Philippa Kaur

Tuber yieldPotato tuber yield increased gradually under 0 to 150 kg ha−1 of applied nitrogen (Fig. 1). Compared with the yield in N0, the yields in N60, N120 and N150 were greater by 16.1%, 21.5% and 67.9%, respectively, in 2017 and 18.2%, 27.4% and 44.9%, respectively, in 2018. However, at nitrogen rates of more than 150 kg ha−1, yield did not significantly differ. Furthermore, the fitted parabolic equation of each dataset from 2017 and 2018 showed maximum tuber yields of 19.7 and 20.4 t ha−1, respectively, where the nitrogen rates were 191 and 227 kg ha−1, respectively.Figure 1Potato tuber yield in treatments with different nitrogen fertilizer rates. Different letters indicate significant differences (P  More

1.Milshteyn, A., Schneider, J. S. & Brady, S. F. Mining the metabiome: identifying novel natural products from microbial communities. Chem. Biol. 21, 1211–1223 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Rutledge, P. J. & Challis, G. L. Discovery of microbial natural products by activation of silent biosynthetic gene clusters. Nat. Rev. Microbiol. 13, 509–523 (2015).CAS 
PubMed 

Google Scholar 
3.Dittmann, E., Gugger, M., Sivonen, K. & Fewer, D. P. Natural product biosynthetic diversity and comparative genomics of the cyanobacteria. Trends Microbiol. 23, 642–652 (2015).CAS 
PubMed 

Google Scholar 
4.Cragg, G. M., Kingston, D. G. & Newman, D. J. Anticancer Agents from Natural Products. (CRC press, 2011).5.Cragg, G. M. & Newman, D. J. Natural products: a continuing source of novel drug leads. Biochimica et. Biophysica Acta 1830, 3670–3695 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Singh, R., Kumar, M., Mittal, A. & Mehta, P. K. Microbial metabolites in nutrition, healthcare and agriculture. 3 Biotech 7, 15 (2017).PubMed 
PubMed Central 

Google Scholar 
7.Bohlmann, J. & Keeling, C. I. Terpenoid biomaterials. Plant J. 54, 656–669 (2008).CAS 
PubMed 

Google Scholar 
8.Kang, A. & Lee, T. S. Biotechnology for Biofuel Production and Optimization 35–71 (Elsevier, 2016).9.Nowruzi, B., Sarvari, G. & Blanco, S. The cosmetic application of cyanobacterial secondary metabolites. Algal Res. 49, 101959 (2020).
Google Scholar 
10.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).CAS 
PubMed 

Google Scholar 
11.Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. Mbio. 11, 1–17 (2020).
Google Scholar 
12.Libis, V. et al. Uncovering the biosynthetic potential of rare metagenomic DNA using co-occurrence network analysis of targeted sequences. Nat. Commun. 10, 1–9 (2019).CAS 

Google Scholar 
13.Haro-Moreno, J. M., López-Pérez, M. & Rodriguez-Valera, F. Enhanced recovery of microbial genes and genomes from a marine water column using long-read metagenomics. Front Microbiol. 2410, 1–15 (2021).
Google Scholar 
14.Navarro-Muñoz, J. C. et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol. 16, 60–68 (2019).PubMed 
PubMed Central 

Google Scholar 
15.Sugimoto, Y. et al. A metagenomic strategy for harnessing the chemical repertoire of the human microbiome. Science 366, 1–11 (2019).
Google Scholar 
16.Katz, M., Hover, B. M. & Brady, S. F. Culture-independent discovery of natural products from soil metagenomes. J. Ind. Microbiol. Biotechnol. 43, 129–141 (2016).CAS 
PubMed 

Google Scholar 
17.Lynch, M. D. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).CAS 
PubMed 

Google Scholar 
18.Amos, G. C. et al. Comparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality. Proc. Natl Acad. Sci. USA 114, E11121–E11130 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Rodriguez-Caballero, E. et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat. Geosci. 11, 185–189 (2018).CAS 

Google Scholar 
20.Reddy, B. V. B. et al. Natural product biosynthetic gene diversity in geographically distinct soil microbiomes. Appl. Environ. Microbiol. 78, 3744–3752 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Starkenburg, S. R. et al. Genome of the cyanobacterium Microcoleus vaginatus FGP-2, a photosynthetic ecosystem engineer of arid land soil biocrusts worldwide. J. Bacteriol. 193, 4569–4570 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Belnap, J., Weber, B. & Büdel, B. Biological Soil Crusts: an Organizing Principle in Drylands 3–13 (Springer, 2016).23.Swenson, T. L., Karaoz, U., Swenson, J. M., Bowen, B. P. & Northen, T. R. Linking soil biology and chemistry in biological soil crust using isolate exometabolomics. Nat. Commun. 9, 1–10 (2018).CAS 

Google Scholar 
24.Adamek, M., Spohn, M., Stegmann, E. & Ziemert, N. Antibiotics 23–47 (Springer, 2017).25.Couradeau, E. et al. Bacteria increase arid-land soil surface temperature through the production of sunscreens. Nat. Commun. 7, 1–7 (2016).
Google Scholar 
26.Martins, T. P. et al. Chemistry, bioactivity and biosynthesis of cyanobacterial alkylresorcinols. Nat. Prod. Rep. 36, 1437–1461 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Luesch, H., Moore, R. E., Paul, V. J., Mooberry, S. L. & Corbett, T. H. Isolation of dolastatin 10 from the marine cyanobacterium Symploca species VP642 and total stereochemistry and biological evaluation of its analogue symplostatin 1. J. Nat. Products 64, 907–910 (2001).CAS 

Google Scholar 
28.Chrapusta, E. et al. Microcystins and anatoxin-a in Arctic biocrust cyanobacterial communities. Toxicon 101, 35–40 (2015).CAS 
PubMed 

Google Scholar 
29.Van Goethem, M. W., Swenson, T. L., Trubl, G., Roux, S. & Northen, T. R. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. Mbio. 10, 1–15 (2019).
Google Scholar 
30.Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).CAS 
PubMed 

Google Scholar 
31.Bowker, M. A., Reed, S. C., Maestre, F. T. & Eldridge, D. J. Biocrusts: the living skin of the earth. (Springer, 2018).32.Büdel, B., Dulić, T., Darienko, T., Rybalka, N. & Friedl, T. Biological Soil Crusts: an Organizing Principle in Drylands 55–80 (Springer, 2016).33.Giraldo‐Silva, A., Nelson, C., Barger, N. N. & Garcia‐Pichel, F. Nursing biocrusts: isolation, cultivation, and fitness test of indigenous cyanobacteria. Restor. Ecol. 27, 793–803 (2019).
Google Scholar 
34.Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).CAS 
PubMed 

Google Scholar 
36.Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Mikheenko, A., Saveliev, V. & Gurevich, A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32, 1088–1090 (2016).CAS 
PubMed 

Google Scholar 
38.Howe, A. C. et al. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl Acad. Sci. USA 111, 4904–4909 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Frank, J. A. et al. Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. Sci. Rep. 6, 1–10 (2016).
Google Scholar 
40.Hiraoka, S. et al. Metaepigenomic analysis reveals the unexplored diversity of DNA methylation in an environmental prokaryotic community. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
41.Blin, K. et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47, W81–W87 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Garcia-Pichel, F., Loza, V., Marusenko, Y., Mateo, P. & Potrafka, R. M. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340, 1574–1577 (2013).CAS 
PubMed 

Google Scholar 
43.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 

Google Scholar 
44.Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 499–509 (2020).45.Fuqua, C. & Greenberg, E. P. Listening in on bacteria: acyl-homoserine lactone signalling. Nat. Rev. Mol. Cell Biol. 3, 685–695 (2002).CAS 
PubMed 

