Philippa Kaur

Scientists with visible and invisible disabilities take on adversity, helping themselves and others.Shigehiro Namiki always wanted to study insects. After his PhD research at the University of Tsukuba, he was a postdoctoral fellow, then a staff scientist at Janelia Research Campus. Among his projects, Namiki worked with others on a method to analyze how the few so-called descending neurons in fruit flies control a wide range of movements and behavior. These neurons run from the brain to the ventral nerve cord and branch out to circuits that control the insect’s neck, legs and wings. More

Knight, H. et al. Impacts of the COVID-19 Pandemic and Self-Isolation on Students and Staff in Higher Education: A Qualitative Study. Int. J. Environ. Res. Public Health 18, 10675 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Higham, J. E., Ramírez, C. A., Green, M. A. & Morse, A. P. UK COVID-19 lockdown: 100 days of air pollution reduction? Air Quality. Atmosphere & Health https://doi.org/10.1007/s11869-020-00937-0 (2020).Article 

Google Scholar 
Office, P. M. s. Slides and datasets to accompany coronavirus press conference. (2020).Organization, W. H. WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary. (2021).Singh, A. et al. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford UK. Environ. Pollut. 293, 118584. https://doi.org/10.1016/j.envpol.2021.118584 (2022).Article 
CAS 
PubMed 

Google Scholar 
Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E. & Palmer, P. I. UK surface NO2 levels dropped by 42% during the COVID-19 lockdown: impact on surface O3. Atmos. Chem. Phys. 20, 15743–15759. https://doi.org/10.5194/acp-20-15743-2020 (2020).Article 
CAS 

Google Scholar 
Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Ropkins, K. & Tate, J. E. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 (2021).Article 
CAS 
PubMed 

Google Scholar 
Nwanaji-Enwerem, J. C., Allen, J. G. & Beamer, P. I. Another invisible enemy indoors: COVID-19, human health, the home, and United States indoor air policy. J Expo Sci Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rasha, A., Karan Jetly, J. & Shqran, S. Indoor Air Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. International Journal of Knowledge-Based Organizations (IJKBO) 11, 1–14, doi:https://doi.org/10.4018/ijkbo.2021070101 (2021).World Health Organization. Regional Office for, E. WHO guidelines for indoor air quality: selected pollutants. xxv, 454 p. (World Health Organization. Regional Office for Europe, 2010).Stafoggia, M. et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: Results from 11 European cohorts within the ESCAPE project. Environ. Health Perspect 122, 919–925. https://doi.org/10.1289/ehp.1307301 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American heart association. Circulation 121, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 (2010).Article 
CAS 
PubMed 

Google Scholar 
Raaschou-Nielsen, O. et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European study of cohorts for air pollution effects (ESCAPE). Lancet Oncol. 14, 813–822. https://doi.org/10.1016/s1470-2045(13)70279-1 (2013).Article 
PubMed 

Google Scholar 
Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: Time for urgent action. Lancet 388, 1939–1951. https://doi.org/10.1016/s0140-6736(16)31597-5 (2016).Article 
PubMed 

Google Scholar 
Atkinson, R. W. et al. Acute effects of particulate air pollution on respiratory admissions: Results from APHEA 2 project. Air pollution and health: A European approach. Am. J. Respir. Crit. Care Med. 164, 1860–1866. https://doi.org/10.1164/ajrccm.164.10.2010138 (2001).Article 
CAS 
PubMed 

Google Scholar 
Stapleton, F. et al. TFOS DEWS II epidemiology report. Ocular Surf. 15, 334–365. https://doi.org/10.1016/j.jtos.2017.05.003 (2017).Article 

Google Scholar 
Starr, C. E. et al. Dry eye disease flares: A rapid evidence assessment. Ocul. Surf. 22, 51–59. https://doi.org/10.1016/j.jtos.2021.07.001 (2021).Article 
PubMed 

Google Scholar 
Torricelli, A. A. et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea 32, e11-15. https://doi.org/10.1097/ICO.0b013e31825e845d (2013).Article 
PubMed 

Google Scholar 
Hwang, S. H. et al. Potential importance of ozone in the association between outdoor air pollution and dry eye disease in South Korea. JAMA Ophthalmol. 134, 503–510. https://doi.org/10.1001/jamaophthalmol.2016.0139 (2016).Article 
PubMed 

