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    First observation of seasonal variations in the meat and co-products of the snow crab (Chionoecetes opilio) in the Barents Sea

    Collection of crabsMale snow crabs of legal size and with hard shells were caught by the commercial SC vessel Northeastern (Opilio AS) using traditional SC pots in the NEAFC area (N 75° 49.2 E 37° 39.2). SCs were stored live onboard the vessel and subsequently delivered to Nofima’s facilities in Tromsø (N 69° 39). The crabs were caught in June, and September 2016, February, April, and December 2017 and will only be referred to by month. Upon slaughtering, data from individual crabs was obtained by recording the weight of the whole animal, clusters + claws, hemolymph, hepatopancreas, and gills (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). Subsequently, different fractions were pooled and analysed as outlined below.Biochemical- and meat content-analysesThe biochemical analyses were performed on each month of analysis (September, December, February, April, and June) and consisted of water, protein, lipid, and ash contents. All analyses were performed on meat (i.e., main product) and the different co-products divided in the following ways: pooled internal organs (mainly hemolymph, hepatopancreas and gonads) with and without added carapace, hemolymph alone and hepatopancreas alone. Lipid class and fatty acid analyses were performed on the lipid storage organ hepatopancreas. Each biochemical analysis consisted of co-products from 10 randomly selected animals. All biochemical analyses (water-, ash-, lipid and protein-content) were determined by Toslab (9266 Tromsø, Norway), lipid classes and fatty acid identifications were performed by Biolab (5141 Fyllingsdalen, Norway). Both are commercial laboratories accredited according to ISO 17025.Water and dry matter content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated drying cabinet at 103 °C ± 1 °C. After precisely 4 h 30 min, the crucible was allowed to cool down in a desiccator before being weighed. Water and dry matter contents were calculated according to Eqs. (1) and (2) respectively:$$Waterleft(%right)=frac{left(a-bright)}{w}times 100$$
    (1)
    $$Dry;matter;content left(%right)=frac{left(b-cright)}{w}times 100$$
    (2)
    where a = weight (g) of crucible with weighed sample; b = weight (g) of crucible with dried sample; c = weight (g) of crucible; w = weight (g) of weighed sample9.Ash content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated muffle furnace at 550 °C ± 20 °C. After 16 h, the crucible was allowed to cool down in a desiccator before being weighed. The ash content was calculated according to Eq. (3):$$Ash left(%right)=frac{left(d-cright)}{{w}^{^{prime}}}times 100$$
    (3)
    where d = weight (g) of crucible with calcinated sample; c = weight (g) of crucible; w′ =  weight (g) of dry matter sample10.Fat contentThe fat in the samples was extracted with a polar solvent consisting of CHCl3, MeOH and H2O in a mixing ratio of 1:2:0.8 to give a single-phase system. 5–20 g of material was weighed into a 250 ml test tube. H2O was added so that water content plus added material corresponded to 16 ml. MeOH (40 ml) and CHCl3 (20 ml) were added. The mix was homogenized for 60 s. CHCl3 (20 ml) was again added and the mix was homogenized for 30 s H2O (20 ml) was added, and the mix was homogenized again for 30 s. The test tube was sealed and cooled in a water bath with ice. The emulsion was quickly filtered out through a small cotton ball in a funnel. The upper layer of the collected liquid consisting of MeOH and H2O was removed by suction. 5–20 ml of the remaining CHCl3 phase was transferred to a tared evaporation dish with a positive displacement pipette. The solvent was evaporated with an infrared lamp. The dish was cooled in a desiccator and weighed. The fat content was calculated according to the Eq. (4):$$Fat;content left(%right)=frac{dtimes b}{W times left(c-frac{d}{mathrm{0,92}}right)}times 100$$
    (4)
    where b = ml CHCl3 added; c = ml CHCl3 transferred; d = weight of fat in evaporation dish (g); 0.92 = specific gravity for fat, g/ml; w = weight (g) of the sample11.Protein contentProtein content analysis was performed with a fully automated Kjeltec 8400 (Foss Analytics, Denmark). 0.5–1 g of nitrogen free paper of previously dried sample was allowed to be digested in a digestion unit with concentrated H2SO4 (17.5 ml) and two catalyser tablets for 2 h 20 min at 420 °C. The digested liquid was transferred to the titration unit after cooling and was titrated fully automated by the equipment.Blanking was performed only with nitrogen free paper, titration with standardized HCl solution and 1% (w/w) boric acid solution containing a pH sensitive indicator. The protein content was calculated according to the Eq. (5):$$Protein;content left(%right)=frac{mathrm{14,007}times N times f times left(a-bright)}{wtimes 1000}times 100$$
    (5)
    where 14.007 = atomic weight of Nitrogen; N = Normality of the titration solution; f = protein factor (6.25); w = weight (g) of weighed sample; a = ml of HCl consumed for sample titration; b = ml of HCl consumed for blank titration12.
    Cis-fatty acid and trans-fatty acids compositionThis method was designed to determine the fatty acid composition of marine oils and marine oil esters in relative (area-%) values, and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in absolute (g/100 g) values using a bonded polyglycol liquid phase in a flexible fused silica capillary column. C23:0 fatty acid was used as an internal standard.For methyl esterification of oil samples for the analysis of cis-fatty acids, two drops of the oil sample were weighed and transferred to a 15 ml test tube with a screw cap. The test amount should be between 20 and 35 mg. Exactly 900 µl of the internal standard solution was added. The solvent was evaporated by nitrogen on a heating block at 80 °C. NaOH solution (1.5 ml, 0.5 N) was added. The mix was incubated in boiling water for 5 min and cooled in cold water. A 15% BF3-solution (2 ml) was added. The mix was again incubated in boiling water for 30 min and cooled to 30–40 °C. Isooctane (1 ml) was added. A cork was set, and the mixture grated with gentle movements for 30 s. Saturated NaCl (5 ml) was added immediately. A cork was set, and the mixture was again grated with gentle movements for 30 s. The isooctane phase was transferred to a test tube with a lid. The test tube was centrifuged at 3000 rpm if phase separation was difficult to achieve. Another 1 ml of isooctane was added to the test tube. A cork was set, and the mixture grated with gentle movements for 30 s. The isooctane phase was transferred to the same test tube with a lid. 5 µl of this transferred isooctane phase was diluted into a new test tube with 1 ml of isooctane.The procedure for methyl esterification of trans-fatty acids was identical to that for methyl esterification of cis-fatty acids, with one exception: incubation time after the addition of BF3-solution was 5 min.For the GC analysis an analytical capillary column (60 m × 0.25 mm × 0.25 µm-70% Cyanopropyl Polysilphenylene-siloxane) was used. (P/N: 054623, manufacturer: SGE). During the analysis the gas valves on the wall panel for synthetic air and hydrogen were left open.Two different GC programs were used for the analysis (Table 1).Table 1 GC-program for analysis of fatty acids.Full size tableThe identification of the different fatty acid methyl esters was performed by comparing the pattern and relative retention times by chromatography of different standards. Empirical response factor was used in quantifying fatty acids, based on calibration solution analysis with equal amounts of included fatty acid methyl esters (GLC-793, Nu-Chek-Prep Inc. Elysian MN, USA). It was calculated according to the Eq. (6):$${RF}_{em }=frac{{A}_{23:0}}{{A}_{FS}}$$
    (6)
    The absolute amount of each fatty acid, calculated as fatty acid methyl ester was calculated according to the Eq. (7):$${C}_{FS}(g/100)=left(frac{{A}_{FS }times {IS}_{W} times {RF}_{em} }{ {A}_{23:0} times W}right)times 100$$
    (7)
    where AFS = Area of the fatty acid A23:0 = Area of internal standard; ISW = Number of milligrams (mg) internal standard added; RFem = Empirical response factor to the fatty acid with reference to 23:0; W = Weighed sample amount in milligrams (mg); 100 = Factor for conversion to g/100 g13,14,15.Lipid classesThe dominant lipid classes were separated by HPLC equipped with a LiChroCART 125-4, diol 5 µm column and a Charged Aerosol Detector (CAD), using a tertiary gradient mobile phase composition. The fat was extracted as previously described (“Fat content”). A suitable amount of CH3Cl was added to the fat sample, the mix was pipetted into a tared test tube and evaporated on a heating block under nitrogen. The temperature of the heating block must be at 60 °C. The test tube with the evaporated sample was weighed and the weight of the fat calculated. The sample was diluted with an appropriate amount of CH3Cl. Prior to injection the CAD detector was programmed with these settings: range = 500, Filter = Med, Offset = 5, T = 30 °C. The gradient profile is shown Table 2.Table 2 Gradient profile for separation of lipid classes.Full size tableThe quantification was based on external standards with a purity ≥ 98%. Triacylglycerols (TAG) in natural marine oils have a large elution range compared to the other lipid classes, therefore a standard control oil (fish oil) for the preparation of the TAG standard curve was used. This provides a better adaptation to real samples compared to a pure TAG compound.Meat contentMeat content was measured on cooked clusters from the middle of the merus on the first walking leg as an area-percentage of meat-to-shell using an elliptic area formula; Internal height and width of shells and external height and width of muscle was measured, width (w) and height (h) were multiplied to each other and π to calculate the elliptical areas (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). The meat content (MC) was defined as the percentage of space occupied by meat according to the Eq. (8) and the hepatopancreas index (HI) was calculated according to the Eq. (9):$$MCleft(%right)=frac{h;muscletimes w;muscletimes pi }{h;shelltimes w;shelltimes pi }times 100$$
    (8)
    $$HIleft(%right)=frac{{W}_{hept}}{{W}_{live}}times 100$$
    (9)
    where Whept is the weight of the hepatopancreas and Wlive is the live weight of the crab (n = 56 September, n = 50 December, n = 70 February, n = 70 April, n = 50 June).Graphs, ordinary one-way ANOVA, and linear regressions were made using GraphPad Prism version 7.03 (CA, USA). Meat content data failed the normality test (Shapiro–Wilk) and was analysed using Kruskal–Wallis One Way Analyses of Variance on Ranks and statistical significance was assumed when P  More

