Philippa Kaur

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

Schwartz, J. J. & Bee, M. A. in Animal communication and noise (ed Henrik Brumm) 91–132 (Springer, 2013).Wollerman, L. Acoustic interference limits call detection in a Neotropical frog Hyla ebraccata. Anim. Behav. 57, 529–536. https://doi.org/10.1006/anbe.1998.1013 (1999).CAS 
Article 
PubMed 

Google Scholar 
Gerhardt, H. C. & Schwartz, J. J. Interspecific interactions in anuran courtship. Amphib. Biol. 2, 603–632 (1995).
Google Scholar 
Gröning, J. & Hochkirch, A. Reproductive interference between animal species. Q. Rev. Biol. 83, 257–282 (2008).Article 

Google Scholar 
Popp, J. W., Ficken, R. W. & Reinartz, J. A. Short-term temporal avoidance of interspecific acoustic interference among forest birds. Auk 102, 744–748. https://doi.org/10.1093/auk/102.4.744 (1985).Article 

Google Scholar 
Luther, D. A. Signaller: Receiver coordination and the timing of communication in Amazonian birds. Biol. Let. 4, 651–654 (2008).Article 

Google Scholar 
Brumm, H. Signalling through acoustic windows: nightingales avoid interspecific competition by short-term adjustment of song timing. J. Comp. Physiol. A. 192, 1279–1285 (2006).Article 

Google Scholar 
Farina, A. Soundscape ecology: principles, patterns, methods and applications. (Springer, 2013).Krause, B. L. The niche hypothesis: a virtual symphony of animal sounds, the origins of musical expression and the health of habitats. Soundscape Newsl. 6, 6–10 (1993).
Google Scholar 
Littlejohn, M. & Martin, A. Acoustic interaction between two species of leptodactylid frogs. Anim. Behav. 17, 785–791. https://doi.org/10.1016/S0003-3472(69)80027-8 (1969).Article 

Google Scholar 
Ficken, R. W., Ficken, M. S. & Hailman, J. P. Temporal pattern shifts to avoid acoustic interference in singing birds. Science 183, 762–763. https://doi.org/10.1126/science.183.4126.762 (1974).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, M. Acoustic niche partitioning in an anuran community inhabiting an Afromontane wetland (Butare, Rwanda). Afr. Zool. 47, 60–73 (2012).Article 

Google Scholar 
Lima, M., Pederassi, J., Pineschi, R. & Barbosa, D. Acoustic niche partitioning in an anuran community from the municipality of Floriano, Piauí Brazil. Brazil. J. Biol. 79, 566–576 (2019).CAS 
Article 

Google Scholar 
Gottsberger, B. & Gruber, E. Temporal partitioning of reproductive activity in a neotropical anuran community. J. Trop. Ecol. 1, 271–280 (2004).Article 

Google Scholar 
Villanueva-Rivera, L. J. Eleutherodactylus frogs show frequency but no temporal partitioning: Implications for the acoustic niche hypothesis. PeerJ 2, e496 (2014).Article 

Google Scholar 
Bignotte-Giró, I. & López-Iborra, G. M. Acoustic niche partitioning in five Cuban frogs of the genus Eleutherodactylus. Amphibia-Reptilia 40, 1–11 (2019).Article 

Google Scholar 
Hödl, W. Call differences and calling site segregation in anuran species from Central Amazonian floating meadows. Oecologia 28, 351–363 (1977).ADS 
Article 

Google Scholar 
Schmidt, A. K., Römer, H. & Riede, K. Spectral niche segregation and community organization in a tropical cricket assemblage. Behav. Ecol. 24, 470–480. https://doi.org/10.1093/beheco/ars187 (2013).Article 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null models in ecology. (1996).Chek, A. A., Bogart, J. P. & Lougheed, S. C. Mating signal partitioning in multi-species assemblages: A null model test using frogs. Ecol. Lett. 6, 235–247 (2003).Article 

