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MaterialsInformation on wood volume and the physical environment of the plots were obtained from the US Forest Service Forest Inventory and Analysis (USFS-FIA)22. The FIA database categorizes each plot into one of 33 forest groups, but 23 groups do not have sufficient data in the control period (before 1990) to enable robust matching and so were dropped from this study. As a result, several western forest groups (e.g., Douglas-fir) were not included in our study. The following ten forest groups [(1) Loblolly/Shortleaf Pine, (2) Slash/Shortleaf Pine, (3) White/Red/Jack Pine, (4) Spruce/Fir, (5) Elm/Ash/Cottonwood, (6) Maple/Beech/Birch, (7) Oak/Hickory, (8) Oak/Gum/Cypress, (9) Aspen/Birch, and (10) Oak/Pine] all had more than 5000 observations and large numbers of observations both from before 1990 and from 2000 on. Data for the 48 conterminous states from evaluation years between 1968 and 2018 were included in the study. We limited our analysis to plots with trees from 1 to 100 years of age, resulting in trees that had been planted somewhere between 1869 and 2018—a period during which atmospheric CO2 increased from roughly 287 to more than 406 ppm32,33,34. The geographic distribution of the ten forest groups presented in Fig. 2 shows in orange all counties in which the USFS recorded in at least one year between 1968 and 2018 the presence of a plot of the respective forest group that met the age requirements for inclusion in this study. Precipitation and temperature data were obtained from the PRISM Climate Group41.MethodsResults in Tables 1 and 2 are based on estimated exponential tree-volume functions of the generalized form shown in Eq. 1. The left-hand side is the natural log of the volume per hectare in the central stem of trees on each plot in cubic meters. Volume is assumed to be a function of age, the logged cumulative lifetime concentration of CO2, and other variables, including plot-specific variables that vary across plots but not time (Xi), weather variables that vary across plots and time (Wit), and time-specific fixed effects that vary across time but not plots (Et).$${{{{mathrm{Ln}}}}},{left(frac{{{{{{rm{Volume}}}}}}}{{{{{{rm{Hectare}}}}}}}right)}_{it}= ,alpha+{beta }_{0}frac{1}{{{{{{{rm{Age}}}}}}}_{{{{{{rm{it}}}}}}}}+{beta }_{1},{{{{mathrm{Ln}}}}}({{{{{rm{CumCO}}}}}}2{{{{{{rm{Life}}}}}}}_{{{{{{rm{t}}}}}}})\ +{beta }_{2}{{{{{{rm{X}}}}}}}_{{{{{{rm{i}}}}}}}+{beta }_{3}{{{{{{rm{W}}}}}}}_{{{{{{rm{it}}}}}}}+{beta }_{4}{{{{{{rm{E}}}}}}}_{{{{{{rm{t}}}}}}}+{varepsilon }_{it}$$
(1)
The nonparametric smearing estimate method was used to transform logged-volume results into a volume in cubic meters per hectare42. The climate variables, obtained from the PRISM Climate Group41 and described in Supplementary Table 1, enter as cubic polynomials of the lifetime seasonal temperature and precipitation averages that a plot of a given age at a given time experienced.The variable for atmospheric carbon was constructed as the logarithmic transformation of the sum of yearly atmospheric CO2 exposures over the lifetime of the stand. Other site-specific covariates were obtained from the FIA data (Supplementary Table 2), such as the availability of water, the quality of the soil, the photoperiod of the plot, whether disturbances had impacted the land, and whether the land was publicly or privately owned43,44.The time-specific fixed effects (Et) in the model control for episodic factors like nitrogen deposition and invasive species, which are correlated with time but cannot be observed over space for the whole time period. These time-dummy variables account for underlying, unobservable systematic differences between the 21st-century period when atmospheric CO2 was higher and the pre-period when levels were much lower. Controlling for these factors aids the identification of the impact of elevated CO2, which varies annually.A potential concern is that wood volume changes over time could be related to an increased number of trees per hectare rather than increased wood volume of the trees. To assess whether controls for the stocking condition were needed, we examined data on the number of trees per acre of each forest type. First, we looked at a group of southern states (Supplementary Table 3) and found double-digit percentage changes in tree stocking between 1974 and 2017 for seven of the nine forest groups. However, the changes were mixed, with four having increased tree density and five decreasing tree density. The FIA data do not record the Aspen/Birch forest group as present in these southern states in these evaluations.Examination of a group of northern states involved a comparison of the average stocking conditions around 1985 with those in 2017. The changes in tree density for these forest types (Supplementary Table 4) were also split with four showing increased stocking and five having less dense stocking. The change for Loblolly/Shortleaf pine was relatively large, with stocking density increasing by 27.2%. Slash/Longleaf was not recorded as present in these states in these evaluations.Next, we analyzed changes, over the period from around 1985 to 2017, in all states east of the 100th meridian, as those states comprised the bulk of the data in our study (Supplementary Table 5). Results for seven of the ten forest groups showed a less dense composition. Loblolly/Shortleaf pine again was shown to have become more densely stocked, with an increase of 13.2%.The last check included all of the 48 conterminous states and compared changes in stocking conditions from years around 1985 to 2017 (Supplementary Table 6). Seven of the ten forest groups showed decreased stocking density over time. Not surprisingly (because most Loblolly/Shortleaf is located in the Eastern US), the change in Loblolly/Shortleaf pine density is the same for this check as was shown in the results in Supplementary Table 5. Based on the results from all these comparisons and given that stocking density has changed over time, we controlled for it both in the matching and in the multivariate-regression analysis.Genetic matching (GM), the primary approach used for this analysis, combines propensity score matching and Mahalanobis matching techniques45. The choice of GM was made after initially considering other approaches, such as nearest-neighbor propensity score matching with replacement and a non-matching, pooled regression approach. These three options were tested on the samples for Loblolly/Shortleaf pine and Oak/Hickory, and the regression results are presented in Supplementary Data 3-4.The results across these different approaches were quite similar, suggesting