Ecology

Concentration of TPHs in surface soilsStatistical results of TPHs concentrations at different geographic oilfields were showed in Fig. 2, and grid regional distribution of TPHs in YC Oilfield surface soils (Y6–Y25) were shown in Fig. 3. Results are given as mean value of triplicate analysis of each sample. The results of TPHs concentration in soil samples showed that the three oilfields all suffered from varying degrees of petroleum pollution, and 60.92% of the 47 sampling points was significantly higher than the soil critical value (500 mg/kg). The average concentration of the TPHs in each study areas conformed to be in the following law: SL Oilfield (average: 5.36 × 103 mg/kg) ( >) NY Oilfield (average: 1.73 × 103 mg/kg) ( >) YC Oilfield (average: 1.37 × 103 mg/kg). The highest concentration of the TPHs were found in SL Oilfield surface soils, ranging from 1.21 × 102 to 6.66 × 104 mg/kg, and NY Oilfield had the second highest TPHs concentrations in the range from 15.82 to 7.42 × 103 mg/kg. The concentrations of TPHs in YC Oilfield ranged from 12.34 to 5.38 × 103 mg/kg. The petroleum contamination mainly derived from abandoned and working oil wells. S4 and S8 soils were collected near the abandoned oil well and working oil well, respectively, and had the highest concentration of TPHs up to 5.28 × 104 and 6.66 × 104 mg/kg. Y1, N8 near the abandoned oil well also had high concentration of TPHs with 5.39 × 103 and 7.42 × 103 mg/kg, respectively. Pollution caused by grounded crude oil in exploitation process has been a serious problem in oilfield area. Our previous research reported that the TPHs content in Dagang Oilfield soils collected adjacent to working oil wells were about 20-folds higher than that in corn soils and living area soils25. Concentration contour map of TPHs in YC Oilfield by grid sampling method showed that regional pollution in the northwest and southeast area are more serious than other sites. Y6 near the gas station and Y15, Y21, Y23 adjacent to the working oil wells have higher concentration (2.12 × 103–5.34 × 103 mg/kg) of TPHs than other farmland and grass soils. Previous study reported that the concentrations of TPHs ranged 7.0 × 102–4.0 × 103 mg/kg in oil exploitation areas of the loess plateau region (34°20′N,107°10′E), showing a similar pollution level with this study26.Figure 2The concentration of TPHs in three oilfield soils.Full size imageFigure 3Grid regional distribution of TPHs in YC Oilfield.Full size imageThe percentage composition of total PAHs, SHs and polar components of petroleum hydrocarbons were shown in Table 1. In general, the dominant petroleum component was saturated hydrocarbons in all soils, accounting more than 50%. Yet, the percentage proportion of PAHs and SHs in contamination soils adjacent to working and abandon oil wells were significantly different (p  BbF (14.16–21.87%) ≫ BaA, Chr, InP, and BkF (less than 10%). This result aligned to the previous study that the contribution of individual PAHs to the TEQs of ∑PAH16 was BaP (45%)  > DBA (33%) in urban surface dust of Xi’an city, China46. Therefore, contamination control should priority focus on the individual PAHs of BaP, DBA, BbF in these areas. In addition, the ecological risk with abandoned time ranging 0–15 years has been assessed, and the descriptive statistic TEQBap of PAHs was shown in Supporting Information, Table S6. The highest TEQs of ∑PAH16 and ∑PAH7 with mean of 1422.27 μg/kg and 1400.48 μg/kg, respectively, were present in soils adjacent to abandoned oil well with abandoned time of 0—5 years. And the TEQs of ∑PAH16 and ∑PAH7 decreased with the abandoned time though the percentage proportion of PAHs increased. The TEQs of ∑PAH16 and ∑PAH7 were close between abandoned time of 5–10 years and 10—15 years while both had high content. It demonstrated that high ecological risk was persistent in abandoned oil well areas over abandoned time of 15 years, and basically stable after 5 years. Therefore, abandoned oil well areas need to be blocked to prevent PAHs entering the external environment, and combine physical–chemical technology for petroleum remediation instead of simple weathering biological processes.Table 3 Descriptive statistic TEQBap of PAHs in different sampling area.Full size tableAs referred the PAHs standard of Dutch soil, TEQs of ∑PAH7 was 32.02 μg/kg, calculated by ten individual PAHs times TEFs. In this study, the mean TEQs of ∑PAH7 were about 35- and 10-folds of Dutch soil in petro-related area soils and grassland soils, indicating a high and medium ecological risk in these soils respectively. However, the mean TEQs of ∑PAH7 in farmland soils (18.80 μg/kg) was below Dutch soil, presenting a low potential ecological risk. It should be noted that the minimum of TEQs of ∑PAH7 in grassland soil was 26.24 μg/kg less than TEQs of ∑PAH7 in Dutch soil, but it was vulnerable affected by the surrounding soils with high TEQs of ∑PAH7. In this study, except the farmland soils, TEQs of ∑PAH7 exhibited higher TEQ values than those reported soils in Santiago, Chile47 and Nepal24, and road dust in Tianjin, China48. Overall, the most threat of ecological risk in petro-related soils caused by the anthropogenic PAHs input, such like oil leakage, oil refining, and fossil energy combustion. Preventing oil spills accident and developing the remediation methods are the main significant ways to reduce the ecological risks in these areas. The medium ecological risk in grassland might result from the migration of PAHs via rainfall pathway. Therefore, establishment the oil-blocking isolation zones is the critical way for medium ecological risk areas to control petroleum inflow. Even though the low ecological risk was identified in farmland soils, PAHs source analysis indicated that the biomass combustion should be controlled in these areas. More

Lipkind, D. et al. Stepwise acquisition of vocal combinatorial capacity in songbirds and human infants. Nature 498, 104–108 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Goldstein, M., King, A. P. & West, M. J. Social interaction shapes babbling: testing parallels between birdsong and speech. Proc. Natl Acad. Sci. USA 100, 8030–8035 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fehér, O., Ljubičić, I., Suzuki, K., Okanoya, K. & Tchernichovski, O. Statistical learning in songbirds: from self-tutoring to song culture. Phil. Trans. R. Soc. B 372, 20160053 (2017).PubMed 
PubMed Central 

Google Scholar 
Tchernichovski, O., Lints, T., Mitra, P. P. & Nottebohm, F. Vocal imitation in zebra finches is inversely related to model abundance. Proc. Natl Acad. Sci. USA 96, 12901–12904 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tchernichovski, O. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564–2569 (2001).CAS 

Google Scholar 
Fehér, O., Wang, H., Saar, S., Mitra, P. P. & Tchernichovski, O. De novo establishment of wild-type song culture in the zebra finch. Nature 459, 564–568 (2009).PubMed 
PubMed Central 

Google Scholar 
Takahashi, D. et al. The developmental dynamics of marmoset monkey vocal production. Science 349, 734–738 (2015).CAS 

Google Scholar 
Takahashi, D. Y., Liao, D. A. & Ghazanfar, A. A. Vocal learning via social reinforcement by infant marmoset monkeys. Curr. Biol. 27, 1844–1852.E6 (2017).Takahashi, D. Y., Fenley, A. R. & Ghazanfar, A. A. Early development of turn-taking with parents shapes vocal acoustics in infant marmoset monkeys. Phil. Trans. R. Soc. B 371, 20150370 (2016).PubMed 
PubMed Central 

Google Scholar 
Gultekin, Y. B. & Hage, S. R. Limiting parental interaction during vocal development affects acoustic call structure in marmoset monkeys. Sci. Adv. 4, eaar4012 (2018).PubMed 
PubMed Central 

Google Scholar 
Gultekin, Y. B. & Hage, S. R. Limiting parental feedback disrupts vocal development in marmoset monkeys. Nat. Commun. 8, 14046 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jarvis, E. D. Evolution of vocal learning and spoken language. Science 366, 50–54 (2019).CAS 

Google Scholar 
Snowdon, C. T. Learning from monkey “talk”. Science 355, 1120–1122 (2017).CAS 

Google Scholar 
Malik, K. Rights and wrongs. Nature 406, 675–676 (2000).
Google Scholar 
Wise, S. M. & Goodall, J. Rattling the Cage: Toward Legal Rights for Animals (Da Capo Press, 2017).Grayson, L. Animals in Research: For and Against (British Library, 2000).Nater, A. et al. Morphometric, behavioral, and genomic evidence for a new orangutan species. Curr. Biol. 27, 3487–3498.E10 (2017).CAS 

Google Scholar 
Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3, e1600946 (2017).PubMed 
PubMed Central 

Google Scholar 
Ross, S. et al. Inappropriate use and portrayal of chimpanzees. Science 319, 1487 (2008).CAS 

