat. 109, 263–264 (1995).
Google Scholar 
Reis, A. da S., Zampaulo, R. de A. & Talamoni, S. A. Frequency of leucism in a colony of Anoura geoffroyi (Chiroptera: Phyllostomidae) roosting in a ferruginous cave in Brazil. Biota Neotropica 19(3): e20180676. https://doi.org/10.1590/1676-0611-BN-2018-0676 (2019).Jehl, J. R. Leucism in Eared Grebes in western north America. Condor 87, 439–441 (1985).
Google Scholar 
Forrest, S. & Naveen, R. Prevalence of leucism in Pygoscelid penguins of the Antarctic peninsula. Waterbirds 23, 283–285 (2000).
Google Scholar 
González-Ortegón, E., Drake, P., Quigley, D. T. G. & Cuesta, J. A. Leucism in the European sardine Sardina pilchardus (Clupeidae). Ecol. Indic. 117, 106544 (2020).
Google Scholar 
David, B. Z. First report of partial leucism in the poison frog Epipedobates anthonyi (Anura: Dendrobatidae) in El Oro, Ecuador. Neotrop. Biodivers. 7, 1–4 (2021).
Google Scholar 
Krecsák, L. Albinism and leucism among European Viperinae: a review. Russ. J. Herpetol. 15, 97–102 (2008).
Google Scholar 
Ritland, K., Newton, C. & Marshall, H. D. Inheritance and population structure of the white-phased “Kermode” black bear. Curr. Biol. 11, 1468–1472 (2001).CAS 
PubMed 

Google Scholar 
Galván, I., Bijlsma, R. G., Negro, J. J., Jarén, M. & Garrido-Fernández, J. Environmental constraints for plumage melanization in the northern goshawk Accipiter gentilis. J. Avian Biol. 41, 523–531 (2010).
Google Scholar 
Pijpe, A., Gardien, K. L. M., Meijeren-Hoogendoorn, R. E. van, Middelkoop, E. & Zuijlen, P. P. M. van. Scar Symptoms: Pigmentation Disorders in Textbook On Scar Management (eds. Téot, L., Mustoe, T. A., Middelkoop, E. & Gauglitz, G. G.) 109–115 (Springer, 2020).Edelaar, P. et al. Apparent selective advantage of leucism in a coastal population of Southern caracaras (Falconidae). Evol. Ecol. Res. 13, 187–196 (2011).
Google Scholar 
Ellegren, H., Lindgren, G., Primmer, C. R. & Møller, A. P. Fitness loss and germline mutations in barn swallows breeding in Chernobyl. Nature 389, 593–596 (1997).ADS 
CAS 
PubMed 

Google Scholar 
Benítez-López, A. & García-Egea, I. First record of an aberrantly colored Pin-tailed Sandgrouse (Pterocles alchata). Wilson J. Ornithol. 127, 755–759 (2015).
Google Scholar 
Zbyryt, A., Mikula, P., Ciach, M., Morelli, F. & Tryjanowski, P. A large-scale survey of bird plumage colour aberrations reveals a collection bias in Internet-mined photographs. Ibis 163, 566–578 (2020).
Google Scholar 
Bensch, S., Hansson, B., Hasselquist, D. & Nielsen, B. Partial albinism in a semi-isolated population of Great Reed Warblers. Hereditas 133, 167–170 (2000).CAS 
PubMed 

Google Scholar 
Izquierdo, L. et al. Factors associated with leucism in the common blackbird Turdus merula. J. Avian Biol. 49, e01778 (2018).
Google Scholar 
Møller, A. P. & Mousseau, T. A. Albinism and phenotype of barn swallows (Hirundo rustica) from Chernobyl. Evolution 55, 2097–2104 (2001).PubMed 

Google Scholar 
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 1–8 (2016).
Google Scholar 
Aragonés, J., Arias de Reyna, L. & Recuerda, P. Visual communication and sexual selection in a nocturnal bird species, Caprimulgus ruficollis, a balance between crypsis and conspicuousness. Wilson Bull. 111, 340–345 (1999).
Google Scholar 
Negro, J. J., Bortolotti, G. R. & Sarasola, J. H. Deceptive plumage signals in birds: manipulation of predators or prey? Biol. J. Linn. Soc. 90, 467–477 (2007).
Google Scholar 
Brooke, M. de L. Unexplained recurrent features of the plumage of birds. Ibis 152, 845–847 (2010).Forero, M. G., Tella, J. L. & García, L. Age related evolution of sexual dimorphism in the Red-necked Nightjar Caprimulgus ruficollis. J. Ornithol. 136, 447–451 (1995).
Google Scholar 
Camacho, C. Early age at first breeding and high natal philopatry in the Red-necked Nightjar Caprimulgus ruficollis. Ibis 156, 442–445 (2014).
Google Scholar 
Camacho, C. et al. The road to opportunities: landscape change promotes body-size divergence in a highly mobile species. Curr. Zool. 62, 7–14 (2016).PubMed 
PubMed Central 

Google Scholar 
Forero, M. G., Tella, J. L. & Oro, D. Annual survival rates of adult Red-necked Nightjars Caprimulgus ruficollis. Ibis 143, 273–277 (2001).
Google Scholar 
Henner, J. et al. Genetic mapping of the (G)-locus responsible for the coat color phenotype “Progressive Greying with Age” in horses (Equus caballus). Mamm. Genome 13, 535–537 (2002).CAS 
PubMed 

Google Scholar 
Edson, J. M. An epidemic of albinism. Auk 45, 377–378 (1928).
Google Scholar 
Camacho, C., Palacios, S., Sáez, P., Sánchez, S. & Potti, J. Human-induced changes in landscape configuration influence individual movement routines: lessons from a versatile, highly mobile species. PLoS ONE 9, e104974 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Enders, F. & Post, W. White-spotting in the genus Ammospiza and other grassland sparrows. Bird-Band. 42, 210–219 (1971).
Google Scholar 
Sage, B. L. Albinism and melanism in birds. Br. Birds 55, 201–225 (1962).
Google Scholar 
O’Sullivan, J. D. B. et al. The biology of human hair greying. Biol. Rev. 96, 107–128 (2021).PubMed 

Google Scholar 
Nichols, J. D., Hines, J. E. & Blums, P. Tests for senescent decline in annual survival probabilities of common pochards, Aythya ferina. Ecology 78, 1009–1018 (1997).
Google Scholar 
Owen, M. & Skimmings, P. The occurrence and performance of leucistic Barnacle Geese Branta leucopsis. Ibis 134, 22–26 (1992).
Google Scholar 
Mulder, T., Campbell, C. J. & Ruxton, G. D. Evaluation of disruptive camouflage of avian cup-nests. Ibis 163, 150–158 (2021).
Google Scholar 
Holyoak, D. Variable albinism of the flight feathers as an adaptation for recognition of individual birds in some Polynesian populations of Acrocephalus warblers. Ardea 66, 112–117 (1978).
Google Scholar 
Griffith, S. C., Parker, T. H. & Olson, V. A. Melanin- versus carotenoid-based sexual signals: is the difference really so black and red? Anim. Behav. 71, 749–763 (2006).
Google Scholar 
Galván, I., Jorge, A., Nielsen, J. T. & Møller, A. P. Pheomelanin synthesis varies with protein food abundance in developing goshawks. J. Comp. Physiol. B 189, 441–450 (2019).PubMed 

Google Scholar 
Zaragoza-Trello, C., Vilà, M., Botías, C. & Bartomeus, I. Interactions among global change pressures act in a non-additive way on bumblebee individuals and colonies. Funct. Ecol. 35, 420–434 (2021).
Google Scholar 
Rollin, N. A note on abnormally marked Song Thrushes and Blackbirds. Trans. Nat. Hist. Soc. Northumberl. Durh. Newctle upon Tyne 10, 183–184 (1953).Guerrero-Bosagna, C. et al. Transgenerational epigenetic inheritance in birds. Environ. Epigenet. 4, dvy008 (2018).Camacho, C., Negro, J. J., Redondo, I., Palacios, S. & Sáez-Gómez, P. Correlates of individual variation in the porphyrin-based fluorescence of red-necked nightjars (Caprimulgus ruficollis). Sci. Rep. 9, 1–9 (2019).
Google Scholar 
Camacho, C. Tropical phenology in temperate regions: extended breeding season in a long-distance migrant. Condor 115, 830–837 (2013).
Google Scholar 
Cleere, N. Nightjars: a guide to nightjars and related birds (A&C Black, London, 2010).
Google Scholar 
Gargallo, G. Flight feather moult in the red-necked nightjar Caprimulgus ruficollis. J. Avian Biol. 25, 119–124 (1994).
Google Scholar 
Jackson, H. D. A field survey to investigate why nightjars frequent roads at night. Ostrich 74, 97–101 (2003).
Google Scholar 
Jackson, H. D. Finding and trapping nightjars. Bokmakierie 36, 86–89 (1984).
Google Scholar 
Sénar, J. C. & Pascual, J. Keel and tarsus length may provide a good predictor of avian body size. Ardea 85, 269–274 (1997).
Google Scholar 
Svensson, L. Identification Guide To European Passerines (Lars Svensson, Cleveland, 1992).
Google Scholar 
van de Pol, M. & Wright, J. A simple method for distinguishing within-versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).
Google Scholar 
Schielzeth, H. & Forstmeier, W. Conclusions beyond support: overconfident estimates in mixed models. Behav. Ecol. 20, 416–420 (2009).PubMed 

Google Scholar 
Rising, J. D. & Somers, K. M. The measurement of overall body size in birds. Auk 106, 666–674 (1989).
Google Scholar 
Magnusson, A. et al. Package “glmmTMB”. R Package Version 0.2.0. (2017).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.2, 4. (2019).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
Barton, K. MuMIn: Multi-Model inference. Model selection and model averaging based on information criteria (AICc and alike). R package version 1.43.17. (2020). More

