Philippa Kaur

The shell of the endangered hawksbill sea turtle (pictured) is prized for trinkets and jewellery.Credit: Reinhard Dirscherl/SPL

Poaching is less of a threat to the survival of sea turtles than it once was, a new analysis suggests1. Illegal sea-turtle catch has dropped sharply since 2000, with most of the current exploitation occurring in areas where turtle populations are relatively healthy.This study is the first worldwide estimate of the number of adult sea turtles moved on the black market. According to the analysis, more than one million sea turtles were illegally harvested between 1990 and 2020. But the researchers also found that the illegal catch from 2010 to 2020 was nearly 30% lower than that in the previous decade.“The silver lining is that, despite the seemingly large illegal take, exploitation is not having a negative impact on sea-turtle populations on a global scale. This is really good news,” says co-author Jesse Senko, a marine conservation scientist at Arizona State University in Tempe. The research was published 7 September in Global Change Biology.Turtles for trinketsFor millennia, humans have used both adult sea turtles and their eggs as a food source and for cultural practices. In the past 200 years, however, many sea turtle populations declined steeply as hunting rose to meet a growing demand for turtle-based goods. In Europe, North America and Asia, sea-turtle shells were used to make combs, jewelry and furniture inlays. Turtles were also hunted for meat and for use in traditional medicine.The rise in turtle hunting meant that, by 2014, an estimated 42,000 sea turtles were legally harvested every year, and an unknown number of sea turtles were sold on the black market. Today, six of the seven sea-turtle species found around the globe are endangered owing to a deadly combination of habitat destruction, poaching and accidental entanglement in fishing gear.To pin down how many sea turtles were illegally harvested, Senko and his colleagues surveyed sea-turtle specialists and sifted through 150 documents, including reports from non-governmental organizations, papers in peer-reviewed journals and news articles.

Source: Ref. 1

By combining this information, the researchers made a conservative estimate that around 1.1 million sea turtles were illegally caught between 1990 and 2020. Nearly 90% of these turtles were funneled into China and Japan, largely from a handful of middle- and low-income countries (see ‘Long-distance turtle transport’). Of the species that could be identified, the most frequently exploited were the endangered green turtles (Chelonia mydas), hunted for meat, and the critically endangered hawksbill turtles (Eretmochelys imbricata), prized for their beautiful shells.However, the data also showed that the number of illegally caught turtles decreased from around 61,000 each year between the start of 2000 and the end of 2009 to around 44,000 in the past decade (see ‘More sea turtles swim free’). And, although there were exceptions, most sea turtles were taken from relatively robust populations that were both large and genetically diverse.

Source: Ref. 1

Although sea turtles seem to be doing well globally, this doesn’t mean that threats to regional populations can be ignored, says Emily Miller, an ecologist at the Monterey Bay Aquarium Research Institute in California. The study pins down where — and for whom — sea turtles are being exploited, which could help conservationists to target communities for advocacy, she says.Overall, the numbers signal that conservation efforts could be working, says Senko. “Contrary to popular belief, most sea-turtle populations worldwide are doing quite well,” he says. “The number of turtles being exploited is a shocker, but the ocean is big, and there are a lot of turtles out there.” More