Google Scholar 
Wich, S. A. et al. Land-cover changes predict steep declines for the Sumatran orangutan (Pongo abelii). Sci. Adv. 2, e1500789 (2016).PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Understanding the impacts of land-use policies on a threatened species: is there a future for the Bornean orangutan? PLoS ONE 7, e49142 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Will oil palm’s homecoming spell doom for Africa’s great apes? Curr. Biol. https://doi.org/10.1016/j.cub.2014.05.077 (2014).Fitch, T. W. Empirical approaches to the study of language evolution. Psychon. Bull. Rev. 24, 3–33 (2017).Hauser, M. D. et al. The mystery of language evolution. Front. Psychol. https://doi.org/10.3389/fpsyg.2014.00401 (2014)Corballis, M. C. Crossing the Rubicon: behaviorism, language, and evolutionary continuity. Front. Psychol. 11, 653 (2020).PubMed 
PubMed Central 

Google Scholar 
Berwick, R. C. & Chomsky, N. All or nothing: no half-merge and the evolution of syntax. PLoS Biol. 17, e3000539 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolhuis, J. J. & Wynne, C. D. Can evolution explain how minds work? Nature 458, 832–833 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hayes, K. J. & Hayes, C. The intellectual development of a home-raised chimpanzee. Proc. Am. Phil. Soc. 95, 105–109 (1951).
Google Scholar 
Premack, D. Language in chimpanzee? Science 172, 808–822 (1971).CAS 
PubMed 
PubMed Central 

Google Scholar 
Terrace, H., Petitto, L., Sanders, R. & Bever, T. Can an ape create a sentence? Science 206, 891–902 (1979).CAS 

Google Scholar 
Patterson, F. & Linden, E. The Education of Koko (Holt, Rinehart and Winston, 1981).Leavens, D. A., Bard, K. A. & Hopkins, W. D. BIZARRE chimpanzees do not represent “the chimpanzee”. Behav. Brain Sci. 33, 100–101 (2010).
Google Scholar 
Lameira, A. R. Bidding evidence for primate vocal learning and the cultural substrates for speech evolution. Neurosci. Biobehav. Rev. 83, 429–439 (2017).
Google Scholar 
Lameira, A. R. et al. Speech-like rhythm in a voiced and voiceless orangutan call. PLoS ONE 10, e116136 (2015).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Shumaker, R. W. Orangutans show active voicing through a membranophone. Sci. Rep. 9, 12289 (2019).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R., Hardus, M. E., Mielke, A., Wich, S. A. & Shumaker, R. W. Vocal fold control beyond the species-specific repertoire in an orangutan. Sci. Rep. 6, 30315 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. et al. Orangutan (Pongo spp.) whistling and implications for the emergence of an open-ended call repertoire: a replication and extension. J. Acoust. Soc. Am. 134, 2326–2335 (2013).
Google Scholar 
Perlman, M. & Clark, N. Learned vocal and breathing behavior in an enculturated gorilla. Anim. Cogn. 18, 1165–1179 (2015).
Google Scholar 
Wich, S. et al. A case of spontaneous acquisition of a human sound by an orangutan. Primates 50, 56–64 (2009).
Google Scholar 
Lameira, A. R., Maddieson, I. & Zuberbuhler, K. Primate feedstock for the evolution of consonants. Trends Cogn. Sci. 18, 60–62 (2014).
Google Scholar 
Lameira, A. R. The forgotten role of consonant-like calls in theories of speech evolution. Behav. Brain Sci. 37, 559–560 (2014).
Google Scholar 
Boë, L.-J. et al. Which way to the dawn of speech? Reanalyzing half a century of debates and data in light of speech science. Sci. Adv. 5, eaaw3916 (2019).PubMed 
PubMed Central 

Google Scholar 
Boë, L. J. et al. Evidence of a vocalic proto-system in the baboon (Papio papio) suggests pre-hominin speech precursors. PLoS ONE 12, e0169321 (2017).PubMed 
PubMed Central 

Google Scholar 
Fitch, T. W., Boer, B., Mathur, N. & Ghazanfar, A. A. Monkey vocal tracts are speech-ready. Sci. Adv. 2, e1600723 (2016).PubMed 
PubMed Central 

Google Scholar 
Pereira, A. S., Kavanagh, E., Hobaiter, C., Slocombe, K. E. & Lameira, A. R. Chimpanzee lip-smacks confirm primate continuity for speech-rhythm evolution. Biol. Lett. 16, 20200232 (2020).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. et al. Proto-consonants were information-dense via identical bioacoustic tags to proto-vowels. Nat. Hum. Behav. 1, 0044 (2017).
Google Scholar 
Lameira, A. R. et al. Orangutan information broadcast via consonant-like and vowel-like calls breaches mathematical models of linguistic evolution. Biol. Lett. 17, 20210302 (2021).PubMed 
PubMed Central 

Google Scholar 
Watson, S. K. et al. Nonadjacent dependency processing in monkeys, apes, and humans. Sci. Adv. 6, eabb0725 (2020).PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Call, J. Time-space–displaced responses in the orangutan vocal system. Sci. Adv. 4, eaau3401 (2018).PubMed 
PubMed Central 

Google Scholar 
Belyk, M. & Brown, S. The origins of the vocal brain in humans. Neurosci. Biobehav. Rev. 77, 177–193 (2017).
Google Scholar 
Crockford, C., Wittig, R. M. & Zuberbuhler, K. Vocalizing in chimpanzees is influenced by social-cognitive processes. Sci. Adv. 3, e1701742 (2017).PubMed 
PubMed Central 

Google Scholar 
Taglialatela, J. P., Reamer, L., Schapiro, S. J. & Hopkins, W. D. Social learning of a communicative signal in captive chimpanzees. Biol. Lett. 8, 498–501 (2012).PubMed 
PubMed Central 

Google Scholar 
Russell, J. L., Joseph, M., Hopkins, W. D. & Taglialatela, J. P. Vocal learning of a communicative signal in captive chimpanzees, Pan troglodytes. Brain Lang. 127, 520–525 (2013).PubMed 
PubMed Central 

Google Scholar 
Hopkins, W. D. et al. Genetic factors and orofacial motor learning selectively influence variability in central sulcus morphology in chimpanzees (Pan troglodytes). J. Neurosci. 37, 5475–5483 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Staes, N. et al. FOXP2 variation in great ape populations offers insight into the evolution of communication skills. Sci. Rep. 7, 16866 (2017).PubMed 
PubMed Central 

Google Scholar 
Martins, P. T. & Boeckx, C. Vocal learning: beyond the continuum. PLoS Biol. 18, e3000672 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, S. K. et al. Vocal learning in the functionally referential food grunts of chimpanzees. Curr. Biol. 25, 495–499 (2015).CAS 

Google Scholar 
Hopkins, W. D., Taglialatela, J. P. & Leavens, D. A. Chimpanzees differentially produce novel vocalizations to capture the attention of a human. Anim. Behav. 73, 281–286 (2007).PubMed 
PubMed Central 

Google Scholar 
Bianchi, S., Reyes, L. D., Hopkins, W. D., Taglialatela, J. P. & Sherwood, C. C. Neocortical grey matter distribution underlying voluntary, flexible vocalizations in chimpanzees. Sci. Rep. 6, 34733 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wich, S. A. et al. Call cultures in orangutans? PLoS ONE 7, e36180 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crockford, C., Herbinger, I., Vigilant, L. & Boesch, C. Wild chimpanzees produce group-specific calls: a case for vocal learning? Ethology 110, 221–243 (2004).
Google Scholar 
Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).CAS 

Google Scholar 
van Schaik, C. P. et al. Orangutan cultures and the evolution of material culture. Science 299, 102–105 (2003).
Google Scholar 
Whiten, A. Culture extends the scope of evolutionary biology in the great apes. Proc. Natl Acad. Sci. USA 114, 7790–7797 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Koops, K., Visalberghi, E. & van Schaik, C. The ecology of primate material culture. Biol. Lett. 10, 20140508 (2014).PubMed 
PubMed Central 

Google Scholar 
Kalan, A. K. et al. Chimpanzees use tree species with a resonant timbre for accumulative stone throwing. Biol. Lett. 15, 20190747 (2019).PubMed 
PubMed Central 

Google Scholar 
Hardus, M., Lameira, A. R., Van Schaik, C. P. & Wich, S. A. Tool use in wild orangutans modifies sound production: a functionally deceptive innovation? Proc. R. Soc. B https://doi.org/10.1098/rspb.2009.1027 (2009).Lameira, A. R. et al. Population-specific use of the same tool-assisted alarm call between two wild orangutan populations (Pongo pygmaeus wurmbii) indicates functional arbitrariness. PLoS ONE 8, e69749 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hohmann, G. & Fruth, B. Culture in bonobos? Between‐species and within‐species variation in behavior. Curr. Anthropol. 44, 563–571 (2003).
Google Scholar 
Robbins, M. M. et al. Behavioral variation in gorillas: evidence of potential cultural traits. PLoS ONE 11, e0160483 (2016).PubMed 
PubMed Central 

