Ecology

1.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
3.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).ADS 
Article 

Google Scholar 
4.Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 
Article 

Google Scholar 
5.Dee, L. E. E. et al. Temperature variability alters the stability and thresholds for collapse of interacting species. Philos. Trans. R. Soc. Biol. Sci. 375, 20190457 (2020).Article 

Google Scholar 
6.Leung, J. Y. S., Russell, B. D. & Connell, S. D. Adaptive responses of marine gastropods to heatwaves. One Earth 1, 374–381 (2019).Article 

Google Scholar 
7.Whiteley, N. M. & Mackenzie, C. L. Physiological responses of marine invertebrates to thermal stress. in Stressors in the Marine Environment (eds. Solan, M. & Whiteley, N. M.) 56–72 (Oxford University Press, 2016). https://doi.org/10.1093/acprof:oso/9780198718826.003.0004.8.Lonhart, S. I., Jeppesen, R., Beas-luna, R., Crooks, J. A. & Lorda, J. Shifts in the distribution and abundance of coastal marine species along the eastern Pacific Ocean during marine heatwaves from 2013 to 2018. Mar. Biodivers. Rec. 8, 1–15 (2019).
Google Scholar 
9.Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B Biol. Sci. 280, 20122829 (2013).Article 

Google Scholar 
10.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).ADS 
Article 

Google Scholar 
11.Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 2015–2018 (2018).Article 
CAS 

Google Scholar 
12.Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 1–10. https://doi.org/10.1038/s41598-020-63650-z (2020).CAS 
Article 

Google Scholar 
13.Caputi, N. et al. Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol. Evolut. https://doi.org/10.1002/ece3.2137 (2016).Article 

Google Scholar 
14.Verdelhos, T., Marques, J. C. & Anastácio, P. Behavioral and mortality responses of the bivalves Scrobicularia plana and Cerastoderma edule to temperature, as indicator of climate change’s potential impacts. Ecol. Indic. 58, 95–103 (2015).Article 

Google Scholar 
15.Shanks, A. L. et al. Marine heat waves, climate change, and failed spawning by coastal invertebrates. Limnol. Oceanogr. 65, 627–636 (2020).ADS 
Article 

Google Scholar 
16.Morgan, E. A., Brown, A., Ciotti, B. J. & Panton, A. Effects of temperature stress on ecological processes. in Stressors in the Marine Environment (eds. Solan, M. & Whiteley, N. M.) 213–227 (Oxford University Press, 2016). https://doi.org/10.1093/acprof:oso/9780198718826.003.0012.17.Beukema, J. J. & Dekker, R. Winters not too cold, summers not too warm: long-term effects of climate change on the dynamics of a dominant species in the Wadden Sea: the cockle Cerastoderma edule L. Mar. Biol. 167, 1–8 (2020).Article 

Google Scholar 
18.Sousa, R. et al. Die-offs of the endangered pearl mussel Margaritifera margaritifera during an extreme drought. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1244–1248 (2018).Article 

Google Scholar 
19.Smale, D. A., Yunnie, A. L. E., Vance, T. & Widdicombe, S. Disentangling the impacts of heat wave magnitude, duration and timing on the structure and diversity of sessile marine assemblages. PeerJ 2015, 1–23 (2015).
Google Scholar 
20.McLusky, D. S. & Elliott, M. The Estuarine Ecosystem: Ecology (Threats and Management. Oxford Press, 2004).Book 

Google Scholar 
21.Johnson, R. G. Temperature variation in the infaunal environment of a sand flat. Limnol. Oceanogr. 10, 114–120 (1965).ADS 
Article 

Google Scholar 
22.Amorim, V. E. et al. Immunological and oxidative stress responses of the bivalve Scrobicularia plana to distinct patterns of heatwaves. Fish Shellfish Immunol. 106, 1067–1077 (2020).CAS 
PubMed 
Article 

Google Scholar 
23.Grilo, T. F. F., Cardoso, P. G. G., Dolbeth, M., Bordalo, M. D. D. & Pardal, M. Â. A. Effects of extreme climate events on the macrobenthic communities’ structure and functioning of a temperate estuary. Mar. Pollut. Bull. 62, 303–311 (2011).CAS 
PubMed 
Article 

Google Scholar 
24.Dolbeth, M. et al. Long-term changes in the production by estuarine macrobenthos affected by multiple stressors. Estuar. Coast. Shelf Sci. 92, 10–18 (2011).ADS 
Article 

Google Scholar 
25.Ouellette, D. et al. Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens. Mar. Ecol. Prog. Ser. 266, 185–193 (2004).ADS 
Article 

Google Scholar 
26.Nagelkerken, I. & Munday, P. L. Animal behaviour shapes the ecological effects of ocean acidification and warming: Moving from individual to community-level responses. Glob. Chang. Biol. 22, 974–989 (2016).ADS 
PubMed 
Article 

Google Scholar 
27.Solan, M., Bennett, E. M., Mumby, P. J., Leyland, J. & Godbold, J. A. Benthic-based contributions to climate change mitigation and adaptation. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190107 (2020).Article 

Google Scholar 
28.Kristensen, E. & Kostka, J. E. Macrofaunal burrows and irrigation in marine sediment: Microbiological and biogeochemical interactions. in Interactions Between Macro‐ and Microorganisms in Marine Sediments (eds. Kristensen, E., Haese, R. R. & Kostka, J. E.), 125–157 (American Geophysical Union, 2013). https://doi.org/10.1029/CE060p0125.29.Cozzoli, F. et al. Biological and physical drivers of bio-mediated sediment resuspension: A flume study on Cerastoderma edule. Estuar. Coast. Shelf Sci. 241, 106824 (2020).Article 

Google Scholar 
30.Soissons, L. M. et al. Sandification vs. muddification of tidal flats by benthic organisms: A flume study. Estuar. Coast. Shelf Sci. 228, 106355 (2019).Article 

Google Scholar 
31.Fernandes, S., Sobral, P. & Costa, M. H. Nereis diversicolor effect on the stability of cohesive intertidal sediments. Aquat. Ecol. 40, 567–579 (2006).CAS 
Article 

Google Scholar 
32.Paramor, O. A. L. & Hughes, R. G. The effects of bioturbation and herbivory by the polychaete Nereis diversicolor on loss of saltmarsh in south-east England. J. Appl. Ecol. 41, 449–463 (2004).Article 

Google Scholar 
33.Dolbeth, M., Crespo, D., Leston, S. & Solan, M. Realistic scenarios of environmental disturbance lead to functionally important changes in benthic species-environment interactions. Mar. Environ. Res. 150, 104770 (2019).CAS 
PubMed 
Article 

Google Scholar 
34.Godbold, J. A. & Solan, M. Long-term effects of warming and ocean acidification are modified by seasonal variation in species responses and environmental conditions. Philos. Trans. R. Soc. B Biol. Sci. 368, 20130186 (2013).Article 
CAS 

Google Scholar 
35.Godbold, J. A., Hale, R., Wood, C. L. & Solan, M. Vulnerability of macronutrients to the concurrent effects of enhanced temperature and atmospheric pCO2 in representative shelf sea sediment habitats. Biogeochemistry 135, 89–102 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Sorte, C. J. B., Fuller, A. & Bracken, M. E. S. Impacts of a simulated heat wave on composition of a marine community. Oikos 119, 1909–1918 (2010).Article 

Google Scholar 
37.Pansch, C. et al. Heat waves and their significance for a temperate benthic community: A near-natural experimental approach. Glob. Change Biol. 24, 4357–4367 (2018).ADS 
Article 

Google Scholar 
38.Queirós, A. M. et al. A bioturbation classification of European marine infaunal invertebrates. Ecol. Evol. 3, 3958–3985 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Wrede, A., Beermann, J., Dannheim, J., Gutow, L. & Brey, T. Organism functional traits and ecosystem supporting services—A novel approach to predict bioirrigation. Ecol. Indic. 91, 737–743 (2018).Article 

Google Scholar 
40.Crespo, D. et al. New climatic targets against global warming: Will the maximum 2 °C temperature rise affect estuarine benthic communities. Sci. Rep. 7, 1–14 (2017).Article 
CAS 

Google Scholar 
41.Galasso, H. L., Richard, M., Lefebvre, S., Aliaume, C. & Callier, M. D. Body size and temperature effects on standard metabolic rate for determining metabolic scope for activity of the polychaete Hediste (Nereis) diversicolor. PeerJ 6, e5675 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Kristensen, E. Ventilation and oxygen uptake by three species of Nereis (Annelida: Polychaeta). I. Effects of hypoxia. Mar. Ecol. Prog. Ser. 12, 289–297 (1983).ADS 
Article 

Google Scholar 
43.Cozzoli, F. et al. The combined influence of body size and density on cohesive sediment resuspension by bioturbators. Sci. Rep. 8, 1–12 (2018).CAS 
Article 

Google Scholar 
44.Cozzoli, F. et al. A process based model of cohesive sediment resuspension under bioturbators’ influence. Sci. Total Environ. 670, 18–30 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Scaps, P. A review of the biology, ecology and potential use of the common ragworm Hediste diversicolor (O.F. Müller) (Annelida: Polychaeta). Hydrobiologia 470, 203–218 (2002).Article 

Google Scholar 
46.Cassidy, C., Grange, L. J., Garcia, C., Bolam, S. G. & Godbold, J. A. Species interactions and environmental context affect intraspecific behavioural trait variation and ecosystem function. Proc. R. Soc. B Biol. Sci. 287, 20192143 (2020).Article 

Google Scholar 
47.Thomsen, M. S. et al. Compensatory responses can alter the form of the biodiversity—function relation curve. Philos. Trans. R. Soc. B 286, 20190287 (2019).CAS 

Google Scholar 
48.Hale, R. et al. Mediation of macronutrients and carbon by post-disturbance shelf sea sediment communities. Biogeochemistry 135, 121–133 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Karlson, K., Bonsdorff, E. & Rosenberg, R. The impact of benthic macrofauna for nutrient fluxes from Baltic Sea sediments. Ambio 36, 161–167 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Thomsen, M. S. et al. Compensatory responses can alter the form of the biodiversity-function relation curve. Proc. R. Soc. B Biol. Sci. 286, 20190287 (2019).CAS 
Article 

Google Scholar 
51.Wohlgemuth, D., Solan, M. & Godbold, J. A. Species contributions to ecosystem process and function can be population dependent and modified by biotic and abiotic setting. Proc. R. Soc. B: Biol. Sci. 284, 20162805 (2017).Article 

Google Scholar 
52.Lillebø, A. I., Neto, J. M., Flindt, M. R., Marques, J. C. & Pardal, M. A. Phosphorous dynamics in a temperate intertidal estuary. Estuar. Coast. Shelf Sci. 61, 101–109 (2004).ADS 
Article 
CAS 

Google Scholar 
53.Lillebø, A. I. et al. Management of a shallow temperate estuary to control eutrophication: The effect of hydrodynamics on the system’s nutrient loading. Estuar. Coast. Shelf Sci. 65, 697–707 (2005).ADS 
Article 

Google Scholar 
54.Verdelhos, T., Cardoso, P. G., Dolbeth, M. & Pardal, M. A. Recovery trends of Scrobicularia plana populations after restoration measures, affected by extreme climate events. Mar. Environ. Res. 98, 39–48 (2014).CAS 
PubMed 
Article 