Google Scholar 
46.Ciemniecki, J. A. & Newman, D. K. The potential for redox-active metabolites to enhance or unlock anaerobic survival metabolisms in aerobes. J. Bacteriol. 202, 1–14 (2020).
Google Scholar 
47.Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J. 7, 2178–2191 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Nunes da Rocha, U. et al. Isolation of a significant fraction of non-phototroph diversity from a desert biological soil crust. Front. Microbiol. 6, 277 (2015).PubMed 
PubMed Central 

Google Scholar 
49.Felde, V. J. M. N. L., Peth, S., Uteau-Puschmann, D., Drahorad, S. & Felix-Henningsen, P. Soil microstructure as an under-explored feature of biological soil crust hydrological properties: case study from the NW Negev Desert. Biodivers. Conserv. 23, 1687–1708 (2014).
Google Scholar 
50.Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. No. LBNL-7065E. (Lawrence Berkeley National Lab (LBNL), Berkeley, CA (United States), 2014).51.Donia, M. S., Ruffner, D. E., Cao, S. & Schmidt, E. W. Accessing the hidden majority of marine natural products through metagenomics. ChemBioChem. 12, 1230–1236 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Van Der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
Google Scholar 
53.Couradeau, E., Giraldo-Silva, A., De Martini, F. & Garcia-Pichel, F. Spatial segregation of the biological soil crust microbiome around its foundational cyanobacterium, Microcoleus vaginatus, and the formation of a nitrogen-fixing cyanosphere. Microbiome 7, 1–12 (2019).
Google Scholar 
54.Magne, F. et al. The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients? Nutrients 12, 1474 (2020).CAS 
PubMed Central 

Google Scholar 
55.Baran, R. et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat. Commun. 6, 8289 (2015).CAS 
PubMed 

Google Scholar 
56.Kupriyanova, E. V. et al. Extracellular β-class carbonic anhydrase of the alkaliphilic cyanobacterium Microcoleus chthonoplastes. J. Photochem. Photobiol. B: Biol. 103, 78–86 (2011).CAS 

Google Scholar 
57.Hernandez, M. & Newman, D. Extracellular electron transfer. Cell. Mol. Life Sci. 58, 1562–1571 (2001).CAS 
PubMed 

Google Scholar 
58.Karaoz, U. et al. Large blooms of Bacillales (Firmicutes) underlie the response to wetting of cyanobacterial biocrusts at various stages of maturity. MBio. 9, e01366–01316 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Rodriguez-R, L. M., Gunturu, S., Tiedje, J. M., Cole, J. R. & Konstantinidis, K. T. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. MSystems. 3, 1–9 (2018).
Google Scholar 
60.Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 

Google Scholar 
61.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 

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

Google Scholar 
64.Van der Walt, A. J. et al. Assembling metagenomes, one community at a time. BMC Genom. 18, 1–13 (2017).
Google Scholar 
65.Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).
Google Scholar 
66.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 

Google Scholar 
67.Arkin, A. P. et al. KBase: the United States department of energy systems biology knowledgebase. Nat. Biotechnol. 36, 566 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Kautsar, S. A. et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 48, D454–D458 (2020).PubMed 

Google Scholar 
69.von Meijenfeldt, F. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H. & Dutilh, B. E. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 20, 217 (2019).
Google Scholar 
70.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
71.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
72.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar  More

