Philippa Kaur

Site descriptionWarra Supersite, (Lat: 43° 5′ 42ʺ S; Long: 146° 39′ 16ʺ E) is located on a floodplain of the Huon River within the Warra Long Term Ecological Research site (https://warra.com/) 60 km southwest of Hobart, Tasmania. The forest at the Supersite is a Eucalyptus obliqua tall forest with a canopy height of 50–55 m, overtopping a 15–40 m tall secondary layer of rainforest and wet sclerophyll tree species. Ferns dominate the ground layer. The forest is very productive with an aboveground biomass of 790 tonnes/ha16 and a leaf area index of 5.7 m2/m247.The Supersite is within the Tasmanian Wilderness World Heritage Area (TWWHA). That part of the TWWHA experiences infrequent, but sometimes intense, wildfire. Except for a small proportion of mature ( > 250 years-old) E. obliqua trees, the current forest resulted from seedling regeneration following the last major wildfire in that part of the landscape in 1898. No timber harvesting has ever been done in the forest at the Supersite.The climate at Warra is classified as temperate, with no dry season and a mild summer48. Mean annual rainfall measured at the nearby Warra Climate Station (Bureau of Meteorology Station 097024) is 1736 mm and the mean daily temperature is 14 °C and 5.6 °C in January and July, respectively. The soil at the site is a Kurosolic Redoxic Hydrosol16.Analysis of historical heatwaves in southern TasmaniaDaily maximum temperature records from the Bureau of Meteorology station at Cape Bruny Lighthouse (station number 94010) were extracted from the Bureau of Meteorology’s online climate data portal (http://www.bom.gov.au/climate/data). Cape Bruny Lighthouse is one of the 112 stations in the ACORN-SAT network of Australia’s reference sites for monitoring climate change49. The station provides a record of daily maximum temperature measurements commencing in 1923 and spanning almost a century. It is the southern-most station in the ACORN-SAT network; is 59 km south-east of the Warra Flux Site; and bounds the south-eastern extent of E. obliqua tall forest in Tasmania.Missing temperature measurements represented less than 0.6% of the 35,795 records collected at Cape Bruny Lighthouse between January 1st 1923 and December 31st 2020. The missing measurements were gap-filled using predicted values calculated from linear regression models constructed from measurements made at nearby Bureau of Meteorology stations (listed in order of proximity to Cape Bruny Lighthouse and priority for gap-filling)—Cape Bruny Automatic Weather Station (1997-present), Hastings Chalet (1947–1987) and Hobart-Ellerslie Road (1892-present).Average, standard deviation and 90th percentiles of daily maximum temperature were calculated for each calendar day of the year. Further analysis of heatwaves was restricted to the period between the beginning of August and the end of February. This period bounds the growing season of the forest at the Warra Supersite when there is normally a net carbon gain by the forest (Wardlaw unpublished data). Heatwaves were identified as three or more consecutive days with maximum temperatures that met or exceeded the 90th percentile value sensu Perkins and Alexander9. For each heatwave event that was identified, the following three statistics were calculated: (1) average daily maximum temperature during the heatwave; (2) summed departures (as standard deviations) from average daily maximum temperature during the heatwave; (3) summed departures (as standard deviations) from average daily maximum temperature of the 21 day period centred on the middle day of the heatwave. The November 2017 heatwave, as described by these three statistics, was ranked against all the other heatwave events identified between 1923 and 2020 at Cape Bruny Lighthouse. In addition, the z-score was calculated to measure the magnitude of the departure of the average daily maximum temperatures during the November 2017 heatwave from the long-term average of this 21-day period. Those statistics were also calculated for the same period in 2016.Weather conditions at Warra Supersite during the 2017 warm spellFour attributes of weather were used to describe the November 2017 warm spell—air temperature, vapor pressure deficit (calculated from temperature and relative humidity), incoming shortwave radiation and soil moisture. Air temperature and relative humidity were measured using an HMP155A probe (Vaisala, Finland) and incoming shortwave radiation was measured using a CNR4 radiometer (Kipp and Zonen, The Netherlands). Both instruments were mounted 80-m above ground level at the top of the Warra Flux tower. Data was processed to 30-min averages and logged onto a CR3000 datalogger (Campbell Scientific, Logan, USA).Soil moisture was measured by time-domain reflectometry using two CS616 soil moisture probes (Campbell Scientific, Logan, USA) each installed at a depth of 20 cm. These probes were installed in two pits approximately 40 m west of the tower. Soil moisture data were processed to 30-min averages and logged onto a CR1000 datalogger (Campbell Scientific, Logan USA).Turbulent fluxes at Warra Supersite during the November 2017 warm spellMeasurement of turbulent fluxes (carbon, water and energy) were done by eddy covariance (EC) using a closed-path infra-red gas analyser (Model EC155, Campbell Scientific Inc., Logan, USA) to measure CO2 and H2O concentrations and a 3-D sonic anemometer (Model CSAT3A, Campbell Scientific Inc, Logan, USA) to measure turbulent wind vectors and virtual air temperature. The sonic anemometer and infra-red gas analyser were mounted at 80-m above the ground at the top of the Warra Flux tower. Storage of CO2 and H2O beneath the forest canopy was measured by a profile system (Model AP200, Campbell Scientific Inc, Logan, USA ), with sampling heights of 2, 4, 8, 16, 30, 42, 54, 70 m. Temperature sensors in aspirated shields (Model 110-ST, Apogee Instruments, Logan, USA) were co-located with the CO2/H2O sample inlets of the profile system. High frequency (10 Hz) measurements of turbulent fluxes were processed to 30-min averages in a datalogger (Model CR3000, Campbell Scientific, Logan USA). High frequency (2 Hz) of CO2 and water concentration measurements were processed to 15-s averages sequentially for each profile sample height in a datalogger (Model CR1000, Campbell Scientific, Logan, USA). Thus, each inlet was sampled for a 15 s interval every 2 min. The rate at which sub-canopy storage of CO2 changed was calculated from changes in the quasi-instantaneous (2-min) vertical profile concentrations beneath the tower at the beginning and end of each 30-min flux averaging period using the method of McHugh50.Soil heat flux (SHF) was measured to enable calculation of energy balance that was needed to partition energy fluxes into latent and sensible heat. SHF was measured using five SHF plates (Model HFP01SC, Hukseflux, Delft, The Netherlands) inserted in the soil at depth 8 cm adjacent to the two pits in which the soil moisture probes were installed. Each of the five SHF plates were allocated to one of the two soil pits in a 2–3 split. Changes in soil temperature was measured by an averaging thermocouple (Model TCAV, Campbell Scientific Inc, Logan, USA) inserted into the soil above each SHF plate at depths of 2 and 6 cm. Soil moisture measurements at 20 cm depth were as described previously. Heat flux, soil temperature and soil moisture data were processed to 30-min averages on a datalogger (Model CR1000, Campbell Scientific Inc, Logan, USA).Raw 30-min flux, CO2 storage and climate data were processed by the standard OzFlux QA/QC processing stream51 using PyFluxPro Version 1.0.2 software. Fluxes (carbon and energy) adjusted for storage were computed at the mid-stage (level 3). At the final stage of data processing (level 6), gap-filled net ecosystem exchange (NEE) data were partitioned into gross primary productivity (GPP) and ecosystem respiration (ER) using the u*-filtered night-time CO2 flux records to calculate ER with the SOLO artificial neural network algorithm as described in51. The standard conventions of the global flux network were adopted in partitioning NEE as described in52.The full period between 10 and 30th November 2017 was defined as the November 2017 warm spell. The climate and fluxes measured during this period were compared with measurements of those made during the same calendar days of the preceding year, 2016. The carbon fluxes measured in the 10 weeks before (1 September–9 November) and the month after (1–31 December) the 2017 warm spell period were also compared with the same periods in 2016. This was done to ascertain whether changes in carbon fluxes during the 2017 warm spell we not due to differences in antecedent weather conditions and, whether or not differences in carbon fluxes arising from the warm spell persisted after the warm spell.Data analysisFor each day of the 10–30 November period, daily sums were calculated for measurements of carbon fluxes and incoming shortwave radiation (Fsd), while daily averages were calculated for air temperature, VPD and soil moisture. Quantile plots, done for Ta and VPD, used 30-min data during daytime hours (when Fsd  > 0). The significance of differences in measurements during the 10–30 November period among the two years of each variable were tested by analysis of variance. Tests were first done to confirm the data for each variable were normally distributed and between-group variances were homoscedastic. Log-transformation was used to correct skewness in the VPD data. Soil moisture data were strongly skewed, and transformation was unable to correct. For this variable, the Kruskal–Wallis method was used to test the significance of differences in medians among the two years. These analyses were repeated for the 10 weeks (1st September–9th November) leading up to the warm spell and the 4 weeks (1st–31st December) following the warm spell to examine antecedent conditions and subsequent recovery from the warm spell, respectively.The energy fluxes were examined for evidence of coupling between GPP, transpiration and canopy conductance. Closure of the energy balance was first determined for the two periods to ensure comparability of the energy fluxes for the 2017 warm spell period and the corresponding period in 2016. This was done by firstly resampling the 30-min data and calculating 2-hourly averages of latent heat flux (Fe), sensible heat flux (Fh), net radiation (Fn) and ground heat flux (Fg), then fitting linear regressions of Fe + Fh against Fn–Fg for dates encompassing the warm spell in each of two years. Peak energy storage of the biomass, Fb, in the forest at Warra was estimated as 40 W m−2 using the method described in17. That estimate used the value of LAI of 5.72 based on the average of periodic measurements of LAI at Warra reported in47 and the value of 22.0 for the quadratic mean radius at breast height (1.3 m) calculated from tree measurements in a 1-ha plot adjacent to the Warra Flux tower (detailed in47). The ratio of energy storage in the biomass and ground heat flux at their respective daily maxima was calculated, assuming their respective diurnal peaks coincided. This ratio was then applied to each 2-h average of ground heat flux measured in the warm spell period in 2017 and the corresponding period in 2016. Available energy was recalculated using the formula Fn–(Fg + Fb). Analysis of variance was used to test the significance of differences between the 2017 warm spell and the corresponding period in 2016 of each component energy fluxes (Fn, Fe, Fh and Fg) for each of the twelve, 2-h periods, in the day. Kruskal–Wallis rank test was used if a variable had a non-normal distribution or exhibited heteroscedasticity. The Bowen ratio, which is the ratio between Fh and Fe, was calculated for each 2-h period during daytime hours. The 2-h average data were non-normal and heteroscedastic so testing the significance of differences in daytime Bowen ratio between the warm spell and comparison period was done using 2-sample t-test with unequal variance.Latent heat flux was converted to evapotranspiration by dividing the measured latent heat flux by the latent heat of vaporisation of water. Evapotranspiration was used as a proxy of transpiration on the assumption that evaporation was a minor component of evapotranspiration in the tall E. obliqua forest at Warra based on measurements of soil and litter evaporation in similar forests by23. An estimate of total canopy conductance of sunlit leaves, Gt, was calculated from transpiration (E) and vapor pressure deficit, VPD, using the Skelton et al.53 adaptation of the method developed by Hogg and Hurdle54, whereby:$${text{G}}_{{text{t}}} = (upalpha /1000){text{E}}/{text{VPD}}$$The atmospheric pressure of water vapor, α, is equivalent to ρwGvTk, where ρw is the density of water (c 1000 kg m−3), Gv is the universal gas constant for water vapor (0.462 m3 kPa kg−1 K−1) and Tk is air temperature (in K = Ta + 273.15). Gt (in mmol m−2 s−1) was calculated for each 2-h period during the 2017 warm spell and the same calendar days in 2016 using each period’s corresponding values of E, VPD and Ta. Records were excluded if rain fell during the 2-h period. The significance of differences in daytime canopy conductance between the 2017 warm spell and the 2016 comparison period was tested using a two-sample t-test as the data were strongly skewed.The diurnal patterns of GPP, ER and canopy conductance were compared with incoming shortwave radiation, air temperature and vapour pressure deficit. Each 30-min record of the six variables was recoded to its corresponding 2-h time interval. Analysis of variance was used to test for significance of differences between the warm spell and comparison period for each of the twelve 2-h diurnal periods of the six variables. Kruskal–Wallis rank test was used in the data were non-normal or heteroscedastic. Time series plots of diurnal 2-hourly averages for each of the six variables were plotted and visually compared. More