Google Scholar 
55.Hale, R., Mavrogordato, M. N., Tolhurst, T. J. & Solan, M. Characterizations of how species mediate ecosystem properties require more comprehensive functional effect descriptors. Sci. Rep. 4, 1–6 (2014).
Google Scholar 
56.Benton, T. G., Solan, M., Travis, J. M. J. & Sait, S. M. Microcosm experiments can inform global ecological problems. Trends Ecol. Evol. 22, 516–521 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Bento, E. G. et al. Climate influence on juvenile European sea bass (Dicentrarchus labrax, L.) populations in an estuarine nursery: A decadal overview. Mar. Environ. Res. 122, 93–104 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Martinho, F. et al. The influence of an extreme drought event in the fish community of a southern Europe temperate estuary. Estuar. Coast. Shelf Sci. 75, 537–546 (2007).ADS 
Article 

Google Scholar 
59.Solan, M. et al. In situ quantification of bioturbation using time-lapse fluorescent sediment profile imaging (f-SPI), luminophore tracers and model simulation. Mar. Ecol. Prog. Ser. 271, 1–12 (2004).ADS 
Article 

Google Scholar 
60.Schiffers, K., Teal, L. R., Travis, J. M. J. & Solan, M. An open source simulation model for soil and sediment bioturbation. PLoS ONE 6, e28028 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (Verlag Chemie, 1983).
Google Scholar 
62.Jones, M. N. Nitrate reduction by shaking with cadmium. Alternative to cadmium columns. Water Res. 18, 643–646 (1984).ADS 
CAS 
Article 

Google Scholar 
63.Hayward, P. J. & Ryland, J. S. Handbook of the Marine Fauna of North-West Europe. (Oxford University Press, 2017). https://doi.org/10.1093/acprof:oso/9780199549443.001.0001.64.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. 214 (PRIMER-E Ltd., Plymouth, UK, 2008).65.Ricotta, C. & Moretti, M. CWM and Rao’s quadratic diversity: A unified framework for functional ecology. Oecologia 167, 181–188 (2011).ADS 
PubMed 
Article 

Google Scholar 
66.Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).Article 

Google Scholar 
67.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
Austria. (2019) https://www.R-project.org/.68.Oksanen, J. et al. vegan: Community Ecology Package. R package 2.5-6. (2019). https://CRAN.Rproject.org/package=vegan.69.Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12. (2014).70.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar  More

CORRESPONDENCE
01 June 2021

French vote for river barriers defies biodiversity strategy

Simon Blanchet

 ORCID: http://orcid.org/0000-0002-3843-589X

0
&

Pablo A. Tedesco

 ORCID: http://orcid.org/0000-0001-5972-5928

1

Simon Blanchet

National Centre for Scientific Research (CNRS), Moulis, France.

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Pablo A. Tedesco

French National Research Institute for Development (IRD), Toulouse, France.

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Europe’s rivers are disrupted by more than one million artificial barriers, including small dams, weirs and fords (see, for example, B. Belleti et al. Nature 588, 436–441; 2020). There is strong scientific evidence that such obstructions can harm both hydrological and ecological systems, yet the French parliament has voted to leave them in place (see go.nature.com/3ck9mxq).By limiting the transfer of sediments and movement of organisms, these small barriers create a succession of reaches of warming, stagnant water that threatens freshwater biodiversity (M. R. Fuller et al. Ann. NY Acad. Sci. 1335, 31–51; 2015). Dismantling such small barriers is the most effective way to restore river connectivity and is now a worldwide objective (J. E. O’Connor et al. Science 348, 496–497; 2015).The French parliament’s decision flies in the face of the EU Biodiversity Strategy. It also has no economic justification. Most small barriers cannot generate hydroelectricity and those that can contribute less than 1% to France’s electricity (see go.nature.com/2rphjch).In our view, the fate of each barrier should be decided by balancing its ecological benefits and socioeconomic costs.

Nature 594, 26 (2021)
doi: https://doi.org/10.1038/d41586-021-01467-0

Competing Interests
The authors declare no competing interests.

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1.Neugschwandter, R. W. & Kaul, H. P. Sowing ratio and N fertilization affect yield and yield components of oat and pea in intercrops. Field Crops Res. 155, 159–163 (2014).Article 

Google Scholar 
2.Hu, F. et al. Low N fertilizer application and intercropping increases N concentration in pea (Pisum sativum L.) grains. Front Plant Sci. 9, 1763 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Jensen, E. S., Carlsson, G. & Hauggaard-Nielsen, H. Intercropping of grain legumes and cereals improves the use of soil N resources and reduces the requirement for synthetic fertilizer N: a global-scale analysis. Agron. Sustain. Dev. 40, 5 (2020).Article 

Google Scholar 
4.Jannoura, R., Joergensen, R. G. & Bruns, C. Organic fertilizer effects on growth, crop yield, and soil microbial biomass indices in sole and intercropped peas and oats under organic farming conditions. Eur. J. Agron. 52, 259–270 (2014).Article 

Google Scholar 
5.Darch, T. et al. Inter- and intra-species intercropping of barley cultivars and legume species, as affected by soil phosphorus availability. Plant Soil 427, 125–138 (2018).CAS 
PubMed 
Article 

Google Scholar 
6.Monti, M., Pellicanò, A., Santonoceto, C., Preiti, G. & Pristeri, A. Yield components and nitrogen use in cereal-pea intercrops in Mediterranean environment. Field Crops Res. 196, 379–388 (2016).Article 

Google Scholar 
7.Scalise, A., Pappa, V. A., Gelsomino, A. & Rees, R. M. Pea cultivar and wheat residues affect carbon/nitrogen dynamics in pea-triticale intercropping: a microcosms approach. Sci. Tot. Environ. 592, 436–450 (2017).CAS 
Article 

Google Scholar 
8.Bedoussac, L. et al. Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron. Sustain. Dev. 35, 911–935 (2015).Article 

Google Scholar 
9.Garcia, K., Doidy, J., Zimmermann, S. D., Wipf, D. & Courty, P. E. Take a trip through the plant and fungal transportome of mycorrhiza. Trends Plant Sci. 21, 937–950 (2016).CAS 
PubMed 
Article 

Google Scholar 
10.Oelbermann, M., Regehr, A. & Echarte, L. Changes in soil characteristics after six seasons of cereal–legume intercropping in the Southern Pampa. Geoderma Reg. 4, 100–107 (2015).Article 

Google Scholar 
11.Wichern, F., Eberhardt, E., Mayer, J., Joergensen, R. G. & Müller, T. Nitrogen rhizodeposition in agricultural crops: methods, estimates and future prospects. Soil Biol. Biochem. 40, 30–48 (2008).CAS 
Article 

Google Scholar 
12.Pausch, J., Tian, J., Riederer, M. & Kuzyakov, Y. Estimation of rhizodeposition at field scale: upscaling of a 14C labeling study. Plant Soil 364, 273–285 (2013).CAS 
Article 

Google Scholar 
13.Fustec, J., Lesuffleur, F., Mahieu, S. & Cliquet, J. B. Nitrogen rhizodeposition of legumes. A review. Agron. Sustain. Dev. 30, 57–66 (2010).CAS 
Article 

Google Scholar 
14.Hupe, A. et al. Get on your boots: estimating root biomass and rhizodeposition of peas under field conditions reveals the necessity of field experiments. Plant Soil 443, 449–462 (2019).CAS 
Article 

Google Scholar 
15.Parniske, M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat. Rev. Microbiol. 6, 763–775 (2008).CAS 
PubMed 
Article 

Google Scholar 
16.Jones, D. L., Hodge, A. & Kuzyakov, Y. Plant and mycorrhizal regulation of rhizodeposition. New Phytol. 163, 459–480 (2004).CAS 
PubMed 
Article 

Google Scholar 
17.Hupe, A. et al. Even flow? Changes of carbon and nitrogen release from pea roots over time. Plant Soil 431, 143–157 (2018).CAS 
Article 

Google Scholar 
18.He, X., Xu, M., Qiu, C. Y. & Zhou, J. Use of 15N stable isotope to quantify nitrogen transfer between mycorrhizal plants. J. Plant Ecol. 2, 107–118 (2009).Article 

Google Scholar 
19.Pepe, A., Giovannetti, M. & Sbrana, C. Lifespan and functionality of mycorrhizal fungal mycelium are uncoupled from host plant lifespan. Sci. Rep. 8, 10235 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Xiao, Y., Li, L. & Zhang, F. Effect of root contact on interspecific competition and N transfer between wheat and faba bean using direct and indirect 15N techniques. Plant Soil 262, 45–54 (2004).CAS 
Article 

Google Scholar 
21.Thilakarathna, M. S., McElroy, M. S., Chapagain, T., Papadopoulos, Y. A. & Raizada, M. N. Belowground nitrogen transfer from legumes to non-legumes under managed herbaceous cropping systems. A review. Agron. Sustain. Dev. 36, 58 (2016).Article 
CAS 

Google Scholar 
22.Meng, L. et al. Arbuscular mycorrhizal fungi and rhizobium facilitate nitrogen uptake and transfer in soybean/maize intercropping system. Front Plant Sci. 6, 339 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
23.Shao, Z. et al. Root contact between maize and alfalfa facilitates nitrogen transfer and uptake using techniques of foliar 15N-labeling. Agronomy 10, 360 (2020).CAS 
Article 

Google Scholar 
24.Duc, G., Trouvelot, A., Gianinazzi-Pearson, V. & Gianinazzi, S. First report of non-mycorrhizal plant mutants (Myc−) obtained in pea (Pisum sativum L.) and fababean (Vicia faba L.). Plant Sci. 60, 215–222 (1989).Article 

Google Scholar 
25.Kleikamp, B. & Joergensen, R. G. Evaluation of arbuscular mycorrhiza with symbiotic and nonsymbiotic pea isolines at three sites in the Alentejo, Portugal. J. Plant Nutr. Soil Sci. 169, 661–669 (2006).CAS 
Article 

Google Scholar 
26.Jannoura, R., Kleikamp, B., Dyckmans, J. & Joergensen, R. G. Impact of pea growth and of arbuscular mycorrhizal fungi on the decomposition of 15N-labeled maize residues. Biol. Fertil. Soils 48, 547–560 (2012).Article 

Google Scholar 
27.Chalk, P. M. et al. Methodologies for estimating nitrogen transfer between legumes and companion species in agro-ecosystems: a review of 15N-enriched techniques. Soil Biol. Biochem. 73, 10–21 (2014).CAS 
Article 

Google Scholar 
28.Wahbi, S. et al. Enhanced transfer of biologically fixed N from faba bean to intercropped wheat through mycorrhizal symbiosis. Appl. Soil Ecol. 107, 91–98 (2016).Article 

Google Scholar 
29.Ingraffia, R., Amato, G., Frenda, A. S. & Giambalvo, D. Impacts of arbuscular mycorrhizal fungi on nutrient uptake, N2 fixation, N transfer, and growth in a wheat/faba bean intercropping system. PLoS ONE 14, e0213672 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Fusconi, A. Regulation of root morphogenesis in arbuscular mycorrhizae, what role do fungal exudates, phosphate, sugars and hormones play in lateral root formation. Ann. Bot. 113, 19–33 (2014).CAS 
PubMed 
Article 

