Philippa Kaur

1.Lalli, C. & Parsons, T. R. Biological Oceanography: An Introduction (Elsevier, 1997).
Google Scholar 
2.Mackas, D. L., Denman, K. L. & Abbott, M. R. Plankton patchiness: Biology in the physical vernacular. Bull. Mar. Sci. 37, 652–674 (1985).
Google Scholar 
3.Kjerfve, B. & Magill, K. E. Geographic and hydrodynamic characteristics of shallow coastal lagoons. Mar. Geol. 88, 187–199 (1989).ADS 
Article 

Google Scholar 
4.Kjerfve, B. Chapter 1, Coastal lagoons. In Elsevier Oceanography Series Vol. 60 (ed. Kjerfve, B.) 1–8 (Elsevier, 1994).
Google Scholar 
5.McManus, M. A. & Woodson, C. B. Plankton distribution and ocean dispersal. J. Exp. Biol. 215, 1008–1016 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Carr, M.-E. Estimation of potential productivity in Eastern Boundary Currents using remote sensing. Deep Sea Res. Part II 49, 59–80 (2001).ADS 
Article 

Google Scholar 
7.Chavez, F. P. & Messié, M. A comparison of Eastern Boundary Upwelling Ecosystems. Prog. Oceanogr. 83, 80–96 (2009).ADS 
Article 

Google Scholar 
8.Schubert, H. & Telesh, I. Estuaries and coastal lagoons. In Biological Oceanography of the Baltic Sea (eds Snoeijs-Leijonmalm, P. et al.) 483–509 (Springer Netherlands, 2017). https://doi.org/10.1007/978-94-007-0668-2_13.Chapter 

Google Scholar 
9.Pecqueur, D. et al. Dynamics of microbial planktonic food web components during a river flash flood in a Mediterranean coastal lagoon. Hydrobiologia 673, 13–27 (2011).CAS 
Article 

Google Scholar 
10.Deininger, A. et al. Simulated terrestrial runoff triggered a phytoplankton succession and changed seston stoichiometry in coastal lagoon mesocosms. Mar. Environ. Res. 119, 40–50 (2016).CAS 
PubMed 
Article 

Google Scholar 
11.Newton, A. & Mudge, S. M. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuar. Coast. Shelf Sci. 57, 73–85 (2003).ADS 
CAS 
Article 

Google Scholar 
12.Huang, J., Gao, J. & Hörmann, G. Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China. Limnologica 42, 7–18 (2012).CAS 
Article 

Google Scholar 
13.Pérez-Ruzafa, A. et al. Connectivity between coastal lagoons and sea: Asymmetrical effects on assemblages’ and populations’ structure. Estuar. Coast. Shelf Sci. 216, 171–186 (2019).ADS 
Article 

Google Scholar 
14.Dube, A., Jayaraman, G. & Rani, R. Modelling the effects of variable salinity on the temporal distribution of plankton in shallow coastal lagoons. J. Hydro-environ. Res. 4, 199–209 (2010).Article 

Google Scholar 
15.Pulina, S., Satta, C. T., Padedda, B. M., Sechi, N. & Lugliè, A. Seasonal variations of phytoplankton size structure in relation to environmental variables in three Mediterranean shallow coastal lagoons. Estuar. Coast. Shelf Sci. 212, 95–104 (2018).ADS 
Article 

Google Scholar 
16.Millet, B. & Cecchi, P. Wind-induced hydrodynamic control of the phytoplankton biomass in a lagoon ecosystem. Limnol. Oceanogr. 37, 140–146 (1992).ADS 
Article 

Google Scholar 
17.Souchu, P. et al. Influence of shellfish farming activities on the biogeochemical composition of the water column in Thau lagoon. Mar. Ecol. Prog. Ser. 218, 141–152 (2001).ADS 
CAS 
Article 

Google Scholar 
18.Paphitis, D. & Collins, M. B. Sediment resuspension events within the (microtidal) coastal waters of Thermaikos Gulf, northern Greece. Cont. Shelf Res. 25, 2350–2365 (2005).ADS 
Article 

Google Scholar 
19.Trombetta, T. et al. Water temperature drives phytoplankton blooms in coastal waters. PLoS One 14, e0214933 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
21.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
22.Trombetta, T., Vidussi, F., Roques, C., Scotti, M. & Mostajir, B. Marine microbial food web networks during phytoplankton bloom and non-bloom periods: Warming favors smaller organism interactions and intensifies trophic cascade. Front. Microbiol. 11, 502336 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).CAS 
PubMed 
Article 

Google Scholar 
24.Hemraj, D. A., Hossain, A., Ye, Q., Qin, J. G. & Leterme, S. C. Anthropogenic shift of planktonic food web structure in a coastal lagoon by freshwater flow regulation. Sci. Rep. 7, 44441 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bec, B., Husseini Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
Article 

Google Scholar 
26.Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
Article 

Google Scholar 
27.Collos, Y. et al. Pheopigment dynamics, zooplankton grazing rates and the autumnal ammonium peak in a Mediterranean lagoon. Hydrobiologia 550(1), 83–93 (2005).CAS 
Article 

Google Scholar 
28.Gangnery, A. et al. Growth model of the Pacific oyster, Crassostrea gigas, cultured in Thau Lagoon (Méditerranée, France). Aquaculture 215, 267–290 (2003).Article 

Google Scholar 
29.Pernet, F. et al. Marine diatoms sustain growth of bivalves in a Mediterranean lagoon. J. Sea Res. 68, 20–32 (2012).ADS 
CAS 
Article 

Google Scholar 
30.Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895 (2007).ADS 
Article 

Google Scholar 
31.Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226 (2011).Article 

Google Scholar 
32.Jones, K. J. & Gowen, R. J. Influence of stratification and irradiance regime on summer phytoplankton composition in coastal and shelf seas of the British Isles. Estuar. Coast. Shelf Sci. 30, 557–567 (1990).ADS 
Article 

Google Scholar 
33.Bosak, S., Godrijan, J. & Šilović, T. Dynamics of the marine planktonic diatom family Chaetocerotaceae in a Mediterranean coastal zone. Estuar. Coast. Shelf Sci. 180, 69–81 (2016).ADS 
Article 

Google Scholar 
34.Wagner, C. & Adrian, R. Consequences of changes in thermal regime for plankton diversity and trait composition in a polymictic lake: A matter of temporal scale. Freshw. Biol. 56, 1949–1961 (2011).Article 

Google Scholar 
35.Rynearson, T. A., Flickinger, S. A. & Fontaine, D. N. Metabarcoding reveals temporal patterns of community composition and realized thermal niches of Thalassiosira spp. (Bacillariophyceae) from the Narragansett Bay long-term plankton time series. Biology 9, 19 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
36.Delaney, M. P. Effects of temperature and turbulence on the predator–prey interactions between a heterotrophic flagellate and a marine bacterium. Microb. Ecol. 45, 218–225 (2003).CAS 
PubMed 
Article 

Google Scholar 
37.Peter, K. H. & Sommer, U. Phytoplankton cell size: Intra- and interspecific effects of warming and grazing. PLoS One 7, e49632 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.van Donk, E. & Kilham, S. S. Temperature effects on silicon- and phosphorus-limited growth and competitive interactions among three diatoms. J. Phycol. 26, 40–50 (1990).Article 

Google Scholar 
39.Stelzer, C.-P. Population growth in planktonic rotifers. Does temperature shift the competitive advantage for different species? In Rotifera VIII: A Comparative Approach (eds Wurdak, E. et al.) 349–353 (Springer Netherlands, 1998).Chapter 

Google Scholar 
40.Arandia-Gorostidi, N., Weber, P. K., Alonso-Sáez, L., Morán, X. A. G. & Mayali, X. Elevated temperature increases carbon and nitrogen fluxes between phytoplankton and heterotrophic bacteria through physical attachment. ISME 11, 641–650 (2017).CAS 
Article 

Google Scholar 
41.Peacock, E. E., Olson, R. J. & Sosik, H. M. Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Sci. 503, 1–10 (2014).ADS 
Article 

Google Scholar 
42.Käse, L. et al. Host-parasitoid associations in marine planktonic time series: Can metabarcoding help reveal them?. PLoS One 16, e0244817 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Caron, D. A., Dennett, M. R., Lonsdale, D. J., Moran, D. M. & Shalapyonok, L. Microzooplankton herbivory in the Ross Sea, Antarctica. Deep Sea Res. Part II 47, 3249–3272 (2000).ADS 
Article 

Google Scholar 
44.Vidussi, F. et al. Effects of experimental warming and increased ultraviolet B radiation on the Mediterranean plankton food web. Limnol. Oceanogr. 56, 206–218 (2011).ADS 
CAS 
Article 

Google Scholar 
45.Balas, L. & Özhan, E. Three-dimensional modelling of stratified coastal waters. Estuar. Coast. Shelf Sci. 54, 75–87 (2002).ADS 
Article 

Google Scholar 
46.Dalu, T., Richoux, N. B. & Froneman, P. W. Distribution of benthic diatom communities in a permanently open temperate estuary in relation to physico-chemical variables. S. Afr. J. Bot. 107, 31–38 (2016).Article 

Google Scholar 
47.Sommer, U., Peter, K. H., Genitsaris, S. & Moustaka-Gouni, M. Do marine phytoplankton follow Bergmann’s rule sensu lato?. Biol. Rev. 92, 1011–1026 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems (Cambridge University Press, 1994).Book 

Google Scholar 
49.Litchman, E., de TezanosPinto, P., Klausmeier, C. A., Thomas, M. K. & Yoshiyama, K. Linking traits to species diversity and community structure in phytoplankton. In Fifty years after the “Homage to Santa Rosalia”: Old and new paradigms on biodiversity in aquatic ecosystems (eds Naselli-Flores, L. & Rossetti, G.) 15–28 (Springer Netherlands, 2010).Chapter 

Google Scholar 
50.Unrein, F., Gasol, J. M., Not, F., Forn, I. & Massana, R. Mixotrophic haptophytes are key bacterial grazers in oligotrophic coastal waters. ISME 8, 164–176 (2014).CAS 
Article 

Google Scholar 
51.Polimene, L. et al. Modelling a light-driven phytoplankton succession. J. Plankton Res. 36, 214–229 (2014).CAS 
Article 