that the results are not strongly driven by methodological choice. We focused on matching rather than a pooled regression approach to help reduce bias and provide estimates closer to those that would be obtained in a randomized controlled trial. When choosing the specific matching approach, we considered that standard matching methods are equal percent bias reducing (EPBR) only in the unlikely case that the covariate distributions are all roughly normal46 and that EPBR may not be desirable, as in the case where one of two covariates has a nonlinear relationship with the dependent variable16. We also noted that GM is a matching algorithm that at each step minimizes the largest bias distance of the covariates24 and that GM has been shown to be a more efficient estimator than other methods like the inverse probability of treatment weighting and one-to-one greedy nearest-neighbor matching24,47,48,49. Additionally, when the distributions of covariates are non-ellipsoidal, this nonparametric method has been shown to minimize bias that may not be captured by simple minimization of mean differences50. Lastly, as sample size increases, this approach will converge to a solution that reduces imbalance more than techniques like full or greedy matching48,51,52. Given the support that this choice has in the literature, we decided to employ GM to create all the matched data used in this study using R software53.Artificial regeneration of forest stands, noted as planting throughout the text is used as the main proxy for the impact of forest management. The other indicator of management activity is what can be described as interventions, which are a range of human on-site activities that the USFS details22. We define unmanaged land as stands with natural regeneration and where no interventions occurred on the plot.To create Table 1, we first excluded all plots on which there had been either planting activities or some type of human intervention. Then, we created treatment and control groups by forming two time periods separated by an intervening period of ten years to ensure a more than a marginal difference between the groups in terms of lifetime exposure to atmospheric CO2. The control period used forest plot data sampled between 1968 and 1990, and the treatment period used forest plots sampled between 2000 and 2018. Note that even though the earlier period contains more years, there are fewer overall observations.Matches were then made to balance the treatment and control groups based on the following observable covariates: (1) Seasonal Temperature, (2) Seasonal Precipitation, (3) Stocking Condition, (4) Aspect, (5) Age, (6) Physiographic Class, and (7) Site Class. The propensity score was defined as a logit function of the above covariates to generate estimates of the probability of treatment. Calipers with widths less than or equal to 0.2 standard deviations of the propensity score were also employed to remove at least 98% of bias49.Balance statistics for the primary covariates are presented in Supplementary Data 1–2 and show a strong balance for all covariates across all forest groups. Thus for each forest group, our sample of plots includes control plots (pre-1990) and treatment plots (post-2000) that are comparable (balanced) in climate and other biophysical attributes.After trimming our sample using this matching process and obtaining strongly balanced matches, we turned to regression analysis, where we employed Stata software54. To confirm that we had the most appropriate model structure, tests of the climate and atmospheric carbon variables were undertaken using various polynomial forms, and the main variable of interest, atmospheric carbon, was tested both using a linear lifetime cumulative CO2 variable and a logarithmic transformation of that variable. Results (Supplementary Data 5–10) show that the climate variables were not improved with complexity beyond cubic form. Moreover, selection tools, like the Akaike and Bayesian information criterion, favored the cubic choice, and so we utilized the cubic formulation throughout this study. Results for the CO2 variable were similar in both sign and significance for the linear and logged form. We use the logged form as it allows easier interpretation of the effect, suppresses heteroscedasticity, and removes the assumption that each unit increase in CO2 exposure will have a linear (constant) effect on volume.The estimated effect of CO2 exposure for each forest group (Supplementary Data 12–21) was estimated using alternate specifications of the independent variables included in Eq. 1. For each forest type, the Model (1) specification (Eq. 2) is the basis for the results presented in Table 1. The β0 coefficient details the impact on the volume of the main variable of interest, atmospheric carbon.$${{{{mathrm{Ln}}}}}left(frac{volume}{hectare}right)= alpha+{beta }_{0},{{{{mathrm{Ln}}}}}({{{{{{rm{Lifetime}}}}}}{{{{{rm{CO}}}}}}}_{2})+{beta }_{1}frac{1}{{{{{{rm{Age}}}}}}}+{beta }_{2}{{{{{rm{Site}}}}}},{{{{{rm{Class}}}}}}\ +{beta }_{3}{{{{{rm{Seasonal}}}}}},{{{{{rm{Temperature}}}}}}+{beta }_{4}{{{{{rm{Seasonal}}}}}},{{{{{{rm{Temp}}}}}}}^{2}+{beta }_{5}{{{{{rm{Seasonal}}}}}},{{{{{{rm{Temp}}}}}}}^{3}\ +{beta }_{6}{{{{{rm{Seasonal}}}}}},{{{{{rm{Precipitation}}}}}}+{beta }_{7}{{{{{rm{Seasonal}}}}}},{{{{{{rm{Precip}}}}}}}^{2}+{beta }_{8}{{{{{rm{Seasonal}}}}}},{{{{{{rm{Precip}}}}}}}^{3}\ +{beta }_{9}{{{{{rm{Stocking}}}}}}+{beta }_{10}{{{{{rm{Disturbances}}}}}}+{beta }_{11}{{{{{rm{Physiographic}}}}}},{{{{{rm{Class}}}}}}+{beta }_{12}{{{{{rm{Aspect}}}}}}\ +{beta }_{13}{{{{{rm{Slope}}}}}}+{beta }_{14}{{{{{rm{Elevation}}}}}}+{beta }_{15}{{{{{rm{Latitude}}}}}}+{beta }_{16}{{{{{rm{Longitude}}}}}}+{beta }_{17}{{{{{rm{Ownership}}}}}}\ +{beta }_{18}{{{{{rm{Time}}}}}},{{{{{rm{Dummies}}}}}}+{beta }_{19}{{{{{rm{Seasonal}}}}}},{{{{{rm{Vapor}}}}}},{{{{{rm{Pressure}}}}}},{{{{{rm{Deficit}}}}}}\ +{beta }_{20}{{{{{rm{Length}}}}}},{{{{{rm{of}}}}}},{{{{{rm{Growing}}}}}},{{{{{rm{Season}}}}}}+{{{{{rm{varepsilon }}}}}}$$
(2)
