Ecology

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

Pawlowski, J. et al. The evolution of early Foraminifera. Proc. Natl. Acad. Sci. 100(20), 11494–11498 (2003).ADS 
CAS 
Article 

Google Scholar 
Gupta, S. Modern Foraminifera (Springer-Verlag, 1999).
Google Scholar 
Narayan, G. R. et al. Response of large benthic foraminifera to climate and local changes: Implications for future carbonate production. Sedimentology 2, 2 (2021).
Google Scholar 
Doo, S. S., Fujita, K., Byrne, M. & Uthicke, S. Fate of calcifying tropical symbiont-bearing large benthic foraminifera: Living sands in a changing ocean. Biol. Bull. 226(3), 169–186 (2014).CAS 
Article 

Google Scholar 
Fujita, K. et al. Effects of ocean acidification on calcification of symbiont-bearing reef foraminifers. Biogeosciences 8(8), 2089–2098 (2011).ADS 
Article 

Google Scholar 
Raja, R., Saraswati, P. K., Rogers, K. & Iwao, K. Magnesium and strontium compositions of recent symbiont-bearing benthic foraminifera. Mar. Micropaleontol. 58(1), 31–44 (2005).ADS 
Article 

Google Scholar 
Murray, J. Ecological experiments on Foraminiferida. J. Mar. Biol. Assoc. U.K. 43(3), 621–642 (1963).Article 

Google Scholar 
Wukovits, J., Enge, A. J., Wanek, W., Watzka, M. & Heinz, P. Increased temperature causes different carbon and nitrogen processing patterns in two common intertidal foraminifera. Biogeosciences 14, 2815–2829 (2017).ADS 
CAS 
Article 

Google Scholar 
Lintner, M., Biedrawa, B., Wukovits, J., Wanek, W., and Heinz, P. Salinity-depending algae uptake and subsequent carbon and nitrogen metabolisms of two intertidal foraminifera (Ammonia tepida and Haynesina germanica). BG, 17, 3723–3732 (2020).Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).ADS 
CAS 
Article 

Google Scholar 
Occhipinti-Ambrogi, A. Global change and marine communities: Alien species and climate change. Mar. Pollut. Bull. 55, 342–352 (2007).CAS 
Article 

Google Scholar 
Hallock, P. Symbiont-bearing foraminifera. In Modern Foraminifera 123–139 (Springer, 1999).Chapter 

Google Scholar 
Renema, W. Large benthic foraminifera in low-light environments. In Mesophotic coral ecosystems 553–561 (Springer, 2019).Chapter 

Google Scholar 
Hallock, P. & Peebles, M. W. Foraminifera with chlorophyte endosymbionts—habitats of 6 species in the Florida Keys. Mar. Micropaleontol. 20, 277–292 (1993).ADS 
Article 

Google Scholar 
Stulpinaite, R., Hyams-Kaphzan, O. & Langer, M. R. Alien and cryptogenic Foraminifera in the Mediterranean Sea: A revision of taxa as part of the EU 2020 marine strategy framework directive. Mediterr. Mar. Sci. 21(3), 719–758 (2020).
Google Scholar 
McCoshum, S., Schlarb, M. A. & Baum, A. K. Direct and indirect effects of sunscreen exposure for reef biota. Rev. Hydrobiology 776, 139–146 (2016).CAS 
Article 

Google Scholar 
Singh, S., Jha, B., Tiwary, N. K. & Agrawal, N. K. Does using a high sun protection factor sunscreen on face, along with physical photoprotection advice, in patients with melasma, change serum vitamin D concentration in Indian conditions? A pragmatic pretest-posttest study. Indian J. Dermatol. Venereol. Leprol. 85, 282–286 (2019).Article 

Google Scholar 
Harjung, A. et al. High anthropogenic organic matter inputs during a festival increase river heterotrophy and refractory carbon load. Environ. Sci. Technol. 54(16), 10039–10048. https://doi.org/10.1021/acs.est.0c02259 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rai, R., Shanmuga, S. C. & Srinivas, C. Update on photoprotection. Indian J. Dermatol. 57, 335–342 (2012).Article 

Google Scholar 
Schiavo, S., Oliviero, M., Phillipe, A. & Manzo, S. Nanoparticles based sunscreens provoke adverse effects on marine microalgae Dunaliella tertiolecta. Environ. Sci. Nano. 12, 2 (2018).
Google Scholar 
Parkhill, J., Mailett, G. & Cullen, J. Fluorescence-based maximal quantim yield fpr PSII as a diagnostic of nutrient stress. J. Phycol. 37, 517–529 (2001).Article 

Google Scholar 
Butler, W. L. Energy distribution in the photochemical apparatus of photosynthesis. Ann. Rev. Plant. Physiol. 29, 345–378 (1978).CAS 
Article 

Google Scholar 
Kroon, B., Prezelin, B. B. & Schonfield, O. Chromatic regulation of quantum yields for photosystem II charge separation, oxygen evolution and carbon fixation in Heterocapsa pygmaea. J. Phycol 29, 453–462 (1993).CAS 
Article 

Google Scholar 
Casas-Beltran, D. A., Hernandez-Pedraza, M. & Alvarado-Flores, J. Estimation of the discharge of sunscreens in aquatic environments of the Mexican caribbean. Environments 7, 15 (2020).Article 

Google Scholar 
Danovaro, R. et al. Sunscreens cause coral bleaching by promoting viral infections. Environ. Health Perspect. 116, 441–447 (2008).CAS 
Article 

Google Scholar 
Brausch, J. M. & Rand, G. M. A review of personal care products in the aquatic environment: Environmental concentrations and toxicity. Chemosphere 82, 1518–1532 (2011).ADS 
CAS 
Article 

Google Scholar 
Balmer, M. E., Buser, H. R., Muller, M. D. & Poiger, T. Occurrence of the organic UV-filter compounds BP-3, 4-MBC, EHMC, and OC in wastewater, surface waters, and in fish from Swiss lakes. Environ. Sci. Technol. 39, 953–962 (2004).ADS 
Article 

Google Scholar 
Godejohann, M., Berset, J. & Muff, D. Non-targeted analysis of wastewater treatment plant effluents by high-performance liquid chromatography–time slice-solid phase extraction-nuclear magnetic resonance/time-of-flight-mass spectrometry. J. Chromatogr. A 1218, 9202–9209 (2011).CAS 
Article 

Google Scholar 
Hallock, P., Lidz, B. H., Cockey-Burkhard, E. M. & Donnelly, K. B. Foraminifera as bioindicators in coral reef assessment and monitoring: The FORAM index. Environ. Monit. Assess. 81(1), 221–238 (2003).Article 

