Ecology

Duggins, D. O., Simenstad, C. A. & Estes, J. A. Magnification of secondary producition by kelp detritus in coastal marine ecosystems. Science 1979(245), 170–173 (1989).Article 
ADS 

Google Scholar 
Hill, R. et al. Can macroalgae contribute to blue carbon? An Australian perspective. Limnol. Oceanogr. 60, 1689–1706 (2015).Article 
ADS 

Google Scholar 
Mann, K. H. Seaweeds: Their productivity and strategy for growth. Science 1979(182), 975–981 (1973).Article 
ADS 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

Google Scholar 
Giordano, M., Beardall, J. & Raven, J. A. CO2 concentrating mechanisms in algae: Mechanisms, environmental modulation, and evolution. Annu. Rev. Plant Biol. 56, 99–131 (2005).Article 
CAS 

Google Scholar 
Raven, J. A. & Beardall, J. The ins and outs of CO2. J. Exp. Bot. 67, 1–13 (2016).Article 
CAS 

Google Scholar 
Raven, J. A. et al. Seaweeds in cold seas: Evolution and carbon acquisition. Ann. Bot. 90, 525–536. https://doi.org/10.1093/aob/mcf171 (2002).Article 
CAS 

Google Scholar 
Raven, J. et al. Ocean Acidification due to Increasing Atmospheric Carbon Dioxide 1–68 (The Royal Society, 2005).
Google Scholar 
Kübler, J. E. & Dudgeon, S. R. Predicting effects of ocean acidification and warming on algae lacking carbon concentrating mechanisms. PLoS ONE 10, 1–19 (2015).Article 

Google Scholar 
Fernández, P. A., Hurd, C. L. & Roleda, M. Y. Bicarbonate uptake via an anion excange protein is the main mechanism of inorganic carbon acquisition by the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae) under variable pH1. J. Phycol. 50, 1–11 (2014).Article 

Google Scholar 
Raven, J. A. et al. Mechanistic interpretation of carbon isotope discrimination by marine macroalgae and seagrasses. Funct. Plant Biol. 29, 355 (2002).Article 
CAS 

Google Scholar 
Raven, J. A., Cockell, C. S. & De La Rocha, C. L. The evolution of inorganic carbon concentrating mechanisms in photosynthesis. Philos. Trans. R. Soc. B 363, 2641–2650 (2008).Article 
CAS 

Google Scholar 
Bidwell, R. G. S. S. & McLachlan, J. Carbon nutrition of seaweeds: Photosynthesis, photorespiration and respiration. J. Exp. Mar. Biol. Ecol. 86, 15–46 (1985).Article 
CAS 

Google Scholar 
Hurd, C. L. Water motion, marine macroalgal physiology and production. J. Phycol. 36, 453–472. https://doi.org/10.1046/j.1529-8817.2000.99139.x (2000).Article 
CAS 

Google Scholar 
Hurd, C. L., Stevens, C. L., Laval, B. E., Lawrence, G. A. & Harrison, P. J. Visualization of seawater flow around morphologically distinct forms of the giant kelp Macrocystis integrifolia from wave-sheltered and exposed sites. Limnol. Oceanogr. 42, 156–163. https://doi.org/10.4319/lo.1997.42.1.0156 (1997).Article 
ADS 

Google Scholar 
Smith, F. A. A. & Walker, N. A. A. Photosynthesis by aquatic plants: Effects of unstirred layers in relation to assimilation of CO2 and HCO3- to carbon isotope discrimination. N. Phytol. 86, 245–259 (1980).Article 
CAS 

Google Scholar 
Wheeler, W. N. Effect of boundary layer transport on the fixation of carbon by the giant kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L., Lenton, A., Tilbrook, B. & Boyd, P. W. Current understanding and challenges for oceans in a higher-CO2 world. Nat. Clim. Chang. 8, 686–694 (2018).Article 
ADS 
CAS 

Google Scholar 
Stocker, T. F. et al. Technical Summary. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 33–115 (2013).Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Chang. Biol. 17, 2488–2497 (2011).Article 
ADS 

Google Scholar 
Beer, S. & Koch, E. Photosynthesis of marine macroalgae and seagrasses in globally changing CO2 environments. Mar. Ecol. Prog. Ser. 141, 199–204 (1996).Article 
ADS 

Google Scholar 
Ihnken, S., Roberts, S. & Beardall, J. Differential responses of growth and photosynthesis in the marine diatom Chaetoceros muelleri to CO2 and light availability. Phycologia 50, 182–193 (2011).Article 
CAS 

Google Scholar 
Gerard, V. A. In situ water motion and nutrient uptake by the giant kelp Macrocystis pyrifera. Mar. Biol. 69, 51–54 (1982).Article 

Google Scholar 
Hepburn, C. D., Holborow, J. D., Wing, S. R., Frew, R. D. & Hurd, C. L. Exposure to waves enhances the growth rate and nitrogen status of the giant kelp Macrocystis pyrifera. Mar. Ecol. Prog. Ser. 339, 99–108 (2007).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L. Shaken and stirred: The fundamental role of water motion in resource acquisition and seaweed productivity. Persp. Phycol. 4, 73–81 (2017).ADS 

Google Scholar 
Sültemeyer, D. F., Miller, A. G., Espie, G. S., Fock, H. P. & Canvin, D. T. Active CO2 transport by the green alga Chlamydomonas reinhardtii. Plant Physiol. 89, 1213–1219 (1989).Article 

Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Chang. Biol. 19, 103–132 (2013).Article 
ADS 

Google Scholar 
Britton, D., Cornwall, C. E., Revill, A. T., Hurd, C. L. C. L. & Johnson, C. R. Ocean acidification reverses the positive effects of seawater pH fluctuations on growth and photosynthesis of the habitat-forming kelp Ecklonia radiata. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Cornwall, C. E. et al. Carbon-use strategies in macroalgae: Differential responses to lowered ph and implications for ocean acidification. J. Phycol. 48, 137–144 (2012).Article 
CAS 

Google Scholar 
Kram, S. L. et al. Variable responses of temperate calcified and fleshy macroalgae to elevated pCO2 and warming. ICES J. Mar. Sci. 73, 693–703 (2016).Article 

Google Scholar 
Kübler, J. E., Johnston, A. M. & Raven, J. A. The effects of reduced and elevated CO2 and O2 on the seaweed Lomentaria articulata. Plant Cell Environ. 22, 1303–1310 (1999).Article 

Google Scholar 
van der Loos, L. M. et al. Responses of macroalgae to CO2 enrichment cannot be inferred solely from their inorganic carbon uptake strategy. Ecol. Evol. 9, 125–140 (2019).Article 

Google Scholar 
Cornwall, C. E. & Hurd, C. L. Variability in the benefits of ocean acidification to photosynthetic rates of macroalgae without CO2-concentrating mechanisms. Mar. Freshw. Res. 71, 275–280 (2019).Article 

Google Scholar 
Cornwall, C. E., Revill, A. T. & Hurd, C. L. High prevalence of diffusive uptake of CO2 by macroalgae in a temperate subtidal ecosystem. Photosynth. Res. 124, 181–190 (2015).
Article 
CAS 

Google Scholar 
Lovelock, C. E., Reef, R., Raven, J. A. & Pandolfi, J. M. Regional variation in δ13C of coral reef macroalgae. Limnol. Oceanogr. 65, 2291–2302 (2020).Article 
ADS 
CAS 

Google Scholar 
Fischer, G. & Wiencke, C. Stable carbon isotope composition, depth distribution and fate of macroalgae from the Antarctic Peninsula region. Polar. Biol. 12, 341–348 (1992).Article 

Google Scholar 
Stephens, T. A. & Hepburn, C. D. Mass-transfer gradients across kelp beds influence Macrocystis pyrifera growth over small spatial scales. Mar. Ecol. Prog. Ser. 515, 97–109 (2014).Article 
ADS 

Google Scholar 
Kregting, L. T., Hepburn, C. D. & Savidge, G. Seasonal differences in the effects of oscillatory and uni-directional flow on the growth and nitrate-uptake rates of juvenile Laminaria digitata (Phaeophyceae). J. Phycol. 51, 1116–1126 (2015).Article 
CAS 

