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

Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth? (2017). https://doi.org/10.1146/annurev-ento-020117.Scudder, G. Insect Biodiversity: Science and Society—Google Books (Wiley-Blackwell, 2009).
Google Scholar 
Lami, F., Boscutti, F., Masin, R., Sigura, M. & Marini, L. Seed predation intensity and stability in agro-ecosystems: Role of predator diversity and soil disturbance. Agric. Ecosyst. Environ. 288, 106720 (2020).
Google Scholar 
Gallai, N., Salles, J. M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).
Google Scholar 
Consoli, F. L., Parra, J. R. P. & Zucchi, R. A. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma (Springer Science, 2010).
Google Scholar 
Sánchez-Guillén, R. A., Córdoba-Aguilar, A., Hansson, B., Ott, J. & Wellenreuther, M. Evolutionary consequences of climate-induced range shifts in insects. Biol. Rev. 91, 1050–1064 (2016).PubMed 

Google Scholar 
Zalucki, M. P. et al. Estimating the economic cost of one of the world’s major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): Just how long is a piece of string?. J. Econ. Entomol. 105, 1115–1129 (2012).PubMed 

Google Scholar 
Dornelas, M. & Daskalova, G. N. Nuanced changes in insect abundance. Science (80-). 368, 368–369 (2020).CAS 
ADS 

Google Scholar 
Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
Google Scholar 
Greenwood, B. M., Bojang, K. & Whitty, C. J. M. Malaria. Lancet 365, 98 (2005).
Google Scholar 
Dangles, O. & Casas, J. Ecosystem services provided by insects for achieving sustainable development goals. Ecosyst. Serv. 35, 109–115 (2019).
Google Scholar 
Burkholder, W. E. & Ma, M. Pheromones for monitoring and control of stored-product insects. Annu. Rev. Entomol. 30, 257–272 (1985).CAS 

Google Scholar 
Morris, R. F. Sampling insect populations. Annu. Rev. Entomol. 5, 243–264 (1960).
Google Scholar 
Strickland, A. H. Sampling crop pests and their hosts. Annu. Rev. Entomol. 6, 201–220 (1961).
Google Scholar 
Bannerman, J. A., Costamagna, A. C., McCornack, B. P. & Ragsdale, D. W. Comparison of relative bias, precision, and efficiency of sampling methods for natural enemies of soybean aphid (Hemiptera: Aphididae). J. Econ. Entomol. 108, 1381–1397 (2015).CAS 
PubMed 

Google Scholar 
Osborne, J. L. et al. Harmonic radar: A new technique for investigating bumblebee and honey bee foraging flight. VII Int. Symp. Pollinat. 437, 159–164 (1996).
Google Scholar 
Zink, A. G. & Rosenheim, J. A. State-dependent sampling bias in insects: Implications for monitoring western tarnished plant bugs. Entomol. Exp. Appl. 113, 117–123 (2004).
Google Scholar 
Rancourt, B., Vincent, C. & De Oliveira, A. D. Circadian activity of Lygus lineolaris (Hemiptera: Miridae) and effectiveness of sampling techniques in strawberry fields. J. Econ. Entomol 93, 1160–1166 (2000).CAS 
PubMed 

Google Scholar 
Binns, M. R. & Nyrop, J. P. Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37, 427–453. https://doi.org/10.1146/annurev.ento.37.1.427 (1992).Article 

Google Scholar 
Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).
Google Scholar 
Montgomery, G. A., Belitz, M. W., Guralnick, R. P. & Tingley, M. W. Standards and best practices for monitoring and benchmarking insects. Front. Ecol. Evolut. 8, 579193 (2021).
Google Scholar 
Bick, E., Dryden, D. M., Nguyen, H. D. & Kim, H. A novel CO2-based insect sampling device and associated field method evaluated in a strawberry agroecosystem. J. Econ. Entomol. 113, 1037–1042 (2020).CAS 
PubMed 

Google Scholar 
Wen, C. & Guyer, D. Image-based orchard insect automated identification and classification method. Comput. Electron. Agric. 89, 110–115 (2012).
Google Scholar 
Chen, Y., Why, A., Batista, G., Mafra-Neto, A. & Keogh, E. Flying insect classification with inexpensive sensors. J. Insect Behav. 27, 657–677 (2014).
Google Scholar 
Potamitis, I. & Rigakis, I. Novel noise-robust optoacoustic sensors to identify insects through wingbeats. IEEE Sens. J. 15, 4621–4631 (2015).CAS 
ADS 