Google Scholar 
31.Wang, W. et al. Nutrient exchange and regulation in arbuscular mycorrhizal symbiosis. Mol. Plant 10, 1147–1158 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Xue, Y. et al. Crop acquisition of phosphorus, iron and zinc from soil in cereal/legume intercropping systems: a critical review. Ann. Bot. 117, 363–377 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Abdelhalim, T., Jannoura, R. & Joergensen, R. G. Mycorrhiza response and phosphorus acquisition efficiency of sorghum cultivars differing in strigolactone composition. Plant Soil 437, 55–63 (2019).CAS 
Article 

Google Scholar 
34.Louarn, G. et al. The amounts and dynamics of nitrogen transfer to grasses differ in alfalfa and white clover-based grass-legume mixtures as a result of rooting strategies and rhizodeposit quality. Plant Soil 389, 289–305 (2015).CAS 
Article 

Google Scholar 
35.Faust, S., Kaiser, K., Wiedner, K., Glaser, B. & Joergensen, R. G. Comparison of different methods to determine lignin concentration and quality in herbaceous and woody plant residues. Plant Soil 433, 7–18 (2018).CAS 
Article 

Google Scholar 
36.Baldrian, P. et al. Production of extracellular enzymes and degradation of biopolymers by saprotrophic microfungi from the upper layers of forest soil. Plant Soil 338, 1–15 (2011).Article 
CAS 

Google Scholar 
37.Wichern, F., Andreeva, D., Joergensen, R. G. & Kuzyakov, Y. Distribution of applied 14C and 15N in legumes using two different labelling methods. J. Plant Nutr. Soil Sci. 174, 732–741 (2011).CAS 
Article 

Google Scholar 
38.Turner, T. R. et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 7, 2248–2258 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Yu, L., Nicolaisen, M., Larsen, J. & Ravnskov, S. Molecular characterization of root-associated fungal communities in relation to health status of Pisum sativum using barcoded pyrosequencing. Plant Soil 357, 395–405 (2012).CAS 
Article 

Google Scholar 
40.Gunina, A. & Kuzyakov, Y. Sugars in soil and sweets for microorganisms: review of origin, content, composition and fate. Soil Biol. Biochem. 90, 87–100 (2015).CAS 
Article 

Google Scholar 
41.Allison, S. D. Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecol. Lett. 8, 626–635 (2005).Article 

Google Scholar 
42.Joergensen, R. G. & Wichern, F. Alive and kicking: why dormant soil microorganisms matter. Soil Biol. Biochem. 116, 419–430 (2018).CAS 
Article 

Google Scholar 
43.IUSS Working Group. WRB World reference base for soil resources 2014 (update 2015), international soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports (2015).44.Mahieu, S., Fustec, J., Jensen, E. S. & Crozat, Y. Does labelling frequency affect N rhizodeposition assessment using the cotton-wick method?. Soil Biol. Biochem. 41, 2236–2243 (2009).CAS 
Article 

Google Scholar 
45.Russell, C. A. & Fillery, I. R. P. Estimates of lupin below-ground biomass nitrogen, drymatter, and nitrogen turnover to wheat. Crop Pasture Sci. 47, 1047–1059 (1996).CAS 
Article 

Google Scholar 
46.Wichern, F., Mayer, J., Joergensen, R. & Müller, T. Evaluation of the wick method for in situ 13C and 15N labelling of annual plants using sugar-urea mixtures. Plant Soil 329, 105–115 (2010).CAS 
Article 

Google Scholar 
47.Phillips, J. M. & Hayman, D. S. Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Transact. Brit. Mycol. Soc. 55, 158–168 (1970).Article 

Google Scholar 
48.Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen. A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).CAS 
Article 

Google Scholar 
49.Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
50.Mueller, T., Joergensen, R. G. & Meyer, B. Estimation of soil microbial biomass C in the p resence of living roots by fumigation-extraction. Soil Biol. Biochem. 24, 179–181 (1992).Article 

Google Scholar 
51.Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation-extraction—an automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).CAS 
Article 

Google Scholar 
52.Hupe, A., Schulz, H., Bruns, C., Joergensen, R. G. & Wichern, F. Digging in the dirt—inadequacy of below-ground plant biomass quantification. Soil Biol. Biochem. 96, 137–144 (2016).CAS 
Article 

Google Scholar  More

Site informationThe study was conducted at the Kenauk Institute, an environmental research site, in western Quebec in July 2018, 2019 and 2020. All surveys occurred between 21h30 and 00h00 at night, with location and time of day randomized for each date of testing. Testing did not occur during inclement weather (rain or winds above 10 km/h). In 2018, during an initial field season, we surveyed bat populations using a traditional method (transect-based surveys) to determine which species were present. Six transects lasting 1.5 h each were laid out, and surveyed three times per season; three transects were located in open-canopy areas, and three were located in rugged, closed-canopy areas. Every 200 m, a flag marked a sampling point where we completed a 2-min static inventory using an Anabat SD2 (Titley Scientific, Columbia, MO). In this pilot study used to develop the main study, we observed all eight species known in Quebec, including the eastern red bat (Lasiurus borealis; 0.005 passes detected per minute in open-canopy habitat; 0.001 in closed-canopy), hoary bat (Lasiurus cinereus; 0.002 passes detected per minute in open-canopy habitat; 0.006 in closed-canopy) and tri-coloured bats (Perimyotis subflavus; 0.026 passes detected per minute in open-canopy habitat; none in closed-canopy). Species in the Eptesicus fuscus/Lasionycteris noctivagans acoustic complex were the most abundant (0.075 passes detected per minute in open-canopy habitat; 0.018 in closed-canopy) followed by Myotis species (Myotis leibii, Myotis septentrionalis, Myotis lucifugus; 0.075 passes detected per minute in open-canopy habitat; none in closed-canopy). Due to small sample sizes per species and because manual identification using spectrographic analyses can be unreliable for the differentiation of some bat species22, we pooled several bat species that had similar spectrograms into complexes. We pooled the big brown bat (Eptesicus fuscus) and the silver-haired bat (Lasionycteris noctivagans), and the Myotis species: little brown bat (Myotis lucifugus), northern long-eared Myotis (M. septentrionalis), and eastern small-footed bat (M. leibii)22. Therefore, these species are grouped together in analyses to minimize identification errors22. The big brown bat and silver-haired bat form the EPNO complex whereas the Myotis species form the MYSP complex. We identified to species the hoary bat (LACI), red bat (LABO), and tri-coloured bat (PESU)22. We identified bat passes visually using the output from the Anabat in the Anabat Insight software17,23.Detection efficiencyBecause total bat passes per minute were seven times higher in open-canopy habitats than in closed-canopy habitats, in 2019 we focused our surveying efforts in relatively open habitats. The Anabat (420 g) is too large to attach to a drone, thus in 2019 and 2020, we used Echometer Touch bat detectors (20 g; Wildlife Acoustics, Maynard, MA), commercially available and inexpensive detectors, attached to iPod 7 s (88 g; Apple Inc., Cupertino, CA). We do not directly compare between surveys done with the Anabat and the Echometer Touch, but merely used the 2018 Anabat surveys as a guide for expected bat species and distributions in 2019 and 2020. The UAV used was a commercially available Phantom 4 quadcopter from DJI (1.3 kg, DJI Technology Co. Inc., Shenzhen, China). To reduce sound interference from the drone, which could reduce the detection range of the instrument, we placed a 2-in. Sonoflat acoustic foam (Auralex, Indianapolis, IN) divider between the recorder and the drone, as recommended by past studies19,21 (Fig. 1).Figure 1Illustration of the three phases of the experiment design. A photograph of the UAV setup used in Phase 2 is presented in the top right corner. The setup consists of an Echometer Touch bat detector from Wildlife Acoustics and 2-inch Sonoflat acoustic foam from Auralex attached to a DJI Phantom 4 quadcopter using zip ties. (Images by Julian Herzog, Symbolon, FontAwesome retrieved from https://commons.wikimedia.org. Picture taken by the author).Full size imageIn both 2019 and 2020, we surveyed in three phases: (1) a 5-min recording from the ground without UAV; (2) a 5-min recording while the detector was attached to the UAV using zip ties and carabiners and while the UAV was manually flown in a 10–15 m diameter circle at canopy height (5––10 m above the pilot), depending on the survey site; and (3), identically to Phase 1, a 5-min recording taken from the ground without UAV (Fig. 1). The ground recorder, used sparsely in 2019 and consistently in 2020, was 1 m above the ground during phase 2. Based on surveys in 2018, seven sites were identified as having higher relative activity and were repeatedly monitored in 2019 and 2020 for bat activity. Of the seven study sites, five were located next to bodies of water and four were located near buildings; all were located in open areas. Open spaces and bodies of water are preferred hunting grounds for most bat species18, and make for an easier and safer drone flight. An additional bat detector (Echometer Touch 2, Wildlife Acoustics, Maynard USA) was used on the ground during Phase 2 to simultaneously monitor bat passes from the air and from the ground, to indicate whether bats were present but not detected due to UAV noise interference. In 2020, ten surveys were conducted with Echometer Touch 2 recorders on (1) the UAV, (2) on the ground, and (3) at a control site > 1 km from the current site. Control sites were only used in 2020. Because different bat detectors, as well as different classification software, detect and identify bats at different rates, we do not directly compare among different detectors or software24,25. In 2019, we used the Kaleidoscope software to identify bats automatically. We removed false identifications manually. In 2020, we used the Kaleidoscope software to identify all bats automatically. We also identified all passes visually and blind to the classification from Kaleidoscope. By classifying all bats using both software and visual identification, we aimed to determine whether our results were robust to identification technique.Data were collected beyond Phase 1 if the site had a bat density above three passes per 5 min (2019: N = 24 without ground detector; N = 5 with ground detector; 2020: N = 10 with ground detector; all sample sizes refer to experiments that included Phases 2 and 3). If insufficient bat activity was recorded at a given site after a 5-min period, data collection moved on to the next site, and data from that site was excluded from any analyses. Phase 1 was done to ensure there was an established bat presence, and to maximize sampling. The length of each phase was extended to 10 min if two passes were detected by the 5-min mark of Phase 1, allowing for the collection of more data, while maintaining the time proportions of each phase. While this process, necessary logistically to obtain a sufficient sample size, could lead to more bats detected during Phase 1, there should be no impact on Phase 3 compared to Phase 2, and thus, we used Tukey tests to examine Phase 3 relative to Phase 2, as well as Phase 1 compared with both other phases26.Each drone flight was performed by two field technicians: a pilot and an assistant. The UAV pilot held a basic operations pilot certificate for a small remotely-piloted aircraft system, visual line-of-sight (certificate number PC1917023611) in accordance with federal regulations enforced by Transport Canada. The assistant held the bat detector during Phases 1 and 3. During Phase 2, the assistant acted as the drone’s elevated launching and landing pad as the additional equipment obstructing the UAV’s landing gear. For take-off, they held the UAV upright above their head and gradually let go as the UAV gained altitude. For landing, the pilot gradually decreased the altitude of the drone until the landing gear was safely grasped by the assistant, who then held the UAV above their head until the propellers stopped moving. All methods were carried out in accordance with the guidelines of the Canadian Council for Animal Care. All experimental protocols were approved by McGill University animal care committee under protocol 2015-7599 and complied with the ARRIVE guidelines for animals.Statistical analyses were conducted using R 3.6.0 base package26. Generalized linear models (glm, Poisson distribution) were performed to determine the effect of phase (i.e., 1, 2, and 3) and detector location (detector on the UAV or on the ground) on the total number of bat passes. Tukey tests were then used to determine what phases and locations were significantly different from one another. To assess interspecific variation in detectability, the difference between the mean detection rate for Phase 1 and 3 and the detection rate in Phase 2 were calculated by species for each survey. A glm was then performed on the difference in detectability by species ([Average of Phases 1 and 3 − Average of Phase 2]–Species). Species were divided into four categories: MYSP (Myotis species complex), EPNO (big brown bat/silver-haired bat complex), LABO (eastern red bat), and LACI (hoary bat). No tri-coloured bats were detected, and are therefore absent from analyses. Detection phases were also divided into four categories in relation to the UAV flight: Phase 1 (pre-flight), Phase 2 from UAV-based detection (during flight), Phase 2 from ground-based detection (ground), and Phase 3 (post-flight).Detection capacityTo estimate the degree to which technological limitations affected the results gathered during the first experiment, a second experiment was conducted to estimate the impact of propeller-noise interference on the range of the bat detector. An Audio Generator SGA-8200 (Circuit-Test, Burnaby, Canada), connected to an Ultra Sound Advice S55/6 amplifier and loudspeaker (Ultra Sound Advice, London, UK) set to broadcast a 40 kHz sine wave at 40 dB SPLA @ 1 m, the highest dB setting, was used to replicate the high amplitude ultrasound reached by most bat species during their echolocation calls22. The Echometer Touch bat detector was moved away from the speaker along a measuring tape until the ultrasonic frequency could no longer be detected by the microphone. The procedure was then repeated with the detector attached to the flying UAV. As ambient sound perception cannot be evaluated when the microphone is attached to the UAV, the spectrogram on the Echometer Touch cellphone app (Wildlife Acoustics) connected to the detector was recorded with the screen video recording feature of the iPod 7 (Apple). These recordings were taken as the drone and bat detector were flown slowly along the ground to three distances (10 m, 15 m, 20 m) away from the ultrasound generator to better approximate the detection range. The videos were later visually assessed qualitatively by estimating the distance at which the signal from the speaker could no longer be distinguished from the noise interference of the drone.To quantify the spectral overlap of the drone with echolocation pulses, a spectral analysis of three 15 s recordings were performed using Avisoft SASLab Pro 4.40 (Avisoft Bioacoustics, Berlin Germany). These recordings included the drone flying, the drone motors running without propellers attached, and the ambient noise from the same location and time (control). Recordings were saved as 16 bit WAV files sampled at 256 kc/s and were normalized to 90% in SASLab Pro prior to parameterization. Spectrographs of those normalized recordings were generated using a Fast Fourier Transform length of 512 points, with a frame size of 100% and 75% overlap of Hann windows. This achieved a frequency resolution of 500 Hz and temporal resolution of 0.5 ms. Frequencies where noise was concentrated are evident from these spectrographs, but were confirmed by generating Logarithmic Power Spectra from each recording using Hann windowing achieving frequency resolution of 0.061 Hz. Noise is described at frequencies where the relative sound pressure level exceeded − 80 dB in those Power Spectra. More