Google Scholar 
Parker, H. S. Influence of relative water motion on the growth, ammonium uptake and carbon and nitrogen composition of Ulva lactuca (Chlorophyta). Mar. Biol. 63, 309–318 (1981).Article 
CAS 

Google Scholar 
Bergstrom, E. et al. Inorganic carbon uptake strategies in coralline algae: Plasticity across evolutionary lineages under ocean acidification and warming. Mar. Environ. Res. 161, 105107 (2020).Article 
CAS 

Google Scholar 
Maberly, S. C., Raven, J. A. & Johnston, A. M. Discrimination between C-12 and C-13 by marine plants. Oecologia 91, 481–492 (1992).Article 
ADS 
CAS 

Google Scholar 
Gattuso, J. P. et al. Package ‘Seacarb ’. Preprint at http://cran.r-project.org/package=seacarb (2015).Raven, J. A., Beardall, J. & Giordano, M. Energy costs of carbon dioxide concentrating mechanisms in aquatic organisms. Photosynth. Res. 121, 111–124 (2014).Article 
CAS 

Google Scholar 
Raven, J. A., Walker, D. I., Johnston, A. M., Handley, L. L. & Kübler, J. E. Implications of 13C natural abundance measurements for photosynthetic performance by marine macrophytes in their natural environment. Mar. Ecol. Prog. Ser. 123, 193–205 (1995).Article 
ADS 

Google Scholar 
Raven, J. A. Inorganic carbon acquisition by marine autotrophs. Adv. Bot. Res. 27, 85–209 (1997).Article 
CAS 

Google Scholar 
Fernández, P. A., Roleda, M. Y. & Hurd, C. L. Effects of ocean acidification on the photosynthetic performance, carbonic anhydrase activity and growth of the giant kelp Macrocystis pyrifera. Photosynth. Res. 124, 293–304 (2015).Article 

Google Scholar 
Bailly, J. & Coleman, J. R. Effect of CO(2) concentration on protein biosynthesis and carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 87, 833–840 (1988).Article 
CAS 

Google Scholar 
Dionisio-Sese, M. L., Fukuzawa, H. & Miyachi, S. Light-induced carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 94, 1103–1110 (1990).Article 
CAS 

Google Scholar 
Pollock, S. V., Colombo, S. L., Prout, D. L., Godfrey, A. C. & Moroney, J. V. Rubisco activase is required for optimal photosynthesis in the green alga Chlamydomonas reinhardtii in a low-CO2 atmosphere. Plant Physiol. 133, 1854–1861 (2003).Article 
CAS 

Google Scholar 
Carlberg, S., Axelsson, L., Larsson, C., Ryberg, H. & Uusitalo, J. Inducible CO2 concentrating mechanisms in green seaweeds I. Taxonomical and physiological aspects. In Current Research in Photosynthesis (ed. Baltscheffsky, M.) (Springer, 1990). https://doi.org/10.1007/978-94-009-0511-5_749.Chapter 

Google Scholar 
Wheeler, W. N. Effect of boundary-layer transport on the fixation of carbon by the giant-kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Johnston, A. M. & Raven, J. A. Effects of culture in high CO2 on the photosynthetic physiology of Fucus serratus. Br. J. Phycol. 25, 75–82 (1990).Article 

Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. Lond. 368, 20120442 (2013).Article 

Google Scholar 
Porter, E. T., Sanford, L. P. & Suttles, S. E. Gypsum dissolution is not a universal integrator of water motion. Limnol. Oceanogr. 45, 145–158 (2000).Article 
ADS 

Google Scholar 
Gerard, V. A. & Mann, K. H. Growth and production of Laminaria longicruris (Phaeophyta) populations exposed to different intensities of water movement. J. Phycol. 15, 33–41 (1979).Article 

Google Scholar 
Bivand, R., Keitt, T. & Rowlingson, B. Package ‘rgdal’. R Package https://doi.org/10.1353/lib.0.0050 (2016).Article 

Google Scholar 
LINZ. LINZ Data Service. https://data.linz.govt.nz/layer/50258-nz-coastlines-topo-150k/history/ Accessed July 2021 (2021).Kirk, J. T. Characteristics of the light field in highly turbid waters: A Monte Carlo study. Limnol. Oceanogr. 39, 702–706 (1994).Article 
ADS 

Google Scholar 
Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
Kohler, K. E. & Gill, S. M. Coral Point Count with Excel extensions (CPCe): A visual basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269 (2006).Article 
ADS 

Google Scholar 
Axelsson, L., Mercado, J. & Figueroa, F. Utilization of HCO3− at high ph by the brown macroalga laminaria saccharina. Eur. J. Phycol. 35, 53–59 (2000).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. Preprint at (2017).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).Article 
MathSciNet 
MATH 

Google Scholar  More

Asevedo, L. et al. Palynological analysis of dental calculus from Pleistocene proboscideans of southern Brazil: a new approach for paleodiet and paleoenvironmental reconstructions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 540, 109523 (2020).Article 

Google Scholar 
Cristiani, E. et al. Wild cereal grain consumption among Early Holocene foragers of the Balkans predates the arrival of agriculture. Elife 10, e72976 (2021).Article 
CAS 

Google Scholar 
Nava, A. et al. Multipronged dental analyses reveal dietary differences in last foragers and first farmers at Grotta Continenza, central Italy (15,500–7000 BP). Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Ottoni, C. et al. Tracking the transition to agriculture in Southern Europe through ancient DNA analysis of dental calculus. Proc. Natl. Acad. Sci. USA 118, e2102116118 (2021).Article 
CAS 

Google Scholar 
Cammidge, T. S., Kooyman, B. & Theodor, J. M. Diet reconstructions for end-Pleistocene Mammut americanum and Mammuthus based on comparative analysis of mesowear, microwear, and dental calculus in modern Loxodonta africana. Palaeogeogr. Palaeoclimatol. Palaeoecol. 538, 109403 (2020).Article 

Google Scholar 
de Oliveira, K. et al. From oral pathology to feeding ecology: the first dental calculus paleodiet study of a South American native megamammal. J. S. Am. Earth Sci. 109, 103281 (2021).Article 

Google Scholar 
Mothé, D. et al. The micro from mega: dental calculus description and the first record of fossilized oral bacteria from an extinct proboscidean. Int. J. Paleopathol. 33, 55–60 (2021).Article 

Google Scholar 
Eglinton, G. & Logan, G. A. Molecular preservation. Philosophical Transactions of the Royal Society of London. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 333, 315–328 (1991).CAS 

Google Scholar 
Romanowski, G., Lorenz, M. G. & Wackernagel, W. Adsorption of plasmid DNA to mineral surfaces and protection against Dnase I. Appl. Environ. Microbiol. 57, 1057–1061 (1991).Article 
CAS 

Google Scholar 
Milanesi, C. et al. Ultrastructural study of archaeological Vitis vinifera L. seeds using rapid-freeze fixation and substitution. Tissue Cell 41, 443–447 (2009).Article 
CAS 

Google Scholar 
Power, R. C., Salazar-García, D. C., Wittig, R. M., Freiberg, M., & Henry, A. G. Dental calculus evidence of Taï Forest chimpanzee plant consumption and life history transitions. Sci. Rep. 5, 15161 (2015).Goude, G. et al. A multidisciplinary approach to Neolithic life reconstruction. J. Archaeol. Method Theory 26, 537–560 (2019).Article 

Google Scholar 
Farrer, A. G. et al. Effectiveness of decontamination protocols when analyzing ancient DNA preserved in dental calculus. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Weyrich, L. S., Dobney, K. & Cooper, A. Ancient DNA analysis of dental calculus. J. Hum. Evol. 79, 119–124 (2015).Article 

Google Scholar 
Ozga, A. T. et al. Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus. Am. J. Phys. Anthropol. 160, 220–228 (2016).Article 

Google Scholar 
Mann, A. E. et al. Do I have something in my teeth? The trouble with genetic analyses of diet from archaeological dental calculus. Quat. Int. https://doi.org/10.1016/j.quaint.2020.11.019 (2020).Wright, S. L., Dobney, K. & Weyrich, L. S. Advancing and refining archaeological dental calculus research using multiomic frameworks. Sci. Technol. Archaeol. Res. 7, 13–30 (2021).
Google Scholar 
Sawafuji, R., Saso, A., Suda, W., Hattori, M. & Ueda, S. Ancient DNA analysis of food remains in human dental calculus from the Edo period, Japan. PLoS One 15, e0226654 (2020).Article 
CAS 

