Philippa Kaur

Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn. (Academic Press, 2008).
Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: The past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 
PubMed 

Google Scholar 
Štajerová, K., Šmilauerová, M. & Šmilauer, P. Arbuscular mycorrhizal symbiosis of herbaceous invasive neophytes in the Czech Republic. Preslia 81, 341–355 (2009).
Google Scholar 
Hempel, S. et al. Mycorrhizas in the Central European flora: Relationships with plant life history traits and ecology. Ecology 94, 1389–1399 (2013).Article 
PubMed 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. New Phytol. 227, 955–966 (2020).Article 
PubMed 

Google Scholar 
Spatafora, J. W. et al. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia 108, 1028–1046 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lekberg, Y., Hammer, E. C. & Olsson, P. A. Plants as resource islands and storage units—Adopting the mycocentric view of arbuscular mycorrhizal networks. FEMS Microbiol. Ecol. 74, 336–345 (2010).Article 
CAS 
PubMed 

Google Scholar 
Newsham, K. K., Fitter, A. H. & Watkinson, A. R. Arbuscular mycorrhiza protect an annual grass from root pathogenic fungi in the field. J. Ecol. 83, 991–1000 (1995).Article 

Google Scholar 
Vigo, C., Norman, J. R. & Hooker, J. E. Biocontrol of the pathogen Phytophthora parasitica by arbuscular mycorrhizal fungi is a consequence of effects on infection loci. Plant Pathol. 49, 509–514 (2000).Article 

Google Scholar 
Pfeffer, P. E., Douds, D. D., Becard, G. & Shachar-Hill, Y. Carbon uptake and the metabolism and transport of lipids in an arbuscular mycorrhiza. Plant Physiol. 120, 587–598 (1999).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bago, B., Pfeffer, P. E. & Shachar-Hill, Y. Carbon metabolism and transport in arbuscular mycorrhizas. Plant Physiol. 124, 949–958 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ramirez, R., Mendoza, B. & Lizaso, J. I. Mycorrhiza effect on maize P uptake from phosphate rock and superphosphate. Commun. Soil Sci. Plant Anal. 40, 13–14 (2009).Article 

Google Scholar 
Pearson, J. N. & Jakobsen, I. The relative contribution of hyphae and roots to phosphorus uptake by arbuscular mycorrhizal plants, measured by dual labeling with 32P and 33P. New Phytol. 124, 489–494 (1993).Article 
CAS 

Google Scholar 
Smith, S. E., Smith, F. A. & Jakobsen, I. Functional diversity in arbuscular mycorrhizal (AM) symbioses: The contribution of the mycorrhizal P uptake pathway is not correlated with mycorrhizal responses in growth or total P uptake. New Phytol. 162, 511–524 (2004).Article 

Google Scholar 
Smith, M. D., Hartnett, D. C. & Wilson, G. W. T. Interacting influence of mycorrhizal symbiosis and competition on plant diversity in tallgrass prairie. Oecologia 121, 574–582 (1999).Article 
CAS 
PubMed 

Google Scholar 
Bennett, J. A. et al. Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).Article 
CAS 
PubMed 

Google Scholar 
Liao, H. et al. Soil microbes regulate forest succession in a subtropical ecosystem in China: Evidence from a mesocosm experiment. Plant Soil 430, 277–289 (2018).Article 
CAS 

Google Scholar 
Awaydul, A. et al. Common mycorrhizal networks influence the distribution of mineral nutrients between an invasive plant, Solidago canadensis, and a native plant, Kummerowa striata. Mycorrhiza 29, 29–38 (2019).Article 
CAS 
PubMed 

Google Scholar 
Callaway, R. M., Newingham, B., Zabinski, C. A. & Mahall, B. E. Compensatory growth and competitive ability of an invasive weed are enhanced by soil fungi and native neighbours. Ecol. Lett. 4, 429–433 (2001).Article 

Google Scholar 
Workman, R. E. & Cruzan, M. B. Common mycelial networks impact competition in an invasive grass. Am. J. Bot. 103, 1041–1049 (2016).Article 
CAS 
PubMed 

Google Scholar 
Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—The role of mutualisms. Biol. Rev. Camb. Philos. Soc. 75, 65–93 (2000).Article 
CAS 
PubMed 

Google Scholar 
Vogelsang, K. M. & Bever, J. D. Mycorrhizal densities decline in association with nonnative plants and contribute to plant invasion. Ecology 90, 399–407 (2009).Article 
PubMed 

Google Scholar 
Horton, T. R. Mycorrhizal Networks, Ecological Studies 224 (Springer, 2015).Book 

Google Scholar 
Lin, G., McCormack, M. L. & Guo, D. Arbuscular mycorrhizal fungal effects on plant competition and community structure. J. Ecol. 103, 1224–1232 (2015).Article 
CAS 

Google Scholar 
Jasper, D. A., Abbott, J. K. & Robson, A. D. The effect of soil disturbance on vesicular-arbuscular mycorrhizal fungi in soils from different vegetation types. New Phytol. 118, 471–476 (1991).Article 

Google Scholar 
Jansa, J. et al. Diversity and structure of AMF communities as affected by tillage in a temperate soil. Mycorrhiza 12, 225–234 (2002).Article 
CAS 
PubMed 

Google Scholar 
van der Heyde, M., Ohsowski, B., Abbott, L. K. & Hart, M. Arbuscular mycorrhizal fungus responses to disturbance are context-dependent. Mycorrhiza 27, 431–440 (2017).Article 
PubMed 

Google Scholar 
Verbruggen, E. & Kiers, E. T. Evolutionary ecology of mycorrhizal functional diversity in agricultural systems. Evol. Appl. 3, 547–560 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Řezáčová, V., Řezáč, M., Gryndlerová, H., Wilson, G. W. T. & Michalová, T. Arbuscular mycorrhizal fungi favor invasive Echinops sphaerocephalus when grown in competition with native Inula conyzae. Sci. Rep. 10, 20287. https://doi.org/10.1038/s41598-020-77030-0 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callaway, R. M., Thelen, G. C., Barth, S., Ramsey, P. W. & Gannon, J. E. Soil fungi alter interactions between the invader Centaurea maculosa and North American natives. Ecology 85, 1062–1071 (2004).Article 

Google Scholar 
Abhilasha, D. & Joshi, J. Enhanced fitness due to higher fecundity, increased defence against a specialist and tolerance towards a generalist herbivore in an invasive annual plant. J. Plant Ecol. 2, 77–86 (2009).Article 

Google Scholar 
Shah, M. A., Reshi, Z. A. & Khasa, D. Arbuscular mycorrhizal status of some Kashmir Himalayan alien invasive plants. Mycorrhiza 20, 67–72 (2009).Article 
PubMed 

Google Scholar 
Shah, M. A., Reshi, Z. A. & Rasool, N. Plant invasions induce a shift in Glomalean spore diversity. Trop. Ecol. 51, 317–323 (2010).
Google Scholar 
Shah, M. A., Beaulieu, M.-E., Reshi, Z. A., Qureshi, S. & Khasa, D. P. A cross-city molecular biogeographic investigation of arbuscular mycorrhizas in Conyza canadensis rhizosphere across native and non-native regions. Ecol. Process. 4, 7. https://doi.org/10.1186/s13717-015-0034-0 (2015).Article 

Google Scholar 
Řezáčová, V., Konvalinková, T. & Řezáč, M. Decreased mycorrhizal colonization of Conyza canadensis (L.) Cronquist in invaded range does not affect fungal abundance in native plants. Biologia 75, 693–699 (2020).Article 

Google Scholar 
Shah, M. A., Reshi, Z. & Rashid, I. Mycorrhizal source and neighbour identity differently influence Anthemis cotula L. invasion in the Kashmir Himalaya, India. Appl. Soil Ecol. 40, 330–337 (2008).Article 

Google Scholar 
Řezáčová, V. et al. Plant invasion alters community structure and decreases diversity of arbuscular mycorrhizal fungal communities. Appl. Soil Ecol. 167, 104039 (2021).Article 

