Philippa Kaur

Automatic feeder productivityTable 1 and Figs. 4, 5 and 6 show the automatic feeder productivity as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the automatic feeder productivity increases with increasing feed pellets size, air flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the automatic feeder productivity significantly increased from 11.16 to 13.87 (by 19.54%) kg min−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the automatic feeder productivity significantly increased from 11.02 to 14.03 (by 21.45%) kg min−1, while the automatic feeder productivity significantly increased from 3.33 to 21.46 (by 84.48%) kg min−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 1 Automatic feeder productivity at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 4Automatic feeder productivity at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 5Automatic feeder productivity at different feed pellet sizes and air flow rates.Full size imageFigure 6Automatic feeder productivity at different rotational speeds of screw and flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to increase the automatic feeder productivity from 3.04 to 3.79, 6.23 to 8.92, 11.86 to 14.10, 15.27 to 18.94 and 19.42 to 23.62 kg min−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the automatic feeder productivity increased from 3.04 to 19.42, 3.16 to 21.36 and 3.79 to 23.62 kg min−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 4.From statistical analysis, there were no significant different between feed pellets sizes 1 and 2 on the automatic feeder productivity, meanwhile, there were significant differences between feed pellets size 3 and sizes 1 and 2 on the productivity. Regarding the effect of air flow rate, there were significant differences between air flow rates on the automatic feeder productivity, the same trend was happened with the effect of rotational speed of screw on productivity. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 1.Regarding the effect of feed pellet size and air flow rate on the automatic feeder productivity, the results indicate that the automatic feeder productivity increases with increasing the feed pellets size and flow rate. It increased from 9.53 to 12.37, 11.23 to 13.82 and 12.73 to 15.43 kg min−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the automatic feeder productivity increased from 9.53 to 12.73, 11.16 to 13.92 and 12.37 to 15.43 kg min−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 5.The results also indicate that the automatic feeder productivity increased from 2.26 to 4.54, 6.39 to 8.90, 11.76 to 14.56, 15.25 to 18.68 and 19.44 to 23.45 kg min−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the automatic feeder productivity increased from 2.26 to 19.44, 3.19 to 21.50 and 4.54 to 23.45 kg min−1 at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 6.Multiple regression analysis was carried out to obtain a relationship between the automatic feeder productivity as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ Pr_{actual} = – 8.457 + 1.354PS + 0.301FR + 0.025RS{text{ R}}^{{2}} = 0.98{ ,} $$
(13)
where PS is the feed pellets size, mm; FR is the air flow rate, m3 min−1; RS is the rotational speed of screw, rpm.This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Automatic feeder efficiencyTable 2, Figs. 7, 8 and 9 show the automatic feeder efficiency as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that, when the feed pellets size increased from 1 to 3 mm, the automatic feeder efficiency significantly increased from 65.30 to 82.14 (by 20.50%) %. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the automatic feeder efficiency significantly increased from 62.58 to 85.07 (by 26.44%) %, while the automatic feeder efficiency significantly increased from 61.58 to 78.69 (by 21.74%) % when the rotational speed of screw increased from 180 to 900 rpm.Table 2 Automatic feeder efficiency at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 7Automatic feeder efficiency at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 8Automatic feeder efficiency at different feed pellet sizes and air flow rates.Full size imageFigure 9Automatic feeder efficiency at different rotational speeds of screw and air flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to increase the automatic feeder efficiency from 55.79 to 69.41, 57.10 to 81.78, 72.48 to 86.13, 69.96 to 86.81 and 71.19 to 86.58% at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the automatic feeder efficiency increased from 55.79 to 71.19, 57.98 to 78.29 and 69.41 to 86.58% at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 7.The statistical analysis showed that the differences between the obtained data of automatic feeder efficiency due to the effect of feed pellets size (A) and air flow rate (B) were significant. Regarding the effect of rotational speed of screw, there were significant differences between rotational speeds of screw 1, 2 and 3, meanwhile, there were no significant differences between rotational speeds of screw 3, 4 and 5. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 2.Regarding the effect of feed pellet size and air flow rate on the automatic feeder productivity, the results indicate that the automatic feeder efficiency increases with increasing the feed pellets size and flow rate. It increased from 53.91 to 70.69, 65.23 to 81.19 and 76.78 to 94.54% for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the automatic feeder efficiency increased from 53.91 to 76.78, 63.14 to 83.89 and 70.69 to 94.54% at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 8.The results also indicate that the automatic feeder efficiency increased from 41.37 to 83.28, 58.53 to 81.54, 71.85 to 84.96, 69.88 to 85.59 and 71.27 to 85.98% at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the automatic feeder efficiency increased from 41.37 to 71.27, 58.53 to 80.82 and 83.28 to 85.98% at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 9.Increasing the parameters seams to increase the productivity but regarding the efficiency, results show that the efficiency increases with increasing this parameter at (540 rpm) started to be constant and 720–900 rpm decreased in all treatments under study (Figs. 7, 9). It is concluded that efficiency with the parameters increased, became constant and decreased.Multiple regression analysis was carried out to obtain a relationship between the automatic feeder efficiency as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ eta = 9.566 + 8.417PS + 2.249FR + 0.025RS{text{ R}}^{{2}} = 0.89{ ,} $$
(14)
where this equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Specific energy consumptionTable 3, Figs. 10, 11 and 12 show the specific energy consumption of automatic feeder as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the specific energy consumption of automatic feeder decreases with increasing feed pellets size, air flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the specific energy consumption of automatic feeder significantly decreased from 8.93 to 6.74 (by 24.52%) W h kg−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the specific energy consumption of automatic feeder significantly decreased from 10.83 to 5.42 (by 49.95%) W h kg−1, while the specific energy consumption significantly decreased from 9.08 to 6.55 (by 27.86%) W h kg−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 3 Specific energy consumption at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 10Specific energy consumption at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 11Specific energy consumption at different feed pellet sizes and air flow rates.Full size imageFigure 12Specific energy consumption at different rotational speeds of screw and air flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to decrease the specific energy consumption from 9.87 to 7.94, 9.18 to 7.63, 9.14 to 7.30, 8.65 to 6.63 and 7.79 to 4.20 W h kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the specific energy consumption decreased from 9.87 to 7.79, 9.42 to 7.65 and 7.94 to 4.20 W h kg−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 10.From statistical analysis, there were no significant differences between feed pellets sizes 1 and 2 on the specific energy consumption, meanwhile, there were significant differences between feed pellets size 3 and 1 and 2 on the specific energy consumption. Regarding the effect of air flow rate, there were significant differences between air flow rates and specific energy consumption. Regarding the effect of rotational speed of screw, there were significant differences between rotational speeds of screw 1, 2, 4 and 5 on the specific energy consumption, meanwhile, there were no significant differences between rotational speeds of screw 2 and 3 on the specific energy consumption. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 3.Regarding the effect of feed pellet size and air flow rate on the specific energy consumption, the results indicate that the specific energy consumption decreases with increasing the feed pellets size and flow rate. It decreased from 12.05 to 9.07, 8.81 to 6.56 and 5.92 to 4.59 W h kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the specific energy consumption decreased 12.05 to 5.92, 11.37 to 5.75 and 9.07 to 4.59 W h kg−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 11.The results also indicate that the specific energy consumption decreased from 12.31 to 6.18, 11.43 to 5.63, 11.21 to 5.63, 10.38 to 5.21 and 8.81 to 4.46 W h kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the specific energy consumption decreased from 12.31 to 8.81, 8.75 to 6.37 and 6.18 to 4.46 W h kg−1 at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown Fig. 12.
Multiple regression analysis was carried out to obtain a relationship between the specific energy consumption of automatic feeder as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ SEC = 20.045 – 1.095PS – 0.541FR – 0.003RS{text{ R}}^{{2}} = 0.92 , {.} $$
(15)
This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Total costs of automatic feederTable 4, Figs. 13, 14 and 15 show the total cost of automatic feeder as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the total cost of automatic feeder decreases with increasing feed pellets size, flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the total cost of automatic feeder significantly decreased from 0.15 to 0.11 (by 26.27%) EGP kg−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the total cost of automatic feeder significantly decreased from 0.16 to 0.09 (by 43.75%) EGP kg−1, while the total cost of automatic feeder significantly decreased from 0.16 to 0.10 (by 37.50%) EGP kg−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 4 Total cost of automatic feeder at different feed pellets sizes, air flow rate and rotational speeds of screw.Full size tableFigure 13Total cost of automatic feeder at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 14Total cost of automatic feeder at different feed pellet sizes and air flow rates.Full size imageFigure 15Total cost of automatic feeder at different rotational speeds of screw and air flow rate.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to decrease the total cost of automatic feeder from 0.18 to 0.14, 0.16 to 0.12, 0.15 to 0.11, 0.13 to 0.09 and 0.12 to 0.08 EGP kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the total cost of automatic feeder decreased from 0.18 to 0.12, 0.17 to 0.10 and 0.14 to 0.08 EGP kg−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 13.From statistical analysis, there were no significant differences between feed pellets sizes 1 and 2 on the total cost of automatic feeder, meanwhile, there were significant differences between feed pellets size 3 and 1 and 2 on the total cost of automatic feeder. Regarding the effect of air flow rate, there were significant differences between air flow rates and specific energy consumption. Regarding the effect of rotational speed of screw, there were no significant differences between rotational speeds of screw 1 and 2, also 3 and 4 on the total cost of automatic feeder, meanwhile, there were significant differences between rotational speeds of screw 2 and 3 on the total cost of automatic feeder.Regarding the effect of feed pellet size and flow rate on the total cost of automatic feeder, the results indicate that the total cost of automatic feeder decreases with increasing the feed pellets size and air flow rate. It decreased from 0.18 to 0.13, 0.16 to 0.11 and 0.10 to 0.08 EGP kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the total cost of automatic feeder decreased from 0.18 to 0.10, 0.16 to 0.10 and 0.13 to 0.08 EGP kg−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 14.The results also indicate that the total cost of automatic feeder decreased from 0.22 to 0.11, 0.18 to 0.10, 0.16 to 0.10, 0.13 to 0.09 and 0.12 to 0.07 EGP kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the total cost of automatic feeder decreased from 0.22 to 0.12, 0.16 to 0.11 and 0.11 to 0.07 EGP kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 15.
Multiple regression analysis was carried out to obtain a relationship between the total costs of automatic feeder as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:$$ TC = 0.315 – 0.020PS – 0.006FR – 8.8 times 10^{ – 5} RS{text{ R}}^{{2}} = 0.87{,} $$
(16)
where: TC is the total cost of automatic feeder, EGP kg−1.This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw. More

