Philippa Kaur

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

Van Gossum, H., Sherratt, T. N., Cordero-Rivera, A. & Córdoba-Aguilar, A. The evolution of sex-limited colour polymorphism. In Dragonflies and Damselflies: Model Organisms for Ecological and Evolutionary Research (ed. Córdoba-Aguilar, A.) 219–231 (Oxford University Press, 2008).
Google Scholar 
Hughes, J. M. & Jones, M. P. Shell colour polymorphism in a mangrove snail Littorina sp. (Prosobranchia: Littorinidae). Biol. J. Linn. Soc. 25, 365–378 (1985).
Google Scholar 
Sinervo, B., Bleay, C. & Adamopoulou, C. Social causes of correlational selection and the resolution of a heritable throat color polymorphism in a lizard. Evolution 55, 2040–2052 (2001).CAS 
PubMed 

Google Scholar 
Westerman, E. L. et al. Does male preference play a role in maintaining female limited polymorphism in a Batesian mimetic butterfly? Behav. Process. 150, 47–58 (2018).CAS 

Google Scholar 
Vane-Wright, R. I. An integrated classification for polymorphism and sexual dimorphism in butterflies. J. Zool. 177, 329–337 (1975).
Google Scholar 
Timmermans, M. J., Srivathsan, A., Collins, S., Meier, R. & Vogler, A. P. Mimicry diversification in Papilio dardanus via a genomic inversion in the regulatory region of engrailed–invected. Proc. R. Soc. B 287, 20200443 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brodie, E. D. III. & Janzen, F. J. Experimental studies of coral snake mimicry: Generalized avoidance of ringed snake patterns by free-ranging avian predators. Funct. Ecol. 9, 186–190 (1995).
Google Scholar 
Banci, K. R., Eterovic, A., Marinho, P. S. & Marques, O. A. Being a bright snake: Testing aposematism and mimicry in a neotropical forest. Biotropica 52, 1229–1241 (2020).
Google Scholar 
Wüster, W. et al. Do aposematism and Batesian mimicry require bright colours? A test, using European viper markings. Proc. R. Soc. B 271, 2495–2499 (2004).PubMed 
PubMed Central 

Google Scholar 
Valkonen, J. K. & Mappes, J. Resembling a viper: Implications of mimicry for conservation of the endangered smooth snake. Conserv. Biol. 28, 1568–1574 (2014).PubMed 

Google Scholar 
Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).ADS 
CAS 

Google Scholar 
Moon, R. M. & Kamath, A. Re-examining escape behaviour and habitat use as correlates of dorsal pattern variation in female brown anole lizards, Anolis sagrei (Squamata: Dactyloidae). Biol. J. Linn. Soc. 126, 783–795 (2019).
Google Scholar 
Le Rouzic, A., Hansen, T. F., Gosden, T. P. & Svensson, E. I. Evolutionary time-series analysis reveals the signature of frequency-dependent selection on a female mating polymorphism. Am. Nat. 185, E182–E196 (2015).PubMed 

Google Scholar 
Udyawer, V. et al. Future directions in the research and management of marine snakes. Front. Mar. Sci. 5, 399 (2018).
Google Scholar 
Goiran, C., Bustamante, P. & Shine, R. Industrial melanism in the seasnake Emydocephalus annulatus. Curr. Biol. 27, 2510–2513 (2017).CAS 
PubMed 

Google Scholar 
Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
Google Scholar 
Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).
Google Scholar 
Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
Google Scholar 
Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
Google Scholar 
Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
Google Scholar 
Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

Google Scholar 
Olsson, M., Stuart-Fox, D. & Ballen, C. Genetics and evolution of colour patterns in reptiles. Semin. Cell Dev. Biol. 24, 529–541 (2013).PubMed 

Google Scholar 
Shine, R., Brischoux, F. & Pile, A. J. A seasnake’s colour affects its susceptibility to algal fouling. Proc. R. Soc. B 277, 2459–2464 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Packard, G. C. & Boardman, T. J. The misuse of ratios, indices, and percentages in ecophysiological research. Physiol. Zool. 61, 1–9 (1988).
Google Scholar 
Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
PubMed Central 

Google Scholar 
Shine, R. All at sea: Aquatic life modifies mate-recognition modalities in sea snakes (Emydocephalus annulatus, Hydrophiidae). Behav. Ecol. Sociobiol. 57, 591–598 (2005).
Google Scholar 
Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021).ADS 

Google Scholar 
Rancurel, P. & Intes, A. Le requin tigre, Galeocerdo cuvieri Lacepede, des eaux neocaledoniennes examen des contenus stomacaux. Tethys 10, 195–199 (1982).
Google Scholar 
Heatwole, H. Predation on sea snakes. In The Biology of Sea Snakes (ed. Dunson, W. A.) 233–250 (University Park Press, 1975).
Google Scholar 
Ineich, I. & Laboute, P. Les serpents marins de Nouvelle-Calédonie (IRD éditions, 2002).
Google Scholar 
Kerford, M. R., Wirsing, A. J., Heithaus, M. R. & Dill, L. M. Danger on the rise: diurnal tidal state mediates an exchange of food for safety by the bar-bellied sea snake Hydrophis elegans. Mar. Ecol. Progr. Ser. 358, 289–294 (2008).ADS 

Google Scholar 
Masunaga, G., Kosuge, T., Asai, N. & Ota, H. Shark predation of sea snakes (Reptilia: Elapidae) in the shallow waters around the Yaeyama Islands of the southern Ryukyus, Japan. Mar. Biodivers. Rec. 1, e96 (2008).
Google Scholar 
Wirsing, A. J. & Heithaus, M. R. Olive-headed sea snakes Disteria major shift seagrass microhabitats to avoid shark predation. Mar. Ecol. Progr. Ser. 387, 287–293 (2009).ADS 