George, T. S. et al. Understanding the genetic control and physiological traits associated with rhizosheath production in barley (Hordeum vulgare). New Phytol. 203, 195–205 (2014).CAS 
PubMed 

Google Scholar 
Delhaize, E., Rathjen, T. M. & Cavanagh, C. R. The genetics of rhizosheath size in a multiparent mapping population of wheat. J. Exp. Bot. 66, 4527–4536 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Duell, R. W. & Peacock, G. R. Rhizosheaths on mesophytic grasses. Crop Sci. 25, 880–883 (1985).
Google Scholar 
Shane, M. W. et al. Summer dormancy and winter growth: Root survival strategy in a perennial monocotyledon. New Phytol. 183, 1085–1096 (2009).CAS 
PubMed 

Google Scholar 
Shane, M. W., McCully, M. E., Canny, M. J. & Pate, J. S. Development and persistence of sandsheaths of Lyginia barbata (Restionaceae): Relation to root structural development and longevity. Ann. Bot. 108, 1307–1322 (2011).PubMed 
PubMed Central 

Google Scholar 
Sprent, J. I. Adherence of sand particles to soybean roots under water stress. New Phytol. 74, 461–463 (1975).
Google Scholar 
Unno, Y., Okubo, K., Wasaki, J., Shinano, T. & Osaki, M. Plant growth promotion abilities and microscale bacterial dynamics in the rhizosphere of Lupin analysed by phytate utilization ability. Environ. Microbiol. 7, 396–404 (2005).PubMed 

Google Scholar 
McCully, M. E. Roots in soil: unearthing the complexities of roots and their rhizospheres. Annu Rev. Plant. Phys. 50, 695–718 (2003).
Google Scholar 
Volkens, G. Die Flora der ægyptisch-arabischen Wuste auf Grundlage anatomisch-physiologischer Forschungen 156 (Gerbruger Borntraeger, 1887).
Google Scholar 
Bailey, C. & Scholes, M. Rhizosheath occurrence in South African grasses. S Afr J Bot 63, 484–490 (1997).
Google Scholar 
Price, S. R. The roots of some north African desert-grasses. New Phytol. 10, 328–340 (1911).
Google Scholar 
Young, I. M. Variation in moisture contents between bulk soil and the rhizosheath of wheat (Triticum aestivum L. cv. Wembly). New Phytol. 130, 125–39 (1995).
Google Scholar 
Pate, J.S., & Dixon, K.W. Convergence and Divergence in the Southwestern Australian Flora in Adaptations of Roots to Limited Availability of Water and Nutrients, Fire and Heat Stress, New South Wales, 1966;249–58.Shane, M. W. et al. Seasonal water relations of Lyginia barbata (southern rush) in relation to root xylem development and summer dormancy of root apices. New Phytol. 185, 1025–37 (2010).PubMed 

Google Scholar 
Benard, P., Kroener, E., Vontobel, P., Kaestner, A. & Carminati, A. Water percolation through the root-soil interface. Adv. Water Res. 95, 190–198 (2016).
Google Scholar 
Lynch, J. P. Roots of the second green revolution. Aust. J. Bot. 55, 493–512 (2007).
Google Scholar 
Brown, L. K., George, T. S., Neugebauer, K. & White, P. J. The rhizosheath—A potential trait for future agricultural sustainability occurs in orders throughout the angiosperms. Plant. Soil 418(1–2), 115–128 (2017).CAS 

Google Scholar 
Zhang, R., Vivanco, J. M. & Shen, Q. The unseen rhizosphere root–soil–microbe interactions for crop production. Curr. Opin. Microbiol. 37, 8 (2017).PubMed 

Google Scholar 
Spaepen, S., Bossuyt, S., Vanderleyden, J., Engelen, K. & Marchal, K. Phenotypical and molecular responses of Arabidopsis thaliana roots as a result of inoculation with the auxin-producing bacterium Azospirillum brasilense. New Phytol. 201, 66 (2014).
Google Scholar 
Vries, F. T. D., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 66 (2020).
Google Scholar 
York, L. M., Carminati, A., Mooney, S. J., Ritz, K. & Bennett, M. J. The holistic rhizosphere: Integrating zones, processes, and semantics in the soil influenced by roots. J. Exp. Bot. 67(12), 3629–3643 (2016).CAS 
PubMed 

Google Scholar 
Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–5 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schneijderberg, M. et al. Quantitative comparison between the rhizosphere effect of Arabidopsis thaliana and co-occurring plant species with a longer life history. ISME J. 14(10), 2433–2448 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21), 2957–2963 (2011).PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10(1), 57–59 (2013).CAS 
PubMed 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7(5), 335–336 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2100 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21(3), 494–504 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Q. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 66, 5261–67 (2007).ADS 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10(10), 996–998 (2013).CAS 
PubMed 

Google Scholar 
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J. & Glöckner, F. O. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res. 41(D1), 66 (2012).
Google Scholar 
Clark, F. E. Soil microorganisms and plant roots. Adv. Agron. 1, 241–288 (1949).CAS 

Google Scholar 
Cook, F. D. & Lochhead, A. G. Growth factor relationships of soil microorganisms as affected by proxmity to the plant root. Can. J. Microbiol. 5, 323–334 (1959).CAS 
PubMed 

Google Scholar 
Bulgarelli, D., Schlaeppi, K., Spaepen, S., Van Themaat, E. V. L. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant. Biol. 64(1), 807–838 (2012).
Google Scholar 
Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. USA 112(8), 911–920 (2015).ADS 

Google Scholar 
Zhang, J. Y. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. Sci. Bus. Biotechnol. 37(6), 1–13 (2019).
Google Scholar 
Fu, Z. Q. et al. Mechanism of controlling cotton Verticillium wilt with endophytic bacterium 73a. Jiangsu J Agric. Sci. 15(4), 211–15 (1999).
Google Scholar 
Van Loon, L. C., Bakker, P. A. H. M. & Pieterse, C. M. J. Systemic resistance induced by rhizosphere bacteria. Annu. Rev. Phytopathol. 36, 453–83 (1998).CAS 
PubMed 

Google Scholar 
Wees, S. C. M. V., Pieteerse, C. M. J., Trijssenaar, A. V., Westende, Y. A. V. & Loon, L. C. V. Differental induction of systemic resistance in Arabidopsis by biocontrol bacterial. Mol. Plant-Microbe Interact. 10, 716–24 (1997).PubMed 

Google Scholar 
Compant, S., Clément, C. & Sessitsch, A. Plant growth-promoting bacteria in the rhizo- and endosphere of plants: Their role, colonization, mechanisms involved and prospects for utilization. Soil Biol. Biochem. 42, 669–678 (2010).CAS 

Google Scholar 
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Vander, P. W. H. Going back to the roots: The microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11(11), 789–799 (2013).CAS 
PubMed 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant. Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S., Nuccio, E. E., Shi, Z. J., He, Z. & Firestone, M. K. The interconnected rhizosphere: High network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).
PubMed 

Google Scholar 
Lambers, H., Mougel, C., Jaillard, B. & Hinsinger, P. Plant–microbe–soil interactions in the rhizosphere: An evolutionary perspective. Plant. Soil 321, 83–115 (2009).CAS 

Google Scholar 
Zhang, Y., Ruyter-Spira, C. & Bouwmeester, H. J. Engineering the plant rhizosphere. Curr. Opin. Biotechnol. 32, 136–142 (2015).CAS 
PubMed 

Google Scholar 
Dessaux, Y., Grandclement, C. & Faure, D. Engineering the rhizosphere. Trends Plant. Sci. 21, 266–278 (2016).CAS 
PubMed 

Google Scholar 
Bergmann, D., Zehfus, M., Zierer, L., Smith, B. & Gabel, M. Grass Rhizosheaths: Associated bacterial communities and potential for nitrogen fixation. Western N. Am. Nat. 69(1), 105–114 (2009).
Google Scholar 
Wullstein, L. H. Nitrogen fixation (acetylene reduction) associated with rhizosheaths of Indian rice–grass used in stabilization of the Slick Rock, Colorado tailings pile. J. Range Manag. 33, 204–206 (1980).
Google Scholar 
Wullstein, L. H., Bruening, M. L. & Bollen, W. B. Fixation associated with sand grain root sheaths (rhizosheaths) of certain Xeric grasses. Physiol. Plant. 46, 1–4 (1979).CAS 

Google Scholar 
Buckley, R. Sand rhizosheath of an arid zone grass. Plant. Soil 66, 417–421 (1982).
Google Scholar  More

Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 

Google Scholar 
Kearns, C. A., Inouye, D. W. & Waser, N. M. ENDANGERED MUTUALISMS: The conservation of plant-pollinator interactions. Annu. Rev. Ecol. Syst. 29, 83–112 (1998).
Google Scholar 
Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Google Scholar 
Labandeira, C. C. A paleobiologic perspective on plant–insect interactions. Curr. Opin. Plant Biol. 16, 414–421 (2013).PubMed 

Google Scholar 
Faegri, K. & Van Der Pijl, L. Principles of Pollination Ecology. (Elsevier Science, 2014).Bhutia, J. & Sharma, B. Diversity of Pollinators/ Visitors in Namchi, South Sikkim, India. 487–498 (2020).Torres-Vanegas, F. et al. Tropical deforestation reduces plant mating quality by shifting the functional composition of pollinator communities. J. Ecol. 109, 1730–1746 (2021).
Google Scholar 
Macgregor, C. J., Pocock, M. J. O., Fox, R. & Evans, D. M. Pollination by nocturnal Lepidoptera, and the effects of light pollution: A review. Ecol. Entomol. 40, 187–198 (2015).PubMed 