Boutaiba, S., Hacene, H., Bidle, K. A. & Maupin-Furlow, J. A. Microbial diversity of the hypersaline Sidi Ameur and Himalatt Salt Lakes of the Algerian Sahara. J. Arid Environ. 75, 909–916. https://doi.org/10.1016/j.jaridenv.2011.04.010 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ventosa, A. Unusual micro-organisms from unusual habitats: hypersaline environments. Symposia Society for General Microbiology (2006).Fukuchi, S., Yoshimune, K., Wakayama, M., Moriguchi, M. & Nishikawa, K. Unique amino acid composition of proteins in halophilic bacteria. J. Mol. Biol. 327, 347–357 (2003).CAS 
Article 

Google Scholar 
Pillai, S. D., Nakatsu, C. H., Miller, R. V. & Yates, M. V. Manual of environmental microbiology. Life High-Salinity Environ. https://doi.org/10.1128/9781555818821 (2015).Article 

Google Scholar 
Poli, A. et al. Microbial diversity in extreme marine habitats and their biomolecules. Microorganisms 5, 25. https://doi.org/10.3390/microorganisms5020025 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Azpiazu-Muniozguren, M., Martinez-Ballesteros, I., Gamboa, J., Seoane, S. & Bikandi, J. Altererythrobacter muriae sp. nov., isolated from hypersaline Aana Salt Valley spring water, a continental thalassohaline-type solar saltern. Int. J. Syst. Evol. Microbiol. 71, 3 (2021).
Google Scholar 
Zhang, J. et al. Bacterial diversity in Bohai Bay Solar Saltworks, China. Curr. Microbiol. 72, 55–63 (2016).CAS 
Article 

Google Scholar 
Highfield, A., Ward, A., Pipe, R. & Schroeder, D. C. Molecular and phylogenetic analysis reveals new diversity of Dunaliella salina from hypersaline environments. J. Mar. Biol. Assoc. UK 101, 27–37. https://doi.org/10.1017/s0025315420001319 (2021).CAS 
Article 

Google Scholar 
Cycil, L. M. et al. Metagenomic insights into the diversity of halophilic microorganisms indigenous to the Karak Salt Mine, Pakistan. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01567 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Jacob, J. H., Hussein, E. I., Shakhatreh, M. A. K. & Cornelison, C. T. Microbial community analysis of the hypersaline water of the Dead Sea using high-throughput amplicon sequencing. Microbiol. Open 6, e00500. https://doi.org/10.1002/mbo3.500 (2017).CAS 
Article 

Google Scholar 
Ben Abdallah, M. et al. Abundance and diversity of prokaryotes in ephemeral hypersaline lake Chott El Jerid using Illumina Miseq sequencing, DGGE and qPCR assay. Extremophiles 22, 811–823. https://doi.org/10.1007/s00792-018-1040-9 (2018).CAS 
Article 
PubMed 

Google Scholar 
Tazi, L., Breakwell, D. P., Harker, A. R. & Crandall, K. A. Life in extreme environments: Microbial diversity in Great Salt Lake, Utah. Extremophiles 18, 525–535. https://doi.org/10.1007/s00792-014-0637-x (2014).Article 
PubMed 

Google Scholar 
Kashi, F. J., Owlia, P., Amoozegar, M. A. & Kazemi, B. Halophilic prokaryotes in Urmia Salt Lake, a hypersaline environment in Iran. Curr. Microbiol. 78(8), 3230–3238 (2021).Article 

Google Scholar 
Sorokin, D. Y., Roman, P. & Kolganova, T. V. Halo(natrono)archaea from hypersaline lakes can utilize sulfoxides other than DMSO as electron acceptors for anaerobic respiration. Extremophiles 25, 173–180 (2021).CAS 
Article 

Google Scholar 
Hwang, K., Choe, H. & Kim, K. M. Complete genome of Nocardioides aquaticus KCTC 9944T isolated from meromictic and hypersaline Ekho Lake, Antarctica. Mar. Genom. 1, 100889 (2021).Article 

Google Scholar 
Didari, M. et al. Diversity of halophilic and halotolerant bacteria in the largest seasonal hypersaline lake (Aran-Bidgol-Iran). J. Environ. Health Sci. Eng. 18, 961–971. https://doi.org/10.1007/s40201-020-00519-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oren, A. Diversity of halophilic microorganisms: Environments, phylogeny, physiology, and applications. J. Ind. Microbiol. Biotechnol. 28, 56–63 (2002).CAS 
Article 

Google Scholar 
Mutlu, M. B. et al. Prokaryotic diversity in Tuz Lake, a hypersaline environment in Inland Turkey. FEMS Microbiol. Ecol. 65, 474–483. https://doi.org/10.1111/j.1574-6941.2008.00510.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Antón, J. et al. Distribution, abundance and diversity of the extremely halophilic bacterium Salinibacter ruber. Saline Syst. 4, 15. https://doi.org/10.1186/1746-1448-4-15 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oren, A. Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Syst. 4, 2. https://doi.org/10.1186/1746-1448-4-2 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Abdeljabbar, H., Badiaa, E., Jean-Luc, C., Marie-Laure, F. & Najla, S. Prokaryotic biodiversity of halophilic microorganisms isolated from Sehline Sebkha Salt Lake (Tunisia). Afr. J. Microbiol. Res. 8, 355–367. https://doi.org/10.5897/ajmr12.1087 (2014).Article 

Google Scholar 
Najjari, A., Elshahed, M. S., Cherif, A., Youssef, N. H. & Löffler, F. E. Patterns and determinants of halophilic archaea (Class Halobacteria) diversity in Tunisian endorheic salt lakes and Sebkhet systems. Appl. Environ. Microbiol. 81, 4432–4441. https://doi.org/10.1128/aem.01097-15 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Aharon, O. The ecology of the extremely halophilic archaea. FEMS Microbiol. Rev. 1, 415–440 (1994).
Google Scholar 
Oren, A. Halophilic Archaea. FEMS Microbiol. Rev. https://doi.org/10.1016/b978-0-12-809633-8.20800-5 (2019).Article 

Google Scholar 
Feng, Y. et al. The evolutionary origins of extreme halophilic archaeal lineages. Genome Biol. Evol. 13, 8. https://doi.org/10.1093/gbe/evab166 (2021).CAS 
Article 

Google Scholar 
Ventosa, A., Nieto, J. J. & Oren, A. Biology of moderately halophilic aerobic bacteria. Microbiol. Mol. Biol. Rev. 62, 504–544 (1998).CAS 
Article 

Google Scholar 
Kushner, D. J. Halophilic bacteria. Adv. Appl. Microbiol. 10, 73–99 (1968).CAS 
Article 

Google Scholar 
Ghozlan, H., Deif, H., Kandil, R. A. & Sabry, S. Biodiversity of moderately halophilic bacteria in hypersaline habitats in Egypt. J. Gen. Appl. Microbiol. 52, 63–72 (2006).CAS 
Article 

Google Scholar 
Ali, I., Prasongsuk, S., Akbar, A., Aslam, M. & Rakshit, S. K. Hypersaline habitats and halophilic microorganisms. Maejo Int. J. Sci. Technol. 10, 330–345 (2016).CAS 

Google Scholar 
Margesin, R. & Schinner, F. Biodegradation and bioremediation of hydrocarbons in extreme environments. Appl. Microbiol. Biotechnol. 56, 650–663. https://doi.org/10.1007/s002530100701 (2001).CAS 
Article 
PubMed 

Google Scholar 
Poosarla, V. G. & Ts, C. Xylanase production by halophilic bacterium Gracilibacillus sp. TSCPVG under solid state fermentation. Res. J. Biotechnol. 16, 92–100 (2021).Article 

Google Scholar 
Foti, M. et al. Diversity, activity, and abundance of sulfate-reducing bacteria in saline and hypersaline soda lakes. Appl. Environ. Microbiol. 73, 2093–2100. https://doi.org/10.1128/aem.02622-06 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boujelben, I. et al. Spatial and seasonal prokaryotic community dynamics in ponds of increasing salinity of Sfax solar saltern in Tunisia. Antonie Van Leeuwenhoek 101, 845–857. https://doi.org/10.1007/s10482-012-9701-7 (2012).CAS 
Article 
PubMed 

Google Scholar 
García-Maldonado, J. Q., Bebout, B. M., Everroad, R. C. & López-Cortés, A. Evidence of novel phylogenetic lineages of methanogenic archaea from hypersaline microbial mats. Microb. Ecol. 69, 106–117. https://doi.org/10.1007/s00248-014-0473-7 (2014).CAS 
Article 
PubMed 

Google Scholar 
Abed, R. M. M., de Beer, D. & Stief, P. Functional-structural analysis of nitrogen-cycle bacteria in a hypersaline mat from the omani desert. Geomicrobiol. J. 32, 119–129. https://doi.org/10.1080/01490451.2014.932033 (2014).CAS 
Article 

Google Scholar 
Coban, O., Rasigraf, O., Jong, A., Spott, O. & Bebout, B. M. Quantifying potential N turnover rates in hypersaline microbial mats by 15 N tracer techniques. Appl. Environ. Microbiol. 87, 8 (2021).Article 

Google Scholar 
Rodriguez-Valera, F. Introduction to Saline Environments (Springer, 1993).
Google Scholar 
Wei, H. C., Qi-Shun, F., Fu-Yuan, A., Fa-Shou, S. & Qin, Z. J. Chemical elements in core sediments of the qarhan salt lake and palaeoclimate evolution during 94–9 ka. Acta Geosci. Sin. (2016).Yu, S., Liu, X., Tan, H. & Cao, G. Sustainable Utilization of Qarhan Salt Lake Resources 27–265 (Science Press, 2009).
Google Scholar 
Zhu, D. et al. An evaluation of the core bacterial communities associated with hypersaline environments in the Qaidam Basin, China. Arch. Microbiol. 202, 2093–2103. https://doi.org/10.1007/s00203-020-01927-7 (2020).CAS 
Article 
PubMed 