Google Scholar 
Kühl, H. S. et al. Human impact erodes chimpanzee behavioral diversity. Science 363, 1453–1455 (2019).
Google Scholar 
van Schaik, C. P. Fragility of Traditions: the disturbance hypothesis for the loss of local traditions in orangutans. Int. J. Primatol. 23, 527–538 (2002).
Google Scholar 
Delgado, R. A. & van Schaik, C. P. The behavioral ecology and conservation of the orangutan (Pongo pygmaeus): a tale of two islands. Evol. Anthropol. 9, 201–218 (2000).
Google Scholar 
van Schaik, C. The socioecology of fission–fusion sociality in orangutans. Primates 40, 69–86 (1999).
Google Scholar 
Nater, A. et al. Sex-biased dispersal and volcanic activities shaped phylogeographic patterns of extant orangutans (genus: Pongo). Mol. Biol. 28, 2275–2288 (2011).CAS 

Google Scholar 
Arora, N. et al. Parentage-based pedigree reconstruction reveals female matrilineal clusters and male-biased dispersal in nongregarious Asian great apes, the Bornean orangutans (Pongo pygmaeus). Mol. Ecol. 21, 3352–3362 (2012).CAS 

Google Scholar 
Kavanagh, E. et al. Dominance style is a key predictor of vocal use and evolution across nonhuman primates. R. Soc. Open Sci. 8, 210873 (2021).PubMed 
PubMed Central 

Google Scholar 
Husson, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 6 (Oxford Univ. Press, 2009).van Noordwijk, M. A. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 12 (Oxford Univ Press, 2009).Singleton, I., Knott, C., Morrogh-Bernard, H., Wich, S. & van Schaik, C. P. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 13 (Oxford Univ. Press, 2009).Wich, S. et al. Life history of wild Sumatran orangutans (Pongo abelii). J. Hum. Evol. 47, 385–398 (2004).CAS 

Google Scholar 
Wich, S. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 5 (Oxford Univ. Press, 2009).Shumaker, R. W., Wich, S. A. & Perkins, L. Reproductive life history traits of female orangutans (Pongo spp.). Primate Reprod. Aging 36, 147–161 (2008).CAS 

Google Scholar 
Freund, C., Rahman, E. & Knott, C. Ten years of orangutan-related wildlife crime investigation in West Kalimantan, Indonesia. Am. J. Primatol. 79, 22620 (2016).
Google Scholar 
van Noordwijk, M. A. & van Schaik, C. P. Development of ecological competence in Sumatran orangutans. Am. J. Phys. Anthropol. 127, 79–94 (2005).
Google Scholar 
Knot, C. D. et al. The Gunung Palung Orangutan Project: Twenty-five years at the intersection of research and conservation in a critical landscape in Indonesia. Biol. Conserv. 255, 108856 (2021).
Google Scholar 
Guillermo, S.-B., Gershenson, C. & Fernández, N. A package for measuring emergence, self-organization, and complexity based on shannon entropy. Front. Robot. AI 4, 174102 (2017).
Google Scholar 
Santamaría-Bonfil, G., Fernández, N. & Gershenson, C. Measuring the complexity of continuous distributions. Entropy 18, 72 (2016).
Google Scholar 
Kalan, A. K., Mundry, R. & Boesch, C. Wild chimpanzees modify food call structure with respect to tree size for a particular fruit species. Anim. Behav. 101, 1–9 (2015).
Google Scholar 
Fedurek, P. & Slocombe, K. E. The social function of food-associated calls in male chimpanzees. Am. J. Primatol. 75, 726–739 (2013).
Google Scholar 
Luef, E., Breuer, T. & Pika, S. Food-associated calling in gorillas (Gorilla g. gorilla) in the wild. PLoS ONE 11, e0144197 (2016).PubMed 
PubMed Central 

Google Scholar 
Clay, Z. & Zuberbuhler, K. Food-associated calling sequences in bonobos. Anim. Behav. 77, 1387–1396 (2009).
Google Scholar 
Hardus, M. E. et al. in Orangutans: Geographic Variation in Behavioral Ecology and Conservation (eds Wich, S. et al.) Ch. 4 (Oxford Univ. Press, 2009).Wich, S. A. et al. Forest fruit production is higher on Sumatra than on Borneo. PLoS ONE 6, e21278 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lameira, A. R. & Wich, S. Orangutan long call degradation and individuality over distance: a playback approach. Int. J. Primatol. 29, 615–625 (2008).
Google Scholar 
Lameira, A. R., Delgado, R. & Wich, S. Review of geographic variation in terrestrial mammalian acoustic signals: human speech variation in a comparative perspective. J. Evolut. Psychol. 8, 309–332 (2010).
Google Scholar 
Lameira, A. R. et al. Predator guild does not influence orangutan alarm call rates and combinations. Behav. Ecol. Sociobiol. 67, 519–528 (2013).
Google Scholar 
Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).
Google Scholar 
Scerri, E. M. et al. Did our species evolve in subdivided populations across Africa, and why does it matter? Trends Ecol. Evol. 33, 582–594 (2018).PubMed 
PubMed Central 

Google Scholar 
Kaya, F. et al. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nat. Ecol. Evol. 2, 241–246 (2018).PubMed 
PubMed Central 

Google Scholar 
Bobe, R. The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 399–420 (2004).
Google Scholar 
Zhu, D., Galbraith, E. D., Reyes-García, V. & Ciais, P. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nat. Ecol. Evol. 5, 1536–1545 (2021).PubMed 
PubMed Central 

Google Scholar 
DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
Google Scholar 
Mauricio, G.-F. & Gardner, A. Inference of ecological and social drivers of human brain-size evolution. Nature 557, 554–557 (2018).
Google Scholar 
Lindenfors, P., Wartel, A. & Lind, J. ‘Dunbar’s number’ deconstructed. Biol. Lett. 17, 20210158 (2021).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C., van Noordwijk, M., Atmoko, S. U. & van Schaik, C. Early sociability fosters later exploratory tendency in wild immature orangutans. Sci. Adv. 6, eaaw2685 (2020).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C. et al. Observational social learning and socially induced practice of routine skills in immature wild orangutans. Anim. Behav. 119, 87–98 (2016).
Google Scholar 
Jaeggi, A. V. et al. Social learning of diet and foraging skills by wild immature Bornean orangutans: implications for culture. Am. J. Primatol. 72, 62–71 (2010).PubMed 
PubMed Central 

Google Scholar 
Schuppli, C. et al. The effects of sociability on exploratory tendency and innovation repertoires in wild Sumatran and Bornean orangutans. Sci. Rep. 7, 15464 (2017).PubMed 
PubMed Central 

Google Scholar 
Ehmann, B. et al. Immature wild orangutans acquire relevant ecological knowledge through sex-specific attentional biases during social learning. PLoS Biol. 19, e3001173 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meijaard, E. et al. Declining orangutan encounter rates from Wallace to the present suggest the species was once more abundant. PLoS ONE 5, e12042 (2010).PubMed 
PubMed Central 

Google Scholar 
Marshall, A. J. et al. The blowgun is mightier than the chainsaw in determining population density of Bornean orangutans (Pongo pygmaeus morio) in the forests of East Kalimantan. Biol. Conserv. 129, 566–578 (2006).
Google Scholar 
Gail, C.-S., Miran, C.-S., Singleton, I. & Linkie, M. Raiders of the lost bark: orangutan foraging strategies in a degraded landscape. PLoS ONE 6, e20962 (2011).
Google Scholar 
Schuppli, C. & van Schaik, C. P. Animal cultures: how we’ve only seen the tip of the iceberg. Evol. Hum. Sci. 1, e2 (2019).
Google Scholar 
Langergraber, K. E. et al. Vigilant, generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and human evolution. Proc. Natl Acad. Sci. USA 109, 15716–15721 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernández, N., Maldonado, C. & Gershenson, C. in Guided Self-Organization: Inception (ed Prokopenko, M.) 19–51 (Springer Berlin Heidelberg, 2014).JAST Team, JASP (Univ. of Amsterdam, 2020).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).Auguie, B. gridExtra: Functions in grid graphics. R version 0.9.1 (2012).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar 
Korthauer, K. et al. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 20, 118 (2019).PubMed 
PubMed Central 