Google Scholar 
Macgregor, C. J., Williams, J. H., Bell, J. R. & Thomas, C. D. Moth biomass increases and decreases over 50 years in Britain. Nat. Ecol. Evol. 3, 1645–1649 (2019).PubMed 

Google Scholar 
Chamorro, S., Heleno, R., Olesen, J. M., McMullen, C. K. & Traveset, A. Pollination patterns and plant breeding systems in the Galápagos: A review. Ann. Bot. 110, 1489–1501 (2012).PubMed 
PubMed Central 

Google Scholar 
Ramirez, N. Pollination specialization and time of pollination on a tropical Venezuelan plain: Variations in time and space. Bot. J. Linn. Soc. 145, 1–16 (2004).
Google Scholar 
Walton, R. E., Sayer, C. D., Bennion, H. & Axmacher, J. C. Nocturnal pollinators strongly contribute to pollen transport of wild flowers in an agricultural landscape. Biol. Lett. 16, 20190877 (2020).PubMed 
PubMed Central 

Google Scholar 
Young, H. J. Diurnal and nocturnal pollination of Silene alba (Caryophyllaceae). Am. J. Bot. 89, 433–440 (2002).PubMed 

Google Scholar 
Maeda, M., Maguchi, S., Nakamaru, Y., Takagi, D. & Fukuda, S. Prospective study of pollen dispersal prediction and identifying the usefulness of different parameters. Nihon Jibiinkoka Gakkai Kaiho 109, 455–460 (2006).PubMed 

Google Scholar 
Bertin, R. I. & Willson, M. F. Effectiveness of diurnal and nocturnal pollination of two milkweeds. Can. J. Bot. 58, 1744–1746 (1980).
Google Scholar 
Morse, D. H. & Fritz, R. S. Contributions of diurnal and nocturnal insects to the pollination of common milkweed (Asclepias syriaca L.) in a pollen-limited system. Oecologia 60, 190–197 (1983).Jennersten, O. & Morse, D. H. The quality of pollination by diurnal and nocturnal insects visiting common milkweed Asclepias syriaca. Am. Midl. Nat. 125, 18 (1991).
Google Scholar 
Miyake, T. & Yahara, T. Why does the flower of Lonicera japonica open at dusk?. Can. J. Bot. 76, 1806–1811 (1998).
Google Scholar 
Atwater, M. M. Diversity and nectar hosts of flower-settling moths within a Florida sandhill ecosystem. J. Nat. Hist. 47, 2719–2734 (2013).
Google Scholar 
Grant, V. & Grant, K. A. Hawkmoth pollination of Mirabilis longiflora (Nyctaginaceae). Proc. Natl. Acad. Sci. 80, 1298–1299 (1983).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Willmott, A. P. & Burquez, A. The pollination of Merremia palmeri (Convolvulaceae): Can Hawk moths be trusted?. Am. J. Bot. 83, 1050 (1996).
Google Scholar 
Wasserthal, L. T. The Pollinators of the Malagasy Star Orchids Angraecum sesquipedale, A. sororium and A. compactum and the Evolution of Extremely Long Spurs by Pollinator Shift. Bot. Acta 110, 343–359 (1997).Miyake, T., Yamaoka, R. & Yahara, T. Floral scents of hawkmoth-pollinated flowers in Japan. J. Plant Res. 111, 199–205 (1998).CAS 

Google Scholar 
Luyt, R. & Johnson, S. D. Hawkmoth pollination of the African epiphytic orchid Mystacidium venosum, with special reference to flower and pollen longevity. Plant Syst. Evol. 228, 49–62 (2001).
Google Scholar 
Rust, R. W., Vaissire, B. E. & Westrich, P. Pollinator biodiversity and floral resource use in Ecballium elaterium (Cucurbitaceae), a Mediterranean endemic. Apidologie 34, 29–42 (2003).
Google Scholar 
Jürgens, A., Witt, T. & Gottsberger, G. Flower scent composition in Dianthus and Saponaria species (Caryophyllaceae) and its relevance for pollination biology and taxonomy. Biochem. Syst. Ecol. 31, 345–357 (2003).
Google Scholar 
Oliveira, P. E., Gibbs, P. E. & Barbosa, A. A. Moth pollination of woody species in the Cerrados of Central Brazil: A case of so much owed to so few?. Plant Syst. Evol. 245, 41–54 (2004).
Google Scholar 
Morimoto, Y., Gikungu, M. & Maundu, P. Pollinators of the bottle gourd (Lagenaria siceraria) observed in Kenya. Int. J. Trop. Insect Sci. 24, (2004).Willmer, P. Pollination and floral ecology. (Princeton University Press, 2011). https://doi.org/10.1515/9781400838943.Mitchell, T. C., Dötterl, S. & Schaefer, H. Hawk-moth pollination and elaborate petals in Cucurbitaceae: The case of the Caribbean endemic Linnaeosicyos amara. Flora Morphol. Distrib. Funct. Ecol. Plants 216, 50–56 (2015).Chakraborty, P., Smith, B. & Basu, P. Pollen transport in the dark: Hawkmoths prefer non crop plants to crop plants in an agricultural landscape. Proc. Zool. Soc. 71, 299–303 (2018).
Google Scholar 
Proctor, M., Yeo, P. & Lack, A. The natural history of pollination. (Timber Press, 1996).Funamoto, D. & Sugiura, S. Settling moths as potential pollinators of Uncaria rhynchophylla (Rubiaceae). Eur. J. Entomol. 113, 497–501 (2016).
Google Scholar 
Funamoto, D. & Sugiura, S. Relative importance of diurnal and nocturnal pollinators for reproduction in the early spring flowering shrub Stachyurus praecox (Stachyuraceae). Plant Species Biol. 36, 94–101 (2021).
Google Scholar 
Buxton, M. N., Anderson, B. J. & Lord, J. M. The secret service—analysis of the available knowledge on moths as pollinators in New Zealand / Te pepe huna—he tātarihaka o te mātauraka rakahau ki kā pepe hai whakaaiai ki Aotearoa me Te Waipounamu. N. Z. J. Ecol. 42, 1–9 (2018).
Google Scholar 
Hahn, M. & Brühl, C. A. The secret pollinators: An overview of moth pollination with a focus on Europe and North America. Arthropod-Plant Interact. 10, 21–28 (2016).
Google Scholar 
Makholela, T. & Manning, J. C. First report of moth pollination in Struthiola ciliata (Thymelaeaceae) in southern Africa. South Afr. J. Bot. 72, 597–603 (2006).CAS 

Google Scholar 
Okamoto, T., Kawakita, A. & Kato, M. Floral adaptations to nocturnal moth pollination in Diplomorpha (Thymelaeaceae). Plant Species Biol. 23, 192–201 (2008).
Google Scholar 
Paul, M. Impact of urbanization on moth (Insecta: Lepidoptera: Heterocera) diversity across different urban landscapes of Delhi India. Acta Ecol. Sin. 41, 204–209 (2021).
Google Scholar 
Subhakar, G. & Sreedevi, K. Nocturnal insect pollinator diversity in bottle gourd and ridge gourd in southern Andhra Pradesh. Curr. Biot. 9, 137–144 (2015).
Google Scholar 
Chakraborty, P., Chatterjee, S., Smith, B. M. & Basu, P. Seasonal dynamics of plant pollinator networks in agricultural landscapes: How important is connector species identity in the network?. Oecologia 196, 825–837 (2021).ADS 
PubMed 

Google Scholar 
Chakraborty, P., Mukherjee, P. A., Laha, S. & Gupta, S. K. The influence of floral traits on insect foraging behaviour on medicinal plants in an urban garden of eastern India. J. Trop. Ecol. 37, 200–207 (2021).CAS 

Google Scholar 
King, C., Ballantyne, G. & Willmer, P. G. Why flower visitation is a poor proxy for pollination: Measuring single-visit pollen deposition, with implications for pollination networks and conservation. Methods Ecol. Evol. 4, 811–818 (2013).
Google Scholar 
Devoto, M., Bailey, S., Craze, P. & Memmott, J. Understanding and planning ecological restoration of plant–pollinator networks. Ecol. Lett. 15, 319–328 (2012).PubMed 

Google Scholar 
Saunders, M. E. Insect pollinators collect pollen from wind-pollinated plants: Implications for pollination ecology and sustainable agriculture. Insect Conserv. Divers. 11, 13–31 (2018).
Google Scholar 
Ssymank, A., Kearns, C. A., Pape, T. & Thompson, F. C. Pollinating Flies (Diptera): A major contribution to plant diversity and agricultural production. Biodiversity 9, 86–89 (2008).
Google Scholar 
Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. 113, 146–151 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Gong, Y.-B. et al. Wind or insect pollination? Ambophily in a subtropical gymnosperm Gnetum parvifolium (Gnetales): Ambophily in Gnetum. Plant Species Biol. 31, 272–279 (2016).
Google Scholar 
Niklas, K. J. A Biophysical Perspective on the Pollination Biology of Ephedra nevadensis and E. trifurca. Bot. Rev. 81, 28–41 (2015).Kato, M., Inoue, T. & Nagamitsu, T. Pollination biology of Gnetum (Gnetaceae) in a LOWLAND MIXED DIPTEROCARP forest in Sarawak. Am. J. Bot. 82, 862–868 (1995).
Google Scholar 
Celedón-Neghme, C., Santamaría, L. & González-Teuber, M. The role of pollination drops in animal pollination in the Mediterranean gymnosperm Ephedra fragilis (Gnetales). Plant Ecol. 217, 1545–1552 (2016).
Google Scholar 
Costa, A. C. G. & Machado, I. C. Flowering dynamics and pollination system of the sedge Rhynchospora ciliata (Vahl) Kükenth (Cyperaceae): does ambophily enhance its reproductive success?: Ambophily in Rhynchospora ciliata. Plant Biol. 14, 881–887 (2012).CAS 
PubMed 

Google Scholar 
Huang, L. et al. Beta diversity partitioning and drivers of variations in fish assemblages in a headwater stream: Lijiang River China. Water 11, 680 (2019).CAS 

Google Scholar 
Schneider, D., Wink, M., Sporer, F. & Lounibos, P. Cycads: their evolution, toxins, herbivores and insect pollinators. Naturwissenschaften 89, 281–294 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Wilson, G. W. Insect Pollination in the Cycad Genus Bowenia Hook, ex Hook. f. (Stangeriaceae)1. Biotropica 34, 438–441 (2002).Terry, L. I. et al. Pollination of Australian Macrozamia cycads (Zamiaceae): effectiveness and behavior of specialist vectors in a dependent mutualism. Am. J. Bot. 92, 931–940 (2005).PubMed 