FLUXNET dataThe data used in this study belong to the FLUXNET LaThuile9 and FLUXNET2015 Tier 1 and Tier 2 datasets10, which make up the global network of CO2, water vapour and energy flux measurements. We merged the two FLUXNET releases and retained the FLUXNET2015 (the most recent and with a robust quality check) version of the data when the site was present in both datasets. Croplands were removed to avoid the inclusion of sites that are heavily managed in the analysis (for example, fertilization and irrigation).The sites used cover a wide variety of climate zones (from tropical to Mediterranean to Arctic) and vegetation types (wetlands, shrublands, grasslands, savanna, evergreen and deciduous forests). It should be noted though that tropical forests are underrepresented in the FLUXNET database (Extended Data Figs. 1, 3).Sites were excluded in cases in which: (i) data on precipitation or radiation were not available or completely gap-filled; (ii) the calculation of functional properties failed because of low availability of measured data (see ‘Calculation of ecosystem functions from FLUXNET’); and (iii) fluxes showed clear discontinuities in time series indicating a change of instrumentation set-up (for example, changes in the height of the ultrasonic anemometer or gas analyser).The final number of sites selected was 203 (1,484 site years). The geographical distribution is shown in Extended Data Fig. 1, the distribution in the climate space is shown in Extended Data Fig. 2 and the fraction of sites for each climate classes is reported in Extended Data Fig. 3.For each site, we downloaded the following variables at half-hourly temporal resolution: (i) gross primary productivity (GPP, μmol CO2 m–2 s–1) derived from the night-time flux partitioning26 (GPP_NT_VUT_50 in FLUXNET 2015 and GPP_f in LaThuile), (ii) net ecosystem exchange (NEE, μmol CO2 m–2 s–1) measurements filtered using annual friction velocity (u*, m s−1) threshold (NEE_VUT_50 in FLUXNET 2015; NEE in LaThuile); (iii) latent heat (LE, W m−2) fluxes, which were converted to evapotranspiration (ET, mm); (iv) sensible heat (H, W m−2) fluxes; (v) air temperature (Tair, °C); (vi) vapour pressure deficit (VPD, hPa); (vii) global shortwave incoming radiation (SWin, W m−2); viii) net radiation (Rn, W m−2); (ix) ground heat flux (G, W m−2); (x) friction velocity u* (m s−1); and (xi) wind speed (u, m s−1). For the energy fluxes (H, LE) we selected the fluxes not corrected for the energy balance closure to guarantee consistency between the two FLUXNET datasets (in the LaThuile dataset energy fluxes were not corrected).The cumulative soil water index (CSWI, mm) was computed as a measure of water availability according to a previous report27. Half-hourly values of transpiration estimates (T, mm) were calculated with the transpiration estimation algorithm (TEA)28. The TEA has been shown to perform well against both model simulations and independent sap flow data28.For 101 sites, ecosystem scale foliar N content (N%, gN 100 g−1) was computed as the community weighted average of foliar N% of the major species at the site sampled at the peak of the growing season or gathered from the literature29,30,31,32. Foliar N% for additional sites was derived from the FLUXNET Biological Ancillary Data Management (BADM) product and/or provided by site principal investigators (Supplementary Table 1, Extended Data Fig. 1). It should be noted that this compilation of N% data might suffer from uncertainties resulting from the scaling from leaves to the eddy covariance footprint, the sampling strategy (including the position along the vertical canopy profile), the species selection and the timing of sampling. About 30% of the data comes from a coordinated effort that minimized these uncertainties29,30, and for the others we collected N% data that were representative for the eddy covariance footprint31,32.Maximum leaf area index (LAImax, m2 m−2) and maximum canopy height (Hc, m) were also collected for 153 and 199 sites, respectively, from the literature32,33, the BADM product, and/or site principal investigators.Earth observation retrievals of above-ground biomass (AGB, tons of dry matter per hectare (t DM ha−1)) were extracted from the GlobBiomass dataset34 at its original resolution (grid cell 100 × 100 m) for each site location. All the grid cells in a 300 × 300 m and 500 × 500 m window around each location were selected to estimate the median and 95th percentiles of AGB for each site. The median of AGB was selected to avoid the contribution of potential outliers to the expected value of AGB. The analysis further explored the contribution of higher percentiles in the local variation of AGB as previous studies have highlighted the contribution of older and larger trees in uneven stand age plots to ecosystem functioning35. According to the evaluation against AGB measured at 71 FLUXNET sites (Extended Data Fig. 10), we decided to use the product with median AGB values extracted from the 500 × 500 m window.A total of 94 sites have all the data on vegetation structure (N%, LAImax, Hc, and AGB).The list of sites is reported in Supplementary Table 1 along with the plant functional type (PFT), Köppen-Geiger classification, coordinates, and when available N%, LAImax, Hc and AGB.In this study we did not make use of satellite information, with the exception of the AGB data product. Future studies will benefit from new missions such as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), the fluorescence explorer (FLEX), hyperspectral, and radar and laser detection and ranging (LiDAR) missions (for example, Global Ecosystem Dynamics Investigation (GEDI)), to characterize a multivariate space of structural and functional properties.Calculation of ecosystem functions from FLUXNETStarting from half-hourly data, we calculated at each site a single value for each of the ecosystem functions listed below. For the calculations of functional properties we used, unless otherwise indicated, good-quality data: quality flag 0 (measured data) and 1 (good-quality gap-filled data) in the FLUXNET dataset.Gross primary productivity at light saturation (GPPsat)GPP at light saturation using photosynthetically active radiation as driving radiation and 2,000 μmol m−2 s−1 as saturating light. GPPsat represents the ecosystem-scale maximum photosynthetic CO2 uptake15,30,36. The GPPsat was estimated from half-hourly data by fitting the hyperbolic light response curves with a moving window of 5 days and assigned at the centre of the moving window30,37. For each site the 90th percentile from the GPPsat estimates was then extracted.Maximum net ecosystem productivity (NEPmax)This was computed as the 90th percentile of the half-hourly net ecosystem production (NEP = −NEE) in the growing season (that is, when daily GPP is higher than 30% of the GPP amplitude). This metric represents the maximum net CO2 uptake of the ecosystem.Basal ecosystem respiration (Rb and Rbmax)Basal ecosystem respiration at reference temperature of 15 °C was derived from night-time NEE measurements26. Daily basal ecosystem respiration (Rbd) was derived by fitting an Arrhenius type equation over a five-day moving window and by keeping the sensitivity to temperature parameter (E0) fixed as in the night-time partitioning algorithms26,38. Rbd varies across seasons because it is affected by short-term variations in productivity33,39, phenology40 and water stress41. For each site, the mean of the Rbd (Rb) and the 95th percentile (Rbmax) were computed. The calculations were conducted with the REddyProc R package v.1.2.2 (ref. 38).Apparent carbon-use efficiency (aCUE)The aCUE as defined in this study is the efficiency of an ecosystem to sequester the carbon assimilated with photosynthesis39. aCUE is an indication of the proportion of respired carbon with respect to assimilated carbon within one season. A previous report6 showed that little of the variability in aCUE can be explained by climate or conventional site characteristics, and suggested an underlying control by plant, faunal and microbial traits, in addition to site disturbance history. Daily aCUE (aCUEd) is defined as aCUEd = 1 − (Rbd/GPPd), where GPPd is daily mean GPP and Rbd is derived as described above. For each site, aCUE was computed as the median of aCUEd.Metrics of water-use efficiency (WUE)Various metrics of WUE are described below: stomatal slope or slope coefficient (G1), underlying water-use efficiency (uWUE), and water-use efficiency based on transpiration (WUEt). The three metrics were used because they are complementary, as shown in previous studies11,42.Stomatal slope or slope coefficient (G1)This is the marginal carbon cost of water to the plant carbon uptake. G1 is the key parameter of the optimal stomatal model derived previously43. G1 is inversely related to leaf-level WUE. At leaf level, G1 is calculated using nonlinear regression and can be interpreted as the slope between stomatal conductance and net CO2 assimilation, normalized for VPD and CO2 concentration43. A previous report42 showed the potential of the use of G1 at ecosystem scale, where stomatal conductance is replaced by surface conductance (Gs), and net assimilation by GPP. The methodology is implemented in the bigleaf R package44. The metric was computed in the following situations: (i) incoming shortwave radiation (SWin) greater than 200 W m−2; (ii) no precipitation event for the last 24 h45, when precipitation data are available; and (iii) during the growing season: daily GPP > 30% of its seasonal amplitude44.Underlying water-use efficiency (uWUE)The underlying WUE was computed following a previous method46. uWUE is a metric of water-use efficiency that is negatively correlated to G1 at canopy scale44:$${rm{uWUE}}=frac{{rm{GPP}}sqrt{{rm{VPD}}}}{{rm{ET}}}.$$uWUE was calculated using the same filtering that was applied for the calculation of G1. The median of the half-hourly retained uWUE values was computed for each site and used as a functional property.Water-use efficiency based on transpiration (WUEt)The WUE based on transpiration (T) was computed to reduce the confounding effect resulting from soil evaporation11,28:$${{rm{WUE}}}_{{rm{t}}}=frac{{rm{GPP}}}{T},$$where T is the mean annual transpiration calculated with the transpiration estimation algorithm (TEA) developed by in a previous study28 and GPP is the mean annual GPP.Maximum surface conductance (G
smax)Surface conductance (Gs) was computed by inverting the Penman–Monteith equation after calculating the aerodynamic conductance (Ga).Among the different formulations of Ga (m s–1) in the literature, we chose to use here the calculation of the canopy (quasi-laminar) boundary layer conductance to heat transfer, which ranges from empirical to physically based (for example, ref. 47). Other studies48,49 suggested an empirical relationship between Ga, the horizontal wind speed (u) and the friction velocity, u*:$${G}_{{rm{a}}}=frac{1}{(frac{u}{{u}^{* 2}}+6.2u{* }^{-0.67})}$$Gs (m s−1) is computed by inverting the Penman–Monteith equation:$${G}_{{rm{s}}}=frac{{{rm{LEG}}}_{{rm{a}}}gamma }{Delta ({R}_{{rm{n}}}-G-S)+rho {C}_{{rm{p}}}{G}_{{rm{a}}}{rm{VPD}}-{rm{LE}}(Delta +gamma )}$$where Δ is the slope of the saturation vapour pressure curve (kPa K−1), ρ is the air density (kg m−3), Cp is the specific heat of the air (J K−1 kg−1), γ is the psychrometric constant (kPa K−1), VPD (kPa), Rn (W m−2), G (W m−2) and S is the sum of all energy storage fluxes (W m−2) and set to 0 as not available in the dataset. When not available, G also was set to 0.Gs represents the combined conductance of the vegetation and the soil to water vapour transfer. To retain the values with a clear physiological interpretation, we filtered the data as we did for the calculation of G1.For each site, the 90th percentile of the half-hourly Gs was calculated and retained as the maximum surface conductance of each site (Gsmax). Gs was computed using the bigleaf R package44.Maximum evapotranspiration in the growing season (ETmax)This metric represents the maximum evapotranspiration computed as the 95th percentile of ET in the growing season and using the data retained after the same filtering applied for the G1 calculation.Evaporative fraction (EF)EF is the ratio between LE and the available energy, here calculated as the sum of H + LE (ref. 50). For the calculation of EF, we used the same filtering strategy as for G1. We first calculated mean daytime EF. We then computed  the EF per site as the growing season average of daytime EF. We also computed the amplitude of the EF in the growing season by calculating the interquartile distance of the distribution of mean daytime EF (EFampl).Principal component analysisA PCA was conducted on the multivariate space of the ecosystem functions. Each variable (ecosystem functional property, EFP) was standardized using z-transformation (that is, by subtracting its mean value and then dividing by its standard deviation). From the PCA results we extracted the explained variance of each component and the loadings of the EFPs, indicating the contribution of each variable to the component. We performed the PCA using the function PCA() implemented in the R package FactoMineR51.We justify using PCA over nonlinear methods because it is an exploratory technique that is highly suited to the analysis of the data volume used in this study, whereas other nonlinear methods applied to such data would be over-parameterized. For the same reason, PCA was used in previous work concerning the global spectrum of leaf and plant traits, and fluxes1,3,52.To test the significance of dimensionality of the PCA, we used a previously described methodology53. We used the R package ade4 (ref. 54) and evaluated the number of significant components of the PCA to be retained to minimize both redundancy and loss of information (Supplementary Information 2). We tested the significance of the PCA loadings using a combination of the bootstrapped eigenvector method55 and a threshold selected using the number of dimensions56 (Supplementary Information 2).Predictive variable importanceA random forests (RF) analysis was used to identify the vegetation structure and climate variables that contribute the most to the variability of the significant principal components, which were identified with the PCA analysis (see ‘Principal component analysis’). In the main text we refer to the results of this analysis as ‘predictive variable importance’ to distinguish this to the ‘causal variable importance’ described below.The analysis was conducted using the following predictor variables: as structural variables, N% (gN 100 g−1), LAImax (m2 m−2), AGB (t DM ha−1) and Hc (m); as climatic variables, mean annual precipitation (P, mm), mean VPD during the growing season (VPD, hPa), mean shortwave radiation (SWin, W m−2), mean air temperature (Tair, °C); and the cumulative soil water index (CSWI, −), as indicator of site water availability.We used partial dependencies of variables to assess the relationship between individual predictors and the response variable (that is, PC1, PC2 and PC3).The results from the partial dependency analysis can be used to determine the effects of individual variables on the response, without the influence of the other variables. The partial dependence function was calculated using the pdp R package57.The partial dependencies were calculated restricted to the values that lie within the convex hull of their training values to reduce the risk of interpreting the partial dependence plot outside the range of the data (extrapolation).Invariant causal regression models and causal variable importanceWe have quantified the dependence of the principal components on the different structural and climatic variables using nonlinear regression. Such dependencies can only be interpreted causally if the regression models are in fact causal regression models (see Supplementary Information 3 for a formal definition), which may not be the case if there are hidden confounders. To see whether the regression models allow for a causal interpretation, we use invariant causal prediction58. This method investigates whether the regression models are stable with respect to different patterns of heterogeneity in the data, encoded by different environments (that is, subsets of the original dataset). The rationale is that a causal model, describing the full causal mechanism for the response variable, should be invariant with respect to changes in the environment if the latter does not directly influence the response variable13,59. Other non-causal models may be invariant, too, but a non-invariant model cannot be considered causal.How to choose the environments is a modelling choice that must satisfy the following criteria. First, it should be possible to assign each data point to exactly one environment. Second, the environments should induce heterogeneity in the data, so that, for example, the predictor variables have different distributions across environments. Third, the environments must not directly affect the response variable, only via predictors, although the distribution of the response may still change between environments. The third criterion can be verified by expert knowledge and is assumed to hold for our analysis. In addition, if it is violated, then, usually, no set is invariant58, which can be detected from data.In our analysis, we assigned each data point (that is, each site) to one of two environments (two subsets of the original dataset): the first includes forest sites in North America, Europe or Asia; and the second includes non-forest and forest ecosystems from South America, Africa or Oceania, and non-forest ecosystems from North America, Europe or Asia (see Supplementary Information 3.1.3.1 for details). Our choice satisfies the method’s assumption that the distribution of the predictors is different between the two environments (that is, they induce heterogeneity in the data; see Supplementary Fig. 3.1). Environments that are too small or too homogeneous do not provide any evidence against the full set of covariates being a candidate for the set of causal predictors. Other choices of environments than the one presented here yield consistent results (Supplementary Information 3.2.1, Supplementary Fig. 3.4).For each subset of predictors, we test whether the corresponding regression model is invariant (yielding the same model fit in each environment). Although many models were rejected and considered non-invariant, the full model (with all the nine predictors and used in the predictive variable importance analysis) was accepted as invariant, establishing the full set of covariates as a reasonable candidate for the set of direct causal predictors. We used both RF (randomForest package in R60) and generalized additive models, GAM61 (mgcv package62 in R) to fit the models. Both methods lead to comparable results but with a better average performance of the RF: GAM led to slightly better results than RF for PC1, whereas for PC2 and PC3 RF showed a much better model performance (Supplementary Table 3.1, Supplementary Information 3.2.2). Therefore, in the main text we showed only the results from the RF (except for PC1).If, indeed, the considered regression models are causal, this allows us to make several statements. First, we can test for the existence of causal effects by testing for statistical significance of the respective predictors in the fitted models. Second, we can use the response curves of the fitted model to define a variable importance measure with a causal interpretation. In the main text we refer to this variable importance as ‘causal variable importance’. For details, see Supplementary Information 3.1.2. More formally, we considered the expected value of the predicted variables (the principal components) under joint interventions on all covariates (AGB, Hc, LAImax, N%, Tair, VPD, SWin, CSWI and P) at once, and then, to define the importance, we quantified how this expected value depends on the different covariates. We applied the same analysis to groups of vegetation structural and climate covariates (see ‘Groupwise variable importance’ in Supplementary Information 3.1.2.3, 3.2.3).The details of the methodology and the results are described in Supplementary Information 3, in which we also provide further details on the choice of environment variable and on the statistical tests that we use to test for invariance. An overview of the invariance-based methodology is shown in Supplementary Fig. 3.1.Land surface model runsWe run two widely used land surface models: Orchidee-CN (OCN) and Jena Scheme for Biosphere Atmosphere Coupling in Hamburg (JSBACH):OCNThe dynamic global vegetation model OCN is a model of the coupled terrestrial carbon and nitrogen cycles63,64, derived from the ORCHIDEE land surface model. It operates at a half-hourly timescale and simulates diurnal net carbon, heat and water exchanges, as well as nitrogen trace gas emissions, which jointly affect the daily changes in leaf area index, foliar nitrogen, and vegetation structure and growth. The main purpose of the model is to analyse the longer-term (interannual to decadal) implication of nutrient cycling for the modelling of land–climate interactions64,65. The model can run offline, driven by observed meteorological parameters, or coupled to the global circulation model.JSBACHJSBACH v.3 is the land surface model of the MPI Earth System Model66,67. The model operates at a half-hourly time step and simulates the diurnal net exchange of momentum, heat, water and carbon with the atmosphere. Daily changes in leaf area index and leaf photosynthetic capacity are derived from a prognostic scheme assuming a PFT-specific set maximum leaf area index and a set of climate responses modulating the seasonal course of leaf area index. Carbon pools are prognostic allowing for simulating the seasonal course of net land–atmosphere carbon exchanges.We selected OCN and JSBACH because they are widely used land surface models with different structures. JSBACH is a parsimonious representation of the terrestrial energy, water and carbon exchanges used to study the coupling of land and atmosphere processes in an Earth system model67. OCN has also been derived from the land surface model ORCHIDEE68, but it includes a more comprehensive representation of plant physiology, including a detailed representation of the tight coupling of the C and N cycling63. Both models contribute to the annual global carbon budget of the Global Carbon Project69 and have shown good performance compared to a number of global benchmarks. OCN was further used in several model syntheses focused on the interaction between changing N deposition and CO2 fertilization70,71,72. Both OCN and JSBACH can operate at a half-hourly timescale and simulate net and gross carbon exchanges, water and energy fluxes, and therefore are ideal for the extraction of ecosystem functional properties, as done with the eddy covariance data.The models were driven by half-hourly meteorological variables (shortwave and longwave downward flux, air temperature and humidity, precipitation, wind speed and atmospheric CO2 concentrations) observed at the eddy covariance sites. OCN was furthermore driven by N deposition fields73. Vegetation type, soil texture and plant available water were prescribed on the basis of site observations, but no additional site-specific parameterization was used. Both models were brought into equilibrium with respect to their ecosystem water storage and biogeochemical pools by repeatedly looping over the available site years. We added random noise (mean equal to 0 and standard deviation of 5% of the flux value) to the fluxes simulated by the models to mimic the random noise of the eddy covariance flux observations. An additional test conducted without noise addition showed only a marginal effect on the calculations of the functional properties and the ecosystem functional space.We used runs of the JSBACH and OCN model for 48 FLUXNET sites (Supplementary Table 1). The simulated fluxes were evaluated against the observation to assess the performance of the models at the selected sites. From the model outputs and from each site we derived the ecosystem functions using the same methodology described above. Then the PCA analysis was performed on the three datasets (FLUXNET, OCN and JSBACH) and restricted to the 48 sites used to run the models. We ran the models only on the subset of sites for which the information for the parameterization and high-quality forcing was available. However, the different ecosystem functions emerge from the model structure and climatological conditions. Therefore, even with a smaller set of site we can evaluate whether models reproduce the key dimensions of terrestrial ecosystem function by comparing the PCA results from FLUXNET and the model runs.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

