Philippa Kaur

Phakamani M’Afrika Xaba speaks at a botanical workshop.Credit: Nong Nooch/Tropical Botanical Garden

For Black communities in today’s South Africa, the legacies of colonialism and apartheid still prevail, shaping social structure and limiting access to opportunities. Colonialism displaced Black South Africans from the mid-seventeenth century, eroding cultural and social systems.From the 1950s, apartheid legalized systematic racial discrimination against disenfranchised, mainly Black, people. It limited their economic opportunities and social standing, prescribing an inferior education system to deliberately shape a poor quality of life. The policy fuelled systemic sexism, sexual-orientation discrimination, ageism, and the use of ethnicity as a divide-and-conquer strategy.Seventy years later, even after more than 25 years of democracy following the end of apartheid in 1994, schools and suburbs are still predominantly segregated, with government funding unevenly allocated in terms of facilities and quality of education.Former South African president Nelson Mandela once said, “In Africa there is a concept known as ubuntu — the profound sense that we are human only through the humanity of others; that if we are to accomplish anything in this world, it will in equal measure be due to the work and achievement of others.”As three past and present employees of the South African National Biodiversity Institute (SANBI), a conservation organization founded in 2004 to manage the country’s biodiversity resources, we have been advocating for a culture of treating others in the way we want to be treated: by applying universal shared human values, redefining institutional culture and systems to be inclusive, and opening safe spaces for a diversity of ideas. We have proposed a ground-up approach that aims to focus on holistic transformation at different levels in our organization.Our approach was to initiate a platform to identify inclusivity challenges, foster awareness and collaboration among staff and collectively develop innovative ideas and solutions. These would be aligned to existing organizational values, such as ubuntu, growth, respect and tolerance, excellence, accountability and togetherness. We strive to bring about institutional cultural change through facilitated, constructive conversations, by strengthening connections and cohesion among staff and through creative and proactive problem-solving across our institution.Mentorship that thrivesInstitutional culture needs to enable successful mentoring by creating a safe space. For example, SANBI’s mentoring programme for interns, students and early-career scientists involves quarterly meetings between them and dedicated human-resources staff — check-ins that provide a space to engage with programme coordinators without early-career researchers’ supervisors being present. In addition to sharing feedback on institutional policies and procedures, early-career scientists have the opportunity to discuss challenges they might face because of their supervisor or work placement. When issues are identified early, transfers or exchanges between work programmes can be arranged.Every year, we each sign up to mentor junior researchers to provide a supportive environment for guidance, counselling and the transfer of skills. We develop structured workplans with specific goals and outputs, and we discuss expectations with our protégés. In addition, we offer shared workspaces for interns and encourage peer learning, so that interns can form a peer support network. In these relationships, trust is crucial and can be a gateway to broader professional and personal networks.

Early-career researchers doing fieldwork training at the Stellenbosch University Experimental Farms in South Africa.Credit: Tlou Masehela

Institutions should recruit outside of their walls, if necessary, to ensure that appropriately skilled mentors are paired with early-career researchers. For mentorship to thrive, institutions must also create an enabling environment. In non-supportive environments, staff — particularly those from under-represented groups — who remain inadequately skilled and work without guidance become frustrated. Some can even feel they don’t belong because they see themselves as lagging behind their peers.Institutions often focus too strongly on outputs — such as publications, products or technologies — at the expense of reflecting on the values that uphold the institution. These values might be outdated and out of touch with those of staff, or with those held by partners, stakeholders or society at large. If staff cannot relate to the institutional culture and systems, job satisfaction and retention rates can suffer.Until a few years ago, for example, venues at our organization were named after former staff, as a way of acknowledging their contributions. Inevitably, these were mostly white, male, senior staff, such as Harold Pearson, the first director of Kirstenbosch National Botanical Garden, and Brian Rycroft, who served as director in the 1950s. But the contributions of staff who were employed in junior positions for 20–30 years also needed to be acknowledged. After an outcry around 2014, then-chief-executive Tanya Abrahamse, the first Black woman to hold the post, decided to acknowledge contributions of staff no matter their position. As a result, we now have Richard Crowie Hall, an exhibition space named after one of SANBI’s longest-serving staff members.The protracted legacy of apartheid in South Africa means that if institutional implicit biases are left unaddressed, they can create a fertile ground for racial, ethnic, tribal, financial and gender tensions. We urge more institutional recognition of the contributions of all.Fostering safe spacesThrough our engagements with each other, we have discovered a set of shared values, aligned with those of our institution, and have set out to establish a space to build our vision of a supportive, safe environment based on these values. Safe spaces are required for expressing controversial or uncomfortable views and to do the hard work of finding solutions to inequities. Confidentiality and trust cultivate such safe spaces, which can be created initially in small groups, then expanded to constructive formal or informal spaces. The conversations and suggestions of informal discussion groups about staff development and transformation can be elevated to management for implementation.
Decolonizing science toolkit
Safe spaces are a necessity for institutions that wish to truly address their legacies of racism and colonialism. Policies alone will not create these spaces — they require dedicated staff, too. Such spaces should ensure that those who speak up can do so without fear of being labelled as troublemakers or victimized.As a first step in pursuing this vision, we met with the senior teams at our organization to share ideas around the need for and benefits of an inclusive culture. We highlighted that inclusivity improves work–life balance, productivity and mental well-being for all employees.Any change, transformative or otherwise, is a process that takes perseverance, patience and determination. For any individual scientist to grow and flourish, they need a supportive environment, rich mentorship, a safe space and an enabling culture. It’s time for those factors to apply to all scientists equitably, no matter their gender, race, ethnicity or tribe. By fostering this mindset, we aim to reframe the narrative of our history and, in doing so, give all South African scientists their chance to thrive. More

Austin, K. G. et al. Land Use Policy 69, 41–48 (2017).Article 

Google Scholar 
Busch, J. et al. Environ. Res. Lett. 17, 014035 (2022).Article 
CAS 

Google Scholar 
Fleiss, S. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01941-6 (2022).Qaim, M. et al. Annu. Rev. Resour. Econ. 12, 321–344 (2020).Article 

Google Scholar 
Haupt, F. et al. Progress on Corporate Commitments and their Implementation (Tropical Forest Alliance, 2018).Brooks, T. et al. Nat. Ecol. Evol. 1, 0099 (2017).Article 

Google Scholar 
Buisson, E. et al. Biol. Rev. 94, 590–609 (2019).Article 
PubMed 

Google Scholar 
López-Ricaurte, L. et al. Biol. Conserv. 213, 225–233 (2017).Article 

Google Scholar 
Furumo, P. R. & Aide, T. M. Environ. Res. Lett. 12, 024008 (2017).Article 

Google Scholar 
RTRS Standard for Responsible Soy Production Version 3.1 (RTRS, 2017). More

Extent and location of critical natural assetsCritical natural assets providing the 12 local NCP (Fig. 1a) occupy only 30% (41 million km2) of total land area (excluding Antarctica) and 24% (34 million km2) of marine Exclusive Economic Zones (EEZs), reflecting the steep slope of the aggregate NCP accumulation curve (Fig. 1b). Despite this modest proportion of global land area, the shares of countries’ land areas that are designated as critical can vary substantially. The 20 largest countries require only 24% of their land area, on average, to maintain 90% of current levels of NCP, while smaller countries (10,000 to 1.5 million km2) require on average 40% of their land area (Supplementary Data 1). This high variability in the NCP–area relationship is primarily driven by the proportion of countries’ land areas made up by natural assets (that is, excluding barren, ice and snow, and developed lands), but even when this is accounted for, there are outliers (Extended Data Fig. 2). Outliers may be due to spatial patterns in human population density (for example, countries with dense population centres and vast expanses with few people, such as Canada and Russia, require far less area to achieve NCP targets) or large ecosystem heterogeneity (if greater ecosystem diversity yields higher levels of diverse NCP in a smaller proportion of area, which may explain patterns in Chile and Australia).The highest-value critical natural assets (the locations delivering the highest magnitudes of NCP in the smallest area, denoted by the darkest blue or green shades in Fig. 1c) often coincide with diverse, relatively intact natural areas near or upstream from large numbers of people. Many of these high-value areas coincide with areas of greatest spatial congruence among multiple NCP (Extended Data Fig. 3). Spatially correlated pairs of local NCP (Supplementary Table 4) include those related to water (flood risk reduction with nitrogen retention and nitrogen with sediment retention); forest products (timber and fuelwood); and those occurring closer to human-modified habitats (pollination with nature access and with nitrogen retention). Coastal risk reduction, forage production for grazing, and riverine fish harvest are the most spatially distinct from other local NCP. In the marine realm, there is substantial overlap of fisheries with coastal risk reduction and reef tourism (though not between the latter two, which each have much smaller critical areas than exist for fisheries).Number of people benefitting from critical natural assetsWe estimate that ~87% of the world’s current population, 6.4 billion people, benefit directly from at least one of the 12 local NCP provided by critical natural assets, while only 16% live on the lands providing these benefits (and they may also benefit; Fig. 2a). To quantify the number of beneficiaries of critical natural assets, we spatially delineate their benefitting areas (which varies on the basis of NCP: for example, areas downstream, within the floodplain, in low-lying areas near the coast, or accessible by a short travel). While our optimization selects for the provision of 90% of the current value of each NCP, it is not guaranteed that 90% of the world’s population would benefit (since it does not include considerations for redundancy in adjacent pixels and therefore many of the areas selected benefit the same populations), so it is notable that an estimated 87% do. This estimate of ‘local’ beneficiaries probably underestimates the total number of people benefitting because it includes only NCP for which beneficiaries can be spatially delineated to avoid double-counting, yet it is striking that the vast majority, 6.1 billion people, live within 1 h travel (by road, rail, boat or foot, taking the fastest path17) of critical natural assets, and more than half of the world’s population lives downstream of these areas (Fig. 2b). Material NCP are often delivered locally, but many also enter global supply chains, making it difficult to delineate beneficiaries spatially for these NCP. However, past studies have calculated that globally more than 54 million people benefit directly from the timber industry18, 157 million from riverine fisheries19, 565 million from marine fisheries20 and 1.3 billion from livestock grazing21, and across the tropics alone 2.7 billion are estimated to be dependent on nature for one or more basic needs22.Fig. 2: People benefitting from and living on critical natural assets (CNA).a,b, ‘Local’ beneficiaries were calculated through the intersection of areas benefitting from different NCP, to avoid double-counting people in areas of overlap; only those NCP for which beneficiaries could be spatially delineated were included (that is, not material NCP that enter global supply chains: fisheries, timber, livestock or crop pollination). Bars show percentages of total population globally and for large and small countries (a) or the percentage of relevant population globally (b). Numbers inset in bars show millions of people making up that percentage. Numbers to the right of bars in b show total relevant population (in millions of people, equivalent to total global population from Landscan 2017 for population within 1 h travel or downstream, but limited to the total population living within 10 km of floodplains or along coastlines 80%) of their populations benefitting from critical natural assets, but small countries have much larger proportions of their populations living within the footprint of critical natural assets than do large countries (Fig. 2a and Supplementary Data 2). When people live in these areas, and especially when current levels of use of natural assets are not sustainable, regulations or incentives may be needed to maintain the benefits these assets provide. While protected areas are an important conservation strategy, they represent only 15% of the critical natural assets for local NCP (Supplementary Table 5); additional areas should not necessarily be protected using designations that restrict human access and use, or they could cease to provide some of the diverse values that make them so critical23. Other area-based conservation measures, such as those based on Indigenous and local communities’ governance systems, Payments for Ecosystem Services programmes, and sustainable use of land- and seascapes, can all contribute to maintaining critical flows of NCP in natural and semi-natural ecosystems24.Overlaps between local and global prioritiesUnlike the 12 local NCP prioritized here at the national scale, certain benefits of natural assets accrue continentally or even globally. We therefore optimize two additional NCP at a global scale: vulnerable terrestrial ecosystem carbon storage (that is, the amount of total ecosystem carbon lost in a typical disturbance event25, hereafter ‘ecosystem carbon’) and vegetation-regulated atmospheric moisture recycling (the supply of atmospheric moisture and precipitation sustained by plant life26, hereafter ‘moisture recycling’). Over 80% of the natural asset locations identified as critical for the 12 local NCP are also critical for the two global NCP (Fig. 3). The spatial overlap between critical natural assets for local and global NCP accounts for 24% of land area, with an additional 14% of land area critical for global NCP that is not considered critical for local NCP (Extended Data Fig. 4). Together, critical natural assets for securing both local and global NCP require 44% of total global land area. When each NCP is optimized individually (carbon and moisture NCP at the global scale; the other 12 at the country scale), the overlap between carbon or moisture NCP and the other NCP exceeds 50% for all terrestrial (and freshwater) NCP except coastal risk reduction (which overlaps only 36% with ecosystem carbon, 5% with moisture recycling; Supplementary Table 4).Fig. 3: Spatial overlaps between critical natural assets for local and global NCP.Red and teal denote where critical natural assets for global NCP (providing 90% of ecosystem carbon and moisture recycling globally) or for local NCP (providing 90% of the 12 NCP listed in Fig. 1), respectively, but not both, occur; gold shows areas where the two overlap (24% of the total area). Together, local and global critical natural assets account for 44% of total global land area (excluding Antarctica). Grey areas show natural assets not defined as ‘critical’ by this analysis, though still providing some values to certain populations. White areas were excluded from the optimization.Full size imageSynergies can also be found between NCP and biodiversity and cultural diversity. Critical natural assets for local NCP at national levels overlap with part or all of the area of habitat (AOH, mapped on the basis of species range maps, habitat preferences and elevation27) for 60% of 28,177 terrestrial vertebrates (Supplementary Data 3). Birds (73%) and mammals (66%) are better represented than reptiles and amphibians (44%). However, these critical natural assets represent only 34% of the area for endemic vertebrate species (with 100% of their AOH located within a given country; Supplementary Data 3) and 16% of the area for all vertebrates if using a more conservative representation target framework based on the IUCN Red List criteria (though, notably, achieving Red List representation targets is impossible for 24% of species without restoration or other expansion of existing AOH; Supplementary Data 4). Cultural diversity (proxied by linguistic diversity) has far higher overlaps with critical natural assets than does biodiversity; these areas intersect 96% of global Indigenous and non-migrant languages28 (Supplementary Data 5). The degree to which languages are represented in association with critical natural assets is consistent across most countries, even at the high end of language diversity (countries containing >100 Indigenous and non-migrant languages, such as Indonesia, Nigeria and India). This high correspondence provides further support for the importance of safeguarding rights to access critical natural assets, especially for Indigenous cultures that benefit from and help maintain them. Despite the larger land area required for maintaining the global NCP compared with local NCP, global NCP priority areas overlap with slightly fewer languages (92%) and with only 2% more species (60% of species AOH), although a substantially greater overlap is seen with global NCP if Red List criteria are considered (36% compared with 16% for local NCP; Supplementary Data 4). These results provide different insights than previous efforts at smaller scales, particularly a similar exercise in Europe that found less overlap with priority areas for biodiversity and NCP29. However, the 40% of all vertebrate species whose habitats did not overlap with critical natural assets could drive very different patterns if biodiversity were included in the optimization.Although these 14 NCP are not comprehensive of the myriad ways that nature benefits and is valued by people23, they capture, spatially and thematically, many elements explicitly mentioned in the First Draft of the CBD’s post-2020 Global Biodiversity Framework13: food security, water security, protection from hazards and extreme events, livelihoods and access to green and blue spaces. Our emphasis here is to highlight the contributions of natural and semi-natural ecosystems to human wellbeing, specifically contributions that are often overlooked in mainstream conservation and development policies around the world. For example, considerations for global food security often include only crop production rather than nature’s contributions to it via pollination or vegetation-mediated precipitation, or livestock production without partitioning out the contribution of grasslands from more intensified feed production.Gaps and next stepsOur synthesis of these 14 NCP represents a substantial advance beyond other global prioritizations that include NCP limited to ecosystem carbon stocks, fresh water and marine fisheries30,31,32, though still falls short of including all important contributions of nature such as its relational values33. Despite the omission of many NCP that were not able to be mapped, further analyses indicate that results are fairly robust to inclusion of additional NCP. Dropping one of the 12 local NCP at a time results in More