Google Scholar 
Wiwatanadate, P. Acute air pollution-related symptoms among residents in Chiang Mai Thailand. J. Environ. Health 76, 76–84 (2014).CAS 
PubMed 

Google Scholar 
Alves, M., Novaes, P., Morraye Mde, A., Reinach, P. S. & Rocha, E. M. Is dry eye an environmental disease? Arq. Bras. Oftalmol. 77, 193–200 https://doi.org/10.5935/0004-2749.20140050 (2014).Bourcier, T. et al. Effects of air pollution and climatic conditions on the frequency of ophthalmological emergency examinations. Br. J. Ophthalmol. 87, 809–811. https://doi.org/10.1136/bjo.87.7.809 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hao, R. et al. Impact of air pollution on the ocular surface and tear cytokine levels: A multicenter prospective cohort study. Front. Med. (Lausanne) 9, 909330. https://doi.org/10.3389/fmed.2022.909330 (2022).Article 
PubMed 

Google Scholar 
Vehof, J., Snieder, H., Jansonius, N. & Hammond, C. J. Prevalence and risk factors of dry eye in 79,866 participants of the population-based lifelines cohort study in the Netherlands. Ocul. Surf. 19, 83–93. https://doi.org/10.1016/j.jtos.2020.04.005 (2021).Article 
PubMed 

Google Scholar 
Wolffsohn, J. S. et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul. Surf. 21, 58–63. https://doi.org/10.1016/j.jtos.2021.05.001 (2021).Article 
PubMed 

Google Scholar 
Núñez-Álvarez, C. & Osborne, N. N. Enhancement of corneal epithelium cell survival, proliferation and migration by red light: Relevance to corneal wound healing. Exp. Eye Res. 180, 231–241. https://doi.org/10.1016/j.exer.2019.01.003 (2019).Article 
CAS 
PubMed 

Google Scholar 
Marek, V. et al. Blue light phototoxicity toward human corneal and conjunctival epithelial cells in basal and hyperosmolar conditions. Free Radic. Biol. Med. 126, 27–40. https://doi.org/10.1016/j.freeradbiomed.2018.07.012 (2018).Article 
CAS 
PubMed 

Google Scholar 
Talens-Estarelles, C., García-Marqués, J. V., Cerviño, A. & García-Lázaro, S. Determining the best management strategy for preventing short-term effects of digital display use on dry eyes. Eye Contact Lens 48, 416–423. https://doi.org/10.1097/icl.0000000000000921 (2022).Article 
PubMed 

Google Scholar 
GOV.UK. COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable, (2020).Joy, M. et al. Reorganisation of primary care for older adults during COVID-19: A cross-sectional database study in the UK. Br. J. Gen. Pract. 70, e540–e547. https://doi.org/10.3399/bjgp20X710933 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the ocular surface disease index. Arch. Ophthalmol. 118, 615–621. https://doi.org/10.1001/archopht.118.5.615 (2000).Article 
CAS 
PubMed 

Google Scholar 
Amparo, F. & Dana, R. Web-based longitudinal remote assessment of dry eye symptoms. Ocul. Surf. 16, 249–253. https://doi.org/10.1016/j.jtos.2018.01.002 (2018).Article 
PubMed 

Google Scholar 
Inomata, T. et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol. 138, 58–68. https://doi.org/10.1001/jamaophthalmol.2019.4815 (2020).Article 
PubMed 

Google Scholar 
Toth, M. & Jokić-Begić, N. Psychological contribution to understanding the nature of dry eye disease: A cross-sectional study of anxiety sensitivity and dry eyes. Health Psychol. Behav. Med. 8, 202–219. https://doi.org/10.1080/21642850.2020.1770093 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Mehra, D. & Galor, A. Digital screen use and dry eye: A review. Asia-Pacific J. Ophthalmol. 9, 491–497. https://doi.org/10.1097/apo.0000000000000328 (2020).Article 

Google Scholar 
Galor, A., Kumar, N., Feuer, W. & Lee, D. J. Environmental factors affect the risk of dry eye syndrome in a United States veteran population. Ophthalmology 121, 972–973. https://doi.org/10.1016/j.ophtha.2013.11.036 (2014).Article 
PubMed 

Google Scholar 
Courtin, R. et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 6, e009675. https://doi.org/10.1136/bmjopen-2015-009675 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Torricelli, A. A. et al. Effects of ambient levels of traffic-derived air pollution on the ocular surface: Analysis of symptoms, conjunctival goblet cell count and mucin 5AC gene expression. Environ. Res. 131, 59–63. https://doi.org/10.1016/j.envres.2014.02.014 (2014).Article 
CAS 
PubMed 