Google Scholar 
Tobias, J. A., Planqué, R., Cram, D. L. & Seddon, N. Species interactions and the structure of complex communication networks. Proc. Natl. Acad. Sci. 111, 1020–1025. https://doi.org/10.1073/pnas.1314337111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sugai, L. S., Llusia, D., Siqueira, T. & Silva, T. S. Revisiting the drivers of acoustic similarities in tropical anuran assemblages. Ecology, e03380 (2021).Hart, P. J. et al. Acoustic niche partitioning in two tropical wet forest bird communities. bioRxiv (2020).Duellman, W. E. & Trueb, L. Biology of amphibians. (McGraw-Hill Book Company, 1986).Wells, K. D. The social behaviour of anuran amphibians. Anim. Behav. 25, 666–693. https://doi.org/10.1016/0003-3472(77)90118-X (1977).Article 

Google Scholar 
Woinarski, J., Fisher, A. & Milne, D. Distribution patterns of vertebrates in relation to an extensive rainfall gradient and variation in soil texture in the tropical savannas of the Northern Territory, Australia. J. Trop. Ecol. 1, 381–398 (1999).Article 

Google Scholar 
Allen-Ankins, S. & Schwarzkopf, L. Spectral overlap and temporal avoidance in a tropical savannah frog community. Anim. Behav. 180, 1–11. https://doi.org/10.1016/j.anbehav.2021.07.024 (2021).Article 

Google Scholar 
Gerhardt, H. C. The evolution of vocalization in frogs and toads. Ann. Rev. Ecol. Syst. 1, 293–324 (1994).Article 

Google Scholar 
Rowley, J. J. & Callaghan, C. T. The FrogID dataset: expert-validated occurrence records of Australia’s frogs collected by citizen scientists. ZooKeys 912, 139 (2020).Article 

Google Scholar 
Zelick, R. & Narins, P. M. Characterization of the advertisement call oscillator in the frogEleutherodactylus coqui. J. Comp. Physiol. A. 156, 223–229 (1985).Article 

Google Scholar 
Schwartz, J. J. & Wells, K. D. An experimental study of acoustic interference between two species of neotropical treefrogs. Anim. Behav. 31, 181–190. https://doi.org/10.1016/S0003-3472(83)80187-0 (1983).Article 

Google Scholar 
Smith, M. J. & Hunter, D. Temporal and geographic variation in the advertisement call of the booroolong frog (Litoria booroolongensis: Anura: Hylidae). Ethology 111, 1103–1115 (2005).Article 

Google Scholar 
Baraquet, M., Grenat, P. R., Salas, N. E. & Martino, A. L. Geographic variation in the advertisement call of Hypsiboas cordobae (Anura, Hylidae). Acta ethologica 18, 79–86 (2015).Ziegler, L., Arim, M. & Bozinovic, F. Intraspecific scaling in frog calls: The interplay of temperature, body size and metabolic condition. Oecologia 181, 673–681 (2016).ADS 
Article 

Google Scholar 
Navas, C. A. & Bevier, C. R. Thermal dependency of calling performance in the eurythermic frog Colostethus subpunctatus. Herpetologica, 384–395 (2001).Lougheed, S. C., Austin, J. D., Bogart, J. P., Boag, P. T. & Chek, A. A. Multi-character perspectives on the evolution of intraspecific differentiation in a neotropical hylid frog. BMC Evol. Biol. 6, 1–16 (2006).Article 

Google Scholar 
Littlejohn, M. Premating isolation in the Hyla ewingi complex (Anura: Hylidae). Evolution, 234–243 (1965).Lemmon, E. M. Diversification of conspecific signals in sympatry: geographic overlap drives multidimensional reproductive character displacement in frogs. Evolution: International Journal of Organic Evolution 63, 1155–1170 (2009).Jansen, M., Plath, M., Brusquetti, F. & Ryan, M. J. Asymmetric frequency shift in advertisement calls of sympatric frogs. Amphibia-Reptilia 37, 137–152 (2016).Article 