Google Scholar 
Liu, W., Jiang, H., Yang, J. & Wu, G. Gammaproteobacterial diversity and carbon utilization in response to salinity in the lakes on the qinghai-tibetan plateau. Geomicrobiol. J. 35, 392–403. https://doi.org/10.1080/01490451.2017.1378951 (2018).CAS 
Article 

Google Scholar 
Zhong, Z.-P. et al. Prokaryotic community structure driven by salinity and ionic concentrations in plateau lakes of the tibetan plateau. Appl. Environ. Microbiol. 82, 1846–1858. https://doi.org/10.1128/aem.03332-15 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
He, C. et al. Synergistic effect of magnetite and zero-valent iron on anaerobic degradation and methanogenesis of phenol. Biores. Technol. 291, 121874. https://doi.org/10.1016/j.biortech.2019.121874 (2019).CAS 
Article 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 17, 10–12 (2011).Article 

Google Scholar 
Zhang, J., Kassian, K., Tomáš, F. & Alexandros, S. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614 (2014).CAS 
Article 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).CAS 
Article 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–1000 (2013).CAS 
Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537 (2009).ADS 
CAS 
Article 

Google Scholar 
Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 35. https://doi.org/10.1186/1471-2105-12-35 (2011).Article 

Google Scholar 
McArdle, B. H. et al. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–290 (2001).Article 

Google Scholar 
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. https://doi.org/10.1038/nbt.2676 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1–3 (2017).
Google Scholar 
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272 (2016).ADS 
CAS 
Article 

Google Scholar 
Junker, B. H. & Schreiber, F. Analysis of Biological Networks 283–304 (Analysis of biological networks, 2008).Book 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550. https://doi.org/10.1038/nrmicro2832 (2012).CAS 
Article 
PubMed 

Google Scholar 
Behzad, H., Ibarra, M. A., Mineta, K. & Gojobori, T. Metagenomic studies of the Red Sea. Gene 576, 717–723. https://doi.org/10.1016/j.gene.2015.10.034 (2016).CAS 
Article 
PubMed 

Google Scholar 
Naghoni, A. et al. Microbial diversity in the hypersaline Lake Meyghan, Iran. Sci. Rep. https://doi.org/10.1038/s41598-017-11585-3 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kambura, A. K. et al. Bacteria and Archaea diversity within the hot springs of Lake Magadi and Little Magadi in Kenya. BMC Microbiol. https://doi.org/10.1186/s12866-016-0748-x (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Paul, D. et al. Exploration of microbial diversity and community structure of Lonar lake: The only hypersaline meteorite crater lake within basalt rock. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01553 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ventosa, A., de la Haba, R. R., Sánchez-Porro, C. & Papke, R. T. Microbial diversity of hypersaline environments: A metagenomic approach. Curr. Opin. Microbiol. 25, 80–87. https://doi.org/10.1016/j.mib.2015.05.002 (2015).CAS 
Article 
PubMed 

Google Scholar 
Liu, F. H. et al. Bacterial and archaeal assemblages in sediments of a large shallow freshwater lake, Lake Taihu, as revealed by denaturing gradient gel electrophoresis. J. Appl. Microbiol. 106, 1022–1032. https://doi.org/10.1111/j.1365-2672.2008.04069.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Song, H., Li, Z., Du, B., Wang, G. & Ding, Y. Bacterial communities in sediments of the shallow Lake Dongping in China. J. Appl. Microbiol. 112, 79–89. https://doi.org/10.1111/j.1365-2672.2011.05187.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Wu, Q. L., Zwart, G., Schauer, M., Agterveld, K. V. & Hahn, M. W. Bacterioplankton community composition along a salinity gradient of sixteen high-mountain lakes located on the Tibetan Plateau, China. Appl. Environ. Microbiol. 72, 5478–5485 (2006).ADS 
CAS 
Article 

Google Scholar 
Xing, P., Hahn, M. W. & Wu, Q. L. Low taxon richness of bacterioplankton in high-altitude lakes of the Eastern Tibetan Plateau, with a predominance of bacteroidetes and Synechococcus spp. Appl. Environ. Microbiol. 75, 7017–7025. https://doi.org/10.1128/aem.01544-09 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Y. et al. Bacterial diversity of freshwater Alpine Lake Puma Yumco on the Tibetan Plateau. Geomicrobiol. J. 26, 131–145. https://doi.org/10.1080/01490450802660201 (2009).CAS 
Article 

Google Scholar 
MounÃc, S., Caumette, P., Matheron, R. & Willison, J. C. Molecular sequence analysis of prokaryotic diversity in the anoxic sediments underlying cyanobacterial mats of two hypersaline ponds in Mediterranean salterns. FEMS Microbiol. Ecol. 44, 117–130. https://doi.org/10.1016/s0168-6496(03)00017-5 (2003).Article 

Google Scholar 
Valenzuela-Encinas, C. et al. Changes in the bacterial populations of the highly alkaline saline soil of the former lake Texcoco (Mexico) following flooding. Extremophiles 13, 609–621. https://doi.org/10.1007/s00792-009-0244-4 (2009).Article 
PubMed 

Google Scholar 
Kim, T. J., Lee, E. Y., Kim, Y. J., Cho, K.-S. & Ryu, H. W. Degradation of polyaromatic hydrocarbons by Burkholderia cepacia 2A–12. World J. Microbiol. Biotechnol. 19, 411–417. https://doi.org/10.1023/A:1023998719787 (2003).CAS 
Article 

Google Scholar 
Gales, G. et al. Preservation of ancestral Cretaceous microflora recovered from a hypersaline oil reservoir. Sci. Rep. https://doi.org/10.1038/srep22960 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kleinsteuber, S., Riis, V., Fetzer, I., Harms, H. & Müller, S. Population dynamics within a microbial consortium during growth on diesel fuel in saline environments. Appl. Environ. Microbiol. 72, 3531–3542. https://doi.org/10.1128/aem.72.5.3531-3542.2006 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela-Encinas, C. et al. The archaeal diversity and population in a drained alkaline saline soil of the former lake Texcoco (Mexico). Geomicrobiol. J. 29, 18–22. https://doi.org/10.1080/01490451.2010.520075 (2012).Article 

Google Scholar 
He, S., Tan, J., Hu, W. & Mo, C. Diversity of archaea and its correlation with environmental factors in the Ebinur Lake Wetland. Curr. Microbiol. 76, 1417–1424. https://doi.org/10.1007/s00284-019-01768-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
Sandaa, R. A., Enger, O. & Torsvik, V. Abundance and diversity of Archaea in heavy-metal-contaminated soils. Appl. Environ. Microbiol. 65, 3293–3297 (1999).ADS 
CAS 
Article 

Google Scholar 
Dave, B. P. & Soni, A. Diversity of halophilic archaea at salt pans around Bhavnagar Coast, Gujarat. Proc. Natl. Acad. Sci. India B 83, 225–232. https://doi.org/10.1007/s40011-012-0124-z (2012).Article 

Google Scholar 
Zafrilla, B., Martínez-Espinosa, R., Alonso, M. A. & Bonete, M. J. Biodiversity of Archaea and floral of two inland saltern ecosystems in the Alto Vinalopó Valley, Spain. Saline Syst. 6, 10 (2010).Article 

Google Scholar 
Costa, M., Santos, H. & Galinski, E. A. An overview of the role and diversity of compatible solutes in Bacteria and Archaea. Adv. Biochem. Eng. Biotechnol. 61, 117 (1998).PubMed 

Google Scholar 
Williams, R. J., Howe, A. & Hofmockel, K. S. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Front. Microbiol. https://doi.org/10.3389/fmicb.2014.00358 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. S. B., MatiasRodrigues, J. F. & von Mering, C. A family of interaction-adjusted indices of community similarity. ISME J. 11, 791–807. https://doi.org/10.1038/ismej.2016.139 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Oyewusi, H. A. et al. Functional profiling of bacterial communities in Lake Tuz using 16S rRNA gene sequences. Biotechnol. Biotechnol. Equip. 35, 1–10. https://doi.org/10.1080/13102818.2020.1840437 (2020).CAS 
Article 

Google Scholar  More

Taylor, M. W., Radax, R., Steger, D. & Wagner, M. Sponge-associated microorganisms: Evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71, 295–347 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webster, N. S. et al. Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts. Environ. Microbiol. 12, 2070–2082 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sipkema, D. et al. Similar sponge-associated bacteria can be acquired via both vertical and horizontal transmission: Microbial transmission in Petrosia ficiformis. Environ. Microbiol. 17, 3807–3821 (2015).CAS 
PubMed 

Google Scholar 
Cleary, D. F. R. et al. The sponge microbiome within the greater coral reef microbial metacommunity. Nat. Commun. 10, 1644 (2019).Björk, J. R., Díez-Vives, C., Astudillo-García, C., Archie, E. A. & Montoya, J. M. Vertical transmission of sponge microbiota is inconsistent and unfaithful. Nat. Ecol. Evol. 3, 1172–1183 (2019).PubMed 
PubMed Central 

Google Scholar 
Webster, N. S. & Taylor, M. W. Marine sponges and their microbial symbionts: Love and other relationships. Environ. Microbiol. 14, 335–346 (2012).CAS 
PubMed 

Google Scholar 
Kennedy, J. et al. Evidence of a putative deep sea specific microbiome in marine sponges. PLoS ONE 9, e91092 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Steinert, G. et al. Compositional and quantitative insights into bacterial and archaeal communities of south pacific deep-sea sponges (Demospongiae and Hexactinellida). Front. Microbiol. 11, 716 (2020).Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).ADS 
CAS 

Google Scholar 
Olson, J. B. & Gao, X. Characterizing the bacterial associates of three Caribbean sponges along a gradient from shallow to mesophotic depths. FEMS Microbiol. Ecol. 85, 74–84 (2013).PubMed 

Google Scholar 
Steinert, G. et al. In four shallow and mesophotic tropical reef sponges from Guam the microbial community largely depends on host identity. PeerJ 4, e1936 (2016).PubMed 
PubMed Central 

