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    China economy-wide material flow account database from 1990 to 2020

    China economy-wide material flow identification: system boundary, processes, and materialsThe first step is to define an economy, i.e., the economic (rather than geographical) territory of a country in which the activities and transactions of producer and consumer units are resident. Additionally, the period is a total of thirty-one years, from 1990 to 2020, for the following reasons: (1) statistics before 1990 are of poor quality and are insufficient to allow us to conduct analyses; and (2) so far, statistics have just recently been updated to cover the year of 2020. Furthermore, the analytical framework (hereinafter referred to as China EW-MFA) is developed to explore material utilisation and its environmental consequences within China’s economy.The general structure of China EW-MFA is depicted in Fig. 1, which comprises seven processes. (1) Input of extracted resources: domestic natural resources are extracted from the environment to the economy through human-controlled means. (2) Output of domestic processed materials: after being processed by manufacturers, materials are released from the economy into the environment in the form of by-products and residues, which can be classified by their destinations (i.e., air, land, and water) and pathways (dissipative use and losses). (3) Input and (4) output by cross-border trade: by imports and exports, materials are transported between China’s economy and the economies of the rest of the world. (5) Input and (6) output of balancing items (BI): sometimes, materials identified in the output processes are not considered by inputs, which needs to be balanced. For example, the utilisation of fossil energy materials by combustion causes the emission of carbon dioxide (CO2) into the air, which is identified as system output, but requirements of oxygen (O2) as system input are not counted. (7) Additions to the system: within the economy, materials would have been added to the economy in the form of buildings, infrastructures, durable goods, and household appliances, which are referred to as the net additions to stock (NAS).Fig. 1The general structure of China EW-MFA. To note, white data cells can be obtained directly from official statistics, whereas grey cells are estimated.Full size imageThe last step is to specify the materials concerned in each process. Four types (in blue boxes in Fig. 1) of natural materials are extracted and input into the economy in China, i.e., harvested biomass (33 items), mined metal ores (28 items), quarried non-metallic minerals (155 items), and mined fossil energy materials (6 items in 3 classes). Materials (green boxes) released into the air are greenhouse gases (e.g., CO2, methane (CH4), dinitrogen oxide (N2O)), air pollutants (e.g., particulate matter 10 (PM10), black carbon (BC)), and toxic contaminants of mercury (Hg) in divalent, gaseous elemental, and particulate forms. Those released into the water are inorganic matters (of nitrogen (N), phosphorus (P), Arsenic (As), and four heavy metals of lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr)) and organic matters of cyanide, petroleum, and volatile phenol. Materials released into the land are waste disposal in uncontrolled landfills, which are illegal in China. Some materials are dissipated by application, for example, fertilisers, compost, sewage sludge being applied to agricultural land, and pesticides being used to cultivate crops. Some would be unintentionally dissipated from abrasion, corrosion, erosion, and leakages. Materials (in red boxes) are BI, which includes the input of O2 and output of water vapour in the fossil energy material combustion process, the input of O2 and output of water vapour and CO2 in the respiration process of human and cultivated livestock, input and output of water in imported and exported beverages, and the output of water from domestically extracting crops.There are some messages needed to be mentioned: (1) Material of water is not included since its flow volume is more substantial than others, which needs to be independently analysed; (2) Activities of foreign tourists, cross-border transfer of emissions through natural media, etc. are excluded. (3) To be clear, we refer to a data cell as a specific flow process of a specific substance in a specific year, e.g., the number of cereals domestically extracted in 2020.Data acquisition: sources and collectionBased on our China EW-MFA, we first analyse accessibility, reliability, completeness, rules of redistribution, etc., for each data source (yellow boxes in Fig. 1), including China national database, China rural statistical yearbooks, USGS mineral yearbooks, etc. The complete list of data sources and descriptions are presented in Table 1. Then, we store the originally retrieved data source files in a semi- or unstructured format (e.g., CSV, PDF). Next, we manually collect these statistics and reorganise them according to China EW-MFA material types and processes. However, only a tiny part of retrieved statistics can be applied directly, as specified in black colour in Fig. 1.Table 1 Data sources and descriptions.Full size tableData compilation: parameter localisation and data estimationA few inconsistencies in statistics were noticed, which would result in data incompleteness. For example, the domestic extraction of vegetables has been accounted for and published since 1995, before which statistics are unavailable. The domestically harvested timber has been measured in the volume unit of cubic metres, which needs to be converted into the mass unit via density conversion factor. Therefore, acquired statistics have to be estimated, which are specified in grey colour in Fig. 1. The following section elaborates on each data cell’s estimation methods, localised parameters, references, etc. In our uploaded data files, the original statistics, data sources, and compilation methods (using formulas) are all implemented, as explained in the Data Records Section.

    The input of natural resources by domestic extraction

    Vegetables in crops: Statistics of vegetable production (WVegetables)16 during 1990–1994 are unavailable, which is estimated based on the relationship between the production yield (PYield) and areas (AVegetables), as shown in Eq. 1. Here, PYield is assumed to remain constant at 27.04 thousand tonnes per thousand hectares from 1990 to 1995, derived by dividing vegetable production (257,267 thousand tonnes) by areas (9,515 thousand hectares) in 1995.$${W}_{Vegetables}={P}_{Yield}times {A}_{Vegetables}$$
    (1)

    Nuts in crops: One of them is chestnuts. The chestnut production in 2020 is unavailable, which is assumed to be the same as in 2019.

    Crop residues in biomass residues: They are referred to as that harvested production of crops that do not reach the market to be sold but are instead employed as raw materials for commercial purposes such as energy generation and livestock husbandry. This number (Wcrop residues) can be calculated by first determining the number of crop residues available from primary crop production (Wcrop) and the harvest factor (Pharvest factor), and then using the recovery rate (Precovery rate) to determine the number of crop residues used by the economy, as shown in Eq. 2. These parameters have been localized by previous studies17,18, which are adopted in this study, i.e., wheat (1.1 for Pharvest factor and 0.463 for Precovery rate), maize (1.2, 0.463), rice (0.9, 0.463), sugar cane (0.5, 0.9), beetroots (0.7, 0.9), tuber (0.5, 0.463), pulse (1.2, 0.7), cotton (3.4, 0.463), fibre crops (1.8, 0.463), silkworm cocoons (1.8, 0.463), and oil-bearing crops (1.8, 0.463).$${W}_{cropresidues}={W}_{crop}times {P}_{harvestfactor}times {P}_{recoveryrate}$$
    (2)

    Roughage of grazed biomass and fodder crops in biomass residues: In China, the grazed biomass for roughage includes annual forage and perennial forage, whereas fodder crops comprise straw feed, processed straw feed, and all other fodder crops. However, information19 on grazed biomass production is only accessible from 2006 to 2018, whereas fodder crop statistics are only available from 2015 to 2017. Equation 3 and Eq. 4 can be used to estimate unavailable statistics. To note, we assume that China’s domestic roughage supply structure has remained unaltered, which has two meanings. The proportion of total domestic roughage production (WDomestic production) in requirement (WRoughage requirement) has remained constant, while the proportion (PSupply fraction) of grazed biomass and fodder crop in domestic roughage production has been unchanged. The requirement (WRoughage requirement) is determined by the quantity of livestock (QLivestock) and their annual feeding amount (PAnnual intake). PAnnual intake (in tonnes per head per year) has been localised for each type of livestock4, with 4.5 for live cattle and buffaloes, 0.5 for sheep and goats, 3.7 for horses, and 2.2 for mules and asses.$${W}_{Roughagerequirement}={Q}_{Livestock}times {P}_{Annualintake}$$
    (3)
    $${W}_{Domesticproduction}={W}_{Roughagerequirement}times {P}_{Supplyfraction}$$
    (4)

    Timber in wood: As illustrated in Eq. 5, wood production16 is reported in volume units of cubic metres (VTimber), which need to be converted into mass units (WTimber) via density (PDensity). The parameter PDensity is assumed to be 0.58 tonnes per cubic metre, calculated by averaging 0.52 for coniferous types and 0.64 for non-coniferous ones4.$${W}_{Timber}={V}_{Timber}times {P}_{Density}$$
    (5)