Google Scholar 
Eliopoulos, P. A., Potamitis, I., Kontodimas, D. C. & Givropoulou, E. G. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J. Econ. Entomol. 108, 2808–2814 (2015).CAS 
PubMed 

Google Scholar 
Ärje, J. et al. Automatic image-based identification and biomass estimation of invertebrates. Methods Ecol. Evol. 11, 922–931 (2020).
Google Scholar 
Hobbs, S. E. & Hodges, G. An optical method for automatic classification and recording of a suction trap catch. Bull. Entomol. Res. 83, 47–51 (1993).
Google Scholar 
O’Neill, M. A., Gauld, I. D., Gaston, K. J. & Weeks, P. Daisy: An automated invertebrate identification system using holistic vision techniques. in Proceedings of the Inaugural Meeting BioNET-INTERNATIONAL Group for Computer-Aided Taxonomy (BIGCAT) 13–22 (1997).Chesmore, E. D. Methodologies for automating the identification of species. in First BioNet-International Work. Gr. Autom. Taxon. 3–12 (2000).Martineau, M. et al. A survey on image-based insect classification. Pattern Recognit. 65, 273–284 (2017).ADS 

Google Scholar 
Silva, D. F., De Souza, V. M. A., Batista Geapa, K. E. & Ellis, D. P. W. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. in Proceedings—2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. (2013).Capinera, J. L. & Walmsley, M. R. Visual responses of some sugarbeet insects to sticky traps and water pan traps of various colors. J. Econ. Entomol., 71(6), 926–927 (1978).
Google Scholar 
Moore, A., Miller, J. R., Tabashnik, B. E. & Gage, S. H. Automated identification of flying insects by analysis of wingbeat frequencies. J. Econ. Entomol. 79, 1703–1706 (1986).
Google Scholar 
Riley, J. R. Angular and temporal variations in the radar cross-sections of insects. Proc. Inst. Electr. Eng. (IET) 120, 1229–1232 (1973).
Google Scholar 
Reed, S. C., Williams, C. M. & Chadwick, L. E. Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics 27, 349 (1942).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: a century of insect acoustic detection and monitoring. Am. Entomol. 57(1), 30–44 (2011).
Google Scholar 
Drake, V. A. & Reynolds, D. R. Radar Entomology: Observing Insect Flight and Migration (Cabi, 2012).
Google Scholar 
Long, T. et al. Entomological radar overview: System and signal processing. IEEE Aerosp. Electron. Syst. Mag. 35, 20–32 (2020).
Google Scholar 
Drake, V. A., Hatty, S., Symons, C. & Wang, H. Insect monitoring radar: Maximizing performance and utility. Remote Sens. 12, 596 (2020).ADS 

Google Scholar 
Brydegaard, M. & Jansson, S. Advances in entomological laser radar. IET Int. Radar Conf. https://doi.org/10.1049/joe.2019.0598 (2018).Article 

Google Scholar 
Jansson, S. Entomological Lidar: Target Characterization and Field Applications (Department of Physics, Lund University, 2020).
Google Scholar 
Malmqvist, E. From Fauna to Flames: Remote Sensing with Scheimpflug-Lidar (Department of Physics, Lund University, 2019).
Google Scholar 
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: A century of insect acoustic detection and monitoring. Am. Entomol. 57, 30–44 (2011).
Google Scholar 
Miller-Struttmann, N. E., Heise, D., Schul, J., Geib, J. C. & Galen, C. Flight of the bumble bee: Buzzes predict pollination services. PLoS ONE 12, 1–14 (2017).
Google Scholar 
Li, Y. et al. Mosquito detection with low-cost smartphones: Data acquisition for malaria research. arXiv:1711.06346 [stat.ML] (2017).Mukundarajan, H., Hol, F. J. H., Castillo, E. A., Newby, C. & Prakash, M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife 6, 1–26 (2017).
Google Scholar 
Osborne, J. L. et al. A landscape-scale study of bumble bee foraging range and constancy, using harmonic radar. J. Appl. Ecol. 36, 519–533 (1999).
Google Scholar 
Smith, A. D., Riley, J. R. & Gregory, R. D. A method for routine monitoring of the aerial migration of insects by using a vertical-looking radar. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 340, 393–404 (1993).
Google Scholar 
Chapman, J. W., Smith, A. D., Woiwod, I. P., Reynolds, D. R. & Riley, J. R. Development of vertical-looking radar technology for monitoring insect migration. Comput. Electron. Agric. 35(2–3), 95–110 (2002).
Google Scholar 
Schaefer, G. W. & Bent, G. A. An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT). Bull. Entomol. Res. 74, 261–278 (1984).
Google Scholar 
Farmery, M. J. Optical studies of insect flight at low altitude. (Doctoral dissertation, University of York, 1981).Farmery, M. J. The effect of air temperature on wingbeat frequency of naturally flying armyworm moth (Spodoptera exempta). Entomol. Exp. Appl. 32, 193–194 (1982).
Google Scholar 
Malmqvist, E. & Brydegaard, M. Applications of KHZ-CW lidar in ecological entomology. EPJ Web Conf. 119, 25016. https://doi.org/10.1051/epjconf/2016I11925016 (2016).Article 