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    Reply to: Old-growth forest carbon sinks overestimated

    1.Gundersen, P. Old-growth forest carbon sinks overestimated. Nature https://doi.org/10.1038/s41586-021-03266-z (2021).2.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    3.Yang, Y., Luo, Y. & Finzi, A. C. Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).CAS 
    Article 

    Google Scholar 
    4.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Fontaine, S. et al. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277–280 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Houlton, B. Z. & Dahlgren, R. A. Convergent evidence for widespread rock nitrogen sources in Earth’s surface environment. Science 62, 58–62 (2018).ADS 
    Article 

    Google Scholar 
    7.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 
    Article 

    Google Scholar 
    8.Hyvönen, R. et al. The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. New Phytol. 173, 463–480 (2006).Article 

    Google Scholar 
    9.Clark, D. A. et al. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecol. Appl. 11, 371–384 (2001).Article 

    Google Scholar 
    10.Wharton, S. & Falk, M. Climate indices strongly influence old-growth forest carbon exchange. Environ. Res. Lett. 11, 044016 (2016).ADS 
    Article 

    Google Scholar 
    11.Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nat. Commun. 7, 13717 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    12.Luyssaert, S. et al. Toward a consistency cross-check of eddy covariance flux-based and biometric estimates of ecosystem carbon balance. Glob. Biogeochem. Cycles 23, https://doi.org/10.1029/2008GB003377 (2009).13.Nord-Larsen, T., Vesterdal, L., Bentsen, N. S. & Larsen, J. B. Ecosystem carbon stocks and their temporal resilience in a semi-natural beech-dominated forest. For. Ecol. Manage. 447, 67–76 (2019).Article 

    Google Scholar 
    14.Kwon, H., Law, B. E., Thomas, C. K. & Johnson, B. G. The influence of hydrological variability on inherent water use efficiency in forests of contrasting composition, age, and precipitation regimes in the Pacific Northwest U.S. Agric. For. Meteorol. 249, 488–500 (2018).ADS 
    Article 

    Google Scholar 
    15.Law, B. E. & Berner, L. T. NACP TERRA-PNW: Forest Plant Traits, NPP, Biomass, and Soil Properties 1999–2014 https://doi.org/10.3334/ORNLDAAC/1292 (ORNL DAAC, 2015).16.Falk, M., Wharton, S., Schroeder, M., Ustin, S. L. & Paw, U. K. T. Flux partitioning in an old-growth forest: seasonal and interannual dynamics. Tree Physiol. 28, 509–520 (2008).CAS 
    Article 

    Google Scholar 
    17.FLUXNET2015 Dataset: Data Processing https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/ (Fluxnet, accessed 25 April 2020).18.Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).ADS 
    Article 

    Google Scholar 
    19.Magnani, F. et al. The human footprint in the carbon cycle of temperate and boreal forests. Nature 447, 849–851 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    20.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Zhou, G. et al. Old-growth forests can accumulate carbon in soils. Science 314, 1417–1417 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    22.Nabuurs, G.-J. et al. First signs of carbon sink saturation in European forest biomass. Nat. Clim. Chang. 3, 792–796 (2013).ADS 
    CAS 
    Article 

    Google Scholar  More

  • in

    A trade-off between plant and soil carbon storage under elevated CO2

    1.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 

    Google Scholar 
    2.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Keenan, T. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

    Google Scholar 
    5.Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long‐term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    6.Norby, R. J. et al. Forest response to elevated CO2 is conserved across a broad range of productivity. Proc. Natl Acad. Sci. USA 102, 18052–18056 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.van Groenigen, K. J., Qi, X., Osenberg, C. W., Luo, Y. & Hungate, B. A. Faster decomposition under increased atmospheric CO2 limits soil carbon storage. Science 344, 508 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

    Google Scholar 
    9.Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).ADS 
    CAS 

    Google Scholar 
    10.Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).ADS 
    CAS 

    Google Scholar 
    11.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Chang. 6, 751–758 (2016).ADS 

    Google Scholar 
    12.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Chang. 9, 684–689 (2019).ADS 
    CAS 

    Google Scholar 
    13.Reich, P. B., Hungate, B. A. & Luo, Y. Carbon-nitrogen interactions in terrestrial ecosystems in response to rising atmospheric carbon dioxide. Annu. Rev. Ecol. Evol. Syst. 37, 611–636 (2006).
    Google Scholar 
    14.Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. 42, 181–203 (2011).
    Google Scholar 
    15.Terrer, C. et al. Ecosystem responses to elevated CO2 governed by plant–soil interactions and the cost of nitrogen acquisition. New Phytol. 217, 507–522 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Olson, J. S. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44, 322–331 (1963).
    Google Scholar 
    17.Hungate, B. A. et al. Assessing the effect of elevated carbon dioxide on soil carbon: a comparison of four meta‐analyses. Glob. Change Biol. 15, 2020–2034 (2009).ADS 

    Google Scholar 
    18.Kuzyakov, Y., Horwath, W. R., Dorodnikov, M. & Blagodatskaya, E. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: no changes in pools, but increased fluxes and accelerated cycles. Soil Biol. Biochem. 128, 66–78 (2019).CAS 

    Google Scholar 
    19.Tian, H. et al. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: current status and future directions. Glob. Biogeochem. Cycles 29, 775–792 (2015).ADS 
    CAS 

    Google Scholar 
    20.Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).ADS 

    Google Scholar 
    21.Nie, M., Lu, M., Bell, J., Raut, S. & Pendall, E. Altered root traits due to elevated CO2: a meta‐analysis. Glob. Ecol. Biogeogr. 22, 1095–1105 (2013).
    Google Scholar 
    22.Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).CAS 

    Google Scholar 
    23.Treseder, K. K. A meta‐analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol. 164, 347–355 (2004).
    Google Scholar 
    24.Jastrow, J. D. et al. Elevated atmospheric carbon dioxide increases soil carbon. Glob. Change Biol. 11, 2057–2064 (2005).ADS 

    Google Scholar 
    25.Carrillo, Y., Dijkstra, F. A., LeCain, D. & Pendall, E. Mediation of soil C decomposition by arbuscular mycorrizhal fungi in grass rhizospheres under elevated CO2. Biogeochemistry 127, 45–55 (2016).CAS 

    Google Scholar 
    26.Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).CAS 

    Google Scholar 
    27.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).ADS 

    Google Scholar 
    28.Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).ADS 
    CAS 

    Google Scholar 
    29.Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).ADS 

    Google Scholar 
    30.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).
    Google Scholar 
    32.Sokol, N. W., Kuebbing, S. E., Karlsen‐Ayala, E. & Bradford, M. A. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytol. 221, 233–246 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Evans, R. D. et al. Greater ecosystem carbon in the Mojave Desert after ten years exposure to elevated CO2. Nat. Clim. Chang. 4, 394–397 (2014).ADS 
    CAS 

    Google Scholar 
    34.Walker, A. P. et al. FACE-MDS Phase 2: Model Output https://www.osti.gov/dataexplorer/biblio/dataset/1480327 (2018).35.Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).ADS 

    Google Scholar 
    36.Sulman, B. N. et al. Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).ADS 
    CAS 

    Google Scholar 
    37.Shi, M., Fisher, J. B., Brzostek, E. R. & Phillips, R. P. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model. Glob. Change Biol. 22, 1299–1314 (2016).ADS 

    Google Scholar 
    38.Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    40.Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Chang. 3, 909–912 (2013).ADS 
    CAS 

    Google Scholar 
    41.Terrer, C. Report of Mutualistic Associations, Nutrients, and Carbon Under eCO2 (ROMANCE) v1.0 Dataset. https://doi.org/10.6084/m9.figshare.11704491.v7 (2020).42.Dieleman, W. I. J. et al. Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob. Change Biol. 18, 2681–2693 (2012).ADS 