    Non-ferrous metals in metal ores: Non-ferrous metal statistics are derived from two sources. China statistics20 are measured in gross ore (WMetal ores in gross ore) but are only available from 1999 to 2017, whereas the USGS statistics21 cover the period of 1990 to 2020 but they are measured in metal or concentrate content (WMetal ores in other units). Therefore, USGS statistics need to be converted with an empirical unit conversion factor (PUnit conversion factor) before being applied to estimate unavailable statistics reported by China, as shown in Eq. 6. Conversion factors are localised for each non-ferrous metal in each year from 2000 to 2017 by using USGS statistics divided by China statistics and then averaged after removing the highest value and the lowest value (i.e., trimmed mean). This factor could capture the general relationship between statistics from two separate sources, which can be used in other long time-series studies on resource management on a particular element in China.$${W}_{Metaloresingrossore}={W}_{Metaloresinotherunits}/{P}_{Unitconversionfactor}$$
    (6)

    Non-metallic minerals: The official China-specific information on non-metallic mineral domestic production is available between 1999 and 201720, the rest of which could be estimated from USGS statistics (1990–2020)21. Also, two differences in reporting standards are observed resulting from the material coverages and reporting units. China statistics contain eighty-eight materials in mineral ores, whereas the USGS only includes twenty in the concentrate unit. Therefore, a conversion factor is developed in this estimation, as shown in Eq. 7. This conversion factor is applied to the total amount of non-metallic mineral production, which is assumed to have been constant from 1990 to 1999 at 11.38% (1999) and 12.56% (2017) from 2017 to 2020.$${W}_{Mineralsingrossore}={W}_{Mineralsinotherunits}/{P}_{Conversionfactor}$$
    (7)

    Coal in fossil energy materials: Coal, mined in China, includes raw coal, peat, stone coal, and oil shale. Except for raw coal, statistics for the rest are only available from 1999 to 201720. The unavailable data (WOther coals) is estimated using Eq. 8 under the assumption that the structure of the coal supply in China barely changes. That is, the proportion (PSupply fraction) of peat, stone coal, and oil shale in raw coal production (WRaw coal) remains constant, so the 1999 proportion is applied to all years before that (earlier years of 1990–1998), while the 2017 proportion is used to the recent years between 2018 and 2020. For example, PSupply fraction for oil shale production was assumed to be 0.014% during 1990–1999, calculated by dividing raw coal production (1,250,000) by oil shale production (179) in 1999. PSupply fraction in the earlier and the recent years are 0.007% and 0.001% for peat, 0.203% and 0.031% for stone coal, and 0.014% and 0.067% for oil shale.

    $${W}_{Othercoals}={W}_{Rawcoal}/{P}_{Supplyfraction}$$
    (8)

    The output of processed materials by release

    Materials released into the air: In China, thirteen materials are released into the air, as shown in Fig. 1. The emission of sulphur dioxide (SO2) is reported in China environmental statistical yearbooks22,23, while the rest is specified in the EDGAR24. However, in EDGAR, statistics for recent years have not yet been updated, which are estimated with the value in the most recent year in our database. For example, nitrous oxide (NOx) records are only available for the years prior to 2016, with 26,365 thousand tonnes in 2015 and 26,837 in 2014. As a result of the observed decreasing trend in NOx emissions, NOx emission data for 2016–2020 is estimated to be 26,000 thousand tonnes. This estimate may be subjective due to constraints, but it would be aligned with European statistics, allowing for international comparisons. Data can be updated after the EDGAR statistics have been updated.

    Materials released into the water: Ten principal materials have been found in China wastewater (both industrial and municipal) that are nitrogen (N), phosphorus (P), organic pollutants of petroleum, volatile phenol and cyanide, heavy metals of mercury (Hg), lead (Pb), cadmium (C·d), and the hexavalent chromium (Cr6+), and arsenic (As). Many statistics22,23 have been of poor quality (e.g., inconsistent material coverages between years). Given that the statistics of pollutants in industrial wastewater cover more periods and contain fewer abnormal observations, the total material emissions can be approximated from those of industrial wastewater. Equations 9 and 10 show the estimation processes. The materials in industrial wastewater (WIndustrial materials) are first identified using material mass concentration (PConcentration) and the weight of industrial wastewater (WIndustrial wastewater), and then the materials in total wastewater (WTotal materials) are identified using the proportion (PContribution) of materials in industrial wastewaters (WIndustrial materials) to the total. The assumption is that PConcentration and PContribution change gradually between years, which enables to use linear interpolation method to estimate unavailable parameters. Consider cyanide: its PConcentration was 23.61 (1‰ ppm) in 2005 and 37.31 in 2002, which was assumed to be 28.18 in 2004 and 32.74 in 2003. PConcentration was assumed to be 100% throughout the years for cyanide because all cyanide emissions in China are driven by industrial wastewater discharges. Later, the total material emissions can be derived by dividing the industrial wastewater mass by PConcentration.$${W}_{Industrialmaterials}={W}_{Industrialwastewater}times {P}_{Concentration}$$
    (9)
    $${W}_{Totalmaterials}={W}_{Industrialmaterials},/,{P}_{Contribution}$$
    (10)

    Materials released to the land: This is zero because uncontrolled landfills are illegal in China.

    Materials dissipated by organic fertiliser use: In China, manure is the primary organic fertiliser, which is excreted by pigs, dairy cows, calves, sheep, horses, asses, mules, camels, chickens, and other animals. As shown in Eq. 11, the manure production (WManure) is estimated through the amounts of raised livestock (QLivestock, heads), the weight of daily manure production (PManure production, kilograms per head per day), the number of days they are raised (PFeeding period, in days per year), and the moisture content of their manure (PDry matter, %) for each type of animal. These parameters are region-specific, which have been localised by Chinese scholars25,26,27 and listed in Table 2.$${W}_{Manure}={Q}_{Livestock}times {P}_{Manureproduction}times {P}_{Feedingperiod}times {P}_{Drymatter}$$
    (11)
    Table 2 Localised parameters for animal manure production.Full size table

    Materials dissipated by mineral fertiliser use: The mineral fertilisers used in China are four types, i.e., nitrogen (N), phosphorus (P), potash (K), and compound. Their usage (WFertiliser usage) is measured in nutrient mass (WNutrient materials), which needs to be converted into the gross mass by dividing their nutrient content (PNutrient content). Equation 12 shows the estimation. This parameter of PNutrient content is localised by the Ministry of Agriculture and Rural Affairs of China28 as 29%, 22%, 35%, and 44% for N- bearing, P- bearing, K-bearing, and compound fertilisers, respectively.$${W}_{Fertiliserusage}={W}_{Nutrientmaterials}/{P}_{Nutrientcontent}$$
    (12)

    Materials dissipated by sewage sludge: Sewage sludge is the residue generated by municipal wastewater treatment. As demonstrated in Eq. 13, its dissipative use (Wss, dissipation) is the untreated amount of production (Wss, production), represented by the parameter of Pss, dissipation rate. Sewage sludge production (Wss, production) statistics are only available for the years 2006–202029, and data for the remaining years can be estimated using Eq. 14 and Eq. 15. In Eq. 14, Pss, production rate represents the relationship between sewage sludge production (Wss, production, 2006–2020) and wastewater treatment (Www, treatment, 2002–2020), and in Eq. 15, Pww, treatment efficiency represents the relationship between the quantity of treated wastewater (Www, treatment, 2002–2020) and the treatment capacity (Www, treatment capacity, 1990–2020). In this estimation, three assumptions are made. The first is to estimate Www, treatment, Pww, treatment efficiency is assumed to be unchanged at 63% during 1990–2001, given it has been increasing from 63% in 2002 to ~80% in recent years. The second is that, in order to estimate Wss, production, Pss, production rate is assumed to be unchanged at 3.5 between 1990 and 2005, suggesting 3.5 tonnes of sewage sludge are generated by processing 10,000 cubic metres of wastewater. This assumption is determined by that Pss, production rate is approximately 3.5 during 2006–2010 while declines sharply and stabilises at around two during 2011–2020. The last is, to estimate the Wss,dissipation, Pss,dissipation rate is assumed to be 5% between 1990 and 2005, given it has been around 5% during 2006–2020.$${W}_{ss,dissipation}={W}_{ss,production}times {P}_{ss,dissipationrate}$$
    (13)
    $${W}_{ss,production}={W}_{ww,treatment}times {P}_{ss,productionrate}$$
    (14)
    $${W}_{ww,treatment}={W}_{ww,treatmentcapacity}times {P}_{ww,treatmentefficiency}$$
    (15)