Google Scholar 
Intachat, J., Holloway, J. D. & Staines, H. Effects of weather and phenology on the abundance and diversity of geometroid moths in a natural Malaysian tropical rain forest. J. Trop. Ecol. 17, 411–429 (2001).
Google Scholar 
Shaheen, H., Ullah, Z., Khan, S. M. & Harper, D. M. Species composition and community structure of western Himalayan moist temperate forests in Kashmir. For. Ecol. Manag. 278, 138–145 (2012).
Google Scholar 
Shaheen, H., Mallik, N. M. & Dar, M. E. U. I. Species composition and community structure of subtropical forest stands in western himalayan foothills of kashmir. Pak. J. Bot. 47, 2151–2160 (2015).CAS 

Google Scholar 
Bhutia, Y., Gudasalamani, R., Ganesan, R. & Saha, S. Assessing forest structure and composition along the altitudinal gradient in the State of Sikkim, Eastern Himalayas India. Forests 10, 633 (2019).
Google Scholar 
Dar, J. A. & Sundarapandian, S. Variation of biomass and carbon pools with forest type in temperate forests of Kashmir Himalaya India. Environ. Monit. Assess. 187, 55 (2015).PubMed 

Google Scholar 
Kandel, P. et al. Plant diversity of the Kangchenjunga Landscape, Eastern Himalayas. Plant Divers. 41, 153–165 (2019).PubMed 
PubMed Central 

Google Scholar 
Leonhardt, S. D. & Blüthgen, N. A sticky affair: Resin collection by bornean stingless bees: resin collection by stingless bees. Biotropica 41, 730–736 (2009).
Google Scholar 
Nyeko, P., Edwards-Jones, G. & Day, R. K. Honeybee, Apis mellifera (Hymenoptera: Apidae), leaf damage on Alnus species in Uganda: A blessing or curse in agroforestry?. Bull. Entomol. Res. 92, 405–412 (2002).CAS 
PubMed 

Google Scholar 
Koch, H., Corcoran, C. & Jonker, M. Honeydew collecting in malagasy stingless bees (Hymenoptera: Apidae: Meliponini) and observations on competition with invasive ants. Afr. Entomol. 19, 36–41 (2011).
Google Scholar 
Santas, L. A. Insects producing honeydew exploited by bees in Greece. Apidologie 14, 93–103 (1983).
Google Scholar 
Banza, P., Belo, A. D. F. & Evans, D. M. The structure and robustness of nocturnal Lepidopteran pollen-transfer networks in a Biodiversity Hotspot. Insect Conserv. Divers. 8, 538–546 (2015).
Google Scholar 
Walton, R. E., Sayer, C. D., Bennion, H. & Axmacher, J. C. Improving the pollinator pantry: Restoration and management of open farmland ponds enhances the complexity of plant-pollinator networks. Agric. Ecosyst. Environ. 320, 107611 (2021).Dormann, C. F. et al. bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices. (2021).Karmawati, E. & Tobing, S. L. Laboratory biology of Achaea janata L. castor large semi-loopers. Ind. Crops Res. J. 1, 37–42 (1988).
Google Scholar 
Labouche, A. & Bernasconi, G. Cost limitation through constrained oviposition site in a plant-pollinator/seed predator mutualism. Funct. Ecol. 27, 509–521 (2013).
Google Scholar 
Ramakrishna & Alfred, J. R. B. Faunal resources of India. (Zoological Survey of India, 2007).Lees, D. C. & Zilli, A. Moths: Their Biology, Diversity and Evolution | NHBS Field Guides & Natural History. (London Natural History Museum, 2020).Holloway, J. D. Moths of Borneo. (Malayan Nature Journal, 2001).Plant diversity in the Himalaya hotspot region: a volume to celebrate the completion of university service of Dr. Abhaya Prasad Das. (Bishen Singh Mahendra Pal Singh, 2018).Hampson, G. F. The Fauna of British India, including Ceylon and Burma. vol. 1 1–560 (Taylor and Francis, 1892).Hampson, G. F. The Fauna of British India, including Ceylon and Burma. vol. 2 1–640 (Taylor and Francis, 1894).Hampson, G. F. The Fauna of British India, including Ceylon and Burma. vol. 3 1–582 (Taylor and Francis, 1895).Hampson, G. F. The Fauna of British India, including Ceylon and Burma. vol. 4 1–632 (Taylor and Francis, 1896).Kirti, J. S. & Singh, N. Arctiid moths of India. (Nature Books India, 2015).Kirti, J. S. & Singh, N. Arctiid moths of India. vol. 2 (Nature Books India, 2016).Moths of India. https://www.mothsofindia.org/.iNaturalist. iNaturalist. iNaturalist https://www.inaturalist.org/users/sign_in.Nieukerken, E. J. V. et al. Order Lepidoptera Linnaeus, 1758. In : Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 212–221 (2011).PalDat. https://www.paldat.org/.Global Pollen Project. Global Pollen Project. https://globalpollenproject.org/.Agashe, S. N. & Caulton, E. Pollen and spores: applications with special emphasis on aerobiology and allergy. (Science Publishers, 2009).Bhattacharya, K. et al. A textbook of palynology. (2014).Stephen, A. Pollen—A microscopic wonder of plant kingdom. Int. J. Adv. Res. Biol. Sci. 1, 45–62 (2014).
Google Scholar 
Halbritter, H. et al. Illustrated Pollen Terminology. (Springer International Publishing, 2018). doi:https://doi.org/10.1007/978-3-319-71365-6.Dunne, J. A., Williams, R. J. & Martinez, N. D. Food-web structure and network theory: The role of connectance and size. Proc. Natl. Acad. Sci. 99, 12917–12922 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodriguez-Girones, M. A. & Santamaria, L. A new algorithm to calculate the nestedness temperature of presence-absence matrices. J. Biogeogr. 33, 924–935 (2006).
Google Scholar 
Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).PubMed 
PubMed Central 

Google Scholar 
Tylianakis, J. M., Tscharntke, T. & Lewis, O. T. Habitat modification alters the structure of tropical host–parasitoid food webs. Nature 445, 202–205 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Blüthgen, N., Menzel, F., Hovestadt, T., Fiala, B. & Blüthgen, N. Specialization, constraints, and conflicting interests in mutualistic networks. Curr. Biol. 17, 341–346 (2007).PubMed 

Google Scholar 
Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).MATH 

Google Scholar 
Poisot, T., Lepennetier, G., Martinez, E., Ramsayer, J. & Hochberg, M. E. Resource availability affects the structure of a natural bacteria–bacteriophage community. Biol. Lett. 7, 201–204 (2011).PubMed 

Google Scholar  More

Verdura, J. et al. Biodiversity loss in a Mediterranean ecosystem due to an extreme warming event unveils the role of an engineering gorgonian species. Sci. Rep. 9, 1–11 (2019).CAS 

Google Scholar 
Pandori, L. L. M. & Sorte, C. J. B. The weakest link: sensitivity to climate extremes across life stages of marine invertebrates. Oikos 128, 621–629 (2019).
Google Scholar 
Palmer, G. et al. Climate change, climatic variation and extreme biological responses. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160144 (2017).Altwegg, R., Visser, V., Bailey, L. D. & Erni, B. Learning from single extreme events. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160141 (2017).
Google Scholar 
McDermott Long, O. et al. Sensitivity of UK butterflies to local climatic extremes: which life stages are most at risk? J. Anim. Ecol. 86, 108–116 (2017).PubMed 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate‐change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Google Scholar 
Suggitt, A. J. et al. Habitat associations of species show consistent but weak responses to climate. Biol. Lett. 8, 590–593 (2012).PubMed 
PubMed Central 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
PubMed 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 

Google Scholar 
Bush, A. et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19, 1468–1478 (2016).PubMed 

Google Scholar 
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).CAS 
PubMed 

Google Scholar 
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).CAS 
PubMed 

Google Scholar 
Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Chang. Biol. 21, 3313–3322 (2015).PubMed 
PubMed Central 

Google Scholar 
Titeux, N. et al. The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Divers. Distrib. 23, 1393–1407 (2017).
Google Scholar 
Haeler, E., Fiedler, K. & Grill, A. What prolongs a butterfly’s life?: trade-offs between dormancy, fecundity and body size. PLoS One 9, e111955 (2014).PubMed 
PubMed Central 

Google Scholar 
Gonzalez-Suarez, M., Gomez, A. & Revilla, E. Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes. Ecosphere 4, 1–16 (2013).
Google Scholar 
Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Chang. 7, 205–208 (2017).
Google Scholar 
Kingsolver, J. G. & Watt, W. B. Thermoregulatory strategies in Colias butterflies: thermal stress and the limits to adaptation in temporally varying environments (Colorado). Am. Nat. 121, 32–55 (1983).
Google Scholar 
MacLean, H. J., Higgins, J. K., Buckley, L. B. & Kingsolver, J. G. Morphological and physiological determinants of local adaptation to climate in Rocky Mountain butterflies. Conserv. Physiol. 4, 1 (2016).Kingsolver, J. G. & Wiernasz, D. C. Seasonal polyphenism in wing-melanin pattern and thermoregulatory adaptation in Pieris butterflies. Am. Nat. 137, 816–830 (1991).
Google Scholar 
Herrando, S. et al. Contrasting impacts of precipitation on Mediterranean birds and butterflies. Sci. Rep. 9, 1–7 (2019).CAS 

Google Scholar 
Thomas, J. A. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 339–357 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roy, D. B., Rothery, P., Moss, D., Pollard, E. & Thomas, J. A. Butterfly numbers and weather: predicting historical trends in abundance and the future effects of climate change. J. Anim. Ecol. 70, 201–217 (2008).
Google Scholar 
Pöyry, J., Luoto, M., Heikkinen, R. K., Kuussaari, M. & Saarinen, K. Species traits explain recent range shifts of Finnish butterflies. Glob. Chang. Biol. 15, 732–743 (2009).
Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang. 2, 121–124 (2012).
Google Scholar 
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol. Lett. 13, 597–605 (2010).PubMed 
PubMed Central 

Google Scholar 
Eskildsen, A. et al. Ecological specialization matters: long-term trends in butterfly species richness and assemblage composition depend on multiple functional traits. Divers. Distrib. 21, 792–802 (2015).
Google Scholar 
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12, 115–134 (1977).
Google Scholar 
Schmucki, R. et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J. Appl. Ecol. 53, 501–510 (2016).
Google Scholar 
Pollard, E., Lakhani, K. H. & Rothery, P. The detection of density-dependence from a series of annual censuses. Ecology 68, 2046–2055 (1987).CAS 
PubMed 

Google Scholar 
Dooley, C. A., Bonsall, M. B., Brereton, T. & Oliver, T. Spatial variation in the magnitude and functional form of density-dependent processes on the large skipper butterfly Ochlodes sylvanus. Ecol. Entomol. 38, 608–616 (2013).
Google Scholar 
Rothery, P., Newton, I., Dale, L. & Wesolowski, T. Testing for density dependence allowing for weather effects. Oecologia 112, 518–523 (1997).PubMed 