Dataset descriptionSample collectionOur research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table 1). Relevant permits and ethics information for each parent study are described in the ‘Permits for sample collection’ section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus). To achieve more even coverage across microbial environments, we devised an ontology of sample types (microbial environments), the EMP Ontology (EMPO) (http://earthmicrobiome.org/protocols-and-standards/empo/)1, and selected samples to fill out EMPO categories as broadly as possible. EMPO recognizes strong gradients structuring microbial communities globally, and thus classifies microbial environments (level 4) on the basis of host association (level 1), salinity (level 2), host kingdom (if host-associated) or phase (if free-living) (level 3) (Fig. 1a). As we anticipated previously1, we have updated the number of levels as well as states therein for EMPO (Fig. 1b) on the basis of an important additional salinity gradient observed among host-associated samples when considering the previously unreported shotgun metagenomic and metabolomic data generated here (Fig. 3c,d). We note that although we were able to acquire samples for all EMPO categories, some categories are represented by a single study.Samples were collected following the Earth Microbiome Project sample submission guide50. Briefly, samples were collected fresh, split into 10 aliquots and then frozen, or alternatively collected and frozen, and subsequently split into 10 aliquots with minimal perturbation. Aliquot size was sufficient to yield 10–100 ng genomic DNA (approximately 107–108 cells). To leave samples amenable to chemical characterization (metabolomics), buffers or solutions for sample preservation (for example, RNAlater) were avoided. Ethanol (50–95%) was allowed as it is compatible with LC–MS/MS although it should also be avoided if possible.Sampling guidance was tailored for four general sample types: bulk unaltered (for example, soil, sediment, faeces), bulk fractionated (for example, sponges, corals, turbid water), swabs (for example, biofilms) and filters. Bulk unaltered samples were split fresh (or frozen), sampled into 10 pre-labelled 2 ml screw-cap bead beater tubes (Sarstedt, 72.694.005 or similar), ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Bulk fractionated samples were fractionated as appropriate for the sample type, split into 10 pre-labelled 2 ml screw-cap bead beater tubes, ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Swabs were collected as 10 replicate swabs using 5 BD SWUBE dual cotton swabs with wooden stick and screw cap (281130). Filters were collected as 10 replicate filters (47 mm diameter, 0.2 um pore size, polyethersulfone (preferred) or hydrophilic PTFE filters), placed in pre-labelled 2 ml screw-cap bead beater tubes, and flash frozen in liquid nitrogen (if possible). All sample types were stored at –80 °C if possible, otherwise –20 °C.To track the provenance of sample aliquots, we employed a QR coding scheme. Labels were affixed to aliquot tubes before shipping when possible. QR codes had the format ‘name.99.s003.a05’, where ‘name’ is the PI name, ‘99’ is the study ID, ‘s003’ is the sample number and ‘a05’ is the aliquot number. QR codes (version 2, 25 pixels × 25 pixels) were printed on 1.125’ × 0.75’ rectangular and 0.437’ circular cap Cryogenic Direct Thermal labels (GA International, DFP-70) using a Zebra model GK420d printer and ZebraDesigner Pro 3 software for Windows. After receipt but before aliquots were stored in freezers, QR codes were scanned into a sample inventory spreadsheet using a QR scanner.Sample metadataEnvironmental metadata were collected for all samples on the basis of the EMP Metadata Guide, which combines guidance from the Genomics Standards Consortium MIxS (Minimum Information about any Sequence) standard74 and the Qiita Database (https://qiita.ucsd.edu)51. The metadata guide provides templates and instructions for each MIxS environmental package (that is, sample type). Relevant information describing each PI submission, or study, was organized into a separate study metadata file (Supplementary Table 1).MetabolomicsLC–MS/MS sample extraction and preparationTo profile metabolites among all samples, we used LC–MS/MS, a versatile method that detects tens of thousands of metabolites in biological samples. All solvents and reactants used were LC–MS grade. To maximize the biomass extracted from each sample, the samples were prepared depending on their sampling method (for example, bulk, swabs, filter and controls). The bulk samples were transferred into a microcentrifuge tube (polypropylene, PP) and dissolved in 7:3 MeOH:H2O using a volume varying from 600 µl to 1.5 ml, depending on the amounts of sample available, and homogenized in a tissue lyser (QIAGEN) at 25 Hz for 5 min. Then, the tubes were centrifuged at 2,000 × g for 15 min, and the supernatant was collected in a 96-well plate (PP). For swabs, the swabs were transferred into a 96-well plate (PP) and dissolved in 1.0 ml of 9:1 ethanol:H2O. The prepared plates were sonicated for 30 min, and after 12 h at 4 °C, the swabs were removed from the wells. The filter samples were dissolved in 1.5 ml of 7:3 MeOH:H2O in microcentrifuge tubes (PP) and sonicated for 30 min. After 12 h at 4 °C, the filters were removed from the tubes. The tubes were centrifuged at 2,000 × g for 15 min, and the supernatants were transferred to 96-well plates (PP). The process control samples (bags, filters and tubes) were prepared by adding 3.0 ml of 2:8 MeOH:H2O and recovering 1.5 ml after 2 min. After the extraction process, all sample plates were dried with a vacuum concentrator and subjected to solid phase extraction (SPE). SPE was used to remove salts that could reduce ionization efficiency during mass spectrometry analysis, as well as the most polar and non-polar compounds (for example, waxes) that cannot be analysed efficiently by reversed-phase chromatography. The protocol was as follows: the samples (in plates) were dissolved in 300 µl of 7:3 MeOH:H2O and put in an ultrasound bath for 20 min. SPE was performed with SPE plates (Oasis HLB, hydrophilic-lipophilic-balance, 30 mg with particle sizes of 30 µm). The SPE beds were activated by priming them with 100% MeOH, and equilibrated with 100% H2O. The samples were loaded on the SPE beds, and 100% H2O was used as wash solvent (600 µl). The eluted washing solution was discarded, as it contains salts and very polar metabolites that subsequent metabolomics analysis is not designed for. The sample elution was carried out sequentially with 7:3 MeOH:H2O (600 µl) and 100% MeOH (600 µl). The obtained plates were dried with a vacuum concentrator. For mass spectrometry analysis, the samples were resuspended in 130 µl of 7:3 MeOH:H2O containing 0.2 µM of amitriptyline as an internal standard. The plates were centrifuged at 30 × g for 15 min at 4 °C. Samples (100 µl) were transferred into new 96-well plates (PP) for mass spectrometry analysis.LC–MS/MS sample analysisThe extracted samples were analysed by ultra-high performance liquid chromatography (UHPLC, Vanquish, Thermo Fisher) coupled to a quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher) operated in data-dependent acquisition mode (LC–MS/MS in DDA mode). Chromatographic separation was performed using a Kinetex C18 1.7 µm (Phenomenex), 100 Å pore size, 2.1 mm (internal diameter) × 50 mm (length) column with a C18 guard cartridge (Phenomenex). The column was maintained at 40 °C. The mobile phase was composed of a mixture of (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid. Chromatographic elution method was set as follows: 0.00–1.00 min, isocratic 5% B; 1.00–9.00 min, gradient from 5% to 100% B; 9.00–11.00 min, isocratic 100% B; followed by equilibration 11.00–11.50 min, gradient from 100% to 5% B; 11.50–12.50 min, isocratic 5% B. The flow rate was set to 0.5 ml min−1.The UHPLC was interfaced to the orbitrap using a heated electrospray ionization source with the following parameters: ionization mode, positive; spray voltage, +3,496.2 V; heater temperature, 363.90 °C; capillary temperature, 377.50 °C; S-lens RF, 60 arbitrary units (a.u.); sheath gas flow rate, 60.19 a.u.; and auxiliary gas flow rate, 20.00 a.u. The MS1 scans were acquired at a resolution (at m/z 200) of 35,000 in the m/z 100–1500 range, and the fragmentation spectra (MS2) scans at a resolution of 17,500 from 0 to 12.5 min. The automatic gain control target and maximum injection time were set at 1.0 × 106 and 160 ms for MS1 scans, and set at 5.0 × 105 and 220 ms for MS2 scans, respectively. Up to three MS2 scans in data-dependent mode (Top 3) were acquired for the most abundant ions per MS1 scans using the apex trigger mode (4–15 s), dynamic exclusion (11 s) and automatic isotope exclusion. The starting value for MS2 was m/z 50. Higher-energy collision induced dissociation (HCD) was performed with a normalized collision energy of 20, 30 and 40 eV in stepped mode. The major background ions originating from the SPE were excluded manually from the MS2 acquisition. Analyses were randomized within plate and blank samples analysed every 20 injections. A quality control mix sample assembled from 20 random samples across the sample types was injected at the beginning, the middle and the end of each plate sequence. The chromatographic shift observed throughout the batch was estimated as less than 2 s, and the relative standard deviation of ion intensity was 15% per replicate.LC–MS/MS data processingThe mass spectrometry data were centroided and converted from the proprietary format (.raw) to the m/z extensible markup language format (.mzML) using ProteoWizard (ver. 3.0.19, MSConvert tool)75. The mzML files were then processed with MZmine 2 toolbox76 using the ion-identity networking modules77 that allow advanced detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18.04 LTS 64-bits workstation (Intel Xeon E5-2637, 3.5 GHz, 8 cores, 64 Gb of RAM) and took ~3 d. The MZmine project, the MZmine batch file (.XML format) and results files (.MGF and .CSV) are available in the MassIVE dataset MSV000083475. The MZmine batch file contains all the parameters used during the processing. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder78 and local minimum search algorithm. The isotopologues were regrouped and the features (peaks) were aligned across samples. The aligned peak list was gap filled and only peaks with an associated fragmentation spectrum and occurring in a minimum of three files were conserved. Peak shape correlation analysis grouped peaks originating from the same molecule and annotated adduct/isotopologue with ion-identity networking77. Finally, the feature quantification table results (.CSV) and spectral information (.MGF) were exported with the GNPS module for feature-based molecular networking analysis on GNPS79 and with SIRIUS export modules.LC–MS/MS data annotationThe results files of MZmine (.MGF and .CSV files) were uploaded to GNPS (http://gnps.ucsd.edu)52 and analysed with the feature-based molecular networking workflow79. Spectral library matching was performed against public fragmentation spectra (MS2) spectral libraries on GNPS and the NIST17 library.For the additional annotation of small peptides, we used the DEREPLICATOR tools available on GNPS80,81. We then used SIRIUS82 (v. 4.4.25, headless, Linux) to systematically annotate the MS2 spectra. Molecular formulae were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns83, and from fragmentation trees analysis84 of MS2. Molecular formula prediction was refined with the ZODIAC module using Gibbs sampling85 on the fragmentation spectra (chimeric spectra or those with poor fragmentation were excluded). In silico structure annotation using structures from biodatabase was done with CSI:FingerID86. Systematic class annotations were obtained with CANOPUS41 and used the NPClassifier ontology87.The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained, 80; molecular formula database, ALL; maximum precursor ion m/z computed, 750; profile, orbitrap; m/z maximum deviation, 10 ppm; ions annotated with MZmine were prioritized and other ions were considered (that is, [M+H3N+H]+, [M+H]+, [M+K]+, [M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+); for ZODIAC: the features were split into 10 random subsets for lower computational burden and computed separately with the following parameters: threshold filter, 0.9; minimum local connections, 0; for CSI:FingerID: m/z maximum deviation, 10 ppm; and biological database, BIO.To establish putative microbially related secondary metabolites, we collected annotations from spectral library matching and the DEREPLICATOR+ tools and queried them against the largest microbial metabolite reference databases (Natural Products Atlas88 and MIBiG89). Molecular networking79 was then used to propagate the annotation of microbially related secondary metabolites throughout all molecular families (that is, the network component).LC–MS/MS data analysisWe combined the annotation results from the different tools described above to create a comprehensive metadata file describing each metabolite feature observed. Using that information, we generated a feature-table including only secondary metabolite features determined to be microbially related. We