Google Scholar 
52.Hatzaki, M. et al. Seasonal aspects of an objective climatology of anticyclones affecting the Mediterranean. J. Clim. 27, 9272–9289 (2014).ADS 
Article 

Google Scholar 
53.Mostajir, B. et al. Experimental test of the effect of ultraviolet-B radiation in a planktonic community. Limnol. Oceanogr. 44, 586–596 (1999).ADS 
Article 

Google Scholar 
54.Lacuna, D. G. & Uye, S.-I. Influence of mid-ultraviolet (UVB) radiation on the physiology of the marine planktonic copepod Acartia omorii and the potential role of photoreactivation. J. Plankton Res. 23, 143–156 (2001).Article 

Google Scholar 
55.Halac, S. et al. An in situ enclosure experiment to test the solar UVB impact on plankton in a high-altitude mountain lake. I. Lack of effect on phytoplankton species composition and growth. J. Plankton Res. 19, 1671–1686 (1997).CAS 
Article 

Google Scholar 
56.Souchu, P. et al. Patterns in nutrient limitation and chlorophyll a along an anthropogenic eutrophication gradient in French Mediterranean coastal lagoons. Can. J. Fish. Aquat. Sci. 67, 743–753 (2010).CAS 
Article 

Google Scholar 
57.Litchman, E. & Klausmeier, C. A. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39, 615–639 (2008).Article 

Google Scholar 
58.Reid, P. C., Lancelot, C., Gieskes, W. W. C., Hagmeier, E. & Weichart, G. Phytoplankton of the North Sea and its dynamics: A review. Neth. J. Sea Res. 26, 295–331 (1990).Article 

Google Scholar 
59.Derolez, V. et al. Two decades of oligotrophication: Evidence for a phytoplankton community shift in the coastal lagoon of Thau (Mediterranean Sea, France). Estuar. Coast. Shelf Sci. 241, 106810 (2020).Article 

Google Scholar 
60.Yool, A., Martin, A. P., Fernández, C. & Clark, D. R. The significance of nitrification for oceanic new production. Nature 447, 999–1002 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
61.Constantin, S., Constantinescu, Ș & Doxaran, D. Long-term analysis of turbidity patterns in Danube Delta coastal area based on MODIS satellite data. J. Mar. Syst. 170, 10–21 (2017).Article 

Google Scholar 
62.de Jorge, V. N. & van Beusekom, J. E. E. Wind- and tide-induced resuspension of sediment and microphytobenthos from tidal flats in the Ems estuary. Limnol. Oceanogr. 40, 776–778 (1995).ADS 
Article 

Google Scholar 
63.Ubertini, M. et al. Spatial variability of benthic-pelagic coupling in an estuary ecosystem: Consequences for microphytobenthos resuspension phenomenon. PLoS One 7, e44155 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Madoni, P. Benthic ciliates in Adriatic Sea lagoons. Eur. J. Protistol. 42, 165–173 (2006).PubMed 
Article 

Google Scholar 
65.Cruz, J. et al. Plankton community and copepod production in a temperate coastal lagoon: What is changing in a short temporal scale?. J. Sea Res. 157, 101858 (2020).Article 

Google Scholar 
66.Audouin, J. Hydrologie de l’étang de Thau. Rev. Trav. Inst. Pêches Marit. 26, 5–104 (1962).
Google Scholar 
67.Byun, D. S., Wang, X. H. & Holloway, P. E. Tidal characteristic adjustment due to dyke and seawall construction in the Mokpo Coastal Zone, Korea. Estuar. Coast. Shelf Sci. 59, 185–196 (2004).ADS 
Article 

Google Scholar 
68.Stefanidou, N., Genitsaris, S., Lopez-Bautista, J., Sommer, U. & Moustaka-Gouni, M. Unicellular eukaryotic community response to temperature and salinity variation in mesocosm experiments. Front. Microbiol. 9, 2444 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Xu, N. et al. Effects of temperature, salinity and irradiance on the growth of the harmful dinoflagellate Prorocentrum donghaiense Lu. Harmful Algae 9, 13–17 (2010).Article 

Google Scholar 
70.Greenwald, G. M. & Hurlbert, S. H. Microcosm analysis of salinity effects on coastal lagoon plankton assemblages. In Saline Lakes V (ed. Hurlbert, S. H.) 307–335 (Springer Netherlands, 1993).Chapter 

Google Scholar 
71.Fiandrino, A., Giraud, A., Robin, S. & Pinatel, C. Validation d’une méthode d’estimation des volumes d’eau échangés entre la mer et les lagunes et définition d’indicateurs hydrodynamiques associés (2012).72.Mostajir, B., Mas, S., Parin, D. & Vidussi, F. High-Frequency physical, biogeochemical and meteorological data of Coastal Mediterranean Thau Lagoon Observatory. SEANOE (2018).73.Données Publiques de Météo-France—Accueil. https://donneespubliques.meteofrance.fr/.74.Kraberg, A., Baumann, M. & Dürselen, C.-D. Coastal phytoplankton: Photo guide for Northern European seas (Univerza v Ljubljani, 2010).
Google Scholar 
75.Bérard-Therriault, L., Poulin, M. & Bossé, L. Guide d’identification du phytoplancton marin de l’estuaire et du golfe du Saint-Laurent incluant également certains protozoaires Canadian Special Publication of Fisheries and Aquatic Sciences No. 128 (NRC Research Press, 1999).
Google Scholar  More

1.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. https://doi.org/10.1111/j.1744-7348.2010.00448.x (2011).Article 

Google Scholar 
2.Jessup, A. J. et al. in Area-Wide Control of Insect Pests: From Research to Field Implementation (eds M. J. B. Vreysen, A. S. Robinson, & J. Hendrichs) 685–697 (Springer Netherlands, 2007).3.HIA. Australian Horticulture Statistics Handbook 2019/20. Horticulture Innovation Australia Limited (2020).4.Fanson, B. G., Sundaralingam, S., Jiang, L., Dominiak, B. C. & 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. https://doi.org/10.1111/eea.12180 (2014)5.Knipling, E. F. Possibilities of Insect Control or Eradication Through the Use of Sexually Sterile Males. J. Econ. Entomol. 48, 459–462. https://doi.org/10.1093/jee/48.4.459 (1955).Article 

Google Scholar 
6.Shelly, T. & McInnis, D. Sterile Insect Technique and Control of Tephritid Fruit Flies: Do Species With Complex Courtship Require Higher Overflooding Ratios?. Ann. Entomol. Soc. Am. 109, 1–11. https://doi.org/10.1093/aesa/sav101 (2015).CAS 
Article 

Google Scholar 
7.Hendrichs, J. & Robinson, A. in Encyclopedia of Insects (Second Edition) (eds Vincent H. Resh & Ring T. Cardé) 953–957 (Academic Press, 2009).8.Dominiak, B., Clifford, C. S. & Nielsen, S. G. Queensland fruit fly (Bactrocera tryoni Froggatt) attraction to and chemical analysis of male annihilation blocks using three concentratios of cuelure at Dubbo, NSW, Australia. Plant Prot. Q. 24, 157–160 (2009).9.Dominiak, B. C., Ekman, J. & Broughton, S. Mass trapping and other management option for mediterranean fruit fly and Queensland fruit fly in Australia. Gen. Appl. Entomol. 44, 1–8 (2016).
Google Scholar 
10.Khan, M. A. M. et al. Semiochemical mediated enhancement of males to complement sterile insect technique in management of the tephritid pest Bactrocera tryoni (Froggatt). Sci. Rep. 7, 13366. https://doi.org/10.1038/s41598-017-13843-w (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Vargas, R. I., Shelly, T. E., Leblanc, L. & Piñero, J. C. in Vitamins & Hormones Vol. 83 (ed Gerald Litwack) Ch. 23, 575–595 (Academic Press, 2010).12.Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: current knowledge and potential applications for Integrated Pest Management. J. Pest. Sci. 87, 385–405. https://doi.org/10.1007/s10340-014-0577-3 (2014).Article 

Google Scholar 
13.Shelly, T. E. Consumption of methyl eugenol by male Bactrocera dorsalis (Diptera: Tephritidae): low incidence of repeat feeding. Florida Entomologist 77, 201–208 (1994).CAS 
Article 

Google Scholar 
14.Shelly, T. E. Trapping male oriental fruit flies (Diptera: Tephritidae): does feeding on a natural source of methyl eugenol reduce capture probability?. Florida Entomologist 83, 109–111 (2000).Article 

Google Scholar 
15.Tan, K. H., & Toong, Y. C. Floral synomone of a wild orchid, Bulbophyllum cheiri, lures Bactrocera fruit flies for pollination. J. Chem. Ecol. 28, 1161–1172 (2002).16.Shelly, T. E. Evaluation of a Genetic Sexing Strain of the Oriental Fruit Fly as a Candidate for Simultaneous Application of Male Annihilation and Sterile Insect Techniques (Diptera: Tephritidae). J. Econ. Entomol. 113, 1913–1921. https://doi.org/10.1093/jee/toaa099 (2020).CAS 
Article 
PubMed 

Google Scholar 
17.Joseph, R. M. & Carlson, J. R. Drosophila Chemoreceptors: A Molecular Interface Between the Chemical World and the Brain. Trends in genetics : TIG 31, 683–695. https://doi.org/10.1016/j.tig.2015.09.005 (2015).CAS 
Article 
PubMed 

Google Scholar 
18.Venthur, H., & Zhou, J.-J. Odorant receptors and odorant-binding proteins as insect pest control targets: A comparative analysis. Front. Physiol. 9, 1. https://doi.org/10.3389/fphys.2018.01163 (2018).19.Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Annu Rev. Entomol. 58, 373–391. https://doi.org/10.1146/annurev-ento-120811-153635 (2013).CAS 
Article 
PubMed 

Google Scholar 
20.Zheng, W. et al. Identification and Expression Profile Analysis of Odorant Binding Proteins in the Oriental Fruit Fly Bactrocera dorsalis. Int. J. Mol. Sci. 14, 14936 (2013).Article 