1.Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Diagne, C. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci. Data 7, 277 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 12986 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Meyerson, L. A. & Reaser, J. K. Biosecurity: moving toward a comprehensive approach. Bioscience 52, 593 (2002).Article 

Google Scholar 
5.Carvajal-Yepes, M. et al. A global surveillance system for crop diseases. Science 364, 1237–1239 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Torres, A., David, M. & Bowman, Q. Risk management of international trade: emergency preparedness. Rev. Sci. Tech. Off. Int. Épizooties 21, 493–496 (2002).CAS 
Article 

Google Scholar 
7.Ricciardi, A. et al. Invasion science: a horizon scan of emerging challenges and opportunities. Trends Ecol. Evol. 32, 464–474 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Giovani, B. et al. Science diplomacy for plant health. Nat. Plants 6, 902–905 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Reaser, J. K. et al. The early detection of and rapid response (EDRR) to invasive species: a conceptual framework and federal capacities assessment. Biol. Invasions 22, 1–19 (2020).Article 

Google Scholar 
10.Delaney, D. G., Sperling, C. D., Adams, C. S. & Leung, B. Marine invasive species: validation of citizen science and implications for national monitoring networks. Biol. Invasions 10, 117–128 (2008).Article 

Google Scholar 
11.Crall, A. W. et al. Improving and integrating data on invasive species collected by citizen scientists. Biol. Invasions 12, 3419–3428 (2010).Article 

Google Scholar 
12.Maistrello, L. et al. Tracking the spread of sneaking aliens by integrating crowdsourcing and spatial modeling: the Italian invasion of halyomorpha halys. Bioscience https://doi.org/10.1093/biosci/biy112 (2018).Article 

Google Scholar 
13.Lepczyk, C. A., Boyle, O. D., Vargo, T. L. V. & Noss, R. F. Handbook of Citizen Science in Ecology and Conservation (University of California Press, Oakland, 2020).14.Devorshak, C. Plant pest risk analysis: concepts and applications. (CAB International, Wallingford, 2012).15.IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge and New York, 2014).16.Hijmans, R. J. & Graham, C. H. The ability of climate envelope models to predict the effect of climate change on species distributions. Glob. Change Biol. 12, 2272–2281 (2006).ADS 
Article 

Google Scholar 
17.Broennimann, O. & Guisan, A. Predicting current and future biological invasions: both native and invaded ranges matter. Biol. Lett. 4, 585–589 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Godefroid, M., Meurisse, N., Groenen, F., Kerdelhué, C. & Rossi, J.-P. Current and future distribution of the invasive oak processionary moth. Biol. Invasions 22, 523–534 (2020).Article 

Google Scholar 
19.Crall, A. W. et al. Citizen science contributes to our knowledge of invasive plant species distributions. Biol. Invasions 17, 2415–2427 (2015).Article 

Google Scholar 
20.Petrovan, S. O., Vale, C. G. & Sillero, N. Using citizen science in road surveys for large-scale amphibian monitoring: are biased data representative for species distribution?. Biodivers. Conserv. 29, 1767–1781 (2020).Article 

Google Scholar 
21.Hannah, L. J. Climate Change Biology (Academic Press, 2015).
Google Scholar 
22.Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).ADS 
Article 

Google Scholar 
23.Porfirio, L. L. et al. Improving the use of species distribution models in conservation planning and management under climate change. PLoS ONE 9, e113749 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Hamilton, G. C., Ahn, J. J., Bu, W., Leskey, T. C., Nielsen, A. L., Park, Y.-L., Rabitsch, W. & Hoelmer, K.A. Halyomorpha halys (Stål). In Invasive stink bugs and related species (Pentatomoidea): biology, higher systematics, semiochemistry, and management (ed McPherson, J. E.) 243–292 (CRC Press, Taylor & Francis, Boca Raton, 2018).25.Bergmann, E. J., Venugopal, P. D., Martinson, H. M., Raupp, M. J. & Shrewsbury, P. M. Host plant use by the invasive Halyomorpha halys (Stål) on woody ornamental trees and shrubs. PLoS ONE 11, e0149975 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Gapon, D. A. First records of the brown marmorated stink bug Halyomorpha halys (Stål, 1855) (Heteroptera, Pentatomidae) in Russia, Abkhazia, and Georgia. Entomol. Rev. 96, 1086–1088 (2016).Article 

Google Scholar 
27.Faúndez, E. I. & Rider, D. A. The brown marmorated stink bug Halyomorpha halys (Stål, 1855) (Heteroptera: Pentatomidae) in Chile. Arq. Entomolóxicos 17, 305–307 (2017).
Google Scholar 
28.McPherson, J. E., ed. Invasive stink bugs and related species (Pentatomoidea): biology, higher systematics, semiochemistry, and management (CRC Press, Taylor & Francis, Boca Raton, 2018).29.Maistrello, L. et al. Halyomorpha halys in Italy: first results of field monitoring in fruit orchards. Integr. Prot. Fruit Crops IOBC-WPRS Bull. 112, 1–5 (2016).
Google Scholar 
30.Bariselli, M., Bugiani, R. & Maistrello, L. Distribution and damage caused by Halyomorpha halys in Italy. EPPO Bull. 46, 332–334 (2016).Article 

Google Scholar 
31.Zhu, G., Bu, W., Gao, Y. & Liu, G. Potential geographic distribution of brown marmorated stink bug invasion (Halyomorpha halys). PLoS ONE 7, e31246 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kriticos, D. J. et al. The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity. J. Pest Sci. 90, 1033–1043 (2017).Article 

Google Scholar 
33.Kistner, E. J. Climate change impacts on the potential distribution and abundance of the brown marmorated stink bug (Hemiptera: Pentatomidae) with special reference to North America and Europe. Environ. Entomol. 46, 1212–1224 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
34.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2021).35.Vaclavik, T., Kanaskie, A., Hansen, E. M., Ohmann, J. L. & Meentemeyer, R. K. Predicting potential and actual distribution of sudden oak death in Oregon: prioritizing landscape contexts for early detection and eradication of disease outbreaks. For. Ecol. Manag. 260, 1026–1035 (2010).Article 

Google Scholar 
36.Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

Google Scholar 
37.Elith, J. et al. A statistical explanation of MaxEnt for ecologists: statistical explanation of MaxEnt. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
38.Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).Article 

Google Scholar 
39.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
40.Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
41.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
42.Wu, T. et al. The Beijing climate center climate system model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).ADS 
Article 

Google Scholar 
43.Voldoire, A. et al. Evaluation of CMIP6 DECK experiments With CNRM-CM6-1. J. Adv. Model. Earth Syst. 11, 2177–2213 (2019).ADS 
Article 

Google Scholar 
44.Séférian, R. et al. Evaluation of CNRM earth system model, CNRM-ESM2-1: role of earth system processes in present-day and future climate. J. Adv. Model. Earth Syst. 11, 4182–4227 (2019).ADS 
Article 

Google Scholar 
45.Swart, N. C. et al. The Canadian earth system model version 5 (CanESM5.0.3). Geosci. Model Dev. 12, 4823–4873 (2019).ADS 
CAS 
Article 

Google Scholar 
46.Hajima, T. et al. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev. 13, 2197–2244 (2020).ADS 
Article 

Google Scholar 
47.Tatebe, H. et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev. 12, 2727–2765 (2019).ADS 
CAS 
Article 

Google Scholar 
48.Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).ADS 
CAS 
Article 

Google Scholar 
49.Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models with Applications in R (Cambridge University Press, 2017).Book 

Google Scholar 
50.Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).Article 

Google Scholar 
52.Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography https://doi.org/10.1111/j.1600-0587.2013.00441.x (2014).Article 

Google Scholar 
53.VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?. Ecol. Model. 220, 589–594 (2009).Article 

Google Scholar 
54.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

Google Scholar 
55.Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012).MATH 