    Materials dissipated by composting: Composting is a natural process that uses microbes to turn organic materials into other products, which are then used for fertilising and entering the environment. In China, composting has been used to treat two materials: feces and municipal waste, whose quantities (WComposting) were only available from 2003 to 201029. The unavailable data can be estimated using Eq. 16. The dry weight of materials treated by composting (WComposting) is proportionally related to the fresh weight of all treated materials (WTotal), the proportion treated by composting (PComposting rate), and the dry content (PDry matter). Considering that China’s composting capacity has been declining since 2001 due to the implementation of waste incineration power generation technologies30, Pcomposting rate is assumed to be the same as it was in 2003 (9.5%) between 1990 and 2002, and 1.5% in 2010 between 2011 and 2020. The parameter of PDry matter is 50%4.$${W}_{Composting}={W}_{Total}times {P}_{Compostingrate}times {P}_{Drymatter}$$
    (16)

    The input and output by cross-border trade. Statistics of imports and exports have been gathered since 1962 and stored in the UN Comtrade database31. However, the data quality issue of outliers, and missing values, especially in weight, is reportedly identified. In our previous work, we addressed these issues, and an improved database32 is provided. Details about our estimation methods can be found in publications33,34,35. As UN Comtrade lists 5,039 different commodity types (in 6-digit HS0 commodity code), yet only 18 material types are specified in the China EW-MFA, UN Comtrade statistics need to be aligned to the China EW-MFA framework. Therefore, we compared each commodity and each material type between them and established a correspondence table to map UN Comtrade commodity types onto our EW-MFA material types. For example, non-ferrous metal materials of China EW-MFA include commodities, such as copper ores and concentrates (260300 HS0 code), silver powder (710610), manganese, articles thereof, and waste or scrap (811100), etc., whereas biomass residues include cereal straw and husks (121300), lucerne meal and pellets (121410), and other fodder and forage products (121410). This correspondence table between HS0 and EW-MFA classification for imports and exports is provided in Supplementary File 1.

    The input of balancing items

    O2 required for combustion: In BI, requirements for materials can be abstracted as equalling exogenous demands minus intrinsic supplies (Eq. 17). Three parts (two demands and one supply) are considered for O2 requirements by the combustion process: (1) demanding exogenous oxygen to oxidise elements (e.g., carbon, sulphur, nitrogen, etc., except for hydrogen) released into the air, (2) demanding exogenous oxygen to oxidise the hydrogen embedded in fossil energy materials, and (3) providing intrinsic oxygen embedded in fossil energy materials. The first part can be estimated via Eq. 18 by multiplying air emissions (WEmissions) of CO2, N2O, NOx, CO, and SO2 by their oxygen content (POxygen content). For the second (Eq. 19), the oxygen demand is estimated based on the principle of mass balance by converting the hydrogen amount of domestically utilised fossil energy materials (WFossil fuel materials × PHydrogen content) via molar mass conversion factor (PMass conversion factor). PMass conversion factor equals 7.92, derived by the molar mass of one oxygen (16 g/mol) divided by that of two hydrogen atoms (2 × 1.01 g/mol). The last is the intrinsic supplies from fossil fuel materials, which is identified via Eq. 20 by multiplying the domestically utilised amount of fossil fuel materials (WFossil fuel materials) by their oxygen content (POxygen content). The parameters in this estimation are presented in Table 3. As a footnote here, the domestically utilised amount is referred to as the domestic material consumption (DMC), which equals domestic extraction (DE) plus imports (IM) and minus exports (EX).$${W}_{Requirements}={W}_{Demands}-{W}_{Supplies}$$
    (17)
    $${W}_{Demands}={W}_{Emissions}times {P}_{Oxygencontent}$$
    (18)
    $${W}_{Demands}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
    (19)
    $${W}_{Supplies}={W}_{Fossilfuelmaterials}times {P}_{Oxygencontent}$$
    (20)
    Table 3 Parameters related to combustion processes4.Full size table

    O2 required for respiration: O2 is required by the metabolic activities of living organisms, the majority of which are humans and livestock. Bacteria are another sort of organism, which are not included in this estimation because their O2 requirements are too small to be quantified. The respiration-required O2 is related to the total quantity (QOrganisms) and their respiration activity by organism types, as shown in Eq. 21. The respiration activity is represented by the respiration requirement coefficient (PRespiration requirement coefficient), which is the average quantity of O2 that each organism utilises to maintain the metabolic activity, as listed in Table 4.$${W}_{Demands}={Q}_{Organisms}times {P}_{Respirationrequirementcoefficient}$$
    (21)
    Table 4 Parameters related to respiration processes4.Full size table

    Water required for the domestic production of exported beverages: The exported beverages are produced domestically using domestically extracted materials, especially a large amount of water. The weight of water is considered in the output by cross-border trade but is not included in the domestic extraction input. The resulted imbalance can be identified by specifying the water weight in beverages, i.e., multiplying the traded beverage weight (WMaterials) by a parameter of the water content (PWater content), as given in Eq. 22. Fruit and vegetable juices (2009 in HS0 code) and beverages (code 22) are covered in the improved UN Comtrade database32, with PWater content of 85% for the first and 90% for the latter4.

    $${W}_{Water}={W}_{Materials}times {P}_{Watercontent}$$
    (22)

    The output of balancing items.

    Water vapour from combustion: Water vapour emissions by domestically combusting fossil fuel materials are contributed by two paths. The direct evaporation of embedded water is the first path (Eq. 23), which can be derived by multiplying the DMC of fossil fuel materials by their moisture content (PMoisture content). The PMoisture content for each type of fossil fuel material is listed in Table 3. The other is the generation of water vapour during hydrogen oxidation, which can be calculated by converting the oxidised weight of hydrogen to the water weight using the molar mass conversion factor (PMass conversion factor), as given in Eq. 24. PMass conversion factor equals 8.92 by dividing the molar mass of water (18.02 g/mol) by that of two hydrogen atoms (2 × 1.01 g/mol).$${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Moisturecontent}$$
    (23)
    $${W}_{Water}={W}_{Fossilfuelmaterials}times {P}_{Hydrogencontent}times {P}_{Massconversionfactor}$$
    (24)

    Water vapour and CO2 from respiration: Respiration activities of organisms will produce water vapour and CO2, whose estimation is similar to that of O2 requirements. As shown in Eq. 25, the respiration-caused gas emissions are related to the number of organisms (QOrganisms) and the respiration activity by organism types. The latter is represented by the parameter of respiration emission coefficient (PRespiration emission coefficient), which is specified in Table 4 for water vapour and CO2 for each type of organism.$${W}_{Emissions}={Q}_{Organisms}times {P}_{Respirationemissioncoefficient}$$
    (25)

    Water from imported beverages: The estimation approach is the same as water by the domestic production of exported beverages, as described in Eq. 16.

    Water in biomass products: Usually, the input of biomass products by domestic extraction16 has been measured in fresh weight, but their corresponding output29 by sewage sludge, composting, etc., are in dry weight, leading to an imbalance in water weight. The water weight in biomass products is calculated by multiplying their domestic extraction amount in fresh weight (WBiomass) by a parameter of moisture content at harvest (PMoisture content), as shown in Eq. 26. The values of PMoisture content by biomass products are presented in Table 5.Table 5 The moisture content at harvest for each biomass product4.Full size table

    $${W}_{Water}={W}_{Biomass}times {P}_{Moisturecontent}$$
    (26)
    Material flow quantificationThe above attempts have quantified material inputs and outputs by flows and presented a detailed profile of material utilisation for each material in China’s economy. In order to depict the economy in a more general way, EW-MFA indicators are assessed by aggregating flows by materials or periods as below.

    Domestic extraction (DE): is referred to as natural materials that are extracted from the domestic environment and are used in the domestic economy, i.e., the total input of natural materials by extraction.

    Domestic processed output (DPO): is referred to as materials that are released to the domestic environment after being processed in the domestic economy, i.e., the total output of processed materials by release.

    Import (IM): is referred to as all goods (in the form of raw materials, semi-finished materials, and final products) that originated from other economies and are further used in the domestic economy. It is calculated as the sum of all imported goods.

    Export (EX): is referred to as all goods that originated from the domestic economy and are transported to other economies to be used. It is calculated as the sum of all exported goods.

    Domestic material input (DMI): is referred to as materials that originated from the domestic environment by extraction and other economies and are available (to be used or to be stored) for the domestic economy. It is calculated as the sum of DE plus IM, as shown in Eq. 27.$$DMI=DE+IM$$
    (27)

    Domestic material consumption (DMC): is referred to as materials that are directly used in the domestic economy after parts of them are exported to other economies. It is calculated as the difference between DMI and EX.