A model of diauxic community assemblyCommunity models studying diauxie should mimic serial dilution cultures instead of chemostats, in order to make their predictions both experimentally and ecologically relevant. Experimentally, microbial community assembly assays frequently utilize serial dilution cultures. Ecologically, diauxic growth is best suited to a “feast and famine” lifestyle, which a serial dilution culture mimics30,31,32. Therefore, throughout this manuscript, we model the assembly of a microbial community undergoing a sequence of growth-dilution cycles (see Fig. 1a). Community assembly occurs gradually through the addition of microbial species from a diverse species pool one at a time. Each species in the pool consumes resources diauxically, i.e., one at a time according to its resource preference.Fig. 1: Model of community assembly with diauxie and serial dilution.a Tables of growth rates and resource preferences of two species α (red) and β (yellow), each capable of consuming all four available resources, R1 to R4. The resource preference sets the sequence in which a microbial species utilizes resources, and the corresponding rates gXi indicate the growth rate while consuming each resource (see “Methods”). b Diauxic growth curve of species α during one serial dilution cycle, which has 4 phases of growth on each individual resource, with rates gα1, gα3, gα2, and gα4, respectively (with a brief lag period between two phases). At the end of each dilution cycle, we dilute the population by a factor D = 100, and supply fresh resources (see “Methods”). c Resource depletion curves corresponding to (b), where each resource is represented by a different color. R1 is exhausted at time T1; then species α consumes R3 which runs out at T3, which is followed by exhaustion of R2 at T2, and so on. d Schematic of serial dilution experiment. During community assembly, new species are added one by one from a species pool. After each successful invasion, the system undergoes several growth-dilution cycles until it reaches a steady state. e Population dynamics corresponding to the assembly process in (d). Panels (b) and (c) correspond to a small section of this process (highlighted in gray), where the community dynamics consist only of species α (red) reaching a steady state.Full size imageWe begin by illustrating the growth of a single species (labeled α) grown in an environment with four resources (Fig. 1a–c). The species first grows on its most preferred resource (R1) with a growth rate gα1 until time T1, when this resource gets exhausted. After a lag period τ, the species switches to growing on its next preferred resource (R3) with growth rate gα3 until time T3, when this resource also gets exhausted. This process of diauxic growth by sequential utilization of resources continues until either all resources are depleted, or the cycle ends at time T. At this point, a fraction 1/D of the medium containing the species is transferred to a fresh medium replete with resources. This corresponds to the dilution of species abundances by a factor D, mimicking serial dilution experiments in the laboratory.After several transfers, species dynamics converge to a steady state, where each species starts a cycle with the same initial abundance as the previous cycle. At this point, we add a small population of a new invader species, chosen randomly from the species pool, to the steady-state community (Fig. 1d, e). (Hence, we assume that species invasions are rare enough such that communities always reach a steady state before the next invasion.) The invader may differ from the resident species in both resource preference order and growth rates on each resource (Fig. 1a). Once introduced, the invader may grow and establish itself in the community in a new steady state (Fig. 1d, e), or it may fail, returning the community to its previous steady state.The growth rates and preference orders completely characterize a species, while the set of resource depletion times (T1, T2, etc.) characterize the current state of the abiotic environment. As we will later show, these resource depletion times are important observables in a community, since they determine the success or failure of an invader.A realistic example of a community captured by our model is the human gut microbiome, specifically the assembly of primary consumers (e.g., Bacteroides species) on the polysaccharides (e.g., starch, cellulose, and mucin) that they consume. Here, there is a significant overlap between the metabolic capabilities of the microbes, but they nevertheless coexist. These species often consume polysaccharides diauxically, and engage in resource competition. Moreover, several of these species have different resource preferences, which others have hypothesized help them coexist26,33.Throughout this paper, we neglect diauxic lag times (τ = 0) for simplicity. We will later show that adding lag times only quantitatively strengthens our main results (see “Discussion” and Fig. 5). We also assume that the supplied resource concentrations are sufficiently large, enabling species to always grow exponentially at their resource-specific growth rates. Further, we assume a balanced supply of resources, i.e., that resources are supplied in equal concentrations (see “Discussion” and Supplementary Text for results in an unbalanced resource supply).We simulated the assembly of 1000 communities, each being colonized from a pool of ~10,000 species (see “Methods”). Species could utilize all 4 supplied resources diauxically. Each species had a random resource preference order and different growth rates on each resource, which were picked randomly from a rectified normal distribution (with mean 0.25 and standard deviation 0.05). We assumed that the growth rate distributions for each of the 4 resources were the same, such that no resource was consistently better than the other. This is a simplifying assumption, but it nevertheless captures a variety of experimental observations showing remarkable growth rate variability of different microbial species on the same carbon sources34,35,36. Community assembly proceeded via introduction of species one at a time, in a random order, until each species had attempted to invade exactly once.Emergent properties of diauxic community assemblyTo study the emergent properties of communities of diauxic species, we followed the assembly process from a species pool via invasion of species one at a time. We used the number of invasion attempts to track time; communities matured over successive invasions. We found that the assembly process became slower over time—successful invasions became rarer as the community matured (Fig. 2a inset). Throughout the assembly process, we recorded four key properties of communities: total resource depletion time, species diversity, complementarity of the community, and prevalence of anomalous species (defined below).Fig. 2: Emergent properties of diauxic microbial communities.In all plots, solid bold lines represent the average over 958 individual community assembly simulations, while gray lines correspond to 100 randomly chosen community assembly simulations. a Total resource depletion time during community assembly (the time taken by the community to deplete all available resources). (Inset) Number of successful invasions during community assembly. b Total species diversity during community assembly (number of surviving species at steady state). c Resource utilization complementarity during community assembly. For each time point, the nth choice complementarity was calculated as a number of unique resources among the n-th preferred choices of all species in the community, divided by the number of unique resources in the environment. For a certain community, the null expectation (complementarity without selection) was defined by the complementarity of a random set of species from the pool that has the same diversity of that community. Colored lines show the average trend of complementarity on each preferred resource choice: top (light blue), second (cyan), third (deep green), and fourth (light green). The red dash-dotted line shows the average trend of null expectation. The gray dash-dotted line at the top corresponds to the perfect complementarity, which is 1. d Frequency of species with anomalous resource preferences during community assembly. The gray dash-dotted line is the expectation of fraction of anomalous species (75%) in the pools.Full size imageResource depletion timeIn each community, resources disappear at specific times and in a well-defined order (Fig. 1c). The total resource depletion time measures how quickly the community consumes all supplied resources. In this way, the total resource depletion time characterizes the overall speed at which a community consumes resources. The total resource depletion time decreased as communities assembled (Fig. 2a, solid line). The rate and degree of this decrease depend on the mean and variance of the growth rate distribution and the number of invasion attempts. In addition, the variability in depletion times between communities reduced over community assembly (Fig. 2a, gray lines; coefficient of variation reduces by 47%, see Fig. S1). Thus the assembly process selects for communities that collectively consume resources quickly.Species diversityThe species diversity was quantified as the number of species coexisting in the steady-state community. In the model, like in other consumer-resource models, the number of coexisting species at steady state is limited by the number of resources, 4 (Fig. 2b, dashed line)10,37. This is a natural consequence of competition for resources in our model (see Supplementary Text, section F for a derivation). Notably, species with the same resource preferences can coexist in the model, as long as the number of species is less than the number of resources (e.g., pairs of E. coli strains can coexist in media with glucose and xylose, see below). We found that the average community diversity increased over time, but the rate slowed as the community matured (Fig. 2b; note the logarithmic x-axis scale). Communities displayed significant variability in the trajectories of increasing diversity (Fig. 2b, gray lines). We discuss the slow increase of diversity, and observed variability, in the next section.Top choice complementarityThe top choice complementarity of a community measured the overlap in the top choice resource of each of the species residing in the community. We defined the top choice complementarity of a community as the number of unique top choice resources among community residents, divided by the number of residents. Thus the top choice complementarity varied between 1, in a maximally complementary community where each resident species had a unique top choice resource (Fig. 2c, right), and 1 divided by the number of coexisting species in the community, where all residents chose the same resource as the top choice (Fig. 2c, left). During community assembly, top choice complementarity stayed close to the maximum value throughout the assembly process (Fig. 2c, blue). This observation was in sharp contrast to the prediction from a null model for the complementarity (Fig. 2c, red). We obtained the null prediction by measuring the complementarity of a group of randomly chosen species from the species pool (group size being the number of coexisting species in the community). This null prediction decreased during the assembly process, due to the increasing community diversity, unlike the top choice complementarity which remained close to the maximum value. We also recorded the complementarity in the second, third, and fourth choice resource of the assembled community (defined similarly to the top choice complementarity). The complementarity of all other choices agreed with the null prediction (Fig. 2c). Together, these observations suggest that communities of coexisting diauxic species exhibit high complementarity on the