Google Scholar 
21.Siciliano, P. et al. Identification of pheromone components and their binding affinity to the odorant binding protein CcapOBP83a-2 of the Mediterranean fruit fly, Ceratitis capitata. Insect Biochem. Mol. Biol. 48, 51–62. https://doi.org/10.1016/j.ibmb.2014.02.005 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Siciliano, P. et al. Sniffing Out Chemosensory Genes from the Mediterranean Fruit Fly, Ceratitis capitata. Plos One 9, e85523https://doi.org/10.1371/journal.pone.0085523 (2014)23.Campanini, E. B. & de Brito, R. A. Molecular evolution of Odorant-binding proteins gene family in two closely related Anastrepha fruit flies. BMC Evol. Biol. 16, 198–198. https://doi.org/10.1186/s12862-016-0775-0 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Park, K., et al. Expression patterns of two putative odorant-binding proteins in the olfactory organs of Drosophila have different implications for their functions. Vol. 300 (2000).25.Shanbhag, S. R. et al. Expression mosaic of odorant-binding proteins in Drosophila olfactory organs. Microsc. Res. Tech. 55, 297–306. https://doi.org/10.1002/jemt.1179 (2001).CAS 
Article 
PubMed 

Google Scholar 
26.Wu, Z. et al. Discovery of chemosensory genes in the oriental fruit fly, Bactrocera dorsalis. PloS One 10, e0129794-e0129794.https://doi.org/10.1371/journal.pone.0129794 (2015)27.Liu, Z., Smagghe, G., Lei, Z. & Wang, J.-J. Identification of male- and female-specific olfaction genes in antennae of the Oriental Fruit Fly (Bactrocera dorsalis). PLoS ONE 11, e0147783–e0147783. https://doi.org/10.1371/journal.pone.0147783 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Zhang, J. et al. Identification and expression profiles of novel odorant binding proteins and functional analysis of OBP99a in Bactrocera dorsalis. Arch. Insect Biochem. Physiol. 98, e21452. https://doi.org/10.1002/arch.21452 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Cheng, J. et al. Identification and expression analysis of chemosensory genes in the citrus fruit fly Bactrocera (Tetradacus) minax. PeerJ Preprints 6, e27297v1. https://doi.org/10.7287/peerj.preprints.27297v1 (2018).30.Kumaran, N., Prentis, P. J., Mangalam, K. P., Schutze, M. K. & Clarke, A. R. Sexual selection in true fruit flies (Diptera: Tephritidae): transcriptome and experimental evidences for phytochemicals increasing male competitive ability. Mol. Ecol. 23, 4645–4657. https://doi.org/10.1111/mec.12880 (2014).CAS 
Article 
PubMed 

Google Scholar 
31.Liu, H. et al. BdorOBP2 plays an indispensable role in the perception of methyl eugenol by mature males of Bactrocera dorsalis (Hendel). Sci. Rep. 7, 15894. https://doi.org/10.1038/s41598-017-15893-6 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Kumaran, N. et al. Plant-Mediated Female Transcriptomic Changes Post-Mating in a Tephritid Fruit Fly, Bactrocera tryoni. Genome biology and evolution 10, 94–107.https://doi.org/10.1093/gbe/evx257 (2017)33.Idrees, A. et al. Protein baits, volatile compounds and irradiation influence the expression profiles of odorant-binding protein genes in Bactrocera dorsalis (Diptera: Tephritidae). Appl. Ecol. Environ. Res. 15, 1883–1899 (2017).Article 

Google Scholar 
34.Pavlidi, N. et al. Transcriptomic responses of the olive fruit fly Bactrocera oleae and its symbiont Candidatus Erwinia dacicola to olive feeding. Sci. Rep. 7, 42633. https://doi.org/10.1038/srep42633 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Arya, G. H. et al. The genetic basis for variation in olfactory behavior in Drosophila melanogaster. Chem Senses 40, 233–243. https://doi.org/10.1093/chemse/bjv001 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Akter, H., Adnan, S., Morelli, R., Rempoulakis, P. & Taylor, P. W. Suppression of cuelure attraction in male Queensland fruit flies provided raspberry ketone supplements as immature adults. PLoS ONE 12, e0184086. https://doi.org/10.1371/journal.pone.0184086 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Gilchrist, A. S. et al. The draft genome of the pest tephritid fruit fly Bactrocera tryoni: resources for the genomic analysis of hybridising species. BMC Genomics 15, 1153. https://doi.org/10.1186/1471-2164-15-1153 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Fleischer, J. & Krieger, J. Insect pheromone receptors—Key elements in sensing intraspecific chemical signals. Front. Cell. Neurosci. 12. https://doi.org/10.3389/fncel.2018.00425 (2018).39.Liu, Z. et al. An antennae-specific odorant-binding protein is involved in Bactrocera dorsalis Olfaction. Front. Ecol. Evol. 8.https://doi.org/10.3389/fevo.2020.00063 (2020).40.Tan, K. H. Recaptures of feral Bactrocera dorsalis and B. umbrosa (Diptera: Tephritidae) males after feeding on methyl eugenol. Bull. Entomol. Res. 110, 15–21, https://doi.org/10.1017/S0007485319000208 (2019).41.Liu, L. et al. Contribution of Drosophila DEG/ENaC genes to salt taste. Neuron 39, 133–146. https://doi.org/10.1016/s0896-6273(03)00394-5 (2003).CAS 
Article 
PubMed 

Google Scholar 
42.Swarup, S., Huang, W., Mackay, T. F. C. & Anholt, R. R. H. Analysis of natural variation reveals neurogenetic networks for Drosophila olfactory behavior. Proc Natl Acad Sci USA 110, 1017–1022. https://doi.org/10.1073/pnas.1220168110 (2013).ADS 
Article 
PubMed 

Google Scholar 
43.Vijayan, V., Thistle, R., Liu, T., Starostina, E. & Pikielny, C. W. Drosophila pheromone-sensing neurons expressing the ppk25 Ion channel subunit stimulate male courtship and female receptivity. PLoS Genet. 10, e1004238. https://doi.org/10.1371/journal.pgen.1004238 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Thistle, R., Cameron, P., Ghorayshi, A., Dennison, L. & Scott, K. Contact chemoreceptors mediate male-male repulsion and male-female attraction during Drosophila courtship. Cell 149, 1140–1151. https://doi.org/10.1016/j.cell.2012.03.045 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Khan, M. A. M. et al. Raspberry ketone accelerates sexual maturation and improves mating performance of sterile male Queensland fruit fly, Bactrocera tryoni (Froggatt). Pest Manag. Sci. 75, 1942–1950. https://doi.org/10.1002/ps.5307 (2019).CAS 
Article 
PubMed 

Google Scholar 
46.Weldon, C. W., Perez-Staples, D. & Taylor, P. W. Feeding on yeast hydrolysate enhances attraction to cue-lure in Queensland fruit flies, Bactrocera tryoni. Entomol. Experim. et Applicata 129, 200–209. https://doi.org/10.1111/j.1570-7458.2008.00768.x (2008)47.Najar-Rodriguez, A. J., Galizia, C. G., Stierle, J. & Dorn, S. Behavioral and neurophysiological responses of an insect to changing ratios of constituents in host plant-derived volatile mixtures. J. Exp. Biol. 213, 3388 (2010).CAS 
Article 

Google Scholar 
48.Bertschy, C., Turlings, T. C., Bellotti, A. C. & Dorn, S. Chemically-mediated attraction of three parasitoid species to mealybug-infested cassava leaves. Florida Entomologist 80, 383–395 (1997).Article 

Google Scholar 
49.Butler, D. G., Cullis, B. R., Gilmour, A. R. & Gogel, B. J. (Queensland Department of Primary Industries and Fisheries, Australia, 2009).50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Zhao, Q.-Y. et al. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinf. 12, S2. https://doi.org/10.1186/1471-2105-12-S14-S2 (2011).CAS 
Article 

Google Scholar 
52.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351 (2015).CAS 
Article 
PubMed 

Google Scholar 
54.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902. https://doi.org/10.1038/nbt.2931 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Al-Shahrour, F., Díaz-Uriarte, R. & Dopazo, J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580. https://doi.org/10.1093/bioinformatics/btg455 (2004).CAS 
Article 
PubMed 

Google Scholar 
59.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47–e47. https://doi.org/10.1093/nar/gkv007 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

ScopeWe compared disposable face masks that were used once with face masks that were sterilized and used five more times (six times in total). Sterilisation and PFE test data of the Aura 1862+ (3M, Saint Paul, Minnesota, USA) face mask indicate that this type of face mask shows good performance after multiple sterilisation cycles10,11,12. In a previous pilot study, the company CSA Services (Utrecht, the Netherlands), a sterilization facility for cleaning, disinfection and sterilization of medical instruments, was rebuild to process FFP2 face masks. In total, 18,166 single use FFP2 masks were sterilised after use in a medical autoclave. As the majority (n = 7993) were Aura 1862+ (3M, Saint Paul, Minnesota, USA), this particular type of face mask was chosen for the LCA.The total weight of the face masks and packaging together during end-of-life consists of incineration for the face masks (97%) and landfill for the carton box packaging of new face masks (3%). There is no recycling potential used in our model since the materials coming from the operating room and its packaging is commonly disposed as medical waste. In the Netherlands, no energy recovery takes place at the incineration of regulated medical waste. Therefore, no co-function was applicable for the end-of-life scenario.Recycling is often a multi-functional process that produces two or more goods. To deal with the multi-functionality in the background processes, the cut-off approach was applied to exclude the allocation of the greenhouse gas emissions to additional goods. This means that potential rest materials such as energy gained during incineration are cut-off and that the greenhouse gas emissions are fully allocated to the waste treatment processes itself.In the LCA, the ‘functional unit’ defines the primary function that is fulfilled by the investigational products and indicates how much of this function is considered18. In this study, we pragmatically chose as a definition for the protection of 100 health care workers against airborne viruses, using one FFP2 certified face mask, each during one working shift of an average of 2 h in a hospital in the Netherlands.Table 1 shows the differences between the two scenarios:

1.

100 masks including packaging, transported from production to the hospital, used and disposed.

2.