    Physical trade balance (PTB): is referred to as a surplus or deficit of materials for the domestic economy. It is calculated as the difference between IM and EX.

    Net additions to stock (NAS): is referred to as materials that remain in the domestic economy. It is calculated by taking BI items into account, as shown in Eq. 28.

    $$NAS=DMC+B{I}_{in}-DPO-B{I}_{out}$$
    (28) More

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    High impact of bacterial predation on cyanobacteria in soil biocrusts

    Tracing the symptomology of predation through macroscopic plaquesA culture bioassay (Expanded Microcoleus Mortality Assay, or EMMA) (Fig. 1 and see Materials and Methods) based on the capacity of a soil to induce complete mortality in the foundational biocrust cyanobacterium Microcoleus vaginatus helped us trace the pathogen detected in biocrust production facilities to the development of cm-sized plaques, or zones of cyanobacterial clearing, in natural biocrusts. These plaques were revealed to the naked eye (Fig. 2) when the soil was wet (i.e., after a rain event), as impacted areas would fail to green up by the migration of cyanobacteria to the surface21, enabling us to detect and quantify them with relative ease. Soil samples obtained from such plaques (n = 30) from different sites (n = 6; Table S1) in the US Southwest were invariably EMMA + , and the pathogens always filterable with pore sizes 0.45–1 µm but not larger, and always insensitive to the eukaryotic inhibitor cycloheximide, indicating the agent’s prokaryotic nature and small size, while paired samples from asymptomatic areas just outside the plaques were always EMMA- (Table S2). These end-point EMMA solutions never gave rise to cyanobacterial re-growth upon further incubation and maintained its infectivity of fresh cyanobacterial cultures for up to 6 months. A one-time, small-scale sampling across a plaque at intervals of 2 mm using microcoring22 showed that the boundary of the visible plaque demarcated exactly the end of infectivity, samples 0–2 mm outside the plaque proving non-infective. Further, inoculation of healthy, natural biocrusts with EMMA + suspensions resulted in the local development of biocrust plaques, and soil from these plaques was itself EMMA + , in partial fulfillment of Koch’s postulates. Yet, standard microbiological plating failed to yield any isolates that were EMMA + (we tested 30 unique isolates), even though standard plating with similar isolation efforts can successfully cultivate a large portion of heterotrophs from biocrusts23.Fig. 1: EMMA bioassay (Expanded Microcoleus Mortality Assay), used to study biocrust pathogens.a Typical visual progression of a positive EMMA inoculated with soil or culture to be tested, as used to test for pathogenicity to Microcoleus vaginatus PPC 9802 in the field and in enrichments. b Typical degradation of cyanobacterial biomass during an EMMA displayed through electron microscopy: healthy Microcoleus vaginatus PPC 9802 filaments (top) display abundant photosynthetic membranes (white arrows), peptidoglycan cross-walls (yellow arrows) and carboxysomes (green arrow). As infection proceeds (downwards), patent degradation of intracellular structures follows, leaving only cellular ghosts in the form of peptidoglycan wall remnants (yellow arrows), including the characteristically enlarged peptidoglycan “bumper” of terminal cells (red arrow). Intracellular bacilloid bacteria can sometimes be observed (blue arrow). Cyanobacterial cultures lose all viability. Scale bars = 1 µm. n = 250 images from 4 independent experiments. c Assay modification used in flow cytometry/cell sorting, showing enrichments positive for predation in the top two rows and those negative for predation below. d Test and controls in EMMA to ensure prokaryotic nature of the disease agent.Full size imageFig. 2: Symptomology in nature: biocrust plaques.Main: Macroscopic view of a soil surface colonized by cyanobacterial biocrusts and impacted by multiple plaques as taken after a rain in a quadrat used for field surveys. Insert: Close-up of a single plaque, showing well-demarcated boundaries and a typical central area of new cyanobacterial colonization.Full size imageCultivation, identification, and salient genomic traits of the cyanobacterial pathogenTo study these organisms, we turned to enrichment of pathogen/prey co-cultures based on repeated passages through EMMA and differential size filtration combined with dilution-to-extinction approaches, followed by purification with flow cytometry/cell sorting. The process was monitored by 16S rRNA gene amplicon sequencing, and eventually yielded a highly enriched co-culture of the cyanobacterium with a genetically homogenous (one single Amplicon Sequence Variant) population that made up more than 80% of reads (Fig. 3 a, b). We name the organism represented by this ASV Candidatus Cyanoraptor togatus. That it corresponds indeed to the predator is supported by the fact that of the 17 ASV’s detected in the final enrichment, only 10 were consistently detected at all infectious stages in the process and, among these, only our candidate ASV steadily increased in relative abundance through the enrichment process (Fig. 3 a, b). This final enrichment of C. togatus, LGM-1, constitutes the basis for downstream biological and molecular analyses. Its ASV was most similar to little-known members of the family Chitinophagaceae in the phylum Bacteroidetes. LGM-1’s genome was sequenced and assembled into a single 3.3 Mb contig with 1,781 putative and 1,328 hypothetical genes (Table S3), though most proteins had low identity (Fig. 4: Compiled paired ratios of functional parameters and compositional (relative) abundance in biocrusts across plaque boundaries (circles), red bars denoting the medians for each group of ratios, and bar background color denoting the p-values that the median is significantly different from unity (Wilcoxon paired ratio two-sided tests), where gray is non-significant (p  >  0.1), light orange is 0.05   > p   p  More

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    Free-living and particle-attached bacterial community composition, assembly processes and determinants across spatiotemporal scales in a macrotidal temperate estuary

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    A Pleistocene Fight Club revealed by the palaeobiological study of the Dama-like deer record from Pantalla (Italy)