    Google Scholar 
    43.Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. in Introduction to Meta‐Analysis 225–238 (John Wiley & Sons, 2009).44.Del Re, A. C. & Hoyt, W. T. MAd: meta-analysis with mean differences. R Package Version 08-2 https://cran.r-project.org/package=MAd (2014).45.Song, J. & Wan, S. A Global Database Of Plant Production And Carbon Exchange From Global Change Manipulative Experiments https://doi.org/10.6084/m9.figshare.7442915.v9 (2020).46.Viechtbauer, W. Conducting meta-analyses in R with the metafor Package. J. Stat. Softw. 36, https://doi.org/10.18637/jss.v036.i03 (2010).47.Osenberg, C. W., Sarnelle, O., Cooper, S. D. & Holt, R. D. Resolving ecological questions through meta-analysis: goals, metrics, and models. Ecology 80, 1105–1117 (1999).
    Google Scholar 
    48.Rubin, D. B. & Schenker, N. Multiple imputation in health‐are databases: an overview and some applications. Stat. Med. 10, 585–598 (1991).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Lajeunesse, M. J. Facilitating systematic reviews, data extraction and meta‐analysis with the METAGEAR package for R. Methods Ecol. Evol. 7, 323–330 (2016).
    Google Scholar 
    50.Van Lissa, C. J. MetaForest: exploring heterogeneity in meta-analysis using random forests. Preprint at https://psyarxiv.com/myg6s/ (2017).51.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, https://doi.org/10.18637/jss.v028.i05 (2008).52.Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, https://doi.org/10.18637/jss.v034.i12 (2010).53.van Groenigen, K. J. et al. Element interactions limit soil carbon storage. Proc. Natl Acad. Sci. USA 103, 6571–6574 (2006).ADS 

    Google Scholar 
    54.Wang, B. & Qiu, Y. L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).CAS 

    Google Scholar 
    55.Maherali, H., Oberle, B., Stevens, P. F., Cornwell, W. K. & McGlinn, D. J. Mutualism persistence and abandonment during the evolution of the mycorrhizal symbiosis. Am. Nat. 188, E113–E125 (2016).
    Google Scholar 
    56.Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).ADS 
    CAS 

    Google Scholar 
    57.Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Chang. 5, 528–534 (2015).ADS 

    Google Scholar 
    58.Zaehle, S. et al. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free‐Air CO2 Enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    60.Walker, A. P. et al. Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO2 enrichment experiments: model performance at ambient CO2 concentration. J. Geophys. Res. Biogeosci. 119, 937–964 (2014).ADS 
    CAS 

    Google Scholar 
    61.Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Schlesinger, W. et al. in Managed Ecosystems and CO2 197–212 (2006).63.Hungate, B. A. et al. Cumulative response of ecosystem carbon and nitrogen stocks to chronic CO2 exposure in a subtropical oak woodland. New Phytol. 200, 753–766 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Jordan, D. N. et al. Biotic, abiotic and performance aspects of the Nevada Desert Free-Air CO2 Enrichment (FACE) Facility. Glob. Change Biol. 5, 659–668 (1999).ADS 

    Google Scholar 
    65.Carrillo, Y., Dijkstra, F., LeCain, D., Blumenthal, D. & Pendall, E. Elevated CO2 and warming cause interactive effects on soil carbon and shifts in carbon use by bacteria. Ecol. Lett. 21, 1639–1648 (2018).
    Google Scholar 
    66.Mueller, K. E. et al. Impacts of warming and elevated CO2 on a semi‐arid grassland are non‐additive, shift with precipitation, and reverse over time. Ecol. Lett. 19, 956–966 (2016).CAS 

    Google Scholar 
    67.Zak, D. R., Pregitzer, K. S., Kubiske, M. E. & Burton, A. J. Forest productivity under elevated CO2 and O3: positive feedbacks to soil N cycling sustain decade‐long net primary productivity enhancement by CO2. Ecol. Lett. 14, 1220–1226 (2011).
    Google Scholar 
    68.Oleson, K. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) Report NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M (2013).69.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

    Google Scholar 
    70.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, https://doi.org/10.1029/2003GB002199 (2005).71.Haverd, V. et al. A new version of the CABLE land surface model (subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).ADS 
    CAS 

    Google Scholar 
    72.Lawrence, D. M. et al. The Community Land Model Version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).ADS 

    Google Scholar 
    73.Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).ADS 
    CAS 

    Google Scholar 
    74.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

    Google Scholar 
    75.Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).ADS 
    CAS 

    Google Scholar 
    76.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

    Google Scholar 
    77.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high‐resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
    Google Scholar 
    78.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    80.Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).ADS 
    CAS 

    Google Scholar 
    81.Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).ADS 

    Google Scholar  More

  • in

    Effects of rising CO2 levels on carbon sequestration are coordinated above and below ground

    In a paper in Nature, Terrer et al.1 reveal an unexpected trade-off between the effects of rising atmospheric carbon dioxide levels on plant biomass and on stocks of soil carbon. Contrary to the assumptions encoded in most computational models of terrestrial ecosystems, the accrual of soil carbon is not positively related to the amount of carbon taken up by plants for biomass growth when CO2 concentrations increase. Instead, the authors show that carbon accumulates in soils when there is a small boost in plant biomass growth in response to CO2, and declines when the growth of biomass is high. Terrer et al. propose that associations of plants with mycorrhizal soil fungi are a key factor in this relationship between the above- and below-ground responses to elevated CO2 levels.
    Read the paper: A trade-off between plant and soil carbon storage under elevated CO2
    Rising levels of atmospheric CO2 are thought to have driven an increase in the amount of carbon absorbed globally by land ecosystems over the past few decades, a phenomenon known as the CO2 fertilization effect2. This occurs because, at the scale of leaves, higher CO2 levels enhance photosynthesis and the efficiency with which resources (water, light and nutrients such as nitrogen) are used to assimilate CO2 and support biomass growth3. Evidence supporting the existence of the CO2 fertilization effect has been observed in experiments in which the atmosphere around plants or plant communities is enriched with CO2. But at the level of whole ecosystems, responses to CO2 enrichment are more difficult to track, because the effects are diluted throughout a chain of connected processes. Constraining estimates of the response of the global land carbon sink to rising CO2 levels therefore remains a major challenge (see go.nature.com/3vgvhj).Changes in soil carbon are inherently difficult to detect, and studies that assess the effects of elevated CO2 levels on soil-carbon stocks have been equivocal4. Terrer and colleagues set out to investigate these effects by carrying out a meta-analysis of 108 CO2-enrichment experiments. The authors estimate that, in these studies, soil-carbon stocks increased in non-forest sites but remained almost unchanged in forests. By evaluating the effects of multiple environmental variables, the authors found that, surprisingly, the best explanation for the observed patterns is that the changes in soil carbon stocks are inversely related to the changes in above-ground plant biomass: high accumulation of carbon in biomass was associated with soil-carbon loss, whereas low biomass accumulation was associated with soil-carbon gain. This relationship was evident only in experiments in which no nutrients had been added to the studied systems, leading the authors to propose that plant nutrient-acquisition strategies are responsible — which, in turn, depend on the mycorrhizal soil fungi associated with the plants.
    Soils linked to climate change
    A previous study reported5 that only a small increase in above-ground biomass occurs in CO2-enriched plants that associate with a particular family of mycorrhizae (arbuscular mycorrhizae; AM). AM-associated plants benefit from the fungi’s extensive network of hyphae (branching filaments that aid vegetative growth), which support the plants’ uptake of nitrogen from the soil solution. However, AM have only a limited ability to ‘mine’ nitrogen from organic matter in the soil. The availability of soil nitrogen therefore limits the increase of biomass growth of AM-associated plants in response to elevated CO2 levels. By contrast, plant species that associate with a different group of soil fungi (the ectomycorrhizae; ECM) exhibit a greater increase in above-ground biomass in CO2-enrichment studies, because some of their carbon is allocated to ECM that can mine for nitrogen5. Mining for nutrients by ECM is, however, thought to accelerate the decomposition of organic matter in soil.Terrer et al. now find that AM-associated plants produce a bigger increase in soil-carbon stocks in CO2-enrichment experiments than do ECM-associated plants. The authors suggest that this is because AM-associated plants allocate more carbon to fine roots and to compounds exuded by the roots, resulting in soil-carbon accrual (Fig. 1a). By contrast, nutrient acquisition by ECM-associated plants results in increased turnover — and therefore loss — of soil organic matter (Fig. 1b). Overall, this would lead to an ecosystem-scale trade-off between the amount of carbon sequestered in plants and that sequestered in soil, in a CO2-enriched atmosphere.