After estimating Eq. 2 for each forest type individually (Supplementary Data 12–21), all plots were pooled across forest groups, with additional forest-group dummy variables, to estimate a general tree-volume function (Supplementary Data 22).Our main Model (1) results are provided in Supplementary Data 12–22, along with three additional models that assess the robustness of the elevated CO2 effect to different specifications. The simplest specification, Model (4), included only stand age, CO2 exposure, and a time-dummy variable. Model (3) took the Model (4) base and added in an array of site-specific variables, including those for the climate. Model (2) was similar to Model (1) in that it included the impact of vapor pressure deficit and the length of the growing season on the variables included in Model (3), but it differed from Model (1) in that it tested an alternate approach to capturing the impact of underlying, unobservable systematic differences like nitrogen deposition.Using the estimated coefficients from the preferred Model (column 1) specification (Eq. 2), the estimated change in growing-stock volume between two CO2 exposure scenarios was calculated at ages 25, 50, and 75. The first scenario examined CO2 exposure up to 1970 (that is, when calculating growing-stock volume for a 25-year-old stand, the CO2 exposure would have the summation of the yearly values for the years from 1946 to 1970 [310 to 326 ppm CO2]). The second scenario examined CO2 exposure up to 2015 (that is, when calculating growing-stock volume for a 25-year-old stand, the CO2 exposure was the summation of the yearly values for the years from 1991 to 2015 [347 to 401 ppm CO2])32,33,34. In both scenarios, climate variables were maintained at their 1970 exposure levels, covering the same historical years (e.g., for a 25-year-old stand, 1946 to 1970 were the years of interest), while using seasonal, not annual values and calculating average values, not lifetime summations.Forest dynamics in the Western US differ from those in the East (e.g., generally drier conditions; greater incidence of large wildfires) and as most of the observations for this study are of forest groups located in the 33 states that the USFS labels as comprising the Eastern US, robustness tests were conducted to assess whether results would differ were only eastern observations utilized. Three forest groups [(1) Loblolly/Shortleaf pine, (2) Oak/Gum/Cypress, and (3) Slash/Longleaf pine] have no observations in the Western US. A fourth, White/Red/Jack Pine, has a slight presence in a few Western states, but no western observations were selected in the original matching process (Supplementary Data 2). For the other six forest groups, all observations from Western US states were dropped. As can be seen from Fig. 2, this had the biggest impact on Aspen/Birch and Elm/Ash/Cottonwood. With this data removed, the GM matching algorithm was again used. Balance statistics are presented in Supplementary Data 23 and again show a strong balance for all covariates across all forest groups. With matches made, the average treatment effect on the treated was estimated using the Model (1) specification used to create Table 1. Regression results are presented in Supplementary Data 24,25, and a revised version of Table 1 for just the observations from the Eastern US is presented as Supplementary Table 7.As an additional robustness check on the results in Table 1, we tested an alternative functional form of the volume function. This alternative volume function is shown in Eq. 3. It has a similar shape as the function used for the main results in the paper, however, this equation cannot be linearized with logs in a similar way. Thus, it was estimated with nonlinear least squares, using the matched samples of naturally regenerated forests for individual forest groups, as well as the aggregated sample.$$frac{{{{{{mathrm{Volume}}}}}}}{{{{{{mathrm{Hectare}}}}}}}=a/(b+exp (-c,ast ,{{{{{rm{Age}}}}}}))$$
(3)
We began by estimating two separate growth functions, one for the pre-1990 (low CO2) period and one for the post-2000 (high CO2) period using Eq. 3. That is, observations from the pre-1990 (low CO2) control period and from the post-2000 (high CO2) treatment period were handled in separate regressions. For this initial analysis with the nonlinear volume function, we did not control for CO2 concentration or other factors that could influence volume across sites (e.g., weather, soils, slope, aspect), and thus, results likely show the cumulative impact of these various factors. Using the regression results (Supplementary Data 26), we calculated the predicted volume for the pre-1990 and post-2000 periods and compared the predicted volumes (Supplementary Table 8).Next, we tested this yield function on the combined sample (containing both control and treatment observations) and all forest groups. Here the model was expanded to better identify the impact of elevated CO2 by including all covariates. Instead of using a dummy variable for each forest group, though, a single dummy variable was used to differentiate hardwoods from softwoods. Once again, the equation was logarithmically transformed for ease of comparison with the results presented in Table 1. All covariates were originally input, but those which were not significant were removed. That process yielded the functional form shown in Eq. 4. Results for the regression are presented in Supplementary Data 27. The predicted change in volume due to CO2 fertilization from 1970 to 2015 is shown in Supplementary Table 9.$$frac{{{{{{mathrm{Volume}}}}}}}{{{{{{mathrm{Hectare}}}}}}}= big(a0+a1,ast ,{{{{{rm{Time}}}}}},{{{{{rm{Dummy}}}}}}+a2,ast ,{{{{mathrm{Ln}}}}}({{{{{rm{LifetimeCO}}}}}}2)+a{3},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Temperature}}}}}})\ +a{4},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Precipitation}}}}}})+a{5},ast ,{{{{{rm{Site}}}}}},{{{{{rm{Class}}}}}}\ +a6,ast ,{{{{{rm{Physiographic}}}}}},{{{{{rm{Dummy}}}}}}+a{7},ast ,{{{{{rm{Aspect}}}}}},{{{{{rm{Dummy}}}}}}+a{8},ast ,{{{{{rm{Stocking}}}}}},{{{{{rm{Code}}}}}}\ +a9,ast ,{{{{{rm{Disturbances}}}}}}+a{10},ast ,{{{{{rm{Hardwood}}}}}}/{{{{{rm{Softwood}}}}}},{{{{{rm{Dummy}}}}}}left.right) /left(right.b{0}+b{1},ast ,{{{{{rm{Time}}}}}},{{{{{rm{Dummy}}}}}}\ +b{2},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Lifetime}}}}}},C{O}_{2})+b3,ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Temperature}}}}}})\ +b{4},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Precipitation}}}}}})+b5,ast ,{{{{{rm{Site}}}}}},{{{{{rm{Class}}}}}}\ +b6,ast ,{{{{{rm{Physiographic}}}}}},{{{{{rm{Dummy}}}}}}+b{7},ast ,{{{{{rm{Aspect}}}}}},{{{{{rm{Dummy}}}}}}+b8,ast ,{{{{{rm{Stocking}}}}}},{{{{{rm{Code}}}}}}\ +b9,ast ,{{{{{rm{Disturbances}}}}}}+b{10},ast ,{{{{{rm{Hardwood}}}}}}/{{{{{rm{Softwood}}}}}},{{{{{rm{Dummy}}}}}}\ +exp left(right.-left(right.c{0}+c{1},ast ,{{{{{rm{Time}}}}}},{{{{{rm{Dummy}}}}}}+c{2},ast ,{{{{{rm{Lifetime}}}}}},{{{{{{rm{CO}}}}}}}_{2}\ +c{3},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Temperature}}}}}})+c{4},ast ,{{{{mathrm{Ln}}}}}({{{{{rm{Seasonal}}}}}},{{{{{rm{Precipitation}}}}}})+c{5},ast ,{{{{{rm{Site}}}}}},{{{{{rm{Class}}}}}}\ +c{6},ast ,{{{{{rm{Physiographic}}}}}},{{{{{rm{Dummy}}}}}}+c{7},ast ,{{{{{rm{Aspect}}}}}},{{{{{rm{Dummy}}}}}}+c{8},ast ,{{{{{rm{Stocking}}}}}},{{{{{rm{Code}}}}}}\ +c{9},ast ,{{{{{rm{Disturbances}}}}}}+c{10},ast ,{{{{{rm{Hardwood}}}}}}/{{{{{rm{Softwood}}}}}},{{{{{rm{Dummy}}}}}}left.right),ast ,{{{{{rm{Age}}}}}}left.right)left.right)$$