top-choice resources, in a manner reminiscent of niche partitioning in consumer-resource models.Prevalence of anomalous speciesIntuition gleaned from experiments with E. coli dictates that microbes often grow fastest on their top choice resource (glucose for E. coli)18,20. However, exceptions to this trend also exist, such as Bacteroides species in the human gut that often prefer polysaccharides that they grow slower on22,26,38. Based on this intuition, we defined anomalous microbes as microbes that do not grow fastest on their top choice resource. To investigate which resource preferences might give microbes a competitive advantage during community assembly, we tracked the fraction of anomalous resident species during community assembly. Despite the majority (75%) of species in the pool being anomalous (since growth rates and preferences were randomly picked; see “Methods”), anomalous species were absent in mature communities. The fraction of anomalous resident species decreased rapidly during assembly (Fig. 2d). Thus, anomalous resource preferences are strongly selected against during community assembly. Further investigation revealed a reduced selection pressure against anomalous species if either resource supply was severely imbalanced (i.e., the imbalance has to be comparable to the dilution factor, D = 100), or if the dilution factor was small (see Figs. S4 and S5; also see Supplementary Text, sections C and H). However, microbes with anomalous resource preferences were eventually outcompeted in all conditions.Top choice resources chiefly drive emergent assembly patternsTo understand what factors drove the maintenance of top choice complementarity—despite the steady increase in species diversity, expected to reduce complementarity—we focused on growth on top choice resources. We hypothesized that the reason for the much higher than expected top choice complementarity was the following: diauxic species derived most of their growth, and spent most of their time growing on their top choice resources. Co-utilizing microbes, instead, grow on multiple resources simultaneously, spending roughly equal time on each utilized resource.To test this hypothesis, we first simulated the growth of a single diauxic species in monoculture using our model. We found that indeed, the species derived the overwhelming majority of its growth (measured in generations of growth) and spent most of its time growing on its top choice resource (54%, Fig. 3a, b, left). For a simpler case, where a single species had the same growth rate g while growing on two resources (both supplied at the same concentration), and preferring resource R1 over R2, we derived the ratio of time spent growing on the top choice resource R1 (T1) versus the second choice R2 (T2 − T1). We obtained the following approximate expression for a large dilution factor D (see Supplementary Text, section A):$$frac{{T}_{1}}{{T}_{2}-{T}_{1}}=frac{,{{mbox{log}}}(D/2)}{{{mbox{log}}},(2)},$$
(1)
which explains that the fraction of time spent growing on the top choice resource increases with the dilution factor.Strikingly, the fraction of time spent growing on the top choice resource became even larger if the species grown in monoculture (Fig. 3b, top row) were instead part of a diverse community (i.e., in co-culture with 3 other species, top choice share 70% versus 54% in monoculture, Fig. 3b, bottom row and top row, respectively). This is because of the following reason. In our model, while a species consumes and grows on all available resources in monoculture, in co-culture, it may not have the opportunity to consume all the resources it can grow on because other species might deplete them first. This further skews growth in favor of the top choice resource. Such a phenomenon only occurs in diauxic species, not co-utilizing species (Supplementary Text, section I).Invader successInterestingly, once we understood that the top choice chiefly drove species growth, we could explain the other emergent patterns in diauxic communities. Importantly, the success of an invader depended on the growth rate on their top choice resource. As community assembly proceeded, the top choice growth rate of successful invaders increased consistently (Fig. 3c, blue line), while their growth rates on all other choices remained constant and close to the average growth rate (Fig. 3c, green lines). Selection on the top choice growth rate in diauxic communities is in striking contrast with co-utilizing communities, which we found select for the average growth rate across all resources instead (Supplementary Text, section I). Further, an invader whose top choice resource coincided with the last depleted resource in the community had the highest probability of invasion success (Fig. 3d). Invaders whose top choice resource was not depleted last had lesser time to grow on it, and thus a lower rate of invasion success. By depleting the last resource faster, invaders reduced the total resource depletion time in the community, thus explaining the trend observed in Fig. 2a. In addition, after a successful invasion, the community’s steady state could have a different resource depletion order.Complementarity and diversitySuccessful invasions could be classified into one of two types based on the “invaded resource”, i.e., the invader’s top choice. If the invaded resource was not the top choice of any other resident community member, we called it an invasion of an “unoccupied” resource (Fig. 3e; in our simulations, 33% of cases). If the invaded resource was instead already the top choice of at least one resident, we called it an invasion of an “occupied” resource (Fig. 3e; 67% of cases). Both types of successful invasions had different effects on species diversity, but interestingly, both maintained complementarity (on the top choice, as in Fig. 2c). invasions of unoccupied resources usually increased community diversity by 1 (62% of cases), and were less likely to result in the extinction of one or more other species (38% of cases). This is because, in that case, the invader did not have to compete with other residents for its top choice resource. For communities with a complementarity {T}_{2},$$
(2)
where gα1 and gα2 are the species α’s growth rates on R1 and R2, respectively. The two triangular regions separated by the diagonal define two complementary scenarios: when T1  T2, R1 is depleted second and the species grows on R1 after R2 is depleted.For a given set of initial resource and species concentrations, community dynamics must converge to a steady state lying on the ZNGI of the surviving species (e.g., the bold purple point in Fig. 4a). This point defines the resource depletion times by the resident species at steady state. Changing the resource supply or dilution factor moves this point along the ZNGI.The ZNGI of a species also separates the resource environment space into two regions: a region inside the ZNGI (towards the origin) where that species grows by a factor D. An invader is successful if it is able to grow by a factor ≥D in the community it invades. Geometrically, the invader’s ZNGI must be closer to the origin than the resource environment corresponding to the invaded community (Fig. 4b). In this way, our geometric approach allows easy visualization of invasion criteria.We can also visualize invasion outcomes. A successful invasion of a single-species community leads to either displacement of the resident or coexistence between the invader and resident. For example, in Fig. 4b, because the ZNGI of the invader (blue) lies fully inside the ZNGI of the resident (purple), the invader displaces the resident. This is because the invader reduces the resource depletion times in the environment to a point where the resident can no longer survive, driving it extinct (bold blue point in Fig. 4b). In contrast, in Fig. 4c, the ZNGI of the invader (orange) intersects with the new resident (blue), in a manner that leads to coexistence between both species (albeit at a new set of resource depletion times, i.e., their intersection point in Fig. 4c). In general, whether two species will coexist depends on various factors, such as the supplied resource concentrations, but whenever two species coexist, they will do so at the intersection of their ZNGIs (Supplementary Text, section A). As a corollary, two species whose ZNGIs do not intersect cannot coexist. Notably, the orange and blue species in Fig. 4c coexist stably with each other; a short perturbation to the resource supply is quickly compensated by species growth, and the resource depletion times returned to the coexistence point (see Supplementary Text, section B for details).The geometric approach provides an alternative explanation to why species with complementary top choices are more likely to coexist than species with the same top choice (Fig. 2c). The ZNGIs of species sharing the same top choice are unlikely to intersect with each other (e.g., the blue and purple species in Fig. 4b). This is because of two reasons: (1) their segments in the yellow region are parallel to each other since both species prefer the same resource (R2), and (2) for the slanted segments in the green region to intersect, the blue species would need a higher growth rate on R1 than the purple species. This is as likely as the outcome of a coin toss, since both growth rates derive from the same distribution. Thus, an invasion of an occupied resource often leads to displacement of the resident, not coexistence (Fig. 4b, d) and no change in community diversity, while an invasion of an unoccupied resource often leads to coexistence (Fig. 4c) and an increase in community diversity (Fig. 3e).Fig. 3: Top choice resources chiefly drive community diversity and complementarity.a (top) Table showing the preferences of a diauxic microbial species (purple) for 4 resources, R1 to R4. (bottom) Plots showing the depletion of the 4 resources by the purple species during one serial dilution cycle, when grown alone in our model. b (top) Bar plots showing the time taken by the purple species in (a) to grow on each of the 4 resources. Percentages on each bar represent the fraction of time spent growing on each resource. (bottom) Bar plots showing the number of generations grown, or the number of doublings by the species when growing on each resource. In both cases, the plots on the left show the quantities when the purple species is in monoculture (growing alone), and those on the right show them when the purple species is in a community with 3 other species. c Mean growth rates of successful invaders during community assembly. The blue line corresponds to the invader’s top choice, while the other colors correspond to all other choices. The horizontal dashed line shows the mean growth rate of the species pool. Each quantity represents a moving average from 958 independent community assembly simulations. Error bars represent s.e.m. d Fraction of the successful invasions as a function of the order in which the invader’s top choice resource is depleted, 1 indicating cases where the invader prefers the earliest depleted resource, and 4 where it prefers the last depleted resource. Each bar represents the mean of such a fraction over 958 independent community assembly simulations, and error bars represent s.e.m. e, f Effect of invasions of community diversity and complementarity, based on whether the invader’s top choice was (e) unoccupied or (f) occupied. Cartoons show the typical effect of an invasion. Pie charts show the fraction of invasions that increase, decrease or maintain a community’s species diversity (middle) and complementarity (right). On unoccupied resources, diversity typically increases (62%), but sometimes stays constant (32%) or decreases (6%). On occupied resources, diversity typically stays the same (68%), but sometimes decreases (26%) and rarely increases (6%). In almost all cases complementarity either stays maintained or increases ( >95%), and very rarely decreases ( More