The European Union’s new (2023–2027) Common Agricultural Policy (CAP) aims to reverse current environmental degradation and biodiversity declines in European farmland1 through the achievement of three green objectives: contribute to climate change mitigation, support efficient natural resource management, and reverse biodiversity loss2,3. Following the outbreak of war in Ukraine, the European Commission proposed a series of short and medium-term relaxations to CAP’s environmental commitments to offset expected shortages in grain imports and enhance food security4.Here, we argue that policy changes to allow cultivation of fallow land will disproportionately impact biodiversity and support further intensification of livestock production. Thus, ultimately, these changes in policy may sacrifice long term biodiversity and agricultural sustainability in Europe, in favour of modest increases in current agricultural production and alleged improvements of food security.A catalyst for reversing green policiesRussia and Ukraine are world-leading producers and exporters of cereal and fodder production (notably, oleo-proteaginous crops)5. The Ukraine war and international sanctions on Russia are threatening the import of these products to the EU. Ukrainian winter cereal, maize and sunflower production is expected to decrease by 20–30%, at least during the 2022–2023 season, and similar reductions in Russian exports are also expected5. Therefore agro-industry lobbies and farmers’ organisations in Brussels, some political parties in the European parliament and some countries’ administrations perceive a need to increase agricultural production6 and, as a means to offset expected shortages, are pressing to relax or remove CAP’s environmental commitments. Mechanisms supporting these commitments include enhanced conditionality (compulsory for all farmers receiving subsidies), voluntary measures of Rural Development Programmes (i.e. agri-environment-climate-measures) and Greening measures (crop diversification, maintenance of permanent grasslands and promotion of Ecological Focus Areas). A call made to mobilise all relevant international groups during the informal meeting held on 2 March 2022 by Member States’ agriculture and food ministers, with the exception of Denmark, Germany and Italy, may reflect such pressure6. Indeed, the European Commission has finally proposed a series of “short- and medium-term actions to enhance global food security and to support farmers and consumers in the EU”4. In regard to land-use, actions refer to the cultivation of fallows, which are protected by green payments for keeping land in good agricultural and environmental conditions and, adequately managed (both long-term and annual), support high levels of biodiversity and ecosystem services7 (Fig. 1). More precisely, the European Commission proposes that “To enlarge the EU’s production capacity, the Commission has today adopted an implementing act to exceptionally and temporarily allow Member States to derogate from certain greening obligations. In particular, they may allow for production of any crops for food and feed on fallow land that is part of Ecological Focus Areas in 2022, while maintaining the full level of the greening payment”4. This measure was recently extended for 2023.Fig. 1: Arable field left fallow and allowed to develop a grassy vegetation cover.Under non-intensive management, fallow areas become a genuine semi-natural habitat, key for the conservation of farmland biodiversity. Credit: Jordi Bas, taken in the cereal steppes of the Lleida plain (Catalonia, Spain).Full size imageConsidering food sovereigntyHowever, the FAO does not draw the same conclusions about the possible world impacts of the conflict and recommends finding alternative suppliers, instead suggesting using existing food stocks, diversifying domestic crops and reducing fertiliser dependence and food waste as mechanisms to help guarantee Europe’s food supplies and sovereignty5. Even the European Commission, while acknowledging the vulnerability of European farmers to animal feed import shortages and increased costs, clearly stated that food supply is not at risk in the EU4. Indeed, EU-based production supplies 79% of the feed proteins consumed in European livestock farming, 90% of feed cereals and 93% of other products such as Dried Distillers’ Grains and Solubles or beet pulp8. In 2020, the EU was completely self-sufficient with respect to dairy products, pork, beef, veal, poultry, and cereals. It remained the largest global exporter of agri-food products, in spite of the COVID-19 pandemic8.Counterproductive policiesAny increase in production from cultivating fallow land will therefore likely be used to feed intensively reared livestock and sustain cattle feed exports. Supporting the increasing trend of feed exports and industrial intensive livestock farming does not align with the EU’s Green Deal due to the negative impacts on air, soil and water quality8,9,10. In addition, cultivating fallow land to support intensive livestock-based agriculture will undermine the EU’s Farm-to-Fork strategy and CAP’s ‘Food and Health’ objective of reducing meat consumption to favour a more sustainable and healthier diet among European consumers2,11. Encouraging the growth of intensive livestock farming through enabling cultivation of fallow lands will increase environmental damage, biodiversity loss and public health risks. Thus, the recent relaxations of the new CAP compromise several of its fundamental objectives, along with those of other elements of the Green Deal, such as the EU’s Nature Restoration Law2,9,12.The duration of the war in Ukraine and its effects on provision of raw materials to Europe is hard to foresee. We acknowledge the uncertainties and input costs faced by farmers but calls for further agricultural intensification may be largely unjustified at this stage. Specifically, cultivating semi-natural habitats like long-term or unploughed annual fallows will have serious environmental costs, including an increase in pesticide and fertiliser application, since fallows often occupy less productive land13. Even a moderate increase in food production at the expense of the semi-natural habitats remaining in farmland landscapes (field margins, grasslands, and fallow land), which support most of Europe’s farmland biodiversity and its associated ecosystem services14, will seriously damage farmland biodiversity and sustainability in European agricultural landscapes3,15. For example, a comprehensive study carried out on 169 farms across 10 European countries showed that semi-natural habitats, including fallows, occupied 23% of the land but hosted 49% of vascular plant, earthworm, spider, and wild bee species; a 10% decrease of these habitats if reclaimed for food production would cause exponential decreases in biodiversity, but only moderate linear increases in production15. Furthermore, the loss of semi-natural habitats in arable systems, fallows among them, would negatively affect arthropod functional diversity and the ecosystem services it supports, which may affect agriculture production14.Sustainable alternativesThere are alternatives to cultivating semi-natural habitats that may (and need to) be assessed to achieve a more strategic European agricultural policy able to meet food demands while maintaining the sustainability principles and improvements of the food-production chain sought by the Farm-to-Fork strategy. Proposals include agro-ecological approaches to increase production through the enhancement of ecosystem services such as pollination and biological control16,17,18, adjusting the amount of cultivated surface in relation to landscape structure and composition19, or relocating crops that are more in demand to areas where production is optimal without increasing the total cultivated area20.After decades of costly implementation and reforms of agricultural and conservation policies1, the EU is at risk of engaging in a hasty and misguided strategy on food production jeopardising the green transition13. As an alternative to such a ‘business as usual’ reaction, the EU has now the opportunity to consolidate the mentioned environmental and social objectives of the new CAP2,3. A more sustainable agriculture, resilient to food supply crises (present and future), should be based on ecological functionality of farmland, which ultimately depends on the conservation of its biodiversity16, along with measures to counter climate change. Responses to this and other challenges on the new CAP should be assessed with a long-term perspective and based on robust scientific evidence before undermining its environmental ambitions3,13. More

Liu, Z., He, C., Zhou, Y. & Wu, J. How much of the world’s land has been urbanized, really? A hierarchical framework for avoiding confusion. Landsc. Ecol. 29, 763–771 (2014).
Google Scholar 
The World’s Cities in 2018: Data Booklet (UN, 2018).Miller, R. W., Hauer, R. J. & Werner, L. P. Urban Forestry: Planning and Managing Urban Greenspaces 3rd edn (Waveland Press, 2015).Escobedo, F. J., Kroeger, T. & Wagner, J. E. Urban forests and pollution mitigation: analyzing ecosystem services and disservices. Environ. Pollut. 159, 2078–2087 (2011).CAS 

Google Scholar 
Keeler, B. L. et al. Social-ecological and technological factors moderate the value of urban nature. Nat. Sustain. 2, 29 (2019).
Google Scholar 
Petri, A. C., Koeser, A. K., Lovell, S. T. & Ingram, D. How green are trees?—using life cycle assessment methods to assess net environmental benefits. J. Environ. Hortic. 34, 101–110 (2016).CAS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).
Google Scholar 
Nowak, D. J. & Greenfield, E. J. Declining urban and community tree cover in the United States. Urban For. Urban Green. 32, 32–55 (2018).
Google Scholar 
Easterling, D. R. et al. Climate extremes: observations, modeling, and impacts. Science 289, 2068–2074 (2000).CAS 

Google Scholar 
Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Google Scholar 
Yan, P. & Yang, J. Performances of urban tree species under disturbances in 120 cities in China. Forests 9, 50 (2018).
Google Scholar 
Hilbert, D., Roman, L., Koeser, A. K., Vogt, J. & Van Doorn, N. S. Urban tree mortality: a literature review. Arboric. Urban For. 45, 167–200 (2019).
Google Scholar 
Young, R. F. & McPherson, E. G. Governing metropolitan green infrastructure in the United States. Landsc. Urban Plan. 109, 67–75 (2013).
Google Scholar 
Esperon-Rodriguez, M. et al. Assessing climate risk to support urban forests in a changing climate. Plants People Planet https://doi.org/10.1002/ppp3.10240 (2022).Esperon-Rodriguez, M. et al. Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants People Planet 1, 387–397 (2019).Gallagher, R. V., Allen, S. & Wright, I. J. Safety margins and adaptive capacity of vegetation to climate change. Sci. Rep. 9, 8241 (2019).
Google Scholar 
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).CAS 