Google Scholar 
Sharma, V. K. Aggregation and toxicity of titanium dioxide nanoparticles in aquatic environment—A Review. J. Environ. Sci. Health Part A. 44(14), 1485–2495 (2009).CAS 
Article 

Google Scholar 
Hutchison, J. E. Greener nanoscience: A proactive approach to advancing applications and reducing implications of nanotechnology. ACSNano. 2(3), 395–402 (2008).CAS 

Google Scholar 
Soto, K., Garza, K. M. & Murr, L. E. Cytosis effects of aggregated nanomaterials. Acta Biomater. 3, 351–358 (2007).CAS 
Article 

Google Scholar 
Deer, W. A., Howie, R. A. & Zussmann, J. An Introduction to the Rock Forming Minerals (Longman Group Limited, 1992).
Google Scholar 
Kaegi, R. et al. Synthetic TiO2 nanoparticle emission from exterior facades into the aquatic environment. Environ. Pollut. 156, 233–239 (2008).CAS 
Article 

Google Scholar 
Mio, A. J. et al. Zinc oxide–engineered nanoparticles: Dissolution and toxicity to marine phytoplankton. Environ. Toxicol. Chem. 29(12), 2814–2822 (2010).Article 

Google Scholar 
Herzog, B. et al. In vivo and in vitro assessment of UVA protection by sunscreen formulations containing either butyl methoxy dibenzoyl methane, methylene bis-benzotriazolyl tetramethylbutylphenol, or microfine ZnO. Int. J. Cosmet. Sci. 24, 170–185 (2002).CAS 
Article 

Google Scholar 
Dhas, S. P., Shiny, P. J., Mukherjee, A. & Chandrasekran, N. Toxic behavior of silver and zinc oxide nanoparticles on environmental microorganisms. J. Basic Microbiol. 53, 1–12 (2013).Article 

Google Scholar 
Lee, J.J. Algal symbiosis in larger foraminifera. Symbiosis. (2006). More

Webb, B. Cognition in insects. Philos. Trans. R. Soc B 367, 2715–2722 (2012).
Google Scholar 
Lorenz, K. The Foundations of Ethology 347–352 (Springer, 1981).
Google Scholar 
Davis, R. L. Olfactory memory formation in Drosophila: From molecular to systems neuroscience. Annu. Rev. Neurosci. 28, 275–302 (2005).CAS 
PubMed 

Google Scholar 
Prokopy, R. J., Averill, A. L., Cooley, S. S. & Roitberg, C. A. Associative learning in egglaying site selection by apple maggot flies. Science 218, 76–77 (1982).ADS 
CAS 
PubMed 

Google Scholar 
Tempel, B. L., Bonini, N., Dawson, D. R. & Quinn, W. G. Reward learning in normal and mutant Drosophila. Proc. Natl Acad. Sci. 80, 1482–1486 (1983).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cook, D. F. Influence of previous mating experience on future mating success in maleLucilia cuprina (Diptera: Calliphoridae). J. Insect Behav. 8, 207–217 (1994).
Google Scholar 
Raubenheimer, D. & Tucker, D. Associative learning by locusts: Pairing of visual cues with consumption of protein and carbohydrate. Anim. Behav. 54, 1449–1459 (1997).CAS 
PubMed 

Google Scholar 
Harari, A. R. & Landolt, P. J. Feeding experience enhances attraction of female Diaprepes abbreviatus (L.) (Coleoptera: Curculionidae) to food plant odors. 8. J. Insect Behav. 12, 415–422 (1999).
Google Scholar 
Menzel, R. Memory dynamics in the honeybee. J. Comp. Physiol. A 185, 323–340 (1999).ADS 

Google Scholar 
McCall, P. J. & Kelly, D. W. Learning and memory in disease vectors. Trends Parasitol. 18, 429–433 (2002).CAS 
PubMed 

Google Scholar 
Alonso, W. J. & Schuck-Paim, C. The ‘ghosts’ that pester studies on learning in mosquitoes: Guidelines to chase them off. Med. Vet. Entomol. 20, 157–165 (2006).CAS 
PubMed 

Google Scholar 
WHO. Global Vector Control Response 20217–22030 (World Health Organization, 2017).
Google Scholar 
Rocklöv, J. & Dubrow, R. Climate change: An enduring challenge for vector-borne disease prevention and control. Nat. Immunol. 21, 479–483 (2020).PubMed 
PubMed Central 

Google Scholar 
Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hemingway, J. et al. Averting a malaria disaster: Will insecticide resistance derail malaria control?. The Lancet 387, 1785–1788 (2016).
Google Scholar 
Martinez-Torres, D. et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae ss. Insect Mol. Biol. 7, 179–184 (1998).CAS 
PubMed 

Google Scholar 
Chandre, F. et al. Current distribution of a pyrethroid resistance gene (kdr) in Anopheles gambiae complex from West Africa and further evidence for reproductive isolation of the Mopti form. Parassitologia 41, 319–322 (1999).CAS 
PubMed 

Google Scholar 
Weill, M. et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol. Biol. 13, 1–7 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Du, W. et al. Independent mutations in the Rdl locus confer dieldrin resistance to Anopheles gambiae and An. arabiensis. Insect Mol. Biol. 14, 179–183 (2005).CAS 
PubMed 

Google Scholar 
Hemingway, J. & Ranson, H. Insecticide resistance in insect vectors of human disease. Annu. Rev. Entomol. 45, 371–391 (2000).CAS 
PubMed 

Google Scholar 
Ranson, H. et al. Pyrethroid resistance in African anopheline mosquitoes: What are the implications for malaria control?. Trends Parasitol. 27, 91–98 (2011).CAS 
PubMed 

Google Scholar 
Liu, N. Insecticide resistance in mosquitoes: Impact, mechanisms, and research directions. Annu. Rev. Entomol. 60, 537–559 (2015).CAS 
PubMed 

Google Scholar 
Wood, O., Hanrahan, S., Coetzee, M., Koekemoer, L. & Brooke, B. Cuticle thickening associated with pyrethroid resistance in the major malaria vector Anopheles funestus. Parasit Vectors 3, 67 (2010).PubMed 
PubMed Central 

Google Scholar 
Balabanidou, V. et al. Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. PNAS 113, 9268–9273 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Balabanidou, V. et al. Mosquitoes cloak their legs to resist insecticides. Proc Biol. Sci. 286, 20191091 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Muirhead-Thomson, R. C. The significance of irritability, behaviouristic avoidance and allied phenomena in malaria eradication. Bull. World Health Organ. 22, 721–734 (1960).CAS 
PubMed 
PubMed Central 