1.Beauchamp, G. Long-distance migrating species of birds travel in larger groups. Biol. Lett. 7, 692–694 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Watts, H. E., Cornelius, J. M., Fudickar, A. M., Pérez, J. & Ramenofsky, M. Understanding variation in migratory movements: A mechanistic approach. Gen. Comp. Endocrinol. 256, 112–122 (2018).CAS 
PubMed 
Article 

Google Scholar 
3.Amezaga, J. M., Santamaría, L. & Green, A. J. Biotic wetland connectivity—Supporting a new approach for wetland policy. Acta Oecol. 23, 213–222 (2002).ADS 
Article 

Google Scholar 
4.O’Connell, M. Threats to waterbirds and wetlands: Implications for conservation, inventory and research. Wildfowl 51, 1–16 (2000).
Google Scholar 
5.Darrah, S. E. et al. Improvements to the wetland extent trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Ind. 99, 294–298 (2019).Article 

Google Scholar 
6.BirdLife International. Waterbirds are Showing Widespread Declines, Particularly in Asia. http://www.birdlife.org (2017).7.Maron, M. et al. The many meanings of no net loss in environmental policy. Nat. Sustain. 1, 19–27 (2018).Article 

Google Scholar 
8.He, Q. Conservation: ‘No net loss’ of wetland quantity and quality. Curr. Biol. 29, R1070–R1072 (2019).CAS 
PubMed 
Article 

Google Scholar 
9.Mander, L., Marie-Orleach, L. & Elliott, M. The value of wader foraging behaviour study to assess the success of restored intertidal areas. Estuar. Coast. Shelf Sci. 131, 1–5 (2013).ADS 
Article 

Google Scholar 
10.Choi, C., Gan, X., Hua, N., Wang, Y. & Ma, Z. The habitat use and home range analysis of Dunlin (Calidris alpina) in Chongming Dongtan, China and their conservation implications. Wetlands 34, 255–266 (2014).Article 

Google Scholar 
11.Xia, S. et al. Identifying priority sites and gaps for the conservation of migratory waterbirds in China’s coastal wetlands. Biol. Cons. 210, 72–82 (2017).Article 

Google Scholar 
12.Ramsar Sites Information Service. Mai Po Marshes and Inner Deep Bay. https://rsis.ramsar.org/ris/750 (2021).13.Environment Bureau. Hong Kong Biodiversity Strategy Action Plan 2016–2021 (The Government of the Hong Kong Special Administrative Region, 2016).
Google Scholar 
14.Sung, Y. H., Tse, I. W. L. & Yu, Y. T. Population trends of the Black-faced Spoonbill Platalea minor: Analysis of data from international synchronised censuses. Bird Conserv. Int. 28, 157–167. https://doi.org/10.1017/s0959270917000016 (2017).Article 

Google Scholar 
15.Wei, P. et al. Impact of habitat management on waterbirds in a degraded coastal wetland. Mar. Pollut. Bull. 124, 645–652 (2017).CAS 
PubMed 
Article 

Google Scholar 
16.Cheung, S. C. The politics of wetlandscape: Fishery heritage and natural conservation in Hong Kong. Int. J. Herit. Stud. 17, 36–45 (2011).Article 

Google Scholar 
17.AFCD. Agriculture, Fisheries and Conservation Department (AFCD). Marine Fish Culture, Pond Fish Culture and Oyster Culture. https://www.afcd.gov.hk/english/fisheries/fish_aqu/fish_aqu_mpo/fish_aqu_mpo.html.18.Yu, Y. T., Li, C. H., Tse, I. W. L. & Fong, H. N. F. International Black-Faced Spoonbill Census 2019 (The Hong Kong Bird Watching Society, 2019).
Google Scholar 
19.Pickett, E. J. et al. Cryptic and cumulative impacts on the wintering habitat of the endangered black-faced spoonbill (Platalea minor) risk its long-term viability. Environ. Conserv. 45, 147–154. https://doi.org/10.1017/s0376892917000340 (2018).Article 

Google Scholar 
20.The Hong Kong Bird Watching Society. Black-Faced Spoonbill Population Hits Record High. Number in HK Continues to Decline. Protection of Deep Bay in Urgent Need. https://cms.hkbws.org.hk/cms/ (2020).21.Swennen, C. & Yu, Y. T. Food and feeding behavior of the black-faced spoonbill. Waterbirds 28, 19–27. https://doi.org/10.1675/1524-4695(2005)028[0019:Fafbot]2.0.Co;2 (2005).Article 

Google Scholar 
22.Nichols, R. V., Åkesson, M. & Kjellander, P. Diet assessment based on rumen contents: A comparison between DNA metabarcoding and macroscopy. PLoS ONE 11, e0157977 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
24.Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).Article 

Google Scholar 
25.McInnes, J. C. et al. Optimised scat collection protocols for dietary DNA metabarcoding in vertebrates. Methods Ecol. Evol. 8, 192–202 (2017).Article 

Google Scholar 
26.Thuo, D. et al. Food from faeces: Evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS ONE 14, e0225805 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.De Sousa, L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystem. Environ. DNA 1, 199–214 (2019).Article 