then excluded very low-intensity features introduced to certain samples during the gap-filling step described above. These features were identified on the basis of presence in negative controls that were universal to all sample types (that is, bulk, filter and swab) and by their relatively low per-sample intensity values. Finally, we excluded features present in positive controls for sampling devices specific to each sample type (that is, bulk, filter or swab). The final feature-table included 618 samples and 6,588 putative microbially related secondary metabolite features that were used for subsequent analysis.We used QIIME 2’s90 (v2020.6) ‘diversity’ plugin to quantify alpha-diversity (that is, feature richness) for each sample and ‘deicode’91 to quantify beta-diversity (that is, robust Aitchison distances, which are robust to both sparsity and compositionality in the data) between each pair of samples. We parameterized our robust Aitchison principal components analysis (RPCA)91 to exclude samples with fewer than 500 features and features present in fewer than 10% of samples. We used the ‘taxa’ plugin to quantify the relative abundance of microbially related secondary metabolite pathways and superclasses (that is, on the basis of NPClassifier) within each environment (that is, for each level of EMPO 4), and ‘songbird’ v1.0.492 to identify sets of microbially related secondary metabolites whose abundances were associated with certain environments. We parameterized our ‘songbird’ model as follows: epochs, 1,000,000; differential prior, 0.5; learning rate, 1.0 × 10−5; summary interval, 2; batch size, 400; minimum sample count, 0; and training on 80% of samples at each level of EMPO 4 using ‘Animal distal gut (non-saline)’ as the reference environment. Environments with fewer than 10 samples were excluded to optimize model training (that is, ‘Animal corpus (non-saline)’, ‘Animal proximal gut (non-saline)’, ‘Surface (saline)’). The output from ‘songbird’ includes a rank value for each metabolite in every environment, which represents the log fold change for a given metabolite in a given environment92. We compared log fold changes for each metabolite from this run to those from (1) a replicate run using the same reference environment and (2) a run using a distinct reference environment: ‘Water (saline)’. We found strong Spearman correlations in both cases (Supplementary Table 8), and therefore focused on results from the original run using ‘Animal distal gut (non-saline)’ as the reference environment, as it has previously been shown to be relatively unique among other habitats. In addition to summarizing the top 10 metabolites for each environment (Supplementary Table 3), we used the log fold change values in our multi-omics analyses described below.We used the RPCA biplot and QIIME 2’s90 EMPeror93 to visualize differences in composition among samples, as well as the association with samples of the 25 most influential microbially related secondary metabolite features (that is, those with the largest magnitude across the first three principal component loadings). We tested for significant differences in metabolite composition across all levels of EMPO using PERMANOVA implemented with QIIME 2’s ‘diversity’ plugin90 and using our robust Aitchison distance matrix as input. In parallel, we used the differential abundance results from ‘songbird’ described above to identify specific microbially related secondary metabolite pathways and superclasses that varied strongly across environments. We then went back to our metabolite feature-table to visualize differences in the relative abundances of those pathways and superclasses within each environment by first selecting features and calculating log-ratios using ‘qurro’94, and then plotting using the ‘ggplot2’ package95 in R96 v4.0.0. We tested for significant differences in relative abundances across environments using Kruskal–Wallis tests implemented with the base ‘stats’ package in R96.GC–MS sample extraction and preparationTo profile volatile small molecules among all samples in addition to what was captured with LC–MS/MS, we used gas chromatography coupled with mass spectrometry (GC–MS). All solvents and reactants were GC–MS grade. Two protocols were used for sample extraction, one for the 105 soil samples and a second for the 356 faecal and sediment samples that were treated as biosafety level 2. The 105 soil samples were received at the Pacific Northwest National Laboratory and processed as follows. Each soil sample (1 g) was weighed into microcentrifuge tubes (Biopur Safe-Lock, 2.0 ml, Eppendorf). H2O (1 ml) and one scoop (~0.5 g) of a 1:1 (v/v) mixture of garnet (0.15 mm, Omni International) and stainless steel (0.9–2.0 mm blend, Next Advance) beads and one 3 mm stainless steel bead (Qiagen) were added to each tube. Samples were homogenized in a tissue lyser (Qiagen) for 3 min at 30 Hz and transferred into 15 ml polypropylene tubes (Olympus, Genesee Scientific). Ice-cold water (1 ml) was used to rinse the smaller tube and combined into the 15 ml tube. Chloroform:methanol (10 ml, 2:1 v/v) was added and samples were rotated at 4 °C for 10 min, followed by cooling at −70 °C for 10 min and centrifuging at 150 × g for 10 min to separate phases. The top and bottom layers were combined into 40 ml glass vials and dried using a vacuum concentrator. Chloroform:methanol (1 ml, 2:1) was added to each large glass vial and the sample was transferred into 1.5 ml tubes and centrifuged at 1,300 × g. The supernatant was transferred into glass vials and dried for derivatization.The remaining 356 samples received from UCSD that included faecal and sediment samples were processed as follows: 100 µl of each sample was transferred to a 2 ml microcentrifuge tube using a scoop (MSP01, Next Advance). The final volume of the sample was brought to 1.5 ml, ensuring that the solvent ratio is 3:8:4 H2O:CHCl3:MeOH by adding the appropriate volumes of H2O, MeOH and CHCl3. After transfer, one 3 mm stainless steel bead (QIAGEN), 400 µl methanol and 300 µl H2O were added to each tube and the samples were vortexed for 30 s. Then, 800 µl chloroform was added and samples were vortexed for 30 s. After centrifuging at 150 × g for 10 min to separate phases, the top and bottom layers were combined in a vial and dried for derivatization.The samples were derivatized for GC–MS analysis as follows: 20 µl of a methoxyamine solution in pyridine (30 mg ml−1) was added to the sample vial and vortexed for 30 s. A bath sonicator was used to ensure that the sample was completely dissolved. Samples were incubated at 37 °C for 1.5 h while shaking at 1,000 r.p.m. N-methyl-N-trimethylsilyltrifluoroacetamide (80 µl) and 1% trimethylchlorosilane solution was added and samples were vortexed for 10 s, followed by incubation at 37 °C for 30 min, with 1,000 r.p.m. shaking. The samples were then transferred into a vial with an insert.An Agilent 7890A gas chromatograph coupled with a single quadrupole 5975C mass spectrometer (Agilent) and an HP-5MS column (30 m × 0.25 mm × 0.25 μm; Agilent) was used for untargeted analysis. Samples (1 μl) were injected in splitless mode, and the helium gas flow rate was determined by the Agilent Retention Time Locking function on the basis of analysis of deuterated myristic acid (Agilent). The injection port temperature was held at 250 °C throughout the analysis. The GC oven was held at 60 °C for 1 min after injection, and the temperature was then increased to 325 °C at a rate of 10 °C min−1, followed by a 10 min hold at 325 °C. Data were collected over the mass range of m/z 50–600. A mixture of FAMEs (C8–C28) was analysed each day with the samples for retention index alignment purposes during subsequent data analysis.GC–MS data processing and annotationThe data were converted from vendor’s format to the .mzML format and processed using GNPS GC–MS data analysis workflow (https://gnps.ucsd.edu)97. The compounds were identified by matching experimental spectra to the public libraries available at GNPS, as well as NIST 17 and Wiley libraries. The data are publicly available at the MassIVE depository (https://massive.ucsd.edu); dataset ID: MSV000083743. The GNPS deconvolution is available in GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d5c5135a59eb48779216615e8d5cb3ac), as is the library search (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59b20fc8381f4ee6b79d35034de81d86).GC–MS data analysisFor multi-omics analyses including GC–MS data, we first removed noisy (that is, suspected background contaminants and artifacts) features by excluding those with balance scores 1.5–2 kb DNA fragments’ (Oxford Nanopore Technologies). The resulting product consists of uniquely tagged rRNA operon amplicons. The uniquely tagged rRNA operons were amplified in a second PCR, where the reaction (100 µl) contained 2 U Platinum SuperFi DNA Polymerase High Fidelity (Thermo Fisher) and a final concentration of 1X SuperFi buffer, 0.2 mM of each dNTP, and 500 nM of each forward and reverse synthetic primer targeting the tailed primers from above. The PCR cycling parameters consisted of an initial denaturation (3 min at 95 °C) and then 25–35 cycles of denaturation (15 s at 95 °C), annealing (30 s at 60 °C) and extension (6 min at 72 °C), followed by final extension (5 min at 72 °C). The PCR product was purified using the custom bead purification protocol above. Batches of 25 amplicon libraries were barcoded and sent for PacBio Sequel II library preparation and sequencing (Sequel II SMRT Cell 8M and 30 h collection time) at the DNA Sequencing Center at Brigham Young University. Circular consensus sequencing (CCS) reads were generated using CCS v.3.4.1 (https://github.com/PacificBiosciences/ccs) using default settings. UMI consensus sequences were generated using the longread_umi pipeline (https://github.com/SorenKarst/longread_umi) with the following command: longread_umi pacbio_pipeline -d ccs_reads.fq -o out_dir -m 3500 -M 6000 -s 60 -e 60 -f CAAGCAGAAGACGGCATACGAGAT -F AGRGTTYGATYMTGGCTCAG -r AATGATACGGCGACCACCGAGATC -R CGACATCGAGGTGCCAAAC -U ‘0.75;1.5;2;0’ -c 2.Amplicon data analysisFor multi-omics analyses including amplicon sequence data, we processed each dataset for comparison of beta-diversity. For all amplicon data except that for bacterial full-length rRNA amplicons, raw sequence data were converted from bcl to fastq, and then multiplexed files for each sequencing run uploaded as separate preparations to Qiita (study: 13114).For each 16S sequencing run, in Qiita, data were demultiplexed, trimmed to 150 bp and denoised using Deblur122 to generate a feature-table of sub-operational taxonomic units (sOTUs) per sample, using default parameters. We then exported feature-tables and denoised sequences from each sequencing run, used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised reads across sequencing runs, and placed all denoised reads into the GreenGenes 13_8 phylogeny123 via fragment insertion using QIIME 2’s90 SATé-Enabled Phylogenetic Placement (SEPP)124 plugin to produce a phylogeny for diversity analyses. To allow for phylogenetically informed diversity analyses, reads not placed during SEPP (that is, 513 sOTUs, 0.1% of all sOTUs) were removed from the merged feature-table. We then used QIIME 2’s ‘feature-table’ plugin to exclude singleton sOTUs and rarefy the data to 5,000 reads per sample. Rarefaction depths for all amplicon analyses were chosen to best normalize sampling effort per sample while maintaining ≥75% of samples representative of Earth’s environments, and also to maintain consistency with the analyses from EMP release 1. We then used QIIME 2’s90 ‘diversity’ plugin to estimate alpha-diversity (that is, sOTU richness) and beta-diversity (that is, unweighted UniFrac distances). The final feature-table for 16S beta-diversity analysis included 681 samples and 93,260 features. We performed a comparative analysis of the data including and excluding the reads not placed during SEPP, and note that both alpha-diversity (that is, sOTU richness) and beta-diversity (that is, sample–sample RPCA distances) were highly correlated between datasets (Spearman r = 1.0) (Supplementary Fig. 5). We thus proceeded with the SEPP-filtered dataset and used phylogenetically informed diversity metrics where applicable.For 18S data, we used QIIME 2’s90 ‘demux’ plugin’s ‘emp-paired’ method125,126 to first demultiplex each sequencing run, and then the ‘cutadapt’ plugin’s127 ‘trim-paired’ method to trim sequencing primers from reads. We then exported trimmed reads, concatenated R1 and R2 read files per sample, and denoised reads using Deblur’s122,128 ‘workflow’ with default settings, trimming reads to 90 bp, and taking the ‘all.biom’ and ‘all.seqs’ output, for each sequencing run. We then used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised sequences across sequencing runs, and then the ‘feature-classifier’ plugin’s ‘classify-sklearn’ method to classify taxonomy for each sOTU via pre-fitted machine-learning classifiers129 and the SILVA 138 reference database130. We then used QIIME 2’s90 ‘feature-table’ plugin to exclude reads assigned to bacteria and archaea, singleton sOTUs and samples with a total frequency of More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
PubMed 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).PubMed 