Google Scholar 
Jang, Y. & Gerhardt, H. Divergence in the calling songs between sympatric and allopatric populations of the southern wood cricket Gryllus fultoni (Orthoptera: Gryllidae). J. Evol. Biol. 19, 459–472 (2006).CAS 
Article 

Google Scholar 
Both, C. & Grant, T. Biological invasions and the acoustic niche: The effect of bullfrog calls on the acoustic signals of white-banded tree frogs. Biol. Let. 8, 714–716 (2012).Article 

Google Scholar 
Hopkins, J. M., Edwards, W., Laguna, J. M. & Schwarzkopf, L. An endangered bird calls less when invasive birds are calling. J. Avian Biol. 52, 1 (2021).Article 

Google Scholar 
Medeiros, C. I., Both, C., Grant, T. & Hartz, S. M. Invasion of the acoustic niche: variable responses by native species to invasive American bullfrog calls. Biol. Invasions 19, 675–690 (2017).Article 

Google Scholar 
Wilczynski, W. & Ryan, M. J. in Geographic Variation in Behavior (eds S. A. Foster & J. A. Endler) 234–261 (Oxford University Press, 1999).Schwartz, J. J. & Gerhardt, H. C. Spatially mediated release from auditory masking in an anuran amphibian. J. Comp. Physiol. A. 166, 37–41 (1989).Article 

Google Scholar 
da Silveira Vasconcelos, T. & de Cerqueira Rossa-Feres, D. Habitat heterogeneity and use of physical and acoustic space in anuran communities in Southeastern Brazil. Phyllomedusa J. Herpetol. 7, 127–142 (2008).Herrick, S. Z., Wells, K. D., Farkas, T. E. & Schultz, E. T. Noisy neighbors: Acoustic interference and vocal interactions between two syntopic species of Ranid frogs, Rana clamitans and Rana catesbeiana. J. Herpetol. 52, 176–184. https://doi.org/10.1670/17-049 (2018).Article 

Google Scholar 
Rowley, J. J. et al. FrogID: citizen scientists provide validated biodiversity data on frogs of Australia. Herpetol. Conserv. Biol. 14, 155–170 (2019).
Google Scholar 
Koehler, J. et al. The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice. Zootaxa 4251, 1–124 (2017).Article 

Google Scholar 
Tonini, J. F. R. et al. Allometric escape from acoustic constraints is rare for frog calls. Ecol. Evol. 10, 3686–3695. https://doi.org/10.1002/ece3.6155 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Anstis, M. et al. Revision of the water-holding frogs, Cyclorana platycephala (Anura: Hylidae), from arid Australia, including a description of a new species. Zootaxa 4126, 451–479 (2016).Article 

Google Scholar 
Cardoso, G. C. Using frequency ratios to study vocal communication. Anim. Behav. 85, 1529–1532 (2013).Article 

Google Scholar 
Narins, P. & Zelick, R. in The evolution of the amphibian auditory system (eds B Fritzsch et al.) 511–536 (John Wiley and Sons, 1988).Amézquita, A., Flechas, S. V., Lima, A. P., Gasser, H. & Hödl, W. Acoustic interference and recognition space within a complex assemblage of dendrobatid frogs. Proc. Natl. Acad. Sci. 108, 17058–17063 (2011).ADS 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Kassambara, A. & Mundt, F. factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.7 (2020). More

Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.PubMed 
PubMed Central 

Google Scholar 
Wietz M, Gram L, Jørgensen B, Schramm A. Latitudinal patterns in the abundance of major marine bacterioplankton groups. Aquat Microbial Ecol. 2010;61:179–89.
Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 

Google Scholar 
González JM, Simó R, Massana R, Covert JS, Casamayor EO, Pedrós-Alió C, et al. Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom. Appli Environ Microbiol. 2000;66:4237–46.
Google Scholar 
González JM, Moran MA. Numerical dominance of a group of marine bacteria in the alpha-subclass of the class Proteobacteria in coastal seawater. Appl Environ Microbiol. 1997;63:4237–42.PubMed 
PubMed Central 