Google Scholar 
Oliver, T. H. et al. Interacting effects of climate change and habitat fragmentation on drought-sensitive butterflies. Nat. Clim. Chang. 5, 941–946 (2015).
Google Scholar 
Stefanescu, C., Carnicer, J. & Peñuelas, J. Determinants of species richness in generalist and specialist Mediterranean butterflies: the negative synergistic forces of climate and habitat change. Ecography 34, 353–363 (2011).
Google Scholar 
Essens, T., van Langevelde, F., Vos, R. A., Van Swaay, C. A. M. & WallisDeVries, M. F. Ecological determinants of butterfly vulnerability across the European continent. J. Insect Conserv. 21, 439–450 (2017).
Google Scholar 
Tolman, T. & Lewington, R. Butterflies of Europe (Harper Collins, 2008).Dapporto, L. et al. Integrating three comprehensive data sets shows that mitochondrial DNA variation is linked to species traits and paleogeographic events in European butterflies. Mol. Ecol. Resour. 19, 1623–1636 (2019).CAS 
PubMed 

Google Scholar 
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. Lond. 68, 87–112 (2008).
Google Scholar 
Dincă, V. et al. High resolution DNA barcode library for European butterflies reveals continental patterns of mitochondrial genetic diversity. Commun. Biol. 4, 1–11 (2021).
Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
Dapporto, L. & Dennis, R. L. H. The generalist–specialist continuum: testing predictions for distribution and trends in British butterflies. Biol. Conserv. 157, 229–236 (2013).
Google Scholar 
MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Glob. Chang. Biol. 23, 4094–4105 (2017).PubMed 

Google Scholar 
Morlon, H. et al. Spatial patterns of phylogenetic diversity. Ecol. Lett. 14, 141–149 (2011).PubMed 
PubMed Central 

Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vanden Broeck, A. et al. Gene flow and effective population sizes of the butterfly Maculinea alcon in a highly fragmented, anthropogenic landscape. Biol. Conserv. 209, 89–97 (2017).
Google Scholar 
Haldane, J. B. S. Theoretical genetics of autopolyploids. J. Genet. 22, 359–372 (1930).
Google Scholar 
Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).PubMed 

Google Scholar 
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Reusch, T. B. H. & Wood, T. E. Molecular ecology of global change. Mol. Ecol. 16, 3973–3992 (2007).CAS 
PubMed 

Google Scholar 
DeLong, J. P. & Gibert, J. P. Gillespie eco-evolutionary models (GEMs) reveal the role of heritable trait variation in eco-evolutionary dynamics. Ecol. Evol. 6, 935–945 (2016).PubMed 
PubMed Central 

Google Scholar 
Atkins, K. E. & Travis, J. M. J. Local adaptation and the evolution of species’ ranges under climate change. J. Theor. Biol. 266, 449–457 (2010).CAS 
PubMed 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).PubMed 

Google Scholar 
Mills, S. C. et al. European butterfly populations vary in sensitivity to weather across their geographical ranges. Glob. Ecol. Biogeogr. 26, 1374–1385 (2017).
Google Scholar 
Van Dyck, H., Bonte, D., Puls, R., Gotthard, K. & Maes, D. The lost generation hypothesis: could climate change drive ectotherms into a developmental trap? Oikos 124, 54–61 (2015).
Google Scholar 
Hu, G. et al. Environmental drivers of annual population fluctuations in a trans-Saharan insect migrant. Proc. Natl Acad. Sci. USA 118, 2102762118 (2021).
Google Scholar 
Merlin, C. & Liedvogel, M. The genetics and epigenetics of animal migration and orientation: birds, butterflies and beyond. J. Exp. Biol. 222, jeb191890 (2019).Wiemers, M. et al. An updated checklist of the European butterflies (Lepidoptera, Papilionoideae). Zookeys 2018, 9–45 (2018).
Google Scholar 
Dennis, E. B., Freeman, S. N., Brereton, T. & Roy, D. B. Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern. Methods Ecol. Evol. 4, 637–645 (2013).
Google Scholar 
Radchuk, V., Turlure, C. & Schtickzelle, N. Each life stage matters: the importance of assessing the response to climate change over the complete life cycle in butterflies. J. Anim. Ecol. 82, 275–285 (2013).PubMed 

Google Scholar 
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).
Google Scholar 
Carnicer, J. et al. A unified framework for diversity gradients: the adaptive trait continuum. Glob. Ecol. Biogeogr. 22, 6–18 (2013).
Google Scholar 
Klok, E. J. & Klein Tank, A. M. G. Updated and extended European dataset of daily climate observations. Int. J. Climatol. 29, 1182–1191 (2009).
Google Scholar 
Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. Atmos. 113, D20119 (2008).
Google Scholar 
Marsh, T. J. The UK drought of 2003: a hydrological review. Weather 59, 224–230 (2004).
Google Scholar 
Voyer, A. G. & Garamszegi, L. Z. An introduction to phylogenetic path analysis. in Modern Phylogenetic Comparative Methods and their Application in Evolutionary Biology (eds Garamszegi, L. Z. & Mundry, R.) 201–229 (Springer Berlin Heidelberg, 2014).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 

Google Scholar 
Pöyry, J. et al. The effects of soil eutrophication propagate to higher trophic levels. Glob. Ecol. Biogeogr. 26, 18–30 (2017).
Google Scholar 
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Bartoń, K. MuMIn: Multi-model inference. R package version 1.10.5. (2014).Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). MEE. 3, 217–223 (2012).
Google Scholar 
Briere, J. F., Pracros, P., Le Roux, A. Y. & Pierre, J. S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).
Google Scholar 
Shi, P. & Ge, F. A comparison of different thermal performance functions describing temperature-dependent development rates. J. Therm. Biol. 35, 225–231 (2010).
Google Scholar 
Angilletta, M. J., Wilson, R. S., Navas, C. A. & James, R. S. Tradeoffs and the evolution of thermal reaction norms. Trends Ecol. Evol. 18, 234–240 (2003).
Google Scholar 
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 1–9 (2014).
Google Scholar  More

Peñuelas, J., Rutishauser, T. & Filella, I. Ecology. Phenology feedbacks on climate change. Science 324, 887–888 (2009).PubMed 

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

Google Scholar 
Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. Meteorol. 169, 156–173 (2013).
Google Scholar 
Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351, 972–976 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Wright, J. S. et al. Rainforest-initiated wet season onset over the southern Amazon. Proc. Natl. Acad. Sci. USA 114, 8481–8486 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl. Acad. Sci. USA 111, 16041–16046 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Girardin, C. A. J. et al. Seasonal trends of Amazonian rainforest phenology, net primary productivity, and carbon allocation. Glob. Biogeochem. Cycles 30, 700–715 (2016).ADS 
CAS 

Google Scholar 
Maeda, E. E. et al. Consistency of vegetation index seasonality across the Amazon rainforest. Int. J. Appl. Earth Obs. Geoinf. 52, 42–53 (2016).ADS 

Google Scholar 
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016). vol.CAS 
PubMed 

Google Scholar 
Chen, X. et al. Vapor pressure deficit and sunlight explain seasonality of leaf phenology and photosynthesis across amazonian evergreen broadleaved forest. Global Biogeochem. Cycles https://doi.org/10.13140/2.1.5019.5520 (2021).Hashimoto, H. et al. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nat. Commun. 12, 684 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl. Acad. Sci. USA 107, 14685–14690 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, J. et al. Seasonality of Central Amazon forest leaf flush using tower-mounted RGB camera. In AGU Fall Meeting https://doi.org/10.13140/2.1.5019.5520 (2014).Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. https://doi.org/10.1029/2005GL025583 (2006).Restrepo-Coupe, N. et al. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agric. Meteorol. 182-183, 128–144 (2013).
Google Scholar 
Manoli, G., Ivanov, V. Y. & Fatichi, S. Dry-season greening and water stress in Amazonia: the role of modeling leaf phenology. J. Geophys. Res. Biogeosci. 123, 1909–1926 (2018).
Google Scholar 
Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).ADS 
CAS 

Google Scholar 
Lopes, A. P. et al. Leaf flush drives dry season green-up of the Central Amazon. Remote Sens. Environ. 182, 90–98 (2016).ADS 

Google Scholar 
Smith, M. N. et al. Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest. N. Phytol. 222, 1284–1297 (2019).
Google Scholar 
Mitchell Aide, T. Herbivory as a selective agent on the timing of leaf production in a tropical understory community. Nature 336, 574–575 (1988).
Google Scholar 
Myneni, R. B. et al. Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl. Acad. Sci. USA 104, 4820–4823 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, J. et al. Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales. Glob. Chang. Biol. 23, 1240–1257 (2017).ADS 
PubMed 

Google Scholar 
Nunes, M. H. et al. Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño. Nat. Commun. 12, 1526 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vasconcelos, H. L. & Luizão, F. J. Litter production and litter nutrient concentrations in a fragmented Amazonian landscape. Ecol. Appl. 14, 884–892 (2004).
Google Scholar 
Laurance, W. F. et al. Rain forest fragmentation and the proliferation of successional trees. Ecology 87, 469–482 (2006).PubMed 

Google Scholar 
Uriarte, M. et al. Impacts of climate variability on tree demography in second growth tropical forests: the importance of regional context for predicting successional trajectories. Biotropica 48, 780–797 (2016).
Google Scholar 
Ewers, R. M. & Banks-Leite, C. Fragmentation impairs the microclimate buffering effect of tropical forests. PLoS One 8, e58093 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chave, J. et al. Regional and seasonal patterns of litterfall in tropical South America. Biogeosciences 7, 43–55 (2010).ADS 

Google Scholar 
Barros, F. et al. Hydraulic traits explain differential responses of Amazonian forests to the 2015 El Niño-induced drought. N. Phytol. 223, 1253–1266 (2019).CAS 

Google Scholar 
Brum, M. et al. Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest. J. Ecol. 107, 318–333 (2019).
Google Scholar 
Signori-Müller, C. et al. Non-structural carbohydrates mediate seasonal water stress across Amazon forests. Nat. Commun. 12, 2310 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Coelho de Souza, F. et al. Evolutionary heritage influences Amazon tree ecology. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2016.1587 (2016).Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Draper, F. C. et al. Amazon tree dominance across forest strata. Nat. Ecol. Evol. 5, 757–767 (2020).
Google Scholar 
Calders, K. et al. Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements. Agric. Meteorol. 203, 158–168 (2015).
Google Scholar 
Disney, M. Terrestrial LiDAR: a three-dimensional revolution in how we look at trees. N. Phytol. 222, 1736–1741 (2019).
Google Scholar 
Tang, H. & Dubayah, R. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl. Acad. Sci. USA 114, 2640–2644 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Laurance, W. F. et al. An Amazonian rainforest and its fragments as a laboratory of global change. Biol. Rev. Camb. Philos. Soc. 93, 223–247 (2018).PubMed 

Google Scholar 
Correction for Tang and Dubayah, Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl. Acad. Sci. USA 116, 9137 (2019).Ma, L. et al. Characterizing the three-dimensional spatiotemporal variation of forest photosynthetically active radiation using terrestrial laser scanning data. Agric. Meteorol. 301-302, 108346 (2021).
Google Scholar 
Laurans, M., Hérault, B., Vieilledent, G. & Vincent, G. Vertical stratification reduces competition for light in dense tropical forests. Ecol. Manag. 329, 79–88 (2014).
Google Scholar 
Garcia, M. N. et al. Importance of hydraulic strategy trade-offs in structuring response of canopy trees to extreme drought in Central Amazon. Oecologia https://doi.org/10.1007/s00442-021-04924-9 (2021).Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).ADS 
CAS 