Google Scholar 
Georghiou, G. P. The evolution of resistance to pesticides. Annu. Rev. Ecol. Syst. 3, 133–168 (1972).CAS 

Google Scholar 
Grieco, J. P. et al. A new classification system for the actions of IRS chemicals traditionally used for malaria control. PLoS ONE 2, e716 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chareonviriyaphap, T. et al. Review of insecticide resistance and behavioral avoidance of vectors of human diseases in Thailand. Parasit Vectors 6, 280 (2013).PubMed 
PubMed Central 

Google Scholar 
Chilaka, N., Perkins, E. & Tripet, F. Visual and olfactory associative learning in the malaria vector Anopheles gambiae sensu stricto. Malar. J. 11, 27 (2012).PubMed 
PubMed Central 

Google Scholar 
Vinauger, C., Lahondère, C., Cohuet, A., Lazzari, C. R. & Riffell, J. A. Learning and memory in disease vector insects. Trends Parasitol. 32, 761–771 (2016).PubMed 
PubMed Central 

Google Scholar 
Carrasco, D. et al. Behavioural adaptations of mosquito vectors to insecticide control. Curr. Opin. Insect Sci. 34, 48–54 (2019).PubMed 

Google Scholar 
Tomberlin, J. K., Rains, G. C., Allan, S. A., Sanford, M. R. & Lewis, W. J. Associative learning of odor with food- or blood-meal by Culex quinquefasciatus Say (Diptera: Culicidae). Naturwissenschaften 93, 551–556 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Menda, G. et al. Associative learning in the dengue vector mosquito, Aedes aegypti: Avoidance of a previously attractive odor or surface color that is paired with an aversive stimulus. J. Exp. Biol. 216, 218–223 (2013).PubMed 
PubMed Central 

Google Scholar 
Vinauger, C., Lutz, E. K. & Riffell, J. A. Olfactory learning and memory in the disease vector mosquito Aedes aegypti. J. Exp. Biol. 217, 2321–2330 (2014).PubMed 
PubMed Central 

Google Scholar 
WHO. Guidelines for laboratory and field-testing of long-lasting insecticidal nets (World Health Organization, 2013).
Google Scholar 
WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes 2nd edn. (World Health Organization, 2016).
Google Scholar 
Rivero, A., Vézilier, J., Weill, M., Read, A. F. & Gandon, S. Insecticide control of vector-borne diseases: When is insecticide resistance a problem?. PLoS Pathog. 6, e1001000 (2010).PubMed 
PubMed Central 

Google Scholar 
Maciel-de-Freitas, R. et al. Undesirable consequences of insecticide resistance following Aedes aegypti control activities due to a dengue outbreak. PLoS ONE 9, e92424 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sherrard-Smith, E. et al. Systematic review of indoor residual spray efficacy and effectiveness against Plasmodium falciparum in Africa. Nat. Commun. 9, 4982 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dusfour, I. et al. Management of insecticide resistance in the major Aedes vectors of arboviruses: Advances and challenges. PLoS Negl. Trop. Dis. 13, e0007615 (2019).PubMed 
PubMed Central 

Google Scholar 
Perrin, A. et al. Variation in the susceptibility of urban Aedes mosquitoes infected with a densovirus. Sci. Rep. 10, 18654 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wilson, A. L. et al. The importance of vector control for the control and elimination of vector-borne diseases. PLoS Negl. Trop. Dis. 14, e0007831 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wills, A. B. et al. Physical durability of PermaNet 2.0 long-lasting insecticidal nets over three to 32 months of use in Ethiopia. Malar. J. 12, 242 (2013).PubMed 
PubMed Central 

Google Scholar 
Gnanguenon, V., Azondekon, R., Oke-Agbo, F., Beach, R. & Akogbeto, M. Durability assessment results suggest a serviceable life of two, rather than three, years for the current long-lasting insecticidal (mosquito) net (LLIN) intervention in Benin. BMC Infect. Dis. 14, 69 (2014).PubMed 
PubMed Central 

Google Scholar 
Boussougou-Sambe, S. T. et al. Physical integrity and residual bio-efficacy of used LLINs in three cities of the South-West region of Cameroon 4 years after the first national mass-distribution campaign. Malar. J. 16, 31 (2017).PubMed 
PubMed Central 

Google Scholar 
Janko, M. M., Churcher, T. S., Emch, M. E. & Meshnick, S. R. Strengthening long-lasting insecticidal nets effectiveness monitoring using retrospective analysis of cross-sectional, population-based surveys across sub-Saharan Africa. Sci. Rep. 8, 17110 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Djènontin, A. et al. The residual life of bendiocarb on different substrates under laboratory and field conditions in Benin, Western Africa. BMC Res Notes 6, 458 (2013).PubMed 
PubMed Central 

Google Scholar 
Mugenyi, L. et al. Estimating the optimal interval between rounds of indoor residual spraying of insecticide using malaria incidence data from cohort studies. PLoS ONE 15, e0241033 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kreppel, K. S. et al. Emergence of behavioural avoidance strategies of malaria vectors in areas of high LLIN coverage in Tanzania. Sci. Rep. 10, 14527 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parker, J. E. A. et al. Infrared video tracking of Anopheles gambiae at insecticide-treated bed nets reveals rapid decisive impact after brief localised net contact. Sci. Rep. 5, 13392 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Spitzen, J., Koelewijn, T., Mukabana, W. R. & Takken, W. Visualization of house-entry behaviour of malaria mosquitoes. Malar. J. 15, 233 (2016).PubMed 
PubMed Central 

Google Scholar 
Spitzen, J. & Takken, W. Keeping track of mosquitoes: A review of tools to track, record and analyse mosquito flight. Parasit. Vectors https://doi.org/10.1186/s13071-018-2735-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Jones, J., Murray, G. & McCall, P. J. A minimal 3D model of mosquito flight behavior around the human baited bed net. Malar. J. 20, (2021)Sougoufara, S., Ottih, E. C. & Tripet, F. The need for new vector control approaches targeting outdoor biting anopheline malaria vector communities. Parasit. Vectors 13, 295 (2020).PubMed 
PubMed Central 

Google Scholar 
Okumu, F. O. & Moore, S. J. Combining indoor residual spraying and insecticide-treated nets for malaria control in Africa: A review of possible outcomes and an outline of suggestions for the future. Malar. J. 10, 208 (2011).PubMed 
PubMed Central 

Google Scholar 
Deletre, E. et al. Repellent, irritant and toxic effects of 20 plant extracts on adults of the malaria vector Anopheles gambiae Mosquito. PLoS One 8, e82103 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar  More