Google Scholar 
Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).Richard, B. et al. The climatic debt is growing in the understory of temperate forests: stand characteristics matter. Global Ecol. Biogeogr. 30, 1474–1487 (2021).IPCC Climate Change 2001: The Scientific Basis (eds Houghton, J. T. et al.) (Cambridge Univ. Press, 2001).Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: biodiversity conservation in a changing climate. Science 332, 53–58 (2011).CAS 

Google Scholar 
Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Change 10, e551 (2019).
Google Scholar 
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–224 (2015).
Google Scholar 
Reisinger, A. et al. The Concept of Risk in the IPCC Sixth Assessment Report: A Summary of Cross-Working Group Discussions (IPCC, 2020).Chen, C. et al. University of Notre Dame Global Adaptation Index: Country Index Technical Report (ND-GAIN, 2015).McPherson, E. G., Berry, A. M. & van Doorn, N. S. Performance testing to identify climate-ready trees. Urban For. Urban Green. 29, 28–39 (2018).
Google Scholar 
Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. 2 https://doi.org/10.17161/bi.v2i0.4 (2005).Pulliam, H. R. On the relationship between niche and distribution. Ecol. Lett. 3, 349–361 (2000).
Google Scholar 
Ordóñez, C. & Duinker, P. Assessing the vulnerability of urban forests to climate change. Environ. Rev. 22, 311–321 (2014).
Google Scholar 
Gallagher, R. V., Beaumont, L. J., Hughes, L. & Leishman, M. R. Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia. J. Ecol. 98, 790–799 (2010).
Google Scholar 
Smith, I. A., Dearborn, V. K. & Hutyra, L. R. Live fast, die young: accelerated growth, mortality, and turnover in street trees. PLoS ONE 14, e0215846 (2019).
Google Scholar 
Hirabayashi, Y., Kanae, S., Emori, S., Oki, T. & Kimoto, M. Global projections of changing risks of floods and droughts in a changing climate. Hydrol. Sci. J. 53, 754–772 (2008).
Google Scholar 
Van der Veken, S., Hermy, M., Vellend, M., Knapen, A. & Verheyen, K. Garden plants get a head start on climate change. Front. Ecol. Environ. 6, 212–216 (2008).
Google Scholar 
Ballinas, M. & Barradas, V. L. Transpiration and stomatal conductance as potential mechanisms to mitigate the heat load in Mexico City. Urban For. Urban Green. 20, 152–159 (2016).
Google Scholar 
Di Baldassarre, G. et al. Water shortages worsened by reservoir effects. Nat. Sustain. 1, 617 (2018).
Google Scholar 
Hoekstra, A. Y. & Mekonnen, M. M. The water footprint of humanity. Proc. Natl Acad. Sci. USA 109, 3232–3237 (2012).CAS 

Google Scholar 
Manoli, G. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573, 55–60 (2019).CAS 

Google Scholar 
Kim, D.-H., Doyle, M. R., Sung, S. & Amasino, R. M. Vernalization: winter and the timing of flowering in plants. Annu. Rev. Cell Dev. Biol. 25, 277–299 (2009).CAS 

Google Scholar 
Kummu, M. & Varis, O. The world by latitudes: a global analysis of human population, development level and environment across the north–south axis over the past half century. Appl. Geogr. 31, 495–507 (2011).
Google Scholar 
Vogt, J. et al. Citree: a database supporting tree selection for urban areas in temperate climate. Landsc. Urban Plan. 157, 14–25 (2017).
Google Scholar 
Paquette, A. et al. Praise for diversity: a functional approach to reduce risks in urban forests. Urban For. Urban Green. 62, 127157 (2021).
Google Scholar 
Esperon-Rodriguez, M. et al. Functional adaptations and trait plasticity of urban trees along a climatic gradient. Urban For. Urban Green. 54, 126771 (2020).
Google Scholar 
Hirons, A. D. et al. Using botanic gardens and arboreta to help identify urban trees for the future. Plants People Planet 3, 182–193 (2021).
Google Scholar 
Watkins, H., Hirons, A., Sjöman, H., Cameron, R. & Hitchmough, J. D. Can trait-based schemes be used to select species in urban forestry? Front. Sustain. Cities 3 https://doi.org/10.3389/frsc.2021.654618 (2021).Populated Places (Natural Earth, accessed 2018); http://www.naturalearthdata.com/downloads/Ossola, A. et al. The Global Urban Tree Inventory: a database of the diverse tree flora that inhabits the world’s cities. Glob. Ecol. Biogeogr. 29, 1907–1914 (2020).
Google Scholar 
Sabatini, F., Lenoir, J. & Bruelheide, H. sPlotOpen—An Environmentally-Balanced, Open-Access, Global Dataset of Vegetation Plots (iDiv, 2021); https://doi.org/10.25829/idiv.3474-40-3292Sabatini, F. M. et al. sPlotOpen—an environmentally balanced, open-access, global dataset of vegetation plots. Global Ecol. Biogeogr. 30, 1740–1764 (2021).Zizka, A. et al. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Taxonstand: Taxonomic standardization of plant species names. R package version 2.4 https://cran.r-project.org/web/packages/Taxonstand/Taxonstand.pdf (2021).Kelso, N. & Patterson, T. World Urban Areas, LandScan, 1:10 Million (2012) (North American Cartographic Information Society, 2012).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States (USGS, 2012).Field, C. et al. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2014).Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).CAS 

Google Scholar 
Zhao, L. et al. Global multi-model projections of local urban climates. Nat. Clim. Change 11, 152–157 (2021).
Google Scholar 
Huang, K., Li, X., Liu, X. & Seto, K. C. Projecting global urban land expansion and heat island intensification through 2050. Environ. Res. Lett. 14, 114037 (2019).
Google Scholar 
Alavipanah, S., Wegmann, M., Qureshi, S., Weng, Q. & Koellner, T. The role of vegetation in mitigating urban land surface temperatures: a case study of Munich, Germany during the warm season. Sustainability 7, 4689–4706 (2015).
Google Scholar 
Corburn, J. Cities, climate change and urban heat island mitigation: localising global environmental science. Urban Stud. 46, 413–427 (2009).
Google Scholar 
Baston, D., ISciences, L.L., Baston, M.D. Package ‘exactextractr’. terra. R package version 0.8.2 (2022).Hijmans, R. J. et al. raster: Geographic data analysis and modeling. R package version 2.3-33 http://cran.r-project.org/web/packages/raster/index.html (2016).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Bivand, R. et al. maptools: Tools for handling spatial objects. R package version 08, 23 https://cran.r-project.org/web/packages/maptools/ (2013). More