Google Scholar 
Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).Article 
CAS 

Google Scholar 
Ottoni, C. et al. Metagenomic analysis of dental calculus in ancient Egyptian baboons. Sci. Rep. 9, 1–10 (2019).Article 

Google Scholar 
Hollingsworth, P. M., Graham, S. W. & Little, D. P. Choosing and using a Plant DNA barcode. PLoS One 6, 1–13 (2011).Article 

Google Scholar 
Gismondi, A., Fanali, F., Labarga, J. M. M., Caiola, M. G. & Canini, A. Crocus sativus L. genomics and different DNA barcode applications. Plant Syst. Evol. 299, 1859–1863 (2013).Article 
CAS 

Google Scholar 
ICSN. The international code for starch nomenclature, accessed 15 September 2021; http://fossilfarm.org/ICSN/Code.html (2011).Gismondi, A. et al. Starch granules: a data collection of 40 food species. Plant Biosyst. 153, 273–279 (2019).Article 

Google Scholar 
Henry, A. G., Brooks, A. S. & Piperno, D. R. Plant foods and the dietary ecology of Neanderthals and early modern humans. J. Hum. Evol. 69, 44–54 (2014).Article 

Google Scholar 
PalDat. A palynological database (2000 onwards), accessed 19 January 2022; https://www.paldat.org/ (2019).Berglund, B. E. & Ralska-Jasiewiczowa, M. Pollen analysis and pollen diagrams. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 455–484 (Wiley, 1986).Faegri, K. & Iversen, J. Textbook of Pollen analysis, 4th edn (eds Faegri, K. et al.) (John Wiley and Sons-Chichester, 1989).Grímsson, F. et al. Fagaceae pollen from the early Cenozoic of West Greenland: revisiting Engler’s and Chaney’s Arcto-Tertiary hypotheses. Plant Syst. Evol. 301, 809–832 (2015).Article 

Google Scholar 
Denk, T. & Tekleva, M. V. Pollen morphology and ultrastructure of Quercus with focus on Group Ilex (= Quercus Subgenus Heterobalanus (Oerst.) Menitsky): Implications for oak systematics and evolution. Grana 53, 255–282 (2014).Article 

Google Scholar 
Grímsson, F. & Zetter, R. Combined LM and SEM study of the middle Miocene (Sarmatian) palynofora from the Lavanttal Basin, Austria: Part II. Pinophyta (Cupressaceae, Pinaceae and Sciadopityaceae). Grana 50, 262–310 (2011).Article 

Google Scholar 
Mohanty, R. P., Buchheim, M. A., Portman, R. & Levetin, E. Molecular and ultrastructural detection of plastids in Juniperus (Cupressaceae) pollen. Phytologia 98, 298–310 (2016).
Google Scholar 
Martin, A. C. & Harvey, W. J. The Global Pollen Project: a new tool for pollen identifcation and the dissemination of physical reference collections. Methods Ecol. Evol. 8, 892–897 (2017).Article 

Google Scholar 
Maciejewska-Rutkowska, I., Bocianowski, J. & Wrońska-Pilarek, D. Pollen morphology and variability of Polish native species from genus Salix L. PLoS One 16, e0243993 (2021).Article 
CAS 

Google Scholar 
Abreu, I., Costa, I., Oliveira, M., Cunha, M. & De Castro, R. Ultrastructure and germination of Vitis vinifera cv. Loureiro pollen. Protoplasma 228, 131–135 (2006).Article 
CAS 

Google Scholar 
Nagels, A. et al. Palynological diversity and major evolutionary trends in Cyperaceae. Plant Syst. Evol. 277, 117 (2009).Article 

Google Scholar 
El Ghazali, G. E. Pollen morphological studies in Amaranthaceae s. lat. (incl. Chenopodiaceae) and their taxonomic significance: a review. Grana 61, 1–7 (2022).Article 

Google Scholar 
Petraco, N., & Kubic, T. Color Atlas and Manual of Microscopy for Criminalists, Chemists, and Conservators (Boca Raton-CRC Press, 2003).D’Agostino, A. et al. Environmental implications and evidence of natural products from dental calculi of a Neolithic–Chalcolithic community (central Italy). Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Frangiote-Pallone, S. & de Souza, L. A. Pappus and cypsela ontogeny in Asteraceae: structural considerations of the tribal category. Rev. Mex. Biodivers. 85, 62–77 (2014).Article 

Google Scholar 
Eglinton, G., Gonzalez, A. G., Hamilton, R. J. & Raphael, R. A. Hydrocarbon constituents of the wax coatings of plant leaves: a taxonomic survey. Phytochemistry 1, 89–102 (1962).Article 
CAS 

Google Scholar 
Buckley, S. A., Stott, A. W. & Evershed, R. P. Studies of organic residues from ancient Egyptian mummies using high temperature-gas chromatography-mass spectrometry and sequential thermal desorption-gas chromatography-mass spectrometry and pyrolysis-gas chromatography-mass spectrometry. Analyst 124, 443–452 (1999).Article 
CAS 

Google Scholar 
Hardy, K. et al. Neanderthal medics? Evidence for food, cooking, and medicinal plants entrapped in dental calculus. Naturwissenschaften 99, 617–626 (2012).Article 
CAS 

Google Scholar 
Luong, S., Tocheri, M. W., Sutikna, T., Saptomo, E. W. & Roberts, R. G. Incorporating terpenes, monoterpenoids and alkanes into multiresidue organic biomarker analysis of archaeological stone artefacts from Liang Bua (Flores, Indonesia). J. Archaeol. Sci. Rep. 19, 189–199 (2018).
Google Scholar 
Luong, S. et al. Combined organic biomarker and use-wear analyses of stone artefacts from Liang Bua, Flores, Indonesia. Sci. Rep. 9, 1–17 (2019).Article 
CAS 

Google Scholar 
Dabney, J., Meyer, M. & Pääbo, S. Ancient DNA damage. Cold Spring Harb. Perspect. Biol. 5, a012567 (2013).Article 

Google Scholar 
Mann, A. E. et al. Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Sci. Rep. 8, 1–15 (2018).Article 

Google Scholar 
Horrocks, M., Nieuwoudt, M. K., Kinaston, R., Buckley, H. & Bedford, S. Microfossil and Fourier Transform InfraRed analyses of Lapita and post-Lapita human dental calculus from Vanuatu, Southwest Pacific. J. R. Soc. N. Z. 44, 17–33 (2014).Article 

Google Scholar 
Radini, A., Nikita, E., Buckley, S., Copeland, L. & Hardy, K. Beyond food: the multiple pathways for inclusion of materials into ancient dental calculus. Am. J. Phys. Anthropol. 162, 71–83 (2017).Article 

Google Scholar 
Henry, A. G. Other microparticles: volcanic glass, minerals, insect remains, feathers, and other plant parts. In Handbook for the Analysis of Micro-Particles in Archaeological Samples 289–295 (Springer, Cham, 2020).MacKenzie, L., Speller, C. F., Holst, M., Keefe, K., & Radini, A. Dental calculus in the industrial age: human dental calculus in the Post-Medieval period, a case study from industrial Manchester. Quat. Int. https://doi.org/10.1016/j.quaint.2021.09.020 (2021).Radini, A., & Nikita, E. Beyond dirty teeth: Integrating dental calculus studies with osteoarchaeological parameters. Quat. Int. https://doi.org/10.1016/j.quaint.2022.03.003 (2022).Dobney, K. & Brothwell, D. A scanning electron microscope study of archaeological dental calculus. In Scanning Electron Microscopy in Archaeology BAR International Series (ed. & Olsen S), vol. 452, pp. 372–385 (Oxford, UK: BAR, 1988).Henry, A. G. & Piperno, D. R. Using plant microfossils from dental calculus to recover human diet: a case study from Tell al-Raqā’i, Syria. J. Archaeol. Sci. 35, 1943–1950 (2008).Article 