Google Scholar 
28.Ueng, Y. T., Perng, J. J., Wang, J. P., Weng, J. H. & Hou, P. C. Diet of the black-faced spoonbill wintering at Chiku Wetland in Southwestern Taiwan. Waterbirds 29, 185–191 (2006).Article 

Google Scholar 
29.Veen, J., Overdijk, O. & Veen, T. The diet of an endemic subspecies of the Eurasian Spoonbill Platalea leucorodia balsaci, breeding at the Banc d’Arguin, Mauritania. Ardea 100, 123–130 (2012).Article 

Google Scholar 
30.Lee, S. Y. The Mangrove Ecosystem of Deep Bay and the Mai Po Marshes, Hong Kong (Hong Kong University Press, 1999).
Google Scholar 
31.Wong, L. C., Corlett, R. T., Young, L. & Lee, J. S. Comparative feeding ecology of Little Egrets on intertidal mudflats in Hong Kong, South China. Waterbirds 23, 214–225 (2000).
Google Scholar 
32.Yang, K. Y., Lee, S. Y. & Williams, G. A. Selective feeding by the mudskipper (Boleophthalmus pectinirostris) on the microalgal assemblage of a tropical mudflat. Mar. Biol. 143, 245–256 (2003).Article 

Google Scholar 
33.Froese, R., Pauly, D. & eds. FishBase. World Wide Web Electronic Publication. https://www.fishbase.org, version 12/2019 (2019).34.Aguilera, E., Ramo, C. & de le Court, C. Food and feeding sites of the Eurasian spoonbill (Platalea leucorodia) in southwestern Spain. Colon. Waterbirds 19, 159–166 (1996).Article 

Google Scholar 
35.Yu, Y. T. & Swennen, C. K. Habitat use of the black-faced spoonbill. Waterbirds 27, 129–135 (2004).Article 

Google Scholar 
36.World Wide Fund Hong Kong. Mai Po Nature Reserve Habitat Management, Monitoring and Research Plan 2013–2018 (World Wide Fund Hong Kong, 2013).
Google Scholar 
37.Sazima, I. Waterbirds catch and release a poisonous fish at a mudflat in southeastern Australia. Ornithol. Res. 27, 126–128 (2019).Article 

Google Scholar 
38.Marchetti, K. & Price, T. Differences in the foraging of juvenile and adult birds: The importance of developmental constraints. Biol. Rev. 64, 51–70 (1989).Article 

Google Scholar 
39.Jiguet, F. Arthropods in diet of Little Bustards Tetrax tetrax during the breeding season in western France. Bird Study 49, 105–109 (2002).Article 

Google Scholar 
40.Birks, J. D. S. & Dunstone, N. Sex-related differences in the diet of the mink Mustela vison. Ecography 8, 245–252 (1985).Article 

Google Scholar 
41.Mata, V. A. et al. Female dietary bias towards large migratory moths in the European free-tailed bat (Tadarida teniotis). Biol. Lett. 12, 20150988 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Carreiro, A. R. et al. Metabarcoding, stables isotopes, and tracking: Unraveling the trophic ecology of a winter-breeding storm petrel (Hydrobates castro) with a multimethod approach. Mar. Biol. 167, 14 (2020).CAS 
Article 

Google Scholar 
43.Rose, L. M. Sex differences in diet and foraging behavior in white-faced capuchins (Cebus capucinus). Int. J. Primatol. 15, 95–114 (1994).Article 

Google Scholar 
44.Beeston, R., Baines, D. & Richardson, M. Seasonal and between-sex differences in the diet of Black Grouse Tetrao tetrix. Bird Study 52, 276–281 (2005).Article 

Google Scholar 
45.Durell, S. L. V. D., Goss-Custard, J. D. & Caldow, R. W. G. Sex-related differences in diet and feeding method in the oystercatcher Haematopus ostralegus. J. Anim. Ecol. 62, 205–215 (1993).Article 

Google Scholar 
46.Faegre, S. K., Nietmann, L., Hannon, P., Ha, J. C. & Ha, R. R. Age-related differences in diet and foraging behavior of the critically endangered Mariana Crow (Corvus kubaryi), with notes on the predation of Coenobita hermit crabs. J. Ornithol. 161, 149–158 (2020).Article 

Google Scholar 
47.Dunn, E. K. Effect of age on the fishing ability of sandwich terns Sterna sandvicensis. Ibis 114, 360–366 (1972).Article 

Google Scholar 
48.Watson, M. J. & Hatch, J. J. Differences in foraging performance between juvenile and adult roseate terns at a pre-migratory staging area. Waterbirds 22, 463–465 (1999).Article 

Google Scholar 
49.AEC Limited. Ecological Monitoring and Adaptive Management Advice Services for Lok Ma Chau and West Rail Wetlands. Lok Ma Chau Habitat Creation and Management Plan (AEC Limited, 2019).
Google Scholar 
50.The Hong Kong Bird Watching Society. Hong Kong Fishpond Conservation Scheme Project. https://cms.hkbws.org.hk/cms/ (2020).51.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Edgar, R. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).CAS 
Article 
PubMed 

Google Scholar 
53.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
54.Andrews, S., Krueger, F. & Segonds-Pichon, A. FastQC a Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).55.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics (Oxford, England) 31, 3476–3482. https://doi.org/10.1093/bioinformatics/btv401 (2015).CAS 
Article 

Google Scholar 
57.Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).Article 

Google Scholar 
58.Edgar, R. SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences. BioRxiv https://doi.org/10.1101/074161 (2016).Article 

Google Scholar 
59.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Machida, R. J., Leray, M., Ho, S. L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027. https://doi.org/10.1038/sdata.2017.27 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Sato, K., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35, 1553–1555 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).CAS 
Article 

Google Scholar 
63.Kahlke, T. & Ralph, P. J. BASTA—Taxonomic classification of sequences and sequence bins using last common ancestor estimations. Methods Ecol. Evol. 10, 100–103. https://doi.org/10.1111/2041-210X.13095 (2019).Article 

Google Scholar 
64.Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406. https://doi.org/10.1111/mec.14734 (2019).Article 
PubMed 

Google Scholar 
65.Lahti, L. & Shetty, S. Microbiome R Package Version 1.6.0. http://microbiome.github.io (2012).66.Oksanen, J. et al. vegan: Community Ecology Package Version 2.5–6. https://cran.r-project.org, https://github.com/vegandevs/vegan (2019).67.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Martinez-Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison Using Adonis. R Package Version 0.3. https://github.com/pmartinezarbizu/pairwiseAdonis (2019).69.Steinberger, A. J. Asteinberger9/seq_scripts: Release v1. https://github.com/asteinberger9/seq_scripts (2018).70.ArcGIS. ArcGIS Version 10.7. https://desktop.arcgis.com/en/arcmap/ (2020). More

1.Lohr, T. A Short Story About the Geological History of the Pamir (University of Mining and Technology Freiberg, 2001).
Google Scholar 
2.Safarov, N. National Strategy and Action Plan on Conservation and Sustainable Use of Biodiversity (Governmental Working Group of the Republic of Tajikistan, 2003).
Google Scholar 
3.Nowak, A., Nowak, S. & Nobis, M. Distribution patterns, ecological characteristic and conservation status of endemic plants of Tadzhikistan: A global hotspot of diversity. J. Nat. Conserv. 19, 296–305 (2011).Article 

Google Scholar 
4.Nowak, A. et al. Red List of vascular plants of Tajikistan: The core area of the Mountains of Central Asia global biodiversity hotspot. Sci. Rep. 10, 6235 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Bagheri, A., Maassoumi, A. A., Rahiminejad, M. R., Brassac, J. & Blattner, F. R. Molecular phylogeny and divergence times of Astragalus section Hymenostegis: An analysis of a rapidly diversifying species group in Fabaceae. Sci. Rep. 7, 14033 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Mittermeier, R. A. et al. Hotspots Revisited: Earth’s Biologically Richest and Most Threatened Terrestrial Ecoregions. (Conservation International, 2005).7.Abramowski, U. et al. Pleistocene glaciations of Central Asia: Results from 10Be surface exposure ages of erratic boulders from the Pamir (Tajikistan), and the Alay-Turkestan range (Kyrgyzstan). Quat. Sci. Rev. 25, 1080–1096 (2006).ADS 
Article 

Google Scholar 
8.Cowling, R. M. & Lombard, A. T. Heterogeneity, speciation/extinction history and climate: Explaining regional plant diversity patterns in the Cape Floristic Region. Divers. Distrib. 8, 163–179 (2002).Article 

Google Scholar 
9.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

Google Scholar 
10.López-Pujol, J., Zhang, F. M., Sun, H. Q., Ying, T. S. & Ge, S. Centres of plant endemism in China: Places for survival or for speciation?. J. Biogeogr. 38, 1267–1280 (2011).Article 

Google Scholar 
11.Chen, X.-Y. & He, F. Speciation and endemism under the model of island biogeography. Ecology 90, 39–45 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Bruchmann, I. & Hobohm, C. Factors that create and increase endemism. In Endemism in Vascular Plants (ed. Hobohm, C.) 51–68 (Springer, 2014).Chapter 

Google Scholar 
13.Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl. Acad. Sci. U. S. A. 97, 9115–9120 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Vetaas, O. R. & Grytnes, J. A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Glob. Ecol. Biogeogr. 11, 291–301 (2002).Article 

Google Scholar 
15.Mucina, L. & Wardell-Johnson, G. W. Landscape age and soil fertility, climatic stability, and fire regime predictability: Beyond the OCBIL framework. Plant Soil 341, 1–23 (2011).CAS 
Article 

Google Scholar 
16.Tzedakis, P. C. Museums and cradles of Mediterranean biodiversity. J. Biogeogr. 36, 1033–1034 (2009).Article 

Google Scholar 
17.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. U. S. A. 104, 5925–5930 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Noroozi, J., Pauli, H., Grabherr, G. & Breckle, S. W. The subnival-nival vascular plant species of Iran: A unique high-mountain flora and its threat from climate warming. Biodivers. Conserv. 20, 1319–1338 (2011).Article 

Google Scholar 
19.Pauli, H., Gottfried, M., Dirnböck, T., Dullinger, S. & Grabherr, G. Assessing the long-term dynamics of endemic plants at summit habitats. In Alpine Biodiversity in Europe (eds Nagy, L. et al.) 195–207 (Springer, 2003).Chapter 

Google Scholar 
20.Agakhanjanz, O. & Breckle, S. W. Origin and evolution of the mountain flora in middle asia and neighbouring mountain regions. In Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences Ecological Studies (Analysis and Synthesis) Vol. 113 (eds Chapin, F. S. & Körner, C.) 63–80 (Springer, 1995).
Google Scholar 
21.Noroozi, J., Akhani, H. & Willner, W. Phytosociological and ecological study of the high alpine vegetation of Tuchal mountains (Central Alborz, Iran). Phytocoenologia 40, 293–321 (2010).Article 

Google Scholar 
22.Goldblatt, P. & Manning, J. C. Plant Diversity of the Cape Region of Southern Africa. Ann. Mo. Bot. Gard. 89, 281–302 (2002).Article 

Google Scholar 
23.Bond, P. & Goldblatt, P. Plants of the Cape fora: a descriptive catalogue. J. S Afr. Bot. Suppl. 13, 1–455 (1984).
Google Scholar 
24.Panda, R. M., Behera, M. D., Roy, P. S. & Biradar, C. Energy determines broad pattern of plant distribution in Western Himalaya. Ecol. Evol. 7, 10850–10860 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Nowak, A., Nowak, S., Nobis, M. & Nobis, A. A report on the conservation status of segetal weeds in Tajikistan. Weed Res. 54, 635–648 (2014).Article 

Google Scholar 
26.Nobis, M., Gudkova, P. D., Nowak, A., Sawicki, J. & Nobis, A. A synopsis of the genus Stipa (Poaceae) in Middle Asia, including a key to species identyfication, an annoted checklist and phytogeographical analyses. Ann. Missouri Bot. Gard. 105, 1–63 (2020).Article 

Google Scholar 
27.Thompson, J. N. The Geographic Mosaic of coevolution (Chicago Univ Press, 2005).Book 

Google Scholar 
28.Thompson, J. N. Four central points about coevolution. Evol. Educ. Outreach 3, 7–13 (2010).Article 

