Philippa Kaur

Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

Google Scholar 
Marvin, D. C. et al. Integrating technologies for scalable ecology and conservation. Glob. Ecol. Conserv. 7, 262–275 (2016).Article 

Google Scholar 
Wall, J., Wittemyer, G., Klinkenberg, B. & Douglas-Hamilton, I. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecol. Appl. 24, 593–601 (2014).Article 

Google Scholar 
Snaddon, J., Petrokofsky, G., Jepson, P. & Willis, K. J. Biodiversity technologies: tools as change agents. Biol. Lett. 9, 20121029 (2013).Article 

Google Scholar 
Pettorelli, N., Safi, K., Turner, W. Satellite remote sensing, biodiversity research and conservation of the future. Philos. Trans. R. Soc. B Biol. Sci. 369, 20130190 (2014).Ripperger, S. P. et al. Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging. PLOS Biol. 18, e3000655 (2020).CAS 
Article 

Google Scholar 
Xu, H., Wang, K., Vayanos, P. & Tambe, M. Strategic coordination of human patrollers and mobile sensors with signaling for security games. 8 (2018).Liu, Y. et al. AI for Earth: Rainforest conservation by acoustic surveillance. 2 (2019).Joppa, L. N. Technology for nature conservation: an industry perspective. Ambio 44, 522–526 (2015).Article 

Google Scholar 
Koh, L. P. & Wich, S. A. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5, 121–132 (2012).Article 

Google Scholar 
Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: a case study. Oryx 51, 513–516 (2017).Article 

Google Scholar 
Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. GigaScience 7, (2018).Van Doren, B. M. & Horton, K. G. A continental system for forecasting bird migration. Science 361, 1115–1118 (2018).ADS 
Article 

Google Scholar 
Howson, P. Building trust and equity in marine conservation and fisheries supply chain management with blockchain. Mar. Policy 115, 103873 (2020).Article 

Google Scholar 
Speaker, T. et al. A global community-sourced assessment of the state of conservation technology. Conserv. Biol. cobi. https://doi.org/10.1111/cobi.13871 (2022).Article 

Google Scholar 
Pearce, J. M. Building research equipment with free Open-Source Hardware. Science 337, 1303–1304 (2012).ADS 
CAS 
Article 

Google Scholar 
Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K. E. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol. Evol. 10, 169–185 (2019).Article 

Google Scholar 
current constraints and future priorities for development. Glover-Kapfer, P., Soto-Navarro, C. A. & Wearn, O. R. Camera-trapping version 3.0. Remote Sens. Ecol. Conserv. 5, 209–223 (2019).Article 

Google Scholar 
Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
Article 

Google Scholar 
Berger-Tal, O. & Lahoz-Monfort, J. J. Conservation technology: the next generation. Conserv. Lett. 11, 1–6 (2018).Article 

Google Scholar 
Hill, A. P. et al. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods Ecol. Evol. 9, 1199–1211 (2018).Article 

Google Scholar 
Zárybnická, M., Kubizňák, P., Šindelář, J. & Hlaváč, V. Smart nest box: a tool and methodology for monitoring of cavity-dwelling animals. Methods Ecol. Evol. 7, 483–492 (2016).Article 

Google Scholar 
Kalmár, G. et al. Animal-Borne Anti-Poaching System. in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services 91–102 (ACM, 2019). https://doi.org/10.1145/3307334.3326080.Weise, F. J. et al. Lions at the gates: Trans-disciplinary design of an early warning system to improve human-lion coexistence. Front. Ecol. Evol. 6, 242 (2019).Article 

Google Scholar 
Beery, S., Van Horn, G. & Perona, P. Recognition in Terra Incognita. in Proceedings of the European Conference on Computer Vision (ECCV) (eds. Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 472–489 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-030-01270-0_28.Crego, R. D., Masolele, M. M., Connette, G. & Stabach, J. A. Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using google earth engine and R. Remote Sens. 13, 4154 (2021).ADS 
Article 