Google Scholar 
Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).
Google Scholar 
Haupt, F., Bakhtary, H., Schulte, I., Galt, H. & Streck, C. Progress on Corporate Commitments and their Implementation (Tropical Forest Alliance, 2018); https://www.tropicalforestalliance.org/assets/Uploads/Progress-on-Corporate-Commitments-and-their-Implementation.pdfAustin, K. G. et al. Mapping and monitoring zero-deforestation commitments. Bioscience 71, 1079–1090 (2021).PubMed 
PubMed Central 

Google Scholar 
Leijten, F. C., Sim, S., King, H. & Verburg, P. H. Which forests could be protected by corporate zero deforestation commitments? A spatial assessment. Environ. Res. Lett. 15, 064021 (2020).
Google Scholar 
Garrett, R. D. et al. Criteria for effective zero-deforestation commitments. Glob. Environ. Change https://doi.org/10.1016/j.gloenvcha.2018.11.003 (2019).Lehmann, C. E. R. & Parr, C. L. Tropical grassy biomes: linking ecology, human use and conservation. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2016.0329 (2016).Miles, L. et al. A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33, 491–505 (2006).
Google Scholar 
Gibbs, H. K. et al. Brazil’s soy moratorium. Science https://doi.org/10.1126/science.aaa0181 (2015).Jopke, P. & Schoneveld, G. C. Corporate Commitments to Zero Deforestation: An Evaluation of Externality Problems and Implementation Gaps (CIFOR, 2018); https://doi.org/10.17528/cifor/006827Parr, C. L., Lehmann, C. E. R., Bond, W. J., Hoffmann, W. A. & Andersen, A. N. Tropical grassy biomes: misunderstood, neglected, and under threat. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2014.02.004 (2014).Ratnam, J. et al. When is a ‘forest’ a savanna, and why does it matter? Glob. Ecol. Biogeogr. https://doi.org/10.1111/j.1466-8238.2010.00634.x (2011).Sanchez-Azofeifa, G. A. et al. Research priorities for neotropical dry forests. Biotropica 37, 477–485 (2005).
Google Scholar 
Vijay, V., Pimm, S. L., Jenkins, C. N. & Smith, S. J. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).PubMed 
PubMed Central 

Google Scholar 
Principles & Criteria for the Production of Sustainable Palm Oil (RSPO, 2018).Rosoman, G. et al. (eds) The HCS Approach Toolkit (HCS Approach Steering Group, 2017).Brown, E. & Senior, M. J. M. (eds) Common Guidance for the Identification of High Conservation Values (HCV Resource Network, 2017).Furumo, P. R. & Aide, T. M. Characterizing commercial oil palm expansion in Latin America: land use change and trade. Environ. Res. Lett. 12, 024008 (2017).
Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience https://doi.org/10.1093/biosci/bix014 (2017).Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data 13, 1211–1231 (2021).
Google Scholar 
Woittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M. & Giller, K. E. Yield gaps in oil palm: a quantitative review of contributing factors. Eur. J. Agron. 83, 57–77 (2017).
Google Scholar 
Kuepper, B., Drost, S. & Piotrowski, M. Latin American Palm Oil Linked to Social Risks, Local Deforestation (Chain Reaction Research, 2021); https://chainreactionresearch.com/wp-content/uploads/2021/12/Latin-American-Palm-Oil-Linked-to-Social-Issues-Local-Deforestation-1.pdfHoyle, D. et al. RSPO New Planting Procedures: Summary Report of ESIA, HCV Assessments and Management Plan (Terea, Proforest and Olam Palm Gabon, 2017).Universal Mill List (World Resources Institute, Rainforest Alliance, Proforest & Daemeter, 2018); https://data.globalforestwatch.org/documents/gfw::universal-mill-list/aboutPirker, J., Mosnier, A., Kraxner, F., Havlík, P. & Obersteiner, M. What are the limits to oil palm expansion? Glob. Environ. Change 40, 73–81 (2016).
Google Scholar 
Fischer, G. et al. Global Agro-Ecological Zones 4 (GAEZ v4) – Model Documentation (FAO, 2021); https://doi.org/10.4060/cb4744enGlobal Agro-Ecological Zoning Version 4 (GAEZ v4) (FAO & IIASA, 2021); http://www.fao.org/gaez/Tao, H. H. et al. Long-term crop residue application maintains oil palm yield and temporal stability of production. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0439-5 (2017).Wei, L., John Martin, J. J., Zhang, H., Zhang, R. & Cao, H. Problems and prospects of improving abiotic stress tolerance and pathogen resistance of oil palm. Plants 10, 2622 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Corley, R. H. & Tinker, P. B. The Oil Palm (Wiley-Blackwell, 2016).Barona, E., Ramankutty, N., Hyman, G. & Coomes, O. T. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ. Res. Lett. 5, 024002 (2010).
Google Scholar 
ten Kate, A., Kuepper, B. & Piotrowski, M. NDPE Policies Cover 83% of Palm Oil Refineries; Implementation at 78% (Chain Reaction Research, 2020); https://chainreactionresearch.com/wp-content/uploads/2020/04/NDPE-Policies-Cover-83-of-Palm-Oil-Refining-Market.pdfThe Trase Yearbook: The State Of Forest Risk Supply Chains (Trase, 2020); https://insights.trase.earth/yearbook/summaryAustin, K. G. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).
Google Scholar 
Furumo, P. R., Rueda, X., Rodríguez, J. S. & Parés Ramos, I. K. Field evidence for positive certification outcomes on oil palm smallholder management practices in Colombia. J. Clean. Prod. 245, 118891 (2020).
Google Scholar 
Carlson, K. M. et al. Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proc. Natl Acad. Sci. USA 115, 121–126 (2018).CAS 
PubMed 

Google Scholar 
Heilmayr, R., Carlson, K. M. & Benedict, J. J. Deforestation spillovers from oil palm sustainability certification. Environ. Res. Lett. 15, 075002 (2020).CAS 

Google Scholar 
Impact (RSPO, 2022); https://www.rspo.org/impactBastos Lima, M. G., Persson, U. M. & Meyfroidt, P. Leakage and boosting effects in environmental governance: a framework for analysis. Environ. Res. Lett. 14, 105006 (2019).
Google Scholar 
Corley, R. H. V. How much palm oil do we need? Environ. Sci. Policy https://doi.org/10.1016/j.envsci.2008.10.011 (2009).FAOSTAT: Food and Agriculture Data (FAO, 2020); https://www.fao.org/faostat/en/Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).
Google Scholar 
Murphy, B. P., Andersen, A. N. & Parr, C. L. The underestimated biodiversity of tropical grassy biomes. Phil. Trans. R. Soc. B 371, 20150319 (2016).PubMed 
PubMed Central 

Google Scholar 
Smith, J. R., Hendershot, J. N., Nova, N. & Daily, G. C. The biogeography of ecoregions: descriptive power across regions and taxa. J. Biogeogr. https://doi.org/10.1111/jbi.13871 (2020).Klink, C. A. & Machado, R. B. Conservation of the Brazilian Cerrado. Conserv. Biol. 19, 707–713 (2005).
Google Scholar 
Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-017-0099 (2017).le Polain de Waroux, Y. et al. The restructuring of South American soy and beef production and trade under changing environmental regulations. World Dev. 121, 188–202 (2019).
Google Scholar 
Nepstad, L. S. et al. Pathways for recent Cerrado soybean expansion: extending the soy moratorium and implementing integrated crop livestock systems with soybeans. Environ. Res. Lett. 14, 044029 (2019).
Google Scholar 
Searchinger, T. D. et al. High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Change https://doi.org/10.1038/nclimate2584 (2015).Cardoso Da Silva, J. M. & Bates, J. M. Biogeographic patterns and conservation in the South American Cerrado: a tropical savanna hotspot. BioScience 52, 225–233 (2002).
Google Scholar 
Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7, 217–240 (2021).Hill, T. C., Williams, M., Bloom, A. A., Mitchard, E. T. A. & Ryan, C. M. Are inventory based and remotely sensed above-ground biomass estimates consistent? PLoS ONE https://doi.org/10.1371/journal.pone.0074170 (2013).Ryan, C. M. et al. Ecosystem services from southern African woodlands and their future under global change. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0312 (2016).Grace, J., Jose, J. S., Meir, P., Miranda, H. S. & Montes, R. A. Productivity and carbon fluxes of tropical savannas. J. Biogeogr. 33, 387–400 (2006).
Google Scholar 
Scharlemann, J. P., Tanner, E. V., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5, 81–91 (2014).CAS 

Google Scholar 
Quezada, J. C., Etter, A., Ghazoul, J., Buttler, A. & Guillaume, T. Carbon neutral expansion of oil palm plantations in the Neotropics. Sci. Adv. 5, eaaw4418 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aleman, J. C., Blarquez, O. & Staver, C. A. Land-use change outweighs projected effects of changing rainfall on tree cover in sub-Saharan Africa. Glob. Change Biol. https://doi.org/10.1111/gcb.13299 (2016).Espírito-Santo, M. M. et al. Understanding patterns of land-cover change in the Brazilian Cerrado from 2000 to 2015. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0435 (2016).Overbeck, G. E. et al. Conservation in Brazil needs to include non-forest ecosystems. Divers. Distrib. https://doi.org/10.1111/ddi.12380 (2015).Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. https://doi.org/10.1111/j.1461-0248.2004.00686.x (2005).RTRS Standard for Responsible Soy Production Version 3.1 (RTRS, 2017); https://responsiblesoy.org/wp-content/uploads/2019/08/RTRS%20Standard%20Responsible%20Soy%20production%20V3.1%20ING-LOW.pdfBatlle-Bayer, L., Batjes, N. H. & Bindraban, P. S. Changes in organic carbon stocks upon land use conversion in the Brazilian Cerrado: a review. Agric. Ecosyst. Environ. 137, 47–58 (2010).CAS 