Google Scholar 
Řezáčová, V. et al. The root-associated arbuscular mycorrhizal fungal assemblages of exotic alien plants are simplified in invaded distribution ranges, but dominant species are retained: A trans-continental perspective. Environ. Microbiol. Rep. https://doi.org/10.1111/1758-2229.13108 (2022).Article 
PubMed 

Google Scholar 
Song, U. et al. Mowing: A cause of invasion, but also a potential solution for management of the invasive, alien plant species Erigeron annuus (L.) Pers.. J. Environ. Manag. 223, 530–536 (2018).Article 

Google Scholar 
Hempel, S. et al. Mycorrhizas in the Central European flora: Relationships with plant life history traits and ecology. Ecology 94, 1389–1399 (2013).Article 
PubMed 

Google Scholar 
Řezáčová, V., Řezáč, M., Líblová, Z., Michalová, T. & Heneberg, P. Stable colonization of native plants and early invaders by arbuscular mycorrhizal fungi after exposure to recent invaders from the Asteraceae family. IPSM 14, 147–155 (2021).Article 

Google Scholar 
Wilson, G. W. T. & Hartnett, D. C. Interspecific variation in plant responses to mycorrhizal colonization in tallgrass prairie. Am. J. Bot. 85, 1732–1738 (1998).Article 
CAS 
PubMed 

Google Scholar 
Rinaudo, V., Barberi, P., Giovanneti, M. & van der Heijden, M. G. A. Mycorrhizal fungi suppress aggressive agricultural weeds. Plant Soil 333, 7–20 (2010).Article 
CAS 

Google Scholar 
Veiga, R. S. L., Jansa, J., Frossard, E. & van der Heijden, M. G. A. Can arbuscular mycorrhizal fungi reduce the growth of agricultural weeds?. PLoS ONE 6, e27825 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Boerner, R. E. J. Plant life span and response to inoculation with vesicular-arbuscular mycorrhizal fungi. Mycorrhiza 1, 153–161 (1992).Article 

Google Scholar 
Wilson, S. D. Tilman plant competition and resource availability in response to disturbance and fertilization. Ecology 74, 599–611 (1993).Article 

Google Scholar 
Xiao-Bin, W., Dian-Xiong, C. A. I., Hoogmoed, W. B., Oenema, O. & Perdok, U. D. Potential effect of conservation tillage on sustainable land use: A review of global long-term studies. Pedosphere 16, 587–595 (2006).Article 

Google Scholar 
Miransari, M. Contribution of arbuscular mycorrhizal symbiosis to plant growth under different types of soil stress. Plant Biol. 12, 563–569 (2010).CAS 
PubMed 

Google Scholar 
Latef, A. A. H. A. et al. Arbuscular mycorrhizal symbiosis and abiotic stress in plants: A review. J. Plant Biol. 59, 407–426 (2016).Article 

Google Scholar 
Richardson, D. M., Allsopp, N., D’Antonio, C. M., Milton, S. J. & Rejmanek, M. Plant invasions—The role of mutualisms. Biol. Rev. 75, 65–93 (2000).Article 
CAS 
PubMed 

Google Scholar 
Řezáčová, V. et al. Mycorrhizal symbiosis induces plant carbon reallocation differently in C3 and C4 Panicum grasses. Plant Soil 425, 441–456 (2018).Article 

Google Scholar 
Newman, E. I. A method of estimating total length of root in a sample. J. Appl. Ecol. 3, 139–145 (1966).Article 

Google Scholar 
Bukovská, P., Gryndler, M., Gryndlerová, H., Püschel, D. & Jansa, J. Organic nitrogen-driven stimulation of arbuscular mycorrhizal fungal hyphae correlates with abundance of ammonia oxidizers. Front. Microbiol. 7, 711. https://doi.org/10.3389/fmicb.2016.00711 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hewitt, E. J. Sand and water culture methods used in the study of plant nutrition. CAB Tech. Commun. 22, 431–432 (1966).
Google Scholar 
Řezáčová, V. et al. Imbalanced carbon-for-phosphorus exchange between European arbuscular mycorrhizal fungi and non-native Panicum grasses—A case of dysfunctional symbiosis. Pedobiologia 62, 48–55 (2017).Article 

Google Scholar 
Řezáčová, V. et al. Little cross-feeding of the mycorrhizal networks shared between C3-Panicum bisulcatum and C4-Panicum maximum under different temperature regimes. Front. Microbiol. 9, 449 (2018).
Google Scholar 
Ohno, T. & Zibilske, L. M. Determination of low concentrations of phosphorus in soil extracts using malachite green. Soil Sci. Soc. Am. J. 55, 892–895 (1991).Article 
CAS 

Google Scholar 
Thonar, C., Erb, A. & Jansa, J. Real-time PCR to quantify composition of arbuscular mycorrhizal fungal communities-marker design, verification, calibration and field validation. Mol. Ecol. Res. 12, 219–232 (2012).Article 
CAS 

Google Scholar 
McGonigle, T. P., Miller, M. H., Evans, D. G., Fairchild, G. L. & Swan, J. A. A new method which gives an objective-measure of colonization of roots by vesicular arbuscular mycorrhizal fungi. New Phytol. 115, 495–501 (1990).Article 
CAS 
PubMed 

Google Scholar 
Koske, R. E. & Gemma, J. N. A modified procedure for staining roots to detect VA-mycorrhizas. Mycol. Res. 92, 486–505 (1989).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2022) More

Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).Article 

Google Scholar 
Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCC, 2015).IPCC Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (IPCC, 2019).Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F. & Erasmi, S. Greenhouse gas emissions from soils—a review. Geochemistry 76, 327–352 (2016).Article 

Google Scholar 
Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry: An Analysis of Global Change 3rd edn (Elsevier, 2013).Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).Article 

Google Scholar 
Du, E. Rise and fall of nitrogen deposition in the United States. Proc. Natl Acad. Sci. USA 113, E3594–E3595 (2016).Article 

Google Scholar 
Schmitz, A. et al. Responses of forest ecosystems in Europe to decreasing nitrogen deposition. Environ. Pollut. 244, 980–994 (2019).Article 

Google Scholar 
Hietz, P. et al. Long-term change in the nitrogen cycle of tropical forests. Science 334, 664–666 (2011).Article 

Google Scholar 
Fang, Y. T., Gundersen, P., Mo, J. M. & Zhu, W. X. Input and output of dissolved organic and inorganic nitrogen in subtropical forests of South China under high air pollution. Biogeosciences 5, 339–352 (2008).Article 

Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).Article 

Google Scholar 
Liu, L. L. & Greaver, T. L. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol. Lett. 13, 819–828 (2010).Article 

Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).Article 

Google Scholar 
Reich, P. B. et al. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecol. Lett. 11, 793–801 (2008).Article 

Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).Article 

Google Scholar 
Mo, J. et al. Nitrogen addition reduces soil respiration in a mature tropical forest in southern China. Glob. Change Biol. 14, 403–412 (2008).Article 

Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).Article 

Google Scholar 
Zhong, Y., Yan, W. & Shangguan, Z. The effects of nitrogen enrichment on soil CO2 fluxes depending on temperature and soil properties. Glob. Ecol. Biogeogr. 25, 475–488 (2016).Article 

Google Scholar 
Deng, L. et al. Soil GHG fluxes are altered by N deposition: new data indicate lower N stimulation of the N2O flux and greater stimulation of the calculated C pools. Glob. Change Biol. 26, 2613–2629 (2020).Article 

Google Scholar 
Hagedorn, F., Kammer, A., Schmidt, M. W. I. & Goodale, C. L. Nitrogen addition alters mineralization dynamics of 13C-depleted leaf and twig litter and reduces leaching of older DOC from mineral soil. Glob. Change Biol. 18, 1412–1427 (2012).Article 

Google Scholar 
Du, Y. et al. Different types of nitrogen deposition show variable effects on the soil carbon cycle process of temperate forests. Glob. Change Biol. 20, 3222–3228 (2014).Article 

