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CORRESPONDENCE
15 February 2022

Brazil opens highly protected caves to mining, risking fauna

Hernani Fernandes Magalhaes de Oliveira

 ORCID: http://orcid.org/0000-0001-7040-8317

0
,

Daiana Cardoso Silva

 ORCID: http://orcid.org/0000-0003-1612-6452

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,

Priscilla Lora Zangrandi

 ORCID: http://orcid.org/0000-0003-1406-944X

2
&

Fabricius Maia Chaves Bicalho Domingos

 ORCID: http://orcid.org/0000-0003-2069-9317

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Hernani Fernandes Magalhaes de Oliveira

Federal University of Paraná, Curitiba, Brazil.

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Daiana Cardoso Silva

Mato Grosso State University, Nova Xavantina, Brazil.

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Priscilla Lora Zangrandi

Toronto, Canada.

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Fabricius Maia Chaves Bicalho Domingos

Federal University of Paraná, Curitiba, Brazil.

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Brazil’s government has changed the designation of caves that warrant top priority for conservation (see go.nature.com/3gy5). Constituting some 13–30% of the country’s 22,000 protected caves, these will now be open to commercial exploitation, which could seriously affect their vulnerable fauna.

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Nature 602, 386 (2022)
doi: https://doi.org/10.1038/d41586-022-00406-x

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Study sitesThe paired 15N-tracer experiments were conducted in 13 forest sites, of which nine were in China, two in Europe and two in the USA. These sites vary in mean annual precipitation (MAP) from 700 to 2500 mm, in mean annual temperature (MAT) from 3 to > 20 °C, and in soil types (Fig. 1, Supplementary Table 1, Supplementary Table 2). Ambient N deposition (bulk/throughfall NH4+ plus NO3−) at the sites ranged from 6 to 54 kg N ha−1 yr−1. Forest types at the experimental sites include tropical forests in southern China, subtropical forests in central China, and temperate forests in northeastern China, Europe, and the USA. Data from the sites in Europe, the USA, and six of the nine sites in China have been reported previously. Detailed descriptions of these sites and the related data source references are summarized in Supplementary Table 1. Data for forests at the other three sites in China (Xishuangbanna, Wuyishan, and Maoershan) are originally presented here. The Xishuangbanna sites, which is located Xishuangbanna National Forest Reserve in Menglun, Mengla County, Yunnan Province, is a primary mixed forest dominated by the typical tropical forest tree species Terminalia myriocarpa and Pometia tomentosa. The Wuyishan forest, which is located in the Wuyi mountains in Jiangxi Province, is also a mature subtropical forest with Tsuga chinensis var. tchekiangensis as the dominant tree species in the canopy layer. Other common tree species in the forest include Betula luminifera and Cyclobalanopsis multinervis. Maoershan is a relatively young (45 years) larch (Larix gmelinii) plantation located at Laoshan Forest Research Station of Northeast Forestry University, Heilongjiang Province. A few tree species- Juglans mandshurica, Quercus mongolica, and Betula platyphylla- coexist with Larix gmelinii in the canopy. More information about these sites is also presented in Supplementary Table 1.
15N-tracer experimentAt all sites, small amounts of 15NH4+ or 15NO3− tracers (generally  20% in a 1-km pixel was defined as forest. Based on this, we estimated the total global forest area to be ≈42 million km2.Calculation of N-induced C sinkThe N-induced C sink was estimated via the stoichiometric upscaling method19, i.e., by multiplying the N retention in woody tissues of stems, branches, and coarse roots and in the soil with the C/N ratios in these compartments. The C sink due to NHx and or NOy deposition was calculated separately using Eq. (4) as follows:$${{{{{{mathrm{C}}}}}}}_{{{{{{mathrm{sink}}}}}}}={{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{dep}}}}}}}times left(,{!}^{15}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{min }}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{min }}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}times {{{{{mathrm{f}}}}}}right)$$
(4)
where Ndep is NHx or NOy deposition (kg N ha−1 yr−1); ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{org}}}}}}}^{{{{{{rm{R}}}}}}}), ({}^{15}{{{{{{rm{N}}}}}}}_{{{min }}}^{{{{{{rm{R}}}}}}}) and ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{wood}}}}}}}^{{{{{{rm{R}}}}}}}) indicate the fraction of deposited NHx or NOy allocated to organic layer, mineral soil, and woody biomass, respectively; and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{org}}}}}}}), ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{min }}}), and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{wood}}}}}}}) indicate C/N ratios in the soil organic layer, soil mineral layer and woody plant biomass, respectively. f is the fraction we applied to account for flexible C/N in response to elevated N deposition. At elevated N deposition, wood C/N ratio may decrease, and N accumulates without stimulating additional ecosystem C storage. To account for this scenario, we adopted a flexible stoichiometry51, in which the effects of N deposition on wood C/N ratios are accounted for by multiplying the C/N ratios of wood with a fraction f (from 1 to 0) depending on plant growth response to different rates of N deposition level (kg N ha−1 yr−1). Results of growth responses to experimental N addition and field N gradient studies show plant growth increased with increasing N deposition, flattening near 15–30 kg N ha−1 yr−1 and a reversal toward no enhanced growth response at about 100 kg N ha−1 yr−1 (ref. 36,52). Therefore, for N deposition More