    Figure 1 | Proposed effects of elevation of atmospheric carbon dioxide levels. Terrer et al.1 suggest that associations of plants with different types of mycorrhizal soil fungi affect plant and soil responses to increases in atmospheric carbon dioxide levels. a, Plants that associate with arbuscular mycorrhizal fungi (grasses and some trees, in this study) do not ‘mine’ nitrogen (N, a nutrient) from the soil, and therefore do not produce much extra above-ground biomass when CO2 levels rise. Instead, they allocate carbon to fine roots and to root-exuded substances, resulting in soil-carbon accrual. Carbon dioxide produced from the respiration of soil microorganisms returns carbon to the atmosphere. b, Plants that associate with ectomycorrhizal fungi (only trees in this study) mine the soil for nitrogen, the uptake of which supports a bigger increase in biomass growth than in a. However, nutrient mining increases the rate of decomposition of organic matter in soil. The amount of carbon in the soil therefore decreases in response to elevated CO2 levels; microbial soil respiration is greater than in a.

    Most Earth-system models that account for land carbon-cycling processes assume that rising levels of atmospheric CO2 will increase plant growth, thus producing more plant litter and thereby increasing stocks of soil carbon6. The authors compared the changes in soil carbon and above-ground plant biomass predicted by various models, both in simulations of six open-air CO2-enrichment experiments, and in global simulations of historical and future increases in atmospheric CO2. None of the models reproduced the negative relationship between carbon sequestration by soil and growth in plant biomass that was observed in the current study.Terrer and co-workers’ findings thus provide another urgent warning that current climate models overestimate the amount of carbon that will be sequestered by land ecosystems as atmospheric CO2 levels increase — not only because the models largely ignore the effects of nutrient limitations, but also because they overestimate the amount of carbon that could be sequestered in soil, particularly in forest ecosystems7. But the new study also reveals that grasslands, shrublands and other ecosystems that already have high soil-carbon stocks have great potential to accumulate more soil carbon as CO2 levels increase. These results thus add weight to previous calls to protect existing soil-carbon stocks to mitigate the effects of climate change8.
    Carbon dioxide loss from tropical soils increases on warming
    There are some limitations to the set of CO2-enrichment experiments included in Terrer and colleagues’ meta-analysis. The experiments are biased towards temperate systems, and most of the forests studied are associated with ECM, whereas the grasslands are all AM-associated. The authors did not find that the type of ecosystem had a substantial effect on the observed responses to CO2, but it remains to be seen whether the reported trade-off between above- and below-ground carbon sequestration for AM- compared with ECM-associated plants applies to forests alone9. Further experiments, especially in tropical ecosystems, are now needed to address these issues.Tropical ecosystems are large contributors to the global terrestrial carbon sink10, but they are notoriously under-studied. Field observations are scarce and few manipulation experiments — such as CO2 enrichment or nutrient additions — have been carried out in these ecosystems11,12. Below-ground processes are particularly challenging to assess in the tropics, where the effects of multiple nutrient scarcities often come into play12. Terrer and colleagues’ study provides a promising framework that can be elaborated to describe diverse plant–soil interactions in various terrestrial ecosystems in the future.CO2-enrichment experiments generally last for just a few years, or just over a decade at most13. Such timescales are unlikely to capture the effects of elevated CO2 levels on plant mortality, plant-species composition and soil-carbon turnover time, all of which can affect the sequestration of carbon by ecosystems in different ways in the longer term. Mechanistic understanding gained from experiments about the coupling between carbon and nutrient cycling can, however, be integrated into computational models. And this will allow us to constrain estimates of the size of the terrestrial carbon sink in the coming decades. The interactions between plants and their associated soil fungi, as well as other crucial below-ground agents and processes such as microbial communities, are already stirring up modelling efforts14,15. Terrer and colleagues’ study now invites researchers to test hypotheses about the processes that drive coordinated above- and below-ground responses to rising CO2 levels. Such studies could be a real step forwards in our understanding of the fate of the terrestrial carbon sink. More

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    Old-growth forest carbon sinks overestimated

    1.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).ADS 
    CAS 
    Article 

    Google Scholar 
    3.Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).5.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar 
    6.Global Soil Organic Carbon Map (GSOCmap) Technical Report http://www.fao.org/3/I8891EN/i8891en.pdf (FAO/ITPS, 2018).7.Belyea, L. R. & Malmer, N. Carbon sequestration in peatland: patterns and mechanisms of response to climate change. Glob. Change Biol. 10, 1043–1052 (2004).ADS 
    Article 

    Google Scholar 
    8.Zhang, J. et al. C:N:P stoichiometry in China’s forests: from organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).Article 

    Google Scholar 
    9.Fang, Y. et al. Atmospheric deposition and leaching of nitrogen in Chinese forest ecosystems. J. For. Res. 16, 341–350 (2011).CAS 
    Article 

    Google Scholar 
    10.Fenn, M. E. et al. Nitrogen excess in North American ecosystems: predisposing factors, ecosystem responses, and management strategies. Ecol. Appl. 8, 706–733 (1998).Article 

    Google Scholar 
    11.MacDonald, J. A. et al. Nitrogen input together with ecosystem nitrogen enrichment predict nitrate leaching from European forests. Glob. Change Biol. 8, 1028–1033 (2002).ADS 
    Article 

    Google Scholar 
    12.Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Glob. Biogeochem. Cycles 20, GB4003 (2006).ADS 
    Article 

    Google Scholar 
    13.Yang, Y., Luo, Y. & Finzi, A. C. Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).CAS 
    Article 

    Google Scholar 
    14.Moffat, A. M. et al. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric. For. Meteorol. 147, 209–232 (2007).ADS 
    Article 

    Google Scholar 
    15.Wu, J. et al. Synthesis on the carbon budget and cycling in a Danish, temperate deciduous forest. Agric. For. Meteorol. 181, 94–107 (2013).ADS 
    Article 

    Google Scholar 
    16.Soloway, A. D., Amiro, B. D., Dunn, A. L. & Wofsy, S. C. Carbon neutral or a sink? Uncertainty caused by gap-filling long-term flux measurements for an old-growth boreal black spruce forest. Agric. For. Meteorol. 233, 110–121 (2017).ADS 
    Article 

    Google Scholar 
    17.McHugh, I. D. et al. Interactions between nocturnal turbulent flux, storage and advection at an “ideal” eucalypt woodland site. Biogeosciences 14, 3027–3050 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nat. Commun. 7, 13717 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Kang, M. et al. New gap-filling strategies for long-period flux data gaps using a data-driven approach. Atmosphere 10, 568 (2019).ADS 
    Article 

    Google Scholar 
    20.Hayek, M. N. et al. A novel correction for biases in forest eddy covariance carbon balance. Agric. For. Meteorol. 250–251, 90–101 (2018).ADS 
    Article 

    Google Scholar 
    21.Wirth, C., Messier, C., Bergeron, Y., Frank, D. & Fankhänel, A. Old-growth forest definitions: a pragmatic view. In Old‐Growth Forests (eds Wirth, C. et al.) Ecological Studies Vol. 207, 1–33 (Springer, 2009).22.Luyssaert, S., Inglima, I. & Jung, M. Global Forest Ecosystem Structure and Function Data for Carbon Balance Research https://doi.org/10.3334/ORNLDAAC/949 (Oak Ridge National Laboratory Distributed Active Archive Center, 2009). More

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    Monitoring respiratory effects of allergenic pollen

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    Local adaptation to continuous mowing makes the noxious weed Solanum elaeagnifolium a superweed candidate by improving fitness and defense traits

    1.Holzner, W. Concepts, categories and characteristics of weeds. Biol. Ecol. Weeds https://doi.org/10.1007/978-94-017-0916-3_1 (1982).Article 

    Google Scholar 
    2.Randall, J. M. Weed control for the preservation of biological diversity. Weed Technol. 10, 370–383 (1996).Article 

    Google Scholar 
    3.Atkinson, I. A. E. Problem Weeds on New Zealand Islands. (Dept. of Conservation, 1997).4.Goslee, S. C., Peters, D. P. C. & Beck, K. G. Modeling invasive weeds in grasslands: the role of allelopathy in Acroptilon repens invasion. Ecological Modelling (2001). https://www.sciencedirect.com/science/article/pii/S0304380001002319. Accessed 2 Oct 2020.5.Dawson, W., Burslem, D. F. R. P. & Hulme, P. E. Factors explaining alien plant invasion success in a tropical ecosystem differ at each stage of invasion. J. Ecol. 97, 657–665 (2009).Article 

    Google Scholar 
    6.Baker, H. G. The evolution of weeds, annual review of ecology, evolution, and systematics. DeepDyve (1974). https://www.deepdyve.com/lp/annual-reviews/the-evolution-of-weeds-YxSFG7LI8J. Accessed 2 Oct 2020.7.Perrins, J., Williamson, M. & Fitter, A. A survey of differing views of weed classification: Implications for regulation of introductions. Biol. Conserv. 60, 47–56 (1992).Article 