Google Scholar 
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
Article 

Google Scholar 
Vulcan. EarthRanger. https://earthranger.com.Ahumada, J. A. et al. Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
Article 

Google Scholar 
Lahoz-Monfort, J. J. et al. A call for international leadership and coordination to realize the potential of conservation technology. Bioscience 69, 823–832 (2019).Article 

Google Scholar 
Group Gets – AudioMoth. https://groupgets.com/manufacturers/open-acoustic-devices/products/audiomoth.Kulits, P., Wall, J., Bedetti, A., Henley, M. & Beery, S. ElephantBook: A semi-automated human-in-the-loop system for elephant re-identification. in ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) 88–98 (ACM, 2021). https://doi.org/10.1145/3460112.3471947.Pardo, L. E. et al. Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity. South Afr. J. Sci. 117, (2021).Iacona, G. et al. Identifying technology solutions to bring conservation into the innovation era. Front. Ecol. Environ. 17, 591–598 (2019).Article 

Google Scholar 
Cooper, R. G. What’s next?: After stage-gate. Res.-Technol. Manag. 57, 20–31 (2014).ADS 

Google Scholar 
Cooper, R. G. The drivers of success in new-product development. Ind. Mark. Manag. 76, 36–47 (2019).Article 

Google Scholar 
Pearce, J. M. The case for open source appropriate technology. Environ. Dev. Sustain. 14, 425–431 (2012).Article 

Google Scholar 
Mair, J., Battilana, J. & Cardenas, J. Organizing for society: A typology of social entrepreneuring models. J. Bus. Ethics 111, 353–373 (2012).Article 

Google Scholar 
Meissner, D. Public-private partnership models for science, technology, and innovation cooperation. J. Knowl. Econ. 10, 1341–1361 (2019).Article 

Google Scholar 
Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22, 1–55.Mayer, A. L. & Wellstead, A. M. Questionable survey methods generate a questionable list of recommended articles. Nat. Ecol. Evol. 2, 1336–1337 (2018).Article 

Google Scholar 
Archie, K. M., Dilling, L., Milford, J. B. & Pampel, F. C. Climate Change and Western Public Lands: a Survey of U.S. Federal Land Managers on the Status of Adaptation Efforts. Ecol. Soc. 17 (2012).Jimenez, M. F. et al. Underrepresented faculty play a disproportionate role in advancing diversity and inclusion. Nat. Ecol. Evol. 3, 1030–1033 (2019).Article 

Google Scholar 
Christensen, R. ordinal – Regression Models for Ordinal Data. R package version 2019.12-10. (2019).R Core Team. R: A language and environment for statistical computing. (2020).Arnold, T. W. Uninformative parameters and model selection using Akaike’s information criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

Google Scholar 
QSR International Pty Ltd. Nvivo 12 Pro. (2020).Glesne, C. Making words fly: Developing understanding through interviewing. Becom. Qual. Res. Introd. 3, (2006).Creswell, J. W. & Creswell, J. D. Research design: Qualitative, quantitative, and mixed methods approaches. (Sage publications 2017). More

Balami, S., Vašutová, M., Godbold, D., Kotas, P. & Cudlín, P. Soil fungal communities across land use types. Forest Biogeosci. For. 13, 548–558 (2020).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2009).
Google Scholar 
Ruiz-Almenara, C., Gándara, E. & Gómez-Hernández, M. Comparison of diversity and composition of macrofungal species between intensive mushroom harvesting and non-harvesting areas in Oaxaca, Mexico. PeerJ 7, e8325 (2019).PubMed 
PubMed Central 