Google Scholar 
Yan, T. et al. Negative effect of nitrogen addition on soil respiration dependent on stand age: evidence from a 7-year field study of larch plantations in northern China. Agr. For. Meteorol. 262, 24–33 (2018).Article 

Google Scholar 
Xing, A. et al. Nonlinear responses of ecosystem carbon fluxes to nitrogen deposition in an old-growth boreal forest. Ecol. Lett. 25, 77–78 (2021).Article 

Google Scholar 
Melillo, J. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).Article 

Google Scholar 
Gao, Q. et al. Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study. Proc. Natl Acad. Sci. USA 117, 33317–33324 (2020).Article 

Google Scholar 
Liu, X. J. et al. Nitrogen deposition and its ecological impact in China: an overview. Environ. Pollut. 159, 2251–2264 (2011).Article 

Google Scholar 
Zhu, F. F., Yoh, M., Gilliam, F. S., Lu, X. K. & Mo, J. M. Nutrient limitation in three lowland tropical forests in southern China receiving high nitrogen deposition: insights from fine root responses to nutrient additions. PLoS ONE 8, e82661 (2013).Article 

Google Scholar 
Wang, C. et al. Responses of soil microbial community to continuous experimental nitrogen additions for 13 years in a nitrogen-rich tropical forest. Soil Biol. Biochem. 121, 103–112 (2018).Article 

Google Scholar 
Priess, J. & Fölster, H. Microbial properties and soil respiration in submontane forests of Venezuelian Guyana: characteristics and response to fertilizer treatments. Soil Biol. Biochem. 33, 503–509 (2001).Article 

Google Scholar 
He, T., Wang, Q., Wang, S. & Zhang, F. Nitrogen addition altered the effect of belowground C allocation on soil respiration in a subtropical forest. PLoS ONE 11, e0155881 (2016).Article 

Google Scholar 
Fan, H. et al. Nitrogen deposition promotes ecosystem carbon accumulation by reducing soil carbon emission in a subtropical forest. Plant Soil 379, 361–371 (2014).Article 

Google Scholar 
Zheng, M. et al. Effects of nitrogen and phosphorus additions on nitrous oxide emission in a nitrogen-rich and two nitrogen-limited tropical forests. Biogeosciences 13, 3503–3517 (2016).Article 

Google Scholar 
Lu, X. et al. Nitrogen deposition accelerates soil carbon sequestration in tropical forests. Proc. Natl Acad. Sci. USA 118, e2020790118 (2021).Article 

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

Google Scholar 
Tian, J. et al. Long-term nitrogen addition modifies microbial composition and functions for slow carbon cycling and increased sequestration in tropical forest soil. Glob. Change Biol. 25, 3267–3281 (2019).Article 

Google Scholar 
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).Article 

Google Scholar 
Lu, X. K. et al. Effect of simulated N deposition on soil exchangeable cations in three forest types of subtropical China. Pedosphere 19, 189–198 (2009).Article 

Google Scholar 
Fang, Y., Gundersen, P., Mo, J. & Zhu, W. Nitrogen leaching in response to increased nitrogen inputs in subtropical monsoon forests in southern China. For. Ecol. Manage. 257, 332–342 (2009).Article 

Google Scholar 
Chen, X. M. et al. Effects of nitrogen deposition on soil organic carbon fractions in the subtropical forest ecosystems of S. China. J. Plant Nutr. Soil Sci. 175, 947–953 (2012).Article 

Google Scholar 
Fang, H. J. et al. 13C abundance, water-soluble and microbial biomass carbon as potential indicators of soil organic carbon dynamics in subtropical forests at different successional stages and subject to different nitrogen loads. Plant Soil 320, 243–254 (2009).Article 

Google Scholar 
Liu, L. et al. Effects of nitrogen and phosphorus additions on soil microbial biomass and community structure in two reforested tropical forests. Sci. Rep. 5, 14378–14378 (2014).Article 

Google Scholar 
Chen, H. et al. Nitrogen saturation in humid tropical forests after 6 years of nitrogen and phosphorus addition: hypothesis testing. Funct. Ecol. 30, 305–313 (2015).Article 

Google Scholar 
Lu, X., Mao, Q., Gilliam, F. S., Luo, Y. & Mo, J. Nitrogen deposition contributes to soil acidification in tropical ecosystems. Glob. Change Biol. 20, 3790–3801 (2014).Article 

Google Scholar 
Mao, Q. G. Impacts of Long-Term Nitrogen and Phosphorus Addition on Understory Plant Diversity in Subtropical Forests in Southern China. Doctoral Thesis, Univ. Chinese Academy of Sciences (2017).Xing, A. J. et al. High-level nitrogen additions accelerate soil respiration reduction over time in a boreal forest. Ecol. Lett. https://doi.org/10.1111/ele.14065 (2022).Cao, J. et al. Plant–bacteria–soil response to frequency of simulated nitrogen deposition has implications for global ecosystem change. Funct. Ecol. 34, 723–734 (2020).Article 

Google Scholar 
Mo, J. M., Brown, S., Peng, S. L. & Kong, G. H. Nitrogen availability in disturbed, rehabilitated and mature forests of tropical China. For. Ecol. Manage. 175, 573–583 (2003).Article 

Google Scholar 
Huang, Z. L., Ding, M. M., Zhang, Z. P. & Yi, W. M. The hydrological processes and nitrogen dynamics in a monsoon evergreen broad-leafed forest of Dinghushan. Acta Phytoecol. Sin. 18, 194–199 (1994).
Google Scholar 
Wright, R. F. & Rasmussen, L. Introduction to the NITREX and EXMAN projects. For. Ecol. Manage. 101, 1–7 (1998).Article 

Google Scholar 
Gundersen, P. et al. Impact of nitrogen deposition on nitrogen cycling in forests: a synthesis of NITREX data. For. Ecol. Manage. 101, 37–55 (1998).Article 

Google Scholar 
Aber, J. D. et al. Plant and soil responses to chronic nitrogen additions at the Harvard Forest, Massachusetts. Ecol. Appl. 3, 156–166 (1993).Article 

Google Scholar 
Cleveland, C. C. & Townsend, A. R. Nutrient additions to a tropical rain forest drive substantial soil carbon dioxide losses to the atmosphere. Proc. Natl Acad. Sci. USA 103, 10316–10321 (2006).Article 

Google Scholar 
Song, X. et al. Nitrogen addition increased CO2 uptake more than non-CO2 greenhouse gases emissions in a Moso bamboo forest. Sci. Adv. 6, eaaw5790 (2020).Article 

Google Scholar 
Lu, X. et al. Long-term nitrogen addition decreases carbon leaching in nitrogen-rich forest ecosystems. Biogeosciences 10, 3931–3941 (2013).Article 

Google Scholar 
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).Article 

Google Scholar 
Tang, X., Liu, S., Zhou, G., Zhang, D. & Zhou, C. Soil–atmospheric exchange of CO2, CH4, and N2O in three subtropical forest ecosystems in southern China. Glob. Change Biol. 12, 546–560 (2006).Article 

Google Scholar 
Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 403 (2021).Article 

Google Scholar  More

Stocks, G., Seales, L., Paniagua, F., Maehr, E. & Bruna, E. M. The geographical and institutional distribution of ecological research in the tropics. Biotropica 40, 397–404 (2008).Article 

Google Scholar 
Bernstein, J. M., Murphy, J. C., Voris, H. K., Brown, R. M. & Ruane, S. Phylogenetics of mud snakes (Squamata: Serpentes: Homalopsidae): A paradox of both undescribed diversity and taxonomic inflation. Mol. Phylogenet. Evol. 160, 107109 (2021).Article 
PubMed 

Google Scholar 
Murphy, J. C., Voris, H. K. & Karns, D. R. The dog-faced water snakes, a revision of the genus Cerberus Cuvier, (Squamata, Serpentes, Homalopsidae), with the description of a new species. Zootaxa 3484, 1–34 (2012).Article 