Google Scholar 
29.Thrall, P. H., Hochberg, M. E., Burdon, J. J. & Bever, J. D. Coevolution of symbiotic mutualists and parasites in a community context. Trends Ecol. Evol. 22, 120–126 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Navarro-Fernández, C. M., Aroca, R. & Barea, J. M. Influence of arbuscular mycorrhizal fungi and water regime on the development of endemic Thymus species in dolomitic soils. Appl. Soil Ecol. 48, 31–37 (2011).Article 

Google Scholar 
31.Zubek, S., Nobis, M., Błaszkowski, J., Mleczko, P. & Nowak, A. Fungal root endophyte associations of plants endemic to the Pamir Alay Mountains of Central Asia. Symbiosis 54, 139–149 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Lambers, H., Chapin, F. S. III. & Pons, T. L. Plant Physiological Ecology (Springer, 2008).Book 

Google Scholar 
33.Lambers, H., Brundrett, M. C., Raven, J. A. & Hopper, S. D. Plant mineral nutrition in ancient landscapes: High plant species diversity on infertile soils is linked to functional diversity for nutritional strategies. Plant Soil 334, 11–31 (2010).CAS 
Article 

Google Scholar 
34.Hopper, S. D. OCBIL theory: Towards an integrated understanding of the evolution, ecology and conservation of biodiversity on old, climatically buffered, infertile landscapes. Plant Soil 322, 49–86 (2009).CAS 
Article 

Google Scholar 
35.Ellison, A. M. & Gotelli, N. J. Energetics and the evolution of carnivorous plants – Darwin’s ‘most wonderful plants in the world’. J. Exp. Bot. 60, 19–42 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Merckx, V., Bidartondo, M. I. & Hynson, N. A. Myco-heterotrophy: When fungi host plants. Ann. Bot. 104, 1255–1261 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Huang, B. H. et al. Differential genetic responses to the stress revealed the mutation-order adaptive divergence between two sympatric ginger species. BMC Genom. 19, 692 (2018).Article 
CAS 

Google Scholar 
38.Turner, J. R. G., Gatehouse, C. M. & Core, C. A. Does solar energy control organic diversity? Butterflies moths and the British climate. Oikos 48, 195–205 (1987).Article 

Google Scholar 
39.Körner, C. Why are there global gradients in species richness? Mountains might hold the answer. Trends Ecol. Evol. 15, 513–514 (2000).Article 

Google Scholar 
40.Makhmadaliev, B., Novikov, V., Kayumov, A., Karimov, U. & Perdomo, M. National Action Plan of the Republic of Tajikistan for Climate Change Mitigation. (Tajik Met Service, 2003).41.Nedzvedskiy, A. P. Geologicheskoe stroenye. In Atlas Tajikskoi SSR (eds Narzikulov, I. K. & Stanyukovich, K. W.) 14–15 (Akademia Nauk Tajikskoi SSR, 1968).
Google Scholar 
42.Latipova, W. A. Kolichestvo osadkov. In Atlas Tajikskoi SSR (eds Narzikulov, I. K. & Stanyukovich, K. W.) 68–69 (Akademia Nauk Tajikskoi SSR, 1968).
Google Scholar 
43.Narzikulov, I. K. & Stanyukovich, K. W. Atlas Tajikskoi SSR. (Akademia Nauk Tajikskoi SSR, 1968).44.Rivas-Martínez, S., Rivas-Sáenz, S. & Penas, Á. Worldwide bioclimatic classification system. Glob. Geobot. 1, 1–638 (2011).
Google Scholar 
45.Djamali, M., Brewer, S., Breckle, S. W. & Jackson, S. T. Climatic determinism in phytogeographic regionalization: A test from the Irano-Turanian region, SW and Central Asia. Flora Morphol. Distrib. Funct. Ecol. Plants 207, 237–249 (2012).
Google Scholar 
46.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. I, Paprotnikoobraznye – Zlaki. (Izdatelstvo Akademii Nauk SSSR, 1957).47.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. II, Osokovye—Orkhidnye. (Izdatelstvo Akademii Nauk SSSR, 1963).48.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. III, Opekhovye—Gvozdichnye. (Izdatelstvo Nauka, 1968).49.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. IV, Rogolistnikovye—Rozotsvetnye. (Izdatelstvo Nauka, 1975).50.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. V, Krestotsvetne—Bobovye. (Izdatelstvo Nauka, 1978).51.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. VI, Bobovye (rod Astragal). (Izdatelstvo Nauka, 1981).52.Kochkareva, T. F. Flora Tadzhikskoi SSR. T. VIII. Kermekovye—Podorozhnikovye. (Izdatelstvo Nauka, 1986).53.Kinzikaeva, G. K. Flora Tadzhikskoi SSR. T. IX. Marenovye – Slozhnotsvetnye. (Izdatelstvo Nauka, 1988).54.Rasulova, M. R. Flora Tadzhikskoi SSR. T. X, Slozhnotsvetnye. (Izdatelstvo Nauka, 1991).55.Grubov, V. I. Schlussbetrachtung zum Florenwerk ‘Rastenija Central’noj Azii’ [Die Pflanzen Zentralasiens] und die Begründung der Eigenständigkeit der mongolischen Flora. Feddes Repert. 121, 7–13 (2010).Article 

Google Scholar 
56.Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org). (2008).57.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Zuur, A. F., Ieno, E. N. & Erik, H. W. G. Meesters A Beginner’s Guide to R (Springer, 2009).MATH 
Book 

Google Scholar 
59.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 027–046 (2013).Article 

Google Scholar 
60.Wood, S. N. Generalized Additive Models An Introduction with R (Chapman and Hall/CRC, 2017).MATH 
Book 

Google Scholar 
61.Therneau, T. & Atkinson, B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-13 (2018).62.De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).Article 

Google Scholar  More

1.Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B 285, 20182047 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Moritz, C. & Agudo, R. The future of species under climate change: Resilience or decline?. Science 341, 504–508 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Parmesan, C. & Hanley, M. E. Plants and climate change: Complexities and surprises. Ann. Bot. 116, 849–864 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Soltis, P. S. & Soltis, D. E. The role of genetic and genomic attributes in the success of polyploids. Proc. Natl. Acad. Sci. U.S.A. 97, 7051–7057 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Barker, M. S., Husband, B. C. & Chris Pires, J. Spreading winge and flying high: The evolutionary importance of polyploidy after a century of study. Am. J. Bot. 103, 1139–1145 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Van De Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Madlung, A. Polyploidy and its effect on evolutionary success: Old questions revisited with new tools. Heredity (Edinb) 110, 99–104 (2013).CAS 
Article 

Google Scholar 
8.Soltis, D. E., Visger, C. J., Marchant, B. D. & Soltis, P. S. Polyploidy: Pitfalls and paths to a paradigm. Am. J. Bot. 103, 1146–1166 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Ramsey, J. Polyploidy and ecological adaptation in wild yarrow. Proc. Natl. Acad. Sci. U.S.A. 108, 7096–7101 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Oswald, B. P. & Nuismer, S. L. Neopolyploidy and diversification in Heuchera grossulariifolia. Evolution 65, 1667–1679 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Kolář, F., Čertner, M., Suda, J., Schönswetter, P. & Husband, B. C. Mixed-ploidy species: Progress and opportunities in polyploid research. Trends Plant Sci. https://doi.org/10.1016/j.tplants.2017.09.011 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Fowler, N. L. & Levin, D. A. Critical factors in the establishment of allopolyploids. Am. J. Bot. 103, 1236–1251 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Husband, B. C., Baldwin, S. J. & Suda, J. The incidence of polyploidy in natural plant populations: Major patterns and evolutionary processes. In Plant Genome Diversity 2: Physical Structure, Behaviour and Evolution of Plant Genomes (eds Leitch, I. et al.) 255–276 (Springer, 2013).Chapter 

Google Scholar 
14.Te Beest, M. et al. The more the better? The role of polyploidy in facilitating plant invasions. Ann. Bot. 109, 19–45 (2012).Article 

Google Scholar 
15.Watanabe, K. The cytogeography of the genus Eupatorium (Compositae)—A review. Plant Species Biol. 1, 99–116 (1986).CAS 
Article 

Google Scholar 
16.Novak, S. J., Soltis, D. E. & Soltis, P. S. Ownbey’s Tragopogons: 40 years later. Am. J. Bot. 78, 1586–1600 (1991).Article 

Google Scholar 
17.Van Dijk, P. & Bakx-Schotman, T. Chloroplast DNA phylogeography and cytotype geography in autopolyploid Plantago media. Mol. Ecol. 6, 345–352 (1997).Article 

Google Scholar 
18.Martin, S. L. & Husband, B. C. Influence of phylogeny and ploidy on species ranges of North American angiosperms. J. Ecol. 97, 913–922 (2009).Article 

Google Scholar 
19.Suda, J., Kron, P., Husband, B. C. & Trávníček, P. Flow cytometry and ploidy: Applications in plant systematics, ecology and evolutionary biology. in Flow Cytometry with Plant Cells 103–130 (Wiley, 2007). https://doi.org/10.1002/9783527610921.ch5.20.Ramsey, J. & Ramsey, T. S. Ecological studies of polyploidy in the 100 years following its discovery. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 1–76 (2014).Article 

Google Scholar 
21.Goldblatt, P. Polyploidy in angiosperms: Monocotyledons. In Polyploidy. Basic Life Sciences Vol. 13 (ed. Lewis, W. H.) 219–239 (Springer, 1980).
Google Scholar 
22.Levy, A. A. & Feldman, M. The impact of polyploidy on grass genome evolution. Plant Physiol. 130, 1587–1593 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Kellogg, A. Flowering Plants Monocots Poaceae Vol. 13 (Springer, 2015).
Google Scholar 
24.Estep, M. C. et al. Allopolyploidy, diversification, and the Miocene grassland expansion. Proc. Natl. Acad. Sci. 111, 15149–15154 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Minaya, M. et al. Contrasting dispersal histories of broad- and fine-leaved temperate Loliinae grasses: Range expansion, founder events, and the roles of distance and barriers. J. Biogeogr. 44, 1980–1993 (2017).Article 

Google Scholar 
26.Torrecilla, P. & Catalán, P. Phylogeny of broad-leaved and fine-leaved Festuca lineages (Poaceae) based on nuclear ITS sequences. Syst. Bot. 27, 241–251 (2002).
Google Scholar 
27.Šmarda, P., Bureš, P., Horová, L., Foggi, B. & Rossi, G. Genome size and GC content evolution of Festuca: Ancestral expansion and subsequent reduction. Ann. Bot. 101, 421–433 (2008).28.Meusel, H., Jäger, E. & Weinert, E. Vergleichende Chorologie der Zentral-europäischen Flora (G. Fischer, 1965).
Google Scholar 
29.Kiedrzyński, M., Zielińska, K. M., Kiedrzyńska, E. & Jakubowska-Gabara, J. Regional climate and geology affecting habitat availability for a relict plant in a plain landscape: The case of Festuca amethystina L. in Poland. Plant Ecol. Divers. 8, 331–341 (2015).Article 

Google Scholar 
30.Kiedrzyński, M., Zielińska, K. M., Rewicz, A. & Kiedrzyńska, E. Habitat and spatial thinning improve the Maxent models performed with incomplete data. J. Geophys. Res. Biogeosci. 122, 1359–1370 (2017).Article 

Google Scholar 
31.Petrova, A. & Kozuharov, S. Citotaxonomicno proucvane na balgarski vidove ot roda Festuca L. in IV Nacionalna Konferencija Po Botanika 1 (ed. Trudova) 16–23 (1987).32.Stählin, A. Morphologische und zytologische Untersuchungen an Gramineen. Wiss. Arch. Landwirtschaft., Abt. A, Pflanzenbau 1, 330–398 (1929).33.Wittmann, H. & Strobl, W. Beitrag zur Kenntnis von Festuca amethystina L. im Bundesland Salzburg. Florist. Mitt. Salzburg 9, 3–8 (1984).34.La Sorte, F. A. & Jetz, W. Projected range contractions of montane biodiversity under global warming. Proc. R. Soc. B Biol. Sci. 277, 3401–3410 (2010).Article 