Google Scholar 
Goiran, C. & Shine, R. The ability of damselfish to distinguish between dangerous and harmless sea snakes. Sci. Rep. 10, 1377 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Norman, M. D., Finn, J. & Tregenza, T. Dynamic mimicry in an Indo-Malayan octopus. Proc. R. Soc. B 268, 1755–1758 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pernetta, J. C. Observations on the habits and morphology of the sea snake Laticauda colubrina (Schneider) in Fiji. Can. J. Zool. 55, 1612–1619 (1977).
Google Scholar 
Randall, J. E. A review of mimicry in marine fishes. Zool. Stud. 44, 299–328 (2005).
Google Scholar 
Dudgeon, C. L. & White, W. T. First record of potential Batesian mimicry in an elasmobranch: Juvenile zebra sharks mimic banded sea snakes? Mar. Freshw. Res. 63, 545–551 (2012).
Google Scholar 
Sullivan Caldwell, G. & Wolff Rubinoff, R. Avoidance of venomous sea snakes by naive herons and egrets. Auk 100, 195–198 (1983).
Google Scholar 
Sanders, K. L., Malhotra, A. & Thorpe, R. S. Evidence for a Müllerian mimetic radiation in Asian pitvipers. Proc. R. Soc. B 273, 1135–1141 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raveendran, D. K., Deepak, V., Smith, E. N. & Smart, U. A new colour morph of Calliophis bibroni (Squamata: Elapidae) and evidence for Müllerian mimicry in Tropical Indian coral snakes. Herpetol. Notes 10, 209–217 (2017).
Google Scholar  More

Chemicals and materialsAnalytical standard ( > 99% purity) of spiromesifen, BSN2060-enol, and chromafenozide were purchased from AB Solution Co., Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea. HPLC grade water and acetonitrile were supplied by Merck, Darmstadt, Germany. QuEChERS kit (4.0 g magnesium sulfate, 1.0 g sodium chloride, 1.0 g sodium citrate tribasic dihydrate, 0.5 g disodium citrate sesquihydrate) were obtained from Phenomenex, California, USA. Individual stock solutions of the target compounds were prepared in acetonitrile and stored at − 20 °C before use.Field experimentsThe trials were carried out in a greenhouse farm during the season 2018 at two different sites (with approximately 24 km distance between both sites) located in Chuncheon and Hongcheon-gun, Gangwon-do, Republic of Korea following the method described by the Organization for Economic Co-operation and Development (OECD)38. The field test of lettuce (Latuca sativa L.) crop was conducted in Chuncheon city, and perilla (Perilla frutescens (L.) Britton) crop in Hongcheon city. The area of each field was divided into two plots (treatment and control). The treatment plots were further divided into three replicates (subplots 33 m2). The control plot was separated by a buffer zone of 3 m2 from the treated site. To minimize spray overlap, buffer zones (1 m) were set up between subplots. The commercial products of spiromesifen 20% SC diluted 2000 times and chromafenozide 5% EC diluted 1000 times were sprayed twice at 7-days intervals using an automatic sprayer. After the second spray samples (lettuce and perilla leaves) were collected from each subplot at 0 (2 h after spraying), 1, 3, 5, and 7 days according to the Korean RDA23 method. Thirty samples 1.0 kg each from the collected crop were placed in polyethylene bag and labeled. After collection, the samples were transported to the laboratory, where they were chopped and homogenized. The homogenized samples were kept frozen at − 20 °C until analysis.We confirm all plant samples used in the current work comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.Samples pretreatmentA QuEChERS method was used for the extraction of the targeted compounds from lettuce and perilla leaves. A 10 g of previously homogenized samples were weighed into a 50 mL polypropylene centrifuge tube and mixed with 10 mL of water followed by 10 mL of acetonitrile. The samples were shaken at 1500 rpm in a shaker machine for 10 min. Then commercial QuEChERS kit was added, and the mixtures were shaken vigorously for 2 min in a shaker. Subsequently, the samples were centrifuged at 3584 g-force for 10 min. After centrifugation, the supernatant was filtered with a 0.22 μm membrane filter and transferred into the glass vial for instrumental analysis.LC-MS/MS analysisQuantitative determination of the tested compounds was carried out by using HPLC system Dionex Ultimate 3000 (Thermo Science, USA) coupled with tandem mass spectrometry (MS/MS) (TSQ Quantum Access Max (Thermo Science, USA). Water (solvent A) and acetonitrile (solvent B) containing 0.1% formic acid and 5 mM ammonium format were used as mobile phase at a flow rate of 0.4 mL/min and injection volume 1.0 µL. To obtain desirable chromatographic peaks, two different instrumental conditions were used. The chromatographic separation of spiromesifen was separated by Capcell core-C18 (2.1 mm I.D. × 150 mm × 2.7 μm, Shiseido Co., Ltd., Tokyo, Japan) and BSN2060-enol was performed by C18 column (Poroshell 120 SB-Ag, 2.1 mm I.D. × 100 mm × 2.7-μm, Agilent Technologies, Santa Clara, California, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5.0%; 2.0 min 5%; 2.5 min, 95%; 6.0 min, 95%; 6.5 min, 5.0%; 10 min, 5.0%. Likewise, chromafenozide was separated by C18 column (Imtakt Unison UK-C18, 2.0 mm I.D. × 100 mm × 3.0-μm, Imtakt, Portland, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5%; 1.0 min, 5.0%; 1.5 min, 90%; 5.0 min, 90%; 7.0 min, 5.0%; 10 min, 5.0%. An MS/MS system (TSQ quantum ultra, Thermo Science, USA) equipped with an electrospray ionization source operating in positive mode (ESI+) was used. The MS/MS parameters and selected product ions are shown in supplementary Tables S2 and S3.The calculation of spiromesifen total residuesThe total residues in lettuce and perilla samples were calculated using Eq. (1)23.Total residues of spiromesifen (mg/kg) = spiromesifen + (BSN2060 residue × 1.36). The conversion factor was calculated as follow;$${1}.{36},{text{(conversion}},{text{factor)}} = frac{{370.49left( {{text{spiromesifen}},{text{MW}}} right)}}{{272.34{ }left( {{text{BSN}}2060,{text{MW}}} right)}}$$
(1)
where MW molecular weight.Initial deposition calculationThe initial residues of spiromesifen and chromafenozide deposition in lettuce and perilla leaves were calculated from 0-day according to Eq. (2) described by Kang et al.12 as follow;$${text{A }},({text{mg}}/{text{kg}}) = {text{B(mg}}/{text{kg)}} times frac{100}{{{text{C}}({text{% }})}} times frac{1}{{text{E}}} times 1000$$
(2)
A: Initial residue (mg/kg), B: Residues (mg/kg) on 0 day, C: active ingredients, E: dilution factor.Method validationThe analytical method was validated in terms of different performance criteria such as linearity, accuracy, precision, and method limit of quantitation (MLOQ). Matrix-matched standards were used to construct the calibration curve by evaporating (0.01, 0.05, 0.1, 0.2, 0.5, 0.7 and 1.0 mg/kg) working solution (1 mL) and re-dissolved in the extract of control sample. The linearity of the matrix-matched calibration curve was evaluated by the values of the correlation coefficient (R2). The accuracy and precision were obtained in terms of recovery (70–120%) and repeatability (n = 3). The recoveries were determined by spiking the analytes at two concentrations levels (0.1 mg/L) and (0.5 mg/L) in 10 g control samples and were quantified by comparing the response of analytes in samples with response in calibration standard solutions prepared in matrix. The repeatability expressed as the relative standard deviation (RSD) of the analyzed samples was calculated from three repetitions. The MLOQ was calculated by Eq. (3) taking into consideration the following factors: the instrument limit of detection, volume of extraction solvent, injection volume, dilution factor, and sample amount39,40.$${text{MLOQ}},{text{(mg}}/{text{kg)}} = {text{A(ng)}} times frac{{text{B(mL)}}}{{{text{C(}}upmu {text{L)}}}} times frac{{text{D}}}{{text{E(g)}}}$$
(3)
where A: instrument limit of detection, B: volume of extraction solvent, C: injection volume, D: dilution factor, E: sample amount.Half-life calculationThe dissipation patterns of spiromesifen and chromafenozide in lettuce and perilla leaves over time were found following the first-order kinetics model28. The half-life was determined by the following equation:$${text{C}}_{{text{t}}} = {text{C}}_{0} times {text{e}}^{{ – {text{kt}}}} ,{text{DT}}_{{{5}0}} = {text{ln2}}/{text{k}}$$where Ct is the concentration of the insecticide, C0 represents the initial residue concentration of insecticide, t is the time (days) after insecticide application, and k is the constant rate.Safety assessmentIn this study, the safety assessments (percent acceptable daily intake; %ADI) of the target insecticides that are consumed with lettuce and perilla leaves were calculated by the ratio of estimated daily intake (EDI) to acceptable daily intake (ADI). The EDI was calculated using insecticide concentration and average consumption of food commodities per person per day. In addition, the theoretical maximum daily intakes (TMDIs) of both insecticides were calculated using the maximum residue limits (MRLs) and average body weight (60 kg) of adults in Republic of Korea. TMDIs were calculated following the equation described by Kim et al.41.$$begin{aligned} & {text{ADI (mg}}/{text{person}}/{text{day)}} = {text{ADI}},({text{mg}}/{text{kg}}/{text{body weight}}/{text{day}}),{text{of target insecticide}} times {text{6}}0,({text{average body weight}}) \ & {text{EDI (mg}}/{text{kg}}/{text{person)}} = {text{concentration of target insecticide (mg}}/{text{kg)}} times {text{ daily food intake (g)}} \ & % {text{ADI}} = {text{EDI}}/{text{ADI}} times {text{1}}00 \ & {text{TMDI}}% = sum % {text{ADI of all registered crops}} \ end{aligned}$$ More