(4)
As the results using the nonlinear volume functions were similar in sign and magnitude to the multivariate-regression results and as the practice of matching and then running a multivariate-regression represents a doubly robust econometric approach that has been shown to yield results that are robust to misspecification in either the matching or the regression model47,55,56,57, the main text results are based on estimations utilizing multivariate-regression analysis post-matching.To develop Table 2, which compares naturally regenerated stands with planted stands, we used the same general approach as was used to create Table 1. The analysis and comparison of planted and naturally regenerated stands was conducted only for stands with enough observations of both to make a comparison: White/Red/Jack, Slash/Longleaf, and Loblolly/Shortleaf pine. We followed the same matching and regression procedures as above, but conducted the matching separately for naturally regenerated and planted stands. We also limited the data to stands less than or equal to 50 years of age, as there are few planted stands of older ages due to the economics of rotational forestry35,36,37,38,39,40. Balance statistics for the matched samples are presented in Supplementary Data 28–30. Again, the matching process resulted in a good balance in observable plot characteristics, which implies that we achieved comparable treatment and control plots.Using the matched data, we estimated the same regression as in Eq. 2. Estimation results, which use the Model (2) specification from Supplementary Data 19–21 that was used with the data for these three forest groups from ages 1–100, are presented in Supplementary Data 30–32. A comparison of the parameter estimates on the natural log of lifetime CO2 exposure between the results for ages 1–50 (from Supplementary Tables 31–33) and those for ages 1–100 (from Supplementary Data 19–21) is presented in Supplementary Table 10.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Essential oils yield and compositionAmong the 14 seed sample populations collected, the content of EOs among populations ranged from 3.16 to 5% (v/w). The lowest and highest EO content was determined in Ghayen (P2) and Fars (P8) populations, respectively (Table 1). Similarly, the percentage of EO in ajwain samples has been reported from Pakistan 3.5–5.2%31, India 2–4%4,32, and Iran 2–6%5,33,34,35. EO yield may vary in plants depending on species, quality (chemotype of the plant), condition (fresh or dry), the layout of plant material (e.g., leaf/stem ratio), harvest time, and also extraction method15,16,36. The EO yield is an important quality factor to bring medicinal plants to the pharmaceutical, and food industries. Seed EO constituents of the 14 ajwain populations and chromatograms are shown in Table 1 and Fig. S1. In this study, eleven constituents were identified in all 14 populations, and thymol was the major constituent ranging from 59.92 to 96.4 percent (Fig. S2). Other major constituents were p-cymene (0.55–21.15%), γ-terpinene (0.23–17.78%), and carvacrol (0.41–2.77%) among populations studied. The highest content of thymol (96.4%) and its structural isomer carvacrol (2.77%) were found in the Ghayen population (P2). Additionally, the lowest thymol content was detected in the Isfahan population (P13) (59.92%). The highest (17.78%) and lowest (0.23%) γ-terpinene content was found in the Isfahan (P13) and Ghayen (P2) populations, respectively. The Birjand population (P3) displayed the highest p-cymene content (21.15%) and (P2) showed the lowest content (0.55%).Table 1 The essential oil composition of the fourteen Trachyspermum ammi populations.Full size tableThe GC–MS spectra obtained from the Hamedan population (P7) are represented in the graphical diagram in Fig. 1. According to our results, the Ghayen population (P2) has the highest levels of thymol and carvacrol and lowest levels of p-cymene and γ-terpinene. So, a higher rate of precursors (γ-terpinene and p-cymene) to final products (thymol/carvacrol) can be converted in isolated EO35. According to the biosynthetic pathway, γ-terpinene precursor converts to thymol and carvacrol during the developmental stages37.Figure 1Represent of graphical design of the present research.Full size imageIn this context, EO compositions of ajwain have been reported from various geographical areas. According to the chemical composition of ajwain oils, major constituents of thymol, γ-terpinene, and p-cymene11,12,33,35 carvone, limonene, and dillapiole13 and carvacrol and p-cymene14 have been documented. Up to now, the high-thymol content populations from Iran were between 34 to 55%33 48.8 to 61.435, and 65.411. However, no chemotype of the plant EO has been reported with a very high percentage of thymol ( > 90%). Thymol and carvacrol percentages of seed EO of 14 populations are shown in Fig. 2. As can be seen in this figure, populations P2 and P8 have the highest thymol content (more than 90% of EO). The presence of a high percentage of thymol in the P8 and P2 can be industrially valuable. Chemotypes are named based on the main constituents in EO within single botanical species38. Normally ajwain oils on the market are those rich in thymol and/or carvacrol with strong antibacterial properties and high antioxidant potential. High purity thymol is interested in the market and will not have the subsequent purification costs. Therefore, chemotypes P2 and P8 with a high percentage of thymol 96.4. 90.57% can be significant respectively.Figure 2Thymol + carvacrol (%) in EO in studied populations. Chemotype determined with hierarchical cluster analysis (HCA).Full size imageEstimation of phyto-constituents of extractSignificant differences were obtained among the population for total phenolic (TPC), total flavonoid (TFC), and total coumarin contents (TCC) (P ≤ 0.01) (Table 2). Natural phenolic compounds are including simple phenolics, phenolic acids, flavonoids, coumarins, tannins, stilbenes, curcuminoids, lignans, quinones, and others39. Phenolic compounds and flavonoids are major bioactive components in medicinal plants and thus can comprise an essential part of the human diet40. The present study assessed the total phenolic, flavonoid, and coumarin contents of ajwain populations, and the results are presented in Fig. 3A–C. Up to now, no studies have reported total phenol, flavonoid, and coumarin contents of Iranian ajwain populations.