Jaiswal, S. K., Msimbira, L. A. & Dakora, F. D. Phylogenetically diverse group of native bacterial symbionts isolated from root nodules of groundnut (Arachis hypogaea L.) in South Africa. Syst. Appl. Microbiol. 40, 215–226. https://doi.org/10.1016/j.syapm.2017.02.002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tahir, M., Lv, Y., Gao, L., Hallett, P. D. & Peng, X. Soil water dynamics and availability for citrus and peanut along a hillslope at the Sunjia Red Soil Critical Zone Observatory (CZO). Soil Tillage Res. 163, 110–118. https://doi.org/10.1016/j.still.2016.05.017 (2016).Article 

Google Scholar 
Xiaogang, L. The composition of root exudates from two different resistant peanut cultivars and their effects on the growth of soil-borne pathogen. Int. J. Biol. Sci. https://doi.org/10.7150/ijbs.5579 (2013).Article 

Google Scholar 
Chen, M. et al. Dynamic succession of soil bacterial community during continuous cropping of peanut (Arachis hypogaea L.). PLoS ONE https://doi.org/10.1371/journal.pone.0101355 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, W. et al. Effects of continuous sugar beet cropping on rhizospheric microbial communities. Genes https://doi.org/10.3390/genes11010013 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Effect of continuous cropping on the rhizosphere soil and growth of common buckwheat. Plant. Prod. Sci. 23, 81–90. https://doi.org/10.1080/1343943X.2019.1685895 (2020).CAS 
Article 

Google Scholar 
Meng, L. B. et al. Changes in soil microbial diversity and control of Fusarium oxysporum in continuous cropping cucumber greenhouses following biofumigation. Emir. J. Food Agric. 30, 644–653. https://doi.org/10.9755/ejfa.2018.v30.i8.1752 (2018).Article 

Google Scholar 
Li, X., Ding, C., Zhang, T. & Wang, X. Fungal pathogen accumulation at the expense of plant-beneficial fungi as a consequence of consecutive peanut monoculturing. Soil Biol. Biochem. 72, 11–18. https://doi.org/10.1016/j.soilbio.2014.01.019 (2014).CAS 
Article 

Google Scholar 
Wang, H. W. et al. Fungal endophyte Phomopsis liquidambari biodegrades soil resveratrol: A potential allelochemical in peanut monocropping systems. J. Sci. Food Agric. 99, 5899–5909. https://doi.org/10.1002/jsfa.9865 (2019).CAS 
Article 
PubMed 

Google Scholar 
Huang, L. et al. Plant-soil feedbacks and soil sickness: From mechanisms to application in agriculture. J. Chem. Ecol. 39, 232–242. https://doi.org/10.1007/s10886-013-0244-9 (2013).CAS 
Article 
PubMed 

Google Scholar 
Deng, J. J. et al. Autotoxicity of phthalate esters in tobacco root exudates: Effects on seed germination and seedling growth. Pedosphere 27, 1073–1082. https://doi.org/10.1016/s1002-0160(17)60374-6 (2017).CAS 
Article 

Google Scholar 
Chen, S. L., Zhou, B. L., Lin, S. S., Li, X. & Ye, X. L. Accumulation of cinnamic acid and vanillin in eggplant root exudates and the relationship with continuous cropping obstacle. Afr. J. Biotechnol. 10, 2659–2665. https://doi.org/10.5897/AJB10.1338 (2011).CAS 
Article 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486. https://doi.org/10.1016/j.tplants.2012.04.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
Wu, L. K. et al. Comparative metagenomic analysis of rhizosphere microbial community composition and functional potentials under Rehmannia glutinosa consecutive monoculture. Int. J. Mol. Sci. https://doi.org/10.3390/ijms19082394 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Galazka, A., Gawryjolek, K., Perzynski, A., Galazka, R. & Ksiezak, J. Changes in enzymatic activities and microbial communities in soil under long-term maize monoculture and crop rotation. Pol. J. Environ. Stud. 26, 39–46. https://doi.org/10.15244/pjoes/64745 (2017).CAS 
Article 

Google Scholar 
Wu, L. K. et al. Modification of rhizosphere bacterial community structure and functional potentials to control Pseudostellaria heterophylla replant disease. Plant Dis. 104, 25–34. https://doi.org/10.1094/pdis-04-19-0833-re (2020).CAS 
Article 
PubMed 

Google Scholar 
Becker, J., Rodibaugh, K., Hahn, D. & Nowlin, W. Bacterial community composition and carbon metabolism in a subtropical riverscape. Hydrobiologia 792, 209–226. https://doi.org/10.1007/s10750-016-3058-2 (2017).CAS 
Article 

Google Scholar 
Zheng, Q. et al. Soil multifunctionality is affected by the soil environment and by microbial community composition and diversity. Soil Biol. Biochem. 136, 107521. https://doi.org/10.1016/j.soilbio.2019.107521 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13. https://doi.org/10.1111/j.1574-6941.2009.00654.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Yang, D., Liu, Y., Wang, Y., Gao, F. & Li, X. Effects of soil tillage, management practices, and mulching film application on soil health and peanut yield in a continuous cropping system. Front. Microbiol. 11, 570924. https://doi.org/10.3389/fmicb.2020.570924 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, J. et al. Variations of rhizospheric soil microbial communities in response to continuous Andrographis paniculata cropping practices. Bot. Stud. https://doi.org/10.1186/s40529-020-00295-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Xiong, W. et al. Distinct roles for soil fungal and bacterial communities associated with the suppression of vanilla Fusarium wilt disease. Soil Biol. Biochem. 107, 198–207. https://doi.org/10.1016/j.soilbio.2017.01.010 (2017).CAS 
Article 

Google Scholar 
Wu, L. et al. Barcoded pyrosequencing reveals a shift in the bacterial community in the rhizosphere and rhizoplane of Rehmannia glutinosa under consecutive monoculture. Int. J. Mol. Sci. 19, 850. https://doi.org/10.3390/ijms19030850 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Zhao, Q. et al. Long-term coffee monoculture alters soil chemical properties and microbial communities. Sci. Rep. https://doi.org/10.1038/s41598-018-24537-2 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Dong, L. et al. High-throughput sequencing technology reveals that continuous cropping of American ginseng results in changes in the microbial community in arable soil. Chin. Med. https://doi.org/10.1186/s13020-017-0139-8 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Dong, L., Xu, J., Feng, G., Li, X. & Chen, S. Soil bacterial and fungal community dynamics in relation to Panax notoginseng death rate in a continuous cropping system. Sci. Rep. https://doi.org/10.1038/srep31802 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Gao, Z. et al. Effects of continuous cropping of sweet potato on the fungal community structure in rhizospheric soil. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.02269 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, L. et al. Effects of consecutive monoculture of Pseudostellaria heterophylla on soil fungal community as determined by pyrosequencing. Sci. Rep. 6, 26601. https://doi.org/10.1038/srep26601 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, Q. et al. Dynamics of soil properties and fungal community structure in continuous-cropped alfalfa fields in Northeast China. PeerJ 7, 7125. https://doi.org/10.7717/peerj.7127 (2019).Article 