Google Scholar  More

Of which at the third instar, the external morphology of larvae is quite similar; thus, the morphological identification used to differentiate between its genera or species, generally includes cephalophalyngeal skeleton, anterior spiracle, and posterior spiracles. The morphology of the posterior spiracle is one of the important characteristics for identification. A typical morphology of the posterior spiracle of third stage larvae was shown in Fig. 2. Based on studying under light microscopy, the posterior spiracle of M. domestica was clearly distinguished from the others. On the other hand, the morphology of the posterior spiracle of C. megacephala and A. rufifacies was quite similar. For C. megacephala and C. rufifacies, the peritreme, a structure encircling the three spiracular openings (slits), was incomplete and slits were straight as shown Fig. 2A,B, respectively. The complete peritreme encircling three slits was found in L. cuprina and M. domestica as shown in Fig. 2C,D, respectively. However, only the slits of M. domestica were sinuous like the M-letter (Fig. 2D). Their morphological characteristics found in this study were like the descriptions in the previous reports23,24,25.Figure 2Morphology of posterior spiracles of four different fly species after inverting the image colors; (A) Chrysomya (Achoetandrus) ruffifacies, (B) Chrysomya megacephala, (C) Lucilia cuprina, (D) Musca domestica.Full size imageFor model training, four of the CNN models used for species-level identification of fly maggots provided 100% accuracy rates and 0% loss. Number of parameter (#Params), model speed, model size, macro precision, macro recall, f1-score, and support value were also presented in Table 1. The result demonstrated that the AlexNet model provided the best performance in all indicators when compared among four models. The AlexNet model used the least number of parameters while the Resnet101 model used the most. For model speed, the AlexNet model provided the fastest speed, while the Densenet161 model provided the slowest speed. For the model size, the AlexNet model was the smallest, while the Resnet101 model was the largest which corresponded to the number of parameters used. Macro precision, macro recall, f1-score and support value of all models were the same.Table 1 Comparison of model size, speed, and performances of each studied model (The text in bold indicates the best value in each category).Full size tableAs the training results presented in the supplementary data (Fig. S1), all models provided 100% accuracy and 0% loss in the early stage of training ( More

Hill, L. et al. The£ 15 billion cost of ash dieback in Britain. Curr. Biol. 29(9), R315–R316 (2019).CAS 
PubMed 

Google Scholar 
Pliûra, A. & Heuertz, M. EUFORGEN Technical Guidelines for Genetic Conservation and Use for Common Ash (Fraxinus excelsior) (Bioversity International, 2003).
Google Scholar 
Dufour, S. & Piégay, H. Geomorphological controls of Fraxinus excelsior growth and regeneration in floodplain forests. Ecology 89(1), 205–215 (2008).CAS 
PubMed 

Google Scholar 
Mitchell, R. J. et al. Ash dieback in the UK: a review of the ecological and conservation implications and potential management options. Biol. Conserv. 175, 95–109 (2014).
Google Scholar 
Przybył, K. Fungi associated with necrotic apical parts of Fraxinus excelsior shoots. For. Pathol. 32(6), 387–394 (2002).
Google Scholar 
Vasaitis, R., & Enderle, R. Dieback of European ash (Fraxinus spp.)-consequences and guidelines for sustainable management. Dieback of European ash (Fraxinus spp.). Report on COST Action FP1103 FRAXBACK. ISBN978-91-576-8696-1. (SLU Swedish University of Agricultural Sciences, 2017).Børja, I. et al. Ash dieback in Norway-current situation. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 166–175 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Ghelardini, L. et al. From the Alps to the Apennines: Possible spread of ash dieback in Mediterranean areas. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 140–149 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Marçais, B., Husson, C., Godart, L. & Cael, O. Influence of site and stand factors on Hymenoscyphus fraxineus-induced basal lesions. Plant. Pathol. 65(9), 1452–1461 (2016).
Google Scholar 
Queloz, V., Hopf, S., Schoebel, C. N., Rigling, D. & Gross, A. Ash dieback in Switzerland: History and scientific achievements. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 68–78 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Orton, E. S. et al. Population structure of the ash dieback pathogen, Hymenoscyphus fraxineus, in relation to its mode of arrival in the UK. Plant. Pathol. 67(2), 255–264 (2018).CAS 
PubMed 

Google Scholar 
Enderle, R., Stenlid, J. & Vasaitis, R. An overview of ash (Fraxinus spp.) and the ash dieback disease in Europe. CAB Rev. 14, 1–12 (2019).
Google Scholar 
Heinze, B., Tiefenbacher, H., Litschauer, R. & Kirisits, T. Ash dieback in Austria: History, current situation and outlook. in Dieback of European Ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management, 33–52 (2017).Coker, T. L. et al. Estimating mortality rates of European ash (Fraxinus excelsior) under the ash dieback (Hymenoscyphus fraxineus) epidemic. Plants People Planet 1(1), 48–58 (2019).
Google Scholar 
Cleary, M., Nguyen, D., Stener, L. G., Stenlid, J., & Skovsgaard, J. P. Ash and ash dieback in Sweden: A review of disease history, current status, pathogen and host dynamics, host tolerance and management options in forests and landscapes. Dieback of European Ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management, 195–208 (2017).Stocks, J. J., Buggs, R. J. & Lee, S. J. A first assessment of Fraxinus excelsior (common ash) susceptibility to Hymenoscyphus fraxineus (ash dieback) throughout the British Isles. Sci. Rep. 7(1), 1–7 (2017).
Google Scholar 
Díaz-Yáñez, O. et al. The invasive forest pathogen Hymenoscyphus fraxineus boosts mortality and triggers niche replacement of European ash (Fraxinus excelsior). Sci. Rep. 10(1), 1–10 (2020).
Google Scholar 
Enderle, R., Metzler, B., Riemer, U. & Kändler, G. Ash dieback on sample points of the national forest inventory in south-western Germany. Forests 9(1), 25 (2018).
Google Scholar 
Klesse, S. et al. Spread and severity of ash dieback in Switzerland: Tree characteristics and landscape features explain varying mortality probability. Front. For. Glob. Change 4, 18 (2021).
Google Scholar 
Timmermann, V., Potočić, N., Ognjenović, M. & Kirchner, T. Tree crown condition in 2020. In Forest Condition in Europe: The 2021 Assessment ICP Forests Technical Report under the UNECE Convention on Long-range Transboundary Air Pollution (Air Convention) (eds Michel, A. et al.) (Thünen Institute, 2021).
Google Scholar 
Chumanová, E. et al. Predicting ash dieback severity and environmental suitability for the disease in forest stands. Scand. J. For. Res. 34(4), 254–266 (2019).
Google Scholar 
Solheim, H. & Hietala, A. M. Spread of ash dieback in Norway. Balt. For. 23(1), 1–6 (2017).
Google Scholar 
Kjær, E. D. et al. Genetics of ash dieback resistance in a restoration context: Experiences from Denmark. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 106–114 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Madsen, C. L. et al. Combined progress in symptoms caused by Hymenoscyphus fraxineus and Armillaria species, and corresponding mortality in young and old ash trees. For. Ecol. Manage. 491, 119177 (2021).
Google Scholar 
Trapiello, E., Schoebel, C. N. & Rigling, D. Fungal community in symptomatic ash leaves in Spain. Balt. For. 23(1), 68–73 (2017).
Google Scholar 
Grosdidier, M., Ioos, R. & Marçais, B. Do higher summer temperatures restrict the dissemination of Hymenoscyphus fraxineus in France?. For. Pathol. 48(4), e12426. https://doi.org/10.1111/efp.12426 (2018).Article 

Google Scholar 
Stroheker, S., Queloz, V. & Nemesio-Gorriz, M. First report of Hymenoscyphus fraxineus causing ash dieback in Spain. New Dis. Rep. 44(2), e12054 (2021).
Google Scholar 
Chandelier, A., Gerarts, F., San Martin, G., Herman, M. & Delahaye, L. Temporal evolution of collar lesions associated with ash dieback and the occurrence of Armillaria in Belgian forests. For. Pathol. 46(4), 289–297. https://doi.org/10.1111/efp.12258 (2016).Article 

Google Scholar 
Gross, A., Holdenrieder, O., Pautasso, M., Queloz, V. & Sieber, T. N. H ymenoscyphus pseudoalbidus, the causal agent of E uropean ash dieback. Mol. Plant Pathol. 15(1), 5–21 (2014).CAS 
PubMed 

Google Scholar 
Clark, J. & Webber, J. The ash resource and the response to ash dieback in Great Britain. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 228–237 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Dandy, N., Marzano, M., Porth, E. F., Urquhart, J. & Potter, C. Who has a stake in ash dieback? A conceptual framework for the identification and categorisation of tree health stakeholders. In Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management (eds Vasaitis, R. & Enderle, R.) 15–26 (Swedish University of Agricultural Sciences, 2017).
Google Scholar 
Kjær, E. D., McKinney, L. V., Nielsen, L. R., Hansen, L. N. & Hansen, J. K. Adaptive potential of ash (Fraxinus excelsior) populations against the novel emerging pathogen Hymenoscyphus pseudoalbidus. Evol. Appl. 5(3), 219–228 (2012).PubMed 