Table 2 Analysis of variance for nine phytochemical traits in fourteen populations of Trachyspermum ammi.Full size tableFigure 3Phyto-constituents analysis of seed samples of 14 studied populations of Trachyspermum ammi (A); total phenolic content (TPC) as mg Gallic acid/g DW equivalent. (B) Total flavonoid content (TFC) quantified based on mg Quercetin/g DW. (C) Total coumarin (mg Coumarin E/g DW).Full size imageTotal phenol content (TPC)The total phenolic content in the evaluated extracts varied from 26.91 (P13) in the Isfahan population to 43.20 (P2) mg GAE/g DW in the Ghayen population, Results demonstrated that TPC in the populations varied as the following the order P2  > P10  > P8  > P1  > P11  > P14  > P6, P9  > P3, P5  > P4  > P7  > P12  > P13 (Fig. 3A). In the few evaluable sources, the total phenolic content of ajwain seeds extracted with CHCl3: MeOH (1: 2) solvent was 69 mg/g DW41. In the present study, the highest phenol content (43.2 mg GAE/g DW) was recorded in the P2 population. The difference in TPC with the available report may be due to genetic diversity and differences in extraction methods. According to the presence of apolar thymol in the seed structure, a combination of polar and non-polar solvents to extract compounds may optimize the extraction performance. Various environmental conditions in different places influence the content and metabolic profile of phenolic compounds in plant populations. It seems that high temperature and high UV radiation levels, and differences in genotypes are the reasons why the Isfahan population has a high content of TPC15,16.Total flavonoid content (TFC)Analysis of variance showed a significant difference in TFC content at levels P ≤ 0.01. The total flavonoid contents ranged from 4.45 (P7) in the Hamedan population to 8.03 (P8) mg QE/g DW in the Fars population. P6 and P10 with 7.38 mg QE/g DW were also among the high content TFC populations (Fig. 3B). It seems that the reason for the lack of total flavonoids in Hamedan is due genetic differences and the low temperature of this region compared to other regions. Also, the reason for the high level of flavonoids in the Fars population may be due to genetic differences and high temperatures during the growing period. It has been reported that seeds and spurts of ajwain contain 0.58 and 1.15 mg/ g FW of TFC respectively42. Also, TFC of methanolic extract of Anethum graveolens L. (dill) seeds from the Apiaceae family have been reported to be 5.07 (mg QE /g)43. Flavonoid accumulation with many protective roles may be influenced by the combination of genetics (i.e., adaptation to local conditions) and environmental effects (i.e., phenotypic plasticity)44,45. Flavonoid accumulation rates among geographically different ajwain populations concerning climate can be correlated positively with temperature and UV-B radiation and negatively with precipitation (Chalker-Scott, 1999; Koski and Ashman, 2015).Total coumarin content (TCC)The TCC content of the T. ammi populations examined ranges from 0.079 (P12) to 0.26 (P1) mg coumarin equivalent to dry weight. The highest coumarin content was obtained from the methanolic extract of Kalat (P1) (0.260 mg CE/g DW) and the lowest value of coumarin was recorded for the population of Ardabil (Fig. 3C). Seed coumarin levels in populations can result from genetic and environmental differences. It seems that coumarin accumulation is decreased due to the coolness condition in Ardabil city during the seed maturation stage. Ajwain is a coumarin-rich source of coumarins (umbelliferone, scopoletin, xanthotoxin, bergapten) mostly found in its sprouts46. However, no literature source was found to report the amount of total coumarin in ajwain seeds. These compounds have valuable medicinal properties, including edema reduction and possible anticancer activity47 Furthermore, they are widely used as a flavoring in foods and pastries. Human exposure to coumarin from the diet has been calculated to be around 0.02 mg/kg/day and its maximum daily intake was estimated to be 0.07 mg/kg BW/day48.Free radical scavenging effects and antioxidant activity of essential oils and extractsThe antioxidant activities of EOs and extracts were assessed using the DPPH, FRAP free-radical scavenging, and total antioxidant capacity (TAC) assays (Fig. 4A–C).Figure 4Antioxidant activities of methanolic extracts and essential oils obtained from Trachyspermum ammi seed populations and seven antioxidant standards (A); Antioxidant activity (DPPH) IC50 (µg/ml) (B); antioxidant activity (FRAP) quantified by µmol Fe+2/g DW (C); total antioxidant capacity (TAC) quantified by mg Ascorbic acid equivalent (AAE).Full size imageIn the DPPH assay, the samples were capable to decrease the DPPH free radical to evaluate their in vitro antioxidant activity. Analysis of variance on DPPH IC50 showed a significant difference in antioxidant activity of EOs and extracts among populations (P  BHT  > RU. Also, this value ranged from 8.3 to 16.6 among EO samples with the highest value in P2. TCA values in extracts were recorded in the range of 1.83–4.59 with the highest value obtained in P11. Other detailed information is shown in Fig. 4C.Antibacterial activityThe antibacterial activity of ajwain EOs was evaluated against two antibiotic resistance bacteria and their ability was compared with Cefixime as a standard. In the present study, we tried to use both gram-positive bacteria and gram-negative bacteria as samples. Staphylococcus aureus is a gram-positive pathogenic and antibiotic-resistant bacteria. It is also one of the most common causes of nosocomial infections. Also, Escherichia coli is available and inexpensive, and easily cultured in the laboratory. It is one of the most common causes of urinary tract infections. Gram-negative bacteria are also resistant to antibiotics and are an important species in the field of microbiology. One of the main problems in the field of microbiology is the resistance of microbes to antibiotics and so introducing new antibiotics is necessary53. The reasons for using Cefixime in the present study are due to its widely used, great therapeutic power, and effectiveness against a wide range of microbes.In this study, EOs exhibited bacteriostatic activities against S. aureus (0.06–64 µg/mL) and E. coli (1–64 µg/mL) (Table 3). High thymol content EO (P2) showed high antibacterial activity (MIC = 0.06 µg/mL) against S. aureus. Also, the EO from the Isfahan population (P13) showed the lowest antibacterial activity with the highest MIC value (64 µg/mL). In the present study, the mean MIC was not significantly different on gram-negative and positive bacteria, and populations with high thymol had a high antibacterial ability, indicating