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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

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Let (y_{itk}) denote the WCR count observed for trap i in week t in year k, and assume it to follow a Poisson distribution with parameter (mu _{itk})$$begin{aligned} y_{itk} | mu _{itk}, sim Poisson(mu _{itk}) end{aligned}$$
(1)
The intensity parameter (mu _{itk}) represents the rate of emergence for a given time period. Instead of allowing it to depend purely on time t, a phenological variable of growing degree days (GDD) is used, as warmer temperatures are required for WCR development25,26,27,28. GDDs reflect the heat accumulation and are defined as an integral of warmth above a base temperature after a given start date:$$begin{aligned} GDD = int (T(t)-T_{base})dt. end{aligned}$$
(2)
The above integral can be approximated by$$begin{aligned} GDD = max left( frac{T_{max} – T_{min}}{2} – T_{base}, 0 right) . end{aligned}$$
(3)
Here (T_{min}) is the minimum daily temperature, (T_{max}) is the maximum daily temperature, and (T_{base}) is a set base temperature. In this study, the base temperature was set to (10,^{circ })C, and the starting date was the beginning of April, which marks the start of the growing season in Austria.The rate of cumulative emergence of the WCR beetle can be described by a Gompertz function. The Gompertz function is a sigmoidal function which describes growth as being slowest at the beginning and the end of a given period and is defined as$$begin{aligned} f(z_t) = alpha exp (-beta exp (-gamma z_t)). end{aligned}$$
(4)
where (alpha) is the upper asymptote, (beta) is a relative starting value, (gamma) is a growth rate coefficient which affects the slope, and (z_t) are the cumulative growing degree days. In this study, one can consider the asymptote as proxy to the saturation level of WCR population growth. Lower values of (beta) suggest an earlier first emergence in the season, while lower values of (gamma) indicate a longer emergence period. To investigate whether there is an association between climate variables and the emergence dynamics, the Gompertz curve parameters were assumed to linearly depend on climate covariates. In this regression modelling framework, a spatially correlated residual structure can be added in either (alpha), (beta), and/or (gamma) if there is evidence to do so.To reflect the nature of the emergence dynamics and to preserve the shape of the increasing Gompertz curve, the parameters of the model were restricted to positive values such that (alpha >0), (beta >0), and (gamma >0). The time at inflection or period of highest growth can be obtained by solving Eq. (4) for the value of t at which the concavity of the function changes. The time at inflection is described as:$$begin{aligned} T_z^* = frac{log (beta )}{gamma } end{aligned}$$
(5)
The Gompertz function describes cumulative emergence. Thus to describe the marginal emergence rate, the derivative of the Gompertz function can be used instead. Consequently, as the WCR trapping data consisted of weekly counts, the rate of emergence (mu _{itk}) is better described by the log of the derivative of the Gompertz function$$begin{aligned} log (mu _{itk}) = log (alpha _{ik}) + log (gamma _{ik}) + log (beta _{ik}) + gamma _i z_{itk} – beta _{ik} exp (-gamma z_{itk}). end{aligned}$$
(6)
The parameters (alpha _{ik}), (beta _{ik}) and (gamma _{ik}) are site and year specific such that:$$begin{aligned}&alpha _{ik} sim N(mu _{alpha _{ik}}, tau _{alpha }) end{aligned}$$
(7)
$$begin{aligned}&gamma _{ik} sim N(mu _{gamma _{ik}}, tau _{gamma }) end{aligned}$$
(8)
$$begin{aligned}&beta _{ik} sim N(mu _{beta _{ik}}, tau _{beta }). end{aligned}$$
(9)
Here, (tau _{alpha }), (tau _{beta }), and (tau _{gamma }) are the precision (inverse variance) parameters of the prior distributions for (alpha), (beta) and (gamma) respectively. Moreover, the means of the distributions (mu _{alpha _{ik}}), (mu _{beta _{ik}}), and (mu _{gamma _{ik}}) can be expressed as functions of known covariates:$$begin{aligned} mu _{alpha _{ik}}= & {} a_{0} + {mathbf {w}}^T X_{alpha _{ik}}, end{aligned}$$
(10)
$$begin{aligned} mu _{beta _{ik}}= & {} b_{0}, end{aligned}$$
(11)
$$begin{aligned} mu _{gamma _{ik}}= & {} g_{0} + {mathbf {u}}^T X_{gamma _{ik}}. end{aligned}$$
(12)
Here (a_{0}) is the intercept, ({mathbf {w}}) is a vector of the regression coefficients, and (X_{alpha _{ik}}) are the location and year specific covariates. The predictors used in the regression of (mu _{alpha _{ik}}) are the average winter temperature, the precipitation sum during winter, the year, the percentage of the agricultural area per Austrian municipality used for cultivating maize crops (maize), and the corresponding centred coordinates of the trap locations; x, y, and their functions (x^2), (y^2), and xy. The parameter (g_{0}) is the intercept for the regression of (mu _{gamma _{ik}}), and u is the corresponding regression coefficient. The predictor used for (mu _{gamma _ik}) is the average yearly spring temperature.The intercepts and regression coefficients ((mathbf {w}) and (mathbf {u})) were given non-informative normal priors N(0, 0.01). The precision parameters (tau _{alpha }), (tau _{beta }) and (tau _{gamma }) were assigned prior distributions Gamma(0.01, 0.01).The model was fitted using WinBUGS through the R2WinBUGS package in R29,30,31. The model was run for 20000 iterations, with a burn-in of 10000 iterations, and a thinning rate of five. Convergence was determined by visual assessments of trace plots and marginal posterior densities. More