Google Scholar 
Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 

Google Scholar 
Egli, S. Mycorrhizal mushroom diversity and productivity—an indicator of forest health?. Ann. For. Sci. 68, 81–88 (2011).
Google Scholar 
Westover, K. M. & Bever, J. D. Mechanisms of plant species coexistence: Roles of rhizosphere bacteria and root fungal pathogens. Ecology 82, 3285–3294 (2001).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2006).
Google Scholar 
Fernandez, C. W., Nguyen, N. H. U. H., Stefanski, A. & Han, Y. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 23, 1598–1609 (2017).ADS 
PubMed 

Google Scholar 
Heilmann-Clausen, J. et al. A fungal perspective on conservation biology. Conserv. Biol. 29, 61–68 (2015).PubMed 

Google Scholar 
Shay, P.-E., Winder, R. S. & Trofymow, J. A. Nutrient-cycling microbes in coastal Douglas-fir forests: Regional-scale correlation between communities, in situ climate, and other factors. Front. Microbiol. 6, 5897 (2015).
Google Scholar 
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 

Google Scholar 
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. For. Ecol. Manag. 409, 250–259 (2018).
Google Scholar 
Monkai, J., Hyde, K. D., Xu, J. & Mortimer, P. E. Diversity and ecology of soil fungal communities in rubber plantations. Fungal Biol. Rev. 31, 1–11 (2017).
Google Scholar 
White, F. Vegetation of Africa—a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa, Natural Resources Research Report XX. U.N. Educational, Scientific and Cultural Organization, Paris (1983).Aynekulu, E. et al. Plant diversity and regeneration in a disturbed isolated dry Afromontane forest in northern Ethiopia. Folia Geobot. 51, 115–127 (2016).
Google Scholar 
Wassie, A., Sterck, F. J. & Bongers, F. Species and structural diversity of church forests in a fragmented Ethiopian Highland landscape. J. Veg. Sci. 21, 938–948 (2010).
Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Survey of macrofungal diversity and analysis of edaphic factors influencing the fungal community of church forests in Dry Afromontane areas of Northern Ethiopia. For. Ecol. Manag. 496, 119391 (2021).
Google Scholar 
Aerts, R. et al. Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. Sci. Total Environ. 551–552, 404–414 (2016).ADS 
PubMed 

Google Scholar 
Wassie, A., Teketay, D. & Powell, N. Church forests in North Gonder administrative zone, Northern Ethiopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Wsaaie, A., Teketay, D. & Powell, N. Church forests in North Gondar Administative Zone, Northern Ethioopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Lemenih, M. & Bongers, F. Dry forests of Ethiopia and their silviculture. In Silviculture in the Tropics, Tropical Forestry 8 (ed. S. G€unter et al.) 261–272 (Springer, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-19986-8_17.Fernández, A., Sánchez, S., García, P. & Sánchez, J. Macrofungal diversity in an isolated and fragmented Mediterranean Forest ecosystem. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 154, 139–148 (2020).
Google Scholar 
Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions. New Phytol. 204, 180–191 (2014).PubMed 

Google Scholar 
Burgess, N. D., Hales, J. D. A., Ricketts, T. H. & Dinerstein, E. Factoring species, non-species values and threats into biodiversity prioritisation across the ecoregions of Africa and its islands. Biol. Conserv. 127, 383–401 (2006).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398, 37–47 (2017).
Google Scholar 
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-. ). 346 (2014).Hawksworth, D. L. Global species numbers of fungi: are tropical studies and molecular approaches contributing to a more robust estimate?. Biodivers. Conserv. 21, 2425–2433 (2012).
Google Scholar 
Crous, P. W. et al. How many species of fungi are there at the tip of Africa?. Stud. Mycol. 55, 13–33 (2006).PubMed 
PubMed Central 

Google Scholar 
Martínez, M. L. et al. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. For. Ecol. Manag. 258, 1856–1863 (2009).
Google Scholar 
Phillips, H. et al. The effects of global change on soil faunal communities: a meta-analytic approach. Res. Ideas Outcomes 5 (2019).Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).CAS 