Google Scholar 
Plumb, W. J. et al. The viability of a breeding programme for ash in the British Isles in the face of ash dieback. Plants People Planet 2(1), 29–40 (2020).
Google Scholar 
Evans, M. R. Will natural resistance result in populations of ash trees remaining in British woodlands after a century of ash dieback disease?. R. Soc. Open Sci. 6(8), 190908 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Buggs, R. J. A. Changing perceptions of tree resistance research. Plants People Planet 2, 2–4. https://doi.org/10.1002/ppp3.10089 (2020).Article 

Google Scholar 
Tomlinson, I. & Potter, C. ‘Too little, too late’? Science, policy and Dutch Elm Disease in the UK. J. Hist. Geogr. 36(2), 121–131 (2010).
Google Scholar 
Kelly, L. J. et al. Convergent molecular evolution among ash species resistant to the emerald ash borer. Nat. Ecol. Evol. 4, 1116–1128. https://doi.org/10.1038/s41559-020-1209-3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sollars, E. S. et al. Genome sequence and genetic diversity of European ash trees. Nature 541(7636), 212–216 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Stocks, J. J. et al. Genomic basis of European ash tree resistance to ash dieback fungus. Nat. Ecol. Evol. 3(12), 1686–1696 (2019).PubMed 
PubMed Central 

Google Scholar 
Volkovitsh, M. G., Bieńkowski, A. O. & Orlova-Bienkowskaja, M. J. Emerald ash borer approaches the borders of the european union and kazakhstan and is confirmed to infest European ash. Forests 12(6), 691 (2021).
Google Scholar 
Eichhorn, J. et al. Part IV: Visual Assessment of Crown Condition and Damaging Agents. in Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests. (Thünen Institute of Forest Ecosystems, 2016). Annex http://www.icp-forests.org/manual.htm.Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality. Nat. Commun. 12(1), 1–13 (2021).
Google Scholar 
Taccoen, A. et al. Climate change impact on tree mortality differs with tree social status. For. Ecol. Manage. 489, 119048 (2021).
Google Scholar 
Therneau, T. A Package for Survival Analysis in R. https://cran.r-project.org/web/packages/survival/vignettes/survival.pdf. Accessed 26 May 2021Godaert, L. et al. Prognostic factors of inhospital death in elderly patients: A time-to-event analysis of a cohort study in Martinique (French West Indies). BMJ Open 8(1), e018838 (2018).PubMed 
PubMed Central 

Google Scholar 
Sargeran, K., Murtomaa, H., Safavi, S. M. R., Vehkalahti, M. M. & Teronen, O. Survival after diagnosis of cancer of the oral cavity. Br. J. Oral Maxillofac. Surg. 46(3), 187–191 (2008).PubMed 

Google Scholar 
Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. B 34(2), 187–202 (1972).MathSciNet 
MATH 

Google Scholar 
Aalen, O. O. A linear regression model for the analysis of life times. Stat. Med. 8(8), 907–925 (1989).CAS 
PubMed 

Google Scholar 
Therneau, T. M., & Grambsch, P. M. The cox model. In Modeling survival data: extending the Cox model, pp. 39–77. (Springer, 2000).Neumann, M., Mues, V., Moreno, A., Hasenauer, H. & Seidl, R. Climate variability drives recent tree mortality in Europe. Glob. Change Biol. 23(11), 4788–4797 (2017).ADS 

Google Scholar 
Senf, C., Buras, A., Zang, C. S., Rammig, A. & Seidl, R. Excess forest mortality is consistently linked to drought across Europe. Nat. Commun. 11(1), 1–8 (2020).
Google Scholar 
Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20 (2008).
Google Scholar 
R Development Core Team. RStudio, R: A Language and Environment for Statistical Computing (R Development Core Team, 2017).Holt, C. C. Forecasting Trends and Season-Als by Exponentially Weighted Averages. (Carnegie Institute of Technology, Pittsburgh ONR memorandum no. 52, 1957)Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3), 1–22 (2008).
Google Scholar  More

Bokulich, N. A. et al. Associations among wine grape microbiome, metabolome, and fermentation behavior suggest microbial contribution to regional wine characteristics. MBio, https://doi.org/10.1128/mBio.00631-16 (2016).Zohary, D. The Domestication of the Grapevine Vitis Vinifera L. in the Near East (Chapter 2) in The Origins and Ancient History of Wine (eds McGovern, P. E., Katz, S. H. & Fleming, S. J.) 21–28. (Routledge, 2003).Whalen, P. ‘Insofar as the ruby wine seduces them’: cultural strategies for selling wine in inter-war Burgundy. Contemp. Eur. Hist. 18, 67–98 (2009).
Google Scholar 
Østerlie, M. & Wicklund, T. In Nutritional and Health Aspects of Food in Nordic Countries (eds Bar, E., Wirtanen, G. & Veslemøy Andersen, V.) Ch. 2 (Elsevier Inc., 2018).Planète Terroirs. The future needs terroirs. https://planeteterroirs.org/ (2010).California Wine-Growing Regions, https://discovercaliforniawines.com/wine-map-winery-directory/Agricultura, M. D. E. & Ambiente. Compendio informativo en relación con las DOPs/IGPs y terminos tradicionales de vino, las indicaciones geograficas de bebidas espirituosas, y las indicaciones geograficas de productos vitivinicolas aromatizados. https://www.mapa.gob.es/es/alimentacion/temas/calidad-diferenciada/relaciondisposicionesdopseigpsdevinosbbeevinosaromatiz_tcm30-432336.pdf (2016).Ballantyne, D., Terblanche, N. S., Lecat, B. & Chapuis, C. Old world and new world wine concepts of terroir and wine: perspectives of three renowned non-French wine makers. J. Wine Res. 30, 122–143 (2019).
Google Scholar 
OIV. Resolution OIV/VITI 333/2010, definition of vitivinicultural “terroir”. https://www.oiv.int/public/medias/379/viti-2010-1-en.pdf (2010).Belda, I., Zarraonaindia, I., Perisin, M., Palacios, A. & Acedo, A. From vineyard soil to wine fermentation: microbiome approximations to explain the ‘terroir’ Concept. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
Zarraonaindia, I. et al. The soil microbiome influences grapevine-associated microbiota. MBio 6, 1–10 (2015).CAS 

Google Scholar 
Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by vineyard management. Soil Biol. Biochem. 103, 337–348 (2016).CAS 

Google Scholar 
Bokulich, N. A., Joseph, C. M. L., Allen, G., Benson, A. K. & Mills, D. A. Next-generation sequencing reveals significant bacterial diversity of botrytized wine. PLoS ONE 7, 3–12 (2012).
Google Scholar 
Portillo, M., del, C., Franquès, J., Araque, I., Reguant, C. & Bordons, A. Bacterial diversity of Grenache and Carignan grape surface from different vineyards at Priorat wine region (Catalonia, Spain). Int. J. Food Microbiol. 219, 56–63 (2016).
Google Scholar 
Mezzasalma, V. et al. Grape microbiome as a reliable and persistent signature of field origin and environmental conditions in Cannonau wine production. PLoS ONE 12, 1–20 (2017).
Google Scholar 
Hermans, S. M. et al. Using soil bacterial communities to predict physico-chemical variables and soil quality. Microbiome 8, 1–13 (2020).
Google Scholar 
OIV. Functional biodiversity in the vineyard. https://www.oiv.int/public/medias/6367/functional-biodiversity-in-the-vineyard-oiv-expertise-docume.pdf (2018).Ortiz-Álvarez, R. et al. Network properties of local fungal communities reveal the anthropogenic disturbance consequences of farming practices in vineyard soils. mSystems 6, e00344-21 (2021).Knight, S., Klaere, S., Fedrizzi, B. & Goddard, M. R. Regional microbial signatures positively correlate with differential wine phenotypes: evidence for a microbial aspect to terroir. Sci. Rep. 5, 1–10 (2015).
Google Scholar 
Belda, I. et al. Unraveling the enzymatic basis of wine ‘flavorome’: a phylo-functional study of wine related yeast species. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
Gilbert, J. A., van der Lelie, D. & Zarraonaindia, I. Microbial terroir for wine grapes. Proc. Natl Acad. Sci. USA 111, 5–6 (2014).CAS 
PubMed 