Study areaThe QTP is located in southwestern China (25° ~ 40°N, 75° ~ 103°E), with a total area of 2.5 million km2 and an average elevation above 4000 m (Fig. 7). The QTP is mainly covered with permafrost and grassland, with areas of glacier and desert48. The QTP, also known as the “Asian Water Tower”49, is the source of 13 major Asian rivers (e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow Rivers). The QTP has a clod, arid climate, with an annual average temperature below 0 °C and an annual mean precipitation of 400 mm. The seasonal distribution of precipitation is uneven, with most precipitation concentrated in the period June to September. There is a decreasing trend in precipitation from the southeast to the northwest of the plateau50. Known as the “Roof of the World” and “Third Pole”, the QTP is also an area that is sensitive to global climate change, showing increasing warming and humidification in recent decades51. In addition, the QTP contains a diversity of ecosystems and fosters a historic ecological security barrier, which nurtures the development of animal husbandry and diverse cultures.Figure 7Geographical location of the QTP. The map was created using ArcMap 10.2, URL: http://www.esri.com.Full size imageData sourcesRCP scenarios and climate change datasetThe RCP scenarios released by the IPCC 5th Assessment Report52 supply a forecasting standard for climate change research. RCP values ranging from 2.6 to 8.5 reflect radiation forcing values in 2100 relative to the beginning of the Industrial Revolution in 175053. Different radiative forcing scenarios represent different future climate scenarios. RCPs consist of one high-emission scenario (8.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP8.5), two medium-emission scenarios (6.0 ({text{W}} cdot {text{m}}^{ – 2}), RCP6.0; 4.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP4.5), and one low-emission scenario (2.6 ({text{W}} cdot {text{m}}^{ – 2}), RCP2.6)54. In this study, we adopted the RCP2.6, RCP4.5 and RCP8.5 climate change scenarios choosing RCP4.5 to represent the medium emission scenario in consideration of increasing activity through global initiatives in response to climate change. Specific descriptions of each scenario are shown in Table 1.Table 1 The characteristics of each RCP scenario.Full size tableWe adopted the climate change dataset outputs from five global circulation models(GCMs) (namely GFDL-ESM2M, HadGEM2-ES, IPSLCM5ALR, MIROC-ESM-CHEM, and NorESM1-M) within the fifth phase of the Coupled Model Intercomparison Project (CMIP5)55. The dataset outputs from GCMs were downscaled to a resolution of 0.5° and bias-corrected with Water and Global Change (WATCH) data (Integrated Project Water and Global Change, http:/www.eu-watch.org/data_availability)56. The baseline period of the dataset is 1950–2005 and the forecast period is 2006–2099.The climate change dataset included daily precipitation, air pressure, solar radiation, air temperature, maximum air temperature, minimum air temperature, wind speed, and relative humidity.Auxiliary dataThe auxiliary data for our research include the following. (1) The land use and land cover (LULC) map was obtained from the Resource and Environment Science and Data Center (RESDC), Chinese Academy of Sciences (https://www.resdc.cn) for 1980, 1990, 1995, 2000, 2005, 2010, 2015 and 2020 at a 1 km resolution. The LULC data have six major classes: cropland, grassland, forestland, water, built-up land and barren land. (2) The spatial distribution of soil type data, digital elevation model (DEM), watershed boundaries and normalized difference vegetation index (NDVI) data with a resolution of 1 km were obtained from the RESDC. (3) Soil physical and chemical property data (available soil water capacity, absolute depth to bedrock, silt content, clay content, sand content and soil organic carbon content) were obtained from the International Soil Reference and Information Centre (ISRIC Data Hub) (https://data.isric.org) with a 1 km spatial resolution. (4) During 1986–2005 and 1986–2098 (RCP2.6; RCP4.5; RCP8.5), the permafrost datasets in the Northern Hemisphere (https://doi.org/10.12072/ncdc.CCI.db0032.2020) and the response of the alpine grassland ecosystem to climate change (RCP2.6, RCP4.5, and RCP8.5) in the permafrost region of the Qinghai-Tibet