    Taxonomy, variation, and biochronologyThe fossils described herein represent one of the most valuable and best-preserved samples of “Dama-like” deer from the European Early Pleistocene. The systematics of these forms has been essentially based on the morphology of the antlers and teeth, with less attention paid to the skull (due to the rarity of well-preserved finds) and postcranial bones.The Pantalla sample shows a combination of characters allowing an unambiguous attribution to ‘P.’ nestii, a species reported confidently so far in the early Late Villafranchian of Italy (several sites) and in the Georgian Homo-bearing locality of Dmanisi (Supplementary Table S1). Based on the literature6,8,12,38, these characters include: four-pointed antler with elongated, slender, and tubular beam; basal tine branching off at a certain distance from the burr forming an acute angle; well-developed middle tine; terminal bifurcation oriented normal to the sagittal plane; cranium with large orbits, preorbital fossae, and ethmoidal vacuities; relatively elongated neurocranium with flat parietals; caudally-oriented pedicles; molarized P2-P3; presence of cingula in upper molars; enlarged i1; un-molarized p4. However, some characters observed in the Pantalla specimens (e.g., rostral edge of the orbit reaching the level of M2; elongated metapodials) do not fit the revised diagnosis of ‘P.’ nestii by Croitor12. The latter author considers nestii as the earliest species of the genus Cervus based on similarities with the extant red deer especially in cranial morphology12,22,23. However, in our opinion, his conclusions are biased by relying mostly on the skull IGF 243 of ‘P.’ nestii from Upper Valdarno6,8, which is heavily deformed and belongs to a juvenile individual (see below for details on ontogenetic variation in ‘Pseudodama’).A broader look at the entire record of ‘P.’ nestii reveals that this species displays a mosaic of characters between Dama and Cervus, but also that the shared characters with Dama are prevalent (as already pointed out by Azzaroli8). The Pantalla sample allows to substantiate these conclusions very well. Our CT-based comparisons between the crania from Pantalla and those of extant red deer and fallow deer (Fig. 3) highlight some morphological similarities with the former, including a relatively longish neurocranium with steep forehead and deep preorbital fossa. On the other hand, ‘P.’ nestii from Pantalla clearly shows Dama-like cranial characters, such as a marked interfrontal crest, horizontal zygomatic arch, high maxilla below the orbit, muzzle more inclined ventrally and less cylindrical in overall shape, sub-horizontal upper cheek tooth row (i.e., the occlusal margin of the row is approximately straight in buccal view), apical surface of the pedicle more inclined dorsocaudally, and overall morphology of the antlers, which in rostral view diverge, rather than converge as in the red deer (Fig. 2).Likewise, the teeth from Pantalla, have a mixture of Dama and Cervus characters although the former are prevalent. All the premolar characters (the complete absence of a lingual grove on P4, the presence of a cingulum on the distolingual wall of P4, the presence of a small paraconid in p2, the entoconid more aligned with the mesiodistal axis in p3-p4, and a weak mesial cingulum on p4) and most of the lower molar characters are Dama-like. The upper molar features are instead more reminiscent of Cervus being either intermediate between the morphology of the latter and that of extant Dama or even matching Cervus (see Supplementary Table S6 and below).The postcranial remains from Pantalla appear more similar to Dama than to Cervus. Of the 23 morphological characters by Lister39 which are present in the preserved bones (axis, metacarpal, tibia, astragalus, calcaneum, cubo-navicular, metatarsal, phalanx I, and phalanx II), 21 scores as fallow deer and only two as red deer (details in Supplementary Table S7).A mixed character suite between Dama and Cervus are revealed also by our palaeoneurological analysis. The brain of ‘P.’ nestii shows Dama-like size and Cervus-like morphology with a prominent cerebellum and a dorsoventrally flattened cerebrum. The latter character is clearly noticeable in ‘Pseudodama’ and Eucladoceros, is less evident in extant Cervus, and is missing in Dama. The hypothesis that depressed and longish cerebra represent a primitive character in Cervini (at least in Pleistocene European forms) is supported by our preliminary data and agree with Azzaroli8.Most interestingly, the two crania from Pantalla actually show some remarkable morphological differences. The neurocranium of 337643 is more lengthened (i.e., more Cervus-like), albeit this shape might be taphonomically modified by the lateral compression of the specimen. This morphology fits that observed in some other ‘P.’ nestii specimens such as IGF 1403 from Olivola (Italy), while the relatively shorter and more rounded neurocranium of 337655 resembles that of other specimens such as IGF 1404 also from Olivola. Moreover, 337643 shows a stronger nuchal crest than 337655. These differences may be related to ontogenesis (see the advanced age of 337643 based on tooth wear). In several cervid species including fallow deer, aging leads to morphological changes in the neurocranium, which tends to elongate and flatten and shows a more developed nuchal crest, probably as a response to the support of larger and heavier antlers18,38. Similarly, in 337643, the pedicles are apparently closer to one another due to their thickening—an expected condition for an old individual as the distance between the pedicles tends to decrease with age8—and markedly shorter than wide. Our comparative data on European Dama-like deer show that the pedicle section can be highly variable both within and between species, although a general trend of laterolateral flattening (i.e., oval shape with major axis oriented anteroposteriorly) can be traced through time (Supplementary Fig. S3), probably as a result of the development of wide, laterally-projecting palmated antlers (in extant deer, D. dama is among those with the heaviest antlers relative to body size40,41). Therefore, the Pantalla sample on the one hand confirms the variation in cranial morphology already observed for ‘P.’ nestii6,8, on the other hand it supports the affinities between this species and the fallow deer. The presence of Cervus-like features especially in cranial morphology may be interpreted as plesiomorphic characters which, associated with some characters of the dentition and of the brain, suggest a basal position of ‘Pseudodama’ in the evolutionary history of the Cervini. This hypothesis may be tested in the future through phylogenetic analyses, currently made difficult by the lack of sufficiently well-preserved material of some species of ‘Pseudodama’ (e.g., ‘P.’ lyra, ‘P.’ perolensis).Compared with other specimens of ‘P.’ nestii6,8, the sample from Pantalla shows some plesiomorphic characters including a high ratio between the premolar and molar lengths, i.e., 0.77–0.82 (n = 2) for upper teeth (LP/LM) and 0.68–0.69 (n = 3) for lower teeth (Lp/Lm). These values are closer to the basal forms of ‘Pseudodama’, such as ‘P.’ lyra from Montopoli (LP/LM = 0.73, n = 1; Lp/Lm = 0.64, n = 2) and ‘P.’ rhenana from Saint Vallier (LP/LM = 0.75, n = 9; Lp/Lm = 0.68, n = 18; data from Valli42), than to ‘P.’ nestii from Olivola and Upper Valdarno (LP/LM = 0.72, n = 10; Lp/Lm = 0.63, n = 17). Other putatively plesiomorphic features of the sample from Pantalla are all those that approach it morphologically to Cervus (see Supplementary Table S6), i.e., the strong development of lingual conids and stylids in lower molars (Char. 439) and of buccal cones and styles in upper molars (Char. 139), the lack of a clear step between 2nd and 3rd lobe of m3 (Char. 1139), the strong lingual cingulum on upper molars (Char. 339), and the lack of the horizontal turning of the buccal columns of upper molars (Char. 439—the so-called buccal “cingulum”43). The strong lingual cingulum on upper molars is constantly present in the earliest species of the ‘Pseudodama’ group, ‘P.’ pardinensis9, and still present, although extremely rare, in ‘P.’ lyra from Montopoli, ‘P.’ rhenana from Saint Vallier and Senèze, ‘P.’ perolensis from Peyrolles, and ‘P.’ nestii from Olivola. However, this feature is back less rare in ‘P.’ nestii from Upper Valdarno and ‘P.’ farnetensis from Selvella, suggesting a certain polymorphism at this stage. The lack of buccal “cingulum” is a constant in the earliest ‘Pseudodama’ populations (Lower Valdarno, Saint Vallier, Senèze), the buccal “cingulum” appearing, although rare, in ‘P.’ perolensis from Peyrolles and ‘P.’ nestii from Olivola and Upper Valdarno but becoming more common only in later ‘P.’ farnetensis, ‘P.’ vallonnetensis, and constant in Dama.The above affinities between the Pantalla deer and the early representatives of ‘Pseudodama’ support the idea that the age of the assemblage may be close to the beginning of the Late Villafranchian (ca. 2.1–2.0 Ma), as already suggested based on the occurrence of Leptobos merlai44 and a primitive form of Equus stenonis35. Thus, the ‘P.’ nestii sample described herein may represent one of the earliest occurrences of the species in Europe.Palaeoecological and palaeoethological inferencesThe Pantalla sample is also noteworthy as it allows opening a window into the behaviour of these extinct deer. The anomalies found on the two male crania are probably the result of different traumas during their life.Deer are well known for the intense fights they engage in during the rutting season using their antlers, as a result of an escalation of a broad repertoire of threats and displays45. Mineralized antlers are solid structures able to withstand the vehemence of the fight46, whereas growing antlers are extremely fragile and any contact with a solid object may result in a serious injury47,48 that may jeopardize the bearer’s ability to compete with conspecifics and, consequently, its dominance status49. Accidents are inevitable in the life of a deer and, in case of the suffered damage not leading to the breakage of the growing beam and consequent loss of its distal part, the antler may continue its growth although, in case of a severe lesion, at a crooked angle45. Thus, if the antler was just cracked and the broken part was held together by the velvet and periosteum, with the blood supply still being guaranteed, the damaged beam would just present a conspicuous swelling around the area of fracture (i.e., a fracture callus)45,50 and a change in the axis of orientation. These features match those seen in the left beam of 337655, which shows a fracture callus between the basal and middle tines corresponding to a change in the orientation of the beam.The supernumerary tine of the right antler of the same individual can be interpreted as the result of a trauma, too. Considering the delicate nature of the growing antlers and the non-negligible risks of occurrence of an injury, it is safe to believe that the right antler has undergone a light traumatic event (most likely concerning the pedicle) at some early stage of its growth. In fact, it is known that limited injuries could result in the growth of supernumerary tines, even in atypical positions51, as it has been documented in other deer species (e.g., reindeer52, sambar53). It is therefore reasonable to hypothesize that both antler anomalies of 337655 derive from traumas suffered by the deer during the antler growth, when the velvet was still present. It is not known whether the two injuries happened at the same time or in two different events. In fact, it cannot even be said that the two events took place during the same season. While the breakage of the left beam must have occurred in the year of the animal’s death (i.e., during the velvet period preceding the period of hard antler in which the individual died), the development of the supernumerary tine on the right may be the result of a trauma suffered in a previous year. This is due to the fact that when unilateral trauma affects the generative region of the antler (i.e., the pedicle area), abnormalities such as supernumerary tines can reappear in next antler cycles even in more intensified forms54, as in the case of 337655 in which the extra-tine is extremely long.The bone anomaly on the right squamosal of 337643 is also likely the outcome of an injury. Although the external portion was artificially smoothed during the preparation of the specimen, the outer and inner morphology matches that of a callus related to the healing of a major lesion and probable intracranial abscessation. Post-traumatic inflammatory processes are known to cause erosion or pitting of cranial bones in deer55 and can be triggered by many factors (e.g., wounds and abrasions of the pedicle56), among which violent sexual competition among males with hard antlers is considered one of the most common55,57. The advanced healing of the injury shown by 337643 suggests that it was not the cause of death, but rather that the individual survived a long time after the trauma albeit with the brain partially compressed by the callus.The six mandibles recovered at Pantalla, all coming from the same bone accumulation hence reasonably referable to a single deer population, represent several age classes, from calves as young as a few months up to very old individuals (i.e., over 15 years; Supplementary Table S4). Unfortunately, no mandible can be safely associated with the two male crania, although 337631 may belong to the same individual as 337643 based on advanced wear and size. Interestingly, the three most significant cranial remains (crania 337655 and 337643 and frontal bone fragment with basal antler base 337625) belong to adult males, which probably died during the hard antler period (i.e., rutting season: 337655 and 337625) or shortly after (i.e., 337643). The absence of females (at least among the remains with certain sex attribution) contrasts with the population structure in the extant fallow deer, in which females represent on average 75% of the herd58. However, the relative abundance of males may increase up to 50% in the rutting season59,60. Therefore, in spite of the relatively low number of fossils available, based on the age and sex structure of the palaeopopulation and by analogy with the extant fallow deer, the most plausible hypothesis is that the Pantalla deer died during or immediately after the rutting season (Fig. 5).Figure 5Life appearance of ‘Pseudodama’ nestii represented during the rutting season. The reconstruction is based on the cranial and postcranial material from the Early Pleistocene of Pantalla (Italy) and on literature data. Artwork by D.A. Iurino.Full size image More