the antibacterial effects of thymol. Some researchers have evaluated the antimicrobial activity of ajwain oil14,54,55. Thymol and carvacrol were found to be more effective in killing bacteria3,4,5,6,7,9. The antibacterial properties of natural products, such as essential oils and their components, are widely explored by both industrial and academic fields56. The antibacterial activity of the EOs is dependent on the composition and concentration, type, and dose of the target microorganism57. The high antibacterial potential of cumin essential oil compared to Ferula essential oil has already been identified due to the high ratio of phenolic monoterpene compounds to other monoterpenes58. It seems that the antibacterial effects of C. copticum are also mainly due to the presence of phenolic monoterpenes such as thymol, carvacrol, p-cymene, and γ-terpinene. Therefore, ajwain EO can be used as a natural agent with antibacterial properties in the food industry and the treatment of infectious diseases, especially antibiotic-resistant strains.Table 3 Minimal Inhibitory Concentrations (MIC) essential oil Iranian 14 populations of Trachyspermum ammi against Escherichia coli and Staphylococcus aureus.Full size tableHierarchical cluster analysis (HCA) of essential oil constituentsHCA was performed by using the 11 identified compounds and 14 populations (Fig. 5A). All used populations were divided into two clusters; Cluster I included P4, P6, P7, P10, P11, P12, P13, and P14 and cluster II consist of P1, P2, P5, P8, and P9 samples. In cluster I the major constituents were thymol (59.92–72.86), p-cymene (15.66–21.15), and γ-terpinene (10.22–17.78). In the second cluster thymol (80.09–96.4) and carvacrol (0.5–2.77) were the major constituents. Cluster analysis can classify studied populations into several groups, according to the chemical composition by ‘magnifying’ their similarities59. Forasmuch as, plant sources from environmentally different origins led to the emergence of new chemotypes to baring domestication and cultivation to obtain uniform chemical plants along with appropriate agricultural features60.Figure 5(A) Heat-map diagram of two-way hierarchical cluster analysis (HCA) of fourteen Trachyspermum ammi populations based on 11 essential oil constituents quantified by GC and GC–MS. Blue color with a great positive share and red color with a great negative share affects cluster formation. (B) Principal component analysis (PCA) based on EO constituents. (C) PCA is based on all studied traits. (D) PCA is based on all studied traits according to populations.Full size imagePrincipal component analysis (PCA)Principal component analysis (PCA) is one of the multivariate statistical techniques used to explain differentiation between populations and to obtain more information on the variables that mainly influence the population’s similarities and differences61. The PCA was performed to identify the most significant variables in the data set (Fig. 5B). The same data set (14 population × 11 components) was used in this section. The PCA showed two components with explain 83.3% of the total variance. The first principal component (PC1) had the most portion of variance (74.5%) which was given by compounds such as γ-Terpinene, α-pinene, α-Thujene, p-cymene, and limonene. The second component (PC2), explaining 8.8% of the total variance, consisted of compounds thymol, carvacrol, and 1, 8-cineol (Fig. 6). The results of PCA agreed with those of the cluster analysis the populations similarly were divided into two distinct groups including high thymol/carvacrol and high thymol/p-cymene/γ-terpinene groups (Fig. 5B). Heat map analyses were drowned to determine how constituents effect on clustering. Based on heat map analysis samples were well-classified.Figure 6Correlation between 24 traits on the studied Trachyspermum ammi populations: TPC: Total phenolic content, TFC: Total flavonoid content, TCC: Total coumarin, EO: Essential Oil yield, TSW: One thousand seed weight (g), MIC: minimum inhibitory concentration, Ec: E. coli, MIC: minimum inhibitory concentration, Sa: S. aureus, DPPH Ext.: DPPH assay Extract is expressed as IC50 index, DPPH EO: DPPH assay EO is expressed as IC50 index, FRAP Ext.: FRAP assay Extract, FRAP EO: FRAP assay Essential oil, TAC Ext: The total antioxidant capacity Extract, TAC EO: The total antioxidant capacity Essential oil.Full size imageAlso, in the analysis of the principal factors (PCA) between all the evaluated traits in the populations, the first principal factor (PC1) showed 53.8% and the second principal factor (PC2) 14.7% of the variance. This analysis determined the principal component, correlation of traits, and their relationship with populations. Accordingly, traits with positive arrows show a positive correlation and two traits with non-directional arrows show a negative correlation. Accordingly, thymol and carvacrol have a high correlation with antioxidant properties and this property is correlated with populations of chemotype 1 (P1, P2, P5, P8, P9). Other relationships and details correlations are shown in Fig. 5C, D.CorrelationSimple correlation estimated the relationship between variables. Simple correlations between 24 studied traits in the present study are shown in Fig. 6. Thymol as the major constituent of EOs showed a high positive correlation with TPC (0.71), carvacrol (0.64), FRAP EO (0.85), and FRAP ext. (0.66). Thymol also had a significant negative correlation with Mic EO (-0.74), Mic Sa (-0.69), α-Thujene (-0.84), α-Pinene (-0.77), β-Pinene (-0.75), β-Myrcene (-0.9), α-Terpinene (-0.85), p-Cymene (-0.98), Limonene (-0.89), γ-Terpinene (-0.97). TPC had a positive correlation with TFC, thymol, carvacrol, FRAP Ext., TAC Ext., and a significant negative correlation with DPPH Ext. The antioxidant methods in extracts DPPH50 vs FRAP (-0.8), DPPH50 vs TAC (-0.67) and FRAP vs TAC (0.59) were highly correlated. Similarly, in estimating the antioxidant activity of essential oil DPPH50 vs FRAP (-0.79), DPPH50 vs TAC (-0.48), and FRAP vs TAC Ext (0.55) were highly correlated. Also, the high correlation of all antioxidant methods with thymol can explain its positive effect on the antioxidant activity of the extracts and EOs. The correlations found between each of the traits can be very important in breeding programs. More