Google Scholar 
35.Elsen, P. R. & Tingley, M. W. Global mountain topography and the fate of montane species under climate change. Nat. Clim. Change 5, 772–776 (2015).ADS 
Article 

Google Scholar 
36.Šmarda, P., Müller, J., Vraná, J. & Kočí, K. Ploidy level variability of some Central European fescues (Festuca subg. Festuca, Poaceae). Biologia 60, 1–6 (2005).
Google Scholar 
37.Rewicz, A. et al. Morphometric traits in the fine-leaved fescues depend on ploidy level: The case of Festuca amethystina L. PeerJ 2018, e5576 (2018).Article 

Google Scholar 
38.Roleček, J., Dřevojan, P. & Šmarda, P. First record of Festuca amethystina L. from the Transylvanian Basin (Romania). Contrib. Bot. 54, 91–97 (2019).Article 

Google Scholar 
39.Phillips, S. J. & Dudík, M. Modeling of species distribution with Maxent: New extensions and a comprehensive evaluation. Ecograpy 31, 161–175 (2008).Article 

Google Scholar 
40.Segraves, K. A., Thompson, J. N., Soltis, P. S. & Soltis, D. E. Multiple origins of polyploidy and the geographic structure of Heuchera grossulariifolia. Mol. Ecol. 8, 253–262 (1999).Article 

Google Scholar 
41.Levin, D. A. Minority cytotype exclusion in local plant populations. TAXON vol. 24. https://eurekamag.com/pdf/000/000139096.pdf (1975).42.Pils, G. Systematics, distribution, and karyology of the Festuca violacea Group (Poaceae) in the Eastern Alps. Plant Syst. Evol. 136, 73–124 (1980).Article 

Google Scholar 
43.Stebbins, G. L. Chromosomal Evolution in Higher Plants (Addison-Wesley, 1971).
Google Scholar 
44.Stutz, H. C. & Sanderson, S. C. Evolutionary studies in Atriplex: Chromosome races of A. confertifolia (shadscale). Am. J. Bot. 70, 1536–1547 (1983).Article 

Google Scholar 
45.Husband, B. C. & Schemske, D. W. Cytotype distribution at a diploid-tetraploid contact zone in Chamerion (Epilobium) angustifolium (Onagraceae). Am. J. Bot. 85, 1688–1694 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Hardy, O. J., Vanderhoeven, S., De Loose, M. & Meerts, P. Ecological, morphological and allozymic differentiation between diploid and tetraploid knapweeds (Centaurea jacea) from a contact zone in the Belgian Ardennes. New Phytol. 146, 281–290 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Gauthier, P., Lumaret, R. & Bédécarrats, A. Genetic variation and gene flow in Alpine diploid and tetraploid populations of Lotus (L. alpinus (DC) Schleicher/L. corniculatus L.). I. Insights from morphological and allozyme markers. Heredity (Edinb) 80, 683–693 (1998).CAS 
Article 

Google Scholar 
48.Schönswetter, P. et al. Sympatric diploid and hexaploid cytotypes of Senecio carniolicus (Asteraceae) in the Eastern Alps are separated along an altitudinal gradient. J. Plant Res. 120, 721–725 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Petit, C., Bretagnolle, F. & Felber, F. Evolutionary consequences of diploid-polyploid hybrid zones in wild species. Trends Ecol. Evol. 14, 306–311 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Chumová, Z., Krejčíková, J., Mandáková, T., Suda, J. & Trávníček, P. Evolutionary and taxonomic implications of variation in nuclear genome size: Lesson from the grass genus Anthoxanthum (Poaceae). PLoS One 10, e0133748 (2015).Article 
CAS 

Google Scholar 
51.Marchant, D. B., Soltis, D. E. & Soltis, P. S. Patterns of abiotic niche shifts in allopolyploids relative to their progenitors. New Phytol. 212, 708–718 (2016).Article 
CAS 

Google Scholar 
52.Arrigo, N. et al. Is hybridization driving the evolution of climatic niche in Alyssum montanum. Am. J. Bot. 103, 1348–1357 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Laport, R. G., Minckley, R. L. & Ramsey, J. Ecological distributions, phenological isolation, and genetic structure in sympatric and parapatric populations of the Larrea tridentata polyploid complex. Am. J. Bot. 103, 1358–1374 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Mosquin, T. Evidence for autopolyploidy in Epilobium angustifolium (Onagraceae). Evolution (N. Y.) 21, 713–719 (1967).
Google Scholar 
55.Szafer, W. The mountain element in the flora of Polish Plain. Rozpr. Wydz. Mat. PAU Ser. 3 Dział B 69, 83–196 (1930).
Google Scholar 
56.Kiedrzyński, M., Zielińska, K. M., Kiedrzyńska, E. & Rewicz, A. Refugial debate: On small sites according to their function and capacity. Evol. Ecol. 31, 815–827 (2017).Article 

Google Scholar 
57.Babić, V. P. et al. Temperature and other microclimate conditions in the oak forests on Fruška Gora (Serbia). Therm. Sci. 19, S415–S425 (2015).Article 

Google Scholar 
58.Jakubowska-Gabara, J. Decline of Potentillo albae-Quercetum Libb. 1933 phytocoenoses in Poland. Vegetatio 124, 45–59 (1996).Article 

Google Scholar 
59.Roleček, J. Formalized classification of thermophilous oak forests in the Czech Republic: What brings the Cocktail method?. Preslia 79, 1–21 (2007).
Google Scholar 
60.Indreica, A. Festuca amethystina in the sessile oak forests from upper basin of Olt River. Contrib. Bot. 42, 11–18 (2007).
Google Scholar 
61.Jakubowska-Gabara, J. Festuca amethystina L. In The Polish Red Book of Plants. Pteridophytes and Vascular Plants (eds Kaźmierczakowa, R. et al.) 616–618 (Institute of Nature Conservation PAS, 2014).
Google Scholar 
62.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
63.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
64.Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (2017).65.Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014). https://doi.org/10.1017/CBO9781139627061.Book 
MATH 

Google Scholar 
66.Wilke, C. O. Ridgeline Plots in ‘ggplot2’. https://wilkelab.org/ggridges/index.html (2021).67.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
68.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography (Cop.) 40, 887–893 (2017).Article 

Google Scholar 
69.Warren, D. L. & Seifert, S. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Soc. Am. 21, 335–342 (2011).
Google Scholar 
70.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
71.Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography (Cop.) 33, 607–611 (2010).
Google Scholar 
72.Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop.) 28, 385–393 (2005).Article 

Google Scholar  More

1.Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
2.Wright, J. S. Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130, 1–14 (2002).ADS 
PubMed 
Article 

Google Scholar 
3.Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

Google Scholar 
4.Connell, J. On the role of natural enemies in preventing competitive exclusion in some marine animals and rain forest trees. Dyn. Popul. 298, 312 (1971).
Google Scholar 
5.Terborgh, J. W. Toward a trophic theory of species diversity. PNAS 112, 11415–11422 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Johnson, D. J., Beaulieu, W. T., Bever, J. D. & Clay, K. Conspecific negative density dependence and forest diversity. Science 336, 904–907 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Chisholm, R. A. & Muller-Landau, H. C. A theoretical model linking interspecific variation in density dependence to species abundances. Theor. Ecol. 4, 241–253 (2011).Article 

Google Scholar 
9.Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
10.Chisholm, R. A. & Fung, T. Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar4685 (2018).PubMed 
Article 
CAS 

Google Scholar 
11.Hülsmann, L. & Hartig, F. Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar2435 (2018).PubMed 
Article 
CAS 

Google Scholar 
12.Detto, M., Visser, M. D., Wright, S. J. & Pacala, S. W. Bias in the detection of negative density dependence in plant communities. Ecol. Lett. 22, 1923–1939 (2019).PubMed 
Article 

Google Scholar 
13.LaManna, J. A. et al. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar3824 (2018).PubMed 
Article 
CAS 

Google Scholar 
14.LaManna, J. A. et al. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar5245 (2018).PubMed 
Article 
CAS 

Google Scholar 
15.LaManna, J. A., Mangan, S. A. & Myers, J. A. Conspecific negative density dependence and why its study should not be abandoned. Ecosphere 12, e03322 (2021).Article 

Google Scholar 
16.Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).CAS 
PubMed 
Article 

Google Scholar 
17.Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: Speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).PubMed 
Article 

Google Scholar 
18.Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
19.Ricklefs, R. E. & He, F. Region effects influence local tree species diversity. PNAS 113, 674–679 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Comita, L. S. et al. Testing predictions of the Janzen-Connell hypothesis: A meta-analysis of experimental evidence for distance- and density-dependent seed and seedling survival. J. Ecol. 102, 845–856 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
21.Currie, D. J. Energy and large-scale patterns of animal- and plant-species richness. Am. Nat. 137, 27–49 (1991).Article 

Google Scholar 
22.Grosso, S. D. et al. Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology 89, 2117–2126 (2008).PubMed 
Article 

Google Scholar 
23.Chase, J. M. Stochastic Community Assembly Causes Higher Biodiversity in More Productive Environments. Science 27, (2010).24.O’Brien, E. M. Climatic gradients in woody plant species richness: Towards an explanation based on an analysis of Southern Africa’s woody flora. J. Biogeography 20, 181–198 (1993).Article 

Google Scholar 
25.McCain, C. M. & Grytnes, J.-A. Elevational Gradients in Species Richness. In eLS (American Cancer Society, 2010).26.Barry, R. G. Mountain Weather and Climate (Cambridge University Press, 2008).Book 

Google Scholar 
27.LaManna, J. A., Walton, M. L., Turner, B. L. & Myers, J. A. Negative density dependence is stronger in resource-rich environments and diversifies communities when stronger for common but not rare species. Ecol. Lett. 19, 657–667 (2016).PubMed 
Article 

Google Scholar 
28.Zhu, K., Woodall, C. W., Monteiro, J. V. D. & Clark, J. S. Prevalence and strength of density-dependent tree recruitment. Ecology 96, 2319–2327 (2015).PubMed 
Article 

Google Scholar 
29.Yao, J. et al. Abiotic niche partitioning and negative density dependence across multiple life stages in a temperate forest in northeastern China. J. Ecol. 108, 1299–1310 (2020).Article 

Google Scholar 
30.Leigh, E. G. et al. Why do some tropical forests have so many species of trees?. Biotropica 36, 447–473 (2004).
Google Scholar 
31.Terborgh, J. Enemies maintain hyperdiverse tropical forests. Am. Nat. 179, 303–314 (2012).PubMed 
Article 

Google Scholar 
32.Altman, J. et al. Linking spatiotemporal disturbance history with tree regeneration and diversity in an old-growth forest in northern Japan. PPEES 21, 1–13 (2016).
Google Scholar 
33.Kubota, Y., Hirao, T., Fujii, S., Shiono, T. & Kusumoto, B. Beta diversity of woody plants in the Japanese archipelago: The roles of geohistorical and ecological processes. J. Biogeogr. 41, 1267–1276 (2014).Article 

Google Scholar 
34.Mori, A. S. Local and biogeographic determinants and stochasticity of tree population demography. J. Ecol. 107, 1276–1287 (2019).Article 

Google Scholar 
35.Oohata, S. Distribution of tree species along the temperature gradient in the Japan archipelago (ii).: Life form and species distribution. Jap. J. Ecol. 40, 71–84 (1990).ADS 

Google Scholar 
36.Kira, T. A Climatological Interpretation of Japanese Vegetation Zones 21–30 (Springer, 1977).
Google Scholar 
37.Mori, A. S. Environmental controls on the causes and functional consequences of tree species diversity. J. Ecol. 106, 113–125 (2018).Article 

Google Scholar 
38.Suzuki, S. N., Ishihara, M. I. & Hidaka, A. Regional-scale directional changes in abundance of tree species along a temperature gradient in Japan. Glob. Chan. Biol. 21, 3436–3444 (2015).ADS 
Article 

Google Scholar 
39.Hara, M. Analysis of seedling banks of a climax beech forest: Ecological importance of seedling sprouts. Vegetatio 71, 67–74 (1987).
Google Scholar 
40.Homma, K. Effects of snow pressure on growth form and life history of tree species in Japanese beech forest. J. Veg. Sci. 8, 781–788 (1997).Article 