1.IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).2.Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).CAS 

Google Scholar 
3.Carton, W., Lund, J. F. & Dooley, K. Undoing equivalence: rethinking carbon accounting for just carbon removal. Front. Clim. 3, 30 (2021).
Google Scholar 
4.Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Change Biol. 27, 1518–1546 (2021).
Google Scholar 
5.Griscom, B. W. et al. We need both natural and energy solutions to stabilize our climate. Glob. Change Biol. 25, 1889–1890 (2019).
Google Scholar 
6.Fargione, J. et al. Natural climate solutions for the United States. Sci. Adv. 4, eaat1869 (2018).
Google Scholar 
7.Drever, C. R. et al. Natural climate solutions for Canada. Sci. Adv. 7, eabd6034 (2021).CAS 

Google Scholar 
8.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 

Google Scholar 
9.Fuss, S. et al. Negative emissions—Part 2: costs, potentials and side effects. Environ. Res. Lett. 13, 63002 (2018).
Google Scholar 
10.Gregorio, N. et al. in Enhancing Food Security Through Forest Landscape Restoration: Lessons from Burkina Faso, Brazil, Guatemala, Viet Nam, Ghana, Ethiopia and Philippines (eds Kumar, C. et al.) 174–217 (IUCN, 2015).11.Meyer, J. M. Gifford Pinchot, John Muir, and the boundaries of politics in American thought. Polity 30, 267–284 (1997).
Google Scholar 
12.Standard on Biodiversity Offsets (BBOP, 2012).13.Performance Standard 6: Biodiversity Conservation and Sustainable Management of Natural Resources (IFC, 2012).14.Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. Bioscience 68, 336–347 (2018).
Google Scholar 
15.Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020).16.Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Phil. Trans. R. Soc. B 375, 20190120 (2020).
Google Scholar 
17.Ellis, P. W. et al. Reduced-impact logging for climate change mitigation (RIL-C) can halve selective logging emissions from tropical forests. Ecol. Manag. 438, 255–266 (2019).
Google Scholar 
18.Martin, D. M. Ecological restoration should be redefined for the twenty-first century. Restor. Ecol. 25, 668–673 (2017).
Google Scholar 
19.Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).
Google Scholar 
20.Supporting Canadians and Fighting COVID-19 (Department of Finance Canada, 2020).21.Roe, S. et al. Contribution of the land sector to a 1.5 °C world. Nat. Clim. Change 9, 817–828 (2019).
Google Scholar 
22.Seddon, N. et al. Nature-Based Solutions in Nationally Determined Contributions: Synthesis and Recommendations for Enhancing Climate Ambition and Action by 2020 (IUCN, 2019).23.Carbon Removal Corporate Action Tracker (Institute for Carbon Removal Law and Policy, accessed 6 July 2021); https://research.american.edu/carbonremoval/2020/05/07/carbon-removal-corporate-action-tracker/24.Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).
Google Scholar 
25.Goal 1 Assessment: Striving to End Natural Forest Loss (NYDF Progress Assessment Secretariat, 2020).26.Smith, B. One year later: The path to carbon negative—a progress report on our climate ‘moonshot’. Microsoft Blog (28 January 2021); https://blogs.microsoft.com/blog/2021/01/28/one-year-later-the-path-to-carbon-negative-a-progress-report-on-our-climate-moonshot/27.Ward, C. et al. Smallholder perceptions of land restoration activities: rewetting tropical peatland oil palm areas in Sumatra. Indonesia. Reg. Environ. Change 21, 1 (2020).
Google Scholar 
28.Jacobson, M. & Ham, C. The (un)broken promise of agroforestry: a case study of improved fallows in Zambia. Environ. Dev. Sustain. 22, 8247–8260 (2020).
Google Scholar 
29.West, T. A. P., Börner, J., Sills, E. O. & Kontoleon, A. Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon. Proc. Natl Acad. Sci USA 117, 24188–24194 (2020).CAS 

Google Scholar 
30.Cook-Patton, S. C. et al. Lower cost and more feasible options to restore forest cover in the contiguous United States for climate mitigation. One Earth 3, 739–752 (2020).
Google Scholar 
31.Petersen, S. O., Højberg, O., Poulsen, M., Schwab, C. & Eriksen, J. Methanogenic community changes, and emissions of methane and other gases, during storage of acidified and untreated pig slurry. J. Appl. Microbiol. 117, 160–172 (2014).CAS 

Google Scholar 
32.Günther, A. et al. Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat. Commun. 11, 1644 (2020).
Google Scholar 
33.Qin, Z. et al. Delayed impact of natural climate solutions. Glob. Change Biol. 27, 215–217 (2021).
Google Scholar 
34.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 

Google Scholar 
35.Pagiola, S., Honey-Rosés, J. & Freire-González, J. Assessing the permanence of land-use change induced by payments for environmental services: evidence from Nicaragua. Trop. Conserv. Sci. https://doi.org/10.1177/1940082920922676 (2020).36.Tseng, T.-W. J. et al. Influence of land tenure interventions on human well-being and environmental outcomes. Nat. Sustain. 4, 242–251 (2021).
Google Scholar 
37.Smith, P. et al. Impacts of land-based greenhouse gas removal options on ecosystem services and the United Nations Sustainable Development Goals. Annu. Rev. Environ. Resour. 44, 255–286 (2019).
Google Scholar 
38.Nunez, S., Verboom, J. & Alkemade, R. Assessing land-based mitigation implications for biodiversity. Environ. Sci. Policy 106, 68–76 (2020).
Google Scholar 
39.Chausson, A. et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Change Biol. 26, 6134–6155 (2020).
Google Scholar 
40.Infield, M., Entwistle, A., Anthem, H., Mugisha, A. & Phillips, K. Reflections on cultural values approaches to conservation: lessons from 20 years of implementation. Oryx 52, 220–230 (2018).
Google Scholar 
41.Rosenstock, T. S. et al. A planetary health perspective on agroforestry in sub-Saharan Africa. One Earth 1, 330–344 (2019).
Google Scholar 
42.Garrett, H. E. et al. Hardwood silvopasture management in North America. Agrofor. Syst. 61, 21–33 (2004).
Google Scholar 
43.Kroeger, T. et al. Returns on investment in watershed conservation: application of a best practices analytical framework to the Rio Camboriú Water Producer program, Santa Catarina, Brazil. Sci. Total Environ. 657, 1368–1381 (2019).CAS 

Google Scholar 
44.Lamb, D., Erskine, P. D. & Parrotta, J. A. Restoration of degraded tropical forest landscapes. Science 310, 1628–1632 (2005).CAS 

Google Scholar 
45.Ferreira, J. et al. Carbon-focused conservation may fail to protect the most biodiverse tropical forests. Nat. Clim. Change 8, 744–749 (2018).CAS 

Google Scholar 
46.Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 

Google Scholar 
47.Wilson, S. J., Schelhas, J., Grau, R., Nanni, A. S. & Sloan, S. Forest ecosystem-service transitions: the ecological dimensions of the forest transition. Ecol. Soc. 22, 38 (2017).
Google Scholar 
48.Funk, J. M. et al. Securing the climate benefits of stable forests. Clim. Policy 19, 845–860 (2019).
Google Scholar 
49.Keith, H. et al. Evaluating nature-based solutions for climate mitigation and conservation requires comprehensive carbon accounting. Sci. Total Environ. 769, 144341 (2021).CAS 

Google Scholar 
50.Moomaw, W. R., Masino, S. A. & Faison, E. K. Intact forests in the United States: proforestation mitigates climate change and serves the greatest good. Front. For. Glob. Change 2, 27 (2019).
Google Scholar 
51.Hiraishi, T. et al. 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (WMO, 2013).52.Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Change 10, 287–295 (2020).CAS 

Google Scholar 
53.Griscom, B. W. et al. National mitigation potential from natural climate solutions in the tropics. Phil. Trans. R. Soc. B 375, 20190126 (2020).CAS 

Google Scholar 
54.Busch, J. et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change 9, 463–466 (2019).CAS 

Google Scholar 
55.Vargas Zeppetello, L. R. et al. Large scale tropical deforestation drives extreme warming. Environ. Res. Lett. 15, 84012 (2020).
Google Scholar 
56.Spalding, M. D. et al. The role of ecosystems in coastal protection: adapting to climate change and coastal hazards. Ocean Coast. Manag. 90, 50–57 (2014).
Google Scholar 
57.Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Google Scholar 
58.Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).59.Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369, 379–381 (2020).CAS 

Google Scholar 
60.Streck, C. REDD+ and leakage: debunking myths and promoting integrated solutions. Clim. Policy 21, 843–852 (2021).
Google Scholar 
61.Brancalion, P. H. S. et al. The cost of restoring carbon stocks in Brazil’s Atlantic Forest. Land Degrad. Dev. 32, 830–841 (2021).
Google Scholar 
62.Bustamante-Sánchez, M. A. & Armesto, J. J. Seed limitation during early forest succession in a rural landscape on Chiloé Island, Chile: implications for temperate forest restoration. J. Appl. Ecol. 49, 1103–1112 (2012).
Google Scholar 
63.Koch, A., Brierley, C. & Lewis, S. L. Effects of Earth system feedbacks on the potential mitigation of large-scale tropical forest restoration. Biogeosciences 18, 2627–2647 (2021).CAS 

Google Scholar 
64.Zickfeld, K., Azevedo, D., Mathesius, S. & Matthews, H. D. Asymmetry in the climate–carbon cycle response to positive and negative CO2 emissions. Nat. Clim. Change 11, 613–617 (2021).CAS 

Google Scholar 
65.Johnson, K. A. et al. A benefit–cost analysis of floodplain land acquisition for US flood damage reduction. Nat. Sustain. 3, 56–62 (2020).
Google Scholar 
66.Nolte, C. High-resolution land value maps reveal underestimation of conservation costs in the United States. Proc. Natl Acad. Sci. USA 117, 29577–29583 (2020).CAS 