Species occurrence recordsWe compiled data on the distribution of 21,252 endemic species of any of the twelve megadiverse countries from four tetrapod (5,757) and four vascular plant groups (15,389) (amphibians, reptiles, birds, mammals, lycophytes, ferns, gymnosperms, and flowering plants). Species occurrence records were obtained from the Global Biodiversity Information Facility (GBIF)27, the International Union of Conservation of Nature (IUCN)28, and BirdLife60,61. We only modeled species with at least 25 unique records at a 5 arc-minute resolution (~10 km at the equator). In many cases, the processing of the IUCN polygons resulted in species with thousands of occurrence records. In these cases, we randomly chose a maximum of 500 records per species. The greater the number of observed records, more problems can be associated with spatial bias in the modeling62. In the case of records coming from IUCN polygons, more records require more computing time and these do not necessarily provide more information into the modeling given that their distribution is quite homogeneous.For tetrapods, we first explored the possibility of using occurrence records from GBIF, but data for megadiverse countries were scarce. Consequently, we decided to use the distribution polygons provided by the IUCN for amphibians, reptiles, and mammals (terrestrial and freshwater species)28, and the distribution polygons provided by BirdLife60. We based this decision on the fact that ecological niche modeling using IUCN polygons has been proven to give robust results20. For the IUCN polygons, we retained species that have been categorized as “extant”, “possibly extinct”, “probably extant”, “possibly extant”, and “presence uncertain”, discarding species considered to be “extinct”. In addition, we did not model species reported by the IUCN as “introduced”, “vagrant”, or those in the “assisted colonization” category; for mammals and birds, we only considered the distribution of “resident” species. Depending on the taxonomic group, and given the information available, we used different approaches to identify species endemic to any of twelve megadiverse countries: Australia, Brazil, China, Colombia, Ecuador, India, Indonesia, Madagascar, Mexico, Peru, Philippines, and Venezuela. For birds, we used BirdLife to identify species listed as “breeding endemic” and then choose the corresponding IUCN polygons. To identify the rest of endemic species in the other groups, we used a 0.08333° buffer around each country to select the IUCN polygons that fall completely within the country limits. We converted all selected species polygons into unique records at a 5 min resolution (~10 km at the equator).For vascular plants, we used geographic occurrence data obtained from the Global Biodiversity Information Facility by querying all records under “Tracheophyta” (we only considered “Preserved Specimens” in our search). Plants records were taxonomically homogenized and cleaned following the procedures described in ref. 63 using Kew’s Plants of the World database64 as the source of taxonomic information. Mostly, we identified endemic species as those with all occurrence records restricted to any given megadiverse country. For countries in which data for vascular plants were scarce or absent (e.g., India), we complemented occurrence information with polygons from the IUCN (although IUCN data for plants remains limited) following the procedure described for tetrapods.Climatic dataWe used the 19 bioclimatic variables available at WorldClim v.2 (Fick 2017) as the baseline (present-day) climatic conditions (1970–2000) (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, the maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter and precipitation of coldest quarter). From this baseline scenario, bioclimatic variables start to vary because of climate change. We used bioclimatic variables derived from the IPSL-CM5-LR ocean-atmospheric model under five scenarios: (i) the high-emissions RCP 8.5 W/m2; and (ii) melting scenarios consisting of four different experiments of freshwater discharge into the North Atlantic from Greenland’s meltwater (see DeFrance16 for details). We acknowledge that using a single GCM does not allow us to estimate inter-GCM variability in the resulting distribution models; however, the melting scenarios do only exist for IPSL-CM5-LR GCM. We applied as control scenario RCP 8.5 because melting scenarios would have been more complicated to support with lower emission scenarios. In addition, we are using well-designed opportunity experiments from ref. 11 and wanted to be consistent with their choice of RCP 8.5. Also, these experiments are based on CMIP5, which shows similar climate impact fingerprints than CMIP665. This might be explained by the fact that CMIP5 and CMIP6 are still relatively close, and that the main climatic effects of the AMOC are already well-represented by the climate dynamics in CMIP5.The four melting scenarios are equivalent to a sea-level rise of 0.5, 1.0, 1.5, and 3.0 meters above the current sea level, and these are named accordingly: Melting 0.5, Melting 1.0, Melting 1.5., and Melting 3.0. These AMOC scenarios are experiments that were superimposed to the RCP 8.5 scenario adding 0.11, 0.22, 0.34, and 0.68 Sv (1 Sv = 106 m3/s) coming from a freshwater release that starts in 2020 and finishes in 2070 (Anthoff et al.14). We obtained debiased bioclimatic variables11 under the five future scenarios for three consecutive time horizons: T1: 2030 (2030–2060); T2: 2050 (2050–2080); and T3: 2070 (2070–2100). The time horizons evaluated represent short, medium, and long terms in order to help decision-makers order conservation priorities.Ecological niche modelingAt their most basic, the algorithms used to construct species distribution models relate species occurrence records with climatic variables to create a climatic profile that can be projected onto other time periods and geographic regions66. The resulting models have proven useful in evaluating the impacts of climate change on biodiversity and to identify varying levels of vulnerability among species32,67,68. Here, we employed a multi-algorithm (ensemble) approach to construct species distribution models as implemented in the “biomod2” package67 in R69 (Supplementary Fig. 33). The underlying philosophy of ensemble modeling is that each model carries a true “signal” about the climate-occurrence relationships we aim to capture, but it also carries “noise” created by biases and uncertainties in the data and model structure32,67. By combining models created with different algorithms, ensemble models aim at capturing the true “signal” while controlling for algorithm-derived model differences; therefore, model uncertainty is accounted for during model construction (see Supplementary Material for further detail).Prior to modeling, we reduced the number of bioclimatic variables per species by estimating collinearity among present-day bioclimatic variables. We employed the “corrSelect” function of the package fuzzySim70 in R69, using a Pearson correlation threshold of 0.8 and variance inflation factors as