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    Metaproteome plasticity sheds light on the ecology of the rumen microbiome and its connection to host traits

    Shotgun sequencing and generation of metagenome-assembled genomesIn our previous study, 78 Holstein Friesian dairy cows were sampled for rumen content, metagenomic shotgun sequencing was carried out, and raw Illumina sequencing reads were assembled into contigs using megahit assembler using default settings [7]. We used a pooled assembly of the original 78 samples to increase the quality of the metagenome-assembled genomes (MAGs) with the syntax: megahit [14] -t 60 -m 0.5 −1 [Illumina R1 files] −2 [Illumina R2 files]. Next, the assembled contigs were indexed using BBMap [15]: bbmap.sh threads = 60 ref = [contigs filename]. Thereafter, reads from each sample were mapped to the assembled contigs using BBTools’ bbwrap.sh script. In order to determine the depth (coverage) of each contig within each sample, the gi_summarize_bam_contig_depths tool was applied with the parameters: gi_summarize_bam_contig_depths –outputDepth depth.txt –pairedContigs paired.txt *.bam –outputDepth depth.txt –pairedContigs paired.txt.Using the depth information, metabat2 [16] was executed to bind genes together into reconstructed genomes, with parameters: metabat2 -t40 -a depth.txt.To evaluate genomic bin quality, we used the CheckM [17] tool, with parameters: checkm lineage_wf [in directory] [out directory] -x faa –genes -t10.Preparing proteomic search libraryWe generated 93 unique high-quality MAGs, and further increased our MAG database by including phyla that were not represented in our set of MAGs. In order to do so, we used the published compendium of 4,941 rumen metagenome-assembled genomes [18] and dereplicated those MAGs using dRep [19]. We then selected MAGs from phylum Spirochaetes, Actinomycetota, Proteobacteria, Firmicutes, Elusimicrobia, Bacillota, Fibrobacteres and Fusobacteria, which had the highest mean coverage in our samples as calculated using BBMap and gi_summarize_bam_contig_depths as described above [15]. This strategy minimized the false discovery rate (FDR), that would have been obtained if larger and unspecific databases would have been employed [20] and allowed the addition of 14 MAGs to our database.In order to create the proteomic search library, genes were identified along the 107 MAGs using the Prodigal tool [21], with parameters: prodigal meta and translated in silico into proteins, using the same tool. Replicates sequences were removed. Protein sequences from the hosting animal (Bos taurus) and common contaminant protein sequences (64,701 in total) were added to the proteomic search library in order to avoid erroneous target protein identification originating from the host or common contaminants. Finally, in order to subsequently assess the percentage of false-positive identifications within the proteomic search [22], the proteomic search library sequences were reversed in order and served as a decoy database.Proteomic analysisThe bacterial fraction from rumen fluid of the 12 selected animals selected from extreme feed efficiency phenotypes, were obtained at the same time as the samples analyzed for metagenomics and stored at −20 °C until extraction. To extract total proteins, a modified protocol from Deusch and Seifert was used [23]. Briefly, cell pellets were resuspended in 100 µl in 50 mM Tris-HCl (pH 7.5; 0.1 mg/ml chloramphenicol; 1 mM phenylmethylsulfonyl fluoride (PMSF)) and incubated for 10 min at 60 °C and 1200 rpm in a thermo-mixer after addition of 150 µl 20 mM Tris-HCl (pH 7.5; 2% sodium dodecyl sulfate (SDS)). After the addition of 500 µl DNAse buffer (20 mM Tris-HCl pH 7.5; 0.1 mg/ml MgCl2, 1 mM PMSF, 1 μg/ml DNAse I), the cells were lysed by ultra-sonication (amplitude 51–60%; cycle 0.5; 4 × 2 min) on ice, incubated in the thermo-mixer (10 min at 37 °C and 1,200 rpm) and centrifuged at 10,000 × g for 10 min at 4 °C. The supernatant was collected and centrifuged again. The proteins in the supernatant were precipitated by adding 20% pre-cooled trichloroacetic acid (TCA; 20% v/v). After centrifugation (12,000 × g; 30 min; 4 °C), the protein pellets were washed twice in pre-cooled (−20 °C) acetone (2 × 10 min; 12,000 × g; 4 °C) and dried by vacuum centrifugation. The protein pellet was resuspended in 2× SDS sample buffer (4% SDS (w/v); 20% glycerin (w/v); 100 mM Tris-HCl pH 6.8; a pinch of bromophenol blue, 3.6% 2‑mercaptoethanol (v/v)) by 5 min sonication bath and vortexing. Samples were incubated for 5 min at 95 °C and separated by 1D SDS-PAGE (Criterion TG 4-20% Precast Midi Gel, BIO-RAD Laboratories, Inc., USA).As previously described, after fixation and staining, each gel line was cut into 10 pieces, destained, desiccated, and rehydrated in trypsin [24]. The in-gel digest was performed by incubation overnight at 37 °C. Peptides were eluted with Aq. dest. by sonication for 15 min The sample volume was reduced in a vacuum centrifuge.Before MS analysis, the tryptic peptide mixture was loaded on an Easy-nLC II or Easy-nLC 1000 (Thermo Fisher Scientific, USA) system equipped with an in-house built 20 cm column (inner diameter 100 µm; outer diameter 360 µm) filled with ReproSil-Pur 120 C18-AQ reversed-phase material (3 µm particles, Dr. Maisch GmbH, Germany). Peptides were eluted with a nonlinear 156 min gradient from 1 to 99% solvent B (95% acetonitrile (v/v); 0.1% acetic acid (v/v)) in solvent A (0.1% acetic acid (v/v)) with a flow rate of 300 ml/min and injected online into an LTQ Orbitrap Velos or Orbitrap Velos Pro (Thermo Fisher Scientific, USA). Overview scan at a resolution of 30,000 in the Orbitrap in a range of 300-2,000 m/z was followed by 20 MS/MS fragment scans of the 20 most abundant precursor ions. Ions without detected charge state as well as singly charged ions were excluded from MS/MS analysis. Original raw spectra files were converted into the common mzXML format, in order to further process it in downstream analysis. The spectra file from each proteomic run of a given sample was searched against the protein search library, using the Comet [25] search engine with default settings.The TPP pipeline (Trans Proteomic Pipeline) [26] was used to further process the Comet [25, 27] search results and produce a protein abundance table for each sample. In detail, PeptideProphet [28] was applied to validate peptide assignments, with filtering criteria set to probability of 0.001, accurate mass binning, non-parametric errors model (decoy model) and decoy hits reporting. In addition, iProphet [28, 29] was applied to refine peptide identifications coming from PeptideProphet. Finally, ProteinProphet [28,29,30] was applied to statistically validate peptide identifications at the protein level. This was carried out using the command: xinteract -N[my_sample_nick].pep.xml -THREADS = 40 -p0.001 -l6 -PPM -OAPd -dREVERSE_ -ip [file1].pep.xml [file2].pep.xml.. [fileN].pep.xml  > xinteract.out 2  > xinteract.err. Then, TPP GUI was used in order to produce a protein table from the resulting ProtXML files (extension ipro.prot.xml).Subsequently, proteins that had an identification probability < 0.9 were also removed as well as proteins supported with less than 2 unique peptides (see Supplementary Table 1).Quantifying metagenomic presence of MAGsA reference database containing all 107 MAGs’ contigs was created (bbmap.sh command, default settings). Then, the paired-end short reads from each sample (FASTQ files) were mapped into the reference database (bbwrap.sh, default settings), producing alignment (SAM) files, which were converted into BAM format. Subsequently, a contig depth (coverage) table was produced using the command jgi_summarize_bam_contig_depths --outputDepth depth.txt --pairedContigs paired.txt *.bam. As each of the MAGs span on more than one contig, MAG depth in each sample was calculated as contig length weighted by the average depth. Finally, to account for unequal sequencing depth, each MAG depth was normalized to the number of short sequencing reads within the given sample.Correlating metagenomic and proteomic structuresIn order to compare metagenomic and proteomic structures, we first calculated the mean coding gene abundance and mean production levels of each of the 1629 detected core proteins over all 12 cows. Both mean gene abundance and mean production level were translated into ranks using the R rank function. The produced proteins were ranked in descending order and the coding genes in the gene abundance vector were reordered accordingly. The two reordered ranked vectors then plotted using the R pheatmap function, and colored using the same color scale.Selection of proteins for downstream analysisAs our goal was to analyze plasticity in microbial protein production in varying environments, e.g., as a function of host state, only MAGs that were identified in all of the 12 proteomic samples were kept for further analysis. Consequently, only proteins that were identified in at least half of the proteomic samples (e.g., in at least six samples) were selected. This last step aimed to reduce spurious correlation results. These filtering