Google Scholar 
56.Godefroid, M., Cruaud, A., Streito, J.-C., Rasplus, J.-Y. & Rossi, J.-P. Xylella fastidiosa: climate suitability of European continent. Sci. Rep. 9, 8844 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Vollering, J., Halvorsen, R. & Mazzoni, S. The MIAmaxent R package: variable transformation and model selection for species distribution models. Ecol. Evol. 9, 12051–12068 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Mazzoni, S., Halvorsen, R. & Bakkestuen, V. MIAT: modular R-wrappers for flexible implementation of MaxEnt distribution modelling. Ecol. Inform. 30, 215–221 (2015).Article 

Google Scholar 
59.Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species: the art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).Article 

Google Scholar 
60.Halvorsen, R., Mazzoni, S., Bryn, A. & Bakkestuen, V. Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography 38, 172–183 (2015).Article 

Google Scholar 
61.Halvorsen, R. A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia 36, 1–132 (2013).Article 

Google Scholar 
62.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
63.Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).Article 

Google Scholar 
64.Jiménez, L. & Soberón, J. Leaving the area under the receiving operating characteristic curve behind: an evaluation method for species distribution modeling applications based on presence-only data. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13479 (2020).Article 

Google Scholar 
65.Chartois, M., Streito, J.-C., Pierre, E., Armand, J.-M., Gaudin, J., Rossi, J.-P. A crowdsourcing approach to track the expansion of the brown marmorated stinkbug Halyomorpha halys (Stål, 1855) in France. Biodivers. Data J. 9, e66335. https://doi.org/10.3897/BDJ.9.e66335 (2021)66.Maurel, J.-P., Blaye G., Valladares L., Roinel, E. & Cochard, P.-O. Halyomorpha halys (Stål, 1855), la punaise diabolique en France, à Toulouse (Heteroptera ; Pentatomidae). Carnets Nat. 3, 21–25 (2016).67.Cherpitel, T. & Casset, L. Halyomorpha halys (Stål, 1855), la Punaise diabolique, atteint la façade atlantique (Heteroptera Pentatomidae). L’Entomologiste 75, 59–60 (2018).
Google Scholar 
68.Pagola-Carte, S. & Zabalegui, I. D. hemípteros asiáticos nuevos para Gipuzkoa, norte de la Península Ibérica (Hemiptera: Pentatomidae, Cicadellidae). Heteropterus Rev. Entomol. 19, 355–360 (2019).
Google Scholar 
69.Streito, J. C., Rossi, J.-P., Haye, T., Hoelmer, K. & Tassus, X. La punaise diabolique à la conquête de la France. Phytoma 677, 26–30 (2014).70.Maistrello, L., Dioli, P., Bariselli, M., Mazzoli, G. L. & Giacalone-Forini, I. Citizen science and early detection of invasive species: phenology of first occurrences of Halyomorpha halys in Southern Europe. Biol. Invasions 18, 3109–3116 (2016).Article 

Google Scholar 
71.Stoeckli, S., Felber, R. & Haye, T. Current distribution and voltinism of the brown marmorated stink bug, Halyomorpha halys, in Switzerland and its response to climate change using a high-resolution CLIMEX model. Int. J. Biometeorol. https://doi.org/10.1007/s00484-020-01992-z (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Leskey, T. C., Lee, D.-H., Glenn, D. M. & Morrison, W. R. Behavioral responses of the invasive Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) to light-based stimuli in the laboratory and field. J. Insect Behav. 28, 674–692 (2015).Article 

Google Scholar 
73.Inkley, D. B. Characteristics of home invasion by the brown marmorated stink bug (Hemiptera: Pentatomidae). J. Entomol. Sci. 47, 125–130 (2012).Article 

Google Scholar 
74.Cambridge, J., Payenski, A. & Hamilton, G. C. The distribution of overwintering brown marmorated stink bugs (Hemiptera: Pentatomidae) in college dormitories. Fla. Entomol. 98, 1257–1259 (2015).Article 

Google Scholar 
75.Hancock, T. J., Lee, D.-H., Bergh, J. C., Morrison, W. R. & Leskey, T. C. Presence of the invasive brown marmorated stink bug Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) on home exteriors during the autumn dispersal period: results generated by citizen scientists: presence of H. halys during the autumn dispersal. Agric. For. Entomol. 21, 99–108 (2019).Article 

Google Scholar 
76.Streito, J.-C., Chartois, M., Pierre, É. & Rossi, J.-P. Beware the brown marmorated stink bug!. IVES Tech Rev. Vine Wine https://doi.org/10.20870/IVES-TR.2020.3304 (2020).Article 

Google Scholar 
77.Haye, T. et al. Range expansion of the invasive brown marmorated stinkbug, Halyomorpha halys: an increasing threat to field, fruit and vegetable crops worldwide. J. Pest Sci. 88, 665–673 (2015).Article 

Google Scholar 
78.Zhu, G., Gariepy, T. D., Haye, T. & Bu, W. Patterns of niche filling and expansion across the invaded ranges of Halyomorpha halys in North America and Europe. J. Pest Sci. 90, 1045–1057 (2017).Article 

Google Scholar 
79.Shabani, F., Kumar, L. & Ahmadi, M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol. Evol. 6, 5973–5986 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Leskey, T. C. & Nielsen, A. L. Impact of the invasive brown marmorated stink bug in North America and Europe: history, biology, ecology, and management. Annu. Rev. Entomol. 63, 599–618 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Jump, A. S. & Penuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).Article 

Google Scholar 
84.Urvois, T., Auger-Rozenberg, M. A., Roques, A., Rossi, J. P. & Kerdelhue, C. Climate change impact on the potential geographical distribution of two invading Xylosandrus ambrosia beetles. Sci. Rep. 11, 1339 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar  More

1.Murphy, P. G. & Lugo, A. E. Ecology of tropical dry forest. Annu. Rev. Ecol. Syst. 17, 67–88 (1986).Article 

Google Scholar 
2.Kishimoto-Yamada, K. & Itioka, T. How much have we learned about seasonality in tropical insect abundance since Wolda (1988)?. Entomol. Sci. 18, 407–419. https://doi.org/10.1111/ens.12134 (2015).Article 

Google Scholar 
3.dos Santos, J. P. D., Iserhard, C. A., Carreira, J. Y. O. & Freitas, A. V. L. Monitoring fruit-feeding butterfly assemblages in two vertical strata in seasonal Atlantic Forest: Temporal species turnover is lower in the canopy. J. Trop. Ecol. 33, 345–355. https://doi.org/10.1017/s0266467417000323 (2017).Article 

Google Scholar 
4.Bonebrake, T. C., Ponisio, L. C., Boggs, C. L. & Ehrlich, P. R. More than just indicators: A review of tropical butterfly ecology and conservation. Biol. Conser. 143, 1831–1841 (2010).Article 

Google Scholar 
5.Molleman, F. Moving beyond phenology: New directions in the study of temporal dynamics of tropical insect communities. Curr. Sci. 114, 982 (2018).Article 

Google Scholar 
6.Frith, C. B. & Frith, D. W. Seasonality of insect abundance in an Australian upland tropical rainforest. Aust. J. Ecol. 10, 237–248 (1985).Article 

Google Scholar 
7.Braby, M. Seasonal-changes in relative abundance and spatial-distribution of Australian lowland tropical satyrine butterflies. Aust. J. Zool. 43, 209–229 (1995).Article 

Google Scholar 
8.Muniz, D. G., Freitas, A. V. & Oliveira, P. S. Phenological relationships of Eunica bechina (Lepidoptera: Nymphalidae) and its host plant, Caryocar brasiliense (Caryocaraceae), in a Neotropical savanna. Stud. Neotrop. Fauna Environ. 47, 111–118 (2012).Article 

Google Scholar 
9.Wolda, H. Insect seasonality: Why?. Annu. Rev. Ecol. Syst. 19, 1–18 (1988).Article 

Google Scholar 
10.Yonow, T. et al. Modelling the population dynamics of the Queensland fruit fly, Bactrocera (Dacus) tryoni: A cohort-based approach incorporating the effects of weather. Ecol. Model. 173, 9–30. https://doi.org/10.1016/s0304-3800(03)00306-5 (2004).Article 

Google Scholar 
11.Baker, R. et al. Bactrocera dorsalis pest report to support ranking of EU candidate priority pests. EFSA https://doi.org/10.5281/zenodo.2786921 (2019).12.Valtonen, A. et al. Tropical phenology: Bi-annual rhythms and interannual variation in an Afrotropical butterfly assemblage. Ecosphere https://doi.org/10.1890/es12-00338.1 (2013).Article 

Google Scholar 
13.Hernández, C. X. P. & Caballero, S. Z. Temporal variation in the diversity of Cantharidae (Coleoptera), in seven assemblages in tropical dry forest in Mexico. Trop. Conserv. Sci. 9, 439–464 (2016).Article 

Google Scholar 
14.Marchioro, C. A. & Foerster, L. A. Biotic factors are more important than abiotic factors in regulating the abundance of Plutella xylostella L., Southern Brazil. Rev. Bras. Entomol. 60, 328–333 (2016).Article 

Google Scholar 
15.Meats, A. The bioclimatic potential of the Queensland fruit fly, Dacus tryoni, Australia. Proc. Ecol. Soc. Aust. 11, 1–61 (1981).
Google Scholar 
16.Sutherst, R. W. & Yonow, T. The geographical distribution of the Queensland fruit fly, Bactrocera (Dacus) tryoni, in relation to climate. Aust. J. Agric. Res. 49, 935–954 (1998).Article 

Google Scholar 
17.Choudhary, J. S. et al. Potential changes in number of generations of oriental fruit fly, Bactrocera Dorsalis (Diptera: Tephritidae) on mango in India in response to climate change scenarios. J. Agrometeorol. 19, 200–206 (2017).
Google Scholar 
18.Clarke, A. R. Biology and Management of Bactrocera and Related Fruit Flies (CABI, 2019).Book 

Google Scholar 
19.Sakai, S. et al. Plant reproductive phenology over four years including an episode of general flowering in a lowland dipterocarp forest, Sarawak, Malaysia. Am. J. Bot. 86, 1414–1436 (1999).CAS 
PubMed 
Article 

Google Scholar 
20.Land, K. C., Yang, Y. & Zeng, Y. Mathematical demography. Handbook of Population 659–717 (Springer, 2005).21.Carey, J. R. & Roach, D. A. Biodemography: An Introduction to Concepts and Methods (Princeton University Press, 2020).MATH 
Book 

Google Scholar 
22.Carey, J. R. Applied Demography for Biologists: With Special Emphasis on Insects (Oxford University Press, 1993).
Google Scholar 
23.Carey, J. R. Insect biodemography. Annu. Rev. Entomol. 46, 79–110 (2001).CAS 
PubMed 
Article 

Google Scholar 
24.Southwood, T. R. E. Ecological Methods: With Particular Reference to the Study of Insect Populations. xviii + 391 (Methuen, London, 1966).25.Udevitz, M. S. & Ballachey, B. E. Estimating survival rates with age-structure data. J. Wildl. Manag. 62, 779–792 (1998).Article 

Google Scholar 
26.Müller, H. G. et al. Demographic window to aging in the wild: constructing life tables and estimating survival functions from marked individuals of unknown age. Aging Cell 3, 125–131 (2004).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Zajitschek, F., Zajitschek, S. & Bonduriansky, R. Senescence in wild insects: Key questions and challenges. Funct. Ecol. 34, 26–37 (2020).Article 