Google Scholar 
Stuart, B. L. The harvest and trade of reptiles at U Minh Thuong National Park, southern Viet Nam. Traffic Bull. 20, 25–34 (2004).
Google Scholar 
Brooks, S. E., Allison, E. H. & Reynolds, J. D. Vulnerability of Cambodian water snakes: Initial assessment of the impact of hunting at Tonle Sap Lake. Biol. Conserv. 139, 401–414 (2007).Article 

Google Scholar 
Murphy, J. C. Homalopsid Snakes (Evolution in the Mud (Krieger Publishing, Malabar, 2007).
Google Scholar 
Karns, D. R., Murphy, J. C. & Voris, H. K. Semi-aquatic snake communities of the central plain region of Thailand. Trop. Nat. Hist. 10, 1–25 (2010).
Google Scholar 
Jayne, B. C., Voris, H. K. & Heang, K. B. Diet, feeding behavior, growth, and numbers of a population of Cerberus rynchops (Serpentes: Homalopsinae) in Malaysia: a contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Fieldiana Zoology, Series 50 (Field Museum of Natural History, Chicago, IL, 1988).Chim, C. K. & Diong, C. H. A mark-recapture study of a dog-faced water snake Cerberus schneiderii (Colubridae: Homalopsidae) population in Sungei Buloh Wetland Reserve Singapore. Raffles Bull. Zool. 61, 811–825 (2013).
Google Scholar 
Shine, R., Ambariyanto, Harlow, P. S. & Mumpuni. Ecological attributes of two commercially-harvested python species in northern Sumatra. J. Herpet. 33, 249–257 (1999).Natusch, D. J., Lyons, J. A., Riyanto, A., Khadiejah, S. & Shine, R. Detailed biological data are informative, but robust trends are needed for informing sustainability of wildlife harvesting: A case study of reptile offtake in Southeast Asia. Biol. Conserv. 233, 83–92 (2019).Article 

Google Scholar 
Natusch, D. J., Lyons, J. A., Riyanto, A. & Shine, R. Harvest effects on blood pythons in North Sumatra. J. Wildl. Manage. 84, 249–255 (2020).Article 

Google Scholar 
Shine, R., Harlow, P. S. & Keogh, J. S. The influence of sex and body size on food habits of a giant tropical snake, Python reticulatus. Funct. Ecol. 12, 248–258 (1988).Article 

Google Scholar 
Shine, R., Harlow, P. S. & Keogh, J. S. The allometry of life-history traits: Insights from a study of giant snakes (Python reticulatus). J. Zool. 244, 405–414 (1998).Article 

Google Scholar 
Shine, R. & Harlow, P. S. Reticulated pythons in Sumatra: biology, harvesting and sustainability. Biol. Conserv. 87, 349–357 (1999).Article 

Google Scholar 
Hoesel, J. K. P. Ophidia Javanica (Museum Zoologicum Bogoriense, Kebun Raya, Indonesia, 1959).Voris, H. K. & Murphy, J. C. The prey and predators of homalopsine snakes. J. Nat. Hist. 36, 1621–1632 (2002).Article 

Google Scholar 
Wall, F. A popular treatise on the common Indian Snakes. Part 26. J. Bombay Nat. Hist. Soc. 26, 89–97 (1918).Gorman, G. C., Licht, P. & McCollum, F. Annual reproductive patterns in three species of marine snakes from the central Philippines. J. Herpetol. 15, 335–354 (1981).Article 

Google Scholar 
Auffenberg, W. The herpetofauna of Komodo, with notes on adjacent areas. Bull. Florida State Mus. Biol. Sci. 25, 39–156 (1980).
Google Scholar 
Alcala, A. C. Guide to Philippine Flora and Fauna. Vol. X. Amphibians and Reptiles (Natural Resource Management Center, Ministry of Natural Resources and the University of the Philippines, Manila, Philippines, 1986).Harlow, P. S. & Taylor, J. E. Reproductive ecology of the jacky dragon (Amphibolurus muricatus): An agamid lizard with temperature-dependent sex determination. Austral. Ecol. 25, 640–652 (2000).Article 

Google Scholar 
Saint Girons, H. & Pfeffer, P. Notes sur l’ecologie des serpents du Cambodge. Zool. Mededelingen 47, 65–87 (1972).Kusrini, M. D. et al. Abundance, demography, and harvesting of water snakes from agricultural landscapes in West Java, Indonesia. Wildl. Res. In review (2022).Shine, R. Sexual differences in morphology and niche utilization in an aquatic snake Acrochordus arafurae. Oecologia 69, 260–267 (1986).Article 
PubMed 

Google Scholar 
Houston, D. & Shine, R. Sexual dimorphism and niche divergence: Feeding habits of the Arafura filesnake. J. Anim. Ecol. 62, 737–748 (1993).Article 

Google Scholar 
Shine, R., Reed, R., Shetty, S. & Cogger, H. Relationships between sexual dimorphism and niche partitioning within a clade of sea-snakes (Laticaudinae). Oecologia 133, 45–53 (2002).Article 
CAS 
PubMed 

Google Scholar 
Vincent, S. E., Herrel, A. & Irschick, D. J. Sexual dimorphism in head shape and diet in the cottonmouth snake (Agkistrodon piscivorus). J. Zool. 264, 53–59 (2004).Article 

Google Scholar 
Perkins, M. W., Cloyed, C. S. & Eason, P. K. Intraspecific dietary variation in niche partitioning within a community of ecologically similar snakes. Evol. Ecol. 34, 1017–1035 (2020).Article 

Google Scholar 
Shine, R. & Goiran, C. Sexual dimorphism in size and shape of the head in the sea snake Emydocephalus annulatus (Hydrophiinae, Elapidae). Sci. Rep. 11, 20026 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shine, R. Intersexual dietary divergence and the evolution of sexual dimorphism in snakes. Am. Nat. 138, 103–122 (1991).Article 

Google Scholar 
Bonnet, X., Shine, R., Naulleau, G. & Vacher-Vallas, M. Sexual dimorphism in snakes: Different reproductive roles favour different body plans. Proc. R. Soc. B 265, 179–183 (1998).Article 
PubMed Central 

Google Scholar 
Shine, R., Olsson, M. M., Moore, I. T., LeMaster, M. P. & Mason, R. T. Why do male snakes have longer tails than females?. Proc. R. Soc. B 266, 2147–2151 (1999).Article 
PubMed Central 