100 times use of reprocessed masks. We calculated that 27.1 masks are being produced and transported from production to the hospital. The 27.1 are being reprocessed five times, taking into account that 20% of the batch cannot be reprocessed. Therefore 80% of the batch could be used for reprocessing after each step resulting in: 27.1 (new) + 21.7 (repro 1) + 17.3 (repro 2) + 13.9 (repro 3) + 11.1 (repro 4) + 8.9 (repro 5) = 100 times of use. For each time of reprocessing the batch is transported from the hospital to the (hospital) Central Sterilization Services Department (CSSD) and disposed after five times of reprocessing.

Table 1 Comparison between reference flow 1 and 2.Full size tableCombining the functional unit with the two alternative scenarios results in the reference flows for the protection of 100 health care workers against airborne viruses, either using a face mask one single time (100 virgin masks produced for the 1st scenario), or reusing a face mask for five additional times (27.1 virgin masks produced for the 2nd scenario). For both reference flows, only FFP2 certified face masks are considered. For the calculations each mask is used for a single two hours working shift in an average hospital in the Netherlands.Life cycle inventory (LCI) analysisThe inventory data includes all phases from production (including material production and part production), transport, sterilisation to end-of-life of the life cycle of the single use and reprocessed face masks. We disassembled one face mask to obtain the weight of each individual component on a precision scale (Fit Evolve, Bangosa Digital, Groningen, the Netherlands) with a calibrated inaccuracy of 1.5%. Component information and materials were obtained from the data fact sheet provided by the manufacturer. We conducted a separate validation experiment to establish the material composition in the filtering fabric (Supplement file).This LCA with the Aura 3M masks was based on steam sterilization by means of a hospital autoclave and therefore part of this study. Therefore, face masks were placed in a sterilization bag that contained up to five masks. A total of 1000 masks were placed into an autoclave (Getinge, GSS6713H-E, Sweden) per cycle. After sterilization, the masks were transported to the hospital. Masks were reprocessed for a maximum of five times before final disposal10,11.The assessment of climate change impact is done following as closely as possible the internationally accepted Life Cycle Assessment (LCA) method following the ISO 14040 and 14044 standards19,20. The LCA examines all the phases of the product’s life cycle from raw material extraction to production, packaging, transport, use and reprocessing until final disposal19. The LCA was modelled using SimaPro 9.1.0.7 (PRé Sustainability, Amersfoort, The Netherlands). The background life cycle inventory data were retrieved from the ecoinvent database (Ecoinvent version 3.6, Zürich, Switzerland)21.To make a valid comparison between the disposable and reprocessing face masks, the system boundaries should be equal in both scenarios. The system boundaries in this study consisted of the production, the use and the disposal and waste treatment of the masks. For the reprocessed face masks, the lifecycle is extended due to the sterilisation process (Fig. 1). Therefore, the additional PPE’s and materials needed to safely process the masks (e.q. masks, gloves and protective sheets) are included in the production phase. The production of machinery for the manufacturing of the face masks and the autoclave were not included in this study.Figure 1System boundary overview of new and reprocessed face masks including waste treatment by incineration.Full size imageThe production facility for the face masks is located in Shanghai, China22,23. Further distribution took place from Bracknell, UK to Neuss, Germany and the final destination was set in Rotterdam, the Netherlands.The packaging materials were disposed in the hospital where the face masks are used primarily. After first use, face masks were transported to the sterilisation department. All masks were manually checked before reprocessing by personnel wearing PPE. Of all used Aura 1862+ facemasks that entered the CSA, approximately 10% was discarded. To remain conservative, the LCA was conducted based on a 20% rejection rate as a result of face masks which could not be reused anymore due to deformities, lipstick, and broken elastic bands.A full overview of the life cycle inventory table for the two scenarios and details on model assumptions are added in the Supplemental file (Supplemental file, Part B).Life cycle impact assessmentThe carbon footprint (kg CO2 eq) was chosen as the primary unit in the impact category. ReCiPe was applied at midpoint level and used to translate greenhouse gas emissions into climate change impact16.Uncertainty analysisThe final LCA model contains several uncertainties based on assumptions and measurement inaccuracies24. The included uncertainties were based on weighted components of the masks as well as the packaging which were measured with 1.5% inaccuracy of the precision scale apparatus. A Monte Carlo sampling25 was conducted for both alternatives (disposable and reprocessing) where input parameters for the LCA were sampled randomly from their respective statistical distributions in for 10,000 ‘runs’. Because input parameters between scenarios were partly overlapping, we compared these two scenarios directly using a discernibility analysis. This technique, establishes which scenario is beneficial for each of 10,000 Monte Carlo runs. We report the percentage of instances where the reprocessing scenario has a lower carbon footprint than the disposable scenario.Sensitivity analysisA sensitivity analysis was conducted to check the sensitivity of the outcome measures to variation in the input parameters. To determine which parameters are interesting to investigate, three aspects were considered: the variations in number of face masks per sterilization cycle (autoclave capacity), rejection rate (number of losses per cycle) and transport distance to the CSSD. Finally, we included the relative contribution of these variations. The following three parameter variations were chosen for the sensitivity analysis:

1.

Rejection percentage. The rejection rate was defined based on experiences from the participating sterilisation department and studies that show that sterilisation of the face masks up to 5 times is possible. Masks were re-used for 5 times, approximately 10% was discarded during the total life cycle. Out of this experience and to remain conservative, the total rejection rate was set on 20%. Therefore it is interesting to investigate whether variation in PFE testing outcomes or differences in user protocols influence the outcomes. This should indicate if masks from higher or lower quality can also be suitable candidates for reprocessing.

2.

Autoclave capacity, which largely depends on the loading of the autoclave. To mimic different loads of the autoclave, it is interesting to know the influence of sterilizing fewer masks per run on the model.

3.

Transport. As it is likely that many hospitals have a Central Sterilisation Services Department (CSSD) it is interesting to know the effect of having zero transportation. Moreover, in case hospitals are not willing to change the routing in their CSSD it is interesting to observe how outcomes are influenced if transportation is set on the maximal realistic value of 200 km.

The parameters have been varied with 250 and 500 face masks per sterilisation batch. A rate varying with 10% and 30% of the face masks being rejected due to quality reasons and variation in transport kilometres of 0–200 km.There is a small difference between the baselines of the sensitivity, LCIA and contribution analyses because all these are performed using separate Monte-Carlo simulations. The output of the different simulations may show minor differences due to statistical distribution.Cost price comparisonA cost analysis was made to give insight in costing from a procurement perspective. The cost analysis is conducted with five face masks that were steam sterilized per batch in a permeable laminate bag, Halyard type CLFP150X300WI-S20 and includes the expenses of energy, depreciation, water consumption, cost of personnel, overhead and compared to the prices for a new disposable 3M Aura face mask during the first and second Corona waves. Five pieces per bag were chosen in order to have enough space between the masks to sterilise each mask properly. The cost analysis is based on actual sterilization as well as associated costs compared to the prices of new disposable face masks. The costs were then related to the functional unit of protecting 100 health care workers by calculating the difference in the amount of Euros per 100 face masks. More