Google Scholar 
Morrow, K. M., Fiore, C. L. & Lesser, M. P. Environmental drivers of microbial community shifts in the giant barrel sponge, Xestospongia muta, over a shallow to mesophotic depth gradient. Environ. Microbiol. 18, 2025–2038 (2016).CAS 
PubMed 

Google Scholar 
Ebada, S. S. & Proksch, P. The chemistry of marine sponges. In Handbook of Marine Natural Products (eds Fattorusso, E. et al.) 191–293 (Springer, 2012). https://doi.org/10.1007/978-90-481-3834-0_4.Chapter 

Google Scholar 
Kornprobst, J.-M. Porifera (Sponges). Encyclopedia of Marine Natural Products (Wiley, 2014).
Google Scholar 
Leal, M. C., Puga, J., Serôdio, J., Gomes, N. C. M. & Calado, R. Trends in the discovery of new marine natural products from invertebrates over the last two decades—Where and what are we bioprospecting?. PLoS ONE 7, e30580 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blunt, J. W., Copp, B. R., Keyzers, R. A., Munro, M. H. G. & Prinsep, M. R. Marine natural products. Nat. Prod. Rep. 34, 235–294 (2017).CAS 
PubMed 

Google Scholar 
Unson, M. D., Holland, N. D. & Faulkner, D. J. A brominated secondary metabolite synthesized by the cyanobacterial symbiont of a marine sponge and accumulation of the crystalline metabolite in the sponge tissue. Mar. Biol. 119, 1–11 (1994).CAS 

Google Scholar 
Bewley, C. A., Holland, N. D. & Faulkner, D. J. Two classes of metabolites from Theonella swinhoei are localized in distinct populations of bacterial symbionts. Experientia 52, 716–722 (1996).CAS 
PubMed 

Google Scholar 
Wilson, M. C. et al. An environmental bacterial taxon with a large and distinct metabolic repertoire. Nature 506, 58–62 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Tianero, M. D., Balaich, J. N. & Donia, M. S. Localized production of defence chemicals by intracellular symbionts of Haliclona sponges. Nat. Microbiol. 4, 1149–1159 (2019).CAS 
PubMed 

Google Scholar 
Ivanišević, J., Thomas, O. P., Lejeusne, C., Chevaldonné, P. & Pérez, T. Metabolic fingerprinting as an indicator of biodiversity: Towards understanding inter-specific relationships among Homoscleromorpha sponges. Metabolomics 7, 289–304 (2011).
Google Scholar 
Pérez, T. et al. Oscarella balibaloi, a new sponge species (Homoscleromorpha: Plakinidae) from the Western Mediterranean Sea: Cytological description, reproductive cycle and ecology: O. balibaloi: Description, reproductive cycle and ecology. Mar. Ecol. (Berl.) 32, 174–187 (2011).ADS 

Google Scholar 
Reveillaud, J. et al. Relevance of an integrative approach for taxonomic revision in sponge taxa: Case study of the shallow-water Atlanto-Mediterranean Hexadella species (Porifera: Ianthellidae: Verongida). Invertebr. Syst. 26, 230–248 (2012).
Google Scholar 
Olsen, E. K. et al. Marine AChE inhibitors isolated from Geodia barretti: Natural compounds and their synthetic analogs. Org. Biomol. Chem. 14, 1629–1640 (2016).CAS 
PubMed 

Google Scholar 
Reverter, M., Perez, T., Ereskovsky, A. V. & Banaigs, B. Secondary metabolome variability and inducible chemical defenses in the Mediterranean Sponge Aplysina cavernicola. J. Chem. Ecol. 42, 60–70 (2016).CAS 
PubMed 

Google Scholar 
Reverter, M., Tribalat, M.-A., Pérez, T. & Thomas, O. P. Metabolome variability for two Mediterranean sponge species of the genus Haliclona: Specificity, time, and space. Metabolomics 14, 114 (2018).Villegas-Plazas, M. et al. Variations in microbial diversity and metabolite profiles of the tropical marine sponge Xestospongia muta with season and depth. Microb. Ecol. 78, 243–256 (2019).CAS 
PubMed 

Google Scholar 
Mohanty, I. et al. Multi-omic profiling of Melophlus sponges reveals diverse metabolomic and microbiome architectures that are non-overlapping with ecological neighbors. Mar. Drugs 18, 124 (2020).CAS 
PubMed Central 

Google Scholar 
Bowerbank, J. S. On the anatomy and physiology of the Spongiadae. Part I. On the spicula. Philos. Trans. R. Soc. Lond. 148, 279–332 (1858).ADS 

Google Scholar 
Vosmaer, G. C. J. The sponges of the ‘Willem Barents’ expedition 1880 and 1881. Bijdragen tot de Dierkunde 12, 1–47 (1885).
Google Scholar 
Radax, R. et al. Metatranscriptomics of the marine sponge Geodia barretti: Tackling phylogeny and function of its microbial community. Environ. Microbiol. 14, 1308–1324 (2012).CAS 
PubMed 

Google Scholar 
Topsent, E. Spongiaires provenant des campagnes scientifiques de la ‘Princesse Alice’ dans les Mers du Nord (1898–1899—1906–1907). Résultats des campagnes scientifiques accomplies par le Prince Albert I. Monaco 45, 1–67 (1913).
Google Scholar 
Yashayaev, I. & Loder, J. W. Further intensification of deep convection in the Labrador Sea in 2016. Geophys. Res. Lett. 44, 1429–1438 (2017).ADS 

Google Scholar 
Gutleben, J. et al. Diversity of tryptophan halogenases in sponges of the genus Aplysina. FEMS Microbiol. Ecol. 95, fiz108 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Indraningrat, A. et al. Cultivation of sponge-associated bacteria from Agelas sventres and Xestospongia muta collected from different depths. Mar. Drugs 17, 578 (2019).CAS 
PubMed Central 

Google Scholar 
Ramiro-Garcia, J. et al. NG-Tax, a highly accurate and validated pipeline for analysis of 16S rRNA amplicons from complex biomes. F1000 Res. 5, 1791 (2018).
Google Scholar 
Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucl. Acids Res. 42, D643–D648 (2014).CAS 
PubMed 

Google Scholar 
Erngren, I., Smit, E., Pettersson, C., Cárdenas, P. & Hedeland, M. The effects of sampling and storage conditions on the metabolite profile of the marine sponge Geodia barretti. Front. Chem. 9:662659 (2021)Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
PubMed 

Google Scholar 
Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).CAS 
PubMed 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2017).Dat, T. T. H., Steinert, G., Thi Kim Cuc, N., Smidt, H. & Sipkema, D. Archaeal and bacterial diversity and community composition from 18 phylogenetically divergent sponge species in Vietnam. PeerJ 6, e4970 (2018).PubMed 
PubMed Central 

Google Scholar 
Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES science gateway for inference of large phylogenetic trees. In 2010 Gateway Computing Environments Workshop (GCE) 1–8 (IEEE, 2010). https://doi.org/10.1109/GCE.2010.5676129.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: Recent updates and new developments. Nucl. Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thévenot, E. A., Roux, A., Xu, Y., Ezan, E. & Junot, C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J. Proteome Res. 14, 3322–3335 (2015).PubMed 

Google Scholar 
Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).
Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Durno, W. E., Hanson, N. W., Konwar, K. M. & Hallam, S. J. Expanding the boundaries of local similarity analysis. BMC Genom. 14, S3 (2013).
Google Scholar 
Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518–1524 (2011).ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Hall, M. M., Torres, D. J. & Yashayaev, I. Absolute velocity along the AR7W section in the Labrador Sea. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 72, 72–87 (2013).
Google Scholar 
Reveillaud, J. et al. Host-specificity among abundant and rare taxa in the sponge microbiome. ISME J. 8, 1198–1209 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. Predicting the HMA-LMA status in marine sponges by machine learning. Front. Microbiol. 8, 752 (2017).Lidgren, G., Bohlin, L. & Bergman, J. Studies of Swedish marine organisms VII. A novel biologically active indole alkaloid from the sponge Geodia barretti. Tetrahedron Lett. 27, 3283–3284 (1986).CAS 

Google Scholar 
Sjögren, M. et al. Antifouling activity of brominated cyclopeptides from the marine sponge Geodia barretti. J. Nat. Prod. 67, 368–372 (2004).PubMed 

Google Scholar 
Sölter, S. Identifizierung und Synthese von Naturstoffen aus Borealen Schwämmen (Universität Hamburg, 2004).
Google Scholar 
Di, X. et al. 6-Bromoindole derivatives from the Icelandic marine sponge Geodia barretti: Isolation and anti-inflammatory activity. Mar. Drugs 16, 437 (2018).CAS 
PubMed Central 

Google Scholar 
Carstens, B. B. et al. Isolation, characterization, and synthesis of the barrettides: Disulfide-containing peptides from the marine sponge Geodia barretti. J. Nat. Prod. 78, 1886–1893 (2015).CAS 
PubMed 

Google Scholar 
Hedner, E. et al. Brominated cyclodipeptides from the marine sponge Geodia barretti as selective 5-HT ligands. J. Nat. Prod. 69, 1421–1424 (2006).CAS 
PubMed 

Google Scholar 
Hedner, E. et al. Antifouling activity of a dibrominated cyclopeptide from the marine sponge Geodia barretti. J. Nat. Prod. 71, 330–333 (2008).CAS 
PubMed 

Google Scholar 
Erwin, P. M., Pita, L., López-Legentil, S. & Turon, X. Stability of sponge-associated bacteria over large seasonal shifts in temperature and irradiance. Appl. Environ. Microbiol. 78, 7358–7368 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cárdenas, C. A., Bell, J. J., Davy, S. K., Hoggard, M. & Taylor, M. W. Influence of environmental variation on symbiotic bacterial communities of two temperate sponges. FEMS Microbiol. Ecol. 88, 516–527 (2014).PubMed 