Plateau from 1981 to 2099 (https://doi.org/10.12072/ncdc.CCI.db0006.2020) were provided by the National Cryosphere Desert Data Center (https://www.ncdc.ac.cn).Future land use simulation and validationIn this study, we used the Future Land Use Simulation model (FLUS) to simulate the LULC in 2030, 2050 and 2100 under the three RCP scenarios. This model was developed by57 and is available for download at (www.geosimulation.cn/flus.html). The FLUS model is an efficient land use simulation tool and has been widely used58,59. We selected the DEM, slope, precipitation, temperature, soil type, and permafrost distribution to calculate the suitability probability. Based on the land use transfer from 2010 to 2015, we calculated the total land use in 2030, 2050 and 2100 under three RCP scenarios by the Markov model. To validate the FLUS model, we set 2010 as the starting year and simulated the land use in 2015. The output results were compared with the real 2015 land use data, and we calculated the Kappa coefficient as follows:$$begin{array}{*{20}c} {Kappa = frac{{P_{0} – P_{C} }}{{P_{P} – P_{C} }}} \ end{array}$$
(1)
where (P_{0}) is the number of pixels converted correctly,(P_{C}) is the correct number of pixels to be converted in the random case, and (P_{P}) is the correct number of pixels to convert under ideal conditions.Assessment of ecosystem services under different RCP scenariosThis study assessed four ESs namely WY, SR, CS, and RMP, under climate change scenarios in 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2030 (short-term); 2050 (medium-term); and 2100 (long-term). We adopted the Integrated Valuation of Environmental Service and Tradeoffs (InVEST)60 model to assess the WY, SR, and CS ecosystem services. The InVEST model developed by the Natural Capital Project(www.naturalcapitalproject.org) is an effective model to evaluate ESs61 and is widely used in ES research on the QTP22,23,24,25. All spatial data were processed into a 1 km resolution and Albers projection by ArcGIS 10.2 before input into the InVEST model. The data requirements of the InVEST model and its processing are shown in Table S1. We use net primary productivity (NPP) to evaluate the RMP, and NPP can be used to represent the richness of biomass and the supply of organic materials. We adopted the Carnegie-Ames-Stanford Approach (CASA)62 model to estimate NPP.Water yieldWater yield is a key ecosystem service. It refers to the annual quantity of water available for human use, as measured by the supply of surface water per unit area63. We adopted the InVEST 3.9.0 water yield model to estimate WY services in the QTP region. The water yield model is based on the water balance principle64. The biophysical parameter table required by the model is shown in Table S2. The parameters in the biophysical table come from the published literature26,63,65. The annual WY is calculated as follows:$$begin{array}{*{20}{c}} {{Y_{xj}} = left( {1 – frac{{AE{T_{xj}}}}{{{P_x}}}} right){P_x}} end{array}$$
(2)
where (Y_{xj}) is the annual WY of land cover type j in pixel x; (P_{x}) is the annual average precipitation of pixel x; and (AET_{xj}) is the actual evapotranspiration of land cover type j in pixel x.$$begin{array}{*{20}c} {frac{{AET_{xj} }}{{P_{x} }} = frac{{1 + omega_{x} R_{xj} }}{{1 + omega_{x} R_{xj} + frac{1}{{R_{xj} }}}}} \ end{array}$$
(3)
where (omega_{x}) is a dimensionless nonphysical parameter representing soil properties under natural climate conditions. The calculation method is as follows:$$begin{array}{*{20}c} {omega_{x} = Zfrac{{AWC_{x} }}{{P_{x} }}} \ end{array}$$
(4)
where Z is a seasonal rainfall factor representing the regional precipitation distribution and other hydrogeological characteristics. The higher the Z value is, the less the seasonal constant Z affects the model results66. Since the QTP region belongs to the arid and cold climate zone in China, the Z value is set as 9. (AWC_{x}) is the soil effective water content of pixel X, which is determined by the soil depth and physical and chemical properties. (R_{xj}) is the Budyko dryness index, which is calculated as follows:$$begin{array}{*{20}c} {R_{xj} = frac{{K_{xj} cdot ET_{0} }}{{P_{x} }}} \ end{array}$$
(5)