Google Scholar 
Brydegaard, M. et al. Lidar reveals activity anomaly of malaria vectors during pan-African eclipse. Sci. Adv. 6, eaay5487 (2020).PubMed 
PubMed Central 
ADS 

Google Scholar 
Malmqvist, E. et al. The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution Scheimpflug Lidar. Roy. Soc. Open Sci. 5(4), 172303 (2018).ADS 

Google Scholar 
Fristrup, K. M., Shaw, J. A. & Tauc, M. J. Development of a wing-beat-modulation scanning lidar system for insect studies. Lidar Remote Sens. Environ. Monit. 2017, 15. https://doi.org/10.1117/12.2274656 (2017).Article 

Google Scholar 
Hoffman, D. S., Nehrir, A. R., Repasky, K. S., Shaw, J. A. & Carlsten, J. L. Range-resolved optical detection of honeybees by use of wing-beat modulation of scattered light for locating land mines. Appl. Opt. 46, 3007–3012 (2007).PubMed 
ADS 

Google Scholar 
Jansson, S., Malmqvist, E. & Mlacha, Y. Real-time dispersal of malaria vectors in rural Africa monitored with lidar. Plos one. 16(3), e0247803 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jansson, S. & Brydegaard, M. Passive kHz lidar for the quantification of insect activity and dispersal. Anim. Biotelemet. 6, 6 (2018).
Google Scholar 
Jansson, S. P. & Sørensen, M. B. An optical remote sensing system for detection of aerial and aquatic fauna. U.S. Patent Application No. 16/346,322 (2019).Malmqvist, E., Jansson, S., Török, S. & Brydegaard, M. Effective parameterization of laser radar observations of atmospheric fauna. IEEE J. Sel. Top. Quant. Electron. 22, 1 (2015).
Google Scholar 
Drake, V. A., Wang, H. K. & Harman, I. T. Insect Monitoring Radar: Remote and network operation. Comput. Electron. Agric. 35, 77–94 (2002).
Google Scholar 
Kirkeby, C. et al. Advances in automatic identification of flying insects using optical sensors and machine learning. Sci. Rep. 11, 1555 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Jacques, S. L. Erratum: Optical properties of biological tissues: A review (Physics in Medicine and Biology (2013) 58). Phys. Med. Biol. 58, 5007–5008 (2013).
Google Scholar 
Li, M. et al. Bark beetles as lidar targets and prospects of photonic surveillance. J. Biophoton. https://doi.org/10.1002/jbio.202000420 (2020).Article 

Google Scholar 
Brydegaard, M. Advantages of shortwave infrared LIDAR entomology. in Laser Applications to Chemical, Security and Environmental Analysis LW2D-6 (Optical Society of America, 2014).
Google Scholar 
Brydegaard, M., Jansson, S., Schulz, M. & Runemark, A. Can the narrow red bands of dragonflies be used to perceive wing interference patterns? Ecol. Evol. 8(11), 5369–5384 (2018).PubMed 
PubMed Central 

Google Scholar 
Gebru, A. et al. Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight. J. Biophotonics 11(8), e201800014 (2018).PubMed 