Study regions and farmsTen European regions from boreal to Mediterranean were selected (Supplementary Table 1). They represented major agricultural land uses such as arable crops including horticulture, mixed farming, grassland and perennial crops (vineyards and olives). Within each region, a pool of ~20–40 farms was selected from which 12–20 farms were randomly selected (169 in total) that belonged to the same farm type, produced under homogeneous climatic and environmental circumstances and fulfilled specific criteria regarding their main production branch. In case the selected farms were not willing to participate, we asked other farms from the pool till the sufficient number has been reached. The selected organic farms had all been certified for at least five years. Farmers were asked if they were willing to participate in the study. If they refused, additional random sampling was conducted. In the region NL, 11 organic farms agreed to participate but only three non-organic farms, whereas seven organic farms and 11 non-organic farms were available in the region HU. During the study, one non-organic farmer in the region CH ceased participation.Habitat maps and farm interviewsThe complete area of all selected farms was mapped, using the BioHab method36. Excluded from the farm area were woody and aquatic habitats larger than 800 m2 and summer pastures. Within the farm area, areal and linear habitats were recorded. For an areal habitat, the minimal mapping unit was 400 m2 with a width of at least 5 m. More narrow habitats, between 0.5 and 5 m wide and at least 30 m long, were mapped as linear habitats. Habitats were distinguished in habitat types according to Raunkiær life forms, environmental conditions and management evidence28. Further, a farmland class was assigned to each habitat that described whether the habitat was managed for agricultural production or other objectives such as e.g. nature conservation. In face-to-face interviews following a standardized questionnaire, farmers provided detailed information on field management and yield.Categorization as production fields and semi-natural habitatsBased on the habitat maps and available information about management intensity, we categorized all habitats as either semi-natural habitats or production fields. In agricultural landscapes, these two categories are often not clearly distinguishable. There is a gradient from more intensively managed production fields to less intensively used semi-natural habitats. In addition, a categorization at the local scale can be different from an approach at a European scale (29 and see p. 45 of37). Here, we applied the same criteria for all ten study regions.In all cases, we categorized as production fields: arable crops, intensively managed grasslands (following main plant species observed, management evidence and objectives, with fertilization and/or two or more cuts a year), horticultural crops, and vineyards.We categorized as semi-natural habitats: linear habitats, habitats that were managed for nature conservation objectives, habitats where mainly geophytes, helophytes or hydrophytes were growing, grasslands with woody vegetation (shrubs and/or trees), and extensively managed grasslands (no fertilization, no or one cut a year).Species samplingVascular plant, earthworm, spider and bee species were sampled in all different habitat types of a farm. One plot per habitat type was randomly selected per farm for species sampling. This resulted in 1402 selected habitat plots on 169 farms (Supplementary Table 2). In the selected habitats, species were sampled during one growing season, using standardized protocols19,38. Plant species were identified in squares of 10 × 10 m in areal habitats and in rectangular strips of 1 × 10 m in linear habitats. Earthworms were collected at three random locations of 30 × 30 cm per habitat. First, a solution of allyl isothiocyanate (AITC) was poured out to extract earthworms from the soil. Afterwards, a 20-cm-deep soil core from the same location was hand sorted to find additional specimens. Identification took place in the lab. Spiders were sampled on three dates at five random locations per habitat within a circle of 0.1 m2. Using a modified vacuum shredder, spiders were taken from the soil surface, transferred to a cool box, frozen, or put in ethanol, sorted and identified in the lab. Bees (wild bees and bumble bees) were sampled on three dates, during dry, sunny and warm weather conditions. They were captured with an entomological aerial net along a 100 m long and 2 m wide transect, transferred to a killing jar and identified in the lab.Grouping of species dataSpecies data were pooled per taxa, habitat and region, and three sub-communities were formed with species (1) exclusively found in semi-natural habitats, i.e. unique to semi-natural habitats, (2) exclusively found in production fields, i.e. unique to production fields, and (3) found in both habitat categories i.e. shared by production fields and semi-natural habitats. For calculations of effects over all four taxa, species richness was the sum of the individual taxa species richnesses.Estimating species richnessSpecies richness was estimated using coverage- and sample-size-based rarefaction and extrapolation curves31,39,40. Rarefaction and extrapolation, including confidence intervals (bootstrap method) and sampling coverage, were calculated in R 3.4.041 using package iNEXT42. Detailed information is provided below for each topic.Estimating richness of unique species to compare semi-natural habitats and production fieldsTo legitimately compare the richness of species unique to semi-natural habitats and to production fields, we used the coverage-based method, i.e. we standardized the samples by their completeness30. The point of comparison was determined by the so-called ‘base coverage’ identified by the following procedure31: (1) select the maximum sample coverage at reference sample size (number of sampling units) of the sub-communities under comparison, (2) select the minimum sample coverage at twice the reference sample size of the sub-communities under comparison, (3) identify the maximum of the results from step (1) and step (2) as ‘base coverage’. The species richness estimates were then read off from the species sample-size-based rarefaction and extrapolation curves at the ‘base coverage’ for each sub-community being compared. If zero or exactly one species was unique to a sub-community at the reference sample size, no sample coverage could be calculated. In this case, we set the species richness at 0 or 1, respectively. The species richness estimate of the other sub-community under comparison was then read off at twice the reference sample size on the curve.The ‘base coverage’ was individually defined for each region and each taxonomic group since the mixed effects models used to analyze the data took into account the variation among regions and taxonomic groups.Differences in species richness unique to semi-natural habitats and production fieldsThe difference between the species richness unique to semi-natural habitats and unique to production fields was tested with mixed effects models using package lme4 (Version 1.1-12) in R43. The data were (Sij | β, b, x) ~ Poisson(µij) from i = 1, …, 10 regions. The model is:$${{{rm{ln}}}}left({mu }_{{ij}}right)={beta }_{0}+{beta }_{1}{x}_{1i}+{b}_{1i}$$
(1)
$${b}_{1} sim N(0,sigma 2)$$where ({beta }_{0}) is a fixed intercept, ({beta }_{1}) a fixed effect sub-community ({x}_{1{ij}}) (species unique to semi-natural habitats versus species unique to production fields), b1i are random intercepts for region i. Random effects are normally distributed with mean 0 and variance σ2. The significance of term ({beta }_{1}) was calculated by log-likelihood ratio tests with one degree of freedom. For the models over all four taxa, an additional random intercept was included, i.e. b2j with mean 0 and variance σ2 for j = 1, …, 4 taxa (Fig. 1b).Differences in species richness between organic and non-organic systemsThe comparison between organic and non-organic systems of species unique to semi-natural habitats and to production fields, and of species shared by the two habitat categories, relied on coverage-based extrapolation as described above. Differences between management systems were tested for significance using mixed-effects models with management system ({beta }_{1}) ({x}_{1{ij}}) as fixed effect in (1).Estimating species loss due to conversion of semi-natural habitats to production fieldsTo predict the species loss due to conversion of semi-natural habitats to production fields, we relied on sample-size-based extrapolations31 with species incidence frequencies. We estimated the richness of the species pool for the total number of mapped habitats including the extrapolated species richness unique to semi-natural habitats and unique to production fields, and the observed richness of shared species for each of the four taxa. This species pool provided the basis for the calculation of the species loss or gain (Table 1 and Supplementary Table 7). To model the species richness decrease for any amount of semi-natural habitats converted to production fields, we calculated and drew backward the curve composed of the accumulation curve for species unique to semi-natural habitats, to which the estimated total species richness unique to production fields (constant) and the corresponding gain of species unique to production fields (increases with increasing area of production fields as semi-natural habitats are converted), and the richness of observed shared species (constant) were added. This is the species decrease curve (Supplementary Fig. 2). If started at the observed species richness, this curve corresponds exactly to a species richness curve calculated by a cumulative random removal of semi-natural habitats one by one from the pool of all habitats. The four taxa decrease curves were added for the curve in Fig. 2. Confidence intervals (CI, 95%) shown in Figs. 2 and 3 are calculated by bootstrapping within the calculation of the species accumulation curves (iNEXT42), upper and lower bounds of the 95% CI of the four taxa being added. From the species decrease curve, we read off the predicted species richness for a conversion of 50% and 90% of the semi-natural habitats, and a conversion required to increase production by 10%.As species were sampled in 20% of all mapped habitats on average per region (min. 8%, max. 35%), extrapolated species accumulation curves used to build the species decrease curve were calculated for more than two to three times the reference sample size, which is the suggested range for reliable extrapolation of the species richness estimator31,44. Obviously, the confidence intervals (CI) of the species richness extrapolations here became wide (Supplementary Fig. 4). As we still wanted to show the impact of a conversion of the whole semi-natural area into production fields on the production gain in the ten regions, we used the uncertainty (upper and lower bounds of the 95% CI of the four taxa added) to define two situations in addition to the average case to predict species richness for a 50% and a 90% semi-natural habitat conversion, and a conversion required to increase production by 10%: (1) a worst case situation with the upper bound of the CI of the expected species richness unique to semi-natural habitats, the lower bound of the CI of the expected species richness unique to production fields, and shared species assumed not to be able to survive without semi-natural habitats and considered like species unique to semi-natural habitats (i.e. upper bound); and (2) a best case situation with the lower bound of the CI of the expected species richness unique to semi-natural habitats, the upper bound of the CI of the expected species richness unique to production fields, and the lower bound of the CI of the expected shared species richness.Estimating production gainFarmer interviews delivered an average yield per crop type per farm for the years 2008–2010 (Supplementary Data45 shows details for organic and non-organic systems separately). Farmers indicated yield in kilograms or tons per hectare. This was transformed into energy units, i.e. mega joules per hectare (MJ ha−1) using standard values46. From this, for each region, the average yield (MJ ha−1) was calculated by first multiplying individual crop type yields by the corresponding crop type areas to obtain the production per crop type, then summing up the production of all crop types, and finally dividing this sum by the total area of the crop types. For livestock farms, the fodder production of grasslands was estimated based on the average requirements per livestock unit, accounting for the amount of feed grain, legumes, silage maize and of imported feedstuff. All yields relate to plant biomass production and do not comprise livestock products. The average yield takes into account the relative cover of the different crop types in the regions. Therefore, the conversion of the semi-natural area to production fields was region-specific. The production of certain semi-natural habitats as e.g. olive groves in Spain was not part of the production calculation. The reason is that data on production for semi-natural habitats were mainly not available and/or negligible, e.g. extensively used grassland in CH or in HU, and we decided to apply the same treatment to all the regions. Consequently, in case of olive groves in Spain the effective increase in production is overestimated. To calculate the production gain per region, the production field area added by the conversion of semi-natural habitat area was multiplied by the average yield. In practice, in many regions it may be impossible to convert semi-natural habitat to productive land due to geomorphological constraints and poor soils, and even if land were converted, yields would be much lower than these averages. The results presented here, especially the 90% scenario, are therefore over-optimistic. On the other hand, our calculations are based on the area of semi-natural habitat available for conversion on existing farms, but in some regions other sources of semi-natural land may be available for conversion, e.g. former agricultural land that has been abandoned.Species loss and production gain for three scenariosWe calculated the change of species richness and the production gain under current day production efficiency for two scenarios: (1) a conversion of 90% of the semi-natural area into production fields. The 10% of semi-natural area remaining is considered unsuitable for agricultural use or even impossible to cultivate; (2) a conversion of 50% of the semi-natural area into production fields, and (3) a necessary conversion of the semi-natural area into production fields to achieve a 10% production increase per region.Standardization for organic and non-organic systemsAlthough the overall mapped area, the number of semi-natural habitats, the number of production fields and the average habitat size did not significantly differ between the two management systems (Supplementary Table 5), we standardized the number and size of habitats to the average across both systems per region to compare the species loss and production gain at current day production efficiency in the organic and non-organic systems. The total production in organic and non-organic systems per region was calculated based on the respective yield and the average mapped area of the production fields across both systems as described in section “Estimation of production gain”. The impact on biodiversity was analyzed for the scenario that organic systems should achieve the same level of production as non-organic systems by converting semi-natural habitats to production fields. We calculated the amount of the required area to be converted into production fields and the corresponding species change.Differences between management systems were again tested for significance using mixed-effects models with management system ({{{{rm{beta }}}}}_{1}) ({{{{rm{x}}}}}_{1{{{rm{ij}}}}}) as fixed effect in (1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Technical criterion for ecosystem status evaluationOn March 3, 2015, the Ministry of Ecology and Environment of the People’s Republic of China approved the “Technical Criterion for Ecosystem Status Evaluation” as the national environmental protection standard. This standard is based on the former standards released in 2006, and 48 relevant documents from 2006 to 2012 were searched to propose new standards and factor weights based on actual utilization effects and expert guidance. The eco-environment assessment uses a comprehensive index (eco-environmental status index, EI) to reflect the overall state of the regional eco-environment. The indicator system includes the biological abundance index, vegetation coverage index, river density index, land stress index, and pollution loading index. These indexes reflect the abundance of organisms in the evaluated area, the level of vegetation coverage, the abundance of water, the intensity of land stress, and the extent of the pollution load. Each indicator was calculated according to its weight to obtain an eco-environment assessment map (Table 1). All parameters involved in the calculation are derived from this standard.Table 1 Weights of the evaluation indicators.Full size tableThe calculation of the eco-environment status is as follows:$$begin{aligned} EI & = 0.35*biological ; abundance ; index + 0.25*vegetation ; coverage ; index hfill \ & quad + 0.15*river ; density ; index + 0.15*(1 – land ; stress ; index) hfill \ & quad + 0.1*(1 – pollution ; loading ; index) hfill \ end{aligned}$$
(9)
Biological abundance indexThe biological abundance index refers to the number of certain organisms in this area. The calculation method is as follows:$$Biological , abundance , index , = , left( {BI , + , HQ} right)/2$$
(10)
In this formula, BI is the biodiversity index and HQ is the habitat quality index. When the biodiversity index does not have dynamic data updates, the change in the biological abundance index is equal to the change in the HQ.Biodiversity is a general term for the complexity of species and their genetic variation and ecosystems in space over time. Biodiversity plays an important role in maintaining soil fertility, ensuring water quality, regulating the climate, stabilizing the environment, and maintaining ecological balance.The BI method is as follows:$$BI = NPP_{mean} *F_{pre} *F_{tem} *(1 – F_{alt} )$$
(11)
NPPmean is the net primary productivity. Fpre is the annual average precipitation. Ftem is the temperature parameter. Falt is the altitude parameter.NPP refers to the amount of organic matter accumulated per unit area and unit time of green plants. NPP is the remainder of the total amount of organic matter produced by photosynthesis after deducting autotrophic respiration and is usually expressed as dry weight. In this study, the estimation of NPP was based on the absorbed photosynthetically active radiation (APAR) and actual light-use efficiency (LUE) (ε) of the CASA ecosystem model40. The CASA model is a process-based remote sensing model that couples ecosystem productivity and soil carbon and nitrogen fluxes, driven by gridded global climate, radiation, soil, and remote sensing vegetation index datasets41. The model can be expressed generally as follows:$$NPP(x,t) = APAR(x,t)*varepsilon (x,t)$$
(12)
The entire study area is divided into 11,303 pixels on a 30 * 30 m grid. x indicates the location of each pixel, and t indicates time; the data were collected once a month. APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in that month (gC * m−2* month−1). Ɛ(x, t) is LUE (gC * MJ−1) of the vegetation42.Estimation of the fraction of APAR using RS data is based on the reflection characteristics of the vegetation on the infrared and near-infrared bands. The value of APAR is determined by the effective radiation of the sun and the absorption ratio of the vegetation to the effective photosynthetic radiation. The formula is as follows:$$APAR(x,t) = SOL(x,t)*FPAR(x,t)*0.5$$
(13)