Google Scholar 
Zhu, B., Wu, J., Ji, Q., Wu, W. & Qin, L. Diversity of rhizosphere and endophytic fungi in Atractylodes macrocephala during continuous cropping. PeerJ 8, e8905. https://doi.org/10.7717/peerj.8905 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Janssen, P. H. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl. Environ. Microb. 72, 1719–1728 (2006).ADS 
CAS 
Article 

Google Scholar 
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-sppressive bacteria. Science https://doi.org/10.1126/science.1203980 (2011).Article 
PubMed 

Google Scholar 
Zhou, H. et al. Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels. Sci. Total Environ. 651, 2281–2291. https://doi.org/10.1016/j.scitotenv.2018.09.336 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Chen, J., Gong, J. L. & Xu, M. G. Implications of continuous and rotational cropping practices on soil bacterial communities in pineapple cultivation. Eur. J. Soil Biol. 97, 103172. https://doi.org/10.1016/j.ejsobi.2020.103172 (2020).CAS 
Article 

Google Scholar 
Li, W., Liu, Q. & Chen, P. Effect of long-term continuous cropping of strawberry on soil bacterial community structure and diversity. J. Integr. Agr. 17, 206–218. https://doi.org/10.1016/S2095-3119(18)61944-6 (2018).Article 

Google Scholar 
Liu, X. et al. Microbial community diversities and taxa abundances in soils along a seven-year gradient of potato monoculture using high throughput pyrosequencing approach. PLoS ONE 9, e86610–e86610. https://doi.org/10.1371/journal.pone.0086610 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Xiong, W. et al. The effect of long-term continuous cropping of black pepper on soil bacterial communities as determined by 454 pyrosequencing. PLoS ONE 10, e0136946. https://doi.org/10.1371/journal.pone.0136946 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tan, Y. et al. Diversity and composition of rhizospheric soil and root endogenous bacteria in Panax notoginseng during continuous cropping practices. J. Basic Microb. 57, 337. https://doi.org/10.1002/jobm.201600464 (2017).CAS 
Article 

Google Scholar 
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364. https://doi.org/10.1890/05-1839 (2007).Article 
PubMed 

Google Scholar 
Yang, Y. et al. Effects of microbiological fertilizer on rhizosphere soil fungus communities under long-term continuous cropping of protected Hami melon. Chin. J. App. Environ. Biol. https://doi.org/10.19675/j.cnki.1006-687x.2017.03014 (2018).Article 

Google Scholar 
Schoch, C. L. et al. The Ascomycota tree of life: A phylum-wide phylogeny clarifies the origin and evolution of fundamental reproductive and ecological traits. Syst. Biol. 58, 224–239. https://doi.org/10.1093/sysbio/syp020 (2009).CAS 
Article 
PubMed 

Google Scholar 
Hayat, R., Ali, S., Amara, U., Khalid, R. & Ahmed, I. Soil beneficial bacteria and their role in plant growth promotion: A review. Ann. Microbiol. 60, 579–598. https://doi.org/10.1007/s13213-010-0117-1 (2010).Article 

Google Scholar 
Jann Lasse, G., Hurek, T., Wiebke, B. & Reinhold-Hurek, B. Bradyrhizobium vignae sp. nov., a nitrogen-fixing symbiont isolated from effective nodules of Vigna and Arachis. Int. J. Syst. Evol. Microbiol. 66, 62. https://doi.org/10.1099/ijsem.0.000674 (2015).CAS 
Article 

Google Scholar 
Ormeo-Orrillo, E. & Esperanza, M.-R. A genomotaxonomy view of the bradyrhizobium genus. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01334 (2019).Article 

Google Scholar 
Palaniappan, P., Chauhan, P. S., Saravanan, V. S., Anandham, R. & Sa, T. Isolation and characterization of plant growth promoting endophytic bacterial isolates from root nodule of Lespedeza sp. Biol. Fertil. Soils 46, 807–816. https://doi.org/10.1007/s00374-010-0485-5 (2010).Article 

Google Scholar 
Wang, H. et al. Impact of soybean nodulation phenotypes and nitrogen fertilizer levels on the rhizosphere bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, M. X. et al. Streptomyces lydicusM01 regulates soil microbial community and alleviates foliar disease caused by Alternaria alternataon cucumbers. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00942 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. S. et al. Biological fertilizer containing Bacillus subtilis BY-2 for control of Sclerotinia sclerotiorum on oilseed rape. Crop Prot. https://doi.org/10.1016/j.cropro.2020.105340 (2020).Article 

Google Scholar 
Kim, M. J. et al. Enhancement of seed dehiscence by seed treatment with talaromyces flavus GG01 and GG04 in ginseng (Panax ginseng). Plant Pathol. J. 33, 1–8. https://doi.org/10.5423/ppj.Oa.06.2016.0146 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, W. et al. Occurrence and characterization of fungi and mycotoxins in contaminated medicinal herbs. Toxins 12, 30. https://doi.org/10.3390/toxins12010030 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Naeem, M. et al. Characterization and pathogenicity of fusarium species associated with soybean pods in maize/soybean strip intercropping. Pathogens 8, 117. https://doi.org/10.3390/pathogens8040245 (2019).CAS 
Article 

Google Scholar 
Desjardins, A. Gibberella from A (Venaceae) to Z (eae). Ann. Rev. Phytopathol. 41, 177–198. https://doi.org/10.1146/annurev.phyto.41.011703.115501 (2003).CAS 
Article 

Google Scholar 
Mingna, C. et al. Soil eukaryotic microorganism succession as affected by continuous cropping of peanut: Pathogenic and beneficial fungi were selected. PLoS ONE 7, e40659. https://doi.org/10.1371/journal.pone.0040659 (2012).CAS 
Article 

Google Scholar 
Arafat, Y. et al. Long-term monoculture negatively regulates fungal community composition and abundance of tea orchards. Agronomy https://doi.org/10.3390/agronomy9080466 (2019).Article 

Google Scholar 
Zhou, X. G. & Wu, F. Z. Changes in soil chemical characters and enzyme activities during continuous monocropping of cucumber (Cucumis sativus). Pak. J. Bot. 47, 691–697 (2015).CAS 