Google Scholar 
Glasl, B., Smith, C. E., Bourne, D. G. & Webster, N. S. Exploring the diversity-stability paradigm using sponge microbial communities. Sci. Rep. 8, 8425 (2018).Schöttner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, e55505 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lurgi, M., Thomas, T., Wemheuer, B., Webster, N. S. & Montoya, J. M. Modularity and predicted functions of the global sponge-microbiome network. Nat. Commun. 10, 992 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Luter, H. M. et al. Microbiome analysis of a disease affecting the deep-sea sponge Geodia barretti. FEMS Microbiol. Ecol. 93, fix074 (2017).Thistle, D. Ecosystems of the Deep Oceans (Elsevier, 2003).
Google Scholar 
Pita, L., Erwin, P. M., Turon, X. & López-Legentil, S. Till death do us part: Stable sponge-bacteria associations under thermal and food shortage stresses. PLoS ONE 8, e80307 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Webster, N. S., Cobb, R. E. & Negri, A. P. Temperature thresholds for bacterial symbiosis with a sponge. ISME J. 2, 830–842 (2008).CAS 
PubMed 

Google Scholar 
Gerringer, M. E., Drazen, J. C. & Yancey, P. H. Metabolic enzyme activities of abyssal and hadal fishes: Pressure effects and a re-evaluation of depth-related changes. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 125, 135–146 (2017).CAS 

Google Scholar 
Yashayaev, I. Hydrographic changes in the Labrador Sea, 1960–2005. Prog. Oceanogr. 73, 242–276 (2007).ADS 

Google Scholar 
Rhein, M., Steinfeldt, R., Kieke, D., Stendardo, I. & Yashayaev, I. Ventilation variability of Labrador Sea Water and its impact on oxygen and anthropogenic carbon: A review. Philos. Trans. A Math. Phys. Eng. Sci. 375, 20160321 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galand, P. E., Potvin, M., Casamayor, E. O. & Lovejoy, C. Hydrography shapes bacterial biogeography of the deep Arctic Ocean. ISME J. 4, 564–576 (2010).PubMed 

Google Scholar 
Frank, A. H., Garcia, J. A. L., Herndl, G. J. & Reinthaler, T. Connectivity between surface and deep waters determines prokaryotic diversity in the North Atlantic Deep Water: North Atlantic dark ocean prokaryotic biogeography. Environ. Microbiol. 18, 2052–2063 (2016).PubMed 
PubMed Central 

Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).PubMed 

Google Scholar 
Djurhuus, A., Boersch-Supan, P. H., Mikalsen, S.-O. & Rogers, A. D. Microbe biogeography tracks water masses in a dynamic oceanic frontal system. R. Soc. Open Sci. 4, 170033 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, O. et al. Spatiotemporal dynamics of ammonia-oxidizing Thaumarchaeota in distinct Arctic water masses. Front. Microbiol. 9, 1–13 (2018).ADS 

Google Scholar 
Kraemer, S., Ramachandran, A., Colatriano, D., Lovejoy, C. & Walsh, D. A. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. https://doi.org/10.1038/s41396-019-0499-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Monier, A. et al. Upper Arctic Ocean water masses harbor distinct communities of heterotrophic flagellates. Biogeosciences 10, 4273–4286 (2013).ADS 

Google Scholar 
Monier, A. et al. Oceanographic structure drives the assembly processes of microbial eukaryotic communities. ISME J. 9, 990–1002 (2015).CAS 
PubMed 

Google Scholar 
Corrège, T. The relationship between water masses and benthic ostracod assemblages in the western Coral Sea, Southwest Pacific. Palaeogeogr. Palaeoclimatol. Palaeoecol. 105, 245–266 (1993).
Google Scholar 
Muhling, B. A., Beckley, L. E., Koslow, J. A. & Pearce, A. F. Larval fish assemblages and water mass structure off the oligotrophic south-western Australian coast: SW Australian larval fish assemblages. Fish. Oceanogr. 17, 16–31 (2007).
Google Scholar 
Eerkes-Medrano, D. et al. A community assessment of the demersal fish and benthic invertebrates of the Rosemary Bank Seamount Marine Protected Area (NE Atlantic). Deep Sea Res. Part 1 Oceanogr. Res. Pap. https://doi.org/10.1016/j.dsr.2019.103180 (2019).Article 

Google Scholar 
Puerta, P. et al. Influence of water masses on the biodiversity and biogeography of deep-sea benthic ecosystems in the North Atlantic. Front. Mar. Sci. 7, 239 (2020).Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic Seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).ADS 

Google Scholar 
Kenchington, E. et al. Connectivity modelling of areas closed to protect vulnerable marine ecosystems in the northwest Atlantic. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 143, 85–103 (2019).
Google Scholar 
Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 

Google Scholar 
McCauley, M., Chiarello, M., Atkinson, C. L. & Jackson, C. R. Gut microbiomes of freshwater mussels (Unionidae) are taxonomically and phylogenetically variable across years but remain functionally stable. Microorganisms 9, 411 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Page, M., West, L., Northcote, P., Battershill, C. & Kelly, M. Spatial and temporal variability of cytotoxic metabolites in populations of the New Zealand Sponge Mycale hentscheli. J. Chem. Ecol. 31, 1161–1174 (2005).CAS 
PubMed 

Google Scholar 
Ternon, E., Perino, E., Manconi, R., Pronzato, R. & Thomas, O. P. How environmental factors affect the production of guanidine alkaloids by the Mediterranean sponge Crambe crambe. Mar. Drugs 15, 181 (2017).PubMed Central 

Google Scholar 
Sacristán-Soriano, O., Banaigs, B. & Becerro, M. A. Temporal trends in the secondary metabolite production of the sponge Aplysina aerophoba. Mar. Drugs 10, 677–693 (2012).PubMed 
PubMed Central 

Google Scholar 
Ivanisevic, J. et al. Biochemical trade-offs: Evidence for ecologically linked secondary metabolism of the sponge Oscarella balibaloi. PLoS ONE 6, e28059 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burg, M. B. & Ferraris, J. D. Intracellular organic osmolytes: Function and regulation. J. Biol. Chem. 283, 7309–7313 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nau-Wagner, G., Boch, J., Le Good, J. A. & Bremer, E. High-affinity transport of choline-O-sulfate and its use as a compatible solute in Bacillus subtilis. Appl. Environ. Microbiol. 65, 560–568 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Popowich, A., Zhang, Q. & Le, X. C. Arsenobetaine: The ongoing mystery. Natl. Sci. Rev. 3, 451–458 (2016).CAS 

Google Scholar 
Connor, K. M. & Gracey, A. Y. High-resolution analysis of metabolic cycles in the intertidal mussel Mytilus californianus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 302, R103–R111 (2012).CAS 
PubMed 

Google Scholar 
Cárdenas, P. Who produces Ianthelline? The Arctic sponge Stryphnus fortis or its sponge Epibiont Hexadella dedritifera: A probable case of sponge–sponge contamination. J. Chem. Ecol. 42, 339–347 (2016).PubMed 

Google Scholar 
Steffen, K. et al. Barrettides: A peptide family specifically produced by the deep-sea sponge Geodia barretti. J. Nat. Prod. 84, 3138–3146 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Abbamondi, G. R., De Rosa, S., Iodice, C. & Tommonaro, G. Cyclic dipeptides produced by marine sponge-associated bacteria as quorum sensing signals. Nat. Prod. Commun. 9, 229–232 (2014).CAS 
PubMed 

Google Scholar 
Kasheverov, I. et al. 6-Bromohypaphorine from Marine Nudibranch Mollusk Hermissenda crassicornis is an agonist of human α7 nicotinic acetylcholine receptor. Mar. Drugs 13, 1255–1266 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. The sponge microbiome project. Gigascience 6, 1–7 (2017).CAS 
PubMed 

Google Scholar 
Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: Moving beyond genes and genomes. Front. Microbiol. 7, 744 (2016).Crits-Christoph, A., Diamond, S., Butterfield, C. N., Thomas, B. C. & Banfield, J. F. Novel soil bacteria possess diverse genes for secondary metabolite biosynthesis. Nature 558, 440–444 (2018).ADS 
CAS 
PubMed 