Google Scholar 
67.Reetz, H., Heffer, P. & Bruulsema, T. in Managing Water and Fertilizer for Sustainable Agricultural Intensification (eds Drechsel, P. et al.) 65–86 (IFA, IWMI, IPNI and IPI, 2015).68.Sharma, P. et al. The role of cover crops towards sustainable soil health and agriculture—a review paper. Am. J. Plant Sci. 09, 1935–1951 (2018).CAS 

Google Scholar 
69.Bergeron, M. et al. Reduced soil nutrient leaching following the establishment of tree-based intercropping systems in eastern Canada. Agrofor. Syst. 83, 321–330 (2011).
Google Scholar 
70.Moore, A. A. & Palmer, M. A. Invertebrate biodiveristy in agricultural and urban headwater streams: implications for conservation and management. Ecol. Appl. 15, 1169–1177 (2005).
Google Scholar 
71.Martin, M. P. et al. People plant trees for utility more often than for biodiversity or carbon. Biol. Conserv. 261, 109224 (2021).
Google Scholar 
72.Mendes, T. P., de Assis Montag, L. F., Alvarado, S. T. & Juen, L. Assessing habitat quality on alpha and beta diversity of Odonata larvae (Insect) in logging areas in Amazon forest. Hydrobiologia 848, 1147–1161 (2021).
Google Scholar 
73.Crouzeilles, R. et al. Achieving cost-effective landscape-scale forest restoration through targeted natural regeneration. Conserv. Lett. 13, e12709 (2020).
Google Scholar 
74.Gilroy, J. J. et al. Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nat. Clim. Change 4, 503–507 (2014).
Google Scholar 
75.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Google Scholar 
76.Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).
Google Scholar 
77.Taillardat, P., Thompson, B. S., Garneau, M., Trottier, K. & Friess, D. A. Climate change mitigation potential of wetlands and the cost-effectiveness of their restoration. Interface Focus 10, 20190129 (2020).
Google Scholar 
78.Xu, S., Liu, X., Li, X. & Tian, C. Soil organic carbon changes following wetland restoration: a global meta-analysis. Geoderma 353, 89–96 (2019).CAS 

Google Scholar 
79.Holl, K. D. & Brancalion, P. H. S. Tree planting is not a simple solution. Science 368, 580–581 (2020).CAS 

Google Scholar 
80.Kroeger, T., McDonald, R. I., Boucher, T., Zhang, P. & Wang, L. Where the people are: current trends and future potential targeted investments in urban trees for PM10 and temperature mitigation in 27 U.S. cities. Landsc. Urban Plan. 177, 277–240 (2018).
Google Scholar 
81.McDonald, R. I., Kroeger, T., Zhang, P. & Hamel, P. The value of US urban tree cover for reducing heat-related health impacts and electricity consumption. Ecosystems 23, 137–150 (2020).
Google Scholar 
82.Heris, M. et al. Piloting urban ecosystem accounting for the United States. Ecosyst. Serv. 48, 101226 (2021).
Google Scholar 
83.Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).
Google Scholar 
84.Li, R. et al. Time and space catch up with restoration programs that ignore ecosystem service trade-offs. Sci. Adv. 7, eabf8650 (2021).
Google Scholar 
85.Gaveau, D. L. A. et al. Rise and fall of forest loss and industrial plantations in Borneo (2000–2017). Conserv. Lett. 12, e12622 (2019).
Google Scholar 
86.Griscom, B. W., Goodman, R. C., Burivalova, Z. & Putz, F. E. Carbon and biodiversity impacts of intensive versus extensive tropical forestry. Conserv. Lett. 11, e12362 (2018).
Google Scholar 
87.Gabon’s Proposed National RED+ Forest Reference Level (Gabonese Republic, 2021).88.Umunay, P., Gregoire, T., Gopalakrishna, T., Ellis, P. & Putz, F. Selective logging emissions and potential emission reductions from reduced-impact logging in the Congo Basin. Ecol. Manag. 437, 360–371 (2019).
Google Scholar 
89.Natural Climate Solutions World Atlas (Nature4Climate, accessed 9 December 2020); https://nature4climate.org/n4c-mapper/90.Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2019); https://doi.org/10.2305/IUCN.CH.2019.06.en91.Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).
Google Scholar 
92.Seymour, F. Seeing the forests as well as the (trillion) trees in corporate climate strategies. One Earth 2, 390–393 (2020).
Google Scholar 
93.Kronenberg, J. & Mieszkowicz, J. Planting trees for publicity—how much are they worth? Sustainability 3, 1022–1034 (2011).
Google Scholar 
94.Microsoft Carbon Removal: Lessons from an Early Corporate Purchase (Microsoft, 2021).95.Toor, I. A., Smith, E. G., Whalen, J. K. & Naseem, A. Tree-based intercropping in southern Ontario, Canada. Can. J. Agric. Econ. 60, 141–154 (2012).
Google Scholar 
96.Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 

Google Scholar 
97.zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).
Google Scholar 
98.Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015). More

1.The State of Food Insecurity in the World—How Does International Price Volatility Affect Domestic Economies and Food Security? (FAO, 2011).2.Catchpoole, D. W. & Blair, G. Forage tree legumes I. Productivity and N economy of leucaena, gliricidia, calliandra and sesbania and tree/green panic mixtures. Aust. J. Agric. Res 41, 521–530 (1990).CAS 
Article 

Google Scholar 
3.Xu, Z. H., Saffigna, P. G., Myers, R. J. K. & Chapman, A. L. Nitrogen cycling in leucaena (Leucaena lecuocephala) alley cropping in semiarid tropics. 1. Mineralization of nitrogen from leucaena residues. Plant Soil 148, 63–72 (1993).CAS 
Article 

Google Scholar 
4.Beer, J., Muschler, R., Kass, D. & Somarriba, E. Shade management in coffee and cacao plantations. Agrofor. Syst. 38, 139–164 (1998).Article 

Google Scholar 
5.Snoeck, D., Zapata, F. & Domenach, A.-M. Isotopic evidence of the transfer of nitrogen fixed by legumes to coffee trees. Biotechnol. Agron. Soc. Environ. 4, 95–100 (2000).CAS 

Google Scholar 
6.Sierra, J. & Nygren, P. Transfer of N fixed by a legume tree to the associated grass in a tropical silvopastoral system. Soil Biol. Biochem. 38, 1893–1903 (2006).CAS 
Article 

Google Scholar 
7.He, X. H., Critchley, C. & Bledsoe, C. Nitrogen transfer within and between plants through common mycorrhizal networks (CMNs). Crit. Rev. Plant Sci. 22, 531–567 (2003).Article 

Google Scholar 
8.Jalonen, R., Nygren, P. & Sierra, J. Transfer of nitrogen from a tropical legume tree to an associated fodder grass via root exudation and common mycelial networks. Plant Cell Environ. 32, 1366–1376 (2009).CAS 
Article 

Google Scholar 
9.Brundrett, M. C. & Tedersoo, L. Evolutionary history of mycorrhizal symbioses and global host plant diversity. New Phytol. 220, 1108–1115 (2018).Article 

Google Scholar 
10.Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn (Academic Press, 2008).11.Giovannetti, M., Sbrana, C., Avio, L. & Strani, P. Patterns of below-ground plant interconnections established by means of arbuscular mycorrhizal networks. New Phytol. 164, 175–181 (2004).Article 

Google Scholar 
12.Newman, E. I. & Ritz, K. Evidence on the pathways of phosphorus transfer between vesicular–arbuscular mycorrhizal plants. New Phytol. 104, 77–87 (1986).CAS 
Article 

Google Scholar 
13.Mikkelsen, B. L., Rosendahl, S. & Jakobsen, I. Underground resource allocation between individual networks of mycorrhizal fungi. New Phytol. 4, 890–898 (2008).Article 

Google Scholar 
14.Saka, A. R., Bunderson, W. T., Itimu, O. A., Phombeya, H. S. K. & Mbekeani, Y. The effects of Acacia albida on soils and maize grain yields under smallholder farm conditions in Malawi. For. Ecol. Manage. 64, 217–230 (1994).Article 