Google Scholar 
Gupta, S. K., Gupta, V., Joshi, S. & Tandon, R. Subclinically dry eyes in urban Delhi: An impact of air pollution?. Ophthalmologica 216, 368–371. https://doi.org/10.1159/000066183 (2002).Article 
CAS 
PubMed 

Google Scholar 
Berg, E. J. et al. Climatic and environmental correlates of dry eye disease severity: A report from the dry eye assessment and management (DREAM) study. Trans. Vision Sci. Technol. 9, 25–25. https://doi.org/10.1167/tvst.9.5.25 (2020).Article 

Google Scholar 
Lang, S.-J., Abel, G. A., Mant, J. & Mullis, R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: A cross-sectional study. BMJ Open 6, e009984. https://doi.org/10.1136/bmjopen-2015-009984 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Denniston, A. K. et al. United Kingdom diabetic retinopathy electronic medical record (UK DR EMR) users group: Report 4, real-world data on the impact of deprivation on the presentation of diabetic eye disease at hospital services. Br. J. Ophthalmol. 103, 837–843. https://doi.org/10.1136/bjophthalmol-2018-312568 (2019).Article 
PubMed 

Google Scholar 
Nessim, M., Denniston, A. K., Nolan, W., Holder, R. & Shah, P. Research into Glaucoma and Ethnicity (ReGAE) 8: Is there a relationship between social deprivation and acute primary angle closure?. Br. J. Ophthalmol. 94, 1304–1306. https://doi.org/10.1136/bjo.2009.160721 (2010).Article 
PubMed 

Google Scholar 
Sharma, H. E. et al. The role of social deprivation in severe neovascular age-related macular degeneration. Br. J. Ophthalmol. 98, 1625–1628. https://doi.org/10.1136/bjophthalmol-2014-304959 (2014).Article 
PubMed 

Google Scholar 
Bo, M., Salizzoni, P., Clerico, M. & Buccolieri, R. Assessment of indoor-outdoor particulate matter air pollution: A review. Atmosphere 8, 136 (2017).Article 

Google Scholar 
Strøm-Tejsen, P., Zukowska, D., Fang, L., Space, D. R. & Wyon, D. P. Advantages for passengers and cabin crew of operating a gas-phase adsorption air purifier in 11-h simulated flights. Indoor Air 18, 172–181. https://doi.org/10.1111/j.1600-0668.2007.00511.x (2008).Article 
CAS 
PubMed 

Google Scholar 
Mandell, J. T., Idarraga, M., Kumar, N. & Galor, A. Impact of air pollution and weather on dry eye. J. Clin. Med. https://doi.org/10.3390/jcm9113740 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Navarro, D. Learning Statistics with R. (Daniel Joseph Navarro, 2015). More