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

Li, F. et al. Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP). Atmos. Chem. Phys. 19, 12545–12567 (2019).CAS 
Article 

Google Scholar 
Ward, D. S. et al. The changing radiative forcing of fires: Global model estimates for past, present and future. Atmos. Chem. Phys. 12, 10857–10886 (2012).CAS 
Article 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).CAS 
Article 

Google Scholar 
McCarty, J. L. et al. Reviews and syntheses: Arctic fire regimes and emissions in the 21st century. Biogeosciences 18, 5053–5083 (2021).CAS 
Article 

Google Scholar 
Kim, J.-S., Kug, J.-S., Jeong, S.-J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6, eaax3308 (2020).Article 

Google Scholar 
Mahowald, N. et al. Global distribution of atmospheric phosphorus sources, concentrations and deposition rates, and anthropogenic impacts. Global Biogeochem. Cy. https://doi.org/10.1029/2008gb003240 (2008).Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, Tropical Atlantic Ocean, and Southern Ocean. Proc. Natl. Acad. Sci. USA 116, 16216–16221 (2019).CAS 
Article 

Google Scholar 
Andreae, M. O. Emission of trace gases and aerosols from biomass burning—an updated assessment. Atmos. Chem. Phys. 19, 8523–8546 (2019).CAS 
Article 

Google Scholar 
Guieu, C., Bonnet, S., Wagener, T. & Loÿe-Pilot, M.-D. Biomass burning as a source of dissolved iron to the open ocean? Geophys. Res. Lett. https://doi.org/10.1029/2005gl022962 (2005).Hamilton, D. S. et al. Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0). Geosci. Model Dev. 12, 3835–3862 (2019).CAS 
Article 

Google Scholar 
Kharol, S. K. et al. Dry deposition of reactive nitrogen from satellite observations of ammonia and nitrogen dioxide over North America. Geophys. Res. Lett. 45, 1157–1166 (2018).CAS 
Article 

Google Scholar 
Wentworth, G. R. et al. Ammonia in the summertime Arctic marine boundary layer: Sources, sinks, and implications. Atmos. Chem. Phys. 16, 1937–1953 (2016).CAS 
Article 

Google Scholar 
Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).CAS 
Article 

Google Scholar 
Mahowald, N. M. et al. Aerosol deposition impacts on land and ocean carbon cycles. Curr. Clim. Change Rep. 3, 16–31 (2017).Article 

Google Scholar 
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

Google Scholar 
Evangeliou, N. et al. Open fires in Greenland in summer 2017: Transport, deposition and radiative effects of BC, OC, and BrC emissions. Atmos. Chem. Phys. 19, 1393–1411 (2019).CAS 
Article 

Google Scholar 
Hamilton, D. S. et al. Earth, wind, fire, and pollution: Aerosol nutrient sources and impacts on ocean biogeochemistry. Annu. Rev. Mar. Sci. 14, 303–330 (2022).Article 

Google Scholar 
Soja, A. J., Shugart, H. H., Sukhinin, A., Conard, S. & Stackhouse, P. W. Satellite-derived mean fire return intervals as indicators of change in Siberia (1995–2002). Mitig. Adapt. Strateg. Glob. Chang. 11, 75–96 (2006).Article 

Google Scholar 
Ito, A. Mega fire emissions in Siberia: Potential supply of bioavailable iron from forests to the ocean. Biogeosciences 8, 1679–1697 (2011).CAS 
Article 

Google Scholar 
Myriokefalitakis, S., Gröger, M., Hieronymus, J. & Döscher, R. An explicit estimate of the atmospheric nutrient impact on global oceanic productivity. Ocean Sci. 16, 1183–1205 (2020).CAS 
Article 

Google Scholar 
Harrison, W. G. & Cota, G. F. Primary production in polar waters: Relation to nutrient availability. Polar Res. 10, 87–104 (1991).Article 

Google Scholar 
Tremblay, J.-É. et al. Global and regional drivers of nutrient supply, primary production and CO2 drawdown in the changing Arctic Ocean. Prog. Oceanogr. 139, 171–196 (2015).Article 

Google Scholar 
Ardyna, M., Gosselin, M., Michel, C., Poulin, M. & Tremblay, J.-É. Environmental forcing of phytoplankton community structure and function in the Canadian High Arctic: contrasting oligotrophic and eutrophic regions. Mar. Ecol. Prog. Ser. 442, 37–57 (2011).CAS 
Article 

Google Scholar 
Rainville, L. & Woodgate, R. A. Observations of internal wave generation in the seasonally ice-free Arctic. Geophys. Res. Lett. 36, L23604 (2009).Article 

Google Scholar 
Ardyna, M. et al. Recent Arctic Ocean sea-ice loss triggers novel fall phytoplankton blooms. Geophys. Res. Lett. 41, 6207–6212 (2014).Article 

Google Scholar 
Baumann, T. M. et al. On the seasonal cycles observed at the continental slope of the Eastern Eurasian Basin of the Arctic Ocean. J. Phys. Oceanogr. 48, 1451–1470 (2018).Article 

Google Scholar 
Bauch, D. & Cherniavskaia, E. Water mass classification on a highly variable Arctic shelf region: Origin of Laptev sea water masses and implications for the nutrient budget. J. Geophys. Res. Oceans 123, 1896–1906 (2018).Article 

Google Scholar 
Pnyushkov, A. V. et al. Heat, salt, and volume transports in the eastern Eurasian Basin of the Arctic Ocean from 2 years of mooring observations. Ocean Sci. 14, 1349–1371 (2018).Article 

Google Scholar 
Hölemann, J. A. et al. The impact of land-fast ice on the distribution of terrestrial dissolved organic matter in the Siberian Arctic shelf seas. Biogeosci. Discuss 2021, 1–30 (2021).
Google Scholar 
Polyakov, I. V. et al. Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean. Science 356, 285–291 (2017).CAS 
Article 

Google Scholar 
Lutsch, E. et al. Unprecedented atmospheric ammonia concentrations detected in the high Arctic from the 2017 Canadian wildfires. J. Geophys. Res. Atmos. 124, 8178–8202 (2019).CAS 
Article 