Google Scholar 
Rantalainen, M., Haimi, J., Fritze, H., Pennanen, T. & Setala, T. Soil decomposer community as a model system in studying the effects of habitat fragmentation and habitat corridors. Soil Biol. Biochem. 40, 853–863 (2008).CAS 

Google Scholar 
Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Chang. 6, 182–186 (2016).ADS 

Google Scholar 
Bahram, M., Põlme, S., Kõljalg, U., Zarre, S. & Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytol. 193, 465–473 (2012).PubMed 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

Google Scholar 
Krüger, C. et al. Plant communities rather than soil properties structure arbuscular mycorrhizal fungal communities along primary succession on a mine spoil. Front. Microbiol. 8, 1–16 (2017).
Google Scholar 
Bahram, M., Peay, K. G. & Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 205, 1454–1463 (2015).CAS 
PubMed 

Google Scholar 
Li, P. et al. Spatial variation in soil fungal communities across paddy fields in Subtropical China. mSystems 5 (2020).Grilli, G., Urcelay, C. & Galetto, L. Forest fragment size and nutrient availability: Complex responses of mycorrhizal fungi in native–exotic hosts. Plant Ecol. 213, 155–165 (2012).
Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Shrub Resprouting Response After Fuel Reduction Treatments: Comparison of Prescribed Burning, Clearing and Mastication (Elsevier, 2013).
Google Scholar 
Tedersoo, L., Sadam, A., Zambrano, M., Valencia, R. & Bahram, M. Low diversity and high host preference of ectomycorrhizal fungi in Western Amazonia, a neotropical biodiversity hotspot. ISME J. 4, 465–471 (2010).PubMed 

Google Scholar 
Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26, 6960–6973 (2017).CAS 
PubMed 

Google Scholar 
Colwell, R. K. EstimateS: statistical estimation of species richness and shared species from samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
Pan, W. et al. DNA polymerase preference determines PCR priming efficiency. BMC Biotechnol. 14, 10 (2014).PubMed 
PubMed Central 

Google Scholar 
Kirk, P. M., Cannon, P. F., Minter, D. W. & J.A, S. Dictionary of the Fungi (The Centre for Agriculture and Bioscience International (CABI), 2008).Rossman, A., Samuel, G., Rogerson, C. & Lowen, R. Genera of bionectriaceae, hypocreaceae and nectriaceae (hypocreales, ascomycetes). Stud. Mycol. 42, 1–260 (1999).
Google Scholar 
Samuels, G. Trichoderma: A review of biology and systematics of the genus. Mycol. Res. 923–935 (1996).Alem, D. et al. Soil fungal communities and succession following wildfire in Ethiopian dry Afromontane forests, a highly diverse underexplored ecosystem. For. Ecol. Manag. 474, 118328 (2020).
Google Scholar 
Muleta, D., Woyessa, D. & Teferi, Y. Mushroom consumption habits of Wacha Kebele residents, southwestern Ethiopia. Glob. Res. J. Agric. Biol. Sci. 4, 6–16 (2013).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Edible wild mushrooms of Ethiopia: Neglected non-timber forest products. Rev. Fitotec. Mex. 40, 391–397 (2017).
Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science (80-) 346, 1052–1053 (2014).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398 (2017).Dang, P. et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere 9 (2018).Gilbert, G. S., Ferrer, A. & Carranza, J. Polypore fungal diversity and host density in a moist tropical forest. Biodivers. Conserv. 11, 947–957 (2002).
Google Scholar 
Kottke, I., Beck, A., Oberwinkler, F., Homeier, J. & Neill, D. Arbuscular endomycorrhizas are dominant in the organic soil of a neotropical montane cloud forest. J. Trop. Ecol. 20, 125–129 (2004).
Google Scholar 
Barnes, C. J., Van der Gast, C. J., Burns, C. A., McNamara, N. P. & Bending, G. D. Temporally variable geographical distance effects contribute to the assembly of root-associated fungal communities. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
Tian, J. et al. Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Sci. Rep. 8, 4–11 (2018).
Google Scholar 
Rosales-Castillo, J. et al. Fungal community and ligninolytic enzyme activities in Quercus deserticola Trel. litter from forest fragments with increasing levels of disturbance. Forests 9, 11 (2017).
Google Scholar 
Kuhar, F., Barroetaveña, C. & Rajchenberg, M. New species of Tomentella (Thelephorales) from the Patagonian Andes forests. Mycologia 108, 780–790 (2016).CAS 
PubMed 

Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A., Geml, J. & Martín-Pinto, P. Soil fungal communities under Pinus patula Schiede ex Schltdl. & Cham. Plantation forests of different ages in Ethiopia. Forests 11, 1109 (2020).
Google Scholar 
Tedersoo, L. et al. Terrestrial and lignicolous macrofungi. ISME J. 10, 1228–1239 (2016).
Google Scholar 
Ruiz, R., Decock, C., Saikawa, M., Gene, J. & Guarro, J. Polyschema obclaviformis sp. Nov., and some new records of hyphomycetes from Cuba. Cryptogam. Mycol. 21, 215–220 (2000).
Google Scholar 
Kaygusuz, O. New locality records of Trichoglossum hirsutum (Geoglossales: Geoglossaceae) based on molecular analyses, and prediction of its potential distribution in Turkey. Curr. Res. Environ. Appl. Mycol. 10, 443–456 (2020).
Google Scholar 
Mayer, P. M. Ecosystem and decomposer effects on litter dynamics along an old field to old-growth forest successional gradient. Acta Oecol. 33, 222–230 (2008).ADS 

Google Scholar 
Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).
Google Scholar 
Kirschbaum, M. U. F. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27, 753–760 (1995).CAS 

Google Scholar 
Mayer, P. M., Tunnell, S. J., Engle, D. M., Jorgensen, E. E. & Nunn, P. Invasive grass alters litter decomposition by influencing macrodetritivores. Ecosystems 8, 200–209 (2005).
Google Scholar 
Epstein, H. E., Burke, I. C. & Lauenroth, W. K. Regional patterns of decomposition and primary production rates in the U.S. great plains. Ecology 83, 320 (2002).
Google Scholar 
Sharon, R., Degani, G. & Warburg, M. Comparing the soil macro-fauna in two oak-wood forests: Does community structure differ under similar ambient conditions?. Pedobiologia (Jena). 45, 355–366 (2001).
Google Scholar 
Clocchiatti, A., Hannula, S. E., van den Berg, M., Korthals, G. & de Boer, W. The hidden potential of saprotrophic fungi in arable soil: Patterns of short-term stimulation by organic amendments. Appl. Soil Ecol. 147, 103434 (2020).
Google Scholar 
Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lauber, C., Hamady, M., Knigh, R. & Fierer, N. Pyrosequencing based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ullah, S. et al. The response of soil fungal diversity and community composition to long-term fertilization. Appl. Soil Ecol. 140, 35–41 (2019).
Google Scholar 
Bååth, E. & Anderson, T.-H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).
Google Scholar 
Zhang, T., Wang, N.-F., Liu, H.-Y., Zhang, Y.-Q. & Yu, L.-Y. Soil pH is a key determinant of soil fungal community composition in the Ny-Ålesund Region, Svalbard (high arctic). Front. Microbiol. 7 (2016).Tian, D. et al. Effects of nitrogen deposition on soil microbial communities in temperate and subtropical forests in China. Sci. Total Environ. 607–608, 1367–1375 (2017).ADS 
PubMed 