Zobell, C. E. & Allen, E. C. The significance of marine bacteria in the fouling of submerged surfaces. J. Bacteriol. 29, 239–251 (1935).CAS 
PubMed 
PubMed Central 

Google Scholar 
Flemming, H. C. et al. Biofilms: An emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).CAS 
PubMed 

Google Scholar 
Shikuma, N. J. & Hadfield, M. G. Marine biofilms on submerged surfaces are a reservoir for Escherichia coli and Vibrio cholerae. Biofouling 26, 39–46 (2010).CAS 
PubMed 

Google Scholar 
Maki, J., Rittschof, D., Schmidt, A., Snyder, A. & Mitchell, R. Factors controlling attachment of bryozoan larvae: A comparison of bacterial films and unfilmed surfaces. Biol. Bull. 177, 295–302 (1989).
Google Scholar 
Satuito, C. G., Natoyama, K., Yamazaki, M. & Fusetani, N. Inductin of attachment and metamorphosis of laboratory cultures mussel Mytilus edulis galloprovincialis larvae by microbial film. Fish. Sci. 61, 223–227 (1995).CAS 

Google Scholar 
Bao, W., Yang, J., Satuito, C. G. & Kitamura, H. Larval metamorphosis of the mussel Mytilus galloprovincialis in response to Alteromonas sp. 1: Evidence for two chemical cues?. Mar. Biol. 152, 657–666 (2007).
Google Scholar 
Liang, X. et al. Polyurethane, epoxy resin and polydimethylsiloxane altered biofilm formation and mussel settlement. Chemosphere 218, 599–608 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Huggett, M. J., Williamson, J. E., De Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).ADS 
PubMed 

Google Scholar 
Yang, J. et al. Larval settlement and metamorphosis of the mussel Mytilus coruscus in response to monospecific bacterial biofilms. Biofouling 29, 247–259 (2013).CAS 
PubMed 

Google Scholar 
Qian, P. Y., Thiyagarajan, V., Lau, S. C. K. & Cheung, S. C. K. Relationship between bacterial community profile in biofilm and attachment of the acorn barnacle Balanus amphitrite. Aquat. Microb. Ecol. 33, 225–237 (2003).
Google Scholar 
Leroy, C., Delbarre, C., Ghillebaert, F., Compere, C. & Combes, D. Effects of commercial enzymes on the adhesion of a marine biofilm-forming bacterium. Biofouling 24, 11–22 (2008).CAS 
PubMed 