Google Scholar 
Brando, P. Tree height matters. Nat. Geosci. 11, 390–391 (2018).ADS 
CAS 

Google Scholar 
Stark, S. C. et al. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol. Lett. 15, 1406–1414 (2012).PubMed 

Google Scholar 
Pyle, E. H. et al. Dynamics of carbon, biomass, and structure in two Amazonian forests. J. Geophys. Res. https://doi.org/10.1029/2007JG000592 (2008).Gorgens, E. B. et al. Resource availability and disturbance shape maximum tree height across the Amazon. Glob. Chang. Biol. 27, 177–189 (2021).ADS 
PubMed 

Google Scholar 
Oliveira, R. S. et al. Linking plant hydraulics and the fast-slow continuum to understand resilience to drought in tropical ecosystems. N. Phytol. 230, 904–923 (2021).
Google Scholar 
Falster, D. S. & Westoby, M. Leaf size and angle vary widely across species: what consequences for light interception? N. Phytol. 158, 509–525 (2003).
Google Scholar 
Chavana-Bryant, C. et al. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. N. Phytol. 214, 1049–1063 (2017).CAS 

Google Scholar 
Brando, P. M. et al. Drought effects on litterfall, wood production and belowground carbon cycling in an Amazon forest: results of a throughfall reduction experiment. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 1839–1848 (2008).PubMed 
PubMed Central 

Google Scholar 
Wang, D., Momo Takoudjou, S. & Casella, E. LeWoS: a universal leaf-wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods Ecol. Evol. 11, 376–389 (2020).
Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).
Google Scholar 
Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6, 1225–1230 (2020).CAS 
PubMed 

Google Scholar 
Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Chang. 9, 384–388 (2019).ADS 

Google Scholar 
Lohbeck, M. et al. Successional changes in functional composition contrast for dry and wet tropical forest. Ecology 94, 1211–1216 (2013).PubMed 

Google Scholar 
Lambers, H. & Oliveira, R. S. in Plant Physiological Ecology (eds. Lambers, H. & Oliveira, R. S.) 385–449 (Springer International Publishing, 2019).Reich, P. B. Key canopy traits drive forest productivity. Proc. Biol. Sci. 279, 2128–2134 (2012).PubMed 
PubMed Central 

Google Scholar 
Albiero-Júnior, A., Venegas-González, A., Camargo, J. L. C., Roig, F. A. & Tomazello-Filho, M. Amazon forest fragmentation and edge effects temporarily favored understory and midstory tree growth. Trees https://doi.org/10.1007/s00468-021-02172-1 (2021).Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
San-José, M., Werden, L., Peterson, C. J., Oviedo-Brenes, F. & Zahawi, R. A. Large tree mortality leads to major aboveground biomass decline in a tropical forest reserve. Oecologia https://doi.org/10.1007/s00442-021-05048-w (2021).Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Chang. 11, 442–448 (2021).Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl. Acad. Sci. USA 112, 13172–13177 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Silva Junior, C. H. L. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3101–3112 (2010).PubMed 
PubMed Central 

Google Scholar 
Park, J. Y. et al. Quantifying leaf phenology of individual trees and species in a tropical forest using unmanned aerial vehicle (UAV) images. Remote Sens. 11, 1534 (2019).ADS 

Google Scholar 
Dubayah, R. et al. The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Egypt. J. Remote Sens. Space Sci. 1, 100002 (2020).
Google Scholar 
Coomes, D. A. et al. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sens. Environ. 194, 77–88 (2017).ADS 

Google Scholar 
Calders, K. et al. Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sens. Environ. 251, 112102 (2020).ADS 

Google Scholar 
Nobre, C. A. et al. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl. Acad. Sci. USA 113, 10759–10768 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Almeida, D. R. A. et al. Persistent effects of fragmentation on tropical rainforest canopy structure after 20 yr of isolation. Ecol. Appl. 29, e01952 (2019).PubMed 

Google Scholar 
Wilkes, P. et al. Data acquisition considerations for terrestrial laser scanning of forest plots. Remote Sens. Environ. 196, 140–153 (2017).ADS 

Google Scholar 
Vincent, G. et al. Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor. Remote Sens. Environ. 198, 254–266 (2017).ADS 

Google Scholar 
Pimont, F., Allard, D., Soma, M. & Dupuy, J.-L. Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR. Remote Sens. Environ. 215, 343–370 (2018).ADS 

Google Scholar 
Vincent, G., Pimont, F. & Verley, P. A note on PAD/LAD Estimators Implemented in AMAPVox 1.7.https://doi.org/10.23708/1AJNMP (2021)Ross, J. The radiation regime and architecture of plant stands (Springer, 1981).Béland, M., Widlowski, J.-L., Fournier, R. A., Côté, J.-F. & Verstraete, M. M. Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements. Agric. Meteorol. 151, 1252–1266 (2011).
Google Scholar 
Almeida, D. R. Ade et al. Optimizing the remote detection of tropical rainforest structure with airborne LiDAR: leaf area profile sensitivity to pulse density and spatial sampling. Remote Sens. 11, 92 (2019).ADS 

Google Scholar 
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Росс, Ю. & Ross, J. The radiation regime and architecture of plant stands (Springer Science & Business Media, 1981).Berry, Z. C. & Goldsmith, G. R. Diffuse light and wetting differentially affect tropical tree leaf photosynthesis. N. Phytol. 225, 143–153 (2020).CAS 

Google Scholar 
Mercado, L. M. et al. Impact of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
USGS. LP DAAC—MCD18A1. https://lpdaac.usgs.gov/products/mcd18a1v006/ (2008).Maeda, E. E. et al. Large-scale commodity agriculture exacerbates the climatic impacts of Amazonian deforestation. Proc. Natl. Acad. Sci. USA 118, e2023787118 (2021).Engelbrecht, B. M. J. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).ADS 
CAS 
PubMed 

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

Google Scholar 
Wild, J. et al. Climate at ecologically relevant scales: a new temperature and soil moisture logger for long-term microclimate measurement. Agric. Meteorol. 268, 40–47 (2019).
Google Scholar 
Camargo, J. L. C. & Kapos, V. Complex edge effects on oil moisture and microclimate in Central Amazonian forest. J. Trop. Ecol. 11, 205–221 (1995).
Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).Malhi, Y., Phillips, O. L. & Laurance, W. F. Forest-climate interactions in fragmented tropical landscapes. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 345–352 (2004).
Google Scholar  More

Variability of N2O concentrations and fluxesAll sampled streams and rivers were supersaturated on all dates (117.9–242.5%, n = 342 samples from 114 site visits) in N2O with respect to the atmosphere. Dissolved N2O concentrations fluctuated between 10.2 and 18.9 nmol L−1 with an average of 12.4 ± 1.7 nmol L−1, which is one-third of the global average3 (37.5 nmol L−1; Supplementary Table 3). Significantly higher N2O concentrations were observed in spring (P  More

Chapman, R. F. Chemoreception: The significance of receptor numbers. Adv. Insect Physiol. 16, 247–356 (1982).CAS 

Google Scholar 
Greenfield, M. D. Signalers and Receivers: Mechanisms and Evolution of Arthropod Communication (Oxford University Press, 2002).
Google Scholar 
Wyatt, T. D. Pheromones and Animal Behavior: Chemical Signals and Signatures (Cambridge University Press, 2014).
Google Scholar 
Elgar, A. et al. Insect antennal morphology: The evolution of diverse solutions to odorant perception. Yale J. Biol. Med. 91, 457–469 (2018).PubMed 
PubMed Central 

Google Scholar 
Dötterl, S. & Vereecken, N. J. The chemical ecology and evolution of bee-flower interactions: a review and perspectives. Can. J. Zool. 88, 668–697 (2010).
Google Scholar 
Leonard, A. S., Dornhaus, A. & Papaj, D. R. Why are floral signals complex, an outline of functional hypotheses. In Evolution of Plant-Pollinator Relationships (ed. Patiny, S.) (Cambridge University Press, USA, 2012).
Google Scholar 
Colazza, S., Peri, E., Salerno, G. & Conti, E. Host Searching by Egg Parasitoids: Exploitation of Host Chemical Cues. In Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma (eds Consoli, F. L. et al.) 97–147 (Springer, 2010).
Google Scholar 
Kelber, A. et al. Light intensity limits the foraging activity in nocturnal and crepuscular bees. Behav. Ecol. 17, 63–72 (2006).
Google Scholar 
Polidori, C., Jorge, A. & Ornosa, C. Antennal morphology and sensillar equipment vary with pollen diet specialization in Andrena bees. Arthropod Struct. Develop. 57, 100950 (2020).
Google Scholar 
Spaethe, J., Brockmann, A., Halbig, C. & Tautz, J. Size determines antennal sensitivity and behavioral threshold to odors in bumblebee workers. Naturwissenschaften. 94, 733–739 (2007).CAS 
PubMed 
ADS 

Google Scholar 
Warrant, E. J., Kelber, A., Wallén, R. & Wcislo, W. The physiological optics of ocelli in nocturnal and diurnal bees and wasps. Arthropod Struct. Dev. 35, 293–305 (2006).PubMed 

Google Scholar 
Keesey, I. W. et al. Inverse resource allocation between vision and olfaction across the genus Drosophila. Nat. Commun. 10, 1162. https://doi.org/10.1038/s41467-019-09087-z (2019).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Keil, T. A. Morphology and Development of the Peripheral Olfactory Organs. In Insect Olfaction (ed. Hansson, B. S.) 5–47 (Springer, 1999).
Google Scholar 
Stöckl, A. et al. Differential investment in visual and olfactory brain areas reflects behavioural choices in hawk moths. Sci. Rep. 6, 26041. https://doi.org/10.1038/srep26041 (2016).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Bulova, S., Purce, K., Khodak, P., Sulger, E. & O’Donnell, S. Into the black and back: The ecology of brain investment in Neotropical army ants (Formicidae: Dorylinae). Sci. Nat. 103, 31. https://doi.org/10.1007/s00114-016-1353-4 (2016).CAS 
Article 

Google Scholar 
Freelance, C. B. et al. The eyes have it: Dim-light activity is associated with the morphology of eyes but not antennae across insect orders. Biol. J. Linn. Soc. 134, 303–315 (2021).
Google Scholar 
Barrett, M. et al. Neuroanatomical differentiation associated with alternative reproductive tactics in male arid land bees, Centris pallida and Amegilla dawsoni. J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol. 207, 497–504 (2021).PubMed 

Google Scholar 
Newland, P. Physiological properties of afferents from tactile hairs on the hindlegs of the locust. J. Exp. Biol. 155, 487–503 (1991).CAS 
PubMed 