Google Scholar 
Rockström, J., Falkenmark, M., Lannerstad, M. & Karlberg, L. The planetary water drama: dual task of feeding humanity and curbing climate change. Geophys. Res. Lett. 39, LXXXXX (2012).
Google Scholar 
Ocampo-Peñuela, N., Garcia-Ulloa, J., Ghazoul, J. & Etter, A. Quantifying impacts of oil palm expansion on Colombia’s threatened biodiversity. Biol. Conserv. https://doi.org/10.1016/j.biocon.2018.05.024 (2018).Gilroy, J. J. et al. Minimizing the biodiversity impact of Neotropical oil palm development. Glob. Change Biol. 21, 1531–1540 (2015).
Google Scholar 
Bonn Challenge 2020 Report (IUCN, 2020); https://www.bonnchallenge.org/resources/bonn-challenge-2020-reportGilroy, 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 
Evans, M. C. et al. Carbon farming via assisted natural regeneration as a cost-effective mechanism for restoring biodiversity in agricultural landscapes. Environ. Sci. Policy 50, 114–129 (2015).CAS 

Google Scholar 
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. BioScience https://doi.org/10.1093/biosci/bix010 (2017).Beyer, R. & Rademacher, T. Species richness and carbon footprints of vegetable oils: can high yields outweigh palm oil’s environmental impact? Sustainability 13, 1813 (2021).
Google Scholar 
Lee, J. S. H., Ghazoul, J., Obidzinski, K. & Koh, L. P. Oil palm smallholder yields and incomes constrained by harvesting practices and type of smallholder management in Indonesia. Agron. Sustain. Dev. 34, 501–513 (2014).
Google Scholar 
Murphy, D. J. The future of oil palm as a major global crop: opportunities and challenges. J. Oil Palm Res. 26, 1–24 (2014).
Google Scholar 
Giam, X., Koh, L. P. & Wilcove, D. S. Tropical crops: cautious optimism. Science https://doi.org/10.1126/science.346.6212.928-a (2014).Villoria, N. B., Golub, A., Byerlee, D. & Stevenson, J. Will yield improvements on the forest frontier reduce greenhouse gas emissions? A global analysis of oil palm. Am. J. Agric. Econ. 95, 1301–1308 (2013).
Google Scholar 
Koh, L. P. & Lee, T. M. Sensible consumerism for environmental sustainability. Biol. Conserv. https://doi.org/10.1016/j.biocon.2011.10.029 (2012).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Harris, N., Goldman, E. & Gibbes, S. Spatial Database of Planted Trees (SDPT) Version 1.0 (World Resources Institute, 2019); https://data.globalforestwatch.org/datasets/tree-plantationsSutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018).
Google Scholar 
Global Land Cover (Copernicus, 2019); https://lcviewer.vito.be/Tsendbazar, N.-E. et al. Copernicus Global Land Operations ‘Vegetation and Energy’ ‘CGLOPS−1’ Validation Report. Moderate Dynamic Land Cover 100m Version 2 (WUR, 2019); https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_VR_LC100m-V2.0_I1.00.pdfSantoro, M. et al. GlobBiomass – global datasets of forest biomass. PANGAEA https://doi.org/10.1594/PANGAEA.894711 (2018).Santoro, M. et al. A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophys. Res. Abstr. https://doi.org/10.1002/joc.5086 (2018).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science https://doi.org/10.1126/science.1244693 (2013).Gumbricht, T. et al. Tropical and Subtropical Wetlands Distribution Version 7 (CIFOR, 2017); https://doi.org/10.17528/CIFOR/DATA.00058The IUCN Red List of Threatened Species Version 2018−1 (IUCN, 2018); https://www.iucnredlist.orgBird Species Distribution Maps of the World Version 6.0 (BirdLife International & Handbook of the Birds of the World, 2016); http://datazone.birdlife.org/species/requestdisR Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Silalertruksa, T. et al. Environmental sustainability of oil palm cultivation in different regions of Thailand: greenhouse gases and water use impact. J. Clean. Prod. 167, 1009–1019 (2017).CAS 

Google Scholar 
Fourcade, Y., Engler, J. O., Rödder, D. & Secondi, J. Mapping species distributions with Maxent using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).PubMed 
PubMed Central 

Google Scholar 
Liu, Z. et al. Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010. Reg. Environ. Change https://doi.org/10.1007/s10113-014-0677-x (2015).Singh, K., McClean, C. J., Büker, P., Hartley, S. E. & Hill, J. K. Mapping regional risks from climate change for rainfed rice cultivation in India. Agric. Syst. 156, 76–84 (2017).PubMed 
PubMed Central 

Google Scholar 
Estes, L. D. et al. Comparing mechanistic and empirical model projections of crop suitability and productivity: implications for ecological forecasting. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.12034 (2013).Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modelling (2016).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Google Scholar 
Merow, C., Smith, M. J., Silander, J. A., Merow, C. & Silander, J. A. A practical guide to Maxent for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol. Modell. 220, 589–594 (2009).
Google Scholar 
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. F. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell. 199, 142–152 (2006).
Google Scholar 
Engler, R., Guisan, A. & Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. https://doi.org/10.1111/j.0021-8901.2004.00881.x (2004).Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1. (International Food Policy Research Institute, 2019); https://doi.org/10.7910/DVN/PRFF8V/M2EMBNHofste, R. W. et al. Aqueduct 3.0: Updated Decision-Relevant Global Water Risk Indicators (World Resources Institute, 2019); https://www.wri.org/research/aqueduct-30-updated-decision-relevant-global-water-risk-indicatorsCarr, M. K. V. The water relations and irrigation requirements of oil palm (Elaeis guineensis): a review. Exp. Agric. 47, 629–652 (2011).
Google Scholar 
Yusop, Z., Hui, C. M., Garusu, G. J. & Katimon, A. Estimation of evapotranspiration in oil palm catchments by short-time period water-budget method. Malays. J. Civ. Eng. 20, 160–174 (2008).
Google Scholar 
Hargreaves, G. H. & Allen, R. G. History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng. 129, 53–63 (2003).
Google Scholar 
Trabucco, A. & Zomer, R. J. Global aridity index and potential evapotranspiration (ET0) climate database v2. Figshare https://doi.org/10.6084/m9.figshare.7504448.v3 (2019).Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC & IUCN, 2020); www.protectedplanet.net/en/thematic-areas/wdpaDudley, N. (ed.) Guidelines for Applying Protected Area Management Categories (IUCN, 2008).Juffe-Bignoli, D. et al. World Database on Protected Areas User Manual 1.5 (UNEP-WCMC, 2017); https://www.protectedplanet.net/en/resources/wdpa-manualChave, J. J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).CAS 
PubMed 

Google Scholar 
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. https://doi.org/10.1371/journal.pbio.0050157 (2007).Beyer, R. M. & Manica, A. Historical and projected future range sizes of the world’s mammals, birds, and amphibians. Nat. Commun. 11, 5633 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Beyer, R. M. & Manica, A. Global and country-level data of the biodiversity footprints of 175 crops and pasture. Data Brief 36, 106982 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cobertura de la Tierra 100K Periodo 2018 (IDEAM, Instituto de Hidrología, Meteorología y Estudios Ambientales, 2021); http://www.siac.gov.co/catalogo-de-mapasSouza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sens. 12, 2735 (2020).
Google Scholar 
MapBiomas Project – Collection 6 of the Annual Series of Land Use and Land Cover Maps of Brazil (MapBiomas, 2021); https://plataforma.brasil.mapbiomas.org/Veldman, J. W. & Putz, F. E. Grass-dominated vegetation, not species-diverse natural savanna, replaces degraded tropical forests on the southern edge of the Amazon Basin. Biol. Conserv. https://doi.org/10.1016/j.biocon.2011.01.011 (2011).Portillo-Quintero, C. A. & Sánchez-Azofeifa, G. A. Extent and conservation of tropical dry forests in the Americas. Biol. Conserv. https://doi.org/10.1016/j.biocon.2009.09.020 (2010).Veldman, J. W. et al. Toward an old-growth concept for grasslands, savannas, and woodlands. Front. Ecol. Environ. https://doi.org/10.1890/140270 (2015).Zaloumis, N. P. & Bond, W. J. Reforestation or conservation? The attributes of old growth grasslands in South Africa. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0310 (2016).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. https://doi.org/10.1038/s41893-018-0100-6 (2018).Djoudi, H., Vergles, E., Blackie, R. R., Koame, C. K. & Gautier, D. Dry forests, livelihoods and poverty alleviation: understanding current trends. Int. For. Rev. https://doi.org/10.1505/146554815815834868 (2015).Ground-Truthing to Improve Due Diligence on Human Rights in Deforestation-Risk Supply Chains (Forest Peoples Programme, 2020); https://www.forestpeoples.org/en/ground-truthing-to-improve-due-diligenceDrost, S., Rijk, G. & Piotrowski, M. Oil Palm Growers Exposed to USD 0.4-5.9B in Social Compensation Risk (Chain Reaction Research, 2019); https://chainreactionresearch.com/wp-content/uploads/2019/12/Social-compensation-risks-for-palm-growers-4.pdf More

United Nations. [World population prospects 2019]. United Nations. Department of Economic and Social Affairs. World Population Prospects 2019. (2019).Consortium, I. & Commission, E. The circular Bio-society in 2050. (2018).Ramaswami, A., Russell, A. G., Culligan, P. J., Rahul Sharma, K. & Kumar, E. Meta-principles for developing smart, sustainable, and healthy cities. Science (1979) 352, 940–943 (2016).CAS 

Google Scholar 
Cooper, C. M., Troutman, J. P., Awal, R., Habibi, H. & Fares, A. Climate change-induced variations in blue and green water usage in U.S. urban agriculture. J. Clean. Prod. 348, 567–579 (2022).Article 

Google Scholar 
Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).Article 
CAS 

Google Scholar 
Paul, S., Dutta, A., Defersha, F. & Dubey, B. Municipal food waste to biomethane and biofertilizer: A circular economy concept. Waste Biomass Valorizat. 9, 601–611 (2018).Article 
CAS 

Google Scholar 
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).Article 
CAS 
PubMed 

Google Scholar 
Bergstrand, K. J. Organic fertilizers in greenhouse production systems—A review. Sci. Hortic. 295, 1–8 (2022).Article 

Google Scholar 
Chiaregato, C. G., França, D., Messa, L. L., dos Santos Pereira, T. & Faez, R. A review of advances over 20 years on polysaccharide-based polymers applied as enhanced efficiency fertilizers. Carbohydr. Polym. 279, 1–10 (2022).Article 

Google Scholar 
Timilsena, Y. P. et al. Enhanced efficiency fertilisers: A review of formulation and nutrient release patterns. J. Sci. Food Agric. 95, 1131–1142 (2015).Article 
CAS 
PubMed 

Google Scholar 
Chen, J. et al. Environmentally friendly fertilizers: A review of materials used and their effects on the environment. Sci. Total Environ. 613–614, 829–839 (2018).Article 
PubMed 

Google Scholar 
Aguilera, E., Lassaletta, L., Sanz-Cobena, A., Garnier, J. & Vallejo, A. The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. A review. Agric. Ecosyst. Environ. 164, 32–52 (2013).Article 
CAS 

Google Scholar 
Lv, G. et al. Biochar-based fertilizer enhanced Cd immobilization and soil quality in soil-rice system. Ecol. Eng. 171, 1–12 (2021).Article 

Google Scholar 
Clark, M. J. & Zheng, Y. Fertilizer rate influences production scheduling of sedum-vegetated green roof mats. Ecol. Eng. 71, 644–650 (2014).Article 

Google Scholar 
Samoraj, M. et al. Biochar in environmental friendly fertilizers—Prospects of development products and technologies. Chemosphere 296, 1–7 (2022).Article 