General methodological approachTo examine if larvae utilize external cues (i.e., oriented movement) to swim in a directional manner (i.e., significant mean vector length), we develop two complementary analyses that compare the empirically observed directional precision (i.e., mean vector length) with the null distribution expected under a strict use of internal cues (i.e., unoriented movement). The empirically observed directional precision is quantified as the mean vector length (R) of larval bearings (θ) (Fig. 2a), herein ({hat{R}}_{theta }). The angular differences between consecutive bearings, herein turning angles (Fig. 2a; Δθt = θt-θt-1), are used to generate two null distributions of Rθ expected under the unoriented movement of Correlated Random Walk (CRW; ({R}_{{theta }_{0}})), based on the two analyses: Correlated Random Walk-von Mises (CRW-vm) and Correlated Random Walk- resampling (CRW-r), described below. The first is theoretical and is based on a von Mises distribution of simulated Δθ (Fig. 2b, c); the second is empirical, and is based on resampling the Δθ within each trial (Fig. 2d, e). These two analyses are complementary because the first can generate an unlimited number of trajectories but is based on a theoretical distribution rather than on observations, whereas the second is based on a finite number of observations. In addition to these two main analyses, we apply a third analysis, the Correlated Random Walk-wrapped Cauchy, herein CRW-wc, which is similar to CRW-vm, with the only difference of using wrapped Cauchy distribution instead of von Mises. The reason for applying CRW-wc is that it was shown to represent well animal movement in some cases33. Notably, we consider the simple cases of undirected movement pattern with a turning angle distribution centered at 0 (CRW), testing if the mean vector length of the trial’s sequence is higher than that expected under CRW. If true, that would be an indication for a directed movement pattern (i.e., BRW or BCRW), or an indication for more complex behaviors (discussed in Supplementary note 4).Statistics and reproducibilityQuantitative analyses are applied to directional trials, i.e., larval bearing sequences ((hat{theta })) that are significantly different from a uniform distribution based on the Rayleigh’s test8 (p  81, 162, 270). Trials with Nobs higher than the maximal Nobs were trimmed to contain the maximal Nobs per species, retaining the later-in-time data. For the scuba-following trials, the number of observations had to be Nobs  > 20 due to the sensitivity of the analysis to a low number of observations. In other words, a low number of observations limits the capacity of the quantitative analyses to distinguish between oriented and unoriented movement patterns (see Supplementary note 3, Supplementary Figure S3). Importantly, both methods were shown to be robust in terms of artifacts and biases55,56, and have been tested together demonstrating high consistency in larval orientation results16,48.Each orientation trial includes a sequence of larval swimming directions, termed bearings (θ) (Fig. 2a). For the DISC trials, θ are the cardinal directions of larval positions within the DISC’s chamber55. The angular differences between θ of consecutive time steps (t) are defined as Δθ (Δθt = θt-θt-1), such that for every θ sequence of a given length (N), there is a respective Δθ sequence of length N-1 (Fig. 2a). Directional precision with respect to external and internal cues is computed as the mean vector length of bearings (Rθ) and of turning angles (RΔθ), respectively54. Values of mean vector length (R) range from 0 to 1, with 0 indicating a uniform distribution of angles and 1 indicating that all angles are the same.We used two quantitative approaches to examine if larvae exhibit oriented movement: the Correlated Random Walk- von Mises and Correlated Random Walk- wrapped Cauchy (CRW-vm and CRW-wc) analyses and the CRW resampling (CRW-r) analysis. Both types of analyses are based on the assumption that trajectories of animals that strictly use internal cues for directional movement are characterized by a CRW pattern. Hence, their capacity for directional movement is exclusively dependent on the distribution of their turning angles (Δθ)57. In contrast, for an external-cues orienting animal, for which movement directions are correlated with an external fixed direction, the mean vector length of the observed bearings, ({hat{R}}_{theta }), is expected to exceed that of a CRW, ({R}_{{theta }_{0}})6. Both analyses compare ({hat{R}}_{theta }) against the expected ({R}_{{theta }_{0}}), but the first type computes ({R}_{{theta }_{0}^{{vm}}})and ({R}_{{theta }_{0}^{{wc}}})using theoretical von Mises and wrapped Cauchy distributions of Δθ, and the second type computes ({R}_{{theta }_{0}^{r}}) by producing 100 new θ sequences per individual trial (larva) by multiple resampling-without-replacement of the Δθ.A key principle for both analyses types stems from the fact that the mean vector length of bearings (Rθ) is inherently dependent on the mean vector length of turning angles (RΔθ)28. In other words, an animal with a high capacity for unoriented directional movement, i.e., a narrow distribution of Δθ, is likely to yield a high Rθ, even if it makes absolutely no use of external cues for oriented movement. Hence, in both analyses ({hat{R}}_{theta }) is gauged against a distribution of ({R}_{{theta }_{0}}), given its respective mean vector length of turning angles ({hat{R}}_{triangle theta }). The open-source software R58 with the package circular59 is used for all analyses in this study.Correlated Random Walk-von Mises (CRW-vm)In this analysis, we first generate the directional precision (R), expected for unoriented CRW movement using the theoretical von Mises distribution (({R}_{{theta }_{0}^{{vm}}})). The CRW bearings sequences (({theta }_{0}^{{vm}})) are generated by choosing a random initial bearing, followed by a series of Nobs-1 turning angles (({triangle theta }_{0}^{{vm}})) in bearing direction; drawn at random (with replacement) from a von Mises distribution (Nrep = 1000). The length of ({theta }_{0}^{{vm}}) sequence is according to the number of observations in our four types of experimental trials: Nobs = 21 for the scuba-following, and 90, 180 and 300 for the DISC (Table 1). The directional precision of the von Mises distribution is dependent on the concentration parameter, kappa. Kappa values ranging from 0 to 399 are applied at 1-unit increments to cover the entire range of directional precision from completely random (kappa = 0), to highly directional (kappa = 399). Next, the directional precision of the bearings (Rθ) and the turning angles (RΔθ) are computed for each simulated sequence of θ (Fig. 2a–c).These respective pairs of values (RΔθ, Rθ) provide the basis for generating the expected relationship between ({R}_{{theta }_{0}^{{vm}}}) and ({R}_{{triangle theta }_{0}^{{vm}}}). Then, for any given kappa value, the following quantiles are computed: 5th, 10th, 20th,….,90th, and 95th (grey vertical distributions in Fig. 2c). Next, smooth spline functions are fitted through all respective quantiles, generating the ({R}_{{theta }_{0}^{{vm}}})quantile contours, which represent the null expectation under CRW. This expected (RΔθ, Rθ) correspondence creates a phase diagram (Fig. 2c), based on which the observed θ patterns are gauged. The procedure is repeated four times to match the among-study differences in the number of θ observations per trial (i.e., Nobs = 21, 90, 180, and 300; see Table 1).To examine if the observed larval movement patterns differ from those expected for unoriented movement (CRW-vm), we compute RΔθ and Rθ for each individual trial (({hat{R}}_{triangle theta }) and ({hat{R}}_{theta })). We then place these values in the phase diagram and examine their positions with respect to ({R}_{{theta }_{0}^{{vm}}}) (Fig. 2c). Larvae with ({hat{R}}_{theta }) substantially higher than ({bar{R}}_{{theta }_{0}^{{vm}}}), are considered to have a higher tendency for a straighter movement than expected under CRW, suggesting oriented movement such as BRW and BCRW (Fig. 2b, c)6,28. Larvae with ({hat{R}}_{theta }) values substantially below ({bar{R}}_{{theta }_{0}^{{vm}}})indicate irregular patterns such as a one-sided drift (right or left). A larva is considered directional if the bearing sequence ((hat{theta })) is significantly different from a uniform distribution based on the Rayleigh’s test (p  More