Google Scholar 
Zhang, J., Li, D., Bian, J. & Bai, Z. Deep stratospheric intrusion and Russian wildfire induce enhanced tropospheric ozone pollution over the northern Tibetan Plateau. Atmos. Res. 259, 105662 (2021).CAS 
Article 

Google Scholar 
Hurrell, J. W. et al. The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteor. Soc. 94, 1339–1360 (2013).Article 

Google Scholar 
Clark, S. K., Ward, D. S. & Mahowald, N. M. The sensitivity of global climate to the episodicity of fire aerosol emissions. J. Geophys. Res.: Atmos. 120, 11,589–511,607 (2015).CAS 
Article 

Google Scholar 
Shi, J.-H. et al. Examination of causative link between a spring bloom and dry/wet deposition of Asian dust in the Yellow Sea, China. J. Geophys. Res. Atmos. https://doi.org/10.1029/2012JD017983 (2012).Wiedinmyer, C. et al. The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641 (2011).Article 

Google Scholar 
Eckhardt, S. et al. Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set. Atmos. Chem. Phys. 15, 9413–9433 (2015).CAS 
Article 

Google Scholar 
Hamilton, D. S. et al. Impact of changes to the atmospheric soluble iron deposition flux on ocean biogeochemical cycles in the anthropocene. Glob. Biogeochem. Cycle 34, e2019GB006448 (2020).CAS 
Article 

Google Scholar 
Kramer, S. J., Bisson, K. M. & Fischer, A. D. Observations of phytoplankton community composition in the Santa Barbara channel during the Thomas fire. J. Geophys. Res. Oceans 125, e2020JC016851 (2020).Article 

Google Scholar 
Kim, Y., Hatsushika, H., Muskett, R. R. & Yamazaki, K. Possible effect of boreal wildfire soot on Arctic sea ice and Alaska glaciers. Atmos. Environ. 39, 3513–3520 (2005).CAS 
Article 

Google Scholar 
Knapp, P. A. & Soulé, P. T. Spatio-temporal linkages between declining Arctic sea-ice extent and increasing wildfire activity in the Western United States. Forests 8, 313 (2017).Article 

Google Scholar 
Horvat, C. et al. The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean. Sci. Adv. https://doi.org/10.1126/sciadv.1601191 (2017).Ardyna, M. et al. Under-ice phytoplankton blooms: Shedding light on the “invisible” part of arctic primary production. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.608032 (2020).Altieri, K. E., Fawcett, S. E. & Hastings, M. G. Reactive nitrogen cycling in the atmosphere and ocean. Annu. Rev. Earth Planet. Sci. https://doi.org/10.1146/annurev-earth-083120-052147 (2021).Baker, A. R. & Jickells, T. D. Atmospheric deposition of soluble trace elements along the Atlantic Meridional Transect (AMT). Prog. Oceanogr. 158, 41–51 (2017).Article 

Google Scholar 
Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl. Acad. Sci. USA 117, 20438–20446 (2020).CAS 
Article 

Google Scholar 
Schmale, J. et al. Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories. Atmos. Chem. Phys. 22, 3067–3096 (2022).CAS 
Article 

Google Scholar 
Lewis, K. M., van Dijken, G. L. & Arrigo, K. R. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science 369, 198–202 (2020).CAS 
Article 

Google Scholar 
Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change 10, 892–903 (2020).CAS 
Article 

Google Scholar 
Tang, W. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021).CAS 
Article 

Google Scholar 
Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).Article 

Google Scholar 
Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: The experience of the Ocean-colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).CAS 
Article 

Google Scholar 
Gordon, H. R. & Wang, M. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Appl. Opt. 33, 443–452 (1994).CAS 
Article 

Google Scholar 
Werdell, P. J. & Bailey, S. W. An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sens. Environ. 98, 122–140 (2005).Article 

Google Scholar 
Hu, C., Lee, Z. & Franz, B. Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. J. Geophys. Res. Oceans https://doi.org/10.1029/2011JC007395 (2012).Tilmes, S. et al. Description and evaluation of tropospheric chemistry and aerosols in the Community Earth System Model (CESM1.2). Geosci. Model Dev. 8, 1395–1426 (2015).Article 

Google Scholar 
Bernstein, D. et al. Short-term impacts of 2017 western North American wildfires on meteorology, the atmosphere’s energy budget, and premature mortality. Environ. Res. Lett. 16, 064065 (2021).Article 

Google Scholar 
Liu, X. et al. Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geosci. Model Dev. 9, 505–522 (2016).CAS 
Article 

Google Scholar 
Suarez, M. J. et al. The GEOS-5 Data Assimilation System – Documentation of Versions 5.0.1, 5.1.0, and 5.2.0. No. NASA/TM-2008-104606-VOL-27 (2008).Janssens-Maenhout, G. et al. HTAP_v2.2: A mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15, 11411–11432 (2015).CAS 
Article 

Google Scholar 
Dentener, F. et al. Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom. Atmos. Chem. Phys. 6, 4321–4344 (2006).CAS 
Article 

Google Scholar 
Inness, A. et al. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. 19, 3515–3556 (2019).CAS 
Article 

Google Scholar 
Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).Article 

Google Scholar 
Carter, T. S. et al. How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America. Atmos. Chem. Phys. 20, 2073–2097 (2020).CAS 
Article 

Google Scholar 
Pan, X. et al. Six global biomass burning emission datasets: Intercomparison and application in one global aerosol model. Atmos. Chem. Phys. 20, 969–994 (2020).CAS 
Article 

Google Scholar 
Reddington, C. L. et al. Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations. Atmos. Chem. Phys. 16, 11083–11106 (2016).CAS 
Article 

Google Scholar 
Kiely, L. et al. New estimate of particulate emissions from Indonesian peat fires in 2015. Atmos. Chem. Phys. 19, 11105–11121 (2019).CAS 
Article 

Google Scholar 
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).Arrigo, K. R. et al. Phytoplankton blooms beneath the sea ice in the Chukchi Sea. Deep Sea Res. Pt. 2 105, 1–16 (2014).Article 

Google Scholar 
Geider, R. J., Maclntyre, H. L. & Kana, T. M. A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature. Limnol. Oceanogr. 43, 679–694 (1998).CAS 
Article 

Google Scholar 
Liefer, J. D., Garg, A., Campbell, D. A., Irwin, A. J. & Finkel, Z. V. Nitrogen starvation induces distinct photosynthetic responses and recovery dynamics in diatoms and prasinophytes. PLoS One 13, e0195705 (2018).Article 
CAS 