Google Scholar 
Shao, S., Chen, M., Liu, W., Hu, X. & Li, Y. Long-term monoculture reduces the symbiotic rhizobial biodiversity of peanut. Syst. Appl. Microbiol. 43, 126101. https://doi.org/10.1016/j.syapm.2020.126101 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y., Zheng, Y. J., Xia, P. G., Xun, L. L. & Liang, Z. S. Impact of continuous Panax notoginseng plantation on soil microbial and biochemical properties. Sci. Rep. https://doi.org/10.1038/s41598-019-49625-9 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, L. C. et al. Comparison of soil enzyme activity and microbial community structure between rapeseed-rice and rice-rice plantings. Int. J. Agric. Biol. 20, 1801–1808. https://doi.org/10.17957/ijab/15.0692 (2018).CAS 
Article 

Google Scholar 
Hansen, J. C., Schillinger, W. F., Sullivan, T. S. & Paulitz, T. C. Soil microbial biomass and fungi reduced with canola introduced into long-term monoculture wheat rotations. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01488 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Z. B. et al. Fertilization regime has a greater effect on soil microbial community structure than crop rotation and growth stage in an agroecosystem. Appl. Soil. Ecol. https://doi.org/10.1016/j.apsoil.2020.103510 (2020).Article 

Google Scholar 
Zhao, H. L. et al. Effect of different straw return modes on soil bacterial community, enzyme activities and organic carbon fractions. Soil Sci. Soc. Am. J. 83, 638–648. https://doi.org/10.2136/sssaj2018.03.0101 (2019).ADS 
CAS 
Article 

Google Scholar 
Agomoh, I. V., Drury, C. F., Phillips, L. A., Reynolds, W. D. & Yang, X. Increasing crop diversity in wheat rotations increases yields but decreases soil health. Soil Sci. Soc. Am. J. https://doi.org/10.1002/saj2.20000 (2020).Article 

Google Scholar 
Liu, Z. X. et al. Long-term continuous cropping of soybean is comparable to crop rotation in mediating microbial abundance, diversity and community composition. Soil Tillage Res. https://doi.org/10.1016/j.still.2019.104503 (2020).Article 

Google Scholar 
Powlson, D. S., Prookes, P. C. & Christensen, B. T. Measurement of soil microbial biomass provides an early indication of changes in total soil organic matter due to straw incorporation. Soil Biol. Biochem. 19, 159–164. https://doi.org/10.1016/0038-0717(87)90076-9 (1987).CAS 
Article 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Magoc, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).CAS 
Article 
PubMed 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).CAS 
Article 
PubMed 

Google Scholar 
Bao, S. Soil and Agricultural Chemistry Analysis (Agriculture Press Publisher, 2013).
Google Scholar 
Guan, S. Y., Zhang, D. & Zhang, Z. Soil Enzyme and its Research Methods (Springer, 1986).
Google Scholar 
Sinha, A. K. Colorimetric assay of catalase. Anal. Biochem. 47, 389–394. https://doi.org/10.1016/0003-2697(72)90132-7 (1972).CAS 
Article 
PubMed 

Google Scholar 
Schinner, F. & Mersi, W. V. Xylanase-, CM-cellulase- and invertase activity in soil: An improved method. Soil Biol. Biochem. 22, 511–515. https://doi.org/10.1016/0038-0717(90)90187-5 (1990).CAS 
Article 

Google Scholar 
Tabatabai, M. A. & Bremner, J. M. Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil Biol. Biochem. 1, 301–307. https://doi.org/10.1016/0038-0717(69)90012-1 (1969).CAS 
Article 

Google Scholar  More

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

Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 359, 183–195 (2004).CAS 

Google Scholar 
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 

Google Scholar 
Abellán, P., Benetti, C. J., Angus, R. B. & Ribera, I. A review of Quaternary range shifts in European aquatic Coleoptera. Glob. Ecol. Biogeogr. 20, 87–100 (2011).
Google Scholar 
Geber, M. A. Ecological and evolutionary limits to species geographic ranges. Am. Naturalist 178, S1–S5 (2011).
Google Scholar 
Miller, T. E. X. et al. Eco-evolutionary dynamics of range expansion. Ecology 101, e03139 (2020).PubMed 

Google Scholar 
Clark, P. U. et al. The last glacial maximum. Science 325, 710 (2009).CAS 
PubMed 

Google Scholar 
Bidegaray-Batista, L. et al. Imprints of multiple glacial refugia in the Pyrenees revealed by phylogeography and palaeodistribution modelling of an endemic spider. Mol. Ecol. 25, 2046–2064 (2016).CAS 
PubMed 

Google Scholar 
Stone, G. N. et al. Tournament ABC analysis of the western Palaearctic population history of an oak gall wasp, Synergus umbraculus. Mol. Ecol. 26, 6685–6703 (2017).PubMed 

Google Scholar 
Walton, W., Stone, G. N. & Lohse, K. Discordant Pleistocene population size histories in a guild of hymenopteran parasitoids. Mol. Ecol. https://doi.org/10.1111/mec.16074 (2021).Grant, K. M. et al. Sea-level variability over five glacial cycles. Nat. Commun. 5, 5076 (2014).CAS 
PubMed 

Google Scholar 
Ye, Z., Zhu, G., Chen, P., Zhang, D. & Bu, W. Molecular data and ecological niche modelling reveal the Pleistocene history of a semi-aquatic bug (Microvelia douglasi douglasi) in East Asia. Mol. Ecol. 23, 3080–3096 (2014).CAS 
PubMed 

Google Scholar 
Wei, S. J. et al. Population genetic structure and approximate Bayesian computation analyses reveal the southern origin and northward dispersal of the oriental fruit moth Grapholita molesta (Lepidoptera: Tortricidae) in its native range. Mol. Ecol. 24, 4094–4111 (2015).PubMed 

Google Scholar 
Petit, R. et al. Glacial refugia: hotspots but not melting pots of genetic diversity. Science 300, 1563–1565 (2003).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 

Google Scholar 
Hewitt, G. M. Speciation, hybrid zones and phylogeography—or seeing genes in space and time. Mol. Ecol. 10, 537–549 (2001).CAS 
PubMed 

Google Scholar 
Bradburd, G. S. & Ralph, P. L. Spatial population genetics: it’s about time. Annu. Rev. Ecol., Evol. Syst. 50, 427–449 (2019).
Google Scholar 
de Lafontaine, G., Ducousso, A., Lefevre, S., Magnanou, E. & Petit, R. J. Stronger spatial genetic structure in recolonized areas than in refugia in the European beech. Mol. Ecol. 22, 4397–4412 (2013).PubMed 