Google Scholar  More

UPRLIMET is our response to a need for a consistent method for predicting the upper extent of trout in all streams across land ownerships within our region. By developing and implementing the model using LiDAR-derived flowline hydrography, we offer a standardized, spatially explicit, spatially contiguous (where LiDAR hydrography is available), and high-quality fish-distribution layer based on the probability of fish presence. UPRLIMET maps both the probability of trout and the upper limit of trout across landscapes, ownerships, and jurisdictions, and better captures the upper extent of fish in headwater reaches relative to previous approaches allowing for a cross-boundary distribution map on which decision-makers and managers can base policies and regulations.This work provides a transferable prediction modeling framework for systematically and comprehensively estimating the upper distribution limit of fish, which could be calibrated and implemented in watersheds and for fish species around the globe. Although the dependency on LiDAR-derived data here may be seen as a limitation to broader implementation of this method, the method is scalable to any resolution, and LiDAR is becoming increasingly ubiquitous in the United States through the U.S. Geological Survey 3D Elevation Program, which is funding LiDAR acquisitions across the United States. Furthermore, LiDAR data is available globally via data from GEDI and ICESAT-2 satellites that offer coarser resolution (~ 25-m) data that are still superior to either ASTER or SRTM derived-DEMs26, 27.Minimizing prediction errors for the upper limit of trout is important to decision support and management planning because it ensures that forest-harvest regulations and management prescriptions are aligned. It is important to note that the prediction error estimates from this study are derived from the NSpCV process, except for models using 20% slope thresholds or unaltered parameterization of Fransen’s model13, because it is likely that the NSpCV estimates are conservative. They tended to overestimate error, as evidenced by the fact that the Refit model (i.e. Fransen’s optimal model13 refit to our data) exhibited a larger MAE than the unchanged optimal Fransen model13. This unexpected result was likely due to applying the NSpCV routine on the Refit model, resulting in the use of many intermediate models to characterize predictive performance using randomized subsets of independent training and test data. In contrast, the optimal Fransen model13 was developed independently using the data in this study and thus error could be evaluated directly without subsampling imposed by NSpCV.The relatively low error for the two-stage model that becomes UPRLIMET suggests that it more accurately characterizes the upper limit of fish than all other models considered in this study, including the Fransen model13, which has been used for estimating upper limit of fish regionally. Although some of the models exhibited relatively small differences in error relative to the model that became UPRLIMET, small differences in predicted upper limit locations when considered in aggregate across multiple watersheds can potentially alter management decisions and expected outcomes. Differences in predictive performance and error between UPRLIMET and the optimal Fransen model13 are likely attributed to high-accuracy hydrography and hydro-topographic data (as LiDAR-derived DEMs were not available in western Washington in 2006), which allowed a finer-scale of analysis (i.e., 5-m vs 10-m reaches). Additionally, the fact that UPRLIMET was fit to data solely from western Oregon likely offers predictive performance gains when applied to western Oregon when compared to the Fransen model13 that was fit to data from western Washington.Quantifying the predicted accuracy associated with applying UPRLIMET to western Washington will require new data and is outside the intended scope of this study. However, we think it is reasonable to infer findings from UPRLIMET across regions with similar climatic and hydro-topographic conditions including northwestern California, western Oregon, western Washington, and southwestern British Columbia, especially given the broad availability of LiDAR-derived DEMs. This conclusion is supported the fact that both the Fransen13 and Refit models produced similar logistic regression coefficients (Data S5) and similar Matthews Correlation Coefficients (Data S6), suggesting that feature space of the two models is similar. This evidence is further corroborated by the high degree of overlap observed among the distributions of each of the four predictor variables for both western Oregon and Washington. We acknowledge that UPRLIMET does not contain identical predictor variables to Fransen’s model13 but maintain that they are similar enough in purpose that it is reasonable to assume that the feature space similarities are retained.When we undertook this study, we hypothesized that a prediction model based on RF would offer superior predictive performance over those based on LR, given the availability of 67 predictor variables and RF’s demonstrated superior predictive performance in ecological applications23,24,25. However, our results suggests no improvement is offered by including more than four of the 67 environmental predictors examined, and that no clear advantage is offered by employing the more complex RF model, as evidenced by the top three of the top five prediction models being four-variable LR model algorithms (Fig. 3; Data S3.) The general importance of these variables to so many models is likely due to the strong linear relationships in the response of fish or no fish in logit space given the slopes of the curves in the partial dependence profiles (Fig. 4). This finding is congruent with the fundamental premise of LR, which is to explain and predict a response with a functional relationship, whereas RF deliberately focuses only on maximizing prediction accuracy with many decision trees28. Additional advantages to prediction models based on LR include the following: relatively better extrapolation performance over RF29, the simplicity of transferring a LR model to another processing platform using the model coefficients (versus the black box of RF decisions), and the immensely reduced computational processing times associated with LR model fitting and prediction. These advantages are especially key to this work, where there may be a desire to implement the model on other landscapes without the requisite expertise in doing so using the R software30. However, there are tradeoffs, as LR is more sensitive to the influence of outliers and multi-collinearity among variables, and overfitting is an increasing concern as the number of predictor variables increase, whereas RF tends to be robust to these concerns, but is more likely to produce a high-variance, low-bias prediction model31.Although there is no single, general explanation for distribution limits of species32, the intersection of stream size, slope, and elevation together locate the upper limit of fish. Stream size corresponds to major ecosystem changes along a stream continuum including for energy sources, ecosystem metabolism, habitat characteristics, and biodiversity33, as well as the upper distribution limit of fish, as shown here. As expected, stream size accounts for the top two variables in the model suggesting that it is the major driver of the upper distribution limit of fish with the probability of trout increasing with increasing upstream stream length and upstream drainage area. Our finding proposes that downstream stream reaches are more likely to have fish. Although the underlying mechanisms have multiple influences, factors related to increasing stream size, such as increasing habitat size, habitat complexity, stability, or temperature variability34 have been shown to be important. Similarly, stream size is the most sensitive factor in intrinsic potential models for Chinook Salmon (O. tshawytscha35). Slope, the next variable of importance influencing the upper extent of fish, exerts control on physical habitats in streams, including channel morphology, hydraulics, sediment transport, substrate, and habitat36. Steep slopes drastically prevent trout from reaching areas above waterfalls or impassable chutes of over 25% slope, but trout can be found in streams channels without barriers at slopes as high as 28%7, 14, 37. Other fishes, such as Coho Salmon (O. kisutch) and steelhead (O. mykiss) are generally not found above 12% slope38. Interestingly, survival of fishes that make it upstream or are introduced above barriers may be facilitated by a geomorphic setting that is less prone to debris flows and other episodic sediment fluxes and has a greater resilience to flooding resulting from wider valley and greater floodplain connectivity39. Elevation or vertical topographic position may indirectly integrate broad influences of other landscape-scale or climate