    Google Scholar 
    8.Mack, R. N. Predicting the identity and fate of plant invaders: Emergent and emerging approaches. Biol. Conserv. 78, 107–121 (1996).Article 

    Google Scholar 
    9.Sutherland, S. What Makes a Weed a Weed: Life History Traits of Native (2004). https://www.jstor.org/stable/pdf/40005745.pdf. Accessed 2 Oct 2020.10.Leather, G. R. Weed control using allelopathic crop plants. J. Chem. Ecol. 9, 983–989 (1983).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Mersie, W. & Singh, M. Allelopathic effect of parthenium (Parthenium hysterophorus L.) extract and residue on some agronomic crops and weeds. J. Chem. Ecol. 13, 1739–1747 (1987).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Derya, E., yildiz, O. & Nelson, E. T. (PDF) Ecology, Competitive Advantages, and Integrated (2006). https://www.researchgate.net/publication/287491753_Ecology_Competitive_Advantages_and_Integrated_Control_of_Rhododendron_An_Old_Ornamental_yet_Emerging_Invasive_Weed_Around_the_Globe. Accessed 2 Oct 2020.13.Clements, D. R. & Ditommaso, A. Climate change and weed adaptation: Can evolution of invasive plants lead to greater range expansion than forecasted?. Weed Res. 51, 227–240 (2011).Article 

    Google Scholar 
    14.Sebasky, M. E., Keller, S. R. & Taylor, D. R. Investigating past range dynamics for a weed of cultivation, Silene vulgaris. Ecol. Evol. 6, 4800–4811 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Hodgins, K. Unearthing the impact of human disturbance on a notorious weed. Mol. Ecol. 23, 2141–2143 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Hobbs, R. J. & Huenneke, L. F. Disturbance, diversity, and invasion: Implications for conservation. Ecosyst. Manag. https://doi.org/10.1007/978-1-4612-4018-1_16 (1992).Article 

    Google Scholar 
    17.Lozon, J. D. & Macisaac, H. J. Biological invasions: Are they dependent on disturbance?. Environ. Rev. 5, 131–144 (1997).Article 

    Google Scholar 
    18.Ditomaso, J. M. Invasive weeds in rangelands: Species, impacts, and management. Weed Sci. 48, 255–265 (2000).CAS 
    Article 

    Google Scholar 
    19.Larson, D. L., Anderson, P. J. & Newton, W. Alien plant invasion in mixed-grass prairie: Effects of vegetation type and anthropogenic disturbance. Ecol. Appl. 11, 128–141 (2001).Article 

    Google Scholar 
    20.Chiuffo, M. C., Cock, M. C., Prina, A. O. & Hierro, J. L. Response of native and non-native ruderals to natural and human disturbance. Biol. Invasions 20, 2915–2925 (2018).Article 

    Google Scholar 
    21.Kariyat, R. R., Scanlon, S. R., Mescher, M. C., De Moraes, C. M. & Stephenson, A. G. Inbreeding depression in Solanum carolinense (Solanaceae) under field conditions and implications for mating system evolution. PLoS ONE (2011). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236180/. Accessed 2 Oct 2020.22.Li, B., Shibuya, T., Yogo, Y. & Hara, T. Effects of ramet clipping and nutrient availability on growth and biomass allocation of yellow nutsedge. Ecol. Res. 19, 603–612 (2004).Article 

    Google Scholar 
    23.Jia, X., Pan, X. Y., Li, B., Chen, J. K. & Yang, X. Z. Allometric growth, disturbance regime, and dilemmas of controlling invasive plants: A model analysis. Biol. Invasions 11, 743–752 (2008).Article 

    Google Scholar 
    24.Ramula, S. Annual mowing has the potential to reduce the invasion of herbaceous Lupinus polyphyllus. Biol. Invasions 22, 3163–3173 (2020).Article 

    Google Scholar 
    25.Liu, X. & Huang, B. Mowing effects on root production, growth, and mortality of creeping bentgrass. Crop Sci. 42, 1241–1250 (2002).Article 

    Google Scholar 
    26.Biazzo, J. & Milbrath, L. R. Response of pale swallowwort (Vincetoxicum rossicum) to multiple years of mowing. Invasive Plant Sci. Manag. 12, 169–175 (2019).Article 

    Google Scholar 
    27.Yong, X.-H. et al. Maternal Mowing Effect on Seed Traits of an Invasive Weed, Erigeron annus in farmland. Sains Malay. 44, 347–354 (2015).Article 

    Google Scholar 
    28.Mithöfer, A., Wanner, G. & Boland, W. Effects of feeding spodoptera littoralis on lima bean leaves. II. Continuous mechanical wounding resembling insect feeding is sufficient to elicit herbivory-related volatile emission. Plant Physiol. 137, 1160–1168 (2005).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    29.Engelberth, J. & Engelberth, M. The Costs of Green Leaf Volatile-Induced Defense Priming: Temporal Diversity in Growth Responses to Mechanical Wounding and Insect Herbivory. Plants 8, 23 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    30.Erfmeier, A. & Bruelheide, H. Invasive and nativeRhododendron ponticumpopulations: Is there evidence for genotypic differences in germination and growth?. Ecography 28, 417–428 (2005).Article 

    Google Scholar 
    31.Milbau, A., Nijs, I., Van Peer, L., Reheul, D. & De Cauwer, B. Disentangling invasiveness and invasibility during invasion in synthesized grassland communities. New Phytol. 159, 657–667 (2003).Article 

    Google Scholar 
    32.Etten, M. L. V., Conner, J. K., Chang, S.-M. & Baucom, R. S. Not all weeds are created equal: A database approach uncovers differences in the sexual system of native and introduced weeds. Ecol. Evol. 7, 2636–2642 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Baker, H. G. Self-compatibility and establishment after “long-distance” dispersal. Evolution 9, 347 (1955).
    Google Scholar 
    34.Tabassum, S. & Leishman, M. R. It doesn’t take two to tango: Increased capacity for self-fertilization towards range edges of two coastal invasive plant species in eastern Australia. Biol. Invasions 21, 2489–2501 (2019).Article 

    Google Scholar 
    35.Pannell, J. R. & Barrett, S. C. H. Baker’s law revisited: reproductive assurance in a metapopulation. Evolution 52, 657–668 (1998).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Pannell, J. R. Evolution of the mating system in colonizing plants. Mol. Ecol. 24, 2018–2037 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Mena-Ali, J. I., Keser, L. H. & Stephenson, A. G. Inbreeding depression in Solanum carolinense (Solanaceae), a species with a plastic self-incompatibility response. BMC Evol. Biol. 8, 10 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Chauhan, B. S., Migo, T., Westerman, P. R. & Johnson, D. E. Post-dispersal predation of weed seeds in rice fields. Weed Res. 50, 553–560 (2010).Article 

    Google Scholar 
    39.Muniappan, R. & Viraktamath, C. A. Invasive alien weeds in the Western Ghats. Curr. Sci. 64, 555–558 (1993).
    Google Scholar 
    40.Ziller S. R. A Estepe Gramineo-Lenhosa no Segundo Plan-alto do Paraná: Diagnóstico Ambiental com Enfoque à Contami-nacão Biológica (PhD Thesis). Universidade Federal doParaná (2000).41.Javaid, A. & Riaz, T. Parthenium hysterophorus L., an alien invasive weed threatening natural vegetations in Punjab, Pakistan. Pak. J. Bot. 44, 123–126 (2012).
    Google Scholar 
    42.Alves, M. T. & Hilker, F. M. Hunting cooperation and Allee effects in predators. J. Theor. Biol. 419, 13–22 (2017).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    43.Kariyat, R. R., Mauck, K. E., Moraes, C. M. D., Stephenson, A. G. & Mescher, M. C. Inbreeding alters volatile signalling phenotypes and influences tri-trophic interactions in horsenettle (Solanum carolinense L..). Ecol. Lett. 15, 301–309 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Nihranz, C. T. et al. Herbivory and inbreeding affect growth, reproduction, and resistance in the rhizomatous offshoots of Solanum carolinense (Solanaceae). Evol. Ecol. 33, 499–520 (2019).Article 

    Google Scholar 
    45.Nihranz, C. T. et al. Transgenerational impacts of herbivory and inbreeding on reproductive output in Solanum carolinense. Am. J. Bot. 107, 286–297 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Wilkens, R. T., Shea, G. O., Halbreich, S. & Stamp, N. E. Resource availability and the trichome defenses of tomato plants. Oecologia 106, 181–191 (1996).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Zaynab, M. et al. Role of secondary metabolites in plant defense against pathogens. Microb. Pathog. 124, 198–202 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Neilson, E. H., Goodger, J. Q., Woodrow, I. E. & Møller, B. L. Plant chemical defense: at what cost?. Trends Plant Sci. 18, 250–258 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Boyd, J. W., Murray, D. S. & Tyrl, R. J. Silverleaf nightshade, Solarium elaeagnifolium, origin, distribution, and relation to man. Econ. Bot. 38, 210–217 (1984).Article 