Google Scholar 
Beigbeder, A. et al. On the effect of carbon nanotubes on the wettability and surface morphology of hydrosilylation-curing silicone coatings. Nanostruct. Polym. Nanocomp 5, 37–43 (2009).
Google Scholar 
Lee, S. H., Pumprueg, S., Moudgil, B. & Sigmund, W. Inactivation of bacterial endospores by photocatalytic nanocomposites. Colloids Surf. B Biointerfaces 40, 93–98 (2005).CAS 
PubMed 

Google Scholar 
Alzieu, C. Tributyltin: Case study of a chronic contaminant in the coastal environment. Ocean Coast. Manag. 40, 23–36 (1998).
Google Scholar 
Yang, J. L. et al. Chromosome-level genome assembly of the hard-shelled mussel Mytilus coruscus, a widely distributed species from the temperate areas of East Asia. GigaScience 10, giab024 (2021).PubMed 
PubMed Central 

Google Scholar 
Liang, X. et al. The flagellar gene regulates biofilm formation and mussel larval settlement and metamorphosis. Int. J. Mol. Sci. 21, 710 (2020).CAS 
PubMed Central 

Google Scholar 
Liang, X. et al. Bacterial cellulose synthesis gene regulates cellular c-di-GMP that control biofilm formation and mussel larval settlement. Int. Biodeterior. Biodegrad. 165, 105330 (2021).CAS 

Google Scholar 
Peng, L. H. et al. A bacterial polysaccharide biosynthesis-related gene inversely regulates larval settlement and metamorphosis of Mytilus coruscus. Biofouling 36, 753–765 (2020).CAS 
PubMed 

Google Scholar 
Chang, R. H. et al. Complete genome sequence of Shewanella marisflavi ECSMB14101, a red pigment synthesizing bacterium isolated from the East China Sea. Mar. Genom. 58, 100846 (2021).
Google Scholar 
Sutherland, I. W. Polysaccharide lyases. FEMS Microbiol. Rev. 16, 323–347 (1995).CAS 
PubMed 

Google Scholar 
Flemming, H. C. & Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 8, 623–633 (2010).CAS 
PubMed 

Google Scholar 
Kristensen, J. B. et al. Antifouling enzymes and the biochemistry of marine settlement. Biotechnol. Adv. 26, 471–481 (2008).CAS 
PubMed 

Google Scholar 
Pettitt, M., Henry, S., Callow, M., Callow, J. & Clare, A. Activity of commercial enzymes on settlement and adhesion of cypris larvae of the barnacle Balanus amphitrite, spores of the green alga Ulva linza, and the diatom Navicula perminuta. Biofouling 20, 299–311 (2004).CAS 
PubMed 

Google Scholar 
McDougald, D., Rice, S. A., Barraud, N., Steinberg, P. D. & Kjelleberg, S. Should we stay or should we go: Mechanisms and ecological consequences for biofilm dispersal. Nat. Rev. Microbiol. 10, 39–50 (2012).CAS 

Google Scholar 
Boyd, A. & Chakrabarty, A. Role of alginate lyase in cell detachment of Pseudomonas aeruginosa. Appl. Environ. Microbiol. 60, 2355–2359 (1994).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaplan, J. B., Ragunath, C., Velliyagounder, K., Fine, D. H. & Ramasubbu, N. Enzymatic detachment of Staphylococcus epidermidis biofilms. Antimicrob. Agents Chemother. 48, 2633–2636 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker, J., Bradshaw, D., Fulford, M. & Marsh, P. Microbiological evaluation of a range of disinfectant products to control mixed-species biofilm contamination in a laboratory model of a dental unit water system. Appl. Environ. Microbiol. 69, 3327–3332 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiater, A., Szczodrak, J. & Rogalski, J. Hydrolysis of mutan and prevention of its formation in streptococcal films by fungal α-d-glucanases. Process Biochem. 39, 1481–1489 (2004).CAS 

Google Scholar 
Dobretsov, S., Xiong, H., Xu, Y., Levin, L. A. & Qian, P.-Y. Novel antifoulants: Inhibition of larval attachment by proteases. Mar. Biotechnol. 9, 388–397 (2007).CAS 

Google Scholar 
Carl, C. et al. Enhancing the efficacy of fouling-release coatings against fouling by Mytilus galloprovincialis using nanofillers. Biofouling 28, 1077–1091 (2012).CAS 
PubMed 

Google Scholar 
Patel, P., Callow, M. E., Joint, I. & Callow, J. A. Specificity in the settlement–modifying response of bacterial biofilms towards zoospores of the marine alga Enteromorpha. Environ. Microbiol. 5, 338–349 (2003).CAS 
PubMed 

Google Scholar 
Thostenson, E. T., Ren, Z. & Chou, T. Advances in the science and technology of carbon nanotubes and their composites: A review. Compos. Sci. Technol. 61, 1899–1912 (2001).CAS 

Google Scholar 
Beigbeder, A. et al. Marine fouling release silicone/carbon nanotube nanocomposite coatings: On the importance of the nanotube dispersion state. J. Nanosci. Nanotechnol. 10, 2972–2978 (2010).CAS 
PubMed 

Google Scholar 
Frogley, M. D., Ravich, D. & Wagner, H. D. Mechanical properties of carbon nanoparticle-reinforced elastomers. Compos. Sci. Technol. 63, 1647–1654 (2003).CAS 

Google Scholar 
G., A. Seawater Composition. Online edition. SBCC Marine Science. Santa Barbara City College. http://www.marinebio.net/marinescience/02ocean/swcomposition.htm. (2004).Shipovskov, S., Ferapontova, E. E., Gazaryan, I. & Ruzgas, T. Recombinant horseradish peroxidase-and cytochrome c-based two-electrode system for detection of superoxide radicals. Bioelectrochemistry 63, 277–280 (2004).CAS 
PubMed 

Google Scholar 
Aehle, W. Enzymes in Industry: Production and Applications (Wiley, 2007).
Google Scholar 
Walker, G. Larval settlement: Historical and future perspectives. Crustacean Issues 10, 69–86 (1995).
Google Scholar 
Tomarelli, R., Charney, J. & Harding, M. L. The use of azoalbumin as a substrate in the colorimetric determination or peptic and tryptic activity. J. Lab. Clin. Med. 34, 428–433 (1949).CAS 
PubMed 