Google Scholar 
Grossart HP, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Alavi M, Miller T, Erlandson K, Schneider R, Belas R. Bacterial community associated with Pfiesteria‐like dinoflagellate cultures. Environ Microbiol. 2001;3:380–96.CAS 
PubMed 

Google Scholar 
Jasti S, Sieracki ME, Poulton NJ, Giewat MW, Rooney-Varga JN. Phylogenetic diversity and specificity of bacteria closely associated with Alexandrium spp. and other phytoplankton. Appl Environ Microbiol. 2005;71:3483–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zubkov MV, Fuchs BM, Archer SD, Kiene RP, Amann R, Burkill PH. Rapid turnover of dissolved DMS and DMSP by defined bacterioplankton communities in the stratified euphotic zone of the North Sea. Deep Sea Res Top Stud Oceanogr. 2002;49:3017–38.CAS 

Google Scholar 
Zubkov MV, Fuchs BM, Archer SD, Kiene RP, Amann R, Burkill PH. Linking the composition of bacterioplankton to rapid turnover of dissolved dimethylsulphoniopropionate in an algal bloom in the North Sea. Environ Microbiol. 2001;3:304–11.CAS 
PubMed 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 

Google Scholar 
Voget S, Wemheuer B, Brinkhoff T, Vollmers J, Dietrich S, Giebel H-A, et al. Adaptation of an abundant Roseobacter RCA organism to pelagic systems revealed by genomic and transcriptomic analyses. ISME J. 2015;9:371–84.CAS 
PubMed 

Google Scholar 
Billerbeck S, Wemheuer B, Voget S, Poehlein A, Giebel H-A, Brinkhoff T, et al. Biogeography and environmental genomics of the Roseobacter-affiliated pelagic CHAB-I-5 lineage. Nat Microbiol. 2016;1:16063.CAS 
PubMed 

Google Scholar 
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 

Google Scholar 
Amin S, Hmelo L, van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 

Google Scholar 
Landa M, Burns AS, Durham BP, Esson K, Nowinski B, Sharma S, et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 2019;13:2536–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.CAS 
PubMed 

Google Scholar 
Howard EC, Henriksen JR, Buchan A, Reisch CR, Bürgmann H, Welsh R, et al. Bacterial taxa that limit sulfur flux from the ocean. Science. 2006;314:649–52.CAS 
PubMed 

Google Scholar 
Levine NM, Toole DA, Neeley A, Bates NR, Doney SC, Dacey JW. Revising upper-ocean sulfur dynamics near Bermuda: new lessons from 3 years of concentration and rate measurements. Environ Chem. 2016;13:302–13.CAS 

Google Scholar 
Kiene RP. Turnover of dissolved DMSP in estuarine and shelf waters of the northern Gulf of Mexico. In Biological and environmental chemistry of DMSP and related sulfonium compounds. Boston, MA: Springer; 1996. pp. 337–49.Kiene RP, Linn LJ. Distribution and turnover of dissolved DMSP and its relationship with bacterial production and dimethylsulfide in the Gulf of Mexico. Limnol Oceanogr. 2000;45:849–61.CAS 

Google Scholar 
Curson AR, Todd JD, Sullivan MJ, Johnston AW. Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes. Nat Rev Microbiol. 2011;9:849–59.CAS 
PubMed 

Google Scholar 
Simó R. Production of atmospheric sulfur by oceanic plankton: biogeochemical, ecological and evolutionary links. Trends Ecol Evol. 2001;16:287–94.PubMed 

Google Scholar 
Charlson RJ, Lovelock JE, Andreae MO, Warren SG. Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature. 1987;326:655–61.CAS 

Google Scholar 
Reisch CR, Moran MA, Whitman WB. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front Microbiol. 2011;2:172.CAS 
PubMed 
PubMed Central 

Google Scholar 
Varaljay VA, Howard EC, Sun S, Moran MA. Deep sequencing of a dimethylsulfoniopropionate-degrading gene (dmdA) by using PCR primer pairs designed on the basis of marine metagenomic data. Appl Environ Microbiol. 2010;76:609–17.CAS 
PubMed 