where, (K_{xj}) is the reference crop evapotranspiration and (ET_{0}) is the reference evapotranspiration in pixel x. We adopted the modified Hargreaves method to calculate (ET_{0}).$$ET_{0} = 0.0013 times 0.408 times RA times (T_{av} + 17) times (TD – 0.0123P)^{0.76}$$
(6)
In the above formula, (T_{av}) represents the average daily maximum temperature and minimum temperature, (TD) represents the difference between the daily maximum temperature and minimum temperature, (RA) represents astronomical radiation (MJm-2d-1) and P represents precipitation (mm/month).Soil retentionSoil retention refers to the ability of various land cover types to prevent soil erosion. The InVEST 3.9.0 sediment delivery ratio (SDR) was employed to estimate SR services in the QTP region. The SDR model is based on the Revised Universal Soil Loss Equation (RUSLE)67, and the model is calculated as follows:$$begin{array}{*{20}c} {SR = R*K*LS – R*K*LS*C*P} \ end{array}$$
(7)
$$begin{array}{*{20}c} {L = left( {frac{gamma }{22.3}} right)^{{frac{beta }{1 + beta }}} } \ end{array}$$
(8)
$$begin{array}{*{20}c} {beta = frac{{sin frac{theta }{0.0896}}}{{left[ {3.0, *,left( {sin theta } right)^{0.8} +, 0.56} right]}}} \ end{array}$$
(9)
$$begin{array}{*{20}c} {S = 65.41*sin^{2} theta + 4.56*sin theta + 0.065} \ end{array}$$
(10)
where SR is the total amount of soil retention (tons ha-1 a-1), LS is the topographic factor, and LS is calculated from the slope length factor (L) and slope steepness factor (S). C is the vegetation and management factor. P is the support practice factor. C and P are shown in Table S2. R is the rainfall erosivity index(MJ mm ha-1 h-1 a-1), which was calculated via monthly precipitation28. K is the soil erodibility, which was calculated from the sand, silt, clay and organic soil moisture contents68. R and K are calculated as follows:$$begin{array}{*{20}c} {R = mathop sum limits_{i = 1}^{12} left( { – 1.5527 + 0.179P_{i} } right)} \ end{array}$$
(11)
$$begin{array}{*{20}c} {K = 0.1317*left{ {0.2 + 0.3*exp left[ { – 0.0256*SANleft( {1 – frac{SIL}{{100}}} right)} right]} right}} \ {*left( {frac{SIL}{{CLA – SIL}}} right)^{0.3} *left( {1 – frac{0.25*SOM}{{SOM + exp 3.72 – 2.95*SOM}}} right)} \ {quad quad*left( {1 – frac{{0.7*1 – frac{SAN}{{100}}}}{{begin{array}{*{20}c} {1 – frac{SAN}{{100}} + exp left( { – 5.51 + 22.9*left( {1 – frac{SAN}{{100}}} right)} right)} \ end{array} }}} right)} \ end{array}$$
(12)
where Pi is the precipitation in month i. SAN, SIL, CLA, and SOM are the contents of sand, silt, clay and organic moisture, respectively. Other parameters are shown in Table S1.Carbon storageCarbon storage services refer to the carbon that ecosystems store in vegetation, soil and debris. The InVEST 3.9.0 carbon model uses a simple method to estimate CS based on land use data. The carbon pools in this model include four categories: aboveground carbon, belowground carbon, soil organic carbon and dead organic matter. This model simplifies the carbon cycle, and the change in carbon storage is mainly caused by change in land use69. The carbon pools for land use types were set according to published literature70,71,72. The carbon storage is calculated as follows:$$begin{array}{*{20}c} {{text{C}}_{{{text{total}}}} = C_{above} + C_{below} + C_{soil} + C_{dead} } \ end{array}$$
(13)
where ({text{C}}_{{{text{total}}}}), (C_{above}), (C_{below}), (C_{soil}) and (C_{dead}) are the total carbon storage, aboveground carbon, belowground carbon, soil organic carbon and dead organic matter, respectively.Raw material provisionRaw material supply refers to the organic matter provided by the ecosystem for human production and life, such as pasture and wood. In this study, RMP was quantified by the annual NPP. The NPP in the QTP region is calculated by the CASA model, which is a light use efficiency model driven by climate and remote sensing data73,74. The CASA model has been widely used to estimate NPP in terrestrial ecosystems75,76. In the CASA model, NPP is calculated as follows:$$begin{array}{*{20}c} {NPPleft( {x,t} right) = APARleft( {x,t} right) times varepsilon left( {x,t} right)} \ end{array}$$
(14)
where, (APARleft( {x,t} right)) is the photosynthetically active radiation(MJ m-2) absorbed by pixel x in month t, (varepsilon left( {x,t} right)) is the actual light energy utilization rate(gC MJ-1), and the (APARleft( {x,t} right)) calculation method is as follows:$$begin{array}{*{20}c} {APARleft( {x,t} right) = SOLleft( {x,t} right) times FPARleft( {x,t} right) times 0.5} \ end{array}$$