Google Scholar 
28.Carey, J. R. et al. Age structure changes and extraordinary lifespan in wild medfly populations. Aging Cell 7, 426–437. https://doi.org/10.1111/j.1474-9726.2008.00390.x (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Rao, A. S. S. & Carey, J. R. Generalization of Carey’s equality and a theorem on stationary population. J. Math. Biol. 71, 583–594 (2015).MathSciNet 
MATH 
Article 

Google Scholar 
30.Carey, J. R. Biodemography of the Mediterranean fruit fly: Aging, longevity and adaptation in the wild. Exp. Gerontol. 46, 404–411. https://doi.org/10.1016/j.exger.2010.09.009 (2011).Article 
PubMed 

Google Scholar 
31.Muller, H. G., Wang, J. L., Yu, W., Delaigle, A. & Carey, J. R. Survival and aging in the wild via residual demography. Theor. Popul. Biol. 72, 513–522. https://doi.org/10.1016/j.tpb.2007.07.003 (2007).Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
32.Vaupel, J. Life lived and left: Carey’s equality. Demogr Res 20, 7–10. https://doi.org/10.4054/DemRes.2009.20.3 (2009).Article 

Google Scholar 
33.Carey, J. R., Papadopoulos, N. T., Papanastasiou, S., Diamantidis, A. & Nakas, C. T. Estimating changes in mean population age using the death distributions of live-captured medflies. Ecol. Entomol. 37, 359–369. https://doi.org/10.1111/j.1365-2311.2012.01372.x (2012).Article 

Google Scholar 
34.Papadopoulos, N. T. et al. Seasonality of post-capture longevity in a medically-important mosquito (Culex pipiens). Front. Ecol. Evol https://doi.org/10.3389/fevo.2016.00063 (2016).Article 

Google Scholar 
35.Behrman, E. L., Watson, S. S., O’Brien, K. R., Heschel, M. S. & Schmidt, P. S. Seasonal variation in life history traits in two Drosophila species. J Evol Biol 28, 1691–1704. https://doi.org/10.1111/jeb.12690 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Drew, R. A. The tropical fruit flies (Diptera: Tephritidae: Dacinae) of the Australasian and Oceanian regions. Mem. Queensland Museum 26, 1 (1989).
Google Scholar 
37.Dominiak, B. C. Components of a systems approach for the management of Queensland fruit fly Bactrocera tryoni (Froggatt) in a post dimethoate fenthion era. Crop prot. 116, 56–67 (2019).Article 

Google Scholar 
38.Boulter, S. L., Kitching, R. L. & Howlett, B. G. Family, visitors and the weather: patterns of flowering in tropical rain forests of northern Australia. J. Ecol. 94, 369–382. https://doi.org/10.1111/j.1365-2745.2005.01084.x (2006).Article 

Google Scholar 
39.Dominiak, B. C. & Mapson, R. Revised distribution of Bactrocera tryoni in eastern Australia and effect on possible incursions of Mediterranean fruit fly: Development of Australia’s eastern trading block. J. Econ. Entomol. 110, 2459–2465 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Bateman, M. Adaptations to temperature in geographic races of the Queensland fruit fly Dacus (Strumenta) tryoni. Aust. J. Zool. 15, 1141–1161 (1967).Article 

Google Scholar 
41.Bateman, M. Determinants of abundance in a population of the Queensland fruit fly. In: Southwood, T.R.E. (ed.) Insect abundance 119–131 (Blackwell Scientific Publications, London, 1968).42.Drew, R., Zalucki, M. & Hooper, G. Ecological studies of eastern Australian fruit flies (Diptera: Tephritidae) in their endemic habitat. Oecologia 64, 267–272 (1984).ADS 
CAS 
PubMed 
Article 

Google Scholar 
43.Muthuthantri, S., Maelzer, D., Zalucki, M. P. & Clarke, A. R. The seasonal phenology of Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) in Queensland. Aust. J. Entomol. 49, 221–233. https://doi.org/10.1111/j.1440-6055.2010.00759.x (2010).Article 

Google Scholar 
44.Lloyd, A. C. et al. Area-wide management of fruit flies (Diptera: Tephritidae) in the Central Burnett district of Queensland. Aust. J. Crop Prot. 29, 462–469. https://doi.org/10.1016/j.cropro.2009.11.003 (2010).CAS 
Article 

Google Scholar 
45.Pritchard, G. The ecology of a natural population of Queensland fruit fly, Dacus tryoni III. The maturation of female flies in relation to temperature. Aust. J. Zool. 18, 77–89 (1970).Article 

Google Scholar 
46.Clarke, A. R., Merkel, K., Hulthen, A. D. & Schwarzmueller, F. Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) overwintering: an overview. Aust. Entomol. 58, 3–8. https://doi.org/10.1111/aen.12369 (2019).Article 

Google Scholar 
47.Merkel, K. et al. Temperature effects on “overwintering” phenology of a polyphagous, tropical fruit fly (Tephritidae) at the subtropical/temperate interface. J. Appl. Entomol. 143, 754–765 (2019).CAS 
Article 

Google Scholar 
48.Raghu, S., Clarke, A. R., Drew, R. A. & Hulsman, K. Impact of habitat modification on the distribution and abundance of fruit flies (Diptera: Tephritidae) in Southeast Queensland. Popul. Ecol. 42, 153–160 (2000).Article 

Google Scholar 
49.Novotny, V., Clarke, A. R., Drew, R. A., Balagawi, S. & Clifford, B. Host specialization and species richness of fruit flies (Diptera: Tephritidae) in a New Guinea rain forest. J. Trop. Ecol. 21, 67–77 (2005).Article 

Google Scholar 
50.Fletcher, B. Temperature-regulated changes in the ovaries of overwintering females of the Queensland Fruit Fly, Dacus tryoni. Aust. J. Zool. 23, 91–102 (1975).Article 

Google Scholar 
51.Meats, A. & Fay, H. The effect of acclimation on mating frequency and mating competitiveness in the Queensland fruit fly, Dacus tryoni, in optimal and cool mating regimes. Physiol. Entomol. 1, 207–212 (1976).Article 

Google Scholar 
52.Balagawi, S. Comparative ecology of Bactrocera Cucumis (French) and Bactrocera Tryoni (Froggatt) (Diptera: Tephritidae)—Understanding the life history consequences of host selection and oviposition behavior. Unpublished Thesis, Griffith University (2006).53.Lee, K. P. et al. Lifespan and reproduction in Drosophila: New insights from nutritional geometry. Proc. Natl. Acad. Sci. 105, 2498–2503 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Carey, J. R., Liedo, P., Müller, H.-G., Wang, J.-L. & Vaupel, J. W. Dual modes of aging in Mediterranean fruit fly females. Science 281, 996–998 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Fanson, B. G. & Taylor, P. W. Protein: carbohydrate ratios explain life span patterns found in Queensland fruit fly on diets varying in yeast: Sugar ratios. Age 34, 1361–1368 (2012).CAS 
PubMed 
Article 

Google Scholar 
56.McElderry, R. M. Seasonal life history trade-offs in two leafwing butterflies: Delaying reproductive development increases life expectancy. J. Insect Physiol. 87, 30–34 (2016).CAS 
PubMed 
Article 

Google Scholar 
57.Werfel, J., Ingber, D. E. & Bar-Yam, Y. Theory and associated phenomenology for intrinsic mortality arising from natural selection. PLoS ONE 12, e0173677 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
58.Kozeretska, I. A., Serga, S. V., Koliada, A. K. & Vaiserman, A. M. Epigenetic regulation of longevity in insects. Adv. Insect Physiol. 53, 87–114 (2017).Article 

Google Scholar 
59.Meats, A. Critical periods for developmental acclimation to cold in the Queensland fruit fly. Dacus tryoni. J. Insect Physiol. 29, 943–946 (1983).Article 

Google Scholar 
60.Kumaran, N. et al. Plant-mediated female transcriptomic changes post-mating in a tephritid fruit fly, Bactrocera tryoni. Genome Biol. Evol. 10, 94–107 (2018).CAS 
PubMed 
Article 

Google Scholar 
61.Dominiak, B. C., Sundaralingam, S., Jiang, L., Jessup, A. & Barchia, I. Production levels and life history traits of mass reared Queensland fruit fly Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) during 1999/2002 in Australia. Plant Prot. Q. 23, 131–135 (2008).
Google Scholar 
62.Fanson, B., Sundaralingam, S., Jiang, L., Dominiak, B. & D’arcy, G. A review of 16 years of quality control parameters at a mass-rearing facility producing Queensland fruit fly, Bactrocera tryoni. Entomol. Exp. Appl. 151, 152–159 (2014).Article 

Google Scholar 
63.Papadopoulos, N., Katsoyannos, B., Carey, J. & Kouloussis, N. Seasonal and annual occurrence of the Mediterranean fruit fly (Diptera: Tephritidae) in northern Greece. Ann. Entomol. Soc. Am. 94, 41–50 (2001).Article 

Google Scholar 
64.Brakefield, P. M. & Reitsma, N. Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi. Ecol. Entomol. 16, 291–303 (1991).Article 

Google Scholar 
65.Molleman, F., Zwaan, B., Brakefield, P. & Carey, J. Extraordinary long life spans in fruit-feeding butterflies can provide window on evolution of life span and aging. Exp. Gerontol. 42, 472–482 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Denlinger, D. L. Dormancy in tropical insects. Ann. Rev. Entomol 31, 239–264 (1986).CAS 
Article 

Google Scholar 
67.Canzano, A. A., Jones, R. E. & Seymour, J. E. Diapause termination in two species of tropical butterfly, Euploea core (Cramer) and Euploea sylvester (Fabricius) (Lepidoptera: Nymphalidae). Aust. J. Entomol 42, 352–356 (2003).Article 

Google Scholar 
68.Lankinen, P. & Forsman, P. Independence of genetic geographical variation between photoperiodic diapause, circadian eclosion rhythm, and Thr-Gly repeat region of the period gene in Drosophila littoralis. J. Biol. Rhythms 21, 3–12 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Kouloussis, N. A. et al. Seasonal trends in Ceratitis capitata reproductive potential derived from live-caught females in Greece. Entomol. Exp. Appl. 140, 181–188. https://doi.org/10.1111/j.1570-7458.2011.01154.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Kouloussis, N. A. et al. Life table assay of field-caught Mediterranean fruit flies, Ceratitis capitata, reveals age bias. Entomol. Exp. Appl. 132, 172–181. https://doi.org/10.1111/j.1570-7458.2009.00879.x (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
71.Tasnin, M. S., Silva, R., Merkel, K. & Clarke, A. R. Response of male Queensland fruit fly (Diptera: Tephritidae) to host fruit odors. J. Econ. Entomol. 113, 1888–1893 (2020).PubMed 
Article 

Google Scholar 
72.Clarke, A. R., Powell, K. S., Weldon, C. W. & Taylor, P. W. The ecology of Bactrocera tryoni (Diptera: Tephritidae): What do we know to assist pest management?. Ann. Appl. Biol. 158, 26–54 (2011).Article 