Google Scholar 
Belda, I. et al. Microbiomics to Define Wine Terroir (Chapter: 3.32) in Comprehensive Foodomics (Ed. Cifuentes, A.) 438–451 (Elsevier, 2021).Van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).Altieri, M. A. In Invertebrate Biodiversity as Bioindicators of Sustainable Landscapes (1999).Brussaard, L., de Ruiter, P. C. & Brown, G. G. Soil biodiversity for agricultural sustainability. Agric. Ecosyst. Environ. 121, 233–244 (2007).Nielsen, U. N., Wall, D. H. & Six, J. Soil biodiversity and the environment. Annu. Rev. Environ. Resour. 40, 63–90 (2015).Wei, Y. J. et al. High-throughput sequencing of microbial community diversity in soil, grapes, leaves, grape juice and wine of grapevine from China. PLoS ONE 13, 1–17 (2018).
Google Scholar 
Liao, J., Xu, Q., Xu, H. & Huang, D. Natural farming improves soil quality and alters microbial diversity in a cabbage field in Japan. Sustain 11, 1–16 (2019).
Google Scholar 
Yan, J. et al. Plant litter composition selects different soil microbial structures and in turn drives different litter decomposition pattern and soil carbon sequestration capability. Geoderma 319, 194–203 (2018).CAS 

Google Scholar 
Qiao, Q. et al. The variation in the rhizosphere microbiome of cotton with soil type, genotype and developmental stage. Sci. Rep. 7, 1–10 (2017).
Google Scholar 
Pacchioni, R. G. et al. Taxonomic and functional profiles of soil samples from Atlantic forest and Caatinga biomes in northeastern Brazil. Microbiologyopen 3, 299–315 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ishaq, S. L. et al. Impact of cropping systems, soil inoculum, and plant species identity on soil bacterial community structure. Microb. Ecol. 73, 417–434 (2017).CAS 
PubMed 

Google Scholar 
Verkley, G. J. M., Da Silva, M., Wicklow, D. T. & Crous, P. W. Paraconiothyrium, a new genus to accommodate the mycoparasite Coniothyrium minitans, anamorphs of Paraphaeosphaeria, and four new species. Stud. Mycol. 50, 323–335 (2004).
Google Scholar 
Thomma, B. P. H. J. Alternaria spp.: from general saprophyte to specific parasite. Mol. Plant Pathol. 4, 225–236 (2003).CAS 
PubMed 

Google Scholar 
Mašínová, T. et al. Drivers of yeast community composition in the litter and soil of a temperate forest. FEMS Microbiol. Ecol. 93, 1–10 (2017).
Google Scholar 
Chen, J., Xu, L., Liu, B. & Liu, X. Taxonomy of Dactylella complex and Vermispora. III. A new genus Brachyphoris and revision of Vermispora. Fungal Divers. 26, 127–142 (2014).Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by geographic features. Soil Biol. Biochem. 91, 232–247 (2015).Bokulich, N. A., Thorngate, J. H., Richardson, P. M. & Mills, D. A. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proc. Natl Acad. Sci. USA 111, 139–148 (2014).
Google Scholar 
Castañeda, L. E. & Barbosa, O. Metagenomic analysis exploring taxonomic and functional diversity of soil microbial communities in Chilean vineyards and surrounding native forests. PeerJ 5, e3098 (2017).PubMed 
PubMed Central 

Google Scholar 
Coller, E. et al. Microbiome of vineyard soils is shaped by geography and management. Microbiome 7, 1–15 (2019).
Google Scholar 
Zhou, J. et al. Wine terroir and the soil bacteria: an amplicon sequencing–based assessment of the Barossa Valley and its sub-regions. Front. Microbiol. 11, 1–15 (2021).
Google Scholar 
Price, C. A. et al. Testing the metabolic theory of ecology. Ecol. Lett. 15, 1465–1474 (2012).PubMed 

Google Scholar 
Jenerette, G. D., Scott, R. L. & Huxman, T. E. Whole ecosystem metabolic pulses following precipitation events. Funct. Ecol. 22, 924–930 (2008).
Google Scholar 
Větrovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Arnold, A. E., Maynard, Z., Gilbert, G. S., Coley, P. D. & Kursar, T. A. Are tropical fungal endophytes hyperdiverse? Ecol. Lett. 3, 267–274 (2000).
Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1052–1053 (2014).
Google Scholar 
Janssen, P. H. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl. Environ. Microbiol. 72, 1719–1728 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bintrim, S. B., Donohue, T. J., Handelsman, J., Roberts, G. P. & Goodman, R. M. Molecular phylogeny of Archaea from soil. Proc. Natl Acad. Sci. USA 94, 277–282 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Simon, H. M., Dodsworth, J. A. & Goodman, R. M. Crenarchaeota colonize terrestrial plant roots. Environ. Microbiol. 2, 495–505 (2000).CAS 
PubMed 

Google Scholar 
Buckley, D. H., Graber, J. R. & Schmidt, T. M. Phylogenetic analysis of nonthermophilic members of the kingdom Crenarchaeota and their diversity and abundance in soils. Appl. Environ. Microbiol. 64, 4333–4339 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ochsenreiter, T., Selezi, D., Quaiser, A., Bonch-Osmolovskaya, L. & Schleper, C. Diversity and abundance of Crenarchaeota in terrestrial habitats studied by 16S RNA surveys and real time PCR. Environ. Microbiol. 5, 787–797 (2003).CAS 
PubMed 

Google Scholar 
Kemnitz, D., Kolb, S. & Conrad, R. High abundance of Crenarchaeota in a temperate acidic forest soil. FEMS Microbiol. Ecol. 60, 442–448 (2007).CAS 
PubMed 

Google Scholar 
Zhalnina, K. et al. Ca. nitrososphaera and bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front. Microbiol. 4, 1–13 (2013).
Google Scholar 
Barata, A., Malfeito-Ferreira, M. & Loureiro, V. The microbial ecology of wine grape berries. Int. J. Food Microbiol. 153, 243–259 (2012).CAS 
PubMed 

Google Scholar 
Yurkov, A. M. Yeasts of the soil—obscure but precious. Yeast 35, 369–378 (2018).CAS 
PubMed 

Google Scholar 
Kachalkin, A. V., Abdullabekova, D. A., Magomedova, E. S., Magomedov, G. G. & Chernov, I. Y. Yeasts of the vineyards in Dagestan and other regions. Microbiology 84, 425–432 (2015).CAS 

Google Scholar 
Čadež, N., Zupan, J. & Raspor, P. The effect of fungicides on yeast communities associated with grape berries. FEMS Yeast Res. 10, 619–630 (2010).PubMed 

Google Scholar 
Comitini, F. & Ciani, M. Influence of fungicide treatments on the occurrence of yeast flora associated with wine grapes. Ann. Microbiol. 58, 489–493 (2008).
Google Scholar 
Kepler, R. M., Maul, J. E. & Rehner, S. A. Managing the plant microbiome for biocontrol fungi: examples from Hypocreales. Curr. Opin. Microbiol. 37, 48–53 (2017).CAS 
PubMed 

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

Google Scholar 
Liu, D. & Howell, K. Community succession of the grapevine fungal microbiome in the annual growth cycle. Environ. Microbiol. 23, 1842–1857 (2021).CAS 
PubMed 

Google Scholar 
Delgado-Baquerizo, M. et al. Bacteria found in soil. Science 325, 320–325 (2018).
Google Scholar 
Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).Alonso, A. et al. Looking at the origin: Some insights into the general and fermentative microbiota of vineyard soils. Fermentation 5, 1–15 (2019).
Google Scholar 
OIV. Resolution OIV-VITI 655-2021. OIV recommendations about valuation and importance of microbial biodiversity in a sustainable vitiviniculture context. https://www.oiv.int/public/medias/8097/en-oiv-viti-655-2021.pdf (2021).Vishnivetskaya, T. A. et al. Commercial DNA extraction kits impact observed microbial community composition in permafrost samples. FEMS Microbiol. Ecol. 87, 217–230 (2014).CAS 
PubMed 

Google Scholar 
Gobbi, A. et al. Quantitative and qualitative evaluation of the impact of the G2 enhancer, bead sizes and lysing tubes on the bacterial community composition during DNA extraction from recalcitrant soil core samples based on community sequencing and qPCR. PLoS One 14, e0200979 (2019).Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ 37, 852–857 (2018).Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 

Google Scholar 
Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3, e00021-18 (2018).Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
Google Scholar 
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).Chen, H. VennDiagram: generate high-resolution Venn and Euler plots. R. Packag. Version 1, 1 (2018).
Google Scholar 
Salonen, A., Salojärvi, J., Lahti, L. & de Vos, W. M. The adult intestinal core microbiota is determined by analysis depth and health status. Clin. Microbiol. Infect. 18, 16–20 (2012).CAS 
PubMed 