(15)
In the formula, (SOLleft( {x,t} right)) is the total solar radiation in pixel x in month t(MJ M-2); (FPARleft( {x,t} right)) is the absorption ratio of photosynthetically active radiation by vegetation, which is determined by the normalized difference vegetation index (NDVI); and the constant 0.5 is the proportion of photosynthetically active radiation to the total radiation. (SOLleft( {x,t} right)) can be calculated by the solar shortwave radiation as follows:$$begin{array}{*{20}c} {SOLleft( {x,t} right) = a_{s} + b_{s} frac{n}{N}R_{s} } \ end{array}$$
(16)
where, (R_{s}) is the solar shortwave radiation(MJ M-2 d-1), n is the actual sunshine time(hours), N is the time of day(hours), and (frac{n}{N}) is the relative sunshine time; The constants (a_{s} = 0.25) and (b_{s} = 0.5).And the (varepsilon left( {x,t} right)) is calculated as follows:$$begin{array}{*{20}c} {varepsilon left( {x,t} right) = T_{varepsilon 1} left( {x,t} right) times T_{varepsilon 2} left( {x,t} right) times W_{varepsilon } left( {x,t} right) times varepsilon_{max} } \ end{array}$$
(17)
where, (T_{varepsilon 1}) and (T_{varepsilon 2}) are the stress factors of cold and heat, respectively; (W_{varepsilon }) is the water stress factor, reflecting the influence of water conditions; (varepsilon_{max}) is the maximum light use efficiency(gC MJ-1) under the optimal conditions, in this study, (varepsilon_{max}) is 0.389.Trend analysisThe Mann–Kendall nonparametric test and Sen’s slope estimator were used to analyze the trend of ESs in the QTP region. The Mann–Kendall method is widely used to analyze climatic and hydrological time series variation trends77. The advantage of the Mann–Kendall test is that it does not require the sample to follow a certain distribution, allows the existence of missing values, is not affected by a small number of outliers, and has strong quantitative ability78. The Mann–Kendall test is as follows:$$begin{array}{*{20}c} {S = mathop sum limits_{i}^{n – 1} mathop sum limits_{j = i + 1}^{n} sgnleft( {x_{j} – x_{i} } right)} \ end{array}$$
(18)
For time series data, i.e., {x1, x2, …, xn}, n is the length of the data, and (sgnleft( {x_{j} – x_{i} } right)) is derived as:$$begin{array}{*{20}c} {sgnleft( {x_{j} – x_{i} } right) = left{ {begin{array}{*{20}c} { + 1,x_{j} – x_{i} > 0} \ {0,x_{j} – x_{i} = 0} \ { – 1,x_{j} – x_{i} < 0} \ end{array} } right.} \ end{array}$$ (19) In this study, we set the significance level of (alpha = 0.05), when (left| Z right| le Z_{1 - alpha /2}) accepts the null hypothesis. Otherwise, the null hypothesis is rejected, and the trend is statistically significant.$$begin{array}{*{20}c} {Z = left{ {begin{array}{*{20}l} frac{S - 1}{{sqrt {VARleft( S right)} }},&quad S > 0 \ 0,&quad S = 0 \ frac{S + 1}{{sqrt {VARleft( S right)} }},&quad S < 0 \ end{array} } right.} \ end{array}$$ (20) $$begin{array}{*{20}c} {VARleft( S right) = left{ {nleft( {n - 1} right)left( {2n + 5} right) - mathop sum limits_{j = 1}^{p} t_{j} left( {t_{j} - 1} right)left( {2t_{j} + 5} right)} right} div 18} \ end{array}$$ (21) where p is the number of nodes in the dataset and (t_{j}) is the length of the nodes.Sen’s slope estimator is an estimation method based on the median and its insensitivity to outliers78.$$begin{array}{*{20}c} {beta = Medianleft( {frac{{x_{j} - x_{i} }}{j - i}} right)} \ end{array}$$ (22) Trade-offs and synergy analysisSynergies and trade-offs were used to describe the relationships among the ESs. A trade-off analysis was conducted to reflect the difference in ESs and their responses to climate change. Trade-offs are when ESs change in the opposite direction. Synergies are when ESs change in the same direction79. Correlation analysis is often used to evaluate trade-offs and synergies between ESs2. To analyze the trade-offs and synergies of ESs at different administrative and natural scales, we allocated the ES values at the 10 km (pixel), county and watershed scales by the “zonal statistic” module of ArcGIS 10.2, and conducted minimum–maximum normalization in R4.0.3 (www.R-project.com). To analyze the relationship between any two of the four ES types, the R package PerformanceAnalytics was adopted to measure the Spearman correlation matrix at different scales. More