Google Scholar 
Wesolowski, V., de Souza, S. M. F. M., Reinhard, K. J. & Ceccantini, G. Evaluating microfossil content of dental calculus from Brazilian sambaquis. J. Archaeol. Sci. 37, 1326–1338 (2010).Article 

Google Scholar 
González-Guarda, E. et al. Multiproxy evidence for leaf-browsing and closed habitats in extinct proboscideans (Mammalia, Proboscidea) from Central Chile. Proc. Natl. Acad. Sci. USA 115, 9258–9263 (2018).Article 

Google Scholar 
Radley, J. A. Starch and its Derivatives (Chapman and Hall, London, 1968).Power, R. C., Salazar-García, D. C., Wittig, R. M. & Henry, A. G. Assessing use and suitability of scanning electron microscopy in the analysis of micro remains in dental calculus. J. Archaeol. Sci. 49, 160–169 (2014).Article 
CAS 

Google Scholar 
Rottoli, M. & Castiglioni, E. Prehistory of plant growing and collecting in northern Italy, based on seed remains from the early Neolithic to the Chalcolithic (c. 5600–2100 cal BC). Veg. Hist. Archaeobot. 18, 91–103 (2009).Article 

Google Scholar 
Fiorentino, G. et al. Climate changes and human–environment interactions in the Apulia region of southeastern Italy during the Neolithic period. Holocene 23, 1297–1316 (2013).Article 

Google Scholar 
Rottoli, M., & Pessina, A. Neolithic agriculture in Italy: an update of archaeobotanical data with particular emphasis on northern settlements. In The Origins and Spread of Domestic Plants in Southwest Asia and Europe 157–170 (Routledge, 2016).Arobba, D., Panelli, C., Caramiello, R., Gabriele, M. & Maggi, R. Cereal remains, plant impressions and 14C direct dating from the Neolithic pottery of Arene Candide Cave (Finale Ligure, NW Italy). J. Archaeol. Sci. Rep. 12, 395–404 (2017).
Google Scholar 
Ucchesu, M., Sau, S. & Lugliè, C. Crop and wild plant exploitation in Italy during the Neolithic period: New data from Su Mulinu Mannu, Middle Neolithic site of Sardinia. J. Archaeol. Sci. Rep. 14, 1–11 (2017).
Google Scholar 
Scorrano, G. et al. Effect of Neolithic transition on an Italian community: Mora Cavorso (Jenne, Rome). Archaeol. Anthropol. Sci. 11, 1443–1459 (2019).Article 

Google Scholar 
De Angelis, F. et al. Exploring mobility in Italian Neolithic and Copper Age communities. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Oxilia, G. et al. Exploring late Paleolithic and Mesolithic diet in the Eastern Alpine region of Italy through multiple proxies. Am. J. Phys. Anthropol. 174, 232–253 (2021).Article 

Google Scholar 
Fahmy, A. G. E. Palaeoethnobotanical studies of the Neolithic settlement in Hidden Valley, Farafra Oasis, Egypt. Veg. Hist. Archaeobot. 10, 235–246 (2001).Article 

Google Scholar 
Reed, K. From the field to the hearth: plant remains from Neolithic Croatia (ca. 6000–4000 cal bc). Veg. Hist. Archaeobot. 24, 601–619 (2015).Article 

Google Scholar 
Lucarini, G., Radini, A., Barton, H. & Barker, G. The exploitation of wild plants in Neolithic North Africa. Use-wear and residue analysis on non-knapped stone tools from the Haua Fteah cave, Cyrenaica, Libya. Quat. Int. 410, 77–92 (2016).Article 

Google Scholar 
García-Granero, J. J., Urem-Kotsou, D., Bogaard, A. & Kotsos, S. Cooking plant foods in the northern Aegean: microbotanical evidence from Neolithic Stavroupoli (Thessaloniki, Greece). Quat. Int. 496, 140–151 (2018).Article 

Google Scholar 
Bouby, L. et al. Early Neolithic (ca. 5850-4500 cal BC) agricultural diffusion in the Western Mediterranean: an update of archaeobotanical data in SW France. PLoS One 15, e0230731 (2020).Article 
CAS 

Google Scholar 
Delhon, C., Binder, D., Verdin, P. & Mazuy, A. Phytoliths as a seasonality indicator? The example of the Neolithic site of Pendimoun, south-eastern France. Veg. Hist. Archaeobot. 29, 229–240 (2020).Article 

Google Scholar 
Lu, H. et al. Phytoliths analysis for the discrimination of foxtail millet (Setaria italica) and common millet (Panicum miliaceum). PLoS One 4, e4448 (2009).Article 

Google Scholar 
Celant, A. Indagini paleobotaniche su macroresti vegetali dai siti neo-eneolitici del territorio di Roma. In Roma prima del mito. Abitati e necropoli dal Neolitico alla prima età dei Metalli nel territorio di Roma (VI-III millennio a.C.) (eds Anzidei, A. P. & Carboni, C.) Vol. 2, 687–704 (Archaeopress Archaeol., 2020).Carra, M. et al. Plant foods in the Late Palaeolithic of Southern Italy and Sicily: Integrating carpological and dental calculus evidence. Quat. Int. https://doi.org/10.1016/j.quaint.2022.06.007 (2022) .Bednar, G. E. et al. Starch and fiber fractions in selected food and feed ingredients affect their small intestinal digestibility and fermentability and their large bowel fermentability in vitro in a canine model. J. Nutr. 131, 276–286 (2001).Article 
CAS 

Google Scholar 
Hoover, R., Hughes, T., Chung, H. J. & Liu, Q. Composition, molecular structure, properties, and modification of pulse starches: a review. Food Res. Int. 43, 399–413 (2010).Article 
CAS 

Google Scholar 
Wani, I. A. et al. Isolation, composition, and physicochemical properties of starch from legumes: a review. Starch‐Stärke 68, 834–845 (2016).Article 
CAS 

Google Scholar 
Tayade, R., Kulkarni, K. P., Jo, H., Song, J. T. & Lee, J. D. Insight into the prospects for the improvement of seed starch in legume—a review. Front. Plant Sci. 10, 1213 (2019).Article 

Google Scholar 
Stafford, H. A. Distribution of tartaric acid in the leaves of certain angiosperms. Am. J. Bot. 46, 347–352 (1959).Article 
CAS 

Google Scholar 
DeBolt, S., Cook, D. R. & Ford, C. M. L-Tartaric acid synthesis from vitamin C in higher plants. Proc. Natl. Acad. Sci. USA 103, 5608–5613 (2006).Article 
CAS 

Google Scholar 
Fernández-García, E. et al. Carotenoids bioavailability from foods: from plant pigments to efficient biological activities. Food Res. Int. 46, 438–450 (2012).Article 

Google Scholar 
Gliszczyńska, A. & Brodelius, P. E. Sesquiterpene coumarins. Phytochem. Rev. 11, 77–96 (2012).Article 

Google Scholar 
Eerkens, J. The preservation and identification of Piñon resins by GC‐MS in pottery from the Western Great Basin. Archaeometry 44, 95–105 (2002).Article 
CAS 

Google Scholar 
Barnard, H. et al. Mixed results of seven methods for organic residue analysis applied to one vessel with the residue of a known foodstuff. J. Archaeol. Sci. 34, 28–37 (2007).Article 

Google Scholar 
Wysocka, W., Przybył, A. & Brukwicki, T. The structure of angustifoline, an alkaloid of Lupinus angustifolius, in solution. Monatsh. Chem. 125, 1267–1272 (1994).Article 
CAS 

Google Scholar 
Ohmiya, S., Saito, K., & Murakoshi, I. Lupine alkaloids. In The alkaloids: Chemistry and Pharmacology Vol. 47, 1–114) (Academic Press, 1995).Mancinotti, D., Frick, K. M. & Geu-Flores, F. Biosynthesis of quinolizidine alkaloids in lupins: mechanistic considerations and prospects for pathway elucidation. Nat. Prod. Rep. 39, 1423–1437 (2022).Article 
CAS 

Google Scholar 
Silvestri, L., Achino, K. F., Gatta, M., Rolfo, M. F. & Salari, L. Grotta Mora Cavorso: physical, material and symbolic boundaries of life and death practices in a Neolithic cave of central Italy. Quat. Int. 539, 29–38 (2020).Article 