Google Scholar 
Somogyi, M. Modifications of two methods for the assay of amylase. Clin. Chem. 6, 23–35 (1960).CAS 
PubMed 

Google Scholar 
Sinegani, A. A. S. & Emtiazi, G. The relative effects of some elements on the DNS method in cellulase assay. J. Appl. Sci. Environ. Manag. 10, 93–96 (2006).
Google Scholar 
Li, Y. et al. Effects of bacterial biofilms on settlement of plantigrades of the mussel Mytilus coruscus. Aquaculture 433, 434–441 (2014).
Google Scholar 
Yang, J. et al. Effects of biofilms on settlement of plantigrades of the mussel Mytilus coruscus. J. Fish. China 37, 904–909 (2013) ((In Chinese with English Abstract)).
Google Scholar 
Hu, X. M. et al. Reduction of mussel metamorphosis by inactivation of the bacterial thioesterase gene via alteration of the fatty acid composition. Biofouling 37, 911–921 (2021).CAS 
PubMed 

Google Scholar  More

Walker, D. A. et al. The circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).Article 

Google Scholar 
Raynolds, M. K. et al. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sens. Environ. 232, 111297 (2019).ADS 
Article 

Google Scholar 
Danell, K. What Is the Arctic? In Which Ways Is the Arctic Different? In Arctic Ecology (ed. Thomas, D. N.) 1–22 (University of Helsinki, 2021).
Google Scholar 
Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochem. Cycles 23(2), 1–11. https://doi.org/10.1029/2008GB003327 (2009).CAS 
Article 

Google Scholar 
Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl. Acad. Sci. U.S.A. 117(34), 20438–20446. https://doi.org/10.1073/pnas.1916387117 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Loisel, J. et al. A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation. Holocene 24(9), 1028–1042. https://doi.org/10.1177/0959683614538073 (2014).ADS 
Article 

Google Scholar 
Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Chang. 8(10), 907–913. https://doi.org/10.1038/s41558-018-0271-1 (2018).ADS 
CAS 
Article 

Google Scholar 
Yu, Z., Beilman, D. W. & Jones, M. C. Sensitivity of Northern Peatland carbon dynamics to holocene climate change. Carbon Cycl. Northern Peatl. C https://doi.org/10.1029/2008GM000822 (2009).Article 

Google Scholar 
Svendsen, J. & Mangerud, J. Paleoclimatic inferences from glacial fluctuations on Svalbard during the last 20 000 years. Clim. Dyn. 6(3–4), 213–220. https://doi.org/10.1007/BF00193533 (1992).Article 

Google Scholar 
Farnsworth, W. R. et al. Holocene glacial history of Svalbard: Status, perspectives and challenges. Earth Sci. Rev. 208(April), 103249. https://doi.org/10.1016/j.earscirev.2020.103249 (2020).CAS 
Article 

Google Scholar 
D’Andrea, W. J. et al. Mild Little Ice Age and unprecedented recent warmth in an 1800 year lake sediment record from Svalbard. Geology 40(11), 1007–1010. https://doi.org/10.1130/G33365.1 (2012).ADS 
CAS 
Article 

Google Scholar 
Miller, G. H., Landvik, J. Y., Lehman, S. J. & Southon, J. R. Episodic Neoglacial snowline descent and glacier expansion on Svalbard reconstructed from the 14C ages of ice-entombed plants. Quatern. Sci. Rev. 155, 67–78. https://doi.org/10.1016/j.quascirev.2016.10.023 (2017).ADS 
Article 

Google Scholar 
Røthe, T. O. et al. Arctic Holocene glacier fluctuations reconstructed from lake sediments at Mitrahalvøya, Spitsbergen. Quatern. Sci. Rev. 109, 111–125. https://doi.org/10.1016/j.quascirev.2014.11.017 (2015).Article 

Google Scholar 
van der Bilt, W. G. M. et al. Reconstruction of glacier variability from lake sediments reveals dynamic Holocene climate in Svalbard. Quatern. Sci. Rev. 126, 201–218. https://doi.org/10.1016/j.quascirev.2015.09.003 (2015).ADS 
Article 

Google Scholar 
Allaart, L. et al. Glacial history of the Åsgardfonna Ice Cap, NE Spitsbergen, since the last glaciation. Quatern. Sci. Rev. https://doi.org/10.1016/j.quascirev.2020.106717 (2021).Article 

Google Scholar 
Humlum, O. et al. Late-Holocene glacier growth in Svalbard, documented by subglacial relict vegetation and living soil microbes. Holocene 15(3), 396–407. https://doi.org/10.1191/0959683605hl817rp (2005).ADS 
Article 

Google Scholar 
Yang, Z., Yang, W., Yuan, L., Wang, Y. & Sun, L. Evidence for glacial deposits during the Little Ice Age in Ny-Alesund, western Spitsbergen. J. Earth Syst. Sci. https://doi.org/10.1007/s12040-019-1274-7 (2020).Article 

Google Scholar 
AMAP – ARCTIC MONITORING AND ASSESSMENT PROGRAMME. (2019). Arctic Climate Change Update 2019: An update to key findings of Snow, Water, Ice, and Permafrost in the Arctic (SWIPA) 2017. Assessment Report, 12. https://www.amap.no/documents/doc/amap-climate-change-update-2019/1761.Nordli, Ø. et al. Polar Res. 39, 3614. https://doi.org/10.33265/polar.v39.3614 (2020).Article 

Google Scholar 
Førland, E. J., Benestad, R., Hanssen-Bauer, I., Haugen, J. E. & Skaugen, T. E. Temperature and precipitation development at svalbard 1900–2100. Adv. Meteorol. 2011, 1–14. https://doi.org/10.1155/2011/893790 (2011).Article 