Inventory analysisBefore computing the environmental impacts, we analyzed inventory data and input them into the software program for simulations. With respect to reusable masks, on-site measurements of raw materials, energy requirements for processing (e.g., laying, cutting, sewing, etc.), packaging material configurations, reuse options, cleaning activities and transport distances were provided by the Italian Social District. In particular, requirements for washing the reusable face mask were adapted from Schmutz et al.9 in compliance with the information provided by the producer. Moreover, waste disposal scenario data for both types was collected from the preprint by Allison et al.23 Finally, inventory data for single-use masks were collected from independent producers via certified laboratories. The final set of background and foreground data are provided in “Supplementary Table S1”.Single-use face masks consist of three layers of polypropylene non-wovens. The inner and outer fabric layers are Spunbond and the middle layer is 99% filtering Meltblown24. Reusable face masks (Type IIR) are also composed of three layers: an internal layer of antibacterial quality cotton, a middle layer of Meltblown, and an external layer of Spunbond. Mask quality is determined by the quality of the component parts and is therefore traceable to the component suppliers. Information on the suppliers and product component types (including certifications and features) is provided in “Supplementary Table S2”. Meltblown (supplied by Ramina) makes up the central part of reusable masks. This component guarantees a filtering performance of more than 99%, which—combined with the high-quality water-repellent anti-drop C6 antibacterial cotton (supplied by Olmetex) of the inner layer—resists up to 10 washes per immersion. These materials, forged together using specialized machinery, enhance Type IIR surgical masks above all others, with respect to their superior performance in the overall trade-off between filtering quality, reusability, and environmental sustainability. Furthermore, the cotton inner fabric of these masks has the same effectiveness as single-use masks in reducing the transmission of respiratory viruses25.Regarding elastic bands, nose clip material (for single-use masks), and fabric layers, no direct datasets are available in the ecoinvent database. Thus, for the present study, non-allergenic latex-free elastic bands, produced using a “polyurethane, flexible foam” process, were assumed. Nose clip material, which is only used for single-use masks, was assumed to be modelled using a “polyvinyl chloride resin (B-PVC)” process. Finally, we assumed that a “polypropylene, granulate” process was used for the TnT Spunbond and Meltblown layers. Regarding packaging materials, reusable face masks are wrapped in biodegradable plastic bags, while single-use masks are packaged in plastic bags. Both types of masks are packaged in sets of 10 and delivered in recycled cardboard boxes. In the present study, packaging materials were introduced to the software as “polyester-complexed starch biopolymer”, “packaging film, low-density polyethylene”, and “corrugated board boxes: 16.6% primary fiber, 83.4% recycled fiber”. For transportation, a “transport, freight, lorry 16–32 metric ton, EURO6” process was assumed from the manufacturing facility and nationwide distribution by road, using Euro 6D vans.To calculate the number of face masks used in Italy in 2020, we estimated the Italian population at 60.6 million, based on Organisation for Economic Co-operation and Development (OECD) statistics26. We assumed one mask per person, per day, for both mask types, according to WHO recommendations27. As reusable face masks can be washed up to 10 times without losing their virus filtration performance (according to the manufacturer’s own specification), we assumed the maximum number of washes for the use phase. Accordingly, the total number of face masks used in Italy was calculated at 2.18 and 22.1 billion for reusable and single-use face masks, respectively. The total amount of waste was calculated in terms of the number of used masks, alongside their packaging materials (i.e., plastic wrap and cardboard boxes) (Table 2). Single-use face masks were found to generate almost 10 times more waste for each waste category, relative to reusable face masks.Table 2 Total waste generated from used face masks in Italy, 2020 (kton/year).Full size tableWith respect to mask use, our basic case scenario was based on WHO recommendations27, which stipulate that reusable face masks should be washed daily with soap/detergent and hot (60 °C) water. We assumed that the entire household (2.3 people for Italian case) masks are washed together with other clothes in a standard 7 kg washing machine, following both the literature9 and producer instructions. Schmutz et al.9 reported that the requirements for a half-full washing machine (a typical situation in Europe) are 84 g detergent, 52.3 L tap water and 1.1 kWh electricity per load. Accordingly, the average washing consumables required for each mask is calculated by normalizing the specified requirements with respect to one mask (i.e., via multiplying a half-full load requirement by 0.2%).It should be noted, however, that user behavior is not easy to predict and the washing machine might not be always considered as the preferred option. Hence, as a further step, we investigated different user behaviors as sensitivity cases. First of all, hand washing was introduced as the main sensitivity scenario9,23,28. In this case, we assumed that the entire household masks will be washed together every day after use, in a bowl of 5 L filled up to 3 L level with water at 60 °C and then rinsed with water without soap/detergent. Approximately 6.24 g of liquid detergent and 6 L of water is required in each manual washing session23. Similar to the machine wash case, the average washing consumables required for each mask is calculated by normalizing the specified requirements with respect to one mask (i.e., the requirements per mask per wash are 2.609 L tap water, 2.713 g detergent, 447.7 kJ energy provided by the gas boiler).Moreover, we also considered other possible user behavior scenarios, assuming that reusable face masks might be washed for more than the recommended lifespan (i.e., 10 washes). Accordingly, a second sensitivity case was modelled for reusable masks washed 15 times prior to disposal. Finally, with reference to single-use masks, we took into consideration a longer period of wearing. Although the recommended face mask use is one mask per day (or 4–8 h), many users wear single-use surgical masks for longer than this recommended period. Thus, in this sensitivity case, we assumed that users would wear the same mask for 2 subsequent days. It should be noted, however, that the latter two sensitivity cases, i.e., concerning longer wearing period of both types, might compromise the protection level of masks and thereby human health.Regarding the packaging and waste disposal activities, the Italian Social District provided some data from their ongoing studies regarding the biodegradability of packaging materials for reusable (Type IIR) face masks. However, the present study could not consider actual waste disposal activities (i.e., recycling, reuse) due to the lack of approved assessments. Thus, waste disposal was based mainly on previous studies indicating incineration and landfilling as viable options23,29. We assumed that contaminated masks and discarded packaging materials would go directly to waste disposal sites, and 43% of mixed waste would be landfilled while 57% of mixed waste would be incinerated23. Regarding alternative disposal activities, we considered two sensitivity cases: one that assumed that all masks from each type would be fully incinerated9,30 and another that assumed that all masks from each type would be fully landfilled31. More