Google Scholar 
Martín-Fernández, J. A., Hron, K., Templ, M., Filzmoser, P. & Palarea-Albaladejo, J. Bayesian-multiplicative treatment of count zeros in compositional data sets. Stat. Model. 15, 134–158 (2015).
Google Scholar 
Oksanen, J. et al. vegan: community ecology package. R package version 2.4-3. Vienna R Found. Stat. Comput. Sch. (2016).Wright, M. N. & Ziegler, A. Ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).Team, R. C. R: a language and environment for statistical computing. (2019).Henschel, A., Anwar, M. Z. & Manohar, V. Comprehensive meta-analysis of ontology annotated 16S rRNA profiles identifies beta diversity clusters of environmental bacterial communities. PLoS Comput. Biol. 11, 1–24 (2015).
Google Scholar 
Lozupone, C. A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl Acad. Sci. USA 104, 11436–11440 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pauvert, C. et al. Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecol. 41, 23–33 (2019).Gobbi, A., Kyrkou, I., Filippi, E., Ellegaard-Jensen, L. & Hansen, L. H. Seasonal epiphytic microbial dynamics on grapevine leaves under biocontrol and copper fungicide treatments. Sci. Rep. 10, 681 (2020).Engelbrektson, A. et al. Experimental factors affecting PCR-based estimates of microbial species richness and evenness. ISME J. 4, 642–647 (2010).CAS 
PubMed 

Google Scholar 
Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407 (2019). More

Study areaWith the advanced observational techniques, abundant data accumulation, and ability to carry on multi-scale experiments, the Huailai Remote Sensing Station and around (for short Huailai Station), located in Huailai, Hebei province, China (40.349°N, 115.785°E), becomes one of the ideal study areas for the observation and validation of the LAI27. The Huailai Station is mainly covered by corn and some weeds. So, we mainly use LAIS to monitor the growth cycle of corn (in April 2015, we submitted an application for plant collection permission to Huailai Remote Sensing Station and obtained approval.)Huailai WSN vegetation monitoring system includes 6 sets of monitoring equipment, and its distribution is shown in Fig. 1 as follows, in which red dot represents LAIS Node, purple frame represents MODIS pixel, red frame represents observation area. The observation system is designed for the application of remote sensing pixel scale authenticity tests. The observation scale is a 1 km MODIS pixel on the pixel scale, and the actual coverage area is 2 km * 1.5 km. The six sets of equipment cover the core area of the test station and the surrounding typical growth plot, which is a good representative of the 1 km pixel scale.Figure 1Equipment distribution of WSN vegetation monitoring network in Huailai (red dot represents LAIS Node; purple frame represents the footprint of a MODIS pixel.Full size imageEach piece of equipment consists of two cameras which were only one camera with two different angles in previous work23 set up at a height of 2.5–4 m above the ground (Fig. 2), one for vertical downward observation and the other for inclined observation, which can take canopy photos regularly every day at its fixed position. The observation system obtained the photos of the corn canopy from May to August, but the corn did not grow in August. Therefore, in this study, we selected the photos taken by the vertical observation camera of the corn sample plot in the experimental station from May to July 2015.Figure 2The design of the LAIS node.Full size imageRelated work—data acquisitionData collection using LAISThe data collection complies with the plant guidelines statement: “LAI-2000 Plant Canopy Analyzer Instrution Manual” (Supplementary Information 2) (https://www.licor.com/env/, Last visit time: 21 October 2021). Existing facilities such as the high poles and the wireless sensor network in the experimental station have proved convenient for the installation of the LAI measurement system. LAIS uses the GEO001 digital serial camera that is suitable for a variety of embedded image acquisition modes. The specification of the camera includes: the total field of view is 120°, the maximum image size is 2176 * 1920 (approximately 5 million pixels), mounted at a height of 3 m, the spatial resolution at ground level is about 3 mm. The acquired image is simultaneously stored in a flash card in two formats: the JPEG format merits in less file size thus suitable for quick wireless transfer; the RAW format, which is the user data in our analysis, contains 3 channel binary image in 10 bits bit-depth. Compared to our previous work, an important new feature of this camera is the programmable cut-off filter. As we know, unlike scientific sensor which has the precise spectral response to each band, the digital camera is cheap and can only acquire the so-called RGB image. Usual digital cameras have one NIR cut-off filter to exclude the near-infrared light. The GEO001 camera, which was a commercial camera produced by Zhongshan Yunteng Photographic Equipment Co., Ltd, has two cut-off filters: one is the NIR cut-off filter, another is a blue cut-off filter. Switching on the NIR cut-off filter results in an ordinary color image as in a usual household digital camera. While the blue cut-off filter is switched on and NIR cut-off filter is switched off, near-infrared light is allowed to reach the detector array and blue light is blocked, resulting in false-color images as in Fig. 3b. Adding near-infrared light can increase illumination in the shadow area, and blocking blue light can alleviate the disturbance of sun glint, so, switching to a blue cut-off filter helps to improve the image quality when the direct sunlight is strong such as around noon time.Figure 3Three images on July 2 of site 1: (a) and (c) are true-color images obtained at 05:31 a.m. and 6:32 p.m., and (b) is a false-color image when the blue filter is removed at 1:28 p.m.Full size imageTo acquire an image in the best illumination condition and avoid the influence of rain or other unsuitable weather, the image acquisition device based on WSN was set up to acquire images three times per day: 5:30 a.m., 1:30 p.m., and 6:30 p.m. According to our experience, when the canopy is open (sparse vegetation), usually images acquired at 6:30 p.m. are the best for classification because the direct sunlight is weak; when the canopy is closed (dense vegetation), the illumination on the soil background is very poor in all time, and classification is difficult. So, the camera is programmed to switch to a blue cut-off filter when acquiring images at 1:30 p.m., while the images acquired at other times were with NIR cut-off filter, resulting in true color images, as shown in Fig. 3.LAILLW data and LAI2000 dataTo evaluate the accuracy of the improved finite length averaging method proposed in this study, a field experiment was carried out to measure LAI by manual sampling (Supplementary Information 3,4). A field sampling scheme covering the corn growing season (late May to early July) was designed (Supplementary Information 1). The LAI of corn in the experimental area was measured by the quadrat harvesting method, and the validation data of LAI of corn in each growth period were obtained. Considering the rapid growth of the corn, the sampling experiment period was set as 1 week, but due to the actual work in summer and the influence of rainfall, six effective measurements were carried out in the field experiment: May 30, June 7, June 13, June 20, July 4 and July 16.The LAILLW method, which is also known as the shape factor method, involves outdoor and indoor measurements. The formulas are:$${text{L}} = {text{S}}*{text{N}}$$
(1)
$${text{f}} = {{text{S}} /{left( {sumlimits_{i = 1}^m {{text{len}}*{text{wid}}} } right)}}$$
(2)