Google Scholar  More

The full-length sequences of PacBio SMRT sequencingBased on PacBio SMRT sequencing, 3,751,089, 3,434,452, 3,900,180, 8,535,019, and 4,435,846 subreads were generated for root, stem, leaf, flower, and seed, with a N50 of 3040, 3367, 2611, 2198, and 4584 bp, respectively (Table S1; Fig. S1). Subreads were processed to generate circular consensus sequences (CCSs). By detecting the primers and poly(A) tail, 238,196, 232,290, 211,535, 257,905, and 231,877 full-length non-chimeric (FLNC) reads were identified for root, stem, leaf, flower, and seed, with a mean length of 2633, 3070, 2561, 1746, and 3762 bp, respectively (Table S2; Fig. S2). After Iterative Clustering for Error Correction (ICE) clustering, polishing, base correction, de-redundancy, and non-plant sequences filtering, 37,789, 34,034, 38,100, 54,937, and 53,906 unigenes were retained for root, stem, leaf, flower, and seed, respectively, with an average unigene length of 1802–3786 bp and N50 of 2238–4707 bp (Table S2). The length of most unigenes from five organs exceeded 2000 bp, accounting for 68.88% of the total number (Table S3; Fig. 1A). Based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, about 88.1% (single-copy: 353; duplicated: 916) of the 1440 core embryophyte genes were found to be complete (90.6% were present when counting fragmented genes), suggesting the high integrity of the M. micrantha transcriptome (Fig. S3).Figure 1Length distribution of unigenes from PacBio SMRT sequencing (A) and Illumina RNA-Seq (B) across five organs.Full size imageDe novo assembly of Illumina RNA-Seq dataBased on Illumina RNA-Seq, 43.23, 40.27, 41.01, 65.85, and 41.09 million clean reads were obtained for root, stem, leaf, flower, and seed, respectively, with Q20 exceeding 96.72%. Using Trinity software, clean reads were de novo assembled into 124,238, 60,232, 63,370, 93,229, and 66,411 unigenes for root, stem, leaf, flower, and seed. After filtering non-plant sequences, 124,233, 60,232, 63,370, 93,228, and 66,410 unigenes were finally retained for the five organs, respectively (Table S4). The length of most unigenes (84.70%) was shorter than 2000 bp (Table S3). In addition, the average length and N50 of unigenes generated by Illumina RNA-Seq were 1067–1312 bp and 1336–1685 bp, respectively, which were shorter than that from PacBio SMRT sequencing (Table S4; Fig. 1B).Functional annotationTo obtain a comprehensive functional annotation of M. micrantha transcriptome, unigenes generated by PacBio SMRT sequencing were annotated in seven public databases, including NCBI non-redundant nucleotide sequences (NT), NCBI non-redundant protein sequences (NR), Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam protein families. For root, stem, leaf, flower, and seed, 35,714 (94.51%), 32,614 (95.83%), 36,134 (94.84%), 49,197 (89.55%), and 50,962 (94.54%) unigenes were annotated to at least one database, respectively, suggesting that our transcriptome is well annotated and that most of unigenes may be functional (Table 1).Table 1 Statistics of annotation of full-length transcripts from five M. micrantha organs in seven databases.Full size tableBased on NR database annotation, the top three homologous species for the five organs were Cynara cardunculus, Vitis vinifera, and Daucus carota (Fig. S4). The top homologous species was a plant of the Asteraceae family. For the GO function annotation, “binding”, “catalytic activities”, “metabolic process”, “cellular process”, “cell”, and “cell part” were functional categories with the most abundant unigenes (Fig. S5). In addition, numerous unigenes were assigned to “response to stimulus”, “response to biotic stimulus”, and “response to oxidative stress” category (Table S5). Positive response to stress stimuli is an important strategy for invasive plants to adapt to the environment. In the KEGG annotation, the top two pathways with the most abundant unigenes were “carbohydrate metabolism” and “translation”. Furthermore, “energy metabolism” and “environmental adaptation” were also worthy of attention, which are important pathways responsible for energy supply and stress responses (Fig. S6).TFs identification and AS analysisUsing the iTAK pipeline, 1776 (root), 1293 (stem), 1627 (leaf), 2529 (flower), and 1733 (seed) unigenes were identified as TFs, which were classified into 68 families (Table S6). C3H (884), C2H2 (525), and bHLH (501) were the most abundant TF families (Fig. S7A). In addition, MYB (333) TFs were also found in the five organs. The differential expression levels of the top 15 TF families were further characterized. We found that the top 15 TF families had a certain amount of expression in the five organs of M. micrantha (Fig. S7B).For root, stem, leaf, flower, and seed, 3300, 2324, 3219, 4730, and 3740 unique transcript models (UniTransModels) were constructed, among which the UniTransModels containing two isoforms were the most common (Fig. S8A). There were 329, 270, 358, 336, and 537 AS events identified in root, stem, leaf, flower, and seed, respectively. Retained introns (RIs) were detected as the most abundant AS event in all five organs, followed by alternative 3′ splice sites (A3) and alternative 5′ splice sites (A5). Mutually exclusive exons (MX) were the least frequent event (Fig. S8B).Gene expression analysisThe number of unigenes in different expression level intervals was similar across the five organs (Fig. 2A). Using FPKM  > 0.3 as the threshold for unigene expression, the total number of unigenes expressed in the five organs was 102,464 (Fig. 2B). Among them, 39,227 unigenes were co-expressed in all five organs. The information of differentially expressed genes (DEGs) identified in pairwise comparisons among the five organs is listed in Table S7. In total, 21,161 DEGs were identified among the five organs (Fig. S9). The number of DEGs between the five organs varied from 3469 (root vs stem) to 10,716 (leaf vs seed) (Fig. 2C). Notably, 933, 428, 1410, 1018, and 1292 DEGs showed significant higher expression in root, stem, leaf, flower, and seed, respectively (Figs. S10 and S11).Figure 2Gene expression patterns in five M. micrantha organs. (A) The FPKM interval distribution in the five organs. (B) Venn diagram of the number of unigenes expressed in five organs. (C) Number of differentially expressed genes in each pairwise comparison of five organs.Full size imageKEGG enrichment of unigenes with higher expression in each organAccording to the KEGG enrichment analysis results, there were obvious differences in enriched pathways in the five organs (Table S8; Fig. 3). The unigenes with higher expression in root were mainly enriched to defense response and protein processing pathways, such as “plant-pathogen interaction” and “protein processing in endoplasmic reticulum”. In stem, unigenes with higher expression were predominantly enriched to pathways related to the secondary metabolite, sugar, and terpenoid biosynthesis, such as “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “diterpenoid biosynthesis”. In flower, unigenes with higher expression were mainly related to “starch and sucrose metabolism”, “phenylpropanoid biosynthesis”, and “cutin, suberine, and wax biosynthesis”. The unigenes with higher expression in seed were mainly enriched in three fatty acid and sugar metabolism pathways, namely “biosynthesis of unsaturated fatty acids”, “galactose metabolism”, and “amino sugar and nucleotide sugar metabolism”. The unigenes with higher expression in leaf were significantly enriched in photosynthesis pathways, including “photosynthesis-antenna proteins”, “photosynthesis”, “porphyrin and chlorophyll metabolism”, and “carbon fixation in photosynthetic organisms”, which are important for the photosynthesis of M. micrantha.Figure 3The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of unigenes with higher expression in each organ. The significantly enriched pathways with corrected p-value (q value)  More

Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 

Google Scholar 
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (European Commission, Publications Office of the European Union, 2016).Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017). This Review provides a comprehensive overview of methods and technologies used to study soil viruses alongside a guide of metrics describing soil viruses across diverse soil ecosystems.CAS 
PubMed 

Google Scholar 
Stefan, G., Cornelia, B., Jörg, R. & Michael, B. Soil water availability strongly alters the community composition of soil protists. Pedobiologia 57, 205–213 (2014).
Google Scholar 
Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).
Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018). This study compiled metagenomic and metabarcoding data from 189 sites to demonstrate global patterns in the structure and function of soil microbial communities as well as the widespread prevalence of bacterial–fungal antagonism as an important structuring force of microbial communities.CAS 
PubMed 

Google Scholar 
He, L. et al. Global biogeography of fungal and bacterial biomass carbon in topsoil. Soil Biol. Biochem. 151, 108024 (2020).CAS 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226 (2018).CAS 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 

Google Scholar 
Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
PubMed 

Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019). This article estimates that more than 50% of SOM may be derived from microbial necromass in grassland and agricultural ecosystems based on extrapolations from amino sugar biomarker data.
Google Scholar 
Angst, G., Mueller, K. E., Nierop, K. G. J. & Simpson, M. J. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol. Biochem. 156, 108189 (2021).CAS 

Google Scholar 
Ludwig, M. et al. Microbial contribution to SOM quantity and quality in density fractions of temperate arable soils. Soil Biol. Biochem. 81, 311–322 (2015). This study uses lipid biomarkers to estimate that at least 50% of SOM may be derived from microbial necromass.CAS 

Google Scholar 
Simpson, A. J., Simpson, M. J., Smith, E. & Kelleher, B. P. Microbially derived inputs to soil organic matter: are current estimates too low? Environ. Sci. Technol. 41, 8070–8076 (2007).CAS 
PubMed 

Google Scholar 
Blazewicz, S. J. et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 14, 1520–1532 (2020). This study used quantitative stable isotope probing to calculate growth and mortality rates of bacteria following the rewetting of a dry Mediterranean soil, and demonstrated that bacterial growth was density independent whereas bacterial mortality was density dependent.PubMed 
PubMed Central 

Google Scholar 
Vieira, S. et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 14, 463–475 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746 (2015).PubMed 
PubMed Central 

Google Scholar 
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, L. Impact of wheat straw decomposition on successional patterns of soil microbial community structure. Soil Biol. Biochem. 41, 262–275 (2009).CAS 

Google Scholar 
Whitman, T. et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ. Microbiol. 20, 4444–4460 (2018).CAS 
PubMed 

Google Scholar 
Maynard, D. S., Crowther, T. W. & Bradford, M. A. Fungal interactions reduce carbon use efficiency. Ecol. Lett. 20, 1034–1042 (2017). This study demonstrated that antagonistic interactions between wood-decay fungi can reduce CUE of the fungal community.PubMed 

Google Scholar 
Crowther, T. W. et al. Environmental stress response limits microbial necromass contributions to soil organic carbon. Soil Biol. Biochem. 85, 153–161 (2015).CAS 

Google Scholar 
Hu, Y., Zheng, Q., Noll, L., Zhang, S. & Wanek, W. Direct measurement of the in situ decomposition of microbial-derived soil organic matter. Soil Biol. Biochem. 141, 107660 (2020).CAS 

Google Scholar 
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016). This review article summarizes how the stoichiometry, morphology and chemistry of microbial necromass affects its decomposition rate in soil.CAS 

Google Scholar 
Buckeridge, K. M. et al. Sticky dead microbes: rapid abiotic retention of microbial necromass in soil. Soil Biol. Biochem. 149, 107929 (2020).CAS 

Google Scholar 
Creamer, C. A. et al. Mineralogy dictates the initial mechanism of microbial necromass association. Geochim. Cosmochim. Acta 260, 161–176 (2019). This study used Raman microspectroscopy and 13C-labelled necromass to demonstrate that different mineral types retained microbial necromass through different mechanisms and with different strengths.CAS 

Google Scholar 
Schurig, C. et al. Microbial cell-envelope fragments and the formation of soil organic matter: a case study from a glacier forefield. Biogeochemistry 113, 595–612 (2013).CAS 

Google Scholar 
Kopittke, P. M. et al. Nitrogen-rich microbial products provide new organo-mineral associations for the stabilization of soil organic matter. Glob. Change Biol. 24, 1762–1770 (2018).
Google Scholar 
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2012).CAS 

Google Scholar 
Kleber, M. et al. Dynamic interactions at the mineral–organic matter interface. Nat. Rev. Earth Environ. 2, 402–421 (2021).
Google Scholar 
Blagodatskaya, E. & Kuzyakov, Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211 (2013).CAS 