where SOL(x,t) represents the total amount of solar radiation at pixel x in month t, FPAR(x,t) represents the absorption ratio of the vegetation layer to the incident photosynthetically active radiation, and a constant of 0.5 indicates the ratio of the effective solar radiation that can be utilized by the vegetation to the total solar radiation.Since there is a linear relationship between FPAR and NDVI within a certain range, this relationship can be determined according to the maximum and minimum values of a certain vegetation type NDVI and the corresponding FPAR maximum and minimum values.$$FPAR(x,t) = frac{{(NDVI(x,t) – NDVI_{i,min } )}}{{(NDVI_{i,max } – NDVI_{i,min } )}}*(FPAR_{max } – FPAR_{min } ) + FPAR_{min }$$
(14)

where NDVImax and NDVImin correspond to the NDVI maximum and minimum values of the ith planting type, respectively. There is also a good linear relationship between FPAR and the simple ratio index (SR) of vegetation, which is represented by the following formula:$$FPAR(x,t) = frac{{(SR(x,t) – SR_{i,min } )}}{{(SR_{i,max } – SR_{i,min } )}}*(FPAR_{max } – FPAR_{min } ) + FPAR_{min }$$
(15)

where the values of FPARmin and FPARmax are independent of vegetation type and are 0.001 and 0.95, respectively; SRi,max and SRi,min correspond to the 95% and 5% percentiles, respectively, of the ith NDVI. SR(x,t) is represented by the following formula:$$SR(x,t) = frac{1 + NDVI(x,t)}{{1 – NDVI(x,t)}}$$
(16)
A comparison of the estimated results of FPAR-NDVI and FPAR-SR shows that the FPAR estimated by NDVI is higher than the measured value, while the FPAR estimated by SR is lower than the measured value, but the error is less than that estimated directly by NDVI. As a result, these two values can be combined, and their weighted average value is taken as an estimate of the estimated FPAR, while ɑ means weight:$$FPAR(x,t) = alpha FPAR_{NDVI} + (1 – alpha )FPAR_{SR}$$
(17)
Light use efficiency (LUE) refers to the ratio of chemical energy contained in organic dry matter produced per unit area over a certain period of time to the photosynthetically active radiation absorbed by plants projected onto the same area at the same time. Different vegetation types and the same types of vegetation have different light energy utilization rates in different living environments43. The differences are mainly due to the characteristics of the vegetation itself, temperature, moisture, and soil44. Vegetation has the highest utilization rate of light energy under ideal conditions, but the maximum light energy utilization rate in the real environment is mainly affected by temperature and moisture, which can be expressed as follows:$$varepsilon left( {x,t} right) = T_{varepsilon 1} left( {x,t} right) cdot T_{varepsilon 2} left( {x,t} right) cdot W_{varepsilon } left( {x,t} right) cdot varepsilon_{max }$$
(18)
where Tε1(x,t) and Tε2(x,t) represent the stress effects of low temperature and high temperature on light energy utilization, respectively, Wε(x,t) is the effect of water stress on the maximum light energy utilization under ideal conditions, and εmax is the maximum light energy utilization under ideal conditions (gC * MJ−1). The maximum solar energy utilization rate εmax varies depending on the vegetation type. In this study, the maximum light energy utilization rate of different land use types simulated by an improved Carnegie-Ames-Stanford Approach (CASA) model is used as the input parameter of light energy utilization in the CASA model (Table 2). The monthly maximum light energy utilization rate is determined in three steps: first, calculate the APAR, temperature, and water stress factors of all pixels; then, select the NPP measured data of the same time period in the study area; finally, simulate the εmax of vegetation according to the principle of minimum error45. Figure 5 shows the calculation process of NPP. The weight of each habitat type in the HQ is shown in Table 3. The weight value is derived from the official document30. To facilitate the calculation, this paper normalizes the calculation results from 0 to 1 (Fig. 6a).Table 2 Maximum LUE rates of different land use types.Full size tableFigure 5NPP calculation process.Full size imageTable 3 Weight of each habitat type in the HQ.Full size tableVegetation coverage indexThe vegetation coverage index was obtained from the NDVI, which is a simple, effective, and empirical measure of surface vegetation status. The vegetation index mainly describes the difference between the reflection of vegetation in the visible and near-infrared bands and the soil background. This index also reduces the solar elevation angle and noise caused by the atmosphere and is thus the most widely used and effective calculation method. Each vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. The expression is as follows:$$NDVI = frac{NIR – R}{{NIR + R}}$$
(19)

where NIR and R are reflectance values in the near-infrared and red bands, respectively.NDVI values are obtained by processing the RS images of the Landsat 8 satellite. This satellite is equipped with an operational land imager (OLI) that includes nine bands with a spatial resolution of 30 m, including a 15-m panchromatic band. To facilitate the calculation, this paper normalizes the calculation results from 0 to 1 (Fig. 6b).Figure 6Eco-environment assessment indexes and evaluation rating map (the first quarter was used as an example). (a) Biological abundance index; (b) vegetation coverage index; (c) river density index; (d) land stress index; (e) pollution loading index; and (f) environmental status classification. The Figure is created using ArcGIS ver.10.3 (https://www.esri.com/).Full size imageRiver density indexThe river density index refers to the total length of rivers, lakes, and water resources in the assessed area as a percentage of the assessed area, which is used to reflect the abundance of water in the assessed area and is calculated as follows:$$begin{gathered} River ; density ; index = (A_{riv} *river ; length / area + A_{lak} *water ; area/area hfill \ + A_{res} {*}amount ; of ; resources/area , )/3 hfill \ end{gathered}$$
(20)

where Ariv is the normalization coefficient of river length, with a reference value of 84.3704, Alak is the normalization coefficient of the lake area, with a reference value of 591.7909, and Ares is the normalization coefficient of water resources, with a reference value of 86.387. Finally, the calculation results were normalized from 0 to 1 (Fig. 6c).Land stress indexThe land stress index is the degree to which the land quality in the assessment area is under stress. The weight of the land stress index evaluation is shown in Table 4.Table 4 Weight of the land stress index evaluation.Full size tableThe calculation method is as follows:$$begin{gathered} Land ; stress ; index = A_{ero} *(0.4*severe ; erosion ; area + 0.2*{text{mod}}erate ; erosion ; area hfill \ + 0.2*construction ; land ; area + 0.2*other ; land ; stress ; area)/area hfill \ end{gathered}$$
(21)