steps retained 79 MAGs coding for a total of 1,629 measurable proteins.Feed efficiency state prediction and ordinationIn order to calculate the accuracy in predicting host feed efficiency state based on the different data layers available (16S rRNA (Supplementary Table 2), metagenomics, metaproteomics), the principal component analysis (PCA) axes for all the samples based on the microbial protein production profiles were calculated. Then, twelve cycles of model building and prediction were made. Each time, the two first PCs of each of five cows along with their phenotype (efficiency state) were used to build a Support Vector Machine (SVM) [R caret package] prediction model and one sample was left out. The model was then used to perform subsequent prediction of the left-out animal phenotype (feed efficiency) by feeding the model with that animal’s first two PCs. This leave-one-out methodology was then repeated over all the samples. Finally, the prediction accuracy was determined as the percent of the cases where the correct label was assigned to the left-out sample. For the proteomics data, this procedure was applied on both the raw protein counts, and the protein production normalized based on MAG abundance, which enabled us to compare the prediction accuracies of the microbial protein production to that of the raw protein counts.Identification proteins associated with a specific host stateIn order to split the proteomics dataset into microbial proteins that tend to be produced differently as a function of the host feed efficiency states, each microbial protein profile was correlated to the sample’s host feed efficiency measure (as calculated by RFI) using the Spearman correlation (R function cor), disregarding the p value. Proteins that had a positive correlation to RFI were grouped as inefficiency associated proteins. In contrast, proteins that presented a negative correlation to RFI were grouped as efficiency associated proteins. To test for equal sizes of these two protein groups, a binomial test was performed (R function binom.test) to examine the probability to get a low number of feed efficient proteins from the overall proteins under examination, when the expected probability was set to 0.5.Functional assignment of proteinsProtein functions were assigned based on the KEGG (Kegg Encyclopedia of Genes and Genomes) [31] database. The entire KEGG genes database was compiled into a Diamond [32] search library. Then, the selected microbial proteins were searched against the database using the Diamond search tool. Significant hits (evalue < 5e-5) were further analyzed to identify the corresponding KO (KEGG Ortholog number). Annotations of glycoside hydrolases were performed using dbcan2 [33].Protein level checkerboard distribution across the feed efficiency groupsThe checkerboard distribution in protein production profiles was estimated separately within the feed efficient and inefficient animal groups. To enable the comparison between the two groups’ checkerboardness level, we chose a standardized C-score estimate (Standardized Effect Size C-score - S.E.S C-Score), based on the comparison of the observed C-score to a null-model distribution derived from simulations. The S.E.S C-score was estimated using the oecosimu function from R vegan package with 100,000 simulated null-model communities.Calculating functional redundancyThe functional redundancy within a given group of proteins was measured as the mean number of times a given KO occurred within a given group, while neglecting proteins that have not been assigned a KO level functional annotation.In order to test whether a given group of proteins exhibits more or less functional redundancy than would have been expected, a null distribution for functional redundancy was created, based on the number of proteins in the given group. A random group of proteins was drawn from the entire set, keeping the same sample size as in the tested group, and the process was repeated 100 times. Then, the functional redundancy for each random protein group was calculated. Thereafter, the null distribution was used to obtain a p value to measure the likelihood of obtaining such a value under the null.Examining functional divergenceExamining the functional divergence between the two groups of proteins, e.g. the feed efficiency and inefficiency associated proteins, was done by first counting the amount of shared functional annotations, in terms of KOs between the two groups. Thereafter, a null distribution for the expected count of KOs was built by randomly splitting in an iterative manner the proteins into groups of the same sizes and calculating the number of shared KOs. A p value for the actual count of shared proteins was obtained by ranking the actual count over the null distribution.Calculating average nearest neighbor ratio (ANN ratio)ANN Ratio analysis was carried out independently for each protein function (KO), containing more than 14 proteins with at least 5 proteins within each feed efficiency group. Initially, all proteins assigned to a given KO were split into two sets, in accordance to their feed efficiency affiliation group. Thereafter, proteins within each set were independently projected into two-dimensional space by PCA applied directly to Sequence Matrix [34]. Average nearest neighbor ratio within each set was then calculated within the minimum enclosing rectangle defined by principal component axes PC1 and PC2, as defined by Clark and Evans [35].MAG feed efficiency score calculationMicroorganism feed efficiency score was calculated for each MAG individually by first ranking each protein being produced by the given microbe along the 12 animals, based on the normalized protein production levels. Thereafter, a representative production value for the microbe in each animal was calculated as the average of the ranked (normalized) protein production levels in that animal (using R rank function). This ranking allowed us to alleviate the potential skewing effect of highly expressed proteins. The microorganism’s Feed Efficiency Score was calculated as the difference between its mean representative production value within feed efficient animals to that within feed inefficient animals. Values close to zero will reflect similar distribution between the two animal groups, positive values will indicate higher expression among efficient animals, and negative values will indicate higher expression among inefficient animals. To calculate significance, the actual feed efficiency score was compared to values in a distribution derived from a permutation based null model. Each of the permuted Feed Efficiency Scores (10,000 for each microbe) was obtained by independently shuffling each of the proteins produced by the MAG between the animals, prior to calculating the actual microorganism feed efficiency score. By positioning the absolute score value over its distribution under permuted assumptions (absolute values), we obtained a significance p value.MAG phylogenetic tree construction and phylogenetic signal estimationIn order to assess the link between phylogenetic similarity between the MAGs and their association with feed efficiency, phylogenetic tree estimating evolutionary relationships between the MAGs was constructed using the PhyloPhlAn pipeline [36]. The phylogenetic signal for Microorganism Feed Efficiency Score was estimated by providing the phylogSignal function from R phylosignal [37] package with MAGs phylogenetic tree and respective values. Pagel’s Lambda statistics was chosen for the analysis, owing to its robustness [38].Plot generationAll bar plots, scatter plots and other point plots were generated with R package ggplot2. Heatmaps were produced by either ggplot2 [39] or pheatmap [https://cran.r-project.org/web/packages/pheatmap/index.html] R packages. KEGG map was produced using the online KEGG Mapper tool [40]. Phylocorrelogram was produced with phyloCorrelogram function from R package phylosignal [37].MAG differential production analysisMAGs that contain a minimal number of proteins (50 functions) were selected for differential protein production analysis, in order to have sufficient data to perform statistical tests. For each MAG, the relative production was used in order to calculate the Jaccard pairwise dissimilarity for core protein production between feed efficient and inefficient cows using the R vegan package. Analysis of similarity between efficiency and inefficiency associated proteins for each MAG (ANOSIM) values and p values were then calculated using the same package.Predicting animal feed efficiency state according to GH family countsUsing all GH annotated proteins, a feature table that sums the count of each GH family within each sample was produced. Thereafter a leave-one-out cross-validation (LOOCV) [R caret package] was performed, each time building a Random Forest (RF) prediction model from the GH family counts and efficiency state of 11 samples, leaving one sample outside. Each one of the RF models, in its turn, was applied on the left-out animal to predict its efficiency state. Model accuracy and AUC curve were calculated based on the LOOCV performance. More