Google Scholar  More

Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834 (2010).Article 

Google Scholar 
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).Article 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).Article 

Google Scholar 
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).Article 

Google Scholar 
Morin, X. et al. Temporal stability in forest productivity increases with tree diversity due to asynchrony in species dynamics. Ecol. Lett. 17, 1526–1535 (2014).Article 

Google Scholar 
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574 (2015).Article 

Google Scholar 
De Boeck, H. J. et al. Patterns and drivers of biodiversity–stability relationships under climate extremes. J. Ecol. 106, 890–902 (2018).Article 

Google Scholar 
Grossiord, C. Having the right neighbors: how tree species diversity modulates drought impacts on forests. N. Phytol. 228, 42–49 (2020).Article 

Google Scholar 
O’Brien, M. J. et al. Resistance of tropical seedlings to drought is mediated by neighbourhood diversity. Nat. Ecol. Evol. 1, 1643–1648 (2017).Article 

Google Scholar 
Gazol, A. & Camarero, J. J. Functional diversity enhances silver fir growth resilience to an extreme drought. J. Ecol. 104, 1063–1075 (2016).Article 

Google Scholar 
Pretzsch, H., Schütze, G. & Uhl, E. Resistance of European tree species to drought stress in mixed versus pure forests: evidence of stress release by inter-specific facilitation. Plant Biol. 15, 483–495 (2013).Article 

Google Scholar 
Grossiord, C. et al. Tree diversity does not always improve resistance of forest ecosystems to drought. P. Natl Acad. Sci. USA 111, 14812–14815 (2014).Article 

Google Scholar 
Grossiord, C. et al. Does drought influence the relationship between biodiversity and ecosystem functioning in boreal forests. Ecosystems 17, 394–404 (2014).Article 

Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. P. Natl Acad. Sci. USA 100, 12765 (2003).Article 

Google Scholar 
Lloret, F. et al. Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests. Ecology 88, 2270–2279 (2007).Article 

Google Scholar 
He, Q. & Bertness, M. D. Extreme stresses, niches, and positive species interactions along stress gradients. Ecology 95, 1437–1443 (2014).Article 

Google Scholar 
Hafner, B. D. et al. Hydraulic redistribution under moderate drought among English oak, European beech and Norway spruce determined by deuterium isotope labeling in a split-root experiment. Tree Physiol. 37, 950–960 (2017).Article 

Google Scholar 
Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. For. Rep. 2, 45–61 (2016).Article 

Google Scholar 
Vitali, V., Forrester, D. I. & Bauhus, J. Know your neighbours: drought response of Norway spruce, silver fir and Douglas fir in mixed forests depends on species identity and diversity of tree neighbourhoods. Ecosystems 21, 1215–1229 (2018).Article 

Google Scholar 
The State of the World’s Forests 2020: Forests, Biodiversity and People (FAO and UNEP, 2020).Zhang, J., Fu, B., Stafford-Smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet Sustainable Development Goal 15. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01332-9 (2020).Schulze, K., Malek, Ž. & Verburg, P. H. Towards better mapping of forest management patterns: a global allocation approach. For. Ecol. Manage. 432, 776–785 (2019).Article 

Google Scholar 
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00976-6 (2021).Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 

Google Scholar 
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).Article 

Google Scholar 
Blackman, C. et al. Leaf hydraulic vulnerability is related to conduit dimensions and drought resistance across a diverse range of woody angiosperms. N. Phytol. 188, 1113–1123 (2010).Article 

Google Scholar 
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Article 

Google Scholar 
Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. P. Natl Acad. Sci. USA 116, 587–592 (2019).Article 

Google Scholar 
Tomppo, E. et al. National Forest Inventories: Pathways for Common Reporting (Springer, 2010).Chirici, G. et al. National Forest Inventories: Contributions to Forest Biodiversity Assessments (Springer, 2011).Magnussen, S., Smith, B. & Uribe, S. National Forest inventories in North America for monitoring forest tree species diversity. Plant Biosyst. 141, 113–122 (2007).Article 

Google Scholar 
Lesiv, M. et al. Global forest management data for 2015 at a 100 m resolution. Sci. Data 9, 199 (2022).Article 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A. Multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850 (2013).Article 

Google Scholar 
Forest Resources Assessment 2015 (FAO, 2015).Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).Article 

Google Scholar 
Didan, K. & Brreto, A. NASA MEaSUREs Vegetation Index and Phenology (VIP) Phenology EVI2 Yearly Global 0.05Deg CMG, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MEaSUREs/VIP/VIPPHEN_EVI2.004 (2016).Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Kline, T. J. B. Sample issues, methodological implications, and best practices. Can. J. Behav. Sci. 49, 71–77 (2017).Article 

Google Scholar 
Gourlet-Fleury, S. et al. Tropical forest recovery from logging: a 24 year silvicultural experiment from Central Africa. Phil. Trans. R. Soc. B 368, 20120302 (2013).Article 

Google Scholar 
Obiang, N. L. E. et al. Spatial pattern of central African rainforests can be predicted from average tree size. Oikos 119, 1643–1653 (2010).Article 

Google Scholar 
Plotkin, J. B. et al. Predicting species diversity in tropical forests. P. Natl Acad. Sci. USA 97, 10850–10854 (2000).Article 

Google Scholar 
Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

Google Scholar 
Tukey, J. W. Exploratory Data Analysis (Addison-Wesley,1977). More

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

Berendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.CAS 
PubMed 
Article 

Google Scholar 
Haas D, Défago G. Biological control of soil-borne pathogens by fluorescent pseudomonads. Nat Rev Microbiol. 2005;3:307–19.CAS 
PubMed 
Article 

Google Scholar 
Whipps JM. Microbial interactions and biocontrol in the rhizosphere. J Exp Bot. 2001;52:487–511.CAS 
PubMed 
Article 

Google Scholar 
Mendes R, Kruijt M, De Bruijn I, Dekkers E, Van Der Voort M, Schneider J, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 2011;332:1097–100.CAS 
PubMed 
Article 

Google Scholar 
Jousset A, Becker J, Chatterjee S, Karlovsky P, Scheu S, Eisenhauer N. Biodiversity and species identity shape the antifungal activity of bacterial communities. Ecology 2014;95:1184–90.PubMed 
Article 