Google Scholar 
Steele, V. J., Stern, B. & Stott, A. W. Olive oil or lard?: distinguishing plant oils from animal fats in the archaeological record of the eastern Mediterranean using gas chromatography/combustion/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 3478–3484 (2010).Article 
CAS 

Google Scholar 
Buonasera, T. Investigating the presence of ancient absorbed organic residues in groundstone using GC–MS and other analytical techniques: a residue study of several prehistoric milling tools from central California. J. Archaeol. Sci. 34, 1379–1390 (2007).Article 

Google Scholar 
Luong, S. et al. Development and application of a comprehensive analytical workflow for the quantification of non-volatile low molecular weight lipids on archaeological stone tools. Anal. Met. 9, 4349–4362 (2017).Article 
CAS 

Google Scholar 
Baeten, J., Jervis, B., De Vos, D. & Waelkens, M. Molecular evidence for the mixing of Meat, Fish and Vegetables in Anglo‐Saxon coarseware from Hamwic, UK. Archaeometry 55, 1150–1174 (2013).Article 
CAS 

Google Scholar 
Evershed, R. P. Chemical composition of a bog body adipocere. Archaeometry 34, 253–265 (1992).Article 
CAS 

Google Scholar 
Garnier, N., Bernal-Casasola, D., Driard, C. & Pinto, I. V. Looking for ancient fish products through invisible biomolecular residues in the roman production vats from the Atlantic. Coast J. Marit. Archaeol. 13, 285–328 (2018).Article 

Google Scholar 
Copley, M. S., Bland, H. A., Rose, P., Horton, M. & Evershed, R. P. Gas chromatographic, mass spectrometric and stable carbon isotopic investigations of organic residues of plant oils and animal fats employed as illuminants in archaeological lamps from Egypt. Analyst 130, 860–871 (2005).Article 
CAS 

Google Scholar 
Reber, E. A. & Hart, J. P. Pine resins and pottery sealing: analysis of absorbed and visible pottery residues from central New York State. Archaeometry 50, 999–1017 (2008).Article 
CAS 

Google Scholar 
Simopoulos, A. P. Omega‐3 fatty acids in wild plants, nuts and seeds. Asia Pac. J. Clin. Nutr. 11, S163–S173 (2002).Article 
CAS 

Google Scholar 
Harris, W. S. et al. Stearidonic acid-enriched soybean oil increased the omega-3 index, an emerging cardiovascular risk marker. Lipids 43, 805–811 (2008).Article 
CAS 

Google Scholar 
Gismondi, A., Rolfo, M. F., Leonardi, D., Rickards, O. & Canini, A. Identification of ancient Olea europaea L. and Cornus mas L. seeds by DNA barcoding. C. R. Biol. 335, 472–479 (2012).Article 
CAS 

Google Scholar 
Steffens, W. & Wirth, M. Freshwater fish-an important source of n-3 polyunsaturated fatty acids: a review. Fish. Aquat. Sci. 13, 5–16 (2005).
Google Scholar 
Swanson, D., Block, R. & Mousa, S. A. Omega-3 fatty acids EPA and DHA: health benefits throughout life. Adv. Nutr. 3, 1–7 (2012).Article 
CAS 

Google Scholar 
Wiermann, R., & Gubatz, S. Pollen wall and sporopollenin. In International Review of Cytology 35–72 (Academic Press, 1992).Cristiani, E., Radini, A., Edinborough, M. & Borić, D. Dental calculus reveals Mesolithic foragers in the Balkans consumed domesticated plant foods. Proc. Natl. Acad. Sci. USA 113, 10298–10303 (2016).Article 
CAS 

Google Scholar 
Hardy, K. et al. Dental calculus reveals potential respiratory irritants and ingestion of essential plant-based nutrients at Lower Palaeolithic Qesem Cave Israel. Quat. Int. 398, 129–135 (2016).Article 

Google Scholar 
Radini, A. et al. Neanderthals, trees and dental calculus: new evidence from El Sidrón. Antiquity 90, 290–301 (2016).Article 

Google Scholar 
Lippi, M. M., Pisaneschi, L., Sarti, L., Lari, M. & Moggi-Cecchi, J. Insights into the Copper-Bronze Age diet in central Italy: plant microremains in dental calculus from Grotta dello Scoglietto (Southern Tuscany, Italy). J. Archaeol. Sci. Rep. 15, 30–39 (2017).
Google Scholar 
Modi, A. et al. Combined metagenomic and archaeobotanical analyses on human dental calculus: a cross-section of lifestyle conditions in a Copper Age population of central Italy. Quat. Int. https://doi.org/10.1016/j.quaint.2021.12.003 (2021).Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. https://doi.org/10.1038/ng.2906 (2014).Lieverse, A. R. Diet and the aetiology of dental calculus. Int. J. Osteoarchaeol. 9, 219–232 (1999).Article 

Google Scholar 
Lukacs, J. R. & Largaespada, L. L. Explaining sex differences in dental caries prevalence: saliva, hormones, and “life‐history” etiologies. Am. J. Hum. Biol. 18, 540–555 (2006).Article 

Google Scholar 
Moore, P. D., Webb, J. A., & Collison, M. E. Pollen Analysis (Blackwell Scientific Publications, 1991).Borojević, K., Forenbaher, S., Kaiser, T. & Berna, F. Plant use at Grapčeva cave and in the eastern Adriatic Neolithic. J. Field Archaeol. 33, 279–303 (2008).Article 

Google Scholar 
Martin, L., Jacomet, S. & Tiebault, S. Plant economy during the Neolithic in a mountain context: the case of “Le Chenet des Pierres” in the French Alps (Bozel-Savoie, France). Veg. Hist. Archaeobot. 17, 113–122 (2008).Article 

Google Scholar 
Moser, D., Di Pasquale, G., Scarciglia, F. & Nelle, O. Holocene mountain forest changes in central Mediterranean: soil charcoal data from the Sila Massif (Calabria, southern Italy). Quat. Int. 457, 113–130 (2017).Article 

Google Scholar 
D’Agostino, A. et al. Pollen record of the Late Pleistocene–Holocene stratigraphic sequence and current plant biodiversity from Grotta Mora Cavorso (Simbruini Mountains, Central Italy). Ecol. Evol. 12, e9486 (2022).Radaeski, J. N., Bauermann, S. G. & Pereira, A. B. Poaceae pollen from Southern Brazil: distinguishing grasslands (campos) from forests by analyzing a diverse range of Poaceae species. Front. Plant Sci. 7, 1833 (2016).Article 

Google Scholar 
Turner, S. D. & Brown, A. G. Vitis pollen dispersal in and from organic vineyards: I. Pollen trap and soil pollen data. Rev. Palaeobot. Palynol. 129, 117–132 (2004).Article 

Google Scholar 
Marvelli, S., De’Siena, S., Rizzoli, E. & Marchesini, M. The origin of grapevine cultivation in Italy: the archaeobotanical evidence. Ann. Bot. 3, 155–163 (2013).
Google Scholar 
Riaz, S. et al. Genetic diversity analysis of cultivated and wild grapevine (Vitis vinifera L.) accessions around the Mediterranean basin and Central Asia. BMC Plant Biol. 18, 1–14 (2018).Article 

Google Scholar 
Arnold, C., Gillet, F., & Gobat, J. M. Situation de la vigne sauvage Vitis vinifera subsp. silvestris en Europe. Vitis 159–170 (1998).Terral, J. F. et al. Evolution and history of grapevine (Vitis vinifera) under domestication: new morphometric perspectives to understand seed domestication syndrome and reveal origins of ancient European cultivars. Ann. Bot. 105, 443–455 (2010).Article 

Google Scholar 
Buckley, S., Usai, D., Jakob, T., Radini, A. & Hardy, K. Dental calculus reveals unique insights into food items, cooking and plant processing in prehistoric central Sudan. PLoS One 9, e100808 (2014).Article 