Ebenhard, T. Colonization in metapopulations: A review of theory and observations. Biol. J. Linn. Soc. 42, 105–121 (1991).Article 

Google Scholar 
Szucs, M., Melbourne, B. A., Tuff, T. & Hufbauer, R. A. The roles of demography and genetics in the early stages of colonization. Proc. R. Soc. B Biol. Sci. 281, 20141073 (2014).Article 

Google Scholar 
Williamson, M. Biological invasions Vol. 15 (Springer, 1996).
Google Scholar 
Dai, Z. C. et al. Synergy among hypotheses in the invasion process of alien plants: A road map within a timeline. Perspect. Plant Ecol. Evol. Syst. 47, 125575 (2020).Article 

Google Scholar 
Briski, E. et al. Beyond propagule pressure: Importance of selection during the transport stage of biological invasions. Front. Ecol. Environ. 16, 345–353 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. & Vitt, D. H. The dynamics of moss establishment: Temporal responses to nutrient gradients. Bryologist 97, 357–364 (1994).Article 

Google Scholar 
Li, Y. & Vitt, D. H. The dynamics of moss establishment: Temporal responses to a moisture gradient. J. Bryol. 18, 677–687 (1995).Article 

Google Scholar 
Wiklund, K. & Rydin, H. Ecophysiological constraints on spore establishment in bryophytes. Funct. Ecol. 18, 907–913 (2004).Article 

Google Scholar 
Zanatta, F. et al. Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities. Nat. Commun. 11, 1–9 (2020).Article 

Google Scholar 
Seaborn, T. J., Goldberg, C. S. & Crespi, E. J. Integration of dispersal data into distribution modeling: What have we done and what have we learned?. Front. Biogeogr. 12, 1–14 (2020).Article 

Google Scholar 
Glime, J. M. Bryophyte Ecology (Vol. 1, Issue Physiological Ecology, Chapter 4–10 Adaptive strategies: vegetative propagules, pp. 1–44). (2021).Guerra, J., Brugués, M., Cano, M. J. & Cros, R. M. Bryum Hedw. in Flora Briofítica Ibérica, Vol. IV, Funariales, Splachnales, Schistostegales, Bryales, Timmiales (eds. Brugués, M. & Cros, R. M.) 105–178 (Universidad de Murcia. Sociedad Española de Briología, 2010).
Google Scholar 
Medina, N. G., Draper, I. & Lara, F. Biogeography of mosses and allies: Does size matter? in Biogeography of microscopic organisms: is everything small everywhere? 209–233 (2011). https://doi.org/10.1017/CBO9780511974878.012Miles, C. J. & Longton, R. E. The role of spores in reproduction in mosses. Bot. J. Linn. Soc. 104, 149–173 (1990).Article 