Google Scholar 
Varaljay VA, Robidart J, Preston CM, Gifford SM, Durham BP, Burns AS, et al. Single-taxon field measurements of bacterial gene regulation controlling DMSP fate. ISME J. 2015;9:1677.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ledyard KM, DeLong EF, Dacey JW. Characterization of a DMSP-degrading bacterial isolate from the Sargasso Sea. Arch Microbiol. 1993;160:312–8.CAS 

Google Scholar 
Todd JD, Kirkwood M, Newton-Payne S, Johnston AW. DddW, a third DMSP lyase in a model Roseobacter marine bacterium, Ruegeria pomeroyi DSS-3. ISME J. 2012;6:223–6.CAS 
PubMed 

Google Scholar 
Todd JD, Curson AR, Kirkwood M, Sullivan MJ, Green RT, Johnston AW. DddQ, a novel, cupin‐containing, dimethylsulfoniopropionate lyase in marine roseobacters and in uncultured marine bacteria. Environ Microbiol. 2011;13:427–38.CAS 
PubMed 

Google Scholar 
Todd J, Curson A, Dupont C, Nicholson P, Johnston A. The dddP gene, encoding a novel enzyme that converts dimethylsulfoniopropionate into dimethyl sulfide, is widespread in ocean metagenomes and marine bacteria and also occurs in some Ascomycete fungi. Environ Microbiol. 2009;11:1376–85.CAS 
PubMed 

Google Scholar 
Todd JD, Curson AR, Nikolaidou‐Katsaraidou N, Brearley CA, Watmough NJ, Chan Y, et al. Molecular dissection of bacterial acrylate catabolism–unexpected links with dimethylsulfoniopropionate catabolism and dimethyl sulfide production. Environ Microbiol. 2010;12:327–43.CAS 
PubMed 

Google Scholar 
Curson A, Rogers R, Todd J, Brearley C, Johnston A. Molecular genetic analysis of a dimethylsulfoniopropionate lyase that liberates the climate‐changing gas dimethylsulfide in several marine α‐proteobacteria and Rhodobacter sphaeroides. Environ Microbiol. 2008;10:757–67.CAS 
PubMed 

Google Scholar 
Delmont TO, Hammar KM, Ducklow HW, Yager PL, Post AF. Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya. Front Microbiol. 2014;5:646.PubMed 
PubMed Central 

Google Scholar 
Stoica E, Herndl GJ. Bacterioplankton community composition in nearshore waters of the NW Black Sea during consecutive diatom and coccolithophorid blooms. Aquat Sci. 2007;69:413–8.CAS 

Google Scholar 
Giebel HA, Brinkhoff T, Zwisler W, Selje N, Simon M. Distribution of Roseobacter RCA and SAR11 lineages and distinct bacterial communities from the subtropics to the Southern Ocean. Environ Microbiol. 2009;11:2164–78.CAS 
PubMed 

Google Scholar 
Landa M, Blain S, Christaki U, Monchy S, Obernosterer I. Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms. ISME J. 2016;10:39–50.CAS 
PubMed 

Google Scholar 
Wemheuer B, Güllert S, Billerbeck S, Giebel H-A, Voget S, Simon M, et al. Impact of a phytoplankton bloom on the diversity of the active bacterial community in the southern North Sea as revealed by metatranscriptomic approaches. FEMS Microbiol Ecol. 2014;87:378–89.CAS 
PubMed 

Google Scholar 
Alonso-Gutiérrez J, Lekunberri I, Teira E, Gasol JM, Figueras A, Novoa B. Bacterioplankton composition of the coastal upwelling system of ‘Ría de Vigo’, NW Spain. FEMS Microbiol Ecol. 2009;70:493–505.PubMed 

Google Scholar 
Brown MV, Van De Kamp J, Ostrowski M, Seymour JR, Ingleton T, Messer LF, et al. Systematic, continental scale temporal monitoring of marine pelagic microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci Data. 2018;5:180130.PubMed 
PubMed Central 