Google Scholar 
15.Rhoades, C. Seasonal pattern of nitrogen mineralization and soil moisture beneath Faidherbia albida (syn Acacia albida) in central Malawi. Agrofor. Syst. 29, 133–145 (1995).Article 

Google Scholar 
16.Sileshi, G. W. et al. in Encyclopedia of Agriculture and Food Systems (ed. van Alfen, N.) 222–234 (Elsevier, 2014).17.Yengwe, J., Gebremikael, M. T., Buchan, D., Lungu, O. & De Neve, S. Effects of Faidherbia albida canopy and leaf litter on soil microbial communities and nitrogen mineralization in selected Zambian soils. Agrofor. Syst. 92, 349–363 (2018).
Google Scholar 
18.Yengwe, J., Amalia, O., Lungu, O. I. & De Neve, S. Quantifying nutrient deposition and yield levels of maize (Zea mays) under Faidherbia albida agroforestry system in Zambia. Eur. J. Agron. 99, 148–155 (2018).CAS 
Article 

Google Scholar 
19.Sida, T. S., Baudron, F., Ndoli, A., Tirfessa, D. & Giller, K. E. Should fertilizer recommendations be adapted to parkland agroforestry systems? Case studies from Ethiopia and Rwanda. Plant Soil 453, 173–188 (2020).CAS 
Article 

Google Scholar 
20.Umar, B. B., Aune, J. B. & Lungu, O. I. Effects of Faidherbia albida on the fertility of soil in smallholder conservation agriculture systems in eastern and southern Zambia. Afr. J. Agric. Res. 8, 173–183 (2013).
Google Scholar 
21.Hadgu, K. M., Kooistra, L., Rossing, W. A. H. & van Bruggen, A. H. C. Assessing the effect of Faidherbia albida based land use systems on barley yield at field and regional scale in the highlands of Tigray, Northern Ethiopia. Food Security 1, 337–350 (2009).Article 

Google Scholar 
22.Dalpé, Y., Diop, T. A., Plenchette, C. & Gueye, M. Glomales species associated with surface and deep rhizosphere of Faidherbia albida in Senegal. Mycorrhiza 10, 125–129 (2000).Article 

Google Scholar 
23.Boddey, R. M., Peoples, M. B., Palmer, B. & Dart, P. J. Use of the 15N natural abundance technique to quantify biological nitrogen fixation by woody perennials. Nutr. Cycl. Agroecosyst. 57, 235–270 (2000).Article 

Google Scholar 
24.Oberson, A. et al. Symbiotic N2 fixation by soybean in organic and conventional cropping systems estimated by 15N dilution and 15N natural abundance. Plant Soil 290, 69–83 (2007).CAS 
Article 

Google Scholar 
25.Snapp, S., Borden, H. & Rohrbach, D. Improving nitrogen efficiency: lessons from Malawi and Michigan. Sci. World 1, 42–48 (2001).
Google Scholar 
26.Akinnifesi, F. K., Wakumba, W. & Kwesiga, F. R. Sustainable maize production using gliricidia/maize intercropping in southern Malawi. Exp. Agric. 42, 441–457 (2006).Article 

Google Scholar 
27.Tovihoudji, P. G., Irenikatché Akponikpè, P. B., Agbossou, E. K., Bertin, P. & Bielders, C. L. Fertilizer microdosing enhances maize yields but may exacerbate nutrient mining in maize cropping systems in northern Benin. Field Crops Res. 213, 130–142 (2017).Article 

Google Scholar 
28.Hill, P. W. et al. Angiosperm symbioses with non-mycorrhizal fungal partners enhance N acquisition from ancient organic matter in a warming maritime Antarctic. Ecol. Lett. 22, 2111–2119 (2019).Article 

Google Scholar 
29.Bueno de Mesquita, C. P. et al. Patterns of root colonization by arbuscular mycorrhizal fungi and dark septate endophytes across a mostly-unvegetated, high-elevation landscape. Fungal Ecol. 36, 63–74 (2018).Article 

Google Scholar 
30.Alexandre, D. Y. & Ouedraogo, S. J. in Faidherbia albida in the West African Semi-arid Tropics: Proceedings of a Workshop (ed. Vandenbeldt, R. J.) 107–110 (International Centre for Research in Agroforestry, 1992).31.Jones, A. et al. Soil Atlas of Africa (European Commission, 2013).32.Dierks, J. et al. Trees enhance abundance of arbuscular mycorrhizal fungi, soil structure, and nutrient retention in low-input maize cropping systems. Agric. Ecosyst. Environ. 318, 107487 (2021).CAS 
Article 

Google Scholar 
33.Mungai, L. M. et al. Smallholder farms and the potential for sustainable intensification. Front. Plant Sci. https://doi.org/10.3389/fpls.2016.01720 (2016).34.Smith, S. E. & Smith, F. A. Roles of arbuscular mycorrhizas in plant nutrition and growth: new paradigms from cellular to ecosystem scales. Annu. Rev. Plant Biol. 62, 227–250 (2011).CAS 
Article 

Google Scholar 
35.Marschner, H. & Dell, B. Nutrient uptake in mycorrhizal symbiosis. Plant Soil 159, 89–102 (1994).CAS 
Article 

Google Scholar 
36.Gryndler, M. et al. Appropriate nonmycorrhizal controls in arbuscular mycorrhiza research: a microbiome perspective. Mycorrhiza 28, 435–450 (2018).Article 

Google Scholar 
37.Rillig, M. C. et al. Suitability of mycorrhiza-defective mutant/wildtype plant pairs (Solanum lycopersicum L. cv Micro-Tom) to address questions in mycorrhizal soil ecology. Plant Soil 308, 267–275 (2008).CAS 
Article 

Google Scholar 
38.Koide, R. T. & Li, M. Appropriate controls for vesicular–arbuscular mycorrhiza research. New Phytol. 111, 35–44 (1989).Article 

Google Scholar 
39.Fitter, A. H. & Nichols, R. The use of benomyl to control infection by vesicular–arbuscular mycorrhizal fungi. New Phytol. 110, 201–206 (1988).CAS 
Article 

Google Scholar 
40.Cavagnaro, T. R., Smith, F. A. & Smith, S. E. Interactions between arbuscular mycorrhizal fungi and a mycorrhiza-defective mutant tomato: does a noninfective fungus alter the ability of an infective fungus to colonise the roots—and vice versa? New Phytol. 164, 485–491 (2004).Article 

Google Scholar 
41.Carey, P. D., Fitter, A. H. & Watkinson, A. R. A field study using the fungicide benomyl to investigate the effect of mycorrhizal fungi on plant fitness. Oecologia 90, 550–555 (1992).Article 

Google Scholar 
42.Merryweather, J. & Fitter, A. Phosphorus nutrition of an obligately mycorrhizal plant treated with the fungicide benomyl in the field. New Phytol. 132, 307–311 (1996).CAS 
Article 

Google Scholar 
43.Shinners, K. J. & Binversie, B. N. Fractional yield and moisture of corn stover biomass produced in northern US Corn Belt. Biomass Bioenergy 31, 576–584 (2007).Article 

Google Scholar 
44.R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); https://www.R-project.org/45.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002). More

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1.Alcolombri, U. et al. Nat. Geosci. 14, 775–780 (2021).CAS 
Article 

Google Scholar 
2.Briggs, N., Dall’Olmo, G. & Claustre, H. Science 367, 791–793 (2020).CAS 
Article 

Google Scholar 
3.Azam, F. Science 280, 694–696 (1998).CAS 
Article 

Google Scholar 
4.Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Nature 568, 327–335 (2019).CAS 
Article 

Google Scholar 
5.Steinberg, D. K. et al. Limnol. Oceanogr. 53, 1327–1338 (2008).Article 