where Aero is the normalization coefficient of the land stress index, with a reference value of 236.0436. According to the “Classification criteria for soil erosion”46, the influencing factors of soil erosion, vegetation, soil texture, landform, and precipitation are ranked according to importance. In the calculation of the land stress index, all the land is divided into three categories, in which the weight of severe erosion is 0.4, the weight of non-erosion is 0, and the other erosion types such as moderate erosion and construction land are 0.2. The areas with severe erosion include vegetation coverage less than 30% and areas of soil erosion greater than 3.7 mm/a due to human activities. These areas are generally developed on highly erosive-sensitive soils. Cinnamon soil and loess soil in the study area are highly erosive-sensitive soils. Therefore, the industrial and mining areas of cinnamon and loess soil types are regarded as severe erosion areas. Areas with vegetation coverage greater than 50% are non-eroded areas, so water bodies and woodlands are divided into non-erodible areas. All areas except these two types have a weight of 0.2. Finally, the calculation results were normalized from 0 to 1 (Fig. 6d).Pollution loading indexThe pollution loading index refers to the load of pollutants in a certain area or an environmental element. In this study, the AQI was used to calculate the pollution loading index, and the results were normalized from 0 to 1 (Fig. 6e).The eco-environmental evaluation score was calculated based on the national environmental protection standard according to the weight of each indicator (Fig. 6f).Improved evaluation system and intelligent evaluation modelImproved evaluation systemConsidering that the evaluation factors in the national environmental protection standards are applicable to ordinary areas, areas affected by mines should have more evaluation factors than those in the standards. Thus, an improved evaluation system was proposed. The improved evaluation system has added factors that affect the environment of the mine based on the factors of the original system. The impact of the mine on the environment is reflected in the pollution of the atmosphere, such as dust from open pits and industrial waste from concentrators; the occupation of land by solid waste, such as ore piles and coal piles; soil pollution, such as the diffusion of heavy metals from coal piles, coal mine concentrator plants, and mines; and the increased likelihood of geological disasters, such as collapse caused by underground mining, spontaneous coal combustion and landslides caused by open-pit mining surfaces. Therefore, the improved evaluation system adds an air pollution range, a solid waste area, a geologic hazard range, and a metallic and non-metallic mine soil pollution buffer to the national environmental protection standards.The area of air pollution in mining areas is generally near open pits and concentrator plants. Therefore, the air pollution range was selected within 50 m around the open pits and the concentrator plants. Due to the dilution and dispersion of the air itself, an estimate of the pollution is the reciprocal of the wind speed (Fig. 7a).Figure 7New factors in the improved evaluation system. (a) Air pollution range; (b) solid waste area; (c) geological hazard range; (d1) non-metallic mine soil pollution buffer; and (d2) metal mine soil pollution buffer. The Figure is created using ArcGIS ver.10.3 (https://www.esri.com/).Full size imageMine solid waste pollution includes a large amount of waste rock from open-pit mining and pit mining, coal gangue produced by coal mining, tailings from beneficiation and slag from smelting. These solid wastes are generally piled up near the mining area. They not only occupy large areas of land and induce geological disasters such as landslides and mudslides but also cause chemical pollution, spontaneous combustion, and radiation from radioactive materials due to long-term stacking. This may affect the health and safety of humans and other biological organisms. The scope of the solid waste area is determined by the ore piles, coal piles, and dumping sites (Fig. 7b).Mine geological disasters are caused by a large number of mining wells and rock and soil deformation, as well as serious changes in the geological, hydrogeological, and natural environments of the mining area, endangering human life and property and destroying mining engineering equipment and mining resources. In this study, the geologic hazard range consists of areas with underground mining stopes and coal piles (Fig. 7c).After the pollutants generated by the mining operation enter the soil, physical and mechanical absorption, retention, colloidal physicochemical adsorption, chemical precipitation, bioabsorption, etc. of the suspended pollutants through the soil continue to accumulate in the upper soil. When pollutants reach a certain maximum, they cause deterioration of the soil composition, cycle, properties, and functions and begin to accumulate in plants, which affects the normal growth and development of plants, decreases crop yield and quality, and ultimately affects human health. Metallic and non-metallic minerals have different effects on soil pollution. The pollution of soil by non-metallic minerals mainly occurs in coal mines and coal piles, and the buffer zone is centred on the coal mines and coal piles. Coal production activities can cause heavy metals in coal piles to enter the soil and cause pollution. Due to different types of heavy metals, the range of soil contamination is different47. Combining the non-metallic mineral industrial squares and coal mine-based non-metallic minerals around the heavy metal soil pollution range, the buffers are graded at 30, 200, and 1000 m (Fig. 7d1)43. The metal mines in Gongyi are mainly aluminium ore and iron ore. Referencing the spread range of heavy metal pollution in the soil of aluminium ore and iron ore mines48,49,50,51, the buffers are graded at 50, 100, 300, and 500 m (Fig. 7d2).The four new elements in the improved evaluation system are normalized from 0 to 1 during the calculation.Intelligent evaluation modelArtificial neural networks, decision trees, and SVMs were calculated using IBM SPSS Modeler software to find an intelligent model suitable for environmental assessment of the mine in the study area. Then, several models with high evaluation accuracy were selected. The SVM, CART, and C5.0 models were chosen for further comparison. The sampling points were selected randomly; 700 sampling points were selected from the area away from the mining area; 100 sampling points were selected from the mining area after random sampling by mine type, and these points were used as training samples. Non-mining evaluation scores were based on the national environmental protection standard, while the mining area scores were based on field investigation. In the field investigation, preliminary scoring of the sampling points was conducted according to mining type, mining intensity, air quality, and surrounding environment. Then, a photo of the field was taken at every sampling point in the mine, and experts were invited to further score the area according to the photo. This score is the relative score obtained by referencing the national environmental protection standard.The index layers of the training samples were used as input, and the scores were used as the output to train the machine learning models. The trained models were applied to the entire study area, and all points except the training sample points were used for verification. After further comparison with SVM, CART, and C5.0, the evaluation accuracy rates of the three methods in the mining area and non-mining area were obtained. In the non-mining area, the model evaluation results of various land use types were compared with the national environmental protection standards. The accuracy in various land use types is shown in Table 5. In the mining area, the model evaluation score is compared with the score from the experts, and the obtained accuracy table is shown in Table 6.Table 5 Accuracy of each algorithm in various land use types in non-mining areas.Full size tableTable 6 Accuracy of each algorithm in the mining area.Full size tableIn non-mining areas, the accuracy of the SVM model is significantly better than that of the other two methods. However, in the mining area, the accuracy of the CART model is higher. Therefore, the SVM model was used to evaluate the area away from the mine, and the CART model was used to evaluate the mining area. The evaluation results of these two models were combined to obtain the evaluation map of the entire study area. More

1.Pan, Y. et al. Science 333, 988–993 (2011).CAS 
Article 

Google Scholar 
2.Vanhaven, H. et al. (eds) Making Boreal Forests Work for People and Nature (IUFRO, 2012); https://www.iufro.org3.Stokland, J. N. For. Ecol. Manage. 488, 119017 (2021).Article 

Google Scholar 
4.Snäll et al. Nat. Sustain. https://doi.org/10.1038/s41893-021-00764-w (2021).5.Assessment Report on Land Degradation and Restoration (IPBES, 2018).6.Felipe-Lucia, M. R. et al. Nat. Commun. 9, 4839 (2018).Article 

Google Scholar 
7.Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

Google Scholar 
8.Holland, R. A. et al. Biodivers. Conserv. 20, 3285–3294 (2011).Article 

Google Scholar 
9.National Forest Inventory: National Forest Monitoring (FAO, 2021); http://www.fao.org/national-forest-monitoring/areas-of-work/nfi/en/10.Simons, N. K. et al. For. Ecosyst. 8, 5 (2021).Article 

Google Scholar 
11.Sweden’s Forest Industry in Brief (FIS, 2021).12.Triviño, M. et al. J. Appl. Ecol. 54, 61–70 (2017).Article 

Google Scholar  More

1.von Humboldt, A. Ansichten der Natur mit Wissenschaftlichen Erlauterungen (J.G. Cotta, 1808).2.Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).Article 

Google Scholar 
3.Badgley, C. et al. Biodiversity and topographic complexity: modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

Google Scholar 
6.Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

Google Scholar 
7.Hughes, C. & Eastwood, R. Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proc. Natl Acad. Sci. USA 103, 10334–10339 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).CAS 
Article 

Google Scholar 
9.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Gillooly, J. F., Allen, A. P., West, G. B. & Brown, J. H. The rate of DNA evolution: effects of body size and temperature on the molecular clock. Proc. Natl Acad. Sci. USA 102, 140–145 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl Acad. Sci. USA 90, 4087–4091 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).Article 

Google Scholar 
13.Allen, A. P., Gillooly, J. F., Savage, V. M. & Brown, J. H. Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Igea, J. & Tanentzap, A. J. Angiosperm speciation cools down in the tropics. Ecol. Lett. 23, 692–700 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schluter, D. Speciation, ecological opportunity, and latitude (American Society of Naturalists address). Am. Nat. 187, 1–18 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

Google Scholar 
18.Clarke, A. & Gaston, K. J. Climate, energy and diversity. Proc. R. Soc. B 273, 2257–2266 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Dowle, E. J., Morgan-Richards, M. & Trewick, S. A. Molecular evolution and the latitudinal biodiversity gradient. Heredity 110, 501–510 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
21.Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

Google Scholar 
22.Boucher-Lalonde, V. & Currie, D. J. Spatial autocorrelation can generate stronger correlations between range size and climatic niches than the biological signal — a demonstration using bird and mammal range maps. PLoS One 11, e0166243 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Cutter, A. D. & Gray, J. C. Ephemeral ecological speciation and the latitudinal biodiversity gradient. Evolution 70, 2171–2185 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Morales‐Barbero, J., Martinez, P. A., Ferrer‐Castán, D. & Olalla‐Tárraga, M. Á. Quaternary refugia are associated with higher speciation rates in mammalian faunas of the Western Palaearctic. Ecography 41, 607–621 (2018).Article 

Google Scholar 
25.Xing, Y. & Ree, R. H. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proc. Natl Acad. Sci. USA 114, E3444–E3451 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Testo, W. L., Sessa, E. & Barrington, D. S. The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytol. 222, 604–613 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Dowsett, H. et al. The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction. Climate 12, 1519–1538 (2016).
Google Scholar 
29.Hartley, A. J. Andean uplift and climate change. J. Geol. Soc. 160, 7–10 (2003).Article 

Google Scholar 
30.Aron, P. G. & Poulsen, C. J. in Mountains, Climate and Biodiversity (eds Hoorn, C., Perrugi, A. & Antonelli, A.) Ch. 8 (2018).31.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Wallis, G. P., Waters, J. M., Upton, P. & Craw, D. Transverse Alpine speciation driven by glaciation. Trends Ecol. Evol. 31, 916–926 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Luebert, F. & Muller, L. A. H. Effects of mountain formation and uplift on biological diversity. Front. Genet. 6, 54 (2015).34.Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).Article 

Google Scholar 
35.Whittaker, R. J., Triantis, K. A. & Ladle, R. J. A general dynamic theory of oceanic island biogeography. J. Biogeogr. 35, 977–994 (2008).Article 

Google Scholar 
36.Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).Article 

Google Scholar 
37.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 
Article 

Google Scholar 
38.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

Google Scholar 
39.Rowley, D. B. & Garzione, C. N. Stable isotope-based paleoaltimetry. Annu. Rev. Earth Planet. Sci. 35, 463–508 (2007).CAS 
Article 

Google Scholar 
40.Mulch, A. Stable isotope paleoaltimetry and the evolution of landscapes and life. Earth Planet. Sci. Lett. 433, 180–191 (2016).CAS 
Article 

Google Scholar 
41.Kuhn, T. S., Mooers, A. Ø. & Thomas, G. H. A simple polytomy resolver for dated phylogenies. Methods Ecol. Evol. 2, 427–436 (2011).Article 