Google Scholar 
Van Der Knaap, W. O. (1988). A pollen diagram from Broggerhalvoya, Spitsbergen: changes in vegetation and environment from ca. 4400 to ca. 800 BP. Arctic & Alpine Research, 20(1), 106–116. Doi: https://doi.org/10.2307/1551703Rozema, J. et al. A vegetation, climate and environment reconstruction based on palynological analyses of high arctic tundra peat cores (5000–6000 years BP) from Svalbard. Plant Ecol. 182(1–2), 155–173. https://doi.org/10.1007/s11258-005-9024-0 (2006).Article 

Google Scholar 
Nakatsubo, T. et al. Carbon accumulation rate of peatland in the High Arctic, Svalbard: Implications for carbon sequestration. Polar Sci. 9(2), 267–275. https://doi.org/10.1016/j.polar.2014.12.002 (2015).ADS 
Article 

Google Scholar 
Magnússon, B., Magnússon, S. & Fridriksson, S. (2009). Developments in plant colonization and succession on Surtsey during 1999–2008. Surtsey Res. pp. 57–76.Zwolicki, A., Zmudczyńska-Skarbek, K. M., Iliszko, L. & Stempniewicz, L. Guano deposition and nutrient enrichment in the vicinity of planktivorous and piscivorous seabird colonies in Spitsbergen. Polar Biol. 36(3), 363–372. https://doi.org/10.1007/s00300-012-1265-5 (2013).Article 

Google Scholar 
Leblans, N. I. W. et al. Effects of seabird nitrogen input on biomass and carbon accumulation after 50 years of primary succession on a young volcanic island Surtsey. Biogeosciences 11(22), 6237–6250. https://doi.org/10.5194/bg-11-6237-2014 (2014).ADS 
Article 

Google Scholar 
Zmudczyńska-Skarbek, K. et al. Transfer of ornithogenic influence through different trophic levels of the Arctic terrestrial ecosystem of Bjørnøya (Bear Island), Svalbard. Soil Biol. Biochem. 115, 475–489. https://doi.org/10.1016/j.soilbio.2017.09.008 (2017).CAS 
Article 

Google Scholar 
Hodkinson, I. D., Coulson, S. J. & Webb, N. R. Community assembly along proglacial chronosequences in the high arctic: vegetation and soil development in north-west Svalbard. J. Ecol. 91(4), 651–663. https://doi.org/10.1046/j.1365-2745.2003.00786.x (2003).Article 

Google Scholar 
Ravolainen, V. et al. High Arctic ecosystem states: Conceptual models of vegetation change to guide long-term monitoring and research. Ambio 49(3), 666–677. https://doi.org/10.1007/s13280-019-01310-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
van der Wal, R. & Brooker, R. W. Mosses mediate grazer impacts on grass abundance in arctic ecosystems. Funct. Ecol. 18(1), 77–86. https://doi.org/10.1111/j.1365-2435.2004.00820.x (2004).Article 

Google Scholar 
Vanderpuye, A. W., Elvebakk, A. & Nilsen, L. Plant communities along environmental gradients of high-arctic mires in Sassendalen Svalbard. J. Veg. Sci. 13(6), 875–884. https://doi.org/10.1111/j.1654-1103.2002.tb02117.x (2002).Article 

Google Scholar 
Le Moullec, M., Pedersen, Å. Ø., Stien, A., Rosvold, J. & Hansen, B. B. A century of conservation: the ongoing recovery of svalbard reindeer. J. Wildl. Manag. 83(8), 1676–1686. https://doi.org/10.1002/jwmg.21761 (2019).Article 

Google Scholar 
Garfelt-Paulsen, I. M. et al. Don’t go chasing the ghosts of the past: habitat selection and site fidelity during calving in an Arctic ungulate. Wildl. Biol. https://doi.org/10.2981/wlb.00740 (2021).Article 

Google Scholar 
Moreau, M., Mercier, D., Laffly, D. & Roussel, E. Impacts of recent paraglacial dynamics on plant colonization: a case study on Midtre Lovénbreen foreland, Spitsbergen (79°N). Geomorphology 95(1–2), 48–60. https://doi.org/10.1016/j.geomorph.2006.07.031 (2008).ADS 
Article 

Google Scholar 
Moreau, M., Laffly, D. & Brossard, T. Recent spatial development of Svalbard strandflat vegetation over a period of 31 years. Polar Res. 28(3), 364–375. https://doi.org/10.1111/j.1751-8369.2009.00119.x (2009).Article 

Google Scholar 
Wietrzyk, P., Wȩgrzyn, M. & Lisowska, M. Vegetation diversity and selected abiotic factors influencing the primary succession process on the foreland of Gåsbreen Svalbard. Pol. Polar Res. 37(4), 493–509. https://doi.org/10.1515/popore-2016-0026 (2016).Article 

Google Scholar 
Divine, D. et al. Thousand years of winter surface air temperature variations in Svalbard and northern norway reconstructed from ice-core data. Polar Res. 30(SUPPL.1), 1–12. https://doi.org/10.3402/polar.v30i0.7379 (2011).ADS 
Article 

Google Scholar 
Van Pelt, W. et al. A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957–2018). Cryosphere 13(9), 2259–2280. https://doi.org/10.5194/tc-13-2259-2019 (2019).ADS 
Article 

Google Scholar 
Johansen, B. E., Karlsen, S. R. & Tømmervik, H. Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data. Polar Rec. 48(1), 47–63. https://doi.org/10.1017/S0032247411000647 (2012).Article 

Google Scholar 
Norwegian Polar Institute. Available online at: https://npolar.no (2021).Norwegian Meteorological Institute. Available online at: https://seklima.met.no (2019).Kelly, T. J. et al. The vegetation history of an Amazonian domed peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468(November), 129–141. https://doi.org/10.1016/j.palaeo.2016.11.039 (2017).Article 

Google Scholar 
Estop-Aragonés, C. et al. Limited release of previously-frozen C and increased new peat formation after thaw in permafrost peatlands. Soil Biol. Biochem. 118, 115–129. https://doi.org/10.1016/j.soilbio.2017.12.010 (2018).CAS 
Article 