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    Disentangling influence over group speed and direction reveals multiple patterns of influence in moving meerkat groups

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    We must get a grip on forest science — before it’s too late

    Climate models need to capture a full spectrum of data from forests such as the Brazilian Amazon.Credit: Florence Goisnard/AFP/Getty

    Humanity’s understanding of how forests are responding to climate change is disconcertingly fragile. Take carbon fertilization, for example — the phenomenon by which plants absorb more carbon dioxide as its concentration in the atmosphere increases. This is one of the principal mechanisms by which nature has so far saved us from the worst of climate change, but there’s little understanding of its future trajectory. In fact, researchers don’t fully understand how climate change interacts with a multitude of forest processes. Complex, unsolved questions include how climate warming affects forest health; how it affects the performance of forests as a carbon sink; and whether it alters the ecosystem services that forests provide. Forests are our life-support system, and we should be more serious about taking their pulse.Six papers in this week’s Nature provide important insights into those questions. They also underline some of the challenges that must be overcome if we are to fully understand forests’ potential in the fight against climate change. These challenges are not only in the science itself, but also relate to how forest scientists collaborate, how they are funded (especially where data collection is concerned) and how they are trained.Forest science is an amalgam of disciplines. Ecologists and plant scientists measure tree growth, soil nutrients and other parameters in thousands of forest plots around the world. Physical scientists monitor factors such as forest height and above-ground forest biomass using remote-sensing data from drones or satellites. Experimental scientists investigate how forests might behave in a warming world by artificially altering factors such as temperature or carbon dioxide levels in experimental plots. Some of the data they generate are absorbed by yet another community: the modellers, who have created dynamic global vegetation models (DGVMs). These simulate how carbon and water cycles change with climate and, in turn, inform broader earth-system and climate models of the type that feed into policymaking.Different DGVMs make different predictions about how long forests will continue to absorb anthropogenic CO2. One reason for these differences is that models are sensitive to assumptions made about the processes in forests. There are many influences — including temperature, moisture, fire and nutrients — that are generally studied in isolation. Yet they interact with each other.Not all DGVMs account for the dampening effect that a lack of soil phosphorus can have on carbon fertilization, for example. Much of central and eastern Amazonia is poor in phosphorus, and research has shown that introducing phosphorus limitation into DGVMs can cut the carbon-fertilization effect1. This week, Hellen Fernanda Viana Cunha at the National Institute for Amazonian Research in Manaus, Brazil, and her colleagues report2 a powerful experimental demonstration of how the soil’s poor phosphorus content limits carbon absorption in an old-growth Amazonian forest.Models simulating the northward spread of boreal forest as temperatures rise are also missing key drivers3, according to Roman Dial at Alaska Pacific University in Anchorage and his colleagues. They report today that a white-spruce population has migrated surprisingly far north into the Arctic tundra. To explain this, it is necessary to take into account winter winds (which facilitate long-distance dispersal) along with the availability of deep snow and soil nutrients (which promote plant growth).Models are often based on a small number of ‘functional tree types’ — for example, ‘evergreen broadleaf’ or ‘evergreen needle leaf’. These are chosen as a proxy for the behaviour of the planet’s more than 60,000 known tree species. Yet ecologists are discovering that the biology of individual species matters when it comes to a tree’s response to climate change.David Bauman at the Environmental Change Institute at the University of Oxford, UK, and his co-workers reported in May that tree mortality on 24 moist tropical plots in northern Australia has doubled in the past 35 years (and life expectancy has halved), apparently owing to the increasing dryness of the air4. But that was an average of the 81 dominant tree species: mortality rates varied substantially between species, a variation that seemed to be related to the density of their wood.Peter Reich at the Institute for Global Change Biology at the University of Michigan in Ann Arbor and his colleagues now report that modest alterations in temperature and rainfall led to varying rates of growth and survival5 for different species in southern boreal-forest trees. The species that prospered were rare.Failure to examine multiple factors simultaneously means that scientists are making findings that challenge the assumptions in models. Spring is coming earlier for temperate forests and most models assume that, by prolonging the growing season, this increases woody-stem biomass. However, observational work carried out in temperate deciduous forests by Kristina Anderson-Teixeira at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, and her colleagues found no sign of this happening6.Modellers are all too aware of the need to incorporate more complexity into their models, and of the potential that increasing amounts of computing power have to assist them in this endeavour. But they need more data.Continuity problemTo obtain comprehensive, valuable data for the models, continuous, long-term observations need to be made, and that depends on the availability of long-term funding. Achieving such continuity is a problem for both remote-sensing and ground-based operations. The former can cost hundreds of millions of dollars, but the value of its long-term data sets is immense, as demonstrated by a team led by Giovanni Forzieri at the University of Florence in Italy. The authors used 20 years of satellite data to show that nearly one-quarter of the world’s intact forests have already reached their critical threshold for abrupt decline7. But even field-based data collection, which costs a pittance by comparison, struggles to achieve financial security.Important ground-based operations include the Forest Global Earth Observatory (ForestGEO), part of the Smithsonian Tropical Research Institute, which is headquartered in Washington DC. This monitors 7.5 million individual trees in plots around the world. The amount of work that goes into this monitoring is formidable. For example, at present, ForestGEO is conducting the eighth five-yearly census of a plot in Peninsular Malaysia. This involves determining the species for each of the 350,000 trees (there are some 800 species growing there) and measuring the circumference of each trunk. It will take 16 skilled people a year to measure all the trees. Delays in the provision of funding to ForestGEO have held up similar censuses at plots in countries including Papua New Guinea, Vietnam, Brunei and Ecuador.

    A ForestGEO researcher making tree measurements at a forest plot in Barro Colorado Island, Panama.Credit: Jorge Aleman, STRI

    The future of the plots in North Queensland, which supplied Bauman with a rare 49 years’ worth of continuous data, is uncertain. They have been monitored since the mid-1970s by the Australian public research-funding agency CSIRO — initially every two years, then, more recently, every five years. In 2019, monitoring of the plots was switched to every 50 years because of funding shortages at CSIRO, leaving scientists searching for new sources of funding.Without continuity of funding, organizations such as ForestGEO can’t equip researchers with the requisite skills or collect data over periods longer than an individual’s time in a specific post or a funder’s cycle. “We have trained people and then lost them due to job insecurity,” says Stuart Davies, who leads ForestGEO.Different groups of forest researchers are trying to address these problems. ForestGEO is coordinating the Alliance for Tropical Forest Science in an effort to make it easier to share data, and to bolster the morale and careers of the skilled technicians and scientists — many of whom live in low- and middle-income countries — who do the bulk of the data collection.But we also need more-imaginative funding mechanisms that lift long-term observational plots out of three- to five-year funding cycles. Space agencies that fund remote-sensing satellites could collaborate with other funding agencies, for example, so that earth-observation missions include a fully funded component for ground-based data collection — which is, after all, crucial for calibrating their results. Journals, too, could do more to value and incentivise the production of long-term data sets.And there is a need for more interdisciplinarity. The US Department of Energy is funding a project called NGEE–Tropics (Next-Generation Ecosystem Experiments–Tropics) in which modellers will work with empirical researchers, both observational and experimental, who study tropical forests to create a full, process-rich model of such forests. This is encouraging, and the idea could be pushed further. What is needed is an initiative that pulls the disciplines together towards a goal of building a better understanding of forest processes. Among other things, such an initiative would encourage researchers in different disciplines to take each other’s data needs into account when planning their projects.For this to work, we need to remember that the edifice of forest science relies on the long-term data that scientists wring from forests over decades. Our chances of overcoming climate change are small, but they will diminish further if we forget the basics of monitoring our home planet. More