Google Scholar 
Potamitis, I. Classifying insects on the fly. Ecol. Inform. 21, 40–49 (2014).
Google Scholar 
Heathcote, G. D. The comparison of yellow cylindrical, flat and water traps, and of Johnson suction traps, for sampling aphids. Ann. Appl. Biol. 45, 133–139 (1957).
Google Scholar 
Vaishampayan, S. M., Kogan, M., Waldbauer, G. P. & Woolley, J. Spectral specific responses in the visual behavior of the greenhouse whitefly, Trialeurodes vaporariorum (Homoptera: Aleyrodidae). Entomol. Exp. Appl. 18, 344–356 (1975).
Google Scholar 
Mound, L. A. Studies on the olfaction and colour sensitivity of Bemisia tabaci (Genn.) (Homoptera, Aleyrodidae). Entomol. Exp. Appl. 5, 99–104 (1962).
Google Scholar 
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Der Kooi, C. J., Stavenga, D. G., Arikawa, K., Belušič, G. & Kelber, A. Evolution of insect color vision: From spectral sensitivity to visual ecology. Annu. Rev. Entomol. 66, 435–461 (2021).PubMed 

Google Scholar  More

Site descriptionThis experiment was conducted in two long-term P-trial grassland sites (Site A and Site B) situated in proximity (~ 350 m) to each other in the dairy farm at Johnstown Castle, Wexford, Co. Wexford, Ireland (6°49′ W, 52°29′ N). The sites were grazed permanent grasslands before establishment. When the experiment was established in 1995, 16 (10 m × 2 m) plots were formed in each site in a fully randomised block design with four replicates. The two sites established were selected to represent different soil types and drainage classes. Site A is a moderately drained brown earth and site B is an imperfectly drained gley soil31. Each year in February, each plot received one of the four phosphorous (P) fertilization rates (16% P superphosphate): 0 (P0), 15 (P15), 30 (P30), and 45 (P45) kg P ha−1 year−1. All plots were initially sown with Lolium perenne and reseeded in 2016 with the same species. However, plant species such as Poa trivialis, Agropyron repens, Trifolium repens were present to a lesser extent. Above-ground biomass is harvested each month between February and August followed by 40 kg N ha−1 fertilizer applications. In the year (2019) of this experiment and the years before, SulCAN as a solid was applied at the first or second week of each month during February-August and potassium (K) as muriate of potash (KCl) was applied in February at a rate of 125 kg K ha−1. SulCAN contains 26.7% N in the form of nitric and ammoniacal nitrogen and 5% water soluble Sulphur. For this study plots receiving P0, P15 and P45 at the two field sites were set up to carry out this experiment. The two sites were selected as they had slightly different soil properties and thus there was an opportunity to consider a soil × treatment effect in the experiment.Experimental designFertilizer N and substrate C were applied on 8 May and 12 June in the experiment undertaken between May and July 2019, which represents the main growing season in Ireland. Within each plot, an area of 1 m × 1 m was selected. Following N fertilizer application (40 kg N ha−1) to all plots, carbon substrate [mixture of glucose (40%), sodium acetate (30%) and methanol (30%)] was applied once within the selected area using a sprayer watering can. Labile C available in animal excreta usually contains carbohydrates, volatile fatty acids, and alcohols32; as such different carbon substrates were applied to mimic this. Our review of the literature also indicated that C source types could differentially affect denitrifying communities and consequently denitrification rate. Thus, a mixture of three C sources was used to decrease bias of one microbial group over another as a result of single substrate use. Carbon was supplied to alleviate C-limitations of denitrification and nitrification processes as observed by O’Neill et al.29 in soils from this trial and to ensure equal substrate availability across all soil P levels. Equivalent C input rate of 0.63 g C m−2 day−1 was added to represent a daily rate of plant carbon input from Lolium perenne dominated ecosystem33. Soil samples were collected on eight occasions throughout the experimental period. Soil was sampled from across each selected area to a depth of 10 cm, sieved through 4 mm sieve and analysed for soil mineral N and microbial biomass.Soil properties, plant biomass and climate parametersPhysico-chemical soil properties were characterized by taking samples from 10 cm depth from each plot in the two sites before the commencement of the experiment. Soil pH was measured in water (2:1, water volume:soil mass) using Sally pH Auto analyser Dilution System (Gilson 215, Gilson, Dunstable, England). Soil organic matter (SOM) content was determined from mass loss on ignition at 550 °C for 7 h. Total C and total N concentrations were measured using a TrueSpec C/N analyser (TruSpec, LECO Corporation, Michigan, USA). Plant available P, potassium (K), and magnesium (Mg) were estimated using Morgan’s extraction34 and analysed using a Lachat QuickChem 8500 Series 2 Flow injection Analyzer (Lachat, QuickChem, 5600 Loveland, Colorado, USA). Particle size analysis was performed using the Pipette method35, where 2 mm sieved dry soil (20 g) was pre-treated with 6% H2O2, 3% NH4OH, and 5% sodium hexametaphosphate before separating soil aliquots into particle sizes. Water Holding Capacity (WHC) was determined from the mass difference between water-saturated and then overnight dried (105 °C) soil. Bulk density was determined by dividing weight of oven-dried soil by the total soil volume.To determine the mineral N concentrations, ten gram fresh soil was extracted with 50 mL 2 M KCl (5:1 solution to soil ratio). The supernatant was filtered through Whatman No. 1 filter paper and filtrates were stored in a cold room at 4 °C for about a week until analysis. Ammonium (NH4+) and nitrate (NO3−) concentrations in the extracts were analysed by the Aquakem 600 discrete analyser.Above-ground plant biomass from each plot of both sites was harvested twice during the experiment period (June 10 and July 11, 2019) to a height of ~ 5 cm using a Haldrup plot harvester. The total harvested biomass weight from each plot was recorded and a 100 g sub-sample was taken for dry matter (DM) analysis. Each fresh herbage sub-sample was weighed and placed in an oven at 70 °C for 3 days, and dry weight of the biomass was determined after re-weighing.Rainfall records for the experiment period were obtained from a Met Éireann weather observing station located in Teagasc dairy farm in Johnstown Castle, Co. Wexford., situated within a 100 m distance from the experimental sites. Volumetric soil moisture content and temperature was measured to 5 cm depth on individual plots on each gas sampling occasion using a handheld theta probe (WET-2 WET Sensor, Delta-T Devices, Cambridge, England). Water-filled pore space (WFPS) were calculated from the soil moisture values, bulk density of the soils, and soil particle density (2.65 g cm−3).Microbial biomass, glomalin-related soil protein and potential denitrification activitySoils were analysed for microbial biomass nitrogen (MBN), phosphorus (MBP) and carbon (MBC) using the fumigation extraction method as described respectively in (Brooks et al.36,37, and Vance et al.38). Five gram fumigated (24 h) and non-fumigated soil samples were extracted with 100 mL 0.5 M NaHCO3 and analysed for P colorimetrically using an Aquakem 600 discrete analyser (Thermo Electron OY, Vantaa, Finland). In order to avoid the spike readings by the instrument due to the effervescent nature of NaHCO3, one millilitre of 10% HCl was added to 10 mL extracts and diluted to 50 mL using distilled water. Microbial P was calculated by subtracting the P concentration of non-fumigated samples from fumigated samples, and dividing the result by an extraction factor of 0.437.Microbial biomass C and N were determined similarly using chloroform fumigation method with extraction period of 48 h with 0.5 M K2SO438. The extracts of the fumigated and non-fumigated samples were analysed for total C and N using a TOC-L CPH/CPN analyser (Shimadzu, Tokyo, Japan), and the differences, divided by correction factors of 0.45 and 0.54, were used to estimate the microbial biomass C and N, respectively.Glomalin is a glycoprotein produced by AMF and can be used as an indicator of mycorrhizal colonization in the plant root-soil interface39. Total glomalin-related soil protein (GRPS) was extracted by 90 min of autoclaving (121 °C) of 1 g air-dried soil in 8 mL of 50 mM sodium citrate adjusted to pH 8.0 with HCl40. Three additional sequential extractions were performed with the sodium citrate solution by autoclaving for 60 min until no red-brown color was visible in the last supernatant. After autoclaving, the samples were centrifuged at 10,000 revolutions per minute (rpm) for 5 min. The amounts of glomalin in the extracts were quantified using the Bradford dye-binding assay with bovine serum albumin (BSA) as the standard (2 mg mL−1). In a 96-well plate, replicated 200 µL of standard or extracts and 50 µL of dye reagent were added in each well and mixed using a microplate mixer. The Bradford-reactive substance was determined by measuring absorbance at 600 nm using Microplate Reader (Modulus Microplate Multimode Reader, Turner BioSystems, Sunnyvale, California, USA). Sample concentrations were determined using the standard curve. Potential denitrification activity (PDA) was determined using the acetylene inhibition method, modified from Pell et al.41. Briefly, replicated 20 g fresh soils were added into two identical flasks from a sample of soil. The flasks were then sealed with a rubber stopper and flushed and filled with helium after evacuating the headspace air. In one of the replicas, 10% of the headspaces were removed and replaced by acetylene. All