    Google Scholar 
    50.EPPO Global Database. Solanum elaeagnifolium (SOLEL)[Documents]| EPPO Global Database. https://gd.eppo.int/taxon/SOLEL/documents. Accessed 5th Nov 2020.51.Travlos, I. S. Responses of invasive silverleaf nightshade (Solanum elaeagnifolium) populations to varying soil water availability. Phytoparasitica 41, 41–48 (2012).Article 

    Google Scholar 
    52.Mekki, M. Biology, distribution and impacts of silverleaf nightshade (Solanum elaeagnifolium Cav.). EPPO Bull. 37, 114–118 (2007).Article 

    Google Scholar 
    53.Cuthbertson, E.G. Morphology of the underground parts of silverleaf nightshade. 5th Australian Weeds Conference (1976).54.Heap, J., Honan, I. & Smith, E. Silverleaf nigthshade: A Technical Handbook for Animal and Plant Control Boards in South Australia (Adelaide, 1997).
    Google Scholar 
    55.Petanidou, T. et al. Self-compatibility and plant invasiveness: Comparing species in native and invasive ranges. Perspect. Plant Ecol. Evol. Syst. 14, 3–12 (2012).Article 

    Google Scholar 
    56.Kariyat, R. R. & Chavana, J. Field data on plant growth and insect damage on the noxious weed Solanum eleaegnifolium in an unexplored native range. Data Brief 19, 2348–2351 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Centibas, M. & Koyuncu, F. The ripening and fruit quality of ‘Monroe’ peaches in response to pre-harvest application gibberellic acid. Akdeniz Üniv. Ziraat Fakült. Dergisi 26, 73–80 (2013).
    Google Scholar 
    58.Pornaro, C., Macolino, S., Menegon, A. & Richardson, M. WinRHIZO technology for measuring morphological traits of Bermudagrass Stolons. Agron. J. 109, 3007–3010 (2017).CAS 
    Article 

    Google Scholar 
    59.Kariyat, R. R. et al. Inbreeding, herbivory, and the transcriptome of Solanum carolinense. Entomol. Exp. Appl. 144, 134–144 (2012).Article 

    Google Scholar 
    60.Kariyat, R. R. et al. Feeding on glandular and non-glandular leaf trichomes negatively affect growth and development in tobacco hornworm (Manduca sexta) caterpillars. Arthropod Plant Interact. 13, 321–333 (2019).Article 

    Google Scholar 
    61.Tayal, M., Chavana, J. & Kariyat, R. R. Efficiency of using electric toothbrush as an alternative to a tuning fork for artificial buzz pollination is independent of instrument buzzing frequency. BMC Ecol. 20, 1 (2020).Article 

    Google Scholar 
    62.Singh, S. & Kariyat, R. R. Exposure to polyphenol-rich purple corn pericarp extract restricts fall armyworm (Spodoptera frugiperda) growth. Plant Signal. Behav. 15, 1784545 (2020).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    63.Kariyat, R. R. et al. Constitutive and herbivore-induced structural defenses are compromised by inbreeding in Solanum carolinense (Solanaceae). Am. J. Bot. 100, 1014–1021 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Paez-Garcia, A. et al. Root traits and phenotyping strategies for plant improvement. Plants 4, 334–355 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Pinke, G., Pál, R. & Botta-Dukát, Z. Effects of environmental factors on weed species composition of cereal and stubble fields in western Hungary. Open Life Sci. 5, 283–292 (2010).Article 

    Google Scholar 
    66.Tremayne, M. A. & Richards, A. J. Seed weight and seed number affect subsequent fitness in outcrossing and selfing Primula species. New Phytol. 148, 127–142 (2000).Article 

    Google Scholar 
    67.Ramesh, K., Matloob, A., Aslam, F., Florentine, S. K. & Chauhan, B. S. Weeds in a changing climate: Vulnerabilities, consequences, and implications for future weed management. Front. Plant Sci. 8, 1 (2017).CAS 
    Article 

    Google Scholar 
    68.Rha, E. S. & Jamil, M. Gibberellic acid (GA3) enhance seed water uptake, germination and early seedling growth in sugar beet under salt stress. Pak. J. Biol. Sci. 10, 654–658 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Stoller, E. W. & Wax, L. M. Periodicity of germination and emergence of some annual weeds. Weed Sci. 21, 574–580 (1973).Article 

    Google Scholar 
    70.Meyer, S. E. & Pendleton, B. K. Factors affecting seed germination and seedling establishment of a long-lived desert shrub (Coleogyne ramosissima: Rosaceae). Plant Ecol. 178, 171–187 (2005).Article 

    Google Scholar 
    71.Milbau, A., Scheerlinck, L., Reheul, D., De Cauwer, B. & Nijs, I. Ecophysiological and morphological parameters related to survival in grass species exposed to an extreme climatic event. Physiol. Plant. 125, 500–512 (2005).CAS 
    Article 

    Google Scholar 
    72.Gioria, M. & Pyšek, P. Early bird catches the worm: Germination as a critical step in plant invasion. Biol. Invasions 19, 1055–1080 (2016).Article 

    Google Scholar 
    73.Mahmood, A. H. et al. Influence of various environmental factors on seed germination and seedling emergence of a noxious environmental weed: Green galenia (Galenia pubescens). Weed Sci. 64, 486–494 (2016).Article 

    Google Scholar 
    74.Mcnaughton, S. J. Grazing lawns: On domesticated and wild grazers. Am. Nat. 128, 937–939 (1986).Article 

    Google Scholar 
    75.McNaughton, S. J. Adaptation of herbivores to seasonal changes in nutrient supply. Nutr. Herb. 1, 391–408 (1987).
    Google Scholar 
    76.Laliberté, E., Lambers, H., Burgess, T. I. & Wright, S. J. Phosphorus limitation, soil-borne pathogens and the coexistence of plant species in hyperdiverse forests and shrublands. New Phytol. 206, 507–521 (2014).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    77.Kramer-Walter, K. R. et al. Root traits are multidimensional: Specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 104, 1299–1310 (2016).Article 

    Google Scholar 
    78.Losapio, G. et al. An invasive plant species enhances biodiversity in overgrazed pastures but inhibits its recovery in protected areas. J. Ecol. https://doi.org/10.1101/2020.08.16.227066 (2020).Article 

    Google Scholar 
    79.Onen, H., Farooq, S., Gunal, H., Ozaslan, C. & Erdem, H. Higher tolerance to abiotic stresses and soil types may accelerate common ragweed (Ambrosia artemisiifolia) invasion. Weed Sci. 65, 115–127 (2016).Article 

    Google Scholar 
    80.Wittstock, U. & Gershenzon, J. Constitutive plant toxins and their role in defense against herbivores and pathogens. Curr. Opin. Plant Biol. 5, 300–307 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Mooney, E. H., Tiedeken, E. J., Muth, N. Z. & Niesenbaum, R. A. Differential induced response to generalist and specialist herbivores by Lindera benzoin (Lauraceae) in sun and shade. Oikos 118, 1181–1189 (2009).Article 

    Google Scholar 
    82.Baldwin, I. T. Plant volatiles. Curr. Biol. 20, 392–397 (2011).Article 
    CAS 

    Google Scholar 
    83.Coley, P. D., Bryant, J. P. & Chapin, F. S. Resource availability and plant antiherbivore defense. Science 230, 895–899 (1985).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    84.Fine, P. V. A. Herbivores promote habitat specialization by trees in amazonian forests. Science 305, 663–665 (2004).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    85.Zandt, P. A. V. Plant defense, growth, and habitat: A comparative assessment of constitutive and induced resistance. Ecology 88, 1984–1993 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Salminen, S. O. & Grewal, P. S. Does decreased mowing frequency enhance alkaloid production in endophytic tall fescue and perennial ryegrass?. J. Chem. Ecol. 28, 939–950 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Freeman. An Overview of Plant Defenses against Pathogens and Herbivores. The Plant Health Instructor (2008). https://doi.org/10.1094/phi-i-2008-0226-01.88.Davis, H. N. et al. Review of Major Crop and Animal Arthropod Pests of South Texas. Subtropical Agriculture and Environments (2020).89.Traw, M. B., Kim, J., Enright, S., Cipollini, D. F. & Bergelson, J. Negative cross-talk between salicylate- and jasmonate-mediated pathways in the Wassilewskija ecotype of Arabidopsis thaliana. Mol. Ecol. 12, 1125–1135 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Bostock, R. M. Signal crosstalk and induced resistance: Straddling the line between cost and benefit. Annu. Rev. Phytopathol. 43, 545–580 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Lefoe, G. et al. Assessing the fundamental host-range of Leptinotarsa texana Schaeffer as an essential precursor to biological control risk analysis. Biol. Control 143, 104165 (2020).CAS 
    Article 