Google Scholar 
Becker J, Eisenhauer N, Scheu S, Jousset A. Increasing antagonistic interactions cause bacterial communities to collapse at high diversity. Ecol Lett. 2012;15:468–74.PubMed 
Article 

Google Scholar 
Hu J, Wei Z, Friman VP, Gu SH, Wang XF, Eisenhauer N, et al. Probiotic diversity enhances rhizosphere microbiome function and plant disease suppression. mBio. 2016;7:e01790–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mehrabi Z, McMillan VE, Clark IM, Canning G, Hammond-Kosack KE, Preston G, et al. Pseudomonas spp. diversity is negatively associated with suppression of the wheat take-all pathogen. Sci Rep. 2016;6:1–10.Article 
CAS 

Google Scholar 
Ma Z, Geudens N, Kieu NP, Sinnaeve D, Ongena M, Martins JC, et al. Biosynthesis, chemical structure, and structure-activity relationship of orfamide lipopeptides produced by Pseudomonas protegens and related species. Front Microbiol. 2016;7:1–16.
Google Scholar 
Yan Q, Philmus B, Chang JH, Loper JE. Novel mechanism of metabolic co-regulation coordinates the biosynthesis of secondary metabolites in Pseudomonas protegens. Elife 2017;6:e22835.PubMed 
PubMed Central 
Article 

Google Scholar 
Ramette A, Moënne-Loccoz Y, Défago G. Prevalence of fluorescent pseudomonads producing antifungal phloroglucinols and/or hydrogen cyanide in soils naturally suppressive or conducive to tobacco black root rot. FEMS Microbiol Ecol. 2003;44:35–43.CAS 
PubMed 
Article 

Google Scholar 
Raaijmakers JM, Weller DM. Natural Plant Protection by 2,4-Diacetylphloroglucinol-Producing Pseudomonas spp. in Take-All Decline Soils. Mol Plant-Microbe Interact. 1998;11:144–52.CAS 
Article 

Google Scholar 
Murata K, Suenaga M, Kai K. Genome Mining Discovery of Protegenins A–D, Bacterial Polyynes Involved in the Antioomycete and Biocontrol Activities of Pseudomonas protegens. ACS Chem Biol. 2021. https://pubs.acs.org/doi/10.1021/acschembio.1c00276. Online ahead of print.Achkar J, Xian M, Zhao H, Frost JW. Biosynthesis of Phloroglucinol. J Am Chem Soc. 2005;127:5332–3.CAS 
PubMed 
Article 

Google Scholar 
Bangera MG, Thomashow LS. Identification and Characterization of a Gene Cluster for Synthesis of the Polyketide Antibiotic 2,4-Diacetylphloroglucinol from Pseudomonas fluorescens Q2-87. J Bacteriol. 1999;181:3155–63.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bottiglieri M, Keel C. Characterization of PhlG, a hydrolase that specifically degrades the antifungal compound 2,4-diacetylphloroglucinol in the biocontrol agent Pseudomonas fluorescens CHA0. Appl Environ Microbiol. 2006;72:418–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yan X, Yang R, Zhao R-X, Han J-T, Jia W-J, Li D-Y, et al. Transcriptional Regulator PhlH Modulates 2,4-Diacetylphloroglucinol Biosynthesis in Response to the Biosynthetic Intermediate and End Product. Appl Environ Microbiol. 2017;83:e01419–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dorrestein PC, Yeh E, Garneau-Tsodikova S, Kelleher NL, Walsh CT. Dichlorination of a pyrrolyl-S-carrier protein by FADH2- dependent halogenase PltA during pyoluteorin biosynthesis. Proc Natl Acad Sci USA. 2005;102:13843–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas MG, Burkart MD, Walsh CT. Conversion of L-proline to pyrrolyl-2-carboxyl-S-PCP during undecylprodigiosin and pyoluteorin biosynthesis. Chem Biol. 2002;9:171–84.CAS 
PubMed 
Article 

Google Scholar 
Schnider-Keel U, Seematter A, Maurhofer M, Blumer C, Duffy B, Gigot-Bonnefoy C, et al. Autoinduction of 2,4-diacetylphloroglucinol biosynthesis in the biocontrol agent Pseudomonas fluorescens CHA0 and repression by the bacterial metabolites salicylate and pyoluteorin. J Bacteriol. 2000;182:1215–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brodhagen M, Henkels MD, Loper JE. Positive autoregulation and signaling properties of pyoluteorin, an antibiotic produced by the biological control organism Pseudomonas fluorescens Pf-5. Appl Environ Microbiol. 2004;70:1758–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maurhofer M, Baehler E, Notz R, Martinez V, Keel C. Cross Talk between 2,4-Diacetylphloroglucinol-Producing Biocontrol Pseudomonads on Wheat Roots. Appl Environ Microbiol. 2004;70:1990–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clifford JC, Buchanan A, Vining O, Kidarsa TA, Chang JH, McPhail KL, et al. Phloroglucinol functions as an intracellular and intercellular chemical messenger influencing gene expression in Pseudomonas protegens. Environ Microbiol. 2016;18:3296–308.CAS 
PubMed 
Article 

Google Scholar 
Kidarsa TA, Goebel NC, Zabriskie TM, Loper JE. Phloroglucinol mediates cross-talk between the pyoluteorin and 2,4-diacetylphloroglucinol biosynthetic pathways in Pseudomonas fluorescens Pf-5. Mol Microbiol. 2011;81:395–414.CAS 
PubMed 
Article 

Google Scholar 
Hassan KA, Johnson A, Shaffer BT, Ren Q, Kidarsa TA, Elbourne LDH, et al. Inactivation of the GacA response regulator in Pseudomonas fluorescens Pf-5 has far-reaching transcriptomic consequences. Environ Microbiol. 2010;12:899–915.CAS 
PubMed 
Article 

Google Scholar 
Dubuis C, Haas D. Cross-species GacA-controlled induction of antibiosis in pseudomonads. Appl Environ Microbiol. 2007;73:650–4.CAS 
PubMed 
Article 