Google Scholar 
Or, D., Smets, B. F., Wraith, J. M., Dechesne, A. & Friedman, S. P. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv. Water Resour. 30, 1505–1527 (2007).
Google Scholar 
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 

Google Scholar 
Finzi, A. C. et al. Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles. Glob. Change Biol. 21, 2082–2094 (2015).
Google Scholar 
Yuan, M. M. et al. Fungal-bacterial cooccurrence patterns differ between arbuscular mycorrhizal fungi and nonmycorrhizal fungi across soil niches. mBio 12, e03509-20 (2015).
Google Scholar 
Zhang, L. & Lueders, T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol. Ecol. 93, fix103 (2017).
Google Scholar 
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).CAS 

Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016). This study used artificial soils to provide empirical evidence that SOM can be entirely microbially derived, and also demonstrated a positive relationship between CUE and SOM formation.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, J. L., Tang, C. & Franks, A. E. Competitive traits are more important than stress-tolerance traits in a cadmium-contaminated rhizosphere: a role for trait theory in microbial ecology. Front. Microbiol. 9, 121 (2018).PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Madin, J. S. et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci. Data 7, 170 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016). This study developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).PubMed 
PubMed Central 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Hasby, F. A., Barbi, F., Manzoni, S. & Lindahl, B. D. Transcriptomic markers of fungal growth, respiration and carbon-use efficiency. FEMS Microbiol. Lett. 368, fnab100 (2021).PubMed 
PubMed Central 

Google Scholar 
Maillard, F., Schilling, J., Andrews, E., Schreiner, K. M. & Kennedy, P. Functional convergence in the decomposition of fungal necromass in soil and wood. FEMS Microbiol. Ecol. 96, fiz209 (2020).CAS 
PubMed 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. N. Phytol. 205, 1525–1536 (2015).CAS 

Google Scholar 
Olivelli, M. S. et al. Unraveling mechanisms behind biomass–clay interactions using comprehensive multiphase nuclear magnetic resonance (NMR) Spectroscopy. ACS Earth Space Chem. 4, 2061–2072 (2020).CAS 

Google Scholar 
Achtenhagen, J., Goebel, M.-O., Miltner, A., Woche, S. K. & Kästner, M. Bacterial impact on the wetting properties of soil minerals. Biogeochemistry 122, 269–280 (2015).CAS 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).CAS 

Google Scholar 
Ahmed, E. & Holmström, S. J. M. Microbe–mineral interactions: The impact of surface attachment on mineral weathering and element selectivity by microorganisms. Chem. Geol. 403, 13–23 (2015).CAS 

Google Scholar 
Chenu, C. Clay- or sand-polysaccharide associations as models for the interface between micro-organisms and soil: water related properties and microstructure. Geoderma 56, 143–156 (1993).CAS 

Google Scholar 
Sher, Y. et al. Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol. Biochem. 143, 107742 (2020).CAS 

Google Scholar 
Lybrand, R. A. et al. A coupled microscopy approach to assess the nano-landscape of weathering. Sci. Rep. 9, 5377 (2019).PubMed 
PubMed Central 

Google Scholar 
Prommer, J. et al. Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob. Change Biol. 26, 669–681 (2020).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).CAS 
PubMed 

Google Scholar 
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).CAS 

Google Scholar 
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol. Biochem. 91, 279–290 (2015).CAS 

Google Scholar 
Buckeridge, K. M. et al. Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization. Commun. Earth Env. 1, 36 (2020).
Google Scholar 
Saifuddin, M., Bhatnagar, J. M., Segrè, D. & Finzi, A. C. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat. Commun. 10, 3568 (2019).PubMed 
PubMed Central 

Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 

Google Scholar 
Mason‐Jones, K., Banfield, C. C. & Dippold, M. A. Compound-specific 13C stable isotope probing confirms synthesis of polyhydroxybutyrate by soil bacteria. Rapid Commun. Mass. Spectrom. 33, 795–802 (2019).PubMed 

Google Scholar 
Bååth, E. The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Microb. Ecol. 45, 373–383 (2003).PubMed 

Google Scholar 
Slessarev, E. W. et al. Cellular and extracellular C contributions to respiration after wetting dry soil. Biogeochemistry 147, 307–324 (2020).CAS 

Google Scholar 
Slessarev, E. W. & Schimel, J. P. Partitioning sources of CO2 emission after soil wetting using high-resolution observations and minimal models. Soil Biol. Biochem. 143, 107753 (2020).CAS 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Brangarí, A. C., Manzoni, S. & Rousk, J. A soil microbial model to analyze decoupled microbial growth and respiration during soil drying and rewetting. Soil Biol. Biochem. 148, 107871 (2020).
Google Scholar 
Zha, J. & Zhuang, Q. Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget. Biogeosciences 17, 4591–4610 (2020).CAS 

Google Scholar 
Anderson, T.-H. Microbial eco-physiological indicators to asses soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).
Google Scholar 
Geyer, K., Schnecker, J., Grandy, A. S., Richter, A. & Frey, S. Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency. Biogeochemistry 151, 237–249 (2020).CAS 

Google Scholar 
Sepehrnia, N. et al. Transport, retention, and release of Escherichia coli and Rhodococcus erythropolis through dry natural soils as affected by water repellency. Sci. Total Environ. 694, 133666 (2019).CAS 
PubMed 

Google Scholar 
Boeddinghaus, R. S. et al. The mineralosphere — interactive zone of microbial colonization and carbon use in grassland soils. Biol. Fertil. Soils 57, 587–601 (2021).CAS 

Google Scholar 
Vieira, S. et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ. Microbiol. 22, 917–933 (2020).CAS 
PubMed 

Google Scholar 
Ma, T. et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat. Commun. 9, 3480 (2018).PubMed 
PubMed Central 

Google Scholar 
Blazewicz, S. J., Schwartz, E. & Firestone, M. K. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology 95, 1162–1172 (2014).PubMed 

Google Scholar 
Ceja-Navarro, J. A. et al. Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop. Microbiome 9, 96 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019). This comprehensive study of RNA viruses detectable in a grassland soil showed how these viruses are shaped by the presence of plant roots and litter.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).PubMed 

Google Scholar 
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. L. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).PubMed 

Google Scholar 
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).CAS 
PubMed 

Google Scholar 
Pett-Ridge, J. et al. in Rhizosphere Biology: Interactions Between Microbes and Plants (eds Gupta, V. V. S. R. & Sharma, A. K.) 51–73 (Springer, 2021).Poll, C., Marhan, S., Ingwersen, J. & Kandeler, E. Dynamics of litter carbon turnover and microbial abundance in a rye detritusphere. Soil Biol. Biochem. 40, 1306–1321 (2008).CAS 

Google Scholar 
Buchkowski, R. W., Bradford, M. A., Grandy, A. S., Schmitz, O. J. & Wieder, W. R. Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol. Lett. 20, 231–245 (2017).PubMed 

Google Scholar 
Erktan, A., Or, D. & Scheu, S. The physical structure of soil: determinant and consequence of trophic interactions. Soil Biol. Biochem. 148, 107876 (2020).CAS 

Google Scholar 
Roesch, L. F. W. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).CAS 
PubMed 

Google Scholar 
Carson, J. K. et al. Low pore connectivity increases bacterial diversity in soil. Appl. Environ. Microbiol. 76, 3936–3942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE 9, e87217 (2014).PubMed 
PubMed Central 

Google Scholar 
Ekelund, F., Rønn, R. & Christensen, S. Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites. Soil Biol. Biochem. 33, 475–481 (2001).CAS 

Google Scholar 
Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. mBio 11, e00416-20 (2020).PubMed 
PubMed Central 

Google Scholar 
Georgiou, K., Abramoff, R. Z., Harte, J., Riley, W. J. & Torn, M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017). This modelling study demonstrated that including a density-dependent microbial mortality term can reduce the oscillatory behaviour of soil carbon models.PubMed 
PubMed Central 

Google Scholar 
Thakur, M. P. & Geisen, S. Trophic regulations of the soil microbiome. Trends Microbiol. 27, 771–780 (2019).CAS 
PubMed 

Google Scholar 
Fanin, N. et al. The ratio of Gram-positive to Gram-negative bacterial PLFA markers as an indicator of carbon availability in organic soils. Soil Biol. Biochem. 128, 111–114 (2019).CAS 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365 (2020).
Google Scholar 
Hungate, B. A. et al. The functional significance of bacterial predators. mBio 12, e00466-21 (2021).PubMed 
PubMed Central 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018). This study identified novel viral genomes from metagenomes and linked many of these viruses in silico to bacterial hosts and carbon metabolisms across the spatial gradient of permafrost thaw.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ren, D., Madsen, J. S., Sørensen, S. J. & Burmølle, M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 9, 81–89 (2015).CAS 
PubMed 

Google Scholar 
Lee, K. W. K. et al. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 8, 894–907 (2014).CAS 
PubMed 

Google Scholar 
Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).
Google Scholar 
Drigo, B. et al. Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proc. Natl Acad. Sci. USA 107, 10938–10942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser, C. et al. Exploring the transfer of recent plant photosynthates to soil microbes: mycorrhizal pathway vs direct root exudation. N. Phytol. 205, 1537–1551 (2015).CAS 

Google Scholar 
Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
Tisserant, E. et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc. Natl Acad. Sci. USA 110, 20117–20122 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).PubMed 
PubMed Central 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).CAS 
PubMed 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 

Google Scholar 
Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).
Google Scholar 
See, C. R. et al. Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. https://doi.org/10.1111/gcb.16073 (2022).Article 

Google Scholar 
Adamczyk, B., Sietiö, O.-M., Biasi, C. & Heinonsalo, J. Interaction between tannins and fungal necromass stabilizes fungal residues in boreal forest soils. N. Phytol. 223, 16–21 (2019).
Google Scholar 
Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 