Google Scholar 
6.Lampitt, R. S., Wishner, K. F., Turley, C. M. & Angel, M. V. Mar. Biol. 116, 689–702 (1993).Article 

Google Scholar 
7.Mende, D. R. et al. Nat. Microbiol. 2, 1367–1373 (2017).CAS 
Article 

Google Scholar 
8.Enke, T. N., Leventhal, G. E., Metzger, M., Saavedra, J. T. & Cordero, O. X. Nat. Commun. 9, 2743 (2018).Article 

Google Scholar 
9.Trull, T. W. et al. 55, 1684–1695 (2008).10.Bressac, M. et al. Nat. Geosci. 12, 995–1000 (2019).CAS 
Article 

Google Scholar  More

Acrylic and polydimethylsiloxane (PDMS) molds preparationThe incubating device for the porous microplate was designed using a CAD software (Solidworks, Dassault Systèmes) and the exported drawing files were used to laser cut 1/4” and 1/8” acrylic sheet (Universal Laser Systems; Supplementary Fig. S2). After washing the cut acrylic parts with deionized water, they were attached by acrylic (Weld-On) and epoxy (3 M) adhesives that were followed by a curing process for ~18 h. Polydimethylsiloxane (PDMS) (Sylgard 184, Dow Corning) was cast onto the acrylic mold and cured at 80 °C for at least 3 h. The PDMS mold was carefully detached from the acrylic surface by dispensing isopropyl alcohol (VWR) into the area between the PDMS and the acrylic molds (Fig. 2a).Fig. 2: Synthesis and characterization of porous microplate.a Procedure to build a porous microplate using polydimethylsiloxane (PDMS) and acrylic molds. b Image of the microplate with an array of culture wells (wall thickness: 0.9 mm). c Scanning electron microscopy image of nanoporous copolymer HEMA–EDMA.Full size imagePorous microplate preparationSynthesis of copolymer HEMA–EDMA was based on previously described protocols [30, 31] and details are given as follows. Prepolymer solution HEMA − EDMA was prepared by mixing 2-hydroxyethyl methacrylate (HEMA; monomer, 24 wt.%, Sigma-Aldrich), ethylene glycol dimethacrylate (EDMA; crosslinker, 16 wt.%, Sigma-Aldrich), 1-decanol (porogen, 12 wt.%, Sigma-Aldrich), cyclohexanol (porogen, 48 wt.%, Sigma-Aldrich) and 2,2-dimethoxy-2-phenylacetophenone (DMPAP; photoinitiator, 1 wt.%). The solution was stored at room temperature without light exposure until further use. Glass slides (75 × 50 mm2, VWR) were chemically cleaned by sequentially soaking in 1 M hydrochloric acid and 1 M sodium hydroxide for one hour, followed by rinsing with deionized water and air drying. The prepolymer solution was cast onto the PDMS mold and a glass slide was placed on the mold. The solution was then polymerized under ultraviolet light with a wavelength 365 nm for 15 min by using a commercial UV lamp (VWR). The photopolymerized device was detached from the PDMS mold and stored in a jar containing methanol (VWR) until further use (Fig. 2a). The jar was refilled with new methanol twice in order to remove the remaining porogen and uncrosslinked monomers from the hydrogel.Upon each incubation experiment with the porous microplate, each device was decontaminated by replacing the solvent with 70% alcohol (VWR) and storing it for 24 h. They were immersed in a pre-autoclaved jar for two weeks with f/2 medium with omitted silicate, where the jar was refilled once with a new sterile medium to adjust its pH for the algal culture and remove any solvent remaining in the hydrogel. Before inoculating microbial cells, each microplate was taken out from the jar and the media remaining on the top surface was removed by absorbing it with a pre-sterilized wipe to minimize the chance for cross-contamination between wells (Fig. 2b).Scanning electron microscopyPhotopolymerized HEMA − EDMA was removed from methanol and dried in air for at least one week to evaporate the excess solvent. A ~5 × 5 mm2 specimen was collected from the dried copolymer and attached to a pin stub. The stub was loaded on a scanning electron microscope (SEM; MERLIN, Carl Zeiss), and the specimen was characterized with imaging software (SmartSEM, Carl Zeiss) with 16,270X magnification and an operating voltage of 1 kV. The SEM imaging was performed at the Electron Microscopy Facility in the MIT Materials Research Science and Engineering Centers (MRSEC; Fig. 2c).Strains and culturing conditionsAxenic P. tricornutum CCMP 2561 was acquired from the National Center for Marine Algae and Microbiota (NCMA) and shown to be axenic via epifluorescence microscopy and sequencing of the 16 S rRNA gene [11]. P. tricornutum was maintained in f/2 medium with 20 g L−1 commercially available sea salts (Instant Ocean, Blacksburg) and with omitted silicate, which we will refer to as f/2-Si [11, 16]. Batch cultures were grown at 20 °C with a 12 h light/12 h dark diurnal cycle and a light intensity of 200 μmol photons m−2 s−1 (Exlenvce). Every 2–3 weeks, axenic cultures were monitored for bacterial contamination by streaking culture samples on marine broth agar [33], that tests for contamination by bacteria that can grow on agar media and is not definitive. Every 6–12 months, every axenic and bacterial co-culture of P. tricornutum was inspected for the absence/presence of bacteria by staining the cellular DNA with 0.1% v/v SYTO BC Green Fluorescent Acid Stain (Thermofisher, Supplementary Fig. S1).Bacterial community samples (referred to as “phycosphere enrichments”) were obtained from mesocosms of P. tricornutum and maintained as previously described [11, 16]. Briefly, an outdoor P. tricornutum mesocosm sample in natural seawater was collected in Corpus Christi, TX and filtered with 0.6–1 µm pores to remove larger algal cells. The bacterial filtrates were inoculated to an axenic algal culture, maintained in f/2-Si media for ~3 months, and washed with a sterile medium to enrich for phycosphere-associated bacteria. These enriched communities were subsequently co-cultured with P. tricornutum in f/2-Si media for ~4 years prior to the start of the experiments.Two bacterial strains, Marinobacter sp. 3-2 and Algoriphagus sp. ARW1R1, were isolated from the phycosphere enrichment samples (Supplementary Table S1). The isolates were either maintained by growing on marine broth agar plates at 30 °C or by co-culturing with P. tricornutum through inoculation of a single colony into the axenic culture.
P. tricornutum culture in porous microplateThree baseline experiments were designed to study how the alga P. tricornutum interacts with its associated bacteria in the porous microplate (Fig. 1). For experiments assessing the algal growth in the microplate, axenic P. tricornutum was acclimated to a copolymer environment in advance by inoculating a stationary phase-culture to a separate microplate. After acclimation for 4 days, the culture was diluted to ~1 × 106 cells ml−1 and transferred to the experimental microplate. Three replicated microplates were placed in a single transparent covered container (128 × 85 × 10 mm3, VWR) which was filled with ~25 ml f/2-Si medium to keep the microplate hydrated throughout the incubation period of 20 days with an initial culture volume of 75 µl (Fig. 1a). The procedures were conducted under a biosafety cabinet to prevent any biological contamination. The cells were incubated under the same conditions as described above for the batch cultures (temperature, light intensity, diurnal cycle).Growth of P. tricornutum was measured by counting cells using a hemocytometer (Electron Microscopy Sciences) or flow cytometry (described later). Specific growth rates were calculated from the natural log of the cell densities in triplicate sampled during an exponential growth phase (day 3 for the batch culture, day 5 for the porous microplate system; Fig. 3a).Fig. 3: Cultivation of P. tricornutum in the porous microplate.a Schematic of a microplate for algal cultivation. b Growth curve and maximal growth rate (inset) comparing the porous microplate with flask culture. Error bars, standard deviation of triplicates. c Cell abundance at center (n = 3) and surrounding (n = 18) wells after incubation. Asterisks denote statistical differences with following levels (two-tailed t-test): ***P  More