Google Scholar 
42.Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).43.Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg Extinction on mammal diversification. Science 334, 521–524 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Redding, D. W. & Mooers, A. Ø. Incorporating evolutionary measures into conservation prioritization. Conserv. Biol. 20, 1670–1678 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Moore, B. R., Höhna, S., May, M. R., Rannala, B. & Huelsenbeck, J. P. Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proc. Natl Acad. Sci. USA 113, 9569–9574 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Meyer, A. L. S., Román-Palacios, C. & Wiens, J. J. BAMM gives misleading rate estimates in simulated and empirical datasets. Evolution 72, 2257–2266 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Mitchell, J. S., Etienne, R. S. & Rabosky, D. L. Inferring diversification rate variation from phylogenies with fossils. Syst. Biol. 68, 1–18 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

Google Scholar 
55.Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Amante, C. & Eakins, B. W. ETOPO1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 (NOAA, 2009).57.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
58.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
60.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, v063i18 (2015).
Google Scholar  More

1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 

Google Scholar 
2.Carpenter, S. R. et al. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proc. Natl Acad. Sci. USA 106, 1305–1312 (2009).CAS 

Google Scholar 
3.Nelson, E. et al. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front. Ecol. Environ. 7, 4–11 (2009).
Google Scholar 
4.Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23 (2016).
Google Scholar 
5.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).
Google Scholar 
6.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).7.Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).8.Martínez-Harms, M. J. & Balvanera, P. Methods for mapping ecosystem service supply: a review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 17–25 (2012).
Google Scholar 
9.Hauck, J. et al. ‘Maps have an air of authority’: potential benefits and challenges of ecosystem service maps at different levels of decision making. Ecosyst. Serv. 4, 25–32 (2013).
Google Scholar 
10.Balvanera, P. et al. Conserving biodiversity and ecosystem services. Science 291, 2047 (2001).CAS 

Google Scholar 
11.Dick, J., Maes, J., Smith, R. I., Paracchini, M. L. & Zulian, G. Cross-scale analysis of ecosystem services identified and assessed at local and European level. Ecol. Indic. 38, 20–30 (2014).
Google Scholar 
12.UK National Ecosystem Assessment. The UK National Ecosystem Assessment Technical Report. (UNEP-WCMC, 2011); http://uknea.unep-wcmc.org/13.Orsi, F., Ciolli, M., Primmer, E., Varumo, L. & Geneletti, D. Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy 99, 104840 (2020).
Google Scholar 
14.Holland, R. A. et al. The influence of temporal variation on relationships between ecosystem services. Biodivers. Conserv. 20, 3285–3294 (2011).
Google Scholar 
15.Renard, D., Rhemtull, J. M. & Bennett, E. M. Historical dynamics in ecosystem service bundles. Proc. Natl Acad. Sci. USA 112, 13411–13416 (2015).CAS 

Google Scholar 
16.Rukundo, E. et al. Spatio-temporal dynamics of critical ecosystem services in response to agricultural expansion in Rwanda, East Africa. Ecol. Indic. 89, 696–705 (2018).
Google Scholar 
17.Stürck, J., Schulp, C. J. E. & Verburg, P. H. Spatio-temporal dynamics of regulating ecosystem services in Europe—the role of past and future land use change. Appl. Geogr. 63, 121–135 (2015).
Google Scholar 
18.Rau, A. L. et al. Temporal patterns in ecosystem services research: a review and three recommendations. Ambio 49, 1377–1393 (2020).
Google Scholar 
19.Sutherland, I. J., Bennett, E. M. & Gergel, S. E. Recovery trends for multiple ecosystem services reveal non-linear responses and long-term tradeoffs from temperate forest harvesting. For. Ecol. Manage. 374, 61–70 (2016).
Google Scholar 
20.Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).CAS 

Google Scholar 
21.Vanhanen, H. et al. Making Boreal Forests Work for People and Nature (IUFRO, 2012).22.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 

Google Scholar 
23.Moen, J. et al. Eye on the Taiga: removing global policy impediments to safeguard the boreal forest. Conserv. Lett. 7, 408–418 (2014).
Google Scholar 
24.Global Forest Industry (Swedish Forest Industries, 2019); https://www.forestindustries.se/forest-industry/statistics/global-forest-industry/25.Saastamoinen, O., Kangas, K. & Aho, H. The picking of wild berries in Finland in 1997 and 1998. Scand. J. For. Res. 15, 645–650 (2000).
Google Scholar 
26.Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).
Google Scholar 
27.Hou, Y., Li, B., Müller, F., Fu, Q. & Chen, W. A conservation decision-making framework based on ecosystem service hotspot and interaction analyses on multiple scales. Sci. Total Environ. 643, 277–291 (2018).CAS 

Google Scholar 
28.Blumstein, M. & Thompson, J. R. Land-use impacts on the quantity and configuration of ecosystem service provisioning in Massachusetts, USA. J. Appl. Ecol. 52, 1009–1019 (2015).
Google Scholar 
29.Fernandez-Campo, M., Rodríguez-Morales, B., Dramstad, W. E., Fjellstad, W. & Diaz-Varela, E. R. Ecosystem services mapping for detection of bundles, synergies and trade-offs: examples from two Norwegian municipalities. Ecosyst. Serv. 28, 283–297 (2017).
Google Scholar 
30.Gissi, E., Fraschetti, S. & Micheli, F. Incorporating change in marine spatial planning: a review. Environ. Sci. Policy 92, 191–200 (2019).
Google Scholar 
31.Maxwell, S. M., Gjerde, K. M., Conners, M. G. & Crowder, L. B. Mobile protected areas for biodiversity on the high seas. Science 367, 252–254 (2020).CAS 

Google Scholar 
32.Willcock, S. et al. Do ecosystem service maps and models meet stakeholders’ needs? A preliminary survey across sub-Saharan Africa. Ecosyst. Serv. 18, 110–117 (2016).
Google Scholar 
33.Jonsson, M., Bengtsson, J., Gamfeldt, L., Moen, J. & Snäll, T. Levels of forest ecosystem services depend on specific mixtures of commercial tree species. Nat. Plants 5, 141–147 (2019).
Google Scholar 
34.Pohjanmies, T. et al. Impacts of forestry on boreal forests: an ecosystem services perspective. Ambio 46, 743–755 (2017).
Google Scholar 
35.Miina, J., Hotanen, J.-P. & Salo, K. Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests. Silva Fenn. 43, 577–593 (2009).
Google Scholar 
36.Hertel, A. G. et al. Berry production drives bottom–up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2018).
Google Scholar 
37.Thiffault, E. Boreal forests and soils. Dev. Soil Sci. 36, 59–82 (2019).
Google Scholar 
38.Jonsson, M., Bengtsson, J., Moen, J., Gamfeldt, L. & Snäll, T. Stand age and climate influence forest ecosystem service delivery and multifunctionality. Environ. Res. Lett. 15, 0940a8 (2020).
Google Scholar 
39.Stokland, J. N. Volume increment and carbon dynamics in boreal forest when extending the rotation length towards biologically old stands. For. Ecol. Manage. 488, 119017 (2021).
Google Scholar 
40.Harmon, M. E., Ferrell, W. K. & Franklin, J. F. Effects on carbon storage of conversion of old-growth forests to young forests. Science 247, 699–702 (1990).CAS 

Google Scholar 
41.Mazziotta, A. et al. Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest. Glob. Change Biol. 21, 637–651 (2015).
Google Scholar 
42.Triviño, M. et al. Optimizing management to enhance multifunctionality in a boreal forest landscape. J. Appl. Ecol. 54, 61–70 (2017).
Google Scholar 
43.Qiu, J. & Turner, M. G. Spatial interactions among ecosystem services in an urbanizing agricultural watershed. Proc. Natl Acad. Sci. USA 110, 12149–12154 (2013).CAS 

Google Scholar 
44.Felipe-Lucia, M. R. et al. Multiple forest attributes underpin the supply of multiple ecosystem services. Nat. Commun. 9, 4839 (2018).
Google Scholar 
45.Eggers, J., Räty, M., Öhman, K. & Snäll, T. How well do stakeholder-defined forest management scenarios balance economic and ecological forest values? Forests 11, 86 (2020).
Google Scholar 
46.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. For. Policy Econ. 92, 119–127 (2018).
Google Scholar 
47.Rusch, A., Bommarco, R., Jonsson, M., Smith, H. G. & Ekbom, B. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J. Appl. Ecol. 50, 345–354 (2013).
Google Scholar 
48.Schipanski, M. E. et al. A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric. Syst. 125, 12–22 (2014).
Google Scholar 
49.Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).
Google Scholar 
50.Guerry, A. D. et al. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 107–121 (2012).
Google Scholar 
51.Wikström, P. et al. The Heureka Forestry Decision Support System: An Overview. Math. Comput. For Nat.-Resour. Sci. 3, 87–94 (2011).
Google Scholar 
52.Forest Statistics (Swedish University of Agricultural Sciences, 2020).53.Eriksson, A., Snäll, T. & Harrison, P. J. Analys av miljöförhållanden ‐ SKA 15. Report 11 (Swedish Forest Agency, 2015).54.Axelsson, A.-L. et al. in National Forest Inventories—Pathways for Common Reporting (eds Tomppo, E. et al.) 541–553 (Springer, 2010).55.Marklund, L. G. Biomass Functions for Pine, Spruce and Birch in Sweden (1988).56.Petersson, H. & Ståhl, G. Functions for below-ground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden. Scand. J. For. Res. 21, 24–83 (2006).
Google Scholar 
57.Miina, J., Pukkala, T. & Kurttila, M. Optimal multi-product management of stands producing timber and wild berries. Eur. J. For. Res. 135, 781–794 (2016).
Google Scholar 
58.Schröter, M. & Remme, R. P. Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation. Landsc. Ecol. 31, 431–450 (2016).
Google Scholar 
59.Wu, J., Feng, Z., Gao, Y. & Peng, J. Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities. Ecol. Indic. 29, 529–537 (2013).CAS 

Google Scholar 
60.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).
Google Scholar 
61.R: A Language and Environment for Statistical Computing (R Development Core Team, 2014); https://www.R-project.org/ More