Google Scholar 
Dahake, A., Stöckl, A., Foster, J., Sane, S. P. & Kelber, A. The roles of vision and antennal mechanoreception in hawkmoth flight control. eLife e37606 (2018).Sane, S. P., Dieudonné, A., Willis, M. A. & Daniel, T. L. Antennal mechanosensors mediate flight control in moths. Science 315, 863–866 (2007).CAS 
PubMed 
ADS 

Google Scholar 
Land, M. F. Compound Eye Structure: Matching Eye to Environment. In Adaptive Mechanisms in the Ecology of Vision (eds Archer, S. et al.) 51–72 (Kluwer Academic Publishers, 1998).
Google Scholar 
Land, M. F. Visual acuity in insects. Ann. Rev. Entomol. 42, 147–177 (1997).CAS 

Google Scholar 
Land, M. F. & Nilsson, D. E. Animal Eyes (Oxford University Press, 2003).
Google Scholar 
Jander, U. & Jander, R. Allometry and resolution of bee eyes (Apoidea). Arthropod Struct. Dev. 30, 179–193 (2002).PubMed 

Google Scholar 
Berry, R., van Kleef, J. & Stange, G. The mapping of visual space by dragonfly lateral ocelli. J. Comp. Physiol. A 193, 495–513 (2007).
Google Scholar 
Hung, Y. S. & Ibbotson, M. R. Ocellar structure and neural innervation in the honeybee. Front. Neuroanat. https://doi.org/10.3389/fnana.2014.00006 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Greiner, B. Visual adaptations in the night-active wasp Apoica pallens. J. Comp. Neurol. 495, 255–262 (2006).PubMed 

Google Scholar 
Klotz, J. H., Reid, B. L. & Gordon, W. C. Variation of ommatidia number as a function of worker size in Camponotus pennsylvanicus (DeGeer) (Hymenoptera, Formicidae). Insect Soc. 39, 233–236 (1992).
Google Scholar 
Narendra, A. et al. Caste-specific visual adaptations to distinct daily activity schedules in Australian Myrmecia ants. Proc. R. Soc. B 278, 1141–1149 (2011).PubMed 

Google Scholar 
Piwczyński, M. et al. Molecular phylogeny of Miltogramminae (Diptera: Sarcophagidae): Implications for classification, systematics and evolution of larval feeding strategies. Mol. Phyl. Evol. 116, 49–60 (2017).
Google Scholar 
Spofford, M. G. & Kurczewski, F. E. Comparative larvipositional behaviors and cleptoparasitic frequencies of Nearctic species of Miltogrammini (Diptera, Sarcophagidae). J. Nat. Hist. 24, 731–755 (1990).
Google Scholar 
Alcock, J. The natural history of a miltogrammine fly, Miltogramma rectangularis (Diptera: Sarcophagidae). J. Kansas Entomol. Soc. 73, 208–219 (2000).
Google Scholar 
Newcomer, E. J. Notes on the habits of a digger wasp and its inquiline flies. Ann. Entomol. Soc. Am. 23, 552–563 (1930).
Google Scholar 
Ristich, S. S. The host relationship of a miltogrammid fly Senotainia trilineata (VDW). Ohio J. Sci. 56, 271–274 (1956).
Google Scholar 
Giordani, G. Contributo alla conoscenza della Senotainia tricuspis Meig, dittero sarcofagide, endoparassita dell’ape domestica. Boll. Istit. Entomol. Univ. Bologna 21, 61–84 (1955).
Google Scholar 
Povolný, D. & Verves, Yu. G. The flesh-flies of Central Europe (Insecta, Diptera, Sarcophagidae). Spixiana (Supplement) 24, 1–260 (1997).
Google Scholar 
Evans, H. E. & O’Neill, K. M. The sand wasps: Natural history and behavior (Harvard University Press, 2007).
Google Scholar 
O’Neill, K. M. Solitary Wasps: Natural History and Behavior (Cornell University Press, 2001).
Google Scholar 
Pape, T. The Sarcophagidae (Diptera) of Fennoscandia and Denmark. Fauna Entomol. Scand. 19, 1–203 (1987).
Google Scholar 
Polidori, C., Ouadragou, M., Gadallah, N. & Andrietti, F. Potential role of evasive flights and nest closures in an African sand wasp, Bembix sp. near capensis Lepeletier 1845 (Hymenoptera Crabronidae), against a parasitic satellite fly. Trop. Zool. 22, 1–14 (2009).
Google Scholar 
Polidori, C. Interactions between the social digger wasp, Cerceris rubida, and its brood parasitic flies at a Mediterranean nest aggregation. J. Insect Behav. 30, 86–102 (2017).
Google Scholar 
Pape, T. A new species of Hoplacephala Macquart (Diptera: Sarcophagidae) from Namibia, with a discussion of generic monophyly. Zootaxa 1183, 57–68 (2006).
Google Scholar 
Haynie, J. L. & Bryant, P. J. Development of the eye-antenna imaginal disc and morphogenesis of the adult head in Drosophila melanogaster. J. Exp. Zool. 237, 293–308 (1986).CAS 
PubMed 

Google Scholar 
Hódar, J. A. The use of regression equations for estimation of arthropod biomass in ecological studies. Acta Oecol. 17, 421–433 (1996).
Google Scholar 
Hogue, J. N. & Hawkins, C. P. Morphological variation in adult aquatic insects: Associations with developmental temperature and seasonal growth patterns. J. N. Am. Benthol. Soc. 10, 309–321 (1991).
Google Scholar 
Seidl, R. & Kaiser, W. Visual field size, binocular domain and the ommatidial array of the compound eyes in worker honey bees. J. Comp. Physiol. A 143, 17–26 (1981).
Google Scholar 
Stuckenberg, B. R. Antennal evolution in the Brachycera (Diptera), with a reassessment of terminology relating to the flagellum. Stud. Dipterol. 6, 33–48 (1999).
Google Scholar 
Lemmon, A. R., Emme, S. A. & Lemmon, E. M. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst. Biol. 61, 727–744 (2012).CAS 
PubMed 

Google Scholar 
Young, A. D. et al. Anchored enrichment dataset for true flies (order Diptera) reveals insights into the phylogeny of flower flies (family Syrphidae). BMC Evol. Biol. 16, 1–13 (2016).
Google Scholar 
Gillung, J. P. et al. Anchored phylogenomics unravels the evolution of spider flies (Diptera, Acroceridae) and reveals discordance between nucleotides and amino acids. Mol. Phyl. Evol. 128, 233–245 (2018).CAS 

Google Scholar 
Buenaventura, E., Szpila, K., Cassel, B. K., Wiegmann, M. & Pape, T. An anchored hybrid enrichment-based dataset challenges the traditional classification of flesh flies (Diptera: Sarcophagidae). Syst. Entomol. 45, 281–301 (2020).
Google Scholar 
Grzywacz, A. et al. Towards a new classification of Muscidae (Diptera): A comparison of hypotheses based on multiple molecular phylogenetic approaches. Syst. Entomol. 46, 508–525 (2021).
Google Scholar 
Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767 (2014).CAS 
PubMed 
ADS 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 

Google Scholar 
Yan, L. et al. A phylotranscriptomic framework for flesh fly evolution (Diptera, Calyptratae, Sarcophagidae). Cladistics https://doi.org/10.1111/cla.12449 (2020).Article 
PubMed 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Maddison, W. P. & Maddison, D. R. Mesquite: A Modular System for Evolutionary analysis. Version 3.61. http://www.mesquiteproject.org (2019).Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).PubMed 

Google Scholar 
Hansen, T. F., Pienaar, J. & Orzack, S. H. A comparative method for studying adaptation to a randomly evolving environment. Evolution 62, 1965–1977 (2008).PubMed 

Google Scholar 
Labra, A., Pienaar, J. & Hansen, T. F. Evolution of thermal physiology in Liolaemus lizards: Adaptation, phylogenetic inertia and niche tracking. Am. Nat. 174, 204–220 (2009).PubMed 

Google Scholar 
Hansen, T. F. Use and Misuse of Comparative Methods in the Study of Adaptation. In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology: Concepts and Practice (ed. Garamszegi, L. Z.) 351–379 (Springer, 2014).
Google Scholar 
Greiner, B., Ribi, W. A. & Warrant, E. J. Retinal and optical adaptations for nocturnal vision in the halictid bee Megalopta genalis. Cell Tissue Res. 316, 377–390 (2004).PubMed 

Google Scholar 
Warrant, E. J. et al. Nocturnal vision and landmark orientation in a tropical halictid bee. Curr. Biol. 14, 1309–1318 (2004).CAS 
PubMed 

Google Scholar 
Somanathan, H., Kelber, A., Wallén, R., Borges, R. M. & Warrant, E. J. Visual ecology of Indian carpenter bees II: Visual adaptations to nocturnal and diurnal lifestyles. J. Comp. Physiol. A 195, 571–583 (2009).
Google Scholar 
Menzi, U. Visual adaptation in nocturnal and diurnal ants. J. Comp. Physiol. 160, 11–21 (1987).
Google Scholar 
Moser, J. C. et al. Eye size and behaviour of day and night-flying leafcutting ant alates. J. Zool. 264, 69–75 (2004).
Google Scholar 
Greiner, B. et al. Eye structure correlates with distinct foraging-bout timing in primitive ants. Curr. Biol. 17, R879–R880 (2007).CAS 
PubMed 

Google Scholar 
Warrant, E. J. Seeing in the dark: Vision and visual behaviour in nocturnal bees and wasps. J. Exp. Biol. 211, 1737–1746 (2008).PubMed 

Google Scholar 
Leys, R. & Hogendoorn, K. Correlated evolution of mating behaviour and morphology in large carpenter bees (Xylocopa). Apidologie 39, 119–132 (2008).
Google Scholar 
Snyder, A. W. Physics of Vision in Compound Eyes. In Handbook of Sensory Physiology: Vision in Invertebrates (ed. Autrum, H. J.) (Springer, 1979).
Google Scholar 
McCorquodale, D. B. Digger wasp provisioning flights as a defense against a nest parasite, Senotainia trilineata. Can. J. Zool. 64, 1620–1627 (1986).
Google Scholar 
Gilbert, C. & Strausfeld, N. J. The functional organization of male-specific visual neurons in flies. J. Comp. Physiol. A 169, 395–411 (1991).CAS 
PubMed 

Google Scholar 
Trischler, C., Boeddeker, N. & Egelhaaf, M. Characterisation of a blowfly male-specific neuron using behaviourally generated visual stimuli. J. Comp. Physiol. A 193, 559–572 (2007).
Google Scholar 
Taylor, G. J. et al. The dual function of orchidbee ocelli as revealed by X-Ray microtomography. Curr. Biol. 26, 1319–1324 (2016).CAS 
PubMed 