flasks were incubated at 15 °C on an orbital shaker at 175 rpm for 30 min followed by the addition of a nutrient solution containing 75 mmol L−1 KNO3, 37.5 mmol L−1 Na-succinate, 25 mmol L−1 glucose, and 75 mM Na-acetate. Gas samples were taken from the headspace every 1 h for 5 h. N2O concentrations were determined using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland), and PDA was calculated from the rate of change of N2O concentrations over time from acetylene amended flasks.N2O and CO2 flux measurementsGas samples (N2O and CO2 fluxes) were measured before and after the application of N fertilizer and C substrates, with a daily sampling for 10 days directly after C + N additions and 3–4 times a week in the third and fourth week and 2–3 times a week in the subsequent weeks. A rectangular (40 × 40 cm) static collar, made of stainless steel (opaque), was anchored 5 cm deep into the soil within the marked area of 1 m × 1 m in each of the selected plots. During gas sampling, a 10 cm tall chamber lid fitted with two septa on top was placed on the collar lined with neoprene rubber band. To ensure hermetic sealing of the headspace during sampling, the ring area of the collar was half-filled with water, and a 10 kg weight was placed on the top of the lid to compress the seal. Gas samples were collected between 09:30 and 11:30 local time using a 10 mL Luer lock syringe fitted with a hypodermic needle via one of the septa at 0, 20, and 40 min after chamber closure. Prior to transferring the final sample into a pre-evacuated 7 mL glass vial, air in the chamber headspace was mixed by flushing the syringe three times. Gas samples were analysed using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland) fitted with an electron capture detector to analyse for N2O concentrations and a thermal conductivity detector to analyse for CO2 concentrations. Daily Fluxes (F) were calculated for each plot using the following equation:$$ F = left( {frac{Delta C}{{Delta t}}} right) times left( {frac{M times P}{{T times R}}} right) times left( frac{V}{A} right) $$where ∆C is the change in gas concentration in the chamber headspace during chamber enclosure period in ppbv, ∆t is chamber closing period in minutes, so ∆C/∆t is the slope of the gas concentration with time. M is the molar mass of N2O-N (28 g mol−1) and CO2-C (12 g mol−1), P and T are the atmospheric pressure (Pa) and temperature (K). Atmospheric pressure values were obtained from the nearby weather station whereas for T, wet sensor values were used. V is the headspace volume of the closed chamber (m3) and A is surface are of the chamber (m3). R is the ideal gas constant (8.314 J K−1 mol−1). Daily flux for each treatment is reported as the average of the replicates.Cumulative N2O and CO2 emissions were calculated over each application period by multiplying the daily N2O and CO2 fluxes by the number of days between two consecutive measurements. A summation of the cumulative flux of each application period is reported as the total cumulative flux.Statistical analysisANOVAs with repeated measures were used to test for the C + N addition effect on N2O and CO2 emissions, MBC, MBN, MBP, NO3−, and NH4+ with P treatment, site, and day of measurement as fixed effects, and individual plots as a random effect. Two-way ANOVA was applied to test for main and interaction effects of P treatment and site on cumulative N2O and CO2 emissions, soil property parameters (Table 1), plant biomass, and GRSP. Prior to analysis, response variables were checked for normality (sphericity for repeated ANOVA) and homogeneity of variance, and log transformed when required. Tukey’s HSD post-hoc tests were conducted to identify pair-wise comparisons of significant effects at P  More

.readcube-buybox { display: none !important;}

The fourteenth-century pandemic known as the Black Death might not have been as devastating as was previously thought, an analysis of ancient pollen suggests1.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribeSubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-022-00435-6

ReferencesIzdebski, A. et al. Nature Ecol. Evol. https://doi.org/10.1038/s41559-021-01652-4 (2022).
Google Scholar 
Download references

Subjects

Ecology

Latest on:

Ecology

Brazil opens highly protected caves to mining, risking fauna
Correspondence 15 FEB 22

Richard Leakey (1944–2022)
Obituary 28 JAN 22

Emphasizing declining populations in the Living Planet Report
Matters Arising 26 JAN 22

Jobs

Director of Microscopy Operations

St. Jude Children’s Research Hospital (St. Jude)
Memphis, TN, United States

POSTDOCTORAL POSITIONS IN NATURAL LANGUAGE PROCESSING

KU Leuven
Leuven, Belgium

MRC Postdoctoral Research Scientist

Medical Research Council
London, United Kingdom

Postdoctoral fellow in X-ray multibeam ptychographic imaging at PETRA III/MAX IV

German Electron Synchrotron (DESY)
Hamburg, Germany More