Google Scholar 
Hoban, S., Dawson, A., Robinson, J. D., Smith, A. B. & Strand, A. E. Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil. Ecography 42, 1991–2011 (2019).
Google Scholar 
Stone, G. N. et al. The phylogeographical clade trade: tracing the impact of human‐mediated dispersal on the colonization of northern Europe by the oak gallwasp Andricus kollari. Mol. Ecol. 16, 2768–2781 (2007).PubMed 

Google Scholar 
McGaughran, A., Laver, R. & Fraser, C. Evolutionary responses to warming. Trends Ecol. Evol. 36, 591–600 (2021).PubMed 

Google Scholar 
van Boheemen, L. A. & Hodgins, K. A. Rapid repeatable phenotypic and genomic adaptation following multiple introductions. Mol. Ecol. 29, 4102–4117 (2020).PubMed 

Google Scholar 
Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).PubMed 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).PubMed 

Google Scholar 
Sun, Y., Bossdorf, O., Grados, R. D., Liao, Z. & Müller-Schärer, H. Rapid genomic and phenotypic change in response to climate warming in a widespread plant invader. Glob. Change Biol. 26, 6511–6522 (2020).
Google Scholar 
Høye, T. T. Arthropods and climate change-arctic challenges and opportunities. Curr. Opin. Insect Sci. 41, 40–45 (2020).PubMed 

Google Scholar 
Maino, J. L., Kong, J. D., Hoffmann, A. A., Barton, M. G. & Kearney, M. R. Mechanistic models for predicting insect responses to climate change. Curr. Opin. Insect Sci. 17, 81–86 (2016).PubMed 

Google Scholar 
Hoffmann, A. A., Weeks, A. R. & Sgrò, C. M. Opportunities and challenges in assessing climate change vulnerability through genomics. Cell 184, 1420–1425 (2021).CAS 
PubMed 

Google Scholar 
van der Geest, L. P. S. & Evenhuis, H. H. World Crop Pests 5: Tortricid Pests Their Biology, Natural Enemies and Control. Vol. 5 (Elsevier, 1991).Wan, F. H. et al. A chromosome-level genome assembly of Cydia pomonella provides insights into chemical ecology and insecticide resistance. Nat. Commun. 10, https://doi.org/10.1038/s41467-41019-12175-41469 (2019).Kirk, H., Dorn, S. & Mazzi, D. Worldwide population genetic structure of the oriental fruit moth (Grapholita molesta), a globally invasive pest. BMC Ecol. 13, 12 (2013).PubMed 
PubMed Central 

Google Scholar 
Torriani, M. V., Mazzi, D., Hein, S. & Dorn, S. Structured populations of the oriental fruit moth in an agricultural ecosystem. Mol. Ecol. 19, 2651–2660 (2010).CAS 
PubMed 

Google Scholar 
Song, W. et al. Multiple refugia from penultimate glaciations in East Asia demonstrated by phylogeography and ecological modelling of an insect pest. BMC Evolut. Biol. 18, 152 (2018).
Google Scholar 
SuomMainen, E. in Chromosome Today Vol. 2 (eds. Darlington, C. D. & Lewis, K. R.) 122–138 (Plenum Press, 1969).Nguyen, P. et al. Neo-sex chromosomes and adaptive potential in tortricid pests. Proc. Natl Acad. Sci. USA 110, 6931–6936 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fuková, I., Nguyen, P. & Marec, F. E. Codling moth cytogenetics: karyotype, chromosomal location of rDNA, and molecular differentiation of sex chromosomes. Genome 48, 1083–1092 (2005).PubMed 

Google Scholar 
Cao, L. J. et al. Local climate adaptation and gene flow in the native range of two co-occurring fruit moths with contrasting invasiveness. Mol. Ecol. 30, 4204–4219 (2021).CAS 
PubMed 

Google Scholar 
Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, 7 (2012).
Google Scholar 
Krabbenhoft, T. J. & Turner, T. F. clock gene evolution: seasonal timing, phylogenetic signal, or functional constraint? J. Heredity 105, 407–415 (2014).
Google Scholar 
Zhang, J. et al. Comparative transcriptomes analysis of the wing disc between two silkworm strains with different size of wings. PLoS ONE 12, e0179560 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhu, Q. S., Arakane, Y., Beeman, R. W., Kramer, K. J. & Muthukrishnan, S. Functional specialization among insect chitinase family genes revealed by RNA interference. Proc. Natl Acad. Sci. USA 105, 6650–6655 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, C., Yang, H., Tang, B., Yang, W.-J. & Jin, D.-C. Identification and functional analysis of chitinase 7 gene in white-backed planthopper, Sogatella furcifera. Comp. Biochem. Physiol. B-Biochem. Mol. Biol. 208, 19–28 (2017).PubMed 

Google Scholar 
Yang, X. et al. Characterization and functional analysis of chitinase family genes involved in nymph-adult transition of Sogatella furcifera. Insect Sci. 28, 901–916 (2021).CAS 
PubMed 

Google Scholar 
Pesch, Y. Y., Riedel, D., Patil, K. R., Loch, G. & Behr, M. Chitinases and Imaginal disc growth factors organize the extracellular matrix formation at barrier tissues in insects. Sci. Rep. 6, 18340 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Charron, Y. et al. The serpin Spn5 is essential for wing expansion in Drosophila melanogaster. Int. J. Dev. Biol. 52, 933–942 (2008).CAS 
PubMed 

Google Scholar 
Charlesworth, B., Campos, J. L. & Jackson, B. C. Faster-X evolution: theory and evidence from Drosophila. Mol. Ecol. 27, 3753–3771 (2018).CAS 
PubMed 

Google Scholar 
Meisel, R. P. & Connallon, T. The faster-X effect: integrating theory and data. Trends Genet. 29, 537–544 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sayres, M. A. W. Genetic diversity on the sex chromosomes. Genome Biol. Evol. 10, 1064–1078 (2018).
Google Scholar 
Ellegren, H. The different levels of genetic diversity in sex chromosomes and autosomes. Trends Genet. 25, 278–284 (2009).CAS 
PubMed 

Google Scholar 
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).CAS 
PubMed 

Google Scholar 
Pool, J. E. et al. Population genomics of sub-saharan Drosophila melanogaster: African diversity and non-african admixture. PLoS Genet. 8, e1003080–e1003080 (2012).PubMed 
PubMed Central 

Google Scholar 
Sackton, T. B. et al. Positive selection drives faster-Z evolution in silkmoths. Evolution 68, 2331–2342 (2014).PubMed 
PubMed Central 