Google Scholar 
41.Gansert, D. Treelines of the Japanese Alps—altitudinal distribution and species composition under contrasting winter climates. Flora 199, 143–156 (2004).Article 

Google Scholar 
42.Hukusima, T. et al. New phytosociological classification of beech forests in Japan. Jpn. J. Ecol. 45, 79–98 (1995).
Google Scholar 
43.Matsui, T. et al. Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan. J. Veg. Sci. 15, 605–614 (2004).Article 

Google Scholar 
44.Johnson, D. J., Condit, R., Hubbell, S. P. & Comita, L. S. Abiotic niche partitioning and negative density dependence drive tree seedling survival in a tropical forest. Proc. R. Soc. B 284, 20172210 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Ishihara, M. I. et al. Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecol. Res. 26, 1007–1008 (2011).Article 

Google Scholar 
46.Law, R. et al. Ecological information from spatial patterns of plants: Insights from point process theory. J. Ecol. 97, 616–628 (2009).Article 

Google Scholar 
47.Wright, S. J. et al. Reproductive size thresholds in tropical trees: Variation among individuals, species and forests. J. Trop. Ecol. 21, 307–315 (2005).Article 

Google Scholar 
48.Zhu, Y., Comita, L. S., Hubbell, S. P. & Ma, K. Conspecific and phylogenetic density-dependent survival differs across life stages in a tropical forest. J. Ecol. 103, 957–966 (2015).Article 

Google Scholar 
49.Ripley, B. D. Spatial point pattern analysis in ecology. In Develoments in Numerical Ecology (eds Legendre, P. & Legendre, L.) 407–429 (Springer, 1987).Chapter 

Google Scholar 
50.Wiegand, T. & Moloney, K. A. Handbook of Spatial Point-Pattern Analysis in Ecology (CRC Press, 2013).Book 

Google Scholar 
51.Loosmore, N. B. & Ford, E. D. Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925–1931 (2006).PubMed 
Article 

Google Scholar 
52.R Core Team. R: A Language and Environment for Statistical Computing (2020).53.Baddeley, A. & Turner, R. spatstat: An R Package for Analyzing Spatial Point Patterns. J. Stat. Soft. 12, 1–42 (2005).Article 

Google Scholar 
54.Wills, C., Condit, R., Foster, R. B. & Hubbell, S. P. Strong density- and diversity-related effects help to maintain tree species diversity in a neotropical forest. PNAS 94, 1252–1257 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Givnish, T. J. On the causes of gradients in tropical tree diversity. J. Ecol. 87, 193–210 (1999).Article 

Google Scholar 
56.Fibich, P., Vítová, A. & Lepš, J. Interaction between habitat limitation and dispersal limitation is modulated by species life history and external conditions: A stochastic matrix model approach. Comm. Ecol. 19, 9–20 (2018).Article 

Google Scholar 
57.Miyawaki, A. A vegetation ecological view of the Japanese archipelago. Bull. Inst. Environ. Sci. Technol. Yokohama Natl. Univ. 11, 85–101 (1984).
Google Scholar 
58.Mori, A. S. et al. Community assembly processes shape an altitudinal gradient of forest biodiversity. Glo. Ecol. Biogeogr. 22, 878–888 (2013).Article 

Google Scholar 
59.Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties (Wiley, 2001).
Google Scholar 
60.Brown, C., Law, R., Illian, J. B. & Burslem, D. F. R. P. Linking ecological processes with spatial and non-spatial patterns in plant communities. J. Ecol. 99, 1402–1414 (2011).Article 

Google Scholar 
61.Bastias, C. C. et al. Species richness influences the spatial distribution of trees in European forests. Oikos 129, 380–390 (2020).Article 

Google Scholar 
62.Hülsmann, L., Chisholm, R. A. & Hartig, F. Is variation in conspecific negative density dependence driving tree diversity patterns at large scales?. Trends Ecol. Evol. 36, 151–163 (2021).PubMed 
Article 

Google Scholar 
63.Damgaard, C. & Weiner, J. It’s about time: A critique of macroecological inferences concerning plant competition. Trends Ecol. Evol. 32, 86–87 (2017).PubMed 
Article 

Google Scholar 
64.Murata, I. et al. Effects of sika deer (Cervus nippon) and dwarf bamboo (Sasamorpha borealis) on seedling emergence and survival in cool-temperate mixed forests in the Kyushu Mountains. J. For. Res. 14, 296–301 (2009).Article 

Google Scholar 
65.Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

Google Scholar  More

Samples collectionThree sampling campaigns were carried out at the two sample sites (A and B) on the coast of north-western Sicily (Fig. 1a) chosen for this study. The main features of the sites and sampling details are summarized in Table S1 (Supplementary Information). A total of 90 specimens of sea urchins Paracentrotus lividus (45 specimen per each site), 30 l of seawater (15 per site), 40 samples (20 per site) of sea grass Posidonia oceanica (less than 5 cm leaf fragments, according to the institutional and national ethical guidelines) were collected and analyzed together with 30 l of brackish water from site B (15 l from each creek).Figure 1Map of the sampling site. (a) Geographic area, (b) bathymetric chart and (c) relative distance between sample sites; (d, e) close ups of sampling sites. (Images obtained by courtesy of Google Earth Pro and map.openseamap.org).Full size imageThe samplings activity was authorized by the Capitaneria di Porto of Palermo with protocol number: 0029430. In the absence of data about PFOA contamination in the most recent report about chemical contamination in the coastal region subjected to this study31, the choice of sample sites was based on supposedly different status of pollution based on the site position or proximity to human activities (e.g. restaurants, pipeline, sewages, etc.).Site A (see Fig. 1d), was chosen assuming a lower state of pollution based on its position in proximity to Capo Zafferano, at the northern extremity of S. Elia’s bay, with an average depth of 11 m and rocky seabed (see Fig. 1b and Supplementary Information: Table S1). Conversely, Site B (see Fig. 1e was chosen in the same coastal area (only 4.7 km away from Site A) assuming a higher state of pollution due to its position located on the southern side of Solanto promontory, nearby a pipeline and the mouths of two small creeks from inland, with a shallow (3 m) sandy seabed and where a bathing prohibition order is in place32 (see Fig. 1b, c and Supplementary Information: Table S1).The biodistribution of PFOA in the various matrices was evaluated by analyzing sea urchin’s coelomocytes (CC) (90 samples) and coelomic fluid (CF) (90 samples), as well as gonads (G) (63 samples from 32 sea urchins collected in site A and 31 sea urchins collected in site B), or mixed organs (MIX) (27 samples from 13 sea urchins collected in site A and 14 sea urchins collected in site B) consisting of a homogenized mixture of urchin’s inner matrices when gonads were not developed enough for sampling. Due to their mutually exclusive nature the latter two datasets (G and MIX) were merged and labelled as “Gonads or Mixed organs” (GoM) for statistical analysis and graphical representations that needed a uniform dataset of 45 items per site. Further details on the collection of matrices and their labelling are described in the Supplementary Information.The size of the sea urchins (horizontal diameter without spines) ranged between 30 and 51 mm indicating specimen that have lived in their respective site approximately from 3 to 5 years25.PFOA extraction and analysisMaterials, equipment and software are described in the Supplementary Information.PFOA extraction procedures were adapted33 to the type of matrix to be analyzed. Recovery percentages (R %) were checked per each batch of analyses by spiking blank samples with different amounts of PFOA analytical standard before the extraction procedure33.Spiked samples underwent the same extraction procedure of unspiked samples and the percentage of recovery R was calculated according to Eq. 1, where Cspike is the known concentration of spiked PFOA, Dspiked is the instrumental (LC–MS) analytical response of the spiked sample (i.e. the “detected” concentration), Dunspiked is the analytical response of the unspiked sample. R was then used in Eq. 2 to calculate the actual values, [PFOA], of PFOA concentrations in unspiked analyzed samples.$$ {text{R}} = 100 times left( {{text{D}}_{{{text{spiked}}}} – {text{D}}_{{{text{unspiked}}}} } right)/{text{C}}_{{{text{spike}}}} $$
(1)
$$ left[ {{text{PFOA}}} right] = 100 times {text{D}}_{{{text{unspiked}}}} /{text{R}} $$
(2)
With the exception of [PFOA]seawater and [PFOA]creek, which are expressed as nanograms per liter (ppt), all other PFOA concentrations are expressed in nanograms per gram of matrix (ppb).The PFOA standard was used for calibration before each batch of analyses and a linear response (R2  > 0.99) was recorded in the concentration range from 0.1 to 1000 ppb. The RSDs on three replicates were below 10%. LOD (0.1 ppb) and LOQ (1.0 ppb) were quantified by IUPAC method. LC–MS analyses were performed in the negative ion-monitoring mode (see Supplementary Information).For the analysis of P. lividus specimens, an estimate of the total PFOA concentration, [PFOA]TOT in ng/g, in each sea urchin has been calculated considering the sampled weight (W) in grams of each matrix (Eq. 3):$$ left[ {{text{PFOA}}} right]_{{{text{TOT}}}} = left( {{text{W}}_{{{text{CF}}}} left[ {{text{PFOA}}} right]_{{{text{CF}}}} + {text{W}}_{{{text{CC}}}} left[ {{text{PFOA}}} right]_{{{text{CC}}}} + {text{W}}_{{{text{GoM}}}} left[ {{text{PFOA}}} right]_{{{text{GoM}}}} } right)/left( {{text{W}}_{{{text{CF}}}} + {text{W}}_{{{text{CC}}}} + {text{W}}_{{{text{GoM}}}} } right) $$
(3)
Water analysisDuring each one of the 3 sampling campaigns, 2 samples of seawater (5 l from Site A and 5 l from site B) and 2 samples of brackish water (5 l from each creek mouths in site B) were collected for a total of 6 seawater samples and 6 brackish water samples.Samples were checked for the presence of PFOA by solid phase extraction (SPE) (see Supplementary Information) followed by LC–MS analysis34.The percentage of recovery, calculated according to Eq. 1, was R = 120%. [PFOA]seawater and [PFOA]creek concentrations (ng/L) were determined from analytical data according to Eq. 2.
Posidonia oceanica analysisA total of 40 samples of leaves were collected from different individuals of P. oceanica (20 samples from site A and 20 samples from site B). Each sample was cut in tiny pieces and homogenized using an agate mortar and pestle, weighed (0.5 g) and transferred to a glass tube for extraction (see Supplementary Information).The percentage of recovery, calculated according to Eq. 1, was R = 70%. [PFOA]P. oceanica concentrations (ng/g) were determined from analytical data according to Eq. 2.Coelomocytes and coelomic fluid analysisThe coelomic fluid, containing also the coelomocyte population, was taken from all the ninety collected specimens (45 per site) by inserting an ultrathin and sharp needle (32G 0.26 mm × 12 mm) of a 1 mL syringe through the peristomal membrane35. All samples were centrifuged at 4 °C and 1500 rpm for 5 min in a 5804R refrigerated centrifuge (Eppendorf, Germany) thus separating the supernatant coelomic fluid (CF) from the coelomocytes (CC). CF and CC were then weighed and placed in different glass tubes for subsequent PFOA extractions (see Supplementary Information).The percentage of recovery, calculated according to Eq. 1, was R = 28% for CF and R = 68% for CC. [PFOA]CF and [PFOA]CC concentrations (ng/g) were determined from analytical data according to Eq. 2.Gonads analysisThe extraction of PFOA from 63 samples of gonads (32 from Site A and 31 from Site B) was performed with LC–MS grade methanol following the same procedure used for extraction from CF and CC (5 mL for samples greater than 0.5 g samples; 2.5 mL for samples between 0.1 g and 0.5 g). In case of undetected PFOA (considered as zero-values in graphics and statistical data treatment), analyses were repeated for confirmation on concentrated sample extracts.Twenty spiked samples were prepared from the most abundant samples of gonads (10 spiked samples per site), by adding 25 µL of an aqueous 1 mg/L stock solution of PFOA to 0.25 g of gonads samples. The percentage of PFOA recovery from gonads, calculated according to Eq. 1, was R = 73%. [PFOA]G concentrations (ng/g) were determined from analytical data according to Eq. 2.Mixed organs analysisIn 27 specimens of sea urchins (13 from Site A and 14 from Site B), the developmental status was not sufficient to collect at least 0.1 g of gonad sample. For these individuals, organs remaining after CF and CC collection, mainly intestine and undeveloped gonads, were mixed together and extracted similarly to the other matrices.Spiked samples were prepared by adding 25 µL of an aqueous 1 mg/L stock solution of PFOA to 0.25 g of mixed organs (MIX) samples. The percentage of PFOA recovery from MIX, calculated according to Eq. 1, was R = 20%. [PFOA]MIX concentrations (ng/g) were determined from analytical data according to Eq. 2.Statistical analyses and graphical data representationThe distribution of PFOA concentrations in all the sampled matrices from collected sea urchins is graphically represented by box and jitter plot (Fig. 2) where the 25–75 percentiles are drawn using a box; minimum and maximum are shown at the end of the thin lines (whiskers), while the median is marked as a horizontal line in the boxfitting. Statistical tests and linear fittings were used to evaluate data significance and correlations (see Supplementary Information).Figure 2Box and jitter plot showing the concentrations of PFOA found in the Coelomic Fluid (CF) Coelomocytes (CC) and Gonads or Mixed organs (GoM), as well as the total PFOA concentration (TOT), in 45 specimens of P. lividus collected from Site A (left side) and in 45 specimens of P. lividus collected from Site B (right side).Full size imageA permutational multivariate analysis of variance PERMANOVA36 was performed to evaluate the differences in the PFOA concentrations between the two groups of sea urchins collected from site A and site B. The experimental design comprised of one factor (Site) two levels (fixed and orthogonal) and four variables corresponding to the concentrations of PFOA in each type of sample analysed (coelomocytes, coelomic fluid, gonad or mixed organs) including the estimated total PFOA concentration. Each term in the analysis was tested by 999 random permutations.Finally, Principal Component Analysis (PCA) (see Supplementary Information: PCA tables and graphs) was performed on a dataset, containing five variables. Specifically sea urchin’s size and PFOA concentrations in each type of sample (CF, CC, and GoM) as well as in the entire sea urchin (TOT), to verify the multivariate nature of data in a relatively small number of dimensions, thus limiting the loss of information. More