Google Scholar 
Estébanez, B., Draper, I. & Bujalance, R. M. Bryophytes: An approximation to the simplest land plants. in Biodiversidad. Aproximación a la diversidad botánica y zoológica de España 19 (2011).Frey, W. & Kürschner, H. Asexual reproduction, habitat colonization and habitat maintenance in bryophytes. Flora Morphol. Distrib. Funct. Ecol. Plants 206, 173–184 (2011).Article 

Google Scholar 
Giordano, S. et al. Regeneration from detached leaves of Pleurochaete squarrosa (Brid.) Lindb. in culture and in the wild. J. Bryol. 19, 219–227 (1996).Article 

Google Scholar 
La Farge, C., Williams, K. H. & England, J. H. Regeneration of Little Ice Age bryophytes emerging from a polar glacier with implications of totipotency in extreme environments. Proc. Natl. Acad. Sci. U. S. A. 110, 9839–9844 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson, S. C. & Miller, N. G. Bryophyte diversity on Adirondack alpine summits is maintained by dissemination and establishment of vegetative fragments and spores. Bryologist 116, 382–391 (2013).Article 

Google Scholar 
Glime, J. M. Chapter 2–1 Meet the bryophytes. in Bryophyte Ecology 1 (2020).Korpelainen, H., Pohjamo, M. & Laaka-Lindberg, S. How efficiently does bryophyte dispersal lead to gene flow?. J. Hattori Bot. Lab. 205, 195–205 (2005).
Google Scholar 
Schuster, R. M. Phytogeography of the Bryophyta. in New manual of Bryology 1, 463–626 (Hattori Bot. Lab, 1983).Löbel, S., Schröder, B. & Snäll, T. Projected shifts in deadwood bryophyte communities under national climate and forestry scenarios benefit large competitors and impair small species. J. Biogeogr. https://doi.org/10.1111/jbi.14278 (2021).Article 

Google Scholar 
Laaka-Lindberg, S., Korpelainen, H. & Pohjamo, M. Dispersal of asexual propagules in bryophytes. J. Hattori Bot. Lab. 330, 319–330 (2003).
Google Scholar 
Miller, N. G. & Mogensen, G. S. Cyrtomnium hymenophylloides (Bryophyta, Mniaceae) in North America and Greenland: Male plants, sex-differential geographical distribution, and reproductive characteristics. Bryologist 100, 499–506 (1997).Article 

Google Scholar 
Muñoz, J., Felicísimo, Á. M., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).Article 
PubMed 

Google Scholar 
Patiño, J. & Vanderpoorten, A. Bryophyte biogeography. CRC. Crit. Rev. Plant Sci. 37, 175–209 (2018).Article 

Google Scholar 
Pasiche-Lisboa, C. J., Booth, T., Belland, R. J. & Piercey-Normore, M. D. Moss and lichen asexual propagule dispersal may help to maintain the extant community in boreal forests. Ecosphere 10, e02823 (2019).Article 

Google Scholar 
Barbé, M., Fenton, N. J. & Bergeron, Y. So close and yet so far away: Long-distance dispersal events govern bryophyte metacommunity reassembly. J. Ecol. 104, 1707–1719 (2016).Article 

Google Scholar 
Hansson, L., Söderström, L. & Solbreck, C. The ecology of dispersal in relation to conservation. in Ecological principles of nature conservation. Conservation Ecology series: principles, practices and management. (ed. Hansson, L.) (Springer, 1992). https://doi.org/10.1007/978-1-4615-3524-9Chapter 

Google Scholar 
Miller, N. G. & Ambrose, L. J. H. Growth in culture of wind-blown bryophyte gametophyte fragments from Arctic Canada. Bryologist 79, 55 (1976).Article 

Google Scholar 
Barbé, M., Fenton, N. J., Caners, R. & Bergeron, Y. Inter-annual variation in bryophyte dispersal: Linking bryophyte phenophases and weather conditions. Botany 95, 1151–1169 (2017).Article 