Google Scholar 
Petrov, P. R., Popova, E. D. & Zlatanova, D. P. Niche partitioning among the red fox Vulpes vulpes (L.), stone marten Martes foina (Erxleben) and pine marten Martes martes (L.) in two mountains in Bulgaria. Acta Zool. Bulg. 68, 375–390 (2016).
Google Scholar 
Mikrjukov, K. A. Revision of genera and species composition of lower Centroheliozoa. II. Family Raphidiophryidae n. tam. Arch. Protistenkd. 147, 205–212 (1996).Article 

Google Scholar 
Cavalier-Smith, T. & von der Heyden, S. Molecular phylogeny, scale evolution and taxonomy of centrohelid heliozoa. Mol. Phylogen. Evol. 44, 1186–1203 (2007).Article 
CAS 

Google Scholar 
Mertens, K. N., Rengefors, K., Moestrup, Ø. & Ellegaard, M. A review of recent freshwater dinoflagellate cysts: taxonomy, phylogeny, ecology and palaeocology. Phycologia 51, 612–619 (2012).Article 

Google Scholar 
Zlatogursky, V. V. Raphidiophrys heterophryoidea sp. nov. (Centrohelida: Raphidiophryidae), the first heliozoan species with a combination of siliceous and organic skeletal elements. Eur. J. Protist. 48, 9–16 (2012).Article 

Google Scholar 
Prokina, K. I. & Mylnikov, A. P. Centrohelid heliozoans from freshwater habitats of different types of South Patagonia and Tierra del Fuego, Chile. Inland Water Biol. 12, 10–20 (2019).Article 

Google Scholar 
Siemensma, F. J. & Roijackers, M. M. A study of new and little- known acanthocystid heliozoans, and a proposed division of the genus Acanthocystis (Actinopoda, Heliozoea). Arch. Protistenkd. 135, 197 (1988a).Article 

Google Scholar 
Siemensma, F. J. & Roijackers, M. M. The genus Raphidiophrys (Actinopoda, Heliozoea): scale morphology and species distinctions. Arch. Protistenkd. 136 237–248 (1988).Taylor, W.D. & Sanders, R. W. PROTOZOA. In Ecology and Classification of North American Freshwater Invertebrates (eds Thorp, J. H. & Covich, A. P.) 43–96 (Academic Press, 2001).Manconi, R., & Pronzato, R. Global diversity of sponges (Porifera: Spongillina) in freshwater. In Freshwater Animal Diversity Assessment 27–33 (Springer, Dordrecht, 2007).Malone, C. & Stoddart, S. The neolithic site of San Marco, Gubbio (Perugia), Umbria: survey and excavation 1985–7. Pap. Br. Sch. Rome 60, 1–69 (1992).Article 

Google Scholar 
Rottoli, M. La Marmotta, Anguillara Sabazia (RM). Scavi 1989. Analisi paletnobotaniche: prime risultanze, Appendice 1 M.A. In La Marmotta” (Anguillara Sabazia, RM). Scavi 1989. Un abitato perilacustre di età neolitica (eds. Fugazzola Delpino, M. A., D’Eugenio, G. & Pessina, A.) Bullettino di Paletnologia Italiana 84, 305–315 (1993).Pini, R. Late Neolithic vegetation history at the pile‐dwelling site of Palù di Livenza (northeastern Italy). J. Quat. Sci. 19, 769–781 (2004).Article 

Google Scholar 
Tinner, W. et al. Holocene environmental and climatic changes at Gorgo Basso, a coastal lake in southern Sicily, Italy. Quat. Sci. Rev. 28, 1498–1510 (2009).Article 

Google Scholar 
Bieniek, A. Archaeobotanical analysis of some early Neolithic settlements in the Kujawy region, central Poland, with potential plant gathering activities emphasized. Veg. Hist. Archaeobot. 11, 33–40 (2002).Article 

Google Scholar 
Tolar, T., Jacomet, S., Velušček, A. & Čufar, K. Plant economy at a late Neolithic lake dwelling site in Slovenia at the time of the Alpine Iceman. Veg. Hist. Archaeobot. 20, 207–222 (2011).Article 

Google Scholar 
D’Agostino, A. et al. Investigating plant micro-remains embedded in dental calculus of the Phoenician inhabitants of Motya (Sicily, Italy). Plants 9, 1395 (2020).Article 

Google Scholar 
Mercader, J. et al. Exaggerated expectations in ancient starch research and the need for new taphonomic and authenticity criteria. Facets 3, 777–798 (2018).Article 

Google Scholar 
Adojoh, O., Fabienne, M., Duller, R. & Osterloff, P. Taxonomy and phytoecology of palynomorphs and non-pollen palynomorphs: a refined compendium from the West Africa Margin. Biodivers. Int. J. 3, 188–200 (2019).Article 

Google Scholar 
Knapp, M., Clarke, A. C., Horsburgh, K. A. & Matisoo-Smith, E. A. Setting the stage building and working in an ancient DNA laboratory. Ann. Anat. 194, 3 (2012).Article 
CAS 

Google Scholar 
Knapp, M., Lalueza-Fox, C. & Hofreiter, M. Re-inventing ancient human DNA. Investig. Genet. 6, 1 (2015).Article 

Google Scholar 
Gismondi, A. et al. Grapevine carpological remains revealed the existence of a Neolithic domesticated Vitis vinifera L. specimen containing ancient DNA partially preserved in modern ecotypes. J. Archaeol. Sci. 69, 75–84 (2016).Article 
CAS 

Google Scholar 
Llamas, B. et al. From the field to the laboratory: controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. Sci. Technol. Archaeol. Res. 3, 1–14 (2017).Le Moyne, C. & Crowther, A. Effects of chemical pre-treatments on modified starch granules: recommendations for dental calculus decalcification for ancient starch research. J. Archaeol. Sci. Rep. 35, 102762 (2021).
Google Scholar 
Rolfo, M. F., Achino, K. F., Fusco, I., Salari, L. & Silvestri, L. Reassessing human occupation patterns in the inner central Apennines in prehistory: the case-study of Grotta Mora Cavorso. J. Archaeol. Sci. Rep. 7, 358–367 (2016).
Google Scholar  More

IPBES. Global assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2019).Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
ADS 
CAS 

Google Scholar 
IPCC. Summary for Policymakers. in Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022).Travis, J. M. J. Climate change and habitat destruction: a deadly anthropogenic cocktail. P. R. Soc. B. 270, 467–473 (2003).Article 
CAS 

Google Scholar 
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. P. R. Soc. B. 285, 20180792 (2018).Article 

Google Scholar 
Anderson, K. J., Allen, A. P., Gillooly, J. F. & Brown, J. H. Temperature-dependence of biomass accumulation rates during secondary succession. Ecol. Lett. 9, 673–682 (2006).Article 

Google Scholar 
Fridley, J. D. & Wright, J. P. Temperature accelerates the rate fields become forests. Proc. Natl Acad. Sci. USA 115, 4702–4706 (2018).Article 
ADS 
CAS 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
ADS 
CAS 

Google Scholar 
Auffret, A. G., Kimberley, A., Plue, J. & Waldén, E. Super-regional land-use change and effects on the grassland specialist flora. Nat. Commun. 9, 3464 (2018).Article 
ADS 

Google Scholar 
Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non-native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).Article 
ADS 

Google Scholar 
Hill, M. O. Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods Ecol. Evol. 3, 195–205 (2012).Article 

Google Scholar 
Isaac, N. J. B., Strien, A. J., van, August, T. A., Zeeuw, M. Pde & Roy, D. B. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).Article 

Google Scholar 
Tyler, T., Herbertsson, L., Olofsson, J. & Olsson, P. A. Ecological indicator and traits values for Swedish vascular plants. Ecol. Indic. 120, 106923 (2021).Article 
CAS 

Google Scholar 
Jiang, M., Bullock, J. M. & Hooftman, D. A. P. Mapping ecosystem service and biodiversity changes over 70 years in a rural English county. J. Appl. Ecol. 50, 841–850 (2013).Article 

Google Scholar 
IPCC. Summary for Policymakers. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2021).Van Calster, H. et al. Unexpectedly high 20th century floristic losses in a rural landscape in northern France. J. Ecol. 96, 927–936 (2008).Article 

Google Scholar 
Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).Article 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 1–10 (2019).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 4455 (2019).Article 
ADS 

Google Scholar 
Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).Article 
ADS 