factors or also indirectly capture stream size, influencing the likelihood of fish presence. Frequently, species richness increases at lower elevations40, and we suggest that elevation also contributes to species distribution limits, as is the case for the Endangered Species Act listed Bull Trout (Salvelinus confluentus)41. The multiple factors associated with elevation correspond to the relationship found for stream size that smaller streams are less likely to have fish. Ultimately, the intersection of stream size, slope, and elevation guide us to finding the upper extent of fish in streams.Physical influences have been proposed to be more limiting to fish distributions upstream, such as near the upper extent of fish, whereas biological factors are probably more important downstream33. Although 67 environmental predictor variables representing geologic, soil, climatic, and hydro-topographic conditions at local and patch scales are evaluated (Data S1), only the hydro-topographic variables of stream size, slope, and elevation are important to predicting the upper limit of fish in UPRLIMET. In fact, the top 9 models (Fig. 3; Data S3) relied on just four to five hydro-topographic variables, most of which were patch-scale variables or elevation at 1000 m, all of which incorporate a broader extent of influence. This suggests that local scale variables that contribute to fish limits, including slope or riparian influences may need to be further explored. In addition, some of the remaining 63 variables present in UPRLIMET, such as precipitation and air temperature, are important drivers of within-network trout distributions and contribute to their connectivity. Some of these predictor variables appear in the 10th ranked 26-variable RF-O-SR1 model (Data S2; Data S4; Data S8), but the influence appears to be dubious for isolating the upper limit and explaining variation in fish occurrence because MAE of upper limit was substantially higher than the 9 models with lower MAEs (Fig. 3; Data S3), and the lower MCC of the associated RF-O sub-model (Data S6). It is likely that other combinations of the 67 predictor variables, including precipitation, may be more important when this model development and evaluation framework is applied elsewhere, especially if those areas contain fishes or are places that are vulnerable to changing water temperatures and streamflow regimes. In addition, biological factors may be a concern in other watersheds, including invasive species and fish stocking which can limit the longitudinal distribution and the upstream extent of fishes.Given the large geographic extent of this study, we expected other variables such as precipitation to be more important drivers, however due to a combination of a wet water year, a lack of precipitation gradient in the study area, coarse grain data, and location of fish in streams this was not the case. For example, 2017 was a wetter than normal water year53, and it may be that the gradient of precipitation variation in western Oregon was not strong enough to explain the variation in the spatial distribution of trout occurrence. All climate data, including the precipitation data were sourced from relatively coarse-scale (800 m) PRISM data. The inability to adequately downscale precipitation to characterize how precipitation truly varies within and between patches, especially along elevational gradients, likely confounded how the model interprets the influence of precipitation. Trout occurrence was on perennial streams, which is likely far enough downstream of locations where variation in precipitation was the dominant influence on streamflow permanence and consequently would not have been a factor.Stream network structure plays a key role in the upper limits of fish. Upper limits for fish can occur at either lateral or terminal points13 and when mapping these points, differences were seen for UPRLIMET relative to other datasets. Lateral limits end in the tributary stream just above where it connects with a mainstem stream. Terminal limits include both mid-stream terminal limits where fish drop out in the middle of a stream channel owing to a soft (i.e., transient barrier or puttering out) or hard (i.e., waterfall) edge, and confluence terminal limits where the upper limit of fish ends at the confluence. For example, when closely examining the 14 watersheds where we have overlapping information across various datasets and models, UPRLIMET and the Fransen optimal model13 exhibit substantial agreement in their lateral limits. However, the largest differences are in their terminal ends, especially terminal mid-stream limits, probably owing to hydro-topographic changes that contribute to fish occurrence at confluences, which are more pronounced than mid-stream. Accordingly, the logic in the stopping rule is likely important in identifying specific upper extent of fish distributions in reaches that end mid-stream.Differences among databases for the upper distribution limits of fish come from both the upper limit points and depiction of fish-bearing reaches, underscoring the importance of having a shared map with common coverage of the fish extent across landscapes and ownerships. Differences among mapped distributions can result from source information, relating to whether it is modeled or occurrence data. Models, such as UPRLIMET, can be applied across a broad extent based on model parameters and training data, thereby offering broad coverage for distributions (and quantifiable error) across the landscape, ownerships, and jurisdictions. However, models are limited by accuracy and fit. As such, they can incorrectly predict distributions in some areas, especially if there are prediction features not yet trained with the model data where prediction would require extrapolation of the model. This makes both the training dataset and modeled extent important considerations, as models are only as good as the data used to develop them. Updating UPRLIMET with new data as it becomes available will help to expand the prediction domain, improve accuracy, and allow the model to do more interpolation than extrapolation.Distributions based on occurrence information depend heavily on data availability, data quality, and access. Differences in data availability can lead to inconsistent coverage across landscapes and ownerships, with high coverage in some watersheds and low to no coverage in others. Inconsistent coverage can lead to errors that are difficult to quantify across landscapes, ownerships, and survey crews. Occurrence information also depends on the ability to survey watersheds and gain access across ownership types, including on private lands that do not have the same assurances of access as public lands, resulting in information asymmetry42, 43. Data quality also depends on the spatial accuracy of the points of uppermost fish, which are a function of GPS quality and error, and can drastically change the modeled results, as these points are used in the training dataset. Differences among mapped distribution limits also result from differences in field protocols on designating last fish. For example, some crews note fish distribution limits where they visually see the last fish, whereas others note it upstream of where they saw last fish, based on habitat features that would limit fish. With the advent of LiDAR-derived DEMs and associated LiDAR-derived stream hydrography, like those available in much of western Oregon, have revealed additional flowlines in watersheds compared to previous topographic maps, which adds more potential tributaries to survey for fish-distribution assessments. When these new previously unmapped tributaries are paired with a model, such as UPRLIMET, a common information set is available across landowners, managers, and agencies for the upper extent of fish. This helps policymakers determine where to apply regulations that support fisheries and forest management, based on the upper fish limit.Next steps for applying and expanding the model include addressing current data gaps. More information and observations about the upper distribution limits of fish beyond western Oregon would be needed to properly expand the spatial scope of the model. The upper extent of fish is at the detection limit of many current technologies, including global nativation satellite system (GNSS), geographic information systems (GIS), and LiDAR, especially in forested landscapes. Better precision of GNSS coordinates from observations would help greatly. From an ecological perspective, we could focus on fish distribution limits that vary seasonally or interannually to better understand which stream features and hydrologic parameters influence those endpoints. We also need information related to locations of barriers, including culverts, waterfalls, and knickpoints to understand their influence on contemporary distributions. Incorporating variables representing riparian conditions as well as leveraging higher-resolution DEMs ( More