Google Scholar 
Dimkpa, C. O., Fugice, J., Singh, U. & Lewis, T. D. Development of fertilizers for enhanced nitrogen use efficiency—Trends and perspectives. Sci. Total Environ. 731, 1–9 (2020).Article 

Google Scholar 
Fertahi, S., Ilsouk, M., Zeroual, Y., Oukarroum, A. & Barakat, A. Recent trends in organic coating based on biopolymers and biomass for controlled and slow release fertilizers. J. Control. Release 330, 341–361 (2021).Article 
CAS 
PubMed 

Google Scholar 
García-Garizábal, I., Causapé, J. & Abrahao, R. Nitrate contamination and its relationship with flood irrigation management. J. Hydrol. (AMST) 442–443, 15–22 (2012).Article 

Google Scholar 
Adu-Poku, D., Ackerson, N. O. B., Devine, R. N. O. A. & Addo, A. G. Climate mitigation efficiency of nitrification and urease inhibitors: Impact on N2O emission—A review. Sci. Afr. 16, 1–7 (2022).
Google Scholar 
Ding, W., Qin, H., Yu, S. & Yu, S. L. The overall and phased nitrogen leaching from a field bioretention during rainfall runoff events. Ecol. Eng. 179, 1–9 (2022).Article 

Google Scholar 
Li, X. et al. Loss of nitrogen and phosphorus from farmland runoff and the interception effect of an ecological drainage ditch in the North China Plain—A field study in a modern agricultural park. Ecol. Eng. 169, 1–10 (2021).Article 

Google Scholar 
Michalsky, R. & Pfromm, P. H. Thermodynamics of metal reactants for ammonia synthesis from steam, nitrogen and biomass at atmospheric pressure. AIChE J. 58, 3203–3213 (2012).Article 
CAS 

Google Scholar 
Pleissner, D. Decentralized utilization of wasted organic material in urban areas: A case study in Hong Kong. Ecol. Eng. 86, 120–125 (2016).Article 

Google Scholar 
Masullo, A. Organic wastes management in a circular economy approach: Rebuilding the link between urban and rural areas. Ecol. Eng. 101, 84–90 (2017).Article 

Google Scholar 
Zeng, Y., de Guardia, A., Ziebal, C., de Macedo, F. J. & Dabert, P. Nitrogen dynamic and microbiological evolution during aerobic treatment of digested sludge. Waste Biomass Valorizat. 5, 441–450 (2014).CAS 

Google Scholar 
Nagarajan, S., Eswaran, P., Masilamani, R. P. & Natarajan, H. Chicken feather compost to promote the plant growth activity by using Keratinolytic Bacteria. Waste Biomass Valorizat. 9, 531–538 (2018).Article 
CAS 

Google Scholar 
Bhat, S. A., Singh, J. & Vig, A. P. Earthworms as organic waste managers and biofertilizer producers. Waste Biomass Valorizat. 9, 1073–1086 (2018).Article 
CAS 

Google Scholar 
Mekki, A., Arous, F., Aloui, F. & Sayadi, S. Treatment and valorization of agro-wastes as biofertilizers. Waste Biomass Valorizat. 8, 611–619 (2017).Article 
CAS 

Google Scholar 
Liu, T. et al. Black soldier fly larvae for organic manure recycling and its potential for a circular bioeconomy: A review. Sci. Total Environ. 833, 1–10 (2022).Article 

Google Scholar 
Siddiqui, S. A. et al. Black soldier fly larvae (BSFL) and their affinity for organic waste processing. Waste Manag. 140, 1–13 (2022).Article 
PubMed 

Google Scholar 
Bortolini, S. et al. Hermetia illucens (L.) larvae as chicken manure management tool for circular economy. J. Clean. Prod. 262, 1–10 (2020).Article 

Google Scholar 
Diener, S., Studt Solano, N. M., Roa Gutiérrez, F., Zurbrügg, C. & Tockner, K. Biological treatment of municipal organic waste using black soldier fly larvae. Waste Biomass Valorizat. 2, 357–363 (2011).Article 
CAS 

Google Scholar 
Cai, M. et al. Rapidly mitigating antibiotic resistant risks in chicken manure by Hermetia illucens bioconversion with intestinal microflora. Environ. Microbiol. 20, 4051–4062 (2018).Article 
CAS 
PubMed 

Google Scholar 
Yang, C. et al. Characteristics and mechanisms of ciprofloxacin degradation by black soldier fly larvae combined with associated intestinal microorganisms. Sci. Total Environ. 811, 1–8 (2022).Article 

Google Scholar 
Pang, W. et al. The influence on carbon, nitrogen recycling, and greenhouse gas emissions under different C/N ratios by black soldier fly. Environ. Sci. Pollut. Res. 27, 42767–42777 (2020).Article 
CAS 

Google Scholar 
Beskin, K. v. et al. Larval digestion of different manure types by the black soldier fly (Diptera: Stratiomyidae) impacts associated volatile emissions. Waste Manag. 74, 213–220 (2018).Gligorescu, A. et al. Pilot scale production of Hermetia illucens (L.) larvae and frass using former foodstuffs. Clean Eng. Technol. 10, 1–10 (2022).Rosa, R. et al. Life cycle assessment of chemical vs enzymatic-assisted extraction of proteins from black soldier fly prepupae for the preparation of biomaterials for potential agricultural use. ACS Sustain. Chem. Eng. 8, 14752–14764 (2020).Article 
CAS 

Google Scholar 
Surendra, K. C. et al. Rethinking organic wastes bioconversion: Evaluating the potential of the black soldier fly (Hermetia illucens (L.)) (Diptera: Stratiomyidae) (BSF). Waste Manag. 117, 58–80 (2020).Hasnol, S. et al. A review on insights for green production of unconventional protein and energy sources derived from the larval biomass of black soldier fly. Processes 8, 1–13 (2020).Article 

Google Scholar 
Wong, C. Y. et al. Rhizopus oligosporus-assisted valorization of coconut endosperm waste by black soldier fly larvae for simultaneous protein and lipid to biodiesel production. Processes 9, 1–14 (2021).Article 

Google Scholar 
Raksasat, R. et al. Blended sewage sludge–palm kernel expeller to enhance the palatability of black soldier fly larvae for biodiesel production. Processes 9, 1–13 (2021).Article 

Google Scholar 
Dortmans B.M.A., Diener S. & Verstappen B.M. Black Soldier Fly Biowaste Processing A Step-by-Step Guide. (2017).European Parliament. Regulation (EC) No 767/2009 of the European Parliament and of the council. (2009).Italian Government. Norme in materia ambientale. (Dlgs, 2006).European Parliament. Regulation (EC) No 178/2002 of the European Parliament and of the Council. Official Journal of the European Communities (2002).Palma, L., Fernandez-Bayo, J., Niemeier, D., Pitesky, M. & VanderGheynst, J. S. Managing high fiber food waste for the cultivation of black soldier fly larvae. NPJ Sci. Food 3, 1–7 (2019).Article 

Google Scholar 
Righi, C. et al. Suitability of porous inorganic materials from industrial residues and bioproducts for use in horticulture: A multidisciplinary approach. Appl. Sci. 12, 5437 (2022).Article 
CAS 

Google Scholar 
Barbi, S. et al. Preliminary study on sustainable NPK slow-release fertilizers based on byproducts and leftovers: A design-of-experiment approach. ACS Omega 5, 27154–27163 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macavei, L. I., Benassi, G., Stoian, V. & Maistrello, L. Optimization of Hermetia illucens (L.) egg laying under different nutrition and light conditions. PLoS ONE 15, 1–12 (2020).Article 

Google Scholar 
Leni, G., Maistrello, L., Pinotti, G., Sforza, S. & Caligiani, A. Production of carotenoid-rich Hermetia illucens larvae using specific agri-food by-products. J. Insects Food Feed 1, 1–12 (2022).
Google Scholar 
Caligiani, A. et al. Composition of black soldier fly prepupae and systematic approaches for extraction and fractionation of proteins, lipids and chitin. Food Res. Int. 105, 812–820 (2018).Article 
CAS 
PubMed 

Google Scholar 
Montgomery, D. C. Design and Analysis of Experiments Eighth Edition. Design vol. 2 (2012).Barbi, S., Messori, M., Manfredini, T., Pini, M. & Montorsi, M. Rational design and characterization of bioplastics from Hermetia illucens prepupae proteins. Biopolymers 110–118, (2019).Eriksson, L., Johansson, E., Kettaneh-Wold, N., WikstrÄom, C. & Wold, S. Design of Experiments: Principles and Applications. (2008).Morris, P. & John, P. W. M. Statistical Design and Analysis of Experiments. Math. Gaz. 83, 189–200 (1999).Article 

Google Scholar 
Kros, J. F. & Mastrangelo, C. M. Comparing multi-response design methods with mixed responses. Qual Reliab Eng Int 20, 527–539 (2004).Article 

Google Scholar 
Fernandez Pulido, C. R., Caballero, J., Bruns, M. A. & Brennan, R. A. Recovery of waste nutrients by duckweed for reuse in sustainable agriculture: Second-year results of a field pilot study with sorghum. Ecol Eng 168, 1–8 (2021).Kaya, M. et al. Biological, mechanical, optical and physicochemical properties of natural chitin films obtained from the dorsal pronotum and the wing of cockroach. Carbohydr. Polym. 163, 162–169 (2017).Article 
CAS 
PubMed 

Google Scholar 
Kaya, M. et al. On chemistry of γ-chitin. Carbohydr. Polym. 176, 177–186 (2017).Article 
CAS 
PubMed 

Google Scholar 
Poerio, A. et al. Extraction and physicochemical characterization of chitin from cicada orni sloughs of the south-eastern French mediterranean basin. Molecules 25, 1–12 (2020).Article 

Google Scholar 
Sagheer, F. A. A., Al-Sughayer, M. A., Muslim, S. & Elsabee, M. Z. Extraction and characterization of chitin and chitosan from marine sources in Arabian Gulf. Carbohydr. Polym. 77, 410–419 (2009).Article 

Google Scholar 
Waśko, A. et al. The first report of the physicochemical structure of chitin isolated from Hermetia illucens. Int. J. Biol. Macromol. 92, 316–320 (2016).Article 
PubMed 

Google Scholar 
Wang, K. et al. Preparation of bacterial cellulose/silk fibroin double-network hydrogel with high mechanical strength and biocompatibility for artificial cartilage. Cellulose 27, 1845–1852 (2020).Article 
CAS 

Google Scholar 
Morin, A. & Dufresne, A. Nanocomposites of Chitin Whiskers from Riftia Tubes and Poly(caprolactone). Macromolecules 35, 2190–2199 (2002).Article 
CAS 

Google Scholar 
George Socrates. Infrared and Raman Characteristic Group Frequencies: Tables and Charts. (John Wiley & Sons, 2004).Chen, P. & Zhang, L. New evidences of glass transitions and microstructures of soy protein plasticized with glycerol. Macromol. Biosci. 5, 237–245 (2005).Article 
CAS 
PubMed 

Google Scholar 
Robertson, N.-L.M., Nychka, J. A., Alemaskin, K. & Wolodko, J. D. Mechanical performance and moisture absorption of various natural fiber reinforced thermoplastic composites. J. Appl. Polym. Sci. 130, 969–980 (2013).Article 
CAS 

Google Scholar 
Chavez, M. The sustainability of industrial insect mass rearing for food and feed production: Zero waste goals through by-product utilization. Curr. Opin. Insect. Sci. 48, 44–49 (2021).Article 
PubMed 

Google Scholar 
Fisher, H. J. et al. Black soldier fly larvae meal as a protein source in low fish meal diets for Atlantic salmon (Salmo salar). Aquaculture 521, 1–12 (2020).Article 

Google Scholar 
Figueiredo, L. R. F., Nepomuceno, N. C., Melo, J. D. D. & Medeiros, E. S. Glycerol-based polymer adhesives reinforced with cellulose nanocrystals. Int. J. Adhes. Adhes. 110, (2021). More