Google Scholar 
Hansen ML, He Z, Wibowo M, Jelsbak L. A Whole-Cell Biosensor for Detection of 2,4- Diacetylphloroglucinol (DAPG)-Producing Bacteria from Grassland Soil. Appl Environ Microbiol. 2021;87:e01400–e01420.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hesse C, Schulz F, Bull CT, Shaffer BT, Yan Q, Shapiro N, et al. Genome‐based evolutionary history of Pseudomonas spp. Environ Microbiol. 2018;20:2142–59.CAS 
PubMed 
Article 

Google Scholar 
Lozano-Andrade CN, Strube ML, Kovács ÁT. Complete genome sequences of four soil-derived isolates for studying synthetic bacterial community assembly. Microbiol Resour Announc. 2021;10:e00848–21.CAS 
PubMed Central 
Article 

Google Scholar 
Le Roux M, Kirkpatrick RL, Montauti EI, Tran BQ, Brook Peterson S, Harding BN, et al. Kin cell lysis is a danger signal that activates antibacterial pathways of Pseudomonas aeruginosa. Elife. 2015;2015:1–65.
Google Scholar 
Tyc O, van den Berg M, Gerards S, van Veen JA, Raaijmakers JM, de Boer W, et al. Impact of interspecific interactions on antimicrobial activity among soil bacteria. Front Microbiol. 2014;5:1–10.
Google Scholar 
Qi SS, Bogdanov A, Cnockaert M, Acar T, Ranty-Roby S, Coenye T, et al. Induction of antibiotic specialized metabolism by co-culturing in a collection of phyllosphere bacteria. Environ Microbiol. 2021;23:2132–51.CAS 
PubMed 
Article 

Google Scholar 
Cornforth DM, Foster KR. Competition sensing: The social side of bacterial stress responses. Nat Rev Microbiol. 2013;11:285–93.CAS 
PubMed 
Article 

Google Scholar 
LeRoux M, Peterson SB, Mougous JD. Bacterial danger sensing. J Mol Biol. 2015;427:3744–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Westhoff S, van Wezel GP, Rozen DE. Distance-dependent danger responses in bacteria. Curr Opin Microbiol. 2017;36:95–101.PubMed 
Article 

Google Scholar 
Davies J, Spiegelman GB, Yim G. The world of subinhibitory antibiotic concentrations. Curr Opin Microbiol. 2006;9:445–53.CAS 
PubMed 
Article 

Google Scholar 
Garbeva P, Silby MW, Raaijmakers JM, Levy SB, Boer WDE. Transcriptional and antagonistic responses of Pseudomonas fluorescens Pf0-1 to phylogenetically different bacterial competitors. ISME J. 2011;5:973–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abrudan MI, Smakman F, Grimbergen AJ, Westhoff S, Miller EL, Van Wezel GP, et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc Natl Acad Sci USA. 2015;112:11054–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kehe J, Ortiz A, Kulesa A, Gore J, Blainey PC, Friedman J. Positive interactions are common among culturable bacteria. Sci Adv. 2021;7:1–10.Article 
CAS 

Google Scholar 
Yang KM, Kim JS, Kim HS, Kim YY, Oh JK, Jung HW, et al. Lactobacillus reuteri AN417 cell-free culture supernatant as a novel antibacterial agent targeting oral pathogenic bacteria. Sci Rep. 2021;11:1–16.Article 
CAS 

Google Scholar 
Dubern JF, Lugtenberg BJJ, Bloemberg GV. The ppuI-rsaL-ppuR quorum-sensing system regulates biofilm formation of Pseudomonas putida PCL1445 by controlling biosynthesis of the cyclic lipopeptides putisolvins I and II. J Bacteriol. 2006;188:2898–906.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wellington S, Peter Greenberg E. Quorum sensing signal selectivity and the potential for interspecies cross talk. mBio. 2019;10:e00146–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Duffy BK, Défago G. Zinc Improves Biocontrol of Fusarium Crown and Root Rot of Tomato by Pseudomonas fluorescens and Represses the Production of Pathogen Metabolites Inhibitory to Bacterial Antibiotic Biosynthesis. Phytopathology. 1997;87:1250–7.CAS 
PubMed 
Article 

Google Scholar 
Li W, Estrada-de los Santos P, Matthijs S, Xie G-L, Busson R, Cornelis P, et al. Promysalin, a Salicylate-Containing Pseudomonas putida Antibiotic, Promotes Surface Colonization and Selectively Targets Other Pseudomonas. Chem Biol. 2011;18:1320–30.CAS 
PubMed 
Article 

Google Scholar 
Parnell JJ, Berka R, Young HA, Sturino JM, Kang Y, Barnhart DM, et al. From the lab to the farm: An industrial perspective of plant beneficial microorganisms. Front Plant Sci. 2016;7:1–12.Article 

Google Scholar 
Berendsen RL, van Verk MC, Stringlis IA, Zamioudis C, Tommassen J, Pieterse CMJ, et al. Unearthing the genomes of plant-beneficial Pseudomonas model strains WCS358, WCS374 and WCS417. BMC Genomics. 2015;16:1–23.CAS 
Article 

Google Scholar 
Niu B, Paulson JN, Zheng X, Kolter R. Simplified and representative bacterial community of maize roots. Proc Natl Acad Sci USA. 2017;114:E2450–E2459.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhuang L, Li Y, Wang Z, Yu Y, Zhang N, Yang C, et al. Synthetic community with six Pseudomonas strains screened from garlic rhizosphere microbiome promotes plant growth. Micro Biotechnol. 2021;14:488–502.CAS 
Article 

Google Scholar 
Zobel S, Benedetti I, Eisenbach L, De Lorenzo V, Wierckx N, Blank LM. Tn7-Based Device for Calibrated Heterologous Gene Expression in Pseudomonas putida. ACS Synth Biol. 2015;4:1341–51.CAS 
PubMed 
Article 

Google Scholar 
Van Gestel J, Weissing FJ, Kuipers OP, Kovács ÁT. Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms. ISME J. 2014;8:2069–79.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hmelo LR, Borlee BR, Almblad H, Love ME, Randall TE, Tseng BS, et al. Precision-engineering the Pseudomonas aeruginosa genome with two-step allelic exchange. Nat Protoc. 2015;10:1820–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yang L, Hengzhuang W, Wu H, Damkiær S, Jochumsen N, Song Z. et al. Polysaccharides serve as scaffold of biofilms formed by mucoid Pseudomonas aeruginosa. FEMS Immunol Med Microbiol. 2012;65:366–76.CAS 
PubMed 
Article 

Google Scholar  More