Pablo Ignacio Plaza and Sergio Agustín Lambertucci from the National University of Comahue and the Argentine Research Council in Argentina quantified the contribution of vultures to reducing greenhouse gas emissions by developing two contrasting scenarios. The first assumes that all the dead animals that the vultures can consume are disposed of, whereas in the second scenario, the dead animals are left to decompose in the environment without scavengers. The results show that the current vulture population can reduce emissions by up to 60.7 teragrams CO2 equivalent per year. A decline in vulture populations decreases their mitigation capacity by 30%. The study highlights that vultures are essential to keep our climate cool. More

Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

Environmental and competitive filtering is most important for future tree survivalWe find that individual functional traits of each tree were most important for individual tree survival (40–87%) for all study sites, followed by forest dynamics (16–28%) and functional diversity (10–26%) (Fig. 2). Nevertheless, importance proportions substantially varied in each study site. While individual functional traits were least important in the mixed mountainous forest under reference climate (no climate warming), individual functional traits showed highest influence in the mixed temperate forest under future climate change (Fig. 2).Figure 2Relative importance of functional diversity, forest dynamics and individual traits for individual tree survival under reference climate and future climate conditions (RCP 4.5). Panels correspond to alpine needle-leaved (A), mixed mountainous (B), mixed temperate (C) and temperate broad-leaved (D) forests. Left bars in each panel illustrate reference climate (no warming) and right bars future climate (RCP4.5). Colours indicate forest dynamics (grey), functional diversity (yellow) and individual functional traits (blue). Forest dynamics include the number of locally competing trees ( > 5 m in height) and local biomass as a proxy for the successional status.Full size imageTree survival depends on a mixture of environmental (e.g. climate) and natural competitive filtering, which excludes trees with trait combinations that underperform under local conditions16. Therefore, the high importance of individual functional traits across all study sites suggests a strong environmental and competitive filtering. Under future climate, the importance of individual functional traits generally increases or remains at high levels (Fig. 2). This shows that environmental and competitive filtering through functional traits are important processes to select best performing trees for the future, although being different for each forest type.Changing forest composition and trait shifts require large functional portfolio to secure forest resistanceUnder future climate, we observe trait shifts within plant functional types and strong changes of the forest composition (Figs. 3, 4). Especially in the alpine and mixed forests, the proportion of broad-leaved trees increases to at least ~ 70% towards the end of the twenty-first century (Fig. 3). The changing climate alters environmental and competitive filtering simultaneously, whereby broad-leaved trees become more productive, survive better and increasingly outcompete needle-leaved trees. For instance, in the two mixed forests survival probabilities of broad-leaved trees (high SLA) increase by about ~ 10%, whereas the survival of needle-leaved (low SLA) trees is reduced by 10% to 30% in a warmer climate (see Supplementary Figs. S7, S8, Panel A). Locally better adapted and competitive broad-leaved trees can replace needle-leaved trees if they die and secure the forest’s overall biomass in the future. Nevertheless, our simulated forests still contain significant amounts of needle-leaved trees in the year 2099 in the two coldest study areas (Fig. 3, red and blue lines). Therefore, mixed tree communities with high functional diversity, where broad- and needle-leaved trees coexist, contain a broad range of functional niches out of which the best suitable plant strategies emerge and result in better resistance to climate change.Figure 3Forest compositions and changes in the proportion of broad-leaved trees (summergreen and evergreen plant functional type combined) under climate change (RCP 4.5) from 2000 to 2099 for each study site. The fraction of broad-leaved trees, as simulated by LPJmL-FIT for each site, increases gradually in almost every forest type reaching at least about 70% by the end of the century. Pictures depict snapshots from visualization of model output in the years 2000 and 2099, respectively. For a full animation of all sites from 2000 to 2099 please see Supplementary Video 1.Full size imageFigure 4Trait distributions of specific leaf area in year 2000 and 2099, respectively, under future climate change (RCP 4.5). Arrows indicate trait shifts within plant functional types: BL-S Broad-leaved summergreen, BL-E Broad-leaved evergreen, T-NL Temperate needle-leaved, B-NL Boreal needle-leaved. For more detailed distributions see Supplementary Figs. S3 and S4.Full size imageSimultaneously, we observe strong trait shifts in SLA within plant functional types across all study sites under climate change (Fig. 4). In general, the community of broad-leaved trees shift to lower SLA, while boreal needle-leaved trees are strongly reduced or slowly replaced by their temperate equivalent with higher SLA (Supplementary Fig. S3A, dark blue colours). In contrast, wood density distributions remain relatively broad and do not shift strongly under climate change (Supplementary Fig. S4). Throughout the century, the increasingly warmer climate filters new trait combinations leading to changes in the community composition within and across PFTs (see Supplementary Discussion B). Those trait shifts emerge from changes in the composition within PFTs and newly establishing PFTs, and could be less drastic if trait adaptation of tree individuals was considered (see “Limitations and outlook” section). The points raised above show, that trait ranges within and between PFTs should be wide to cover potential future trait shifts that secure future forest resistance.All this suggests that functionally diverse forests are more resistant to future climate changes, due to their rich portfolio of traits. Broad trait distributions both within and between PFTs form the fundament for environmental and competitive filtering to select the most productive trees, securing the forest’s overall biomass under changing conditions. But can functional diversity further strengthen forest resistance beyond portfolio effects?Functional complementarity helps young trees to surviveWe find that, in addition to port-folio effects, functional diversity increases forest resistance by supporting the survival of young trees to changing climate conditions via trait complementarity. Our results indicate, that trees benefit from functional diversity if they grow in tree communities with high FR, high FDv and low FE (Supplementary Figs. S6–S9, Panel D–F in each figure). Here, functional traits lay highly separated (FDv and FE) and span a broad range in the functional trait space (FR), enabling functional complementarity. Under these conditions the survival of trees increases up to + 16.8% (± 1.6%) depending on the study site and climate (Table 1). This effect is highest in the alpine and mountainous forests (14–17%), whereas it is less prominent or has an opposite effect in the two temperate forests (− 7% to 6%). That suggests, that complementarity effects are stronger in cold-limited and mixed forests where a marked cold winter season fosters a co-existence between broadleaved and needle-leaved trees. Both PFTs are specialized in fixing carbon during different times of the annual cycle: Due to their leaf phenology, needle-leaved trees can already be productive when broad-leaved trees are still in progress of unfolding or shedding their leaves. On the other hand, broad-leaved trees are more productive than needle-leaved trees during warmer months. If coexistence is given, these phenological differences enable complementarity and reduce competition among PFTs. That overall increases tree survival, because trees can invest more carbon in their stems and defensive structures if competition is lower. Therefore, we argue that phenological complementary can enhance tree survival and thus forest resistance. An in-depth discussion of those mechanisms is further found in Supplementary Discussion A.Table 1 Additional survival probabilities for trees in each forest site under reference climate (central column) and future climate (RCP4.5, right column) in case FR and FDv are high, while FE is low.Full size tableSurprisingly, our results show that those complementary effects are much more important for small trees ( 10 m) in right panel, respectively. Functional diversity and forest dynamics are more important for small trees compared to large trees, whereas individual functional traits matter most for large trees. This pattern was found to be consistent across all sites (see Supplementary Table S6).Full size imageFunctionally diverse understoreys unlock the synergy of filtering and complementary effectsOur findings underline the role of functionally diverse trees in the understoreys for forest resistance. On the one hand, functional diversity supports the survival of understorey trees via functional trait complementarity. On the other hand, they form the fundament for competitive and environmental filtering. Only diverse tree communities have trait pools large enough to ensure that their tree portfolio holds trait combinations best suited for changing climate conditions. Therefore, we argue that functional diversity does not only support tree survival through complementarity, but is a prerequisite for filtering resistant trees in the first place.To profit constantly from functional diversity of the understory and ensure constant adaptation, a diverse age structure is a prerequisite. Depending on the forest type, trees are distributed in a broad range of different height and age classes in our study (Supplementary Fig. S5, Supplementary Video 1). This multi-aged structure is preserved under climate change (Supplementary Fig. S5) and allows gradual changes through constant environmental and competitive filtering in the future.In this study, we simulate forests without any human interference or management. Our results are therefore to be interpreted in the context of environmental and competitive filtering as observed in natural forests. Most managed forests lack this natural filtering effect as they are less dense and diverse in their age-structure. Functionally diverse trees in the understorey could provide the fundament for climate adapted multi-aged forests, as they constantly form new better adapted tree generations with natural competition and succession allowed. Therefore, we fully underline the importance of functionally diverse understorey trees and natural competition as the fundament for future forest resistance.Management implicationsThe results of this study highlight the importance of functionally diverse understorey trees. However, browsing by game might damage new tree saplings and limit tree diversity in the understorey. In addition, invasive species like Prunus serotina or herbaceous competition might hinder forest succession and the establishment of woody native species in European forests20,21. Therefore, regulating game, limiting the spread of invasive species and controlling herbaceous vegetation should be considered in future management practices where tree diversity in the understorey seeks to be increased or maintained. Moreover, insufficient dispersal of functionally different tree species might limit the establishment of functionally diverse trees in the understorey. Future forest management may consider to artificially plant functionally different tree species if dispersal from surrounding forests cannot be guaranteed. On the other hand, forests that already contain functionally diverse trees in the understorey should be preserved.In this study, a clear trend from needle-leaved to broad-leaved trees is captured at all sites, whereas within broad-leaved PFTs a shift to lower specific leaf area and higher leaf longevities indicates that future forests might especially benefit from longer vegetation periods (earlier leaf onset, later senescence). Therefore, forests containing broad-leaved tree individuals with high phenological plasticity could be more resistant. The broad simulated wood density ranges, which persist under climate change, imply beneficial effects for forest communities entailing a range of different growing strategies, i.e. early to late successional species. Therefore, we argue that forest fragmentation should be reduced or reversed to foster some natural dispersal of early and late successional species.This study intended to explore the potentials of functionally diverse forests as a possibility to stabilize forests under climate change over a large climatic gradient. The model used in this study operates on the more general level of functional traits and their diversity rather than on species level (see “Methods” section and Supplementary Methods A). Consequently, management implications regarding suitable specific tree species are beyond the scope of this study. However, we think that our results will stimulate the discussion on the importance of functional tree traits and their diversity for species selection.Limitations and outlookThis study focussed on identifying the importance of functional diversity for future tree survival to advance our understanding on the role of biodiversity for future forest resistance using the flexible-trait Dynamic Global Vegetation Model LPJmL-FIT. The general approach of LPJmL-FIT is to simulate biogeographic dynamics purely based on environmental and competitive filtering (see “Methods” section, Supplementary Methods A). Due to missing processes in the model and the ambiguity of former human influence, drawing site-specific implications on future forest dynamics must be taken with caution (see Supplementary Discussion D).Moreover, processes not yet captured in the LPJmL-FIT model, might play a role and could lower forest resistance in the future, which is why we recommend relying on a trait space as broad as possible.Including more climate scenarios would widen the envelope of possible future pathways by considering climate model uncertainties. Insect outbreaks and pathogens might put pressure to the already drought- and temperature-stressed trees and heavily accelerate mortality especially of needle-leaved trees, although functionally diverse forests are less vulnerable to bark beetle outbreaks22,23. Multi-layered forests showed higher growth resilience to structural disturbances such as wind-throw24, likely enhancing the importance of individual tree height and reduce the survival probability of large trees25. Belowground competition and trait plasticity could favour complementarity effects further. Variable rooting strategies could further reduce competition for soil water and thereby increase individual drought resistance of trees26. Trait plasticity can contribute to tree survival by widening niche, further increasing complementarity effects. However, trait plasticity remains one of the most challenging objectives in vegetation modelling as observational data and modelling approaches are scarce27, leaving it open how far trait relationships would hold under climate change. Considering more functional traits in our analysis might increase the overall predictive power of the random forest models. Even though the explained variance increased with the number of analysed traits explaining ecosystem properties in long-term grassland experiments, such improvement is limited as abiotic factors and their interactions with plant traits might be more important for prediction28. We conclude that simulating future forest dynamics dominated by environmental and natural competitive filtering requires to integrate both, abiotic and biotic drivers on forest dynamics. Machine learning techniques are increasingly used in forest ecological research—but mainly applied in the processing of field and forest inventory data29,30. Machine learning can help to understand the complexity of interactions and provide deeper insights into the underlying ecological process in a modelling study as we have shown here using LPJmL-FIT simulation results. Random forest analyses are suitable for a variety of data and applications because they are relatively robust to different data structures. Importance analysis can help to identify the role of underlying processes in complex models and to visualize their changes in a simple way. In doing so, model development is advanced by making use of large data sets, opening the door to further theory building and deeper understanding of plant trait ecology. More

Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
CAS 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
CAS 

Google Scholar 
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).Article 

Google Scholar 
Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30–36 (2013).Article 

Google Scholar 
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).Article 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).Article 
CAS 

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 
CAS 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).Article 
CAS 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402 (2017).Article 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).Article 
CAS 

Google Scholar 
Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).Article 
CAS 

Google Scholar 
Anderegg, W. R., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Change 10, 1091–1095 (2020).Article 

Google Scholar 
Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).Article 
CAS 

Google Scholar 
Engelbrecht, B. M. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).Article 
CAS 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).Article 
CAS 

Google Scholar 
Au, T. F. et al. Demographic shifts in eastern US forests increase the impact of late‐season drought on forest growth. Ecography 43, 1475–1486 (2020).Article 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
Lindenmayer, D. B., Laurance, W. F. & Franklin, J. F. Global decline in large old trees. Science 338, 1305–1306 (2012).Article 
CAS 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).Article 
CAS 

Google Scholar 
Ellsworth, D. & Reich, P. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest. Oecologia 96, 169–178 (1993).Article 
CAS 

Google Scholar 
Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).Article 
CAS 

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

Google Scholar 
Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).Article 

Google Scholar 
Piovesan, G. & Biondi, F. On tree longevity. N. Phytol. 231, 1318–1337 (2021).Article 

Google Scholar 
Jucker, T. et al. Tallo: a global tree allometry and crown architecture database. Glob. Change Biol. 28, 5254–5268 (2022).Article 
CAS 

Google Scholar 
Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).Article 

Google Scholar 
D’orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).Article 

Google Scholar 
Luo, Y. & Chen, H. Y. Observations from old forests underestimate climate change effects on tree mortality. Nat. Commun. 4, 1655 (2013).Article 

Google Scholar 
Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).Article 

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

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 
CAS 

Google Scholar 
Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).Article 
CAS 

Google Scholar 
Phillips, R. P. et al. A belowground perspective on the drought sensitivity of forests: towards improved understanding and simulation. For. Ecol. Manage. 380, 309–320 (2016).Article 

Google Scholar 
Meinzer, F. C., Lachenbruch, B. & Dawson, T. E. Size- and Age-Related Changes in Tree Structure and Function Vol. 4 (Springer, 2011).Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article 
CAS 

Google Scholar 
Klein, T. The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Funct. Ecol. 28, 1313–1320 (2014).Article 

Google Scholar 
Cavender-Bares, J. & Bazzaz, F. Changes in drought response strategies with ontogeny in Quercus rubra: implications for scaling from seedlings to mature trees. Oecologia 124, 8–18 (2000).Article 
CAS 

Google Scholar 
Gallé, A., Haldimann, P. & Feller, U. Photosynthetic performance and water relations in young pubescent oak (Quercus pubescens) trees during drought stress and recovery. N. Phytol. 174, 799–810 (2007).Article 

Google Scholar 
Keith, H., Mackey, B. G. & Lindenmayer, D. B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl Acad. Sci. USA 106, 11635–11640 (2009).Article 
CAS 

Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).Article 
CAS 

Google Scholar 
Zhao, S. et al. The International Tree‐Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).Article 

Google Scholar 
Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).Article 

Google Scholar 
Rayback, S. A. et al. The DendroEcological Network: a cyberinfrastructure for the storage, discovery and sharing of tree-ring and associated ecological data. Dendrochronologia 60, 125678 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions. Climate of the Past 16, 1901–1916 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Higher CO2 concentrations and lower acidic deposition have not changed drought response in tree growth but do influence iWUE in hardwood trees in the Midwestern USA. J. Geophys. Res. Biogeosci. 124, 3798–3813 (2019).Article 
CAS 

Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Springer, 2013).Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
Google Scholar 
Fritts, H. Tree Rings and Climate (Academic Press, 1976).
Google Scholar 
Wilson, R. et al. Last millennium Northern Hemisphere summer temperatures from tree rings: part I: the long term context. Quat. Sci. Rev. 134, 1–18 (2016).Article 

Google Scholar 
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 
Holmes, R. Program COFECHA User’s Manual (Univ. Arizona Laboratory of Tree-Ring Research, 1983).Palmer, J. G. et al. Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Pacific Oscillation. Environ. Res. Lett. 10, 124002 (2015).Article 

Google Scholar 
Cook, E. R. et al. Asian monsoon failure and megadrought during the last millennium. Science 328, 486–489 (2010).Article 
CAS 

Google Scholar 
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M. & Stahle, D. W. Long-term aridity changes in the western United States. Science 306, 1015–1018 (2004).Article 
CAS 

Google Scholar 
Cook, E. R. et al. Megadroughts in North America: placing IPCC projections of hydroclimatic change in a long‐term palaeoclimate context. J. Quat. Sci. 25, 48–61 (2010).Article 

Google Scholar 
Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era. Sci. Adv. 1, e1500561 (2015).Article 

Google Scholar 
Morales, M. S. et al. Six hundred years of South American tree rings reveal an increase in severe hydroclimatic events since mid-20th century. Proc. Natl Acad. Sci. USA 117, 16816–16823 (2020).Article 
CAS 

Google Scholar 
Stokes, M. & Smiley, T. An Introduction to Tree-Ring Dating. (Univ. Chicago Press, 1968).
Google Scholar 
Lockwood, B. R., Maxwell, J. T., Robeson, S. M, & Au, T. F. Assessing bias in diameter at breast height estimated from tree rings and its effects on basal area increment and biomass. Dendrochronologia 67, 125844 (2021).Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl Acad. Sci. USA 117, 33358–33364 (2020).Article 
CAS 

Google Scholar 
Rozas, V., DeSoto, L. & Olano, J. M. Sex‐specific, age‐dependent sensitivity of tree‐ring growth to climate in the dioecious tree Juniperus thurifera. N. Phytol. 182, 687–697 (2009).Article 

Google Scholar 
Carrer, M. & Urbinati, C. Age‐dependent tree‐ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85, 730–740 (2004).Article 

Google Scholar 
Gazol, A., Camarero, J., Anderegg, W. & Vicente‐Serrano, S. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).Article 

Google Scholar 
Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

Google Scholar 
Pardos, M. et al. The greater resilience of mixed forests to drought mainly depends on their composition: analysis along a climate gradient across Europe. For. Ecol. Manage. 481, 118687 (2021).Article 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: thestandardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).Rollinson, C. R. et al. Climate sensitivity of understory trees differs from overstory trees in temperate mesic forests. Ecology 102, e03264 (2021).Article 

Google Scholar 
Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: effects of successive low‐growth episodes in old ponderosa pine forests. Oikos 120, 1909–1920 (2011).Article 

Google Scholar 
Li, X. et al. Reply to: Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 736–737 (2021).Article 

Google Scholar 
Zheng, T. et al. Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 733–735 (2021).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Long, J. A. jtools: Analysis and Presentation of Social Scientific Data R Package v.2.2.0 https://cran.r-project.org/package=jtools (2022).Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on AIC R Package v.2.3-1 https://cran.r-project.org/package=AICcmodavg (2020).Au, T. F. Au_et_al_NCC.R. Figshare https://doi.org/10.6084/m9.figshare.21263676.v1 (2022). More