Google Scholar 
Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).Article 

Google Scholar 
Cannone, N. & Pignatti, S. Ecological responses of plant species and communities to climate warming: upward shift or range filling processes? Climatic Change 123, 201–214 (2014).Article 
ADS 

Google Scholar 
Wiens, J. J. Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species. PLOS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Hill, M. O. & Preston, C. D. Disappearance of boreal plants in southern Britain: habitat loss or climate change? Biol. J. Linn. Soc. 115, 598–610 (2015).Article 

Google Scholar 
Lynn, J. S., Klanderud, K., Telford, R. J., Goldberg, D. E. & Vandvik, V. Macroecological context predicts species’ responses to climate warming. Glob. Change Biol. 27, 2088–2101 (2021).Article 
ADS 
CAS 

Google Scholar 
Liu, D. et al. Species selection under long-term experimental warming and drought explained by climatic distributions. N. Phytol. 217, 1494–1506 (2018).Article 

Google Scholar 
Buitenwerf, R., Sandel, B., Normand, S., Mimet, A. & Svenning, J.-C. Land surface greening suggests vigorous woody regrowth throughout European semi-natural vegetation. Glob. Change Biol. 24, 5789–5801 (2018).Article 

Google Scholar 
Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 
ADS 

Google Scholar 
De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).Article 

Google Scholar 
Ash, J. D., Givnish, T. J. & Waller, D. M. Tracking lags in historical plant species’ shifts in relation to regional climate change. Glob. Change Biol. 23, 1305–1315 (2017).Article 
ADS 

Google Scholar 
Savage, J. & Vellend, M. Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming. Ecography 38, 546–555 (2015).Article 

Google Scholar 
Gerstner, K., Dormann, C. F., Stein, A., Manceur, A. M. & Seppelt, R. Effects of land use on plant diversity—a global meta-analysis. J. Appl. Ecol. 51, 1690–1700 (2014).Article 

Google Scholar 
Kempel, A. et al. Nationwide revisitation reveals thousands of local extinctions across the ranges of 713 threatened and rare plant species. Conserv. Lett. 13, e12749 (2020).Article 

Google Scholar 
Bilz, M., Kell, S. P., Maxted, N. & Lansdown, R. V. European Red List of Vascular Plants (Publications Office of the EU, 2011).Timmermann, A., Damgaard, C., Strandberg, M. T. & Svenning, J.-C. Pervasive early 21st-century vegetation changes across Danish semi-natural ecosystems: more losers than winners and a shift towards competitive, tall-growing species. J. Appl. Ecol. 52, 21–30 (2015).Article 

Google Scholar 
Staude, I. R. et al. Directional turnover towards larger-ranged plants over time and across habitats. Ecol. Lett. 25, 466–482 (2022).Article 

Google Scholar 
Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
Christiansen, D. M., Iversen, L. L., Ehrlén, J. & Hylander, K. Changes in forest structure drive temperature preferences of boreal understorey plant communities. J. Ecol. 110, 631–643 (2022).Article 

Google Scholar 
Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540, 266–269 (2016).Article 
ADS 
CAS 

Google Scholar 
Duprè, C. et al. Changes in species richness and composition in European acidic grasslands over the past 70 years: the contribution of cumulative atmospheric nitrogen deposition. Glob. Change Biol. 16, 344–357 (2010).Article 
ADS 

Google Scholar 
Tyler, T. et al. Climate warming and land‐use changes drive broad‐scale floristic changes in Southern Sweden. Glob. Change Biol. 24, 2607–2621 (2018).Article 
ADS 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231 (2018).Article 
ADS 
CAS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 12283 (2016).Article 
ADS 
CAS 

Google Scholar 
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).Article 
ADS 
CAS 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 

Google Scholar 
Plue, J. et al. Buffering effects of soil seed banks on plant community composition in response to land use and climate. Glob. Ecol. Biogeogr. 30, 128–139 (2021).Article 

Google Scholar 
Honnay, O. & Bossuyt, B. Prolonged clonal growth: escape route or route to extinction? Oikos 108, 427–432 (2005).Article 

Google Scholar 
Ozinga, W. A. et al. Dispersal failure contributes to plant losses in NW Europe. Ecol. Lett. 12, 66–74 (2009).Article 

Google Scholar 
Svenning, J.-C., Normand, S. & Skov, F. Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31, 316–326 (2008).Article 

Google Scholar 
Lenoir, J., Gégout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).Article 
ADS 
CAS 

Google Scholar 
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).Article 
ADS 
CAS 

Google Scholar 
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).Article 
CAS 

Google Scholar 
Garrido, P. et al. Experimental rewilding may restore abandoned wood-pastures if policy allows. Ambio 50, 101–112 (2021).Article 

Google Scholar 
Kowalczyk, R., Kamiński, T. & Borowik, T. Do large herbivores maintain open habitats in temperate forests? For. Ecol. Manag. 494, 119310 (2021).Article 

Google Scholar 
Auffret, A. G., Schmucki, R., Reimark, J. & Cousins, S. A. O. Grazing networks provide useful functional connectivity for plants in fragmented systems. J. Veg. Sci. 23, 970–977 (2012).Article 

Google Scholar 
Fricke, E. C., Ordonez, A., Rogers, H. S. & Svenning, J.-C. The effects of defaunation on plants’ capacity to track climate change. Science 375, 210–214 (2022).Article 
ADS 
CAS 

Google Scholar 
Blomgren, E., Falk, E. & Herloff, B. Bohusläns Flora (Föreningen Bohusläns Flora, 2011).Fries, H. Göteborgs och Bohus Läns Fanerogamer och Ormbunkar (Elanders Boktryckeri, 1945).Lidberg, R. & Lindström, H. Medelpads Flora (The vascular plants of Medelpad) (SBF Förlaget, 2010).Sterner, R. Flora der insel Öland Vol. IX (Almqvist & Wiksells, 1938).Almquist, E. Upplands vegetation och flora. Acta Phytogeogr. Suec. 1, 1–622 (1929).
Google Scholar 
Jonsell, L. Upplands Flora (SBF Förlaget, 2010).Maad, J., Sundberg, S., Stolpe, P. & Jonsell, L. Floraförändringar i Uppland under 1900-talet—en analys från Projekt Upplands flora [Floristic changes during the 20th century in Uppland, east central Sweden; with English summary]. Sven. Botanisk Tidskr. 103, 67–104 (2009).
Google Scholar 
Auffret, A. G. et al. HistMapR: Rapid digitization of historical land-use maps in R. Methods Ecol. Evol. 8, 1453–1457 (2017).Article 

Google Scholar 
August, T. et al. sparta: Trend analysis for unstructured data. R package version 0.1.44 (2018).Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).Article 
ADS 
CAS 

Google Scholar 
Redhead, J. W. et al. Potential landscape-scale pollinator networks across Great Britain: structure, stability and influence of agricultural land cover. Ecol. Lett. 21, 1821–1832 (2018).Article 

Google Scholar 
Gillings, S. et al. Breeding and wintering bird distributions in Britain and Ireland from citizen science bird atlases. Glob. Ecol. Biogeogr. 28, 866–874 (2019).Article 

Google Scholar 
Stroh, P. A., Walker, K. J., Humphrey, T. A., Pescott, O. L. & Burkmar, R. J. Plant Atlas 2020: Mapping Changes in the Distribution of the British and Irish Flora (Princeton, planned publication date: 21/03/2023).Pearce-Higgins, J. W., Ausden, M. A., Beale, C. M., Oliver, T. H. & Crick, H. Q. P. Research on the assessment of risks & opportunities for species in England as a result of climate change – NECR175. Natural England Commissioned Reports Vol. 175 (2015).R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Telfer, M. G., Preston, C. D. & Rothery, P. A general method for measuring relative change in range size from biological atlas data. Biol. Conserv. 107, 99–109 (2002).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. M. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lme4 (2014).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2009).Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Borcard, D. & Legendre, P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68 (2002).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community ecology package. R package version 2.3-5. http://CRAN.R-project.org/package=vegan (2016).Meineri, E. & Hylander, K. Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography 40, 1003–1013 (2017).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).Article 
ADS 