Google Scholar 
Fraisse, C., Picard, M. A. L. & Vicoso, B. The deep conservation of the Lepidoptera Z chromosome suggests a non-canonical origin of the W. Nat. Commun. 8, https://doi.org/10.1038/s41467-017-01663-5 (2017).Sahara, K., Yoshido, A. & Traut, W. Sex chromosome evolution in moths and butterflies. Chromosome Res. 20, 83–94 (2012).CAS 
PubMed 

Google Scholar 
Ma, C. et al. Mitochondrial genomes reveal the global phylogeography and dispersal routes of the migratory locust. Mol. Ecol. 21, 4344–4358 (2012).PubMed 

Google Scholar 
Zhang, B., Edwards, O., Kang, L. & Fuller, S. Russian wheat aphids (Diuraphis noxia) in China: native range expansion or recent introduction? Mol. Ecol. 21, 2130–2144 (2012).CAS 
PubMed 

Google Scholar 
Provan, J. & Bennett, K. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571 (2008).PubMed 

Google Scholar 
Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migration in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 
PubMed 

Google Scholar 
Zhang, S. P., Xu, X. L., Wang, W. W., Yang, W. Y. & Liang, W. Clock gene is associated with individual variation in the activation of reproductive endocrine and behavior of Asian short toed lark. Sci. Rep. 7, 8 (2017).CAS 

Google Scholar 
Liedvogel, M., Szulkin, M., Knowles, S. C. L., Wood, M. J. & Sheldon, B. C. Phenotypic correlates of Clock gene variation in a wild blue tit population: evidence for a role in seasonal timing of reproduction. Mol. Ecol. 18, 2444–2456 (2009).PubMed 

Google Scholar 
Saino, N. et al. Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Sci. Rep. 7, 10 (2017).
Google Scholar 
e Silva, O. A. B. N., Bernardi, D., Botton, M. & Garcia, M. S. Biological characteristics of Grapholita molesta (Lepidoptera: Tortricidae) induced to diapause in laboratory. J. Insect Sci. 14, 217 (2014).
Google Scholar 
Renfree, M. B. & Shaw, G. Diapause. Annu. Rev. Physiol. 62, 353–375 (2000).CAS 
PubMed 

Google Scholar 
Ochocki, B. M. & Miller, T. E. X. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 8 (2017).
Google Scholar 
Ochocki, B. M., Saltz, J. B. & Miller, T. E. X. Demography-dispersal trait correlations modify the eco-evolutionary dynamics of range expansion. Am. Naturalist 195, 231–246 (2020).
Google Scholar 
Travis, J. M. J. & Dytham, C. Dispersal evolution during invasions. Evolut. Ecol. Res. 4, 1119–1129 (2002).
Google Scholar 
Phillips, B. L., Brown, G. P. & Shine, R. Life-history evolution in range-shifting populations. Ecology 91, 1617–1627 (2010).PubMed 

Google Scholar 
Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than through time. Proc. Natl Acad. Sci. USA 108, 5708–5711 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).PubMed 

Google Scholar 
Phillips, B. L. & Perkins, T. A. Spatial sorting as the spatial analogue of natural selection. Theor. Ecol. 12, 155–163 (2019).
Google Scholar 
Angert, A. L., Bontrager, M. G. & Ågren, J. What do we really know about adaptation at range edges? Annu. Rev. Ecol., Evol. Syst. 51, 341–361 (2020).
Google Scholar 
Hoffmann, A. A. & Rieseberg, L. H. Revisiting the impact of inversions in evolution: From population genetic markers to drivers of adaptive shifts and speciation? Annu. Rev. Ecol. Evol. Syst. 39, 21–42 (2008).PubMed 
PubMed Central 

Google Scholar 
Wellenreuther, M. & Bernatchez, L. Eco-evolutionary genomics of chromosomal inversions. Trends Ecol. Evol. 33, 427–440 (2018).PubMed 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vurture, G. W. et al. GenomeScope: Fast reference-free genome profiling from short reads. Bioinformatics (Oxford, England) 33, https://doi.org/10.1093/bioinformatics/btx153 (2017).Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptivek-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).PubMed 
PubMed Central 

Google Scholar 
Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinforma. 19, 460 (2018).CAS 

Google Scholar 
Neva, C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
Google Scholar 
Dudchenko et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).CAS 
PubMed 

Google Scholar 
Cheng, T. et al. Genomic adaptation to polyphagy and insecticides in a major East Asian noctuid pest. Nat. Ecol. Evol. 1, 1747–1756 (2017).PubMed 

Google Scholar 
Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinforma. 25, unit 4.10 (2009).
Google Scholar 
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5, 59 (2004).
Google Scholar 
Stanke, M. & Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19, ii215–ii225 (2003).PubMed 

Google Scholar 
Brian, J. H. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 31, 5654–5666 (2003).
Google Scholar 
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).CAS 
PubMed 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Knaus, B. J. & Grünwald, N. J. vcfr: a package to manipulate and visualize variant call format data in R. Mol. Ecol. Resour. 17, 44–53 (2017).CAS 
PubMed 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. (Austin) 6, 80–92 (2012).CAS 

Google Scholar 
Zhang, C., Dong, S. S., Xu, J. Y., He, W. M. & Yang, T. L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35, 1786–1788 (2019).CAS 
PubMed 

Google Scholar 
Gautier, M. & Vitalis, R. Inferring Population Histories Using Genome-Wide Allele Frequency Data. Mol. Biol. Evol. 30, 654–668 (2013).CAS 
PubMed 

Google Scholar 
Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
PubMed 

Google Scholar 
Keightley, P. D. et al. Estimation of the spontaneous mutation rate in Heliconius melpomene. Mol. Biol. Evol. 32, 239–243 (2015).CAS 
PubMed 

Google Scholar 
Ahn, J. J., Yang, C. Y. & Jung, C. Model of Grapholita molesta spring emergence in pear orchards based on statistical information criteria. J. Asia-Pac. Entomol. 15, 589–593 (2012).
Google Scholar 
Amat, C., Bosch-Serra, D., Avilla, J. & Escudero Colomar, L. A. Different Population Phenologies of Grapholita molesta (Busck) in Two Hosts and Two Nearby Regions in the NE of Spain. Insects 12, https://doi.org/10.3390/insects12070612 (2021).Li, H. & Ralph, P. Local PCA shows how the effect of population structure differs along the genome. Genetics 211, 289–304 (2019).CAS 
PubMed 

Google Scholar 
Todesco, M. et al. Massive haplotypes underlie ecotypic differentiation in sunflowers. Nature 584, 602–607 (2020).CAS 
PubMed 

Google Scholar 
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wei, S. J. et al. Population genomic signatures of the oriental fruit moth related to the Pleistocene climates. Dryad Digital Repository. https://doi.org/10.5061/dryad.6wwpzgmzm (2021). 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