Seed collectionMature seeds of G. officinalis were collected from the natural-growing plant community at the Hezuo alpine meadow and wetland ecosystem research station of Lanzhou University on the southeast Qinghai-Tibet Plateau (lat. 34°53′ N, long. 101°53′ E, alt. 2900 m) in 2014 and grown in a nursery. Robust seedlings were selected and transplanted to the Haibei Alpine Meadow Ecosystem Research Station of the Chinese Academy of Sciences on the northeast Qinghai-Tibet Plateau (lat. 37°37′N, long. 101°19′ E, alt. 3200 m) and Datong ecological agriculture experimental station of the Northwest Institute of Plateau Biology (lat. 34°53′ N, long. 101°53′ E, alt. 2900 m). Transplantation was also performed in a natural environment (the Hezuo alpine meadow and a wetland ecosystem research station of Lanzhou University).Study plots and transplantingThe naturally studied population is located at the Hezuo alpine meadow and wetland ecosystem research station of Lanzhou University on the southeast Qinghai-Tibet Plateau (henceforth referred to as the natural environment (NE)), China (lat. 34°53′ N, long. 101°53′ E, alt. 2900 m). The third transplantation site was created in a natural environment and was termed “natural transplant” (NT). The average annual air temperature is 2 °C, with extremes of 11.5 °C (maximum) and –8.9 °C (minimum). The annual precipitation is approximately 550 mm, 80% of which falls in the short summer growing season between May and September. Hezuo station is dominated by Kobresia humilis, Pedicularis kansuensis, Heteropappus altaicus, Stellera chamaejasme, Aconitum gymnandrum and Nepeta pratti, which bloom at the same time as G. officinalis.The higher-elevation transplanted plot was located at the Haibei Alpine Meadow Ecosystem Research Station of the Chinese Academy of Sciences on the northeast Qinghai-Tibet Plateau (henceforth referred to as the high-elevation environment (HE) (lat. 37°37′N, long. 101°19′ E, alt. 3200 m). The average annual air temperature was –1.7 °C, with extremes of 27.6 °C (maximum) and –37.1 °C (minimum). The annual precipitation ranged between 426 and 860 mm, mainly in July and August.The lower-elevation transplanted plot was located at the Datong ecological agriculture experimental station of the Northwest Institute of Plateau Biology on the transition zone between Qinghai-Tibet Plateau and loess plateau (henceforth referred to as the low-elevation environment (LE)) (lat. 34°53′ N, long. 101°53′ E, alt. 2900 m). The average annual air temperature was 7.6 °C, with extremes of 34.6 °C (maximum) and –18.9 °C (minimum). The annual precipitation was approximately 380 mm, mainly in July and August. The study area was dominated by cultivated crops.Robust seedlings with floral buds were selected for transplantation. The density of G. officinalis under the NE was approximately 1.5 plants/m2; therefore, we planted individuals at the same plant density in all transplanted plots. Moreover, more than 300 robust seedlings of G. officinalis were transplanted to each transplanted plot. The total planting area was greater than 200 m2 at each plot. The transplanted seedlings flowered in the summer, and we conducted our experiments during the following 2 years (2016–2017).Flowing phenology and flower durationTo observe flowering phenology, three 1 × 10-m areas were created within each experimental plot in 2016. In each plot, flower opening and duration were monitored and recorded every morning until all flowers withered.At the full anthesis phase of G. officinalis in 2016, 10 plants from each plot were randomly selected. On each plant, two buds at the middle position of the inflorescence were selected, and the floral duration of all the selected buds was monitored and recorded. The pollen (male phase) and stigma (female phase) presentations were monitored and recorded.Floral display and reproductive allocationAt the full-bloom stage, 50 single plants were selected from each plot to test the inflorescence traits. Stem length (the distance from the stem base to apex) was measured by a straightedge. The number of sprays on each plant and the average flower numbers (including buds and fruits) on each spray were counted.We selected 100–150 fully open flowers on different plants in each population to test the flower sizes at each plot. To avoid the position effect as much as possible, we did not choose terminal flowers. The length and width (diameter) of the flowers in each plot were measured by Vernier calipers. To test the sexual allocation changes in G. officinalis among the three plots, 30 buds on different plants in each plot were selected randomly. Then, the pollen numbers (PNs) and ovule numbers (ONs) were counted. The pollen/ovule ratios (P/O) were calculated as P/O = pollen numbers in all five anthers/ovule numbers21.Sampling dates corresponded to the height of the flowering season at each site (mid-August in the LE and early September in the NE and HE) before