Google Scholar 
Ajani P, Hallegraeff G, Allen D, Coughlan A, Richardson A, Armand L, et al. Establishing baselines: a review of eighty years of phytoplankton diversity and biomass in southeastern Australia. Oceanogr Mar Biol. 2016;54:387–412.
Google Scholar 
Matear R, Chamberlain M, Sun C, Feng M. Climate change projection of the Tasman Sea from an eddy‐resolving ocean model. J Geophys Res Oceans. 2013;118:2961–76.
Google Scholar 
Ostrowski M, Seymour J, Messer L, Varkey D, Goosen K, Smith M, et al. Status of Australian marine microbial assemblages. In State and Trends of Australia’s Ocean Report, Integrated Marine Observing System (IMOS). 2020. https://doi.org/10.26198/5e16aa3e49e7f.Lynch TP, Morello EB, Evans K, Richardson AJ, Rochester W, Steinberg CR, et al. IMOS National Reference Stations: a continental-wide physical, chemical and biological coastal observing system. PloS ONE. 2014;9:e113652.PubMed 
PubMed Central 

Google Scholar 
Lynch T, Roughan M, Mclaughlan D, Hughes D, Cherry D, Critchley G, et al. A national reference station infrastructure for Australia – Using telemetry and central processing to report multi-disciplinary data streams for monitoring marine ecosystem response to climate change. In: OCEANS 2008. 2008. https://doi.org/10.1109/OCEANS.2008.5151856.Appleyard SA, Abell G, Watson R. Tackling microbial related issues in cultured shellfish via integrated molecular and water chemistry approaches. Clayton: CSIRO Marine and Atmospheric Research; 2013.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 

Google Scholar 
Duarte CM. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol Oceanogr Bull. 2015;24:11–4.
Google Scholar 
Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:1–7.
Google Scholar 
Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environm Microbiol. 2014;16:2659–71.CAS 

Google Scholar 
Dadon-Pilosof A, Conley KR, Jacobi Y, Haber M, Lombard F, Sutherland KR, et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat Microbiol. 2017;2:1608–15.PubMed 

Google Scholar 
Lane D. 16S/23S rRNA sequencing. In: Nucleic acid techniques in bacterial systematics. New York: John Wiley & Sons; 1991, pp. 115–75.Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci. 1985;82:6955–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Piredda R, Tomasino M, D’erchia A, Manzari C, Pesole G, Montresor M, et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol Ecol. 2017;93.Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner HW, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
PubMed 

Google Scholar 
Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010.Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gordon A, Hannon G. Fastx-toolkit. FASTQ/A short-reads preprocessing tools. 2010;5. http://hannonlab.cshl.edu/fastx_toolkit/.Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucl Acids Res. 2014;42:D643–8.CAS 
PubMed 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucl Acids Res. 2012;41:D597–604.PubMed 
PubMed Central 

Google Scholar 
Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 2017;11:1483–99.PubMed 
PubMed Central 

Google Scholar 
Brinkhoff T, Giebel H-A, Simon M. Diversity, ecology, and genomics of the Roseobacter clade: a short overview. Arch Microbiol. 2008;189:531–9.CAS 
PubMed 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 

Google Scholar 
Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Felsenstein J. Evolutionary trees from gene frequencies and quantitative characters: finding maximum likelihood estimates. Evolution. 1981:1229–42.Tamura K, Nei M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol. 1993;10:512–26.CAS 
PubMed 

Google Scholar 
emcparland. emcparland/dmspOTUs: first release. 2021. https://doi.org/10.5281/zenodo.5090864.Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.PubMed 
PubMed Central 

Google Scholar 
Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.
Google Scholar 
McParland EL, Levine NM. The role of differential DMSP production and community composition in predicting variability of global surface DMSP concentrations. Limnol Oceanogr. 2019;64:757–73.CAS 

Google Scholar 
Reshef DN, Reshef YA, Sabeti PC, Mitzenmacher M. An empirical study of the maximal and total information coefficients and leading measures of dependence. Ann Appl Stat. 2018;12:123–55.
Google Scholar 
Hammer Ø, Harper DA, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:9.
Google Scholar 
Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
Moran MA, González JM, Kiene RP. Linking a bacterial taxon to sulfur cycling in the sea: studies of the marine Roseobacter group. Geomicrobiol J. 2003;20:375–88.CAS 