Variation in environmental parameters across space and time in Bohai BayThe environmental parameters of samples collected near the Tianjin coastal area from different stations and seasons exhibited high temporal and spatial heterogeneity. The seawater temperature was 28.09 ± 0.53 °C in Aug, 17.48 ± 2.36 °C in May, and 19.55 ± 1.26 °C in Oct (Table 1). The seasonal variation in seawater temperature corresponded to the meteorological characteristics in Bohai Bay, with warm seawater in summer and relatively cool seawater in spring. The salinity was 29.69 ± 2.71‰ in Aug, 33.19 ± 0.33‰ in May, and 30.15 ± 1.63‰ in Oct. Seasonal variations in salinity may be mainly related to freshwater loading. According to the precipitation observed data of Bohai Bay in previous years, the rainfall amount and days in summer are the most19, which may lead to the increase in runoff and the relatively low salinity in summer. Chlorophyll a (Chl a) was highest in May, with lower levels in Aug and Oct. The dissolved inorganic nitrogen (DIN) was significantly higher in May and Aug than in Oct. The higher level of DIN in May and Aug may be related to terrestrial input and supply for the demand of phytoplankton growth. In October, the temperature and DIN content were both not suitable for phytoplankton growth, and Chl_a showed the lowest value. Spatially, the DIN distribution across the three seasons was rather similar, with high values observed in nearshore waters and low values in offshore waters (Dataset S1 & Fig. S1), which suggested that terrestrial input was an important source of DIN. The pH, soluble reactive phosphate (SRP) and chemical oxygen demand (COD) showed relatively higher values in October than in August and May, which may be caused by the dead phytoplankton release and terrestrial loadings through coasts and rivers. The dissolved oxygen (DO), conductivity and depth did not show significant variation among sampling times (Table 1), while the conductivity and depth had relatively higher values at offshore stations (Dataset S1) since the more remote the sampling water was, the greater the depth was in Bohai Bay and the closer it was to the open sea with higher salinity and conductivity. The ordination plot showed distinct partitioning of samples from nearshore and offshore sites along principal component axis 1 (PC1) (Fig. 1). The ordination plot could explain 73.49% of the total variation in the geo-physical–chemical parameters and revealed a linear positive correlation between different parameters (Fig. 1). AN, DIN, nitrate and Chl_a were most crucial in the partitioning of samples from May and the other 2 months; salinity, longitude, depth and conductivity were crucial for the partitioning of samples from offshore and nearshore stations; pH, COD, SRP, nitrite and temperature were crucial for the partitioning of samples from nearshore stations in August and October and samples from offshore stations. Overall, the principal component analysis (PCA) plot clearly showed both the temporal and spatial variation of the measured environmental parameters, indicating that complex biogeochemical processes and hydrodynamic conditions lead to the variation among sites and seasons.Table 1 The independent-samples t test of environmental variables and α-diversity among different months.Full size tableFigure 1Biplot of the principal component analysis (PCA) for environmental parameters in the seawater samples of the Bohai Bay coastal area across different seasons and sites. The two principal components (PC1 and PC2) explained 73.49% of the total variation in the environmental data and showed clear partitioning of offshore samples (in blue font) from other nearshore samples along PC1 and partitioning of May samples from August and October along PC2. The variables transparency and latitude were strongly correlated with PC1, and the variables ammonia nitrogen (AN), COD, pH, soluble reactive phosphate (SRP), and nitrite were strongly correlated with PC2. Chlorophyll a (Chl_a), dissolved inorganic nitrogen (DIN), nitrate and DO were mainly positively correlated with samples from May, while salinity, longitude, depth and conductivity were mainly positively correlated with offshore samples. Blue arrows represent environmental parameters, and circles in color represent sampling points.Full size imageProkaryotic α/β-diversity variationMeasures of α-diversity showed significant differences in shannon, evenness, faith_pd and OTU richness between samples from May/Aug and Oct (Fig. 2, Table 1). Principal coordinates analysis (PCoAs) based on weighted UniFrac (WUF) distance and unweighted UniFrac (UUF) distance showed that the PCC from different sampling months separated across the first and second principal coordinates (Fig. 3A-B). Both the analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA/ADONIS) results indicated that the prokaryotic communities varied significantly across different sampling months when using a WUF distance metric (ANOSIM, r = 0.709, P = 0.001; ADONIS, R2 = 40.0%, P = 0.001) and UUF distance metric (ANOSIM, r = 0.934, P = 0.001; ADONIS, R2 = 38.7%, P = 0.001). At the same time, the prokaryotic α– and β-diversity both showed high within-month variability in Aug (Figs. 2, 3C–D), which indicated that the community varied greatly among different sites in Aug.Figure 2Alpha diversity of shannon, eveness, faith_pd (phylogenetic diversity) and OTU richness value of the prokaryotic community of all the samples from different stations at different sampling times.Full size imageFigure 3Principal coordinate analysis (PCoA) based on unweighted (A) and weighted (B) UniFrac distances for prokaryotic communities in the surface waters; box plots showing the unweighted (C) and weighted (D) UniFrac distances among each station at different sampling times.Full size imageCorrelation between prokaryotic α/β-diversity and physical, chemical and geographic factorsThe α-diversity measurements exhibited significant positive correlations with temperature, pH, SRP, AN and un_ionN (Dataset S2). The correlation between α-diversity indexes and geo factors (longitude and latitude) was not strong or significant both in samples across the three sampling times or from each sampling time (Dataset S2).The environmental variation significantly correlated with β-diversity among the three seasons (r_weighted = 0.4558, r_unweighted = 0.4631, P = 0.001, Table 2), with pH, AN, temperature, un_ionN, COD, nitrite, SRP, salinity, DO and DIN as the main individual determinants. However, it did not show significant correlations with β-diversity at any sampling time except in Oct (Table S1).Table 2 Spearman’s rank correlation between environmental/spatial variability (Euclidean distance) and prokaryotic β-diversity (weighted/unweighted UniFrac distance) among all samples from different season.Full size tableThe geographic distance was not correlated with prokaryotic β-diversity (variation in community composition; r  0.05; Table 2) among the three sampling times. However, samples from Aug and Oct exhibited a significant correlation between β-diversity and geographic distance (Table S1).Factors driving the PCC variationPERMANOVA using the UUF/WUF distance indicated that temperature variation explained the largest part of community variation among the investigated factors (34.90%/19.83%, P = 0.001, Dataset S3), with AN (31.84%/13.56%, P = 0.001) and salinity (12.91%/6.21%, P = 0.001) as the second and third most significant sources of variation.The variance partitioning analysis (VPA) conducted on both UUF/WUF distances showed that almost 100% percent of the variation in PCC among all three sampling times was explained by the detected environmental factors. In May, no environmental or spatial factors could be selected as significantly explain the PCC variation; in Aug, the joint effects of environmental and spatial factors could explain 49.5% of the variation; in Oct, based on WUF distance, the spatial factors could purely explain 10.5%, environmental factors could purely explain 38.8%, their joint effects could explain 28.2%, and based on UUF distance, the joint effects of environmental factors and trend could explain 13.7% of the PCC variation. These results indicated dramatic shifts in the spatial or environmental factor effects on the PCC variation at different sampling times in Bohai Bay (Table 3).Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).Full size tableDistinct seasonal features at the phylum and OTU levelsThere were notable differences in the proportions of various phyla among different seasons (sampling month). In May, there was a greater proportion of Alphaproteobacteria (41.41%), Planctomycetes (6.42%), Actinobacteria (3.86%), Firmicutes (1.48%), Acidobacteria (0.45%), TM7 (0.16%), Tenericutes (0.16%), OD1 (0.13%), and WPS-2 (0.09%) than in Aug and Oct, whereas Gammaproteobacteria (44.23%), GN02 (0.08%) and SAR406 (0.04%) were depleted in May and Aug but enriched in Oct. In Aug, Bacteroidetes (13.98%), Deltaproteobacteria (6.93%), Verrucomicrobia (4.5%), Chloroflexi (0.36%), Lentisphaerae (0.97%), TM6 (0.25%), Nitrospirae (0.08%), Chlamydiae (0.07%), Chlorobi (0.07%), Spirochaetes (0.04%) and OP8 (0.03%) were significantly enriched than in the other two sampling times (Duncan test; Table S2).At the OTU level, OTUs with relative abundance  > 0.01% (1040 OTUs) were used to perform the difference analysis, and 175 OTUs in May, 281 OTUs in Aug, and 210 OTUs in Oct were identified as seasonal specific OTUs (ssOTUs). The cooccurrence network showed that the ssOTUs were clustered separately (Fig. 4A). Furthermore, the separation of the three modules contained most of the ssOTUs specific to different seasons (Fig. 4A-B). All the ssOTUs of different seasons comprised a taxonomically broad set of prokaryotes at the phylum (phylum Proteobacteria is grouped at the class level) level (Fig. 4C) belonging to various phyla with different proportions. Betaproteobacteria, Verrucomicrobia, Gemmatimonadetes, Epsilonproteobacteria, PAUC34f., and Euryarchaeota did not show significant differences among the three sampling times at the phylum level, but features belonging to these phyla showed differences at the OTU level (Fig. 4C, Dataset S4). In addition, the phylum ssOTUs belonging to, such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Actinobacteria, and Deltaproteobacteria, were not only enriched at one sampling time (Dataset S4) but also enriched at the other two sampling times (Fig. 4C, Dataset S4). These results revealed that different seasons do not strictly select specific microbial lineages at the phylum level, but a finer level analysis could more strictly reflect the seasonal variation.Figure 4Co-occurrence patterns of seasonal sensitive OTUs (A). Co-occurrence network visualizing significant correlations (ρ  > 0.7, P  0.01%. Different colors represent ssOTUs in May (green), Aug (red) and Oct (blue). Cumulative relative abundance (as counts per million, CPM; y-axis in × 1000) of all the sensitive modules in the networks (B). The phylum attribution of ssOTUs in each season (C). The y-axis is the percentage of the number of OTUs that belong to a particular phylum that accounts for the total number of all the OTUs.Full size imageRegression analysis between the relative abundance of modules to which the ssOTUs belonged and the environmental factors was also conducted, and module 1 abundance, to which the Aug-ssOTUs belonged, showed a significant positive correlation with temperature (R2 = 0.77, P = 6.609e−62), AN (R2 = 0.43, P = 7.416e−25), and un_ionN (R2 = 0.75, P = 1.366e−58) and a negative correlation with SRP (R2 = 0.81, P = 6.762e-17). This may be caused by the functional role of the microbes in Aug. In the Aug-ssOTUs, Deltaproteobacteria showed a higher ratio than in the other 2 months (Fig. 4c), and in the following functional analysis, Deltaproteobacteria contributed to the genes related to nitrogen fixation, which may help to explain why there was a positive correlation of Aug-ssOTUs to AN and un_ionN. The module 2 abundance to which the May-ssOTUs belonged showed a significant negative correlation with pH (R2 = 0.65, P = 4.026e−44), temperature (R2 = 0.19, P = 2.325e−10), un_ionN (R2 = 0.025, P = 0.01779), and SRP (R2 = 0.12, P = 4.104e−07) and a positive correlation with AN (R2 = 0.26, P = 5.174e−14). In the May-ssOTUs, the ratio of Alphaproteobacteria was the highest, and Alphaproteobacteria were reported to be pH-sensitive groups in marine environments20, which prefer neutral pH environments21. In this study, the pH in May was 8.04 ± 0.07, in Aug was 8.39 ± 0.09, in Oct was 8.38 ± 0.07, and the pH in May was the closest to neutral, and the ratio decreased with increasing pH in Oct and Aug. The abundance of module 3, to which the Oct-ssOTUs belonged, showed a significant positive correlation with SRP (R2 = 0.81, P = 0.16e-10) and pH (R2 = 0.054, P = 0.00075) and a negative correlation with temperature (R2 = 0.44, P = 2.276e−25), AN (R2 = 0.75, P = 4.51e−58), and un_ionN (R2 = 0.6, P = 3.995e-39) (Fig. S2). Phosphate has been identified to limit primary productivity22, which is of great importance in the structure of dominant bacterial taxa in marine environments23. In the Oct-ssOTUs, the ratio of Gammaproteobacteria was the highest, as reported. Gammaproteobacteria was significantly explained by SRP during the seasonal variation in the Western English Channel, with Rho equal to 0.7523, which suggested the sensitivity of it to SRP, and in that study, it also showed a negative correlation between temperature and Gammaproteobacteria and a positive correlation between SRP and Gammaproteobacteria. Although the correlation was not significant, the variation trend was consistent, which indicates that the phenomenon observed in this study was not unexpected. In addition, most ammonia-oxidizing bacteria belong to the Betaproteobacteria and Gammaproteobacteria classes are chemolithoautotrophs that oxidize ammonia to nitrite24. Gammaproteobacteria and Betaproteobacteria both had higher ratios in Oct-ssOTUs, and the functional prediction results also showed that pmoA/amoA and pmoB/amoB, which encode ammonia monooxygenase, were mainly contributed by OTUs from Gammaproteobacteria and Betaproteobacteria (Dataset S10). The utilization of ammonia may explain the negative correlation between the Oct-ssOTUs and AN.Community assembly processes across different sampling months and sitesBased on the analysis of phylogenetic turnover, unweighted βNTI mostly ranged from -2 to 2 across different sites at a single sampling time in May, Aug and Oct, revealing that PCC variations across different sampling sites at a single time were mostly affected by stochastic processes. The unweighted βNTI was greater than 2 across May–Aug, May–Oct and Aug-Oct (Fig. 5A), which revealed that the variations in PCC across different sampling times were mostly affected by deterministic processes. The RCbray values across any two sampling times were equal to 1, and in each sampling time, the RCbray values ranged from − 1 to 1 (Fig. 5B). Combining the βNTI and RCbray values, the community assembly processes were quantified at each sampling time and at any two sampling times. As shown in Fig. 5C, turning over of the community during different sampling times was mainly governed by selection; among the different sites in May and Oct, it was mainly governed by “undominated” processes; community turn over in Aug was mainly governed by the influence of “Dispersal Limitation”. These results indicated that the shifts in the assembly of prokaryotic communities during different sampling times were caused by strong “heterogeneous selection” (βNTI  > 2), and the community variation at each sampling time was mainly caused by stochastic processes.Figure 5Patterns of distribution of unweighted βNTI (A) and RCbray (B) values across different sampling times. Quantification of the features that impose community assembly processes in and among different sampling times. (C) Pie charts give the percent of turnover in community composition governed primarily by Selection acting alone (white fill), Dispersal Limitation (green line fill), Homogenizing Dispersal (blue line fill) and undominated process (cyan fill).Full size imagePrediction of the metabolic potential at different sampling timesThe NSTI scores of each sample ranged from 0.033 to 0.096, with a mean of 0.058 (Dataset S5). Microbial functions were detected in all the samples from the three sampling times, and it was found that the relative abundances of 242 pathways were significantly changed between samples from May and samples from Aug (Dataset S6). The relative abundances of 321 pathways were significantly changed between samples from May and Oct (Dataset S7), and the relative abundances of 370 pathways were significantly changed between samples from Aug and Oct (Dataset S8).Genes related to energy metabolism were given more attention. For nitrogen metabolism genes relevant with nitrogen fixation (nifD, nifK) were detected only enriched in Aug, while genes relevant with nitrate reduction and denitrification (narG, narZ, nxrA, narH, narY, nxrB, narI, narV, nirD, nasA, nasB) were detected enriched in May, genes related with ammonia oxidation were both detected enriched in Oct and Aug. For sulfur metabolism, genes relevant with thiosulfate oxidation (soxA, soxB, soxC, soxX, soxY and soxZ) were only enriched in Aug, while genes relevant with sulfate and sulfite reduction (cysNC, aprA, aprB, cysJ, cysI, cysK, dsrA) were detected enriched in May and Oct (Fig. 6).Figure 6The LEfSe analysis indicated significantly differential abundances of PICRUSt predicted genes relevant to energy metabolism in different months of samples.Full size imageProkaryotic taxa contributed to the significantly varied functional genes related to nitrogen and sulfur metabolism at different sampling times. At the species level, the taxa contributing to nifK and nifD mainly belonged to Deltaproteobacteria and Firmicutes, and the taxa contributing to the sox-series genes mainly belonged to Alphaproteobacteria and Gammaproteobacteria (Fig. S3). The denitrification-related functional genes that were enriched in May were mainly contributed by members from Alphaproteobacteria and Gammaproteobacteria. The taxa contributing to dsrA, aprA and aprB were mainly from Deltaproteobacteria, including members of Desulfarculaceae, Desulfobacteraceae, Desulfobulbaceae, Desulfovibrionaceae and Syntrophobacteraceae (Fig. S4). More