    Google Scholar 
    92.Chung, S. H. & Felton, G. W. Specificity of induced resistance in tomato against specialist lepidopteran and coleopteran species. J. Chem. Ecol. 37, 378–386 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Korpita, T., Gómez, S. & Orians, C. M. Cues from a specialist herbivore increase tolerance to defoliation in tomato. Funct. Ecol. 28, 395–401 (2013).Article 

    Google Scholar 
    94.Yang, Q. et al. Plant–soil biota interactions of an invasive species in its native and introduced ranges: Implications for invasion success. Soil Biol. Biochem. 65, 78–85 (2013).CAS 
    Article 

    Google Scholar 
    95.Blair, A. C. & Wolfe, L. M. The evolution of an invasive plant: An experimental study with Silene latifolia. Ecology 85, 3035–3042 (2004).Article 

    Google Scholar 
    96.Kariyat, R. R., Smith, J. D., Stephenson, A. G., Moraes, C. M. D. & Mescher, M. C. Non-glandular trichomes of Solanum carolinense deter feeding by Manduca sexta caterpillars and cause damage to the gut peritrophic matrix. Proc. R. Soc. B 284, 20162323 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    97.Kariyat, R. R. et al. Leaf trichomes affect caterpillar feeding in an instar-specific manner. Commun. Integr. Biol. 11, 1–6 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    98.Karabourniotis, G., Liakopoulos, G., Nikolopoulos, D. & Bresta, P. Protective and defensive roles of non-glandular trichomes against multiple stresses: Structure–function coordination. J. For. Res. 31, 1–12 (2019).Article 
    CAS 

    Google Scholar 
    99.Kang, J.-H., Shi, F., Jones, A. D., Marks, M. D. & Howe, G. A. Distortion of trichome morphology by the hairless mutation of tomato affects leaf surface chemistry. J. Exp. Bot. 61, 1053–1064 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    100.Tian, D., Tooker, J., Peiffer, M., Chung, S. H. & Felton, G. W. Role of trichomes in defense against herbivores: Comparison of herbivore response to woolly and hairless trichome mutants in tomato (Solanum lycopersicum). Planta 236, 1053–1066 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.An, F. et al. Ethylene-induced stabilization of ETHYLENE INSENSITIVE3 and EIN3-LIKE1 is mediated by proteasomal degradation of EIN3 binding F-Box 1 and 2 That requires EIN2 in arabidopsis. Plant Cell 22, 2384–2401 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    102.Lämke, J. & Bäurle, I. Epigenetic and chromatin-based mechanisms in environmental stress adaptation and stress memory in plants. Genome Biol. 18, 1 (2017).Article 
    CAS 

    Google Scholar 
    103.Weinhold, A. Transgenerational stress-adaption: an opportunity for ecological epigenetics. Plant Cell Rep. 37, 3–9 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    104.Miryeganeh, M. & Saze, H. Epigenetic inheritance and plant evolution. Popul. Ecol. 62, 17–27 (2019).Article 

    Google Scholar  More

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    Herbaceous perennial plants with short generation time have stronger responses to climate anomalies than those with longer generation time