Google Scholar 
73.Chinajariyawong, A., Drew, R., Meats, A., Balagawi, S. & Vijaysegaran, S. Multiple mating by females of two Bactrocera species (Diptera: Tephritidae: Dacinae). Bull. entomol. research 100, 325 (2010).CAS 
Article 

Google Scholar 
74.Pike, N. & Meats, A. Potential for mating between Bactrocera tryoni (Froggatt) and Bactrocera neohumeralis (hardy) (Diptera: Tephritidae). Aust. J. Entomol. 41, 70–74 (2002).Article 

Google Scholar 
75.Tasnin, M. S., Merkel, K. & Clarke, A. R. Effects of advanced age on olfactory response of male and female Queensland fruit fly, Bactrocera tryoni (Froggatt) (Diptera: Tephritidae). J. Insect Physiol. 122, 104024 (2020).CAS 
PubMed 
Article 

Google Scholar 
76.Perez-Staples, D., Prabhu, V. & Taylor, P. W. Post-teneral protein feeding enhances sexual performance of Queensland fruit flies. Physiol. Entomol. 32, 225–232 (2007).Article 

Google Scholar  More

1.Erisman, J. W. et al. Consequences of human modification of the global nitrogen cycle. Philos. Trans. RSoc. Lond. B Biol. Sci. 368, 20130116 (2013).Article 
CAS 

Google Scholar 
2.Fowler, D. et al. The global nitrogen cycle in the Twenty-First Century. Philos. Trans. RSoc. Lond. B Biol. Sci. 368, 20130164 (2013).Article 
CAS 

Google Scholar 
3.Francis, C. A., Beman, J. M. & Kuypers, M. M. M. New processes and players in the nitrogen cycle: the microbial ecology of anaerobic and archaeal ammonia oxidation. ISME J. 1, 19–27 (2007).CAS 
PubMed 
Article 

Google Scholar 
4.Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol. 16, 263–276 (2018).CAS 
PubMed 
Article 

Google Scholar 
5.Canfield, D. E., Glazer, A. N. & Falkowski, P. G. The evolution and future of Earth’s nitrogen cycle. Science 330, 192–196 (2010).CAS 
PubMed 
Article 

Google Scholar 
6.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Gruber, N. & Galloway, J. N. An Earth-system perspective of the global nitrogen cycle. Nature 451, 293–296 (2008).CAS 
PubMed 
Article 

Google Scholar 
8.Beeckman, F., Motte, H. & Beeckman, T. Nitrification in agricultural soils: impact, actors and mitigation. Curr.Opin. Biotechnol. 50, 166–173 (2018).CAS 
PubMed 
Article 

Google Scholar 
9.Könneke, M. et al. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546 (2005).PubMed 
Article 
CAS 

Google Scholar 
10.Tourna, M. et al. Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. Proc. Natl Acad. Sci. USA. 108, 8420–8425 (2011).CAS 
PubMed 
Article 

Google Scholar 
11.Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature. 528, 504–509 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.van Kessel, M. A. H. J. et al. Complete nitrification by a single microorganism. Nature 528, 555–559 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Leininger, S. et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442, 806–809 (2006).CAS 
PubMed 
Article 

Google Scholar 
14.Prosser, J. I. & Nicol, G. W. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ. Microbiol. 10, 2931–2941 (2008).CAS 
PubMed 
Article 

Google Scholar 
15.Prosser, J. I. & Nicol, G. W. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 20, 523–531 (2012).CAS 
PubMed 
Article 

Google Scholar 
16.Meinhardt, K. A. et al. Ammonia-oxidizing bacteria are the primary N2O producers in an ammonia-oxidizing archaea dominated alkaline agricultural soil. Environ. Microbiol. 20, 2195–2206 (2018).CAS 
PubMed 
Article 

Google Scholar 
17.Di, H. J. et al. Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nat. Geosci. 2, 621–624 (2009).CAS 
Article 

Google Scholar 
18.Prosser, J. I., Hink, L., Gubry-Rangin, C. & Nicol, G. W. Nitrous oxide production by ammonia oxidizers: physiological diversity, niche differentiation and potential mitigation strategies. Glob. Change Biol. 26, 103–118 (2020).Article 

Google Scholar 
19.Norton, J. & Ouyang, Y. Controls and adaptive management of nitrification in agricultural soils. Front. Microbiol. 10, 1931 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Pjevac, P. et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front. Microbiol. 8, 1508 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
21.Lawson, C. E. & Lücker, S. Complete ammonia oxidation: an important control on nitrification in engineered ecosystems? Curr. Opin. Biotechnol. 50, 158–165 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Orellana, L. H., Chee-Sanford, J. C., Sanford, R. A., Löffler, F. E. & Konstantinidis, K. T. Year-round shotgun metagenomes reveal stable microbial communities in agricultural soils and novel ammonia oxidizers responding to fertilization. Appl. Environ. Microbiol. 84, e01646–01617 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Kits, K. D. et al. Low yield and abiotic origin of N2O formed by the complete nitrifier Nitrospira inopinata. Nat. Commun. 10, 1836 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature. 549, 269–272 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Stein, L. Y. Insights into the physiology of ammonia-oxidizing microorganisms. Curr. Opin. Chem. Biol. 49, 9–15 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Lehtovirta-Morley, L. E. Ammonia oxidation: ecology, physiology, biochemistry and why they must all come together. FEMS Microbiol. Lett. 365, fny058–fny058 (2018).Article 
CAS 

Google Scholar 
27.Lu, X., Taylor, A. E., Myrold, D. D. & Neufeld, J. D. Expanding perspectives of soil nitrification to include ammonia-oxidizing archaea and comammox bacteria. Soil Sci. Soc. Am J. 84, 287–302 (2020).CAS 
Article 

Google Scholar 
28.Taylor, A. E., Zeglin, L. H., Wanzek, T. A., Myrold, D. D. & Bottomley, P. J. Dynamics of ammonia-oxidizing archaea and bacteria populations and contributions to soil nitrification potentials. ISME J. 6, 2024–2032 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Taylor, A. E. et al. Use of aliphatic n-alkynes to discriminate soil nitrification activities of ammonia-oxidizing Thaumarchaea and Bacteria. Appl. Environ. Microbiol. 79, 6544–6551 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Taylor, A. E., Giguere, A. T., Zoebelein, C. M., Myrold, D. D. & Bottomley, P. J. Modeling of soil nitrification responses to temperature reveals thermodynamic differences between ammonia-oxidizing activity of archaea and bacteria. ISME J. 11, 896–908 (2017).CAS 
PubMed 
Article 

Google Scholar 
31.Hink, L., Gubry-Rangin, C., Nicol, G. W. & Prosser, J. I. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 12, 1084–1093 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Hink, L., Nicol, G. W. & Prosser, J. I. Archaea produce lower yields of N2O than bacteria during aerobic ammonia oxidation in soil. Environ. Microbiol. 19, 4829–4837 (2017).CAS 
PubMed 
Article 

Google Scholar 
33.Levičnik-Höfferle, Š., Nicol, G. W., Ausec, L., Mandić-Mulec, I. & Prosser, J. I. Stimulation of Thaumarchaeal ammonia oxidation by ammonia derived from organic nitrogen but not added inorganic nitrogen. FEMS Microbiol. Ecol. 80, 114–123 (2012).PubMed 
Article 
CAS 

Google Scholar 
34.Stopnisek, N. et al. Thaumarchaeal ammonia oxidation in an acidic forest peat soil is not Influenced by ammonium amendment. Appl. Environ. Microbiol. 76, 7626–7634 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Verhamme, D. T., Prosser, J. I. & Nicol, G. W. Ammonia concentration determines differential growth of ammonia-oxidising archaea and bacteria in soil microcosms. ISME J. 5, 1067–1071 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Rodriguez, A. F., Gerber, S. & Daroub, S. H. Modeling soil subsidence in a subtropical drained peatland. The case of the everglades agricultural Area. Ecol. Modelling. 415, 108859 (2020).Article 

Google Scholar 
37.Terry, R. E. Nitrogen mineralization in Florida histosols. Soil Sci. Soc. Am. J. 44, 747–750 (1980).CAS 
Article 

Google Scholar 
38.Zhalnina, K. et al. Ca. Nitrososphaera and Bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front. Microbiol. 4, 104 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Hart S. C., Stark, J. M., Davidson, E. A., Firestone, M. K. Nitrogen mineralization, immobilization, and nitrification. In Methods of soil analysis (eds, Weaver, R.W., Angle, S., Bottomley, P., Bezdicek, D., Smith, S., Tabatabai, A. et al). pp 985–1018. (Soil Science Society of America, 1994).40.Martens-Habbena, W. et al. The production of nitric oxide by marine ammonia-oxidizing archaea and inhibition of archaeal ammonia oxidation by a nitric oxide scavenger. Environ. Microbiol. 17, 2261–2274 (2015).CAS 
PubMed 
Article 

Google Scholar 
41.Tourna, M., Freitag, T. E., Nicol, G. W. & Prosser, J. I. Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ. Microbiol. 10, 1357–1364 (2008).CAS 
PubMed 
Article 

Google Scholar 
42.Rotthauwe, J. H., Witzel, K. P. & Liesack, W. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl. Environ. Microbiol. 63, 4704–4712 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Hill, J. T. et al. Poly peak parser: Method and software for identification of unknown indels using sanger sequencing of polymerase chain reaction products. Devel Dyn. 243, 1632–1636 (2014).CAS 
Article 

Google Scholar 
44.Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
46.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
Article 

Google Scholar 
48.Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
49.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Oksanen J., et al. vegan: Community ecology package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan. (2019).53.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).54.Wickham H. ggplot2: Elegant graphics for data analysis. (Springer, 2016).55.Ouyang, Y., Norton, J. M. & Stark, J. M. Ammonium availability and temperature control contributions of ammonia oxidizing bacteria and archaea to nitrification in an agricultural soil. Soil Biol. Biochem. 113, 161–172 (2017).CAS 
Article 

Google Scholar 
56.Ouyang, Y., Evans, S. E., Friesen, M. L. & Tiemann, L. K. Effect of nitrogen fertilization on the abundance of nitrogen cycling genes in agricultural soils: A meta-analysis of field studies. Soil Biol. Biochem. 127, 71–78 (2018).CAS 
Article 

Google Scholar 
57.Ouyang, Y., Norton, J. M., Stark, J. M., Reeve, J. R. & Habteselassie, M. Y. Ammonia-oxidizing bacteria are more responsive than archaea to nitrogen source in an agricultural soil. Soil Biol. Biochem. 96, 4–15 (2016).CAS 
Article 

Google Scholar 
58.Norton, J. M., Alzerreca, J. J., Suwa, Y. & Klotz, M. G. Diversity of ammonia monooxygenase operon in autotrophic ammonia-oxidizing bacteria. Arch. Microbiol. 177, 139–149 (2002).CAS 
PubMed 
Article 

Google Scholar 
59.Shen, T., Stieglmeier, M., Dai, J., Urich, T. & Schleper, C. Responses of the terrestrial ammonia-oxidizing archaeon Ca. Nitrososphaera viennensis and the ammonia-oxidizing bacterium Nitrosospira multiformis to nitrification inhibitors. FEMS Microbiol. Lett. 344, 121–129 (2013).CAS 
PubMed 
Article 