Google Scholar 
Kallenbach, C. M., Wallenstein, M. D., Schipanksi, M. E. & Grandy, A. S. Managing agroecosystems for soil microbial carbon use efficiency: ecological unknowns, potential outcomes, and a path forward. Front. Microbiol. 10, 1146 (2019).PubMed 
PubMed Central 

Google Scholar 
Blagodatskaya, E., Blagodatsky, S., Anderson, T.-H. & Kuzyakov, Y. microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).PubMed 
PubMed Central 

Google Scholar 
Domeignoz-Horta, L. A. et al. Microbial diversity drives carbon use efficiency in a model soil. Nat. Commun. 11, 3684 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 

Google Scholar 
Nicolas, A. M. et al. Soil candidate phyla radiation bacteria encode components of aerobic metabolism and co-occur with nanoarchaea in the rare biosphere of rhizosphere grassland communities. mSystems 6, e0120520 (2021).PubMed 

Google Scholar 
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122 (2018).PubMed 
PubMed Central 

Google Scholar 
Pace, M. L. Bacterial mortality and the fate of bacterial production. Hydrobiologia 159, 41–49 (1988).
Google Scholar 
Cram, J. A., Parada, A. E. & Fuhrman, J. A. Dilution reveals how viral lysis and grazing shape microbial communities. Limnol. Oceanogr. 61, 889–905 (2016).
Google Scholar 
Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014). This study demonstrated that in a marine environment, the mechanism of death (that is, phage infection) altered the biochemistry of microbial necromass relative to uninfected cells.CAS 
PubMed 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).
Google Scholar 
Clarholm, M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181–187 (1985).CAS 

Google Scholar 
Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 8, 625–635 (2014).CAS 
PubMed 

Google Scholar 
Lee, X., Wu, H.-J., Sigler, J., Oishi, C. & Siccama, T. Rapid and transient response of soil respiration to rain. Glob. Change Biol. 10, 1017–1026 (2004).
Google Scholar 
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Google Scholar 
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).CAS 
PubMed 

Google Scholar 
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
Google Scholar 
Sierra, C. A. & Müller, M. A general mathematical framework for representing soil organic matter dynamics. Ecol. Monogr. 85, 505–524 (2015).
Google Scholar 
Wang, G. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).CAS 
PubMed 

Google Scholar 
Wieder, W., Grandy, S., Kallenbach, M. & Bonan, B. Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. Biogeosciences 11, 3899–3917 (2014).
Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012). This paper described one of the first trait-based modelling approaches to link microbial community composition with physiological and enzymatic traits to predict litter decomposition in soil.CAS 
PubMed 

Google Scholar 
Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 

Google Scholar 
Ebrahimi, A. & Or, D. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles – upscaling an aggregate biophysical model. Glob. Change Biol. 22, 3141–3156 (2016). This paper presented a demonstration of how to upscale results from a mechanistic model of microbial activity in soil aggregates to scales of practical interest for hydrological and climate models.
Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019). This opinion article discussed trait-based approaches in microbial ecology with a focus on utilization of large-scale datasets for improved ecological understanding.CAS 
PubMed 

Google Scholar 
Wang, G., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).PubMed 

Google Scholar 
Moorhead, D. L. & Sinsabaugh, R. L. A theoretical model of litter decay and microbial interaction. Ecol. Monogr. 76, 151–174 (2006).
Google Scholar 
Kooijman, S. A. L. M., Muller, E. B. & Stouthamer, A. H. Microbial growth dynamics on the basis of individual budgets. Antonie Van Leeuwenhoek 60, 159–174 (1991).CAS 
PubMed 

Google Scholar 
Evans, S., Dieckmann, U., Franklin, O. & Kaiser, C. Synergistic effects of diffusion and microbial physiology reproduce the Birch effect in a micro-scale model. Soil Biol. Biochem. 93, 28–37 (2016).CAS 

Google Scholar 
Allison, S. D. Modeling adaptation of carbon use efficiency in microbial communities. Front. Microbiol. 5, 571 (2014).PubMed 
PubMed Central 

Google Scholar 
Hawkes, C. V. & Keitt, T. H. Resilience vs. historical contingency in microbial responses to environmental change. Ecol. Lett. 18, 612–625 (2015).PubMed 

Google Scholar 
Tang, J. & Riley, W. J. Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Change 5, 56–60 (2015).CAS 

Google Scholar 
Zhang, Y. et al. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model. Biogeosciences 18, 3147–3171 (2021).CAS 

Google Scholar 
Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140, 1–13 (2018).CAS 

Google Scholar 
Ebrahimi, A. N. & Or, D. Microbial dispersal in unsaturated porous media: Characteristics of motile bacterial cell motions in unsaturated angular pore networks. Water Resour. Res. 50, 7406–7429 (2014).
Google Scholar 
Tang, J. & Riley, W. J. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124, 918–940 (2019).
Google Scholar 
Manzoni, S., Schaeffer, S. M., Katul, G., Porporato, A. & Schimel, J. P. A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils. Soil Biol. Biochem. 73, 69–83 (2014).CAS 

Google Scholar 
Brangarí, A. C., Fernàndez-Garcia, D., Sanchez-Vila, X. & Manzoni, S. Ecological and soil hydraulic implications of microbial responses to stress – a modeling analysis. Adv. Water Resour. 116, 178–194 (2018).
Google Scholar 
Alster, C. J., Weller, Z. D. & von Fischer, J. C. A meta-analysis of temperature sensitivity as a microbial trait. Glob. Change Biol. 24, 4211–4224 (2018).
Google Scholar 
Wang, G., Li, W., Wang, K. & Huang, W. Uncertainty quantification of the soil moisture response functions for microbial dormancy and resuscitation. Soil Biol. Biochem. 160, 108337 (2021).CAS 

Google Scholar 
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S. & Janssens, I. Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture. J. Adv. Model. Earth Syst. 7, 335–356 (2015).
Google Scholar 
Nunan, N., Schmidt, H. & Raynaud, X. The ecology of heterogeneity: soil bacterial communities and C dynamics. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190249 (2020).CAS 

Google Scholar 
Kaiser, C., Franklin, O., Richter, A. & Dieckmann, U. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nat. Commun. 6, 8960 (2015).CAS 
PubMed 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Glob. Change Biol. 27, 2633–2644 (2021).
Google Scholar 
Fan, X. et al. Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool. ISME J. 15, 2248–2263 (2021).CAS 
PubMed 

Google Scholar 
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018). This paper addressed key uncertainties in the representation of microbial degradation and mineral stabilization in five microbially explicit soil carbon models.CAS 

Google Scholar 
Marschmann, G. L., Pagel, H., Kügler, P. & Streck, T. Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environ. Model. Softw. 122, 104518 (2019).
Google Scholar 
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 

Google Scholar 
Malik, A. A., Thomson, B. C., Whiteley, A. S., Bailey, M. & Griffiths, R. I. Bacterial physiological adaptations to contrasting edaphic conditions identified using landscape scale metagenomics. mBio 8, e00799-17 (2017).PubMed 
PubMed Central 

Google Scholar 
Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).PubMed 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 

Google Scholar 
Kempes, C. P., Wang, L., Amend, J. P., Doyle, J. & Hoehler, T. Evolutionary tradeoffs in cellular composition across diverse bacteria. ISME J. 10, 2145–2157 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dethlefsen, L. & Schmidt, T. M. Performance of the translational apparatus varies with the ecological strategies of bacteria. J. Bacteriol. 189, 3237–3245 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).CAS 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, e2016810118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, G., Rabe, K. S., Nielsen, J. & Engqvist, M. K. M. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8, 1411–1420 (2019).CAS 
PubMed 

Google Scholar 
Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e0008521 (2021).PubMed 

Google Scholar 
Rousk, J. & Bååth, E. Fungal and bacterial growth in soil with plant materials of different C/N ratios. FEMS Microbiol. Ecol. 62, 258–267 (2007).CAS 
PubMed 

Google Scholar 
Koechli, C., Campbell, A. N., Pepe-Ranney, C. & Buckley, D. H. Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing. Soil Biol. Biochem. 130, 150–158 (2019).CAS 

Google Scholar 
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 

Google Scholar 
Neurath, R. A. et al. Root carbon interaction with soil minerals is dynamic, leaving a legacy of microbially derived residues. Environ. Sci. Technol. 55, 13345–13355 (2021).CAS 
PubMed 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).CAS 

Google Scholar 
Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2016).PubMed 

Google Scholar 
Sharma, K., Palatinszky, M., Nikolov, G., Berry, D. & Shank, E. A. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. eLife 9, e56275 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arellano-Caicedo, C., Ohlsson, P., Bengtsson, M., Beech, J. P. & Hammer, E. C. Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Commun. Biol. 4, 1226 (2021).PubMed 
PubMed Central 

Google Scholar 
Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Env. 2, 507–517 (2021).
Google Scholar 
Schulz, F. et al. Hidden diversity of soil giant viruses. Nat. Commun. 9, 4881 (2018).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils. PeerJ 7, e7265 (2019).PubMed 
PubMed Central 

Google Scholar 
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Sommers, P., Chatterjee, A., Varsani, A. & Trubl, G. Integrating viral metagenomics into an ecological framework. Annu. Rev. Virol. 8, 133–158 (2021).PubMed 

Google Scholar 
Pratama, A. A. & van Elsas, J. D. The ‘neglected’ soil virome–potential role and impact. Trends Microbiol. 26, 649–662 (2018).CAS 
PubMed 

Google Scholar 
Ghosh, D. et al. Prevalence of lysogeny among soil bacteria and presence of 16S rRNA and trzN genes in viral-community DNA. Appl. Environ. Microbiol. 74, 495–502 (2008).CAS 
PubMed 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 

Google Scholar 
Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 

Google Scholar 
Van Goethem, M. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. mBio 10, e02287-19 (2019).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 9, 208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc. Natl Acad. Sci. USA 118, e2105124118 (2021). This study used stable isotope probing metagenomics to connect, in situ, active virus–host infections with the biogeochemical process of methane oxidation in soil.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2017).PubMed 

Google Scholar  More