    Demographic dataTo address our hypotheses, we used matrix population models (MPMs) or integral projection models (IPMs) from the COMPADRE Plant Matrix Database (v. 5.0.156) and the PADRINO IPM Database57, which we amended with a systematic literature search. First, we selected density-independent models from COMPADRE and PADRINO which described the transition of a population from 1 year to the next. Among these, we selected studies with at least six annual transition matrices, to balance the needs of adequate yearly temporal replicates and sufficient sample size for a quantitative synthesis. This yielded data from 48 species and 144 populations.We then performed a systematic literature search for studies linking climate drivers to structured population models in the form of either MPMs or IPMs. We performed this search on ISI Web of Science for studies published between 1997 and 2017. We used a Boolean expression containing keywords related to plant form, structured demographic models, and environmental drivers (Supplementary Methods). We only considered studies linking macro-climatic drivers to natural populations (e.g., transplant experiments and studies focused on local climatic factors such as soil moisture, light due to treefall gaps, etc. were excluded). Finally, we used the same criteria used to filter studies in COMPARE and PARDINO, by selecting studies with at least six, density-independent, annual projection models. This search brought two additional species, belonging to three additional populations, which we entered in the COMPADRE database.One of the studies we excluded from the literature search because it contained density-dependent IPMs, also provided raw data with high temporal replication (14–32 years of sampling) for 12 species from 15 populations58. Therefore, we re-analyzed these freely available data to produce density-independent MPMs that were directly comparable to the other studies in our dataset (Supplementary Methods).The resulting dataset consisted of 46 studies, 62 species, 162 populations, and a total of 3761 MPMs and 52 IPMs (Supplementary Data 1). The analyzed plant populations were tracked for a mean of 16 (median of 12) annual transitions. To our knowledge, this is the largest open-access dataset of long-term structured population projection models. However, this dataset is taxonomically and geographically biased. Specifically, among our 62 species, this dataset contains 54 herbaceous perennials (11 of which graminoids), and eight woody species: five shrubs, two trees, and one woody succulent (Opuntia imbricata). Moreover, almost all of these studies were conducted in North America and Europe (Supplementary Fig. 1), in temperate biomes that are cold, dry, or both cold and dry (Supplementary Fig. 1, inset). Our geographic and taxonomic bias reflects the rarity of long-term plant demographic data in general. This dearth of long-term demographic data is particularly evident in the tropics. The ForestGEO network59 is an exception to this rule, but to date, no matrix population models or integral projection models using these data have been published.We used the MPMs and IPMs in this dataset to calculate the response variable of our analyses: the yearly asymptotic population growth rate (λ). This measure is one of the most widely used summary statistics in population ecology60, as it integrates the response of multiple interacting vital rates. Specifically, λ reflects the population growth rate that a population would attain if its vital rates remained constant through time61. This metric therefore distills the effect of underlying vital rates on population dynamics, free of other confounding factors (e.g., transient dynamics arising from population structure62). We calculated λ of each MPM or IPM with standard methods61,63. Because our MPMs and IPMs described the demography of a population transitioning from one year to the next, our λ values were comparable in time units. Finally, we identified and categorized any non-climatic driver associated with these MPMs and IPMs. Data associated with 21 of our 62 species explicitly quantified a non-climatic driver (e.g., grazing, neighbor competition), for a total of 60 of our 162 populations. Of the datasets associated with these species, 19 included discrete drivers, and only three included a continuous driver.Climatic dataTo test the effect of temporal climatic variation on demography, we gathered global climatic data. We downloaded 1 km2 gridded monthly values for maximum temperature, minimum temperature, and total precipitation between 1901 and 2016 from CHELSAcruts64, which combines the CRU TS 4.0165, and CHELSA66 datasets. Gridded climatic data are especially suited to estimate annual climatic means45. These datasets include values from 1901 to 2016, which are necessary to cover the temporal extent of all 162 plant populations considered in our analysis. For our temperature analyses, we calculated the mean monthly temperature as the mean of the minimum and maximum monthly temperatures. We used monthly values to calculate the time series of mean annual temperature and total annual precipitation at each site. We then used this dataset to calculate our annual anomalies for each census year, defined as the 12 months preceding a population census. Our annual anomalies are standardized z-scores. For example, if X is a vector of 40 yearly precipitation or temperature values, E() calculates the mean, and σ() calculates the standard deviation, we compute annual anomalies as A = [X − E(X)]/σ(X). Therefore, an anomaly of one refers to a year where precipitation or temperature was one standard deviation above the 40-year mean. In other words, anomalies represent how infrequent annual climatic conditions are at a site. Specifically, if we assume that A values are normally distributed, values exceeding one and two should occur every 6 and 44 years, respectively. We used 40-year means because the minimum number of years suggested to calculate climate averages is 3067.Z-scores are commonly used in global studies on vegetation responses to climate8,68, and they reflect the null hypothesis that species are adapted to the climatic variation at their respective sites. Across our populations, the standard deviations of annual precipitation and temperature anomalies change by 300% and 60%, respectively (Supplementary Fig. 2). Thus, a z-score of one refers to a precipitation anomaly of 50 or 160 mm and to a temperature anomaly of 0.5 or 0.8 °C. Our null hypothesis posits that species are adapted to these conditions, regardless of the absolute magnitude of the standard deviation in annual climatic anomalies. If this null hypothesis were true, each species would respond similarly to z-scores. Z-scores are more easily interpreted when calculated on normally distributed variables. We found our temperature and precipitation z-scores were highly skewed (skewness above 1) only in, respectively, 2 (for temperature) and three (for precipitation) of our 162 populations. We concluded that this degree of skewness should not bias our z-scores substantially.To test how the response of plant populations to climate changes based on biome we used two proxies of water and temperature limitation. For each study population, we computed a proxy for water limitation, water availability index (WAI), and temperature limitation using mean annual temperature. To compute these metrics, we downloaded data at 1 km2 resolution for mean annual potential evapotranspiration, mean annual precipitation, and mean annual temperature referred to the 1970–2000 period. We obtained potential evapotranspiration data from the CGIAR-CSI consortium (http://www.cgiar-csi.org/). This dataset calculates potential evapotranspiration using the Hargreaves method69. We obtained mean annual precipitation and mean annual temperature from Worldclim70. Here, we used WorldClim rather than CHELSA climatic data because the CGIAR-CSI potential evapotranspiration data were computed from the former. We calculated the WAI values at each of our sites by subtracting mean annual potential evapotranspiration from the mean annual precipitation. Such proxy is a coarse measure of plant water availability that ignores information such as soil characteristics and plant rooting depth. However, WAI is useful to compare water availability among disparate environments, so that it is often employed in global analyses68,71. As our proxy of temperature limitation, we use mean annual temperature. While growing degree days would be a more mechanistic measure of temperature limitation48, this requires daily weather data. However, we could not find a global, downscaled, daily gridded weather dataset to calculate this metric.The overall effect of climate on plant population growth rateTo test H1, we estimated the overall effect sizes of responses to anomalies in temperature, precipitation, and their interaction with a linear mixed-effect model.$${mathrm{log}}left( lambda right) = alpha + beta P + eta T + theta P{mathrm{x}}T + varepsilon$$
    (1)
    where log(λ) is the log of the asymptotic population growth rate of plant population P is precipitation, T is temperature. We included random population effects on the intercept and the slopes to account for the nonindependence of measurements within populations. We then compared the mean absolute effect size of precipitation, temperature, and their interaction. This final model did not include a quadratic term of temperature and precipitation because these additional terms led to convergence issues. This likely occurred because single data sets did not include enough years of data.Population-level effect of climate on plant population growth ratesTo test our remaining three hypotheses, we carried out meta-regressions where the response variable was the slope (henceforth “effect size”) of climatic anomalies on the population growth rate for each of our populations. Before carrying out our meta-regression, we first estimated the effect size of our two climatic anomalies on the population growth rate of each population separately. We initially fit population-level and meta-regression simultaneously, in a hierarchical Bayesian framework. However, these Bayesian models shrunk the uncertainty of the noisiest population–level relationships, resulting in unrealistically strong meta-regressions. We, therefore, chose to fit population models separately, resulting in more conservative results.For each population, we fit multiple regressions with an autoregressive error term, and we evaluated the potential for nonlinear effects in the datasets longer than 14 years. We fit multiple regressions because temperature and precipitation anomalies were negatively correlated, so that fitting separate models for temperature and precipitation would yield biased results72. We fit an autoregressive error term because density dependence and autocorrelated climate anomalies can produce autocorrelated plant population growth rates. The form of our baseline model was$${rm{log}}(lambda )_y = alpha + beta _pP_y + beta _tT_y + varepsilon _y$$
    (2)
    $$varepsilon _y = rho varepsilon _{y – 1} + eta _y$$
    (3)
    The model in Eq. 2 is a linear regression relating each log(λ) data point observed in year y, to the corresponding precipitation (P) and temperature (T) anomalies observed in year y, via the intercept α, the effect sizes, β, and an error term, εy, which depends on white noise, ηy, and on the correlation with the error term of the previous year, ρ. When multiple spatial replicates per each population were available each year, we estimated the ρ autocorrelation value separately for each replicate. This happened in the few cases when a study contained contiguous populations, with no ecologically meaningful (e.g., habitat) differences.We compared the baseline model in Eqs. 2 and 3 to models including a quadratic climatic effect and non-climatic covariates. We estimated quadratic climatic effects only for time series longer than 14 years. We choose this threshold because when using a model selection approach to select a quadratic or linear regression model, the recommended minimum sample size is between 8 and 25 data points73. We fit models including a quadratic effect of temperature, precipitation, or both (Supplementary Table 1).Finally, we also tested whether non-climatic covariates could bias the effects of climate on log(λ) estimated in our analysis. Such bias, either upwards or downwards, could result in the case non-climatic co-variates interacted with climate. For example, harvest can have multiplicative, rather than additive effects on the climate responses of forest understory herbs74. We tested for an interaction between a covariate and climate anomaly in 17 of the 21 studies that included a non-climatic covariate. In the remaining three studies, discrete covariates corresponded with the single populations. Because Eqs. 2 and 3 is fit on separate populations, it implicitly accounted for these covariates. For the 17 studies above, we fit a linear effect of the non-climatic covariate and its interaction with one of the two linear climatic anomalies. Thus, including the linear model in Eqs. 2 and 3, the nonlinear models, and the covariate interaction models, we tested up to six alternative models for each one of our populations (Supplementary Table 1). We selected the best model according to the Akaike Information Criterion corrected for small sample sizes (AICc75). We carried out these and subsequent analyses in R version 3.6.176.In the populations for which AICc selected one of the model alternatives to the baseline in Eqs. 2 and 3, we calculated the effect size of climate by adding the effect of the new terms to the linear climatic terms. For example, when a quadratic precipitation model was selected, we calculated the effect size of precipitation as β = βp + βp2. For models including an interaction between temperature and a non-climatic covariate, we evaluated the effect of the interaction at the mean value of the covariate. Therefore, we calculated the effect size as β = βt + βxE(Ci) for continuous covariates. For categorical variables, we calculated the effect size as βp + βx0.5: that is, we calculated the mean effect size between the two categories. We quantified the standard error of the resulting effect sizes by adding the standard errors of the two terms.The effect of biome on the response of plants to climateWe used a simulation procedure to run two meta-regressions to test for the correlation between the effect size of climate drivers on λ, and our measures of water or temperature limitation. These meta-regressions accounted for the uncertainty, measured as the standard error, in the effect sizes of climate drivers. We represented the effect of biome using a proxy of water (WAI) and temperature (mean annual temperature) limitation. For each of our 162 populations, the response data of this analysis were the effect sizes (βp or βt values) estimated by Eqs. 2 and 3 or their modifications in case a quadratic or non-climatic covariate model were selected. In these meta-regressions, the weight of each effect size was inversely proportional to its standard error. To test H2 and H3 on how water and temperature limitation should affect the response of populations to climate, we used linear meta-regressions. These two hypotheses tested both the sign and magnitude of the effect of climate. Therefore, we used the effect sizes as a response variable which could take negative or positive values. As predictors, we used population-specific WAI (H2, only for effect sizes quantifying the effect of precipitation), and mean annual temperature (H3, only for effect sizes quantifying the effect of temperature). The null hypothesis of these meta-regressions is that plant species are adapted to the climatic variation at their respective sites. Such an adaptation implies that a precipitation z-score of one should produce effects on log(λ) of similar magnitude and sign across different climates. This should happen across average climatic values that are connected to substantially different absolute climatic anomalies (Supplementary Fig. 2). On the other hand, our hypotheses posit that at low WAI and MAT values, species are more responsive to z-scores than expected under the null hypothesis.We performed these two meta-regressions by exploiting the standard error of each effect size. We simulated 1000 separate datasets where each effect size was independently drawn from a normal distribution whose mean was the estimated β value, and the standard deviation was the standard error of this β. These simulated datasets accounted for the uncertainty in the β values. We fit 1000 linear models, extracting for each its slope, βmeta. Each one of these slopes had in turn an uncertainty, quantified by its standard error, σmeta. For each βmeta, we then drew 1000 values from a normal distribution with mean βmeta and standard deviation σmeta. We used the resulting 1 × 106 values to estimate the confidence intervals of βmeta. This procedure assumes that the distribution of βmeta values is normally distributed. We performed one-tailed hypothesis tests, considering meta-regression slopes significant when over 95% of simulated values were below zero.The effect of generation time on the response of plants to climateTo test H4 on how the generation time of a species should mediate its responses to climate, we used a gamma meta-regression. We fitted gamma meta-regressions because our response variables were the absolute effect sizes of precipitation and temperature anomalies, |β|, which are bounded between 0 and infinity. To test H4, we therefore fit gamma meta-regressions with a log link, using |β| values as response variable and generation time (T) as predictor. We calculated T directly from the MPMs and IPMs (Supplementary Methods). We log-transformed T to improve model fit. We carried out these meta-regressions using the same simulation procedure described for testing H2 and H3. We also carried out one-tailed hypothesis tests, by verifying whether 95% of βmeta values were below zero.The effect of plant types on estimates of climate effectsWe verified whether certain plant types could bias our results by subdividing our species as graminoids, herbaceous perennials, ferns, woody species (shrubs and trees), and succulents. We ran ANOVA tests to verify whether the effect sizes of precipitation and temperature anomalies differed between plant types. We then tested for significant differences in pairwise contrasts between plants types by running Tukey’s honestly significant difference tests. We carried out these tests on the average effects of climate, without accounting for differences in parameter uncertainty. If Tukey’s test identified significant differences among plant types, we ran additional tests of H2–H4 excluding the plant type, or plant types, whose response to climate differed.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More