Google Scholar 
Harris G, Nilsson C, Clementson L, Thomas D. The water masses of the east coast of Tasmania: seasonal and interannual variability and the influence on phytoplankton biomass and productivity. Mar Freshw Res. 1987;38:569–90.CAS 

Google Scholar 
Kiene RP, Linn LJ, González J, Moran MA, Bruton JA. Dimethylsulfoniopropionate and methanethiol are important precursors of methionine and protein-sulfur in marine bacterioplankton. Appl Environ Microbiol. 1999;65:4549–58.CAS 
PubMed 
PubMed Central 

Google Scholar 
Raina J-B, Tapiolas D, Willis BL, Bourne DG. Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl Environ Microbiol. 2009;75:3492–501.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brinkmeyer R, Rappé M, Gallacher S, Medlin L. Development of clade-(Roseobacter and Alteromonas) and taxon-specific oligonucleotide probes to study interactions between toxic dinoflagellates and their associated bacteria. Eur J Phycol. 2000;35:315–29.
Google Scholar 
Töpel M, Pinder MI, Johansson ON, Kourtchenko O, Clarke AK, Godhe A. Complete genome sequence of novel Sulfitobacter pseudonitzschiae Strain SMR1, isolated from a culture of the marine diatom Skeletonema marinoi. J Genomics. 2019;7:7.PubMed 
PubMed Central 

Google Scholar 
Hong Z, Lai Q, Luo Q, Jiang S, Zhu R, Liang J, et al. Sulfitobacter pseudonitzschiae sp. nov., isolated from the toxic marine diatom Pseudo-nitzschia multiseries. Int J Syst Evol Microbiol. 2015;65:95–100.CAS 
PubMed 

Google Scholar 
Yang Q, Ge Y-M, Iqbal NM, Yang X, Zhang X-l. Sulfitobacter alexandrii sp. nov., a new microalgae growth-promoting bacterium with exopolysaccharides bioflocculanting potential isolated from marine phycosphere. Antonie Van Leeuwenhoek. 2021;114:1091–106.CAS 
PubMed 

Google Scholar 
Ankrah NY, Lane T, Budinoff CR, Hadden MK, Buchan A. Draft genome sequence of Sulfitobacter sp. CB2047, a member of the Roseobacter clade of marine bacteria, isolated from an emiliania huxleyi bloom. Genome Announc. 2014;2:e01125–14.PubMed 
PubMed Central 

Google Scholar 
Kwak M-J, Lee J-S, Lee KC, Kim KK, Eom MK, Kim BK, et al. Sulfitobacter geojensis sp. nov., Sulfitobacter noctilucae sp. nov., and Sulfitobacternoctilucicola sp. nov., isolated from coastal seawater. Int J Syst Evol Microbiol. 2014;64:3760–7.PubMed 

Google Scholar 
Zhang F, Fan Y, Zhang D, Chen S, Bai X, Ma X, et al. Effect and mechanism of the algicidal bacterium Sulfitobacter porphyrae ZFX1 on the mitigation of harmful algal blooms caused by Prorocentrum donghaiense. Environ Pollut. 2020;263:114475.CAS 
PubMed 

Google Scholar 
Keller MD. Dimethyl sulfide production and marine phytoplankton: the importance of species composition and cell size. Biol Oceanogr. 1989;6:375–82.
Google Scholar 
McParland EL, Lee MD, Webb EA, Alexander H, Levine NM. DMSP synthesis genes distinguish two types of DMSP producer phenotypes. Environ Microbiol. 2021;23:1656–69.CAS 
PubMed 

Google Scholar 
Galí M, Simó R. A meta‐analysis of oceanic DMS and DMSP cycling processes: disentangling the summer paradox. Glob Biogeochem Cycles. 2015;29:496–515.
Google Scholar 
Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation analyses in microbial ecology. ISME J. 2019;13:2647–55.PubMed 
PubMed Central 