Keenan, T. F. & Williams, C. A. The terrestrial carbon sink. Annu. Rev. Environ. Resour. 43, 219–243 (2018).Article 

Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).Article 

Google Scholar 
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. N. Phytol. 229, 2413–2445 (2021).Article 

Google Scholar 
Fossum, C. et al. Belowground allocation and dynamics of recently fixed plant carbon in a California annual grassland. Soil Biol. Biochem. 165, 108519 (2022).Article 

Google Scholar 
Rasse, D. P., Rumpel, C. & Dignac, M.-F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269, 341–356 (2005).Article 

Google Scholar 
Sokol, N. W., Kuebbing, Sara, E., Karlsen-Ayala, E. & Bradford, M. A. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. N. Phytol. 221, 233–246 (2019).Article 

Google Scholar 
Calvo, O. C., Franzaring, J., Schmid, I. & Fangmeier, A. Root exudation of carbohydrates and cations from barley in response to drought and elevated CO2. Plant Soil 438, 127–142 (2019).Article 

Google Scholar 
Fransson, P. M. A. & Johansson, E. M. Elevated CO2 and nitrogen influence exudation of soluble organic compounds by ectomycorrhizal root systems. FEMS Microbiol. Ecol. 71, 186–196 (2009).Article 

Google Scholar 
Johansson, E. M., Fransson, P. M. A., Finlay, R. D. & van Hees, P. A. W. Quantitative analysis of soluble exudates produced by ectomycorrhizal roots as a response to ambient and elevated CO2. Soil Biol. Biochem. 41, 1111–1116 (2009).Article 

Google Scholar 
Phillips, R. P., Finzi, A. C. & Bernhardt, E. S. Enhanced root exudation induces microbial feedbacks to N cycling in a pine forest under long-term CO2 fumigation. Ecol. Lett. 14, 187–194 (2011).Article 

Google Scholar 
Jilling, A., Keiluweit, M., Gutknecht, J. L. M. & Grandy, A. S. Priming mechanisms providing plants and microbes access to mineral-associated organic matter. Soil Biol. Biochem. 158, 108265 (2021).Article 

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).Article 

Google Scholar 
Sokol, N. W., Sanderman, J. & Bradford, M. A. Pathways of mineral-associated soil organic matter formation: integrating the role of plant carbon source, chemistry, and point of entry. Glob. Change Biol. 25, 12–24 (2019).Article 

Google Scholar 
Bradford, M. A., Keiser, A. D., Davies, C. A., Mersmann, C. A. & Strickland, M. S. Empirical evidence that soil carbon formation from plant inputs is positively related to microbial growth. Biogeochemistry 113, 271–281 (2013).Article 

Google Scholar 
Keiluweit, M. et al. Mineral protection of soil carbon counteracted by root exudates. Nat. Clim. Change 5, 588–595 (2015).Article 

Google Scholar 
Kuzyakov, Y., Friedel, J. K. & Stahr, K. Review of mechanisms and quantification of priming effects. Soil Biol. Biochem. 32, 1485–1498 (2000).Article 

Google Scholar 
Jones, D. L., Dennis, P. G., Owen, A. G. & van Hees, P. A. W. Organic acid behavior in soils—misconceptions and knowledge gaps. Plant Soil 248, 31–41 (2003).Article 

Google Scholar 
Cleveland, C. C. & Liptzin, D. C:N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235–252 (2007).Article 

Google Scholar 
Meier, I. C., Finzi, A. C. & Phillips, R. P. Root exudates increase N availability by stimulating microbial turnover of fast-cycling N pools. Soil Biol. Biochem. 106, 119–128 (2017).Article 

Google Scholar 
Canarini, A., Kaiser, C., Merchant, A., Richter, A. & Wanek, W. Root exudation of primary metabolites: mechanisms and their roles in plant responses to environmental stimuli. Front. Plant Sci. 10, 157 (2019).Article 

Google Scholar 
Koo, B.-J., Adriano, D. C., Bolan, N. S. & Barton, C. D. in Encyclopedia of Soils in the Environment (ed. Hillel, D.) 421–428 (Elsevier, 2005); https://doi.org/10.1016/B0-12-348530-4/00461-6Oldfield, E. E., Crowther, T. W. & Bradford, M. A. Substrate identity and amount overwhelm temperature effects on soil carbon formation. Soil Biol. Biochem. 124, 218–226 (2018).Article 

Google Scholar 
Mason-Jones, K., Schmücker, N. & Kuzyakov, Y. Contrasting effects of organic and mineral nitrogen challenge the N-mining hypothesis for soil organic matter priming. Soil Biol. Biochem. 124, 38–46 (2018).Article 

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).Article 

Google Scholar 
Drake, J. E. et al. Stoichiometry constrains microbial response to root exudation—insights from a model and a field experiment in a temperate forest. Biogeosciences 10, 821–838 (2013).Article 

Google Scholar 
Falchini, L., Naumova, N., Kuikman, P. J., Bloem, J. & Nannipieri, P. CO2 evolution and denaturing gradient gel electrophoresis profiles of bacterial communities in soil following addition of low molecular weight substrates to simulate root exudation. Soil Biol. Biochem. 35, 775–782 (2003).Article 

Google Scholar 
Rasmussen, C., Southard, R. J. & Horwath, W. R. Soil mineralogy affects conifer forest soil carbon source utilization and microbial priming. Soil Sci. Soc. Am. J. 71, 1141–1150 (2007).Article 

Google Scholar 
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395–398 (2013).Article 

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).Article 

Google Scholar 
Craig, M. E. et al. Fast-decaying plant litter enhances soil carbon in temperate forests but not through microbial physiological traits. Nat. Commun. 13, 1229 (2022).Article 

Google Scholar 
Blagodatsky, S., Blagodatskaya, E., Yuyukina, T. & Kuzyakov, Y. Model of apparent and real priming effects: linking microbial activity with soil organic matter decomposition. Soil Biol. Biochem. 42, 1275–1283 (2010).Article 

Google Scholar 
Hill, P. W., Farrar, J. F. & Jones, D. L. Decoupling of microbial glucose uptake and mineralization in soil. Soil Biol. Biochem. 40, 616–624 (2008).Article 

Google Scholar 
Asmar, F., Eiland, F. & Nielsen, N. E. Interrelationship between extracellular enzyme activity, ATP content, total counts of bacteria and CO2 evolution. Biol. Fertil. Soils 14, 288–292 (1992).Article 

Google Scholar 
Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: a question of microbial competition? Soil Biol. Biochem. 35, 837–843 (2003).Article 

Google Scholar 
McFarlane, K. J. et al. Comparison of soil organic matter dynamics at five temperate deciduous forests with physical fractionation and radiocarbon measurements. Biogeochemistry 112, 457–476 (2013).Article 

Google Scholar 
Post, W. M., Emanuel, W. R., Zinke, P. J. & Stangenberger, A. G. Soil carbon pools and world life zones. Nature 298, 156–159 (1982).Article 

Google Scholar 
Smith, W. H. Character and significance of forest tree root exudates. Ecology 57, 324–331 (1976).Article 

Google Scholar 
Dong, J. et al. Impacts of elevated CO2 on plant resistance to nutrient deficiency and toxic ions via root exudates: a review. Sci. Total Environ. 754, 142434 (2021).Article 

Google Scholar 
White, M. A., Running, S. W. & Thornton, P. E. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. Int. J. Biometeorol. 42, 139–145 (1999).Article 

Google Scholar 
Giasson, M.-A. et al. Soil respiration in a northeastern US temperate forest: a 22-year synthesis. Ecosphere 4, 140 (2013).Article 

Google Scholar 
Mrak, T. et al. Elevated ozone prevents acquisition of available nitrogen due to smaller root surface area in poplar. Plant Soil 450, 585–599 (2020).Article 

Google Scholar 
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).Article 

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).Article 

Google Scholar 
Haney, R. L., Franzluebbers, A. J., Hons, F. M. & Zuberer, D. A. Soil C extracted with water or K2SO4: pH effect on determination of microbial biomass. Can. J. Soil Sci. 79, 529–533 (1999).Article 

Google Scholar 
Ahmed, M. J. & Hossan, J. Spectrophotometric determination of aluminium by morin. Talanta 42, 1135–1142 (1995).Article 

Google Scholar 
Viollier, E., Inglett, P. W., Hunter, K., Roychoudhury, A. N. & Van Cappellen, P. The ferrozine method revisited: Fe(II)/Fe(III) determination in natural waters. Appl. Geochem. 15, 785–790 (2000).Article 

Google Scholar  More