Google Scholar 
Hengstenberg, R. Multisensory Control in Insect Oculomotor Systems. In Visual Motion and Its Role in the Stabilization of Gaze (eds Miles, F. A. & Wallmann, J.) (Elsevier, 1993).
Google Scholar 
Schuppe, H. & Hengstenberg, R. Optical properties of the ocelli of Calliphora erythrocephala and their role in the dorsal light response. J. Comp. Physiol. A 173, 143–149 (1993).
Google Scholar 
Crosskey, R. W. & Lane, R. P. Introduction to Diptera. In Medical Insects and Arachnids (eds Lane, R. P. & Crosskey, R. W.) (Chapman and Hall, 1993).
Google Scholar 
Abouzied, E. M. Antennal and maxillary palp sensillae of male and female Liosarcophaga babiyari Lehrer (Diptera: Sarcophagidae). Bull. Ent. Soc. Egypt 85, 29–48 (2008).
Google Scholar 
Wasserman, S. L. & Itagaki, H. The olfactory responses of the antenna and maxillary palp of the fleshfly, Neobellieria bullata (Diptera: Sarcophagidae), and their sensitivity to blockage of nitric oxide synthase. J. Insect Physiol. 49, 271–280 (2003).CAS 
PubMed 

Google Scholar 
Khedre, A. M. Olfactory sensilla on the antennae and maxillary palps of the fleshfly Wohlfahrtia nuba (Wied.) (Diptera: Sarcophagidae). J. Egypt Ger. Soc. Zool. 24, 171–193 (1997).
Google Scholar 
Pezzi, M. et al. Ultrastructural morphology of the antenna and maxillary palp of Sarcophaga tibialis (Diptera: Sarcophagidae). J. Med. Entomol. 53, 807–814 (2016).CAS 
PubMed 

Google Scholar 
Smallegange, R. C., Kelling, F. J. & Den Otter, C. J. Types and numbers of sensilla on antennae and maxillary palps of small and large houseflies, Musca domestica (Diptera, Muscidae). Microsc. Res. Tech. 71, 880–886 (2008).PubMed 

Google Scholar 
Zhang, D., Wang, Q. K., Yang, Y. Z., Chen, Y. O. & Li, K. Sensory organs of the antenna of two Fannia species (Diptera: Fanniidae). Parasitol. Res. 112, 2177–2185 (2013).CAS 
PubMed 

Google Scholar 
Been, T. H., Schomaker, C. H. & Thomas, G. Olfactory sensilla on the antenna and maxillary palp of the sheep head fly, Hydrotaea irritans (Fallen) (Diptera: Muscidae). Int. J. Insect Morphol. Embryol. 17, 121–133 (1998).
Google Scholar 
Zacharuk, R. Y. & Antennal, S. Comparative Insect Physiology, Biochemistry and Pharmacology. In Pergamon Press (eds Kerkut, G. A. & Gilbert, L. I.) (1985).
Google Scholar 
Sukontason, K. et al. Antennal sensilla of some forensically important flies in families Calliphoridae Sarcophagidae and Muscidae. Micron 35, 671–679 (2004).PubMed 

Google Scholar 
Mamiya, A., Straw, A. D., Tómasson, E. & Dickinson, M. H. Active and passive antennal movements during visually guided steering in flying Drosophila. J Neurosci 31, 6900–6914 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fuller, S. B., Straw, A. D., Peek, M. Y., Murray, R. M. & Dickinson, M. H. Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae. Proc. Nat. Acad. Sci. 111, E1182–E1191 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Nalbach, G. Extremely non-orthogonal axes in a sense organ for rotation: Behavioural analysis of the dipteran haltere system. Neuroscience 61, 149–163 (1994).CAS 
PubMed 

Google Scholar 
Rozanski, A. N. et al. Differential investment in visual and olfactory brain regions is linked to the sensory needs of a wasp social parasite and its host. J. Comp. Neurol. https://doi.org/10.1002/cne.25242 (2021).Article 
PubMed 

Google Scholar  More

Tropical forest restoration areaTo determine the geographic distribution of land available for tropical forest restoration, we used a widely applied global forest restoration map2. This dataset limits potential restoration area to regions that are biogeophysically suitable for forest, and excludes croplands. To define the tropics, we masked the potential restoration map with the following three ecoregions from the Ecoregions2017 vegetation map34: ‘Tropical and Subtropical Moist Broadleaf Forests’, ‘Tropical and Subtropical Dry Broadleaf Forests’, and ‘Tropical and Subtropical Coniferous Forests’. The resulting restoration mask includes all tropical and subtropical forest ecoregions with some that are outside the tropical latitudes, but excludes wetlands and high mountain areas (Extended Data Fig. 4). The restoration mask was converted from a presence–absence raster at its native ~350 m resolution to a 0.5° geographical grid by aggregating to the fraction of each 0.5° grid cell available for restoration. Any uncertainties in the allocation of restorable area, distinguishing crop and pasture, and forest to non-forest classification from the original forest restoration map were also implicitly included in our restoration extent. While the resulting restoration area is relatively small, its spatial distribution is representative for most of the humid tropics.To prioritize for carbon uptake capacity, we selected all grid cells with restoration area greater than 1 ha and ranked these by carbon storage density (above ground and below ground; g m−2) at 2100 under the default scenario. We then selected the top n grid cells with greatest carbon density until cumulatively 64 Mha of restored area was reached. Similarly, for cost we calculated the restoration cost for each grid cell following ref. 27 and sorted the grid cells by their cost, beginning with the lowest value, until 64 Mha were reached. To consider the combined impact of carbon uptake and restoration costs, we divided our restoration cost layer by the total carbon uptake per grid cell from restoration and ranked the cost per carbon uptake from cheapest to most expensive, selecting the n grid cells with the lowest values until 64 Mha were reached. We then used the selected grid cells to mask carbon uptake under the various climate change and CO2 fertilization scenarios. To factor in climate change in the prioritization process, we used the same restoration cost layer but used the carbon density and total carbon uptake layers with climate change impacts in CO22014 for the year 2100.Vegetation modelWe used the LPJ-LMfire DGVM19, a version of the Lund-Potsdam-Jena DGVM (LPJ)35. LPJ-LMfire is driven by gridded fields of climate, soil texture and topography at 0.5° resolution, and with a time series of atmospheric CO2 concentrations (see Supplementary Information). To simulate land use, LPJ-LMfire separates grid cells into fractional tiles of ‘unmanaged’ land that has never been under land use, ‘managed’ land, and areas ‘recovering’ from land use36. Restoration removes land from the ‘managed’ tile and transfers it to the ‘recovering’ tile; land is never reallocated to the ‘unmanaged’ tile. The tiles are treated differently with respect to wildfire: on the ‘unmanaged’ and ‘recovering’ tiles, lightning-ignited wildfires are not suppressed, while fire is excluded from ‘managed’ tiles. For our analysis of total carbon (above and below ground), we only used the ‘recovering’ tile.Climate dataClimate forcing used to drive LPJ-LMfire comes from the output of 13 GCMs in simulations produced for the CMIP6 Supplementary Table 2 (refs. 37,38). For each GCM, we obtained simulations for the historical period (1850–2014) and four future SSPs (SSP1-26, SSP2-45, SSP3-70 and SSP5-85 covering 2015–2100). We used only GCMs that archived all seven climate variables needed to run LPJ-LMfire: 2 m temperature (tas, K), precipitation (pr, kg m−2 s−1), convective precipitation (prc, kg m−2 s−1), cloud cover (clt, %), minimum and maximum daily temperature (tmin, tmax, K), and 10 m surface wind speed (sfcWind, m s−1) (Supplementary Fig. 2). For each model, we concatenated the historical simulation with a future scenario, calculated anomalies with respect to 1971–1990 and added those to observed 30 year climatologies to create bias-corrected monthly climate time series covering 1850–2100 (see Supplementary Information). Where multiple ensemble members were available from a GCM, we chose the first simulation.Simulation protocolWe drove LPJ-LMfire with the GCM simulations described in the previous section, and the same atmospheric CO2 concentrations and land use boundary conditions as those used in the CMIP6 simulations. All forcings cover the historical period (1850–2014) and the individual future SSPs (2015–2100). Each LPJ-LMfire simulation was initialized for 1,020 years with 1850 atmospheric CO2 and land use, and the 1850s climatology of each CMIP6 GCM. This was followed by simulations with transient climate from 1850 to 2100 for each CMIP6 GCM under each of the four SSPs. For each the 13 CMIP6 GCMs running each of the SSP scenarios, we conducted two CO2 experiments (CO22014 and CO2free) and two fire experiments. In total, we ran 221 vegetation model simulations covering the range of future climate, CO2 and fire scenarios.Atmospheric CO2 in these simulations either followed the CMIP6 historical and SSP trajectory for the entire 1850–2100 run (CO2free), or followed the historical CMIP6 trajectory until 2014, and was then fixed at 2014 concentrations for the remainder of the simulation (CO22014). This allowed us to test the vegetation response to future climate change in the absence of additional CO2 fertilization of photosynthesis. Our simulations ended with the standard SSP projections in 2100, 80 years after restoration begins. We therefore could not assess the fate of restored carbon beyond that point. On the basis of the trends in the multi-model mean carbon uptake rates, we estimated that only under severe climate change will carbon storage be reduced shortly after 2100 in CO22014.In control simulations, land use followed the historical CMIP6 trajectory until 2014, after which it was fixed under 2014 conditions until 2100. Land use after 2014 was fixed at 2014 levels because it is the last year with common land use between all scenarios, which allowed us to identify future climate change impacts on restoration permanence and avoid influences from land abandonment and expansion prescribed in the different SSP scenarios.In the restoration experiments, land use also followed the historical CMIP6 trajectory until 2014, but then diverged: cropland extent remained at 2014 levels until 2100, while pasture (or non-cropland land use) remained constant from 2014 to 2020 and was then linearly reduced by the restoration area from 2020 to 2030. From 2030, land use remained constant at that lower level until 2100. The amount of restoration in a grid cell was limited by the pasture area, that is, once all of the available pasture area had been restored, no additional restoration took place. Because it is highly unlikely to be practical to restore the entire target area of tropical forest at once, we linearly increased the restoration area from 2020 to 2030, which caused an expansion-driven increase in carbon uptake over the 11 year period (Extended Data Fig. 1). This means that two factors controlled carbon uptake over time in our experimental design: first the expansion of the restoration area, accounting for approximately 19.7 Pg C, and second the long-term effect of carbon accumulation (Extended Data Fig. 5).Primary climate change impacts, such as drought and heat stress that reduce carbon uptake, were implicitly included in the climate forcing data, while secondary climate change impacts from wildfire were simulated by LPJ-LMfire on the basis of climate. To quantify the contribution of wildfire on the carbon storage from restoration, we repeated the simulations described above with fires turned off in LPJ-LMfire.Restoration opportunity indexWe created a restoration opportunity index to evaluate the suitability of locations for restoration on the basis of the ability for restoration to result in net carbon uptake over 2020–2100 and to store this carbon without episodes of major loss. For each of the 13 realizations of the four SSPs in the CO22014 experiment, we identified all restoration grid cells (1) that had a net carbon uptake by 2100 relative to 2030, and (2) where temporal reductions in total carbon storage over 2030–2100 were More