Google Scholar 
Blaauw, M., Christen, J. A. & Aquino-Lopez, M. A. rplum: Bayesian Age-Depth Modelling of Cores Dated by Pb-210. R package version 0.2.2. https://CRAN.R-project.org/package=rplum (2021).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020).Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: Reproducibility and comparability of results. J. Paleolimnol. 25(1), 101–110. https://doi.org/10.1023/A:1008119611481 (2001).ADS 
Article 

Google Scholar 
Booth, R. K., Lamentowicz, M. & Charman, D. J. Preparation and analysis of testate amoebae in peatland palaeoenvironmental studies. Mires and Peat 7(2), 1–7 (2010).
Google Scholar 
Charman, D., Hendon, D. & Woodland, W. A. The Identification of Testate Amoebae (Protozoa: Rhizopoda) in Peats (Quaternary Research Association, 2000).
Google Scholar 
Siemensma, F. J. Microworld, world of Amoeboid Organisms. World-Wide Electronic Publication, Kortenhoef, the Netherlands. Available online at: https://www.arcella.nl (2019).Payne, R. J. & Mitchell, E. A. D. How many is enough? Determining optimal count totals for ecological and palaeoecological studies of testate amoebae. J. Paleolimnol. 42(4), 483–495. https://doi.org/10.1007/s10933-008-9299-y (2009).ADS 
Article 

Google Scholar 
Swindles, G. T. et al. Testing peatland water-table depth transfer functions using high-resolution hydrological monitoring data. Q. Sci. Rev. 120, 107–117. https://doi.org/10.1016/j.quascirev.2015.04.019 (2015).ADS 
Article 

Google Scholar 
Amesbury, M. J. et al. Development of a new pan-European testate amoeba transfer function for reconstructing peatland palaeohydrology. Quatern. Sci. Rev. 152, 132–151. https://doi.org/10.1016/j.quascirev.2016.09.024 (2016).ADS 
Article 

Google Scholar 
Amesbury, M. J. et al. Towards a Holarctic synthesis of peatland testate amoeba ecology: Development of a new continental-scale palaeohydrological transfer function for North America and comparison to European data. Quatern. Sci. Rev. 201, 483–500. https://doi.org/10.1016/j.quascirev.2018.10.034 (2018).ADS 
Article 

Google Scholar 
Zhang, H. et al. Testate amoeba as palaeohydrological indicators in the permafrost peatlands of north-east European Russia and Finnish Lapland. J. Quat. Sci. 32(7), 976–988. https://doi.org/10.1002/jqs.2970 (2017).Article 

Google Scholar 
Sim, T. G. et al. Pathways for Ecological Change in Canadian High Arctic Wetlands Under Rapid Twentieth Century Warming. Geophys. Res. Lett. 46(9), 4726–4737. https://doi.org/10.1029/2019GL082611 (2019).ADS 
Article 

Google Scholar 
Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: Heterogeneity over space and time. Ecol. Lett. 15(2), 164–175. https://doi.org/10.1111/j.1461-0248.2011.01716.x (2012).Article 
PubMed 

Google Scholar 
Lupascu, M. et al. High Arctic wetting reduces permafrost carbon feedbacks to climate warming. Nat. Clim. Chang. 4(1), 51–55. https://doi.org/10.1038/nclimate2058 (2014).ADS 
CAS 
Article 

Google Scholar 
Bjorkman, A. D. et al. Status and trends in Arctic vegetation: Evidence from experimental warming and long-term monitoring. Ambio 49(3), 678–692. https://doi.org/10.1007/s13280-019-01161-6 (2020).MathSciNet 
Article 
PubMed 

Google Scholar 
Egli, M., Mavris, C., Mirabella, A. & Giaccai, D. Soil organic matter formation along a chronosequence in the Morteratsch proglacial area (Upper Engadine, Switzerland). CATENA 82(2), 61–69. https://doi.org/10.1016/j.catena.2010.05.001 (2010).CAS 
Article 

Google Scholar 
Prach, K. & Rachlewicz, G. Succession of vascular plants in front of retreating glaciers in central Spitsbergen. Polish Polar Research 33(4), 319–328. https://doi.org/10.2478/v10183-012-0022-3 (2012).Article 

Google Scholar 
Låg, J. Special Peat Formations in Svalbard. Acta Agric. Scand. 30(2), 205–210. https://doi.org/10.1080/00015128009435267 (1980).Article 

Google Scholar 
Serebryannyy, L. P., Tishkov, A. A., Malyasova, Y. S., Solomina, O. N. & Il’ves, E. O.,. Reconstruction of the development of vegetation in Arctic high latitudes. Polar Geogr. Geol. 9(4), 308–320. https://doi.org/10.1080/10889378509377261 (1985).Article 

Google Scholar 
Surova, T. G., Troitskiy, L. S., Skobeyeva, Y. I. & Punning, Y. M. K. Glacioclimatic conditions in the european arctic in the late holocene. Polar Geogr. Geol. 11(1), 50–57. https://doi.org/10.1080/10889378709377310 (1987).Article 

Google Scholar 
Surova, T. G., Troitskiy, L. S., Skobeyeva, Y. I. & Troitskiy, Y. M. K. Changes in glacioclimatic conditions on svalbard during the subboreal period. Polar Geogr. Geol. 12(3), 221–226. https://doi.org/10.1080/10889378809377366 (1988).Article 

Google Scholar 
Låg, J. Peat Accumulation in Steep Hills at Alkhornet Spitsbergen. Acta Agric. Scand. 40(3), 217–219. https://doi.org/10.1080/00015129009438554 (1990).Article 

Google Scholar 
Oliva, M. et al. Sedimentological characteristics of ice-wedge polygon terrain in adventdalen (Svalbard) environmental and climatic implications for the late Holocene. Solid Earth 5(2), 901–914. https://doi.org/10.5194/se-5-901-2014 (2014).ADS 
Article 

Google Scholar 
Van der Knaap, W. O. Past Vegetation and Reindeer on Edgeoya (Spitsbergen) Between c. 7900 and c. 3800 BP, Studied by Means of Peat Layers and Reindeer Faecal Pellets. J. Biogeogr. 16(4), 379. https://doi.org/10.2307/2845229 (1989).Article 