1.Chittka, L. & Raine, N. E. Recognition of flowers by pollinators. Curr. Opin. Plant Biol. 9, 428–435 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Raguso, R. A. Flowers as sensory billboards: Progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Goulson, D., Stout, J. C. & Hawson, S. A. Can flower constancy in nectaring butterflies be explained by Darwin’s interference hypothesis?. Oecologia 112, 225–231 (1997).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Goulson, D. & Wright, N. P. Flower constancy in the hoverflies Episyrphus balteatus (Degeer) and Syrphus ribesii (L.) (Syrphidae). Behav. Ecol. 9, 213–219 (1998).Article 

Google Scholar 
5.Von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl. Acad. Sci. 109, 9471–9476 (2012).ADS 
Article 

Google Scholar 
6.Leonard, A. S., Dornhaus, A. & Papaj, D. R. Forget-me-not: Complex floral displays, inter-signal interactions, and pollinator cognition. Curr. Zool. 57, 215–224 (2011).Article 

Google Scholar 
7.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. The role of electrostatic forces in pollination. Plant Syst. Evol. 222, 133–142. https://doi.org/10.1007/bf00984099 (2000).Article 

Google Scholar 
8.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 2: Simulations of pollen capture. Atmos. Environ. 41, 1596–1603 (2007).ADS 
CAS 
Article 

Google Scholar 
9.Erickson, E. Surface electric potentials on worker honeybees leaving and entering the hive. J. Apic. Res. 14, 141–147 (1975).Article 

Google Scholar 
10.Edwards, D. Electrostatic charges on insects due to contact with different substrates. Can. J. Zool. 40, 579–584 (1962).Article 

Google Scholar 
11.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. Pollen and Pollination 133–142 (Springer, 2000).Book 

Google Scholar 
12.Eskov, E. & Sapozhnikov, A. Mechanism of generation and perception of electric fields by honey bees. Biofizika 21, 1097–1102 (1976).CAS 

Google Scholar 
13.Clarke, D., Whitney, H., Sutton, G. & Robert, D. Detection and learning of floral electric fields by bumblebees. Science 340, 66–69 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 1: Measurement of the electrostatic charge on pollen. Atmos. Environ. 41, 1587–1595 (2007).ADS 
CAS 
Article 

Google Scholar 
15.Gan-Mor, S., Schwartz, Y., Bechar, A., Eisikowitch, D. & Manor, G. Relevance of electrostatic forces in natural and artificial pollination. Can. Agric. Eng. 37, 189–194 (1995).
Google Scholar 
16.Colin, M., Richard, D. & Chauzy, S. Measurement of electric charges carried by bees: Evidence of biological variations. J. Bioelectr. 10, 17–32 (1991).Article 

Google Scholar 
17.Pinillos, V. & Cuevas, J. Artificial pollination in tree crop production. Horticult. Rev. 2, 2 (2008).
Google Scholar 
18.Corbet, S. A., Beament, J. & Eisikowitch, D. Are electrostatic forces involved in pollen transfer?. Plant Cell Environ. 5, 125–129 (1982).Article 

Google Scholar 
19.Inouye, D. W., Larson, B. M., Ssymank, A. & Kevan, P. G. Flies and flowers III: Ecology of foraging and pollination. J. Pollin. Ecol. 16, 115–133 (2015).Article 

Google Scholar 
20.Kanstrup, J. & Olesen, J. M. Plant-flower visitor interactions in a neotropical rain forest canopy: Community structure and generalisation level. The Scand. Assoc. Pollin. Ecol. honours knut Fægri 2, 33–42 (2000).
Google Scholar 
21.Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: The importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B Biol. Sci. 282, 20142934 (2015).Article 

Google Scholar 
22.Sakurai, A. & Takahashi, K. Flowering phenology and reproduction of the Solidago virgaurea L. complex along an elevational gradient on M t N orikura, central Japan. Plant Sp. Biol. 32, 270–278 (2017).Article 

Google Scholar 
23.Forup, M. L., Henson, K. S., Craze, P. G. & Memmott, J. The restoration of ecological interactions: Plant–pollinator networks on ancient and restored heathlands. J. Appl. Ecol. 45, 742–752 (2008).Article 

Google Scholar 
24.Solomon, M. & Kendall, D. Pollination by the syrphid fly, Eristalis tenax. (1970).25.Kendall, D., Wilson, D., Guttridge, C. & Anderson, H. Testing Eristalis as a pollinator of covered crops. Long Ashton Res. Stn. Rep. 1971, 120–121 (1971).
Google Scholar 
26.Ohsawa, R. & Namai, H. The effect of insect pollinators on pollination and seed setting in Brassica campestris cv. Nozawana and Brassica juncea cv Kikarashina. Jpn. J. Breed. 37, 453–463 (1987).ADS 
Article 

Google Scholar 
27.Jauker, F. & Wolters, V. Hover flies are efficient pollinators of oilseed rape. Oecologia 156, 819–823 (2008).ADS 
PubMed 
Article 

Google Scholar 
28.Rader, R. et al. Alternative pollinator taxa are equally efficient but not as effective as the honeybee in a mass flowering crop. J. Appl. Ecol. 46, 1080–1087 (2009).Article 

Google Scholar 
29.Kalmijn, A. J. The electric sense of sharks and rays. J. Exp. Biol. 55, 371–383 (1971).CAS 
PubMed 
Article 

Google Scholar 
30.Clarke, D., Morley, E. & Robert, D. The bee, the flower, and the electric field: Electric ecology and aerial electroreception. J. Comp. Physiol. A. 203, 737–748 (2017).Article 

Google Scholar 
31.Greggers, U. et al. Reception and learning of electric fields in bees. Proc. R. Soc. B Biol. Sci. 280, 20130528 (2013).Article 

Google Scholar 
32.Casas, J. & Dangles, O. Physical ecology of fluid flow sensing in arthropods. Annu. Rev. Entomol. 55, 505–520 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Tautz, J. & Rostás, M. Honeybee buzz attenuates plant damage by caterpillars. Curr. Biol. 18, R1125–R1126 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Bathellier, B., Steinmann, T., Barth, F. G. & Casas, J. Air motion sensing hairs of arthropods detect high frequencies at near-maximal mechanical efficiency. J. R. Soc. Interface 9, 1131–1143 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Newland, P. L. et al. Static electric field detection and behavioural avoidance in cockroaches. J. Exp. Biol. 211, 3682–3690 (2008).PubMed 
Article 

Google Scholar 
36.Sutton, G. P., Clarke, D., Morley, E. L. & Robert, D. Mechanosensory hairs in bumblebees (Bombus terrestris) detect weak electric fields. Proc. Natl. Acad. Sci. 113, 7261–7265 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Wędzony, M. & Filek, M. Changes of electric potential in pistils of Petunia hybrida Hort. and Brassica napus L. during pollination. Acta Physiol. Plantarum 20, 291–297 (1998).Article 

Google Scholar 
38.Stout, J. C. & Goulson, D. The use of conspecific and interspecific scent marks by foraging bumblebees and honeybees. Anim. Behav. 62, 183–189 (2001).Article 

Google Scholar 
39.Weiss, M. R. Floral color change: A widespread functional convergence. Am. J. Bot. 82, 167–185 (1995).Article 

Google Scholar 
40.Waser, N. M. & Price, M. V. Pollinator behaviour and natural selection for flower colour in Delphinium nelsonii. Nature 302, 422 (1983).ADS 
Article 

Google Scholar 
41.Shimozawa, T., Murakami, J. & Kumagai, T. Sensors and Sensing in Biology and Engineering 145–157 (Springer, 2003).Book 

Google Scholar 
42.Khan, S. & Hanif, H. Diversity and fauna of hoverflies (Syrphidae) in Chakwal, Pakistan. Int. J of Zool. Stud. 1, 22–23 (2016).
Google Scholar 
43.Khan, S. A. & Hanif, H. First record and redescription of Cheilosia albipila syrphid flies from Punjab, Pakistan. Int. J. Zool. Res. 1, 2 (2016).
Google Scholar 
44.Shehzad, A. et al. Faunistic study of hover flies (Diptera: Syrphidae) of Pakistan. Orient. Insects 51, 197–220 (2017).Article 

Google Scholar 
45.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of eristalis tenax hoverflies for research studies. JoVE https://doi.org/10.3791/57711 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Gilbert, F. S. Foraging ecology of hoverflies: Morphology of the mouthparts in relation to feeding on nectar and pollen in some urban species. Ecol. Entomol. 2, 2 (1981).
Google Scholar 
47.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of Eristalis tenax hoverflies for research studies. J. Vis. Exp. JoVE 2, 2 (2018).
Google Scholar 
48.Hogg, B. N., Bugg, R. L. & Daane, K. M. Attractiveness of common insectary and harvestable floral resources to beneficial insects. Biol. Control 56, 76–84 (2011).Article 

Google Scholar 
49.McGonigle, D. F., Jackson, C. W. & Davidson, J. L. Triboelectrification of houseflies (Musca domestica L.) walking on synthetic dielectric surfaces. J. Electrostat. 54, 167–177 (2002).Article 

Google Scholar 
50.Koh, K., Montgomery, C., Clarke, D., Morley, E. & Robert, D. in Journal of Physics: Conference Series. 012001 (IOP Publishing).51.Rycroft, M., Israelsson, S. & Price, C. The global atmospheric electric circuit, solar activity and climate change. J. Atmos. Solar Terr. Phys. 62, 1563–1576 (2000).ADS 
CAS 
Article 

Google Scholar 
52.Whitney, H. M., Dyer, A., Chittka, L., Rands, S. A. & Glover, B. J. The interaction of temperature and sucrose concentration on foraging preferences in bumblebees. Naturwissenschaften 95, 845–850 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Stanković, B. & Davies, E. Both action potentials and variation potentials induce proteinase inhibitor gene expression in tomato. FEBS Lett. 390, 275–279 (1996).PubMed 
Article 

Google Scholar  More