fruiting had occurred. While fresh, the aboveground parts of 30 fully flowering plants per site were dissected into inflorescences, peduncles, leaves, and stems. Plant material was oven-dried at 70 °C for 3 days, and the dry weights were obtained to the nearest 0.1 mg on an analytical balance (Ohaus). The inflorescence and peduncle fractions of each plant were summed to provide a measure of reproductive biomass (R), and the leaf and stem fractions of each plant were summed to provide a measure of vegetative biomass (V). The reproductive allocation (RA) was calculated as RA = R/(R + V).Observation of pollinatorsThe floral visitors to G. officinalis were recorded in the three plots. Ten neighbouring inflorescences on different individual plants were selected at random and labelled. Before observation, we counted all the open flowers on one inflorescence and then recorded the number of flowers visited by pollinators. We observed these flowers between 9:00 a.m. and 6:00 p.m. in each plot during 2016 and 2017. In total, observations were carried out for 65 h in each plot over the 2 years. While carrying out these observations, we stayed 2 m away from the focal flowers to observe all of the floral visitors without disturbing their foraging behaviours. The visitor species, behaviour in the flower, and visiting times of each species were recorded, and the visit frequencies of each visitor species were calculated. The visit frequency was calculated as visit frequency = visit times/visit flower numbers/hour.To identify whether flower visitors were legitimate pollinators of G. officinalis, collected visitors were observed and photographed with a stereomicroscope to identify whether G. officinalis pollen was attached to their bodies. Additionally, each visitor was observed to determine whether the reproductive structures of flowers had been touched. Visitors that were positive for all these factors were considered legitimate pollinators.Seed productionTo test the self-compatibility of G. officinalis, flowers subjected to self-pollination treatment (unopened flowers were isolated with paper bags) in 2017 on the three plots were subjected. To further analyse self-compatibility, we conducted outcrossing pollination. In addition, 30 individual inflorescences on different plants were bagged, and two buds at the same position on each inflorescence were selected. Both buds on each inflorescence were emasculated before the flowers opened. When the stigma opened, one flower was pollinated with fresh pollen from the same inflorescence or different inflorescences on the same plant (selfing), and the other was pollinated with fresh pollen from a plant 5 m away (outcrossing). To test whether facilitated selfing occurred, 30 individual plants in each plot were tagged. On each tagged plant, two individual buds were selected: one was assigned to natural pollination, and the other was assigned to emasculation (removal of all anthers before stigma lobe opening). To test whether agamospermy occurred, the flowers were subjected to emasculation treatment and isolated in three plots. Thirty buds on different plants were randomly selected, and all the anthers were removed before the flowers opened, and then all the buds were isolated with paper bags. At maturity, all fruits were collected, and all of the seeds (including mature and abortive seeds) were counted. Seed-set ratios were used to assess the reproductive success of each treatment, which were calculated by the number of mature seeds divided by the total ovules in each ovary. The facilitated selfing data were calculated as the natural seed-set ratio minus the emasculated seed-set ratio.Similarly, 30 inflorescences were tagged on different plants in each plot, and two buds were then tagged at the same position on each inflorescence; one bud was assigned to natural pollination, and the other was assigned to supplemental hand pollination when stigmas opened. For supplemental hand pollination, pollen was collected randomly from unmarked individuals at a minimum distance of 5 m from the recipient individual. Supplemental hand pollination events were conducted every day until the flower was permanently closed. When mature, all seeds were counted, and seed-set ratios were calculated. For each plot, we calculated an index of pollen limitation (IPL): IPL = 1 − (Po/Ps), where Po is the natural seed-set ratio and Ps is the supplemental hand-pollination seed-set ratio. As the seed-set ratios showed no significant difference between natural and supplemental hand pollination in the natural environment, we considered the IPL at this plot to be 0. The IPL data at the other two plots were compared using an independent-samples t test.Statistical analysisThe normality of the data was tested using one-sample Kolmogorov–Smirnov (1-K-S) tests, and then one-way ANOVAs (with Tukey’s multiple contrasts) were used to test differences in all traits among the three environments. More