where L represents the leaf area index, S refers to the area of a single plant, and N refers to the number of plants in a unit area. The shape factor ƒ is the ratio of the S to the value multiplied by the length and width of all leaves in the plant.To reduce measurement errors, 10 plants were selected in the sample, and the length and width of each leaf on each corn were recorded with a ruler. To obtain the shape factor, representative corn plants were cut next to the sample (not in the image coverage area) and the true area of each leaf was obtained by software, and the shape factor was derived from this23. Through the length and width of 10 strains measured in the field, and the shape factor obtained, the total leaf area of 10 corns can be calculated, and the average leaf area of one plant is finally obtained. The LAI value under the LAILLW method is obtained.Using the difference between the solar radiation values of the upper and lower canopies, the LAI2000 canopy analyzer can obtain LAI and set up a corresponding point folder to save the measured data for subsequent collation. 10 measurement points were selected for each site, and the average value was the final result for each site. To reduce the effects of the solar altitude angle on measurement accuracy, the experiments were repeated every two hours.To make it easier to record the date of data acquisition, the data were summarized in the order day of the year (DOY). For example, 30 May 2015 is the 150th day in the year and its DOY is 150. The DOY information of data acquisition using the LAILLW method and LAI2000 is specifically shown in Table 1.Table 1 The DOY information of data acquisition using the LAILLW and LAI2000.Full size tableMODIS LAI dataMODIS leaf area index data was downloaded from the United States Geological Survey (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php), named MCD15A2Hv006. It is an 8-day composite dataset with a 500-m pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 8 days.In the comparison of MODIS LAI data, as the pixel of the satellite product is in 500 m resolution, it is not recommended to directly compare single node LAIS measurement with the MODIS LAI product because of the scale mismatch. Though complicated upscaling approaches have been discussed and implemented in Huailai station for other parameters28, it is not the purpose of this study So, we simply averaged the LAI in all the LAIS nodes to compare to the average MODIS LAI product in the 3 * 3 nearest pixels (1.5 km * 1.5 km), referred to as MODIS LAI_Mean in a later context, which approximately covers the area of all LAIS nodes. Time matching was carried out by selecting the date of the MODIS product closest to the date of the handheld LAI2000 measurement. The following Table 2 is obtained by taking 3 * 3 pixels closest to the LAIS Nodes.Table 2 MODIS leaf area index of 3 * 3 pixels around Huailai experimental station.Full size tableImproved LAIS methodsIn previous work, we have deployed sensors and cameras, and also have an automatic image processing and preliminary method of calculating LAI23. Figure 4 is a flow chart of our work. The previous articles focused on hardware and system implementation but did not pay much attention to performance. On this basis, we upgrade the image classification method and LAI calculation method, which will be explained in detail below.Figure 4Flow chart of leaf area index measurement system based on WSN.Full size imageImage preprocessing and classification methodsBecause of weather-related factors such as water vapor and dust or inaccurate exposure, a small number of the photographs are not clear. Besides, some of the image data cannot be decoded because of unstable communications and other factors. Therefore, it is necessary to check and select the photographs that meet the processing requirements before binary image processing. Currently, the selection process is carried out by human visual inspection based on the following principles: (1) when the canopy is open (sparse vegetation), the image at 6:30 p.m. is preferred, when the vegetation the canopy is closed (sparse vegetation), the image at 1:30 p.m. is preferred; (2) if the preferred image is not clear, other clear image acquired on the same day should be used; if all the images are not clear, then this day is marked as a failure.If we decided to use the image acquired at 1:30 p.m. It is also necessary to convert it from a false-color image to a true-color-like image (as shown in Fig. 3b) in which the leaves are shown in green color. The conversion is carried out by multiplying the vector of DN (digital number) of 3 bands with a coefficient matrix which is provided by the camera manufacturer. Another preprocessing is to choose the near nadir-view area of the image for further processing. As the off-nadir-view area of the image is subject to large geometric distortion as well as saturation of fraction of vegetation cover (FVC), they are not used in this study. The images are clipped to an ROI (region of interest) of about 2 * 2 square meters in ground area, with a maximum view zenith angle less than 30°.The study of the color spatial distributions of the crop images is helpful for the classification of the images and extraction of the image information. The color of the image pixel is the most direct and effective element that can be used to describe the image29. Because the red–green–blue (RGB) color space has the characteristic of a clear and convenient expression of information. When corn leaves are small, the crops in the fields are sparse, and most of them are soil background in the images. The soil in a lower hue is similar to the corn in terms of R and B components, while it has an overlap with the corn in G components when soil is in a higher hue. This makes it difficult to classify sparse corn scenes only by RGB space, so it is necessary to consider the characteristics of hue, luminosity, and saturation (HLS) spatial components.Statistical analysis showed that the component values of the crop leave in the RGB color space were in the ranges of G  > R and G  > B while the corresponding values for the soil follow the law that B  More

Kim, J. K., Choi, M. B. & Moon, T. Y. Occurrence of Vespa velutina Lepeletier from Korea, and a revised key for Korean Vespa species (Hymenoptera: Vespidae). Entomol. Res. 36, 112–115 (2006).
Google Scholar 
Choi, M. B., Martin, S. J. & Lee, J. W. Distribution, spread, and impact of the invasive hornet Vespa velutina in South Korea. J. Asia-Pac. Entomol. 15, 473–477 (2012).
Google Scholar 
Do, Y. et al. Quantitative analysis of research topics and public concern on V. velutina as invasive species in Asian and European countries. Entomol. Res. 49, 456–461 (2019).
Google Scholar 
Kwon, O. & Choi, M. B. Interspecific hierarchies from aggressiveness and body size among the invasive alien hornet, Vespa velutina nigrithorax, and five native hornets in South Korea. PLoS ONE 15, e0226934 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Choi, M. B. Foraging behavior of an invasive alien hornet (Vespa velutina) at Apis mellifera hives in Korea: Foraging duration and success. Entomol. Res. 51, 143–148 (2021).
Google Scholar 
Turchi, L. & Derijard, B. Options for the biological and physical control of Vespa velutina nigrithorax (Hym.: Vespidae) in Europe: A review. J. Appl. Entomol. 142, 553–562 (2018).CAS 

Google Scholar 
Bessa, A. S., Carvalho, J., Gomes, A. & Santarém, F. Climate and land-use drivers of invasion: Predicting the expansion of Vespa velutina nigrithorax into the Iberian Peninsula. Insect Conserv. Divers. 9, 27–37 (2016).
Google Scholar 
Rodríguez-Flores, M. S., Seijo-Rodríguez, A., Escuredo, O. & del Carmen Seijo-Coello, M. Spreading of Vespa velutina in northwestern Spain: Influence of elevation and meteorological factors and effect of bait trapping on target and non-target living organisms. J. Pest Sci. 92, 557–565 (2019).
Google Scholar 
Robinet, C., Darrouzet, E. & Suppo, C. Spread modelling: A suitable tool to explore the role of human-mediated dispersal in the range expansion of the yellow-legged hornet in Europe. Int. J. Pest Manag. 65, 258–267 (2019).
Google Scholar 
Saunders, D. A., Hobbs, R. J. & Margules, C. R. Biological consequences of ecosystem fragmentation: A review. Conserv. Biol. 5, 18–32 (1991).
Google Scholar 
Ellstrand, N. C. & Elam, D. R. Population genetic consequences of small population size: Implications for plant conservation. Annu. Rev. Ecol. Evol. Syst. 24, 217–242 (1993).
Google Scholar 
Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 
PubMed 

Google Scholar 
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl. Acad. Sci. 101, 8998–9002 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dudley, R. The Biomechanics of Insect Flight: Form, Function, Evolution (Princeton University Press, 2002).
Google Scholar 
Porporato, M., Manino, A., Laurino, D. & Demichelis, D. Vespa velutina Lepeletier (Hymenoptera Vespidae): A first assessment 2 years after its arrival in Italy. Redia 97, 189–194 (2014).
Google Scholar 
Sauvard, D., Imbault, V. & Darrouzet, É. Flight capacities of yellow-legged hornet (Vespa velutina nigrithorax, Hymenoptera: Vespidae) workers from an invasive population in Europe. PLoS ONE 13, e0198597 (2018).PubMed 
PubMed Central 

Google Scholar 
Monceau, K., Bonnard, O., Moreau, J. & Thiéry, D. Spatial distribution of Vespa velutina individuals hunting at domestic honeybee hives: Heterogeneity at a local scale. Insect Sci. 21, 765–774 (2014).PubMed 

Google Scholar 
Choi, M. B., Lee, S. A., Suk, H. Y. & Lee, J. W. Microsatellite variation in colonizing populations of yellow-legged Asian hornet, Vespa velutina nigrithorax, South Korea. Entomol. Res. 43, 208–214 (2013).
Google Scholar 
Jeong, J. S. et al. Tracing the invasion characteristics of the yellow-legged hornet, Vespa velutina nigrithorax (Hymenoptera: Vespidae), in Korea using newly detected variable mitochondrial DNA sequences. J. Asia-Pac. Entomol. 24(2), 135–147 (2021).MathSciNet 

Google Scholar 
Villemant, C. et al. Predicting the invasion risk by the alien bee-hawking Yellow-legged hornet Vespa velutina nigrithorax across Europe and other continents with niche models. Biol. Conserv. 144, 2142–2150 (2011).
Google Scholar 
Kishi, S. & Goka, K. Review of the invasive yellow-legged hornet, Vespa velutina nigrithorax (Hymenoptera: Vespidae), in Japan and its possible chemical control. Appl. Entomol. Zool. 52, 361–368 (2017).
Google Scholar 
Arca, M. et al. Development of microsatellite markers for the yellow-legged Asian hornet, Vespa velutina, a major threat for European bees. Conserv. Genet. Resour. 4, 283–286 (2012).
Google Scholar 
Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Res. 8, 103–106 (2008).
Google Scholar 
Peakall, P. & Smouse, R. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
PubMed Central 

Google Scholar 
Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
Oksanen, J. et al. The vegan package. 10, 719 (2007).Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, S. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. Resour. 14, 2611–2620 (2005).CAS 

Google Scholar 
Earl, D. A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 

Google Scholar 
Jombart, T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Waraniak, J. M., Fisher, J. D., Purcell, K., Mushet, D. M. & Stockwell, C. A. Landscape genetics reveal broad and fine-scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota. Ecol. Evol. 9, 1041–1060 (2019).PubMed 
PubMed Central 

Google Scholar 
Rohlf, F. J. tpsDig, version 2.10. http://life.bio.sunysb.edu/morph/index.html (2006).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).
Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More