Google Scholar 
60.Sauder, L. A., Ross, A. A. & Neufeld, J. D. Nitric oxide scavengers differentially inhibit ammonia oxidation in ammonia-oxidizing archaea and bacteria. FEMS Microbiol. Lett. 363, fnw052 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
61.Stieglmeier, M. et al. Aerobic nitrous oxide production through N-nitrosating hybrid formation in ammonia-oxidizing archaea. ISME J. 8, 1135–1146 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Erguder, T. H., Boon, N., Wittebolle, L., Marzorati, M. & Verstraete, W. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol. Rev. 33, 855–869 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Zhalnina, K., Dörr de Quadros, P., Camargo, F. A. O. & Triplett, E. W. Drivers of archaeal ammonia-oxidizing communities in soil. Front Microbiol. 3, 210 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Thion, C. E. et al. Plant nitrogen-use strategy as a driver of rhizosphere archaeal and bacterial ammonia oxidiser abundance. FEMS Microbiol. Ecol. 92, fiw091 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
65.Coskun, D., Britto, D. T., Shi, W. & Kronzucker, H. J. How plant root exudates shape the nitrogen cycle. Trends Plant Sci. 22, 661–673 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Marschner P. Mineral nutrition of higher plants. (Academic Press, 2012).67.Coskun, D., Britto, D. T., Shi, W. & Kronzucker, H. J. Nitrogen transformations in modern agriculture and the role of biological nitrification inhibition. Nat. Plants. 3, 17074 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Masclaux-Daubresse, C. et al. Nitrogen uptake, assimilation and remobilization in plants: challenges for sustainable and productive agriculture. Ann. Bot. 105, 1141–1157 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Button, D. K., Robertson, B. R., Lepp, P. W. & Schmidt, T. M. A small, dilute-cytoplasm, high-affinity, novel bacterium isolated by extinction culture and having kinetic constants compatible with growth at ambient concentrations of dissolved nutrients in seawater. Appl. Environ. Microbiol. 64, 4467–4476 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Martens-Habbena, W., Berube, P. M., Urakawa, H., Torre, J. R. & Stahl, D. A. Ammonia oxidation kinetics determine niche separation of nitrifying archaea and bacteria. Nature. 461, 976–979 (2009).CAS 
PubMed 
Article 

Google Scholar 
71.Ferreira, D. A. et al. Contribution of N from green harvest residues for sugarcane nutrition in Brazil. GCB Bioenergy. 8, 859–866 (2016).Article 

Google Scholar 
72.Li, J., Pei, J., Pendall, E., Fang, C. & Nie, M. Spatial heterogeneity of temperature sensitivity of soil respiration: a global analysis of field observations. Soil Biol. Biochem. 141, 107675 (2020).CAS 
Article 

Google Scholar 
73.Maathuis, F. J. M. Physiological functions of mineral macronutrients. Curr. Opin. Plant Biol. 12, 250–258 (2009).CAS 
PubMed 
Article 

Google Scholar 
74.Song, G. C. et al. Plant growth-promoting archaea trigger induced systemic resistance in Arabidopsis thaliana against Pectobacterium carotovorum and Pseudomonas syringae. Environ. Microbiol. 21, 940–948 (2019).CAS 
PubMed 
Article 

Google Scholar  More

1.Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Rulli, M. C., Santini, M., Hayman, D. T. & D’Odorico, P. The nexus between forest fragmentation in Africa and Ebola virus disease outbreaks. Sci. Rep. 7, 41613 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Espinosa, R., Tago, D. & Treich, N. Infectious diseases and meat production. Environ. Resource Econ. 76, 1019–1044 (2020).4.Young, H., Griffin, R. H., Wood, C. L. & Nunn, C. L. Does habitat disturbance increase infectious disease risk for primates? Ecol. Lett. 16, 656–663 (2013).
Google Scholar 
5.Gottdenker, N. L., Streicker, D. G., Faust, C. L. & Carroll, C. R. Anthropogenic land use change and infectious diseases: a review of the evidence. EcoHealth 11, 619–632 (2014).
Google Scholar 
6.Rohr et al. Emerging human infectious diseases and the links to global food production. Nat. Sustain. 2, 445–456 (2019).PubMed 
PubMed Central 

Google Scholar 
7.Zhou, P. et al. A pnemonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Lam, T. T. et al. Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).9.Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).ADS 
CAS 

Google Scholar 
10.Hassell, J. M., Begon, M., Ward, M. J. & Fèvre, E. M. Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface. Trends Ecol. Evol. 32, 55–67 (2017).PubMed 
PubMed Central 

Google Scholar 
11.Shah, H. A., Huxley, P., Elmes, J. & Murray, K. A. Agricultural land-uses consistently exacerbate infectious disease risks in Southeast Asia. Nat. Commun. 10, 4299 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
12.Godfray, H. C. J. et al. Meat consumption, health, and the environment. Science 361, eaam5324 (2018).
Google Scholar 
13.Delgado, C., Rosegrant, M., Steinfeld, H., Ehui, S. & Courbois, C. Livestock to 2020: The Next Food Revolution. Food, Agriculture, and the Environment Discussion Paper 28 (International Food Policy Research Institute, 1999).14.Coker, R. et al. Towards a conceptual framework to support one-health research for policy on emerging zoonoses. Lancet Infect. Dis. 11, P326–P331 (2011).
Google Scholar 
15.Wu et al. Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: a review. Ambio 46, 18–29 (2017).CAS 

Google Scholar 
16.Wilkinson, D. A., Marshall, J. C., French, N. P. & Hayman, D. T. Habitat fragmentation, biodiversity loss and the risk of novel infectious disease emergence. J. R. Soc. Interface 15, 20180403 (2018).PubMed 
PubMed Central 

Google Scholar 
17.Johnson, C. K. et al. Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proc. R. Soc. B 287, 20192736 (2020).
Google Scholar 
18.Bloomfield, L. S. P., McIntosh, T. L. & Lambin, E. F. Habitat fragmentation, livelihood behaviors, and contact between people and nonhuman primates in Africa. Landsc. Ecol. 35, 985–1000 (2020).
Google Scholar 
19.Pulliam, J. R. et al. Agricultural intensification, priming for persistence and the emergence of Nipah virus: a lethal bat-borne zoonosis. J. R. Soc. Interface 9, 89–101 (2012).
Google Scholar 
20.Zhou, P. et al. Fatal swine acute diarrhoea syndrome caused by an HKU2-related coronavirus of bat origin. Nature 5556, 255–258 (2018).ADS 

Google Scholar 
21.Allen, T. et al. Global hotspots and correlates of emerging zoonotic diseases. Nat. Commun. 8, 1124 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
22.Meyer, C. F., Struebig, M. J. & Willig, M. R. in Bats in the Anthropocene: Conservation of Bats in a Changing World (eds Voigt, C.C. & Kingston, T.) 63–103 (Springer, 2016).23.Gibb, R. et al. Zoonotic host diversity increases in human-dominated ecosystems. Nature 584, 398–402 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Cui, J., Li, F. & Shi, Z. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 17, 181–192 (2019).CAS 

Google Scholar 
25.Hul V. et al. A novel SARS-CoV-2 related coronavirus in bats from Cambodia. Preprint at https://doi.org/10.1101/2021.01.26.428212 (2021).26.Murakami, S. et al. Detection and characterization of bat Sarbecovirus phylogenetically related to SARS-CoV-2, Japan. Emerg. Infect. Dis. 26, 3025 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Soman Pillai, V., Krishna, G. & Valiya Veettil, M. Nipah virus: past outbreaks and future containment. Viruses. 12, 465 (2020).PubMed 
PubMed Central 

Google Scholar 
29.Weingartl, H. M. et al. Susceptibility of pigs and chickens to SARS coronavirus. Emerg. Infect. Dis. 10, 179–184 (2004).PubMed 
PubMed Central 

Google Scholar 
30.Schlottau, K. et al. SARS-CoV-2 in fruit bats, ferrets, pigs, and chickens: an experimental transmission study. Lancet Microbe 1, e218–e225 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Munnink, B. B. O. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021).ADS 

Google Scholar 
32.Zhou, L. et al. The re‐emerging of SADS‐CoV infection in pig herds in southern China. Transbound. Emerg. Dis. 66, 2180–2183 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
Google Scholar 
34.Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368, 395–400 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Yang, Q. et al. Assessing the role of live poultry trade in community-structured transmission of avian influenza in China. Proc. Natl Acad. Sci. USA 117, 5949–5954 (2020).ADS 
CAS 

Google Scholar 
36.D’Odorico, P. et al. The global food–energy–water nexus. Rev. Geophys. 56, 456–531 (2018).
Google Scholar 
37.Meyfroidt, P., Lambin, E. F., Erb, K. H. & Hertel, T. W. Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr. Opin. Environ. Sustain. 5, 438–444 (2013).
Google Scholar 
38.Ning, J. et al. Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. In J. Geogr. Sci. 28, 547–562 (2018).
Google Scholar 
39.Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
PubMed Central 

Google Scholar 
40.Liu, J. et al. Forest fragmentation in China and its effect on biodiversity. Biol. Rev. 94, 1636–1657 (2019).
Google Scholar 
41.Whitmee, S. et al. Safeguarding human health in the Anthropocene epoch: report of the Rockefeller Foundation–Lancet Commission on planetary health. Lancet 386, 1973–2028 (2015).
Google Scholar 
42.Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. & Garry, R. F. The proximal origin of SARS-CoV-2. Nat. Med. 26, 450–452 (2020).CAS 

Google Scholar 
43.Dietz, C., Dietz, I., Ivanova, T. & Siemers, B. M. Seasonal and regional scale movements of horseshoe bats (Rhinolophus, Chiroptera: Rhinolophidae) in northern Bulgaria. Nyctalus NF 14, 52–64 (2009).
Google Scholar 
44.Wang, J. et al. Seasonal habitat use by greater horseshoe bat Rhinolophus ferrumequinum (Chiroptera: Rhinolophidae) in Changbai Mountain temperate forest, northeast China. Mammalia 74, 257–266 (2010).
Google Scholar 
45.Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
46.Teluguntla, P. et al. in Land Resources: Monitoring, Modelling, and Mapping, Remote Sensing Handbook Vol. II (eds Prasad, S. & Thenkabail, P. S.) Ch. 7 (CRC Press Inc, 2014).47.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 

Google Scholar 
48.Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).PubMed 
PubMed Central 

Google Scholar 
49.Congalton, R. G. et al. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m: Cropland Extent Validation (GFSAD30VAL) (NASA EOSDIS Land Processes DAAC, 2017); https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30VAL.00150.Nieves, J. J. et al. Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. Comput. Environ. Urban Syst. 80, 101444 (2020).PubMed 
PubMed Central 

Google Scholar 
51.Vogt, P., Riitters, K. H., Estreguil, C. J., Kozak, T. G. & Wade, J. D. Wickham mapping spatial patterns with morphological image processing. Landsc. Ecol. 22, 171–177 (2007).
Google Scholar 
52.Assuncao, R. M., Neves, M. C., Camara, G. & Da Costa Freitas, C. Efficient regionalisation techniques for socio-economic geographical units using minimum spanning trees. Int. J. Geogr. Inf. Sci. 20, 797–811 (2006).
Google Scholar  More