Google Scholar 
Røthe, T. O., Bakke, J., Støren, E. W. N. & Bradley, R. S. Reconstructing holocene glacier and climate fluctuations from lake sediments in Vårfluesjøen Northern Spitsbergen. Front. Earth Sci. 6(July), 1–20. https://doi.org/10.3389/feart.2018.00091 (2018).Article 

Google Scholar 
Alsos, I. G. et al. Sedimentary ancient DNA from Lake Skartjørna, Svalbard: assessing the resilience of arctic flora to Holocene climate change. Holocene 26(4), 627–642. https://doi.org/10.1177/0959683615612563 (2016).ADS 
Article 

Google Scholar 
Klimowicz, Z., Melke, J. & Uziak, S. Peat soils in the Bellsund region Spitsbergen. Pol. Polar Res. 18(1), 25–39 (1997).
Google Scholar 
Yang, Z. et al. Total photosynthetic biomass record between 9400 and 2200 BP and its link to temperature changes at a High Arctic site near Ny-Ålesund Svalbard. Polar Biol. 42(5), 991–1003. https://doi.org/10.1007/s00300-019-02493-5 (2019).Article 

Google Scholar 
Vickers, H. et al. Changes in greening in the high arctic: insights from a 30-year AVHRR max NDVI dataset for Svalbard. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/11/10/105004 (2016).Article 

Google Scholar 
Van Der Knaap, W. O. Human influence on natural Arctic vegetation in the 17th century and climatic change since AD 1600 in northwest Spitsbergen: a paleobotanical study. Arct. Alp. Res. 17(4), 371–387. https://doi.org/10.2307/1550863 (1985).Article 

Google Scholar 
Martín-Moreno, R., Allende Álvarez, F. & Hagen, J. O. ‘Little Ice Age’ glacier extent and subsequent retreat in Svalbard archipelago. Holocene 27(9), 1379–1390. https://doi.org/10.1177/0959683617693904 (2017).ADS 
Article 

Google Scholar 
Rachlewicz, G., Szczuziński, W. & Ewertowski, M. Post-“Little Ice Age” retreat rates of glaciers around Billefjorden in central Spitsbergen Svalbard. Pol. Polar Res. 28(3), 159–186 (2007).
Google Scholar 
Matthews, J. A. & Whittaker, R. J. Vegetation succession on the storbreen glacier foreland, Jotunheimen, Norway : a review. Arct. Alp. Res. 19(4), 385–395 (1987).Article 

Google Scholar 
Beyens, L. & Chardez, D. Evidence from testate amoebae for changes in some local hydrological conditions between c. 5000 BP and c. 3800 BP on Edgeøya (Svalbard). Polar Res. 5(2), 165–169. https://doi.org/10.1111/j.1751-8369.1987.tb00619.x (1987).Article 

Google Scholar 
Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. G. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/10/9/094011 (2015).Article 

Google Scholar 
Isaksen, K., Benestad, R. E., Harris, C. & Sollid, J. L. Recent extreme near-surface permafrost temperatures on Svalbard in relation to future climate scenarios. Geophys. Res. Lett. 34(17), 1–5. https://doi.org/10.1029/2007GL031002 (2007).Article 

Google Scholar 
Cable, S., Elberling, B. & Kroon, A. Holocene permafrost history and cryostratigraphy in the High-Arctic Adventdalen Valley, central Svalbard. Boreas 47(2), 423–442. https://doi.org/10.1111/bor.12286 (2018).Article 

Google Scholar 
König, M., Kohler, J. & Nuth, C. Glacier Area Outlines–Svalbard, v1.0, http://data.npolar.no/dataset/89f430f8-862f-11e2-8036-005056ad0004 Delivered by CryoClim service (2013).Box, J. E. et al. Key indicators of Arctic climate change: 1917–2017. Environ. Res. Lett. 14(4), 045010. https://doi.org/10.1088/1748-9326/aafc1b (2019).ADS 
CAS 
Article 

Google Scholar 
Zhang, H. et al. Decreased carbon accumulation feedback driven by climate-induced drying of two southern boreal bogs over recent centuries. Glob. Change Biol. 26(4), 2435–2448. https://doi.org/10.1111/gcb.15005 (2020).ADS 
Article 

Google Scholar 
Szymański, W., Wojtuń, B., Stolarczyk, M., Siwek, J. & Waścińska, J. Organic carbon and nutrients (N, P) in surface soil horizons in a non-glaciated catchment SW Spitsbergen. Pol. Polar Res. 37(1), 49–66. https://doi.org/10.1515/popore-2016-0006 (2016).Article 

Google Scholar 
Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11(23), 6573–6593. https://doi.org/10.5194/bg-11-6573-2014 (2014).ADS 
Article 

Google Scholar 
Palmtag, J. et al. Storage, landscape distribution, and burial history of soil organic matter in contrasting areas of continuous permafrost. Arct. Antarct. Alp. Res. 47(1), 71–88. https://doi.org/10.1657/AAAR0014-027 (2015).Article 

Google Scholar 
Siewert, M. B. et al. Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution. J. Geophys. Res. Biogeosci. 120, 1973–1994 (2015).CAS 
Article 

Google Scholar 
Wojcik, R., Palmtag, J., Hugelius, G., Weiss, N. & Kuhry, P. Land cover and landform-based upscaling of soil organic carbon stocks on the Brøgger Peninsula, Svalbard. Arct. Antarct. Alp. Res. 51(1), 40–57. https://doi.org/10.1080/15230430.2019.1570784 (2019).Article 

Google Scholar 
Yoshitake, S. et al. Vegetation development and carbon storage on a glacier foreland in the High Arctic, Ny-Ålesund Svalbard. Polar Sci. 5(3), 391–397. https://doi.org/10.1016/j.polar.2011.03.002 (2011).ADS 
Article 

Google Scholar 
Mack, M. C. et al. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475(7357), 489–492. https://doi.org/10.1038/nature10283 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cooper, E. J., Dullinger, S. & Semenchuk, P. Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. Plant Sci. 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005 (2011).CAS 
Article 
PubMed 

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