Google Scholar 
Chmielewski, M. W. & Eppley, S. M. Forest passerines as a novel dispersal vector of viable bryophyte propagules. Proc. R. Soc. B Biol. Sci. 286, 20182253 (2019).Article 
CAS 

Google Scholar 
Davison, G. W. H. Role of birds in moss dispersal. Br. Birds 69, 65–66 (1976).
Google Scholar 
Heinken, T., Lees, R., Raudnitschka, D. & Runge, S. Epizoochorous dispersal of bryophyte stem fragments by roe deer (Capreolus capreolus) and wild boar (Sus scrofa). J. Bryol. 23, 293–300 (2001).Article 

Google Scholar 
Parsons, J. G. et al. Bryophyte dispersal by flying foxes: A novel discovery. Oecologia 152, 112–114 (2007).Article 
CAS 
PubMed 

Google Scholar 
Glime, J. M. Bryophyte Ecology (Vol. 2, Issue Bryological Interaction) (2021).Ware, C., Bergstrom, D. M., Müller, E. & Alsos, I. G. Humans introduce viable seeds to the Arctic on footwear. Biol. Invasions 14, 567–577 (2012).Article 

Google Scholar 
Shacklette, H. T. Unattached moss polsters on Amchitka Island, Alaska. Bryologist 69, 346–352 (1966).Article 

Google Scholar 
Moles, A. T. & Westoby, M. Seedling survival and seed size: A synthesis of the literature. J. Ecol. 92, 372–383 (2004).Article 

Google Scholar 
Kimmerer, R. W. Patterns of dispersal and establishment of bryophytes colonizing natural and experimental treefall mounds in northern hardwood forests. Bryologist 108, 391–401 (2005).Article 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Stieha, C. R., Middleton, A. R., Stieha, J. K., Trott, S. H. & Mcletchie, D. N. The dispersal process of asexual propagules and the contribution to population persistence in Marchantia (Marchantiaceae). Am. J. Bot. 101, 348–356 (2014).Article 
PubMed 

Google Scholar 
Hugonnot, V. Comparative investigations of niche, growth rates and reproduction between the native moss Campylopus pilifer and the invasive C. introflexus. J. Bryol. 39, 79–84 (2017).Article 

Google Scholar 
Benscoter, B. W. Post-fire bryophyte establishment in a continental bog. J. Veg. Sci. 17, 647–652 (2006).Article 

Google Scholar 
Esposito, A., Mazzoleni, S. & Strumia, S. Post-fire bryophyte dynamics in Mediterranean vegetation. J. Veg. Sci. 10, 261–268 (1999).Article 

Google Scholar 
Naeth, M. A. & Wilkinson, S. R. Establishment of restoration trajectories for upland tundra communities on diamond mine wastes in the Canadian arctic. Restor. Ecol. 22, 534–543 (2014).Article 

Google Scholar 
Lamarre, J. J. M. Tundra bryophyte revegetation: novel methods for revegetating northern ecosystems (University of Alberta, 2016).Dierßen, K. Distribution, ecological amplitude and phytosociological characterization of European bryophytes. (Bryophytorum Bibliotheca 56. J. Cramer, Berlin, 289 pp., 2001).Smith, A. J. E. The moss flora of Britain and Ireland (Cambridge University Press, 2004).Book 

Google Scholar 
Casas, C., Brugués, M., Cros, R. M. & Sérgio, C. Handbook of Mosses of the Iberian Peninsula and the Balearic Islands. (Instituts d’Estudis Catalans, 2006).Medina, N., Mazimpaka Nibarere, V., Hortal, J. & Lara García, F. Catálogo de los briófitos epífitos que crecen en bosques de quercíneas del cuadrante noroccidental ibérico. Boletín la Soc. Esp. Briol. 30, 1–30 (2015).
Google Scholar 
Ron Alvarez, M. E. & Vicente, J. Contribución al conocimiento de la flora briológica de Canencia, Sierra de Guadarrama (Madrid). Bot. Complut. https://doi.org/10.5209/BOCM.7415 (1989).Article 

Google Scholar 
Pressel, S., Matcham, H. W. & Duckett, J. G. Studies of protonemal morphogenesis in mosses. XI. Bryum and allied genera: A plethora of propagules. J. Bryol. 29, 241–258 (2007).Article 