Google Scholar 
Zhao, A. et al. Influences of canopy nitrogen and water addition on am fungal biodiversity and community composition in a mixed deciduous forest of China. Front. Plant Sci. 9 (2018).He, J. et al. Greater diversity of soil fungal communities and distinguishable seasonal variation in temperate deciduous forests compared with subtropical evergreen forests of eastern China. FEMS Microbiol. Ecol. 93, 1–12 (2017).
Google Scholar 
Shi, L. et al. Variation in forest soil fungal diversity along a latitudinal gradient. Fungal Divers. 64, 305–315 (2014).
Google Scholar 
Gebeyehu, G., Soromessa, T., Bekele, T. & Teketay, D. Plant diversity and communities along environmental, harvesting and grazing gradients in dry afromontane forests of Awi Zone, northwestern Ethiopia. Taiwania 64, 307–320 (2019).
Google Scholar 
Zegeye, H., Teketay, D. & Kelbessa, E. Diversity and regeneration status of woody species in Tara Gedam and Abebaye forests, northwestern Ethiopia. J. For. Res. 22, 315–328 (2011).
Google Scholar 
Abere, F., Belete, Y., Kefalew, A. & Soromessa, T. Carbon stock of Banja forest in Banja district, Amhara region, Ethiopia: An implication for climate change mitigation. J. Sustain. For. 36, 604–622 (2017).
Google Scholar 
Masresha, G., Soromessa, T. & Kelbessa, E. Status and species diversity of Alemsaga Forest, Northwestern Ethiopia 14 (2015).Rudolph, S., Maciá-Vicente, J. G., Lotz-Winter, H., Schleuning, M. & Piepenbring, M. Temporal variation of fungal diversity in a mosaic landscape in Germany. Stud. Mycol. 89, 95–104 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
De la Varga, H., Águeda, B., Martínez-Peña, F., Parladé, J. & Pera, J. Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity. Mycorrhiza 22, 59–68 (2012).PubMed 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

Google Scholar 
Reeuwijk, L. Procedures for Soil Analysis (International Soil Reference and Information Centre, 2002).
Google Scholar 
Walkley, A. & Black, I. A. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 34, 29–38 (1934).ADS 

Google Scholar 
Kim, J., Kreller, C. R. & Greenberg, M. M. Preparation and analysis of oligonucleotides containing the C4’-oxidized abasic site and related mechanistic probes. J. Org. Chem. 70, 8122–8129 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. T. Soil sampling, preparation and analysis. 139–145 (1996).Bouyoucos, G. H. A reclamation of the hydrometer for making mechanical analysis. Soil. Agro. J. 43, 434–438 (1951).CAS 

Google Scholar 
Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).CAS 
PubMed 

Google Scholar 
White, T. ., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols: A Guide to Methods and Applications (eds. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (Academic Press, 1990).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105 (2020).Hedberg, I. & Edwards, S. Flora of Ethiopia and Eritria (1989).Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
PubMed Central 

Google Scholar 
Schön, M. E., Nieselt, K. & Garnica, S. Belowground fungal community diversity and composition associated with Norway spruce along an altitudinal gradient. PLoS ONE 13, e0208493 (2018).PubMed 
PubMed Central 

Google Scholar 
Castaño, C. et al. Changes in fungal diversity and composition along a chronosequence of Eucalyptus grandis plantations in Ethiopia. Fungal Ecol. 39, 328–335 (2019).
Google Scholar 
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

Google Scholar 
Kent, M. & Coker, P. Vegetation Description and Analysis: A Practical Approach (Belhaven Press, 1993).
Google Scholar 
Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
Google Scholar 
Jost, L., Chao, A. & Chazdon, R. Compositional similarity and β (beta) diversity. in Biological Diversity. Frontiers in Measurement and Assessment (eds. A.E., Magurran & B.J., M.) 66–84 (Oxford University Press, 2011).Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. (World Agroforestry Centre (ICRAF), 2005).R Core Team. A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-128. http://CRAN.R-project.org/package=nlme (2016).Tóthmérész, B. Comparison of different methods for diversity ordering. J. Veg. Sci. 6, 283–290 (1995).
Google Scholar 
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E, Plymouth, 2014).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar  More

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

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

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

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

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

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