criteria to select variables. Given the number of species evaluated and the ecological information scarcity, we did not select a set of variables based on ecological knowledge by each of the species modeled. Instead, for the variables pre-selection, we used the statistical approach described above that has been proven to give models with good performance71,72. We used seven algorithms with a good predictive performance (evaluated with the TSS and ROC statistics; Supplementary Fig. 1): Maxent (MAXENT.Phillips), Generalized Additive Models (GAM), Classification Trees Analysis (CTA), Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), and Random Forest (RF). Because occurrence datasets consisted of presence-only data, for each model, we randomly generated 10,000 pseudo-absences within the model calibration area; we gave presences and absences the same importance during the calibration process (BIOMOD’s prevalence = 0.5). For each species, we selected a calibration area (i.e., the accessible area or M)73 using a spatial intersection between a 4° buffer around species occurrences and the terrestrial ecoregions occupied by the species73 (Supplementary Fig. 33). The projected M (i.e., the area accessible for species in future scenarios) was defined using a 2° buffer around the present-day calibration area (M). By limiting the M, we incorporated information about dispersal and ecological limitations of each species into the modeling66. We did this to take into account a more realistic dispersal scenario given the velocity with which climatic changes are happening and because there are geographic and ecological barriers, which is the reason why we used ecoregions to limit our M. We assumed climatic niche conservatism across time; and inside the projected M we also assumed full dispersal. Consequently, inside the projected M, the evaluated species can win or lose suitable climatic conditions.We calibrated each algorithm using a random sample of 70% of occurrence records and evaluated the resulting models using the remaining 30% of records. To validate the predictive power of the ecological niche models, we used the True Skill Statistics (TSS) and the Receiver Operating Characteristics (ROC) and performed 10 replicates for every model, providing a tenfold internal cross-validation. To account for uncertainty, we constructed the ensemble models (seven algorithms × ten replicates) using a total consensus rule, where models from different algorithms were assembled using a weighted mean of replicates with an evaluation threshold of AUC  > 0.7 (Supplementary Fig. 1). However, as shown by the distribution of validation statistic in Supplementary Fig. 1, most ensemble models presented a very good predictive power (AUC  > 0.8). In some cases, modeling issues in some insular species required that we change the calibration area (M) to the entire country.We used the resulting ensemble models to project the potential distribution of each species under both current and future climatic conditions (Supplementary Fig. 34). We then examined the frequency in which different bioclimatic variables appeared to have the highest contribution during model construction for each species. The algorithms used (Maxent, GAM, CTA, ANN, SRE, FDA, and RF) identify these variables by iteratively testing combinations of all the available variables (i.e., those selected based on low correlation values) until reaching a set of variables that was most informative on the distribution of species; this set of variables had the highest predictive power of species occurrence. For every species, we retrieved the two variables with the largest model contribution (Supplementary Figs. 34 and 35).Species geographic rangeWe converted ensemble probability maps into binary maps of presence/absence using the TSS threshold; these binary maps reflect the distribution of climatic suitability of species, where values of 0 and 1 represent grid cells with non-suitable and suitable climates, respectively. In order to approximate the vulnerability of individual species to climate change, we estimated the temporal changes in the extent of the area of climatic suitability (geographic range) for every species relative to the present-day distribution. We estimated species’ geographic ranges by identifying and counting those grid cells with suitable climatic conditions (values of 1) in the present-day and under future scenarios. We then estimated the proportion of range changes through time, quantifying the proportion of grid cells either lost or gained for each species. This allowed us to estimate the proportion of species (by country and group) projected to have a complete loss of geographic ranges in the future.Species richness, differences in species richness, potential species hotspots (PSH), and temporal dissimilarityWe used binary maps to construct presence-absence matrices (PAM), which contain information on the presence (values of 1) or absence (values of 0) of species across grid cells. Using these PAMs, we estimated species richness (SR) as the sum of species present in each grid cell; to visualize SR across space, we generated 16 species richness maps corresponding to the present-day and the four future scenarios at each of the three temporal horizons. We used these maps to estimate and visualize temporal differences in species richness (ΔSR) over time by subtracting the estimated SR in the future from the current SR, for every grid cell; for visualization, we standardized SR per country to the range 0–1. We assumed full dispersal ability of species in all analyses, meaning that all suitable areas in the future had the same probability of being occupied, irrespective of the distance to the present-day distribution.By calculating species richness (SR) across grid cells, we defined Potential Species Hotspots (PSH) within each country as those grid cells with the highest levels of SR. For this, we defined the PSH by calculating the maximum present-day species richness (maxSR) observed in each country and then identified grid cells with richness values above a threshold of maxSR*0.6. Considering only those grid cells with a SR above this threshold, we estimated the geographic extent of PSH across time periods and scenarios and estimated changes to the extent of PSH relative to present-day conditions. Given that we use the threshold to define PSHs, we tested two additional thresholds (20 and 90%) to define and quantify the extent of PSHs. However, these additional results agree with the general trend. We chose not to base our threshold on the distribution of SR values (i.e., quantiles, median) due to the high proportion of grid cells with SR  More

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