Google Scholar 
Söderström, L. & Herben, T. Dynamics of bryophyte metapopulations. in Advances in Briology 6. Population studies (ed. Longton, R. E.) 6, 205–240 (International Association of Briologists. Schweizerbart Science Publishers, 1997).
Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cox, E. P. A method of assigning numerical and percentage values to the degree of roundness of sand grains. J. Paleontol. 1, 179–183 (1927).
Google Scholar 
R Core Team. R: A language and environment for Statistical Computing (2021).Kassambara, A. rstatix: Pipe-friendly framework for basic statistical tests (2020).Zeileis, A., Meyer, D. & Hornik, K. Residual-based shadings for visualizing (conditional) independence. J. Comput. Graph. Stat. 16, 507–525 (2007).Article 
MathSciNet 

Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A grammar of Data Manipulation (2022).Fox, J. & Weisberg, S. An R Companion to Applied Regression (2019).Maechler, M. et al. robustbase: Basic Robust Statistics (2022).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots (2020).Revelle, W. psych: Procedures for psychological, psychometric, and personality research (2021).Kuhn, M., Jackson, S. & Cimentada, J. corrr: correlations in R. R package version 0.4.3 (2020).Wei, T. & Simko, V. R package ‘corrplot’: visualization of a correlation matrix (Version 0.84) (2017).Wilke, C. O. ggtext: improved text rendering support for ‘ggplot2’ (2020).Auguie, B. gridExtra: miscellaneous functions for ‘Grid’ graphics (2017).Wilke, C. O. cowplot: streamlined plot theme and plot annotations for ‘ggplot2’. R package version 1.1.1 (2020).Stark, L. R., Nichols, L. II., McLetchie, D. N., Smith, S. D. & Zundel, C. Age and sex-specific rates of leaf regeneration in the Mojave Desert moss Syntrichia caninervis. Am. J. Bot. 91, 1–9 (2004).Article 
PubMed 

Google Scholar 
Fernandez-Mendoza, F., Estebanez, B., Gomez-Sanz, D. & Ron, E. Sporophyte-bearing specimens of Pleurochaete squarrosa in Zamora, Spain. Cryptogam. Bryol. 23, 211–215 (2002).
Google Scholar 
Chen, K. H., Liao, H. L., Arnold, A. E., Bonito, G. & Lutzoni, F. RNA-based analyses reveal fungal communities structured by a senescence gradient in the moss Dicranum scoparium and the presence of putative multi-trophic fungi. New Phytol. 218, 1597–1611 (2018).Article 
CAS 
PubMed 

Google Scholar 
Kruijer, H. J. D., Raes, N. & Stech, M. Modelling the distribution of the moss species Hypopterygium tamarisci (Hypopterygiaceae, Bryophyta) in Central and South America. Nov. Hedwigia 91, 399–420 (2010).Article 

Google Scholar 
Van Zanten, B. O. Preliminary report on germination experiments designed to estimate the survival chances of moss spores during aerial trans-oceanic long-range dispersal in the Southern Hemisphere, with particular reference to New Zealand. J. Hattori Bot. Lab. 41, 133–140 (1976).
Google Scholar 
Van Zanten, B. O. Experimental studies on trans-oceanic long-range dispersal of moss spores in the Southern Hemisphere. J. Hattori Bot. Lab. 44, 455–482 (1978).
Google Scholar 
De Meester, L., Gómez, A., Okamura, B. & Schwenk, K. The monopolization hypothesis and the dispersal-gene flow paradox in aquatic organisms. Acta Oecologica 23, 121–135 (2002).Article 

Google Scholar 
Izquieta-Rojano, S. et al. Pleurochaete squarrosa (Brid.) Lindb. as an alternative moss species for biomonitoring surveys of heavy metal, nitrogen deposition and δ15N signatures in a Mediterranean area. Ecol. Indic. 60, 1221–1228 (2016).Article 
CAS 

Google Scholar 
Kimmerer, R. W. & Young, C. C. Effect of gap size and regeneration niche on species coexistence in bryophyte communities. J. Torrey Bot. Soc. 123, 16–24 (1996).Article 

Google Scholar 
Refoyo, P., Peláez, M., García-Rodríguez, M., López-Sánchez, A. & Perea, R. Moss cover and browsing scores as sustainability indicators of mountain ungulate populations in Mediterranean environments. Biodivers. Conserv. https://doi.org/10.1007/s10531-022-02454-1 (2022).Article 

Google Scholar  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

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

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Sasaki, M. et al. Greater evolutionary divergence of thermal limits within marine than terrestrial species. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01534-y (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