Google Scholar 
Amin S, Hmelo L, Van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98.CAS 
PubMed 

Google Scholar 
Miller TR, Hnilicka K, Dziedzic A, Desplats P, Belas R. Chemotaxis of Silicibacter sp. strain TM1040 toward dinoflagellate products. Appl Environ Microbiol. 2004;70:4692–701.CAS 
PubMed 
PubMed Central 

Google Scholar 
Seymour JR, Simó R, Ahmed T, Stocker R. Chemoattraction to dimethylsulfoniopropionate throughout the marine microbial food web. Science. 2010;329:342–5.CAS 
PubMed 

Google Scholar  More

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).CAS 
Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Muir, P. R. et al. Conclusions of low extinction risk for most species of reef-building corals are premature. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01659-5 (2022).Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 884–895 (2013).Article 

Google Scholar 
Zvuloni, A., Artzy-Randrup, Y., Stone, L., van Woesik, R. & Loya, Y. Ecological size–frequency distributions: how to prevent and correct biases in spatial sampling. Limnol. Oceanogr. Methods 6, 144–153 (2008).Article 

Google Scholar  More

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Richards, Z. T., van Oppen, M. J., Wallace, C. C., Willis, B. L. & Miller, D. J. Some rare Indo-Pacific coral species are probable hybrids. PLoS ONE 3, e3240 (2008).Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 84–895 (2013).Article 

Google Scholar 
Hoeksema, B. W. & Cairns, S. World List of Scleractinia (accessed 6 May 2021); http://www.marinespecies.org/scleractiniaHughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 493–496 (2018).Article 

Google Scholar 
Thurba, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 575927 (2020).Article 

Google Scholar 
Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef building corals: shall the deep inherit the reef? Proc. R. Soc. B 284, 20171551 (2017).Article 

Google Scholar 
van Woesik, R., Sakai, K., Ganase, A. & Loya, Y. Revisiting the winners and the losers a decade after coral bleaching. Mar. Ecol. Prog. Ser. 434, 67–76 (2011).Article 

Google Scholar 
Chen, Y.-H., Shertzer, K. W. & Viehman, T. S. Spatio-temporal dynamics of the threatened elkhorn coral Acropora palmata: implications for conservation. Divers. Distrib. 26, 1582–1597 (2020).Article 

Google Scholar 
Sheppard, C., Sheppard, A. & Fenner, D. Coral mass mortalities in the Chagos Archipelago over 40 years: regional species and assemblage extinctions and indications of positive feedbacks. Mar. Poll. Bull. 154, 111075 (2020).CAS 
Article 

Google Scholar 
DeVantier, L. & Turak, E. Species richness and relative abundance of reef-building corals in the Indo-West Pacific. Diversity 9, 25 (2017).Article 

Google Scholar 
Wallace, C. C. Staghorn Corals of the World (CSIRO, 1999).Benzoni, F., Stefani, F., Pichon, M. & Galli, P. The name game: morpho-molecular species boundaries in the genus Psammocora (Cnidaria, Scleractinia). Zool. J. Linn. Soc. 160, 421–456 (2010).Article 

Google Scholar 
Richards, Z. T., Berry, O. & van Oppen, M. J. H. Cryptic genetic divergence within threatened species of Acropora coral from the Indian and Pacific Oceans. Conserv. Genet. 17, 577–591 (2016).Article 

Google Scholar 
Sheets, E. A., Warner, P. A. & Palumbi, S. R. Accurate population genetic measurements require cryptic species identification in corals. Coral Reefs 37, 549–563 (2018).Article 

Google Scholar 
Bongaerts, P. et al. Morphological stasis masks ecologically divergent coral species on tropical reefs. Curr. Biol. 31, 2286–2298 (2021).CAS 
Article 

Google Scholar 
Frankham, R. Effective population size/adult population size ratios in wildlife: a review. Genet. Res. 89, 491–503 (2008).Article 

Google Scholar  More