Google Scholar 
Breheny, P. & Burchett, W. Visualization of regression models using visreg. R. J. 9, 57–71 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 2.5-8. http://CRAN.R-project.org/package=raster (2016). More

Orr, M. R. & Smith, T. B. Ecology and speciation. Trends Ecol. Evol. 13, 502–506 (1998).Article 
CAS 

Google Scholar 
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).
Google Scholar 
Gillespie, R. G. Adaptive radiation: Convergence and non-equilibrium. Curr. Biol. 23, R71–R74 (2013).Article 
CAS 

Google Scholar 
Price, T. Speciation in Birds (Roberts and Company Publishers, 2008).
Google Scholar 
Schluter, D. Evidence for ecological speciation and its alternative. Science 323, 737–741 (2009).Article 
ADS 
CAS 

Google Scholar 
Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

Google Scholar 
Jønsson, K. A. et al. Ecological and evolutionary determinants for the adaptive radiation of the Madagascan vangas. Proc. Natl. Acad. Sci. 109, 6620–6625 (2012).Article 
ADS 

Google Scholar 
Wiens, J. J. Speciation and ecology revisited: Phylogenetic niche conservatism and the origin of species. Evolution 58, 193–197 (2004).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

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

Google Scholar 
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).Article 
ADS 
CAS 

Google Scholar 
Winger, B. M., Barker, F. K. & Ree, R. H. Temperate origins of long-distance seasonal migration in New World songbirds. Proc. Natl. Acad. Sci. 111, 12115–12120 (2014).Article 
ADS 
CAS 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: Evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
Gómez, C., Tenorio, E. A., Montoya, P. & Cadena, C. D. Niche-tracking migrants and niche-switching residents: Evolution of climatic niches in New World warblers (Parulidae). Proc. R. Soc. B Biol. Sci. 283, 20152458 (2016).Article 

Google Scholar 
Menchaca, A., Arteaga, M. C., Medellin, R. A. & Jones, G. Conservation units and historical matrilineal structure in the tequila bat (Leptonycteris yerbabuenae). Glob. Ecol. Conserv. 23, e01164 (2020).Article 

Google Scholar 
Medellín, R. A. et al. Follow me: Foraging distances of Leptonycteris yerbabuenae (Chiroptera: Phyllostomidae) in Sonora determined by fluorescent powder. J. Mammal. 99, 306–311 (2018).Article 

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

Google Scholar 
Martínez-Meyer, E., Peterson, A. T. & Hargrove, W. W. Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity. Glob. Ecol. Biogeogr. 13, 305–314 (2004).Article 

Google Scholar 
Soto-Centeno, J. A. & Steadman, D. W. Fossils reject climate change as the cause of extinction of Caribbean bats. Sci. Rep. 5, 7971 (2015).Article 
ADS 
CAS 

Google Scholar 
Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, 2000).Book 

Google Scholar 
Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).Article 
CAS 

Google Scholar 
Pahad, G., Montgelard, C. & Jansen van Vuuren, B. Phylogeography and niche modelling: Reciprocal enlightenment. Mammalia 84, 10–25 (2019).Article 

Google Scholar 
Flanders, J. et al. Phylogeography of the greater horseshoe bat, Rhinolophus ferrumequinum: Contrasting results from mitochondrial and microsatellite data. Mol. Ecol. 18, 306–318 (2009).Article 
CAS 

Google Scholar 
Machado, A. F. et al. Integrating phylogeography and ecological niche modelling to test diversification hypotheses using a Neotropical rodent. Evol. Ecol. 33, 111–148 (2019).Article 

Google Scholar 
Kalkvik, H. M., Stout, I. J., Doonan, T. J. & Parkinson, C. L. Investigating niche and lineage diversification in widely distributed taxa: Phylogeography and ecological niche modeling of the Peromyscus maniculatus species group. Ecography 35, 54–64 (2012).Article 

Google Scholar 
Wang, Y. et al. Ring distribution patterns—diversification or speciation? Comparative phylogeography of two small mammals in the mountains surrounding the Sichuan Basin. Mol. Ecol. 30, 2641–2658 (2021).Article 

Google Scholar 
Soto-Centeno, J. A., Barrow, L. N., Allen, J. M. & Reed, D. L. Reevaluation of a classic phylogeographic barrier: New techniques reveal the influence of microgeographic climate variation on population divergence. Ecol. Evol. 3, 1603–1613 (2013).Article 

Google Scholar 
Amador, L. I., Moyers Arévalo, R. L., Almeida, F. C., Catalano, S. A. & Giannini, N. P. Bat systematics in the light of unconstrained analyses of a comprehensive molecular supermatrix. J. Mamm. Evol. 25, 37–70 (2018).Article 

Google Scholar 
Rojas, D., Warsi, O. M. & Dávalos, L. M. Bats (Chiroptera: Noctilionoidea) challenge a recent origin of extant neotropical diversity. Syst. Biol. 65, 432–448 (2016).Article 

Google Scholar 
Shi, J. J. & Rabosky, D. L. Speciation dynamics during the global radiation of extant bats. Evolution 69, 1528–1545 (2015).Article 

Google Scholar 
Dumont, E. R. et al. Morphological innovation, diversification and invasion of a new adaptive zone. Proc. Biol. Sci. 279, 1797–1805 (2012).
Google Scholar 
Leiser-Miller, L. B. & Santana, S. E. Morphological diversity in the sensory system of phyllostomid bats: Implications for acoustic and dietary ecology. Funct. Ecol. 34, 1416–1427 (2020).Article 

Google Scholar 
Hedrick, B. P. & Dumont, E. R. Putting the leaf-nosed bats in context: A geometric morphometric analysis of three of the largest families of bats. J. Mammal. 99, 1042–1054 (2018).Article 

Google Scholar 
Clare, E. L. Cryptic species? Patterns of maternal and paternal gene flow in eight neotropical bats. PLoS One 6, e21460 (2011).Article 
ADS 
CAS 

Google Scholar 
Chaverri, G. et al. Unveiling the hidden bat diversity of a neotropical montane forest. PLoS One 11, e0162712 (2016).Article 

Google Scholar 
Calahorra-Oliart, A., Ospina-Garcés, S. M. & León-Paniagua, L. Cryptic species in Glossophaga soricina (Chiroptera: Phyllostomidae): Do morphological data support molecular evidence?. J. Mammal. 102, 54–68 (2021).Article 

Google Scholar 
Lim, B. K., Loureiro, L. O. & Garbino, G. S. T. Cryptic diversity and range extension in the big-eyed bat genus Chiroderma (Chiroptera, Phyllostomidae). Zookeys 918, 41–63 (2020).Article 

Google Scholar 
Loureiro, L. O., Engstrom, M., Lim, B., González, C. L. & Juste, J. Not all Molossus are created equal: Genetic variation in the mastiff bat reveals diversity masked by conservative morphology. Acta Chiropterologica 21, 51 (2019).Article 

Google Scholar 
Morales, A., Villalobos, F., Velazco, P. M., Simmons, N. B. & Piñero, D. Environmental niche drives genetic and morphometric structure in a widespread bat. J. Biogeogr. 43, 1057–1068 (2016).Article 

Google Scholar 
Hedrick, B. P. et al. Morphological diversification under high integration in a hyper diverse mammal clade. J. Mamm. Evol. 27, 563–575 (2020).Article 

Google Scholar 
Morales, A. E. & Carstens, B. C. Evidence that myotis lucifugus “subspecies” are five nonsister species, despite gene flow. Syst. Biol. 67, 756–769 (2018).Article 

Google Scholar 
Simmons, N. B. & Cirranello, A. L. Bat species of the world: A taxonomic and geographic database. https://batnames.org.Russell, A. L., Pinzari, C. A., Vonhof, M. J., Olival, K. J. & Bonaccorso, F. J. Two tickets to paradise: Multiple dispersal events in the founding of hoary bat populations in Hawai’i. PLoS One 10, 1–13 (2015).
Google Scholar 
Shump, K. A. & Shump, A. U. Lasiurus cinereus. Mamm. Species 185, 1–5 (1982).
Google Scholar 
Ziegler, A. C., Howarth, F. G. & Simmons, N. B. A second endemic land mammal for the Hawaiian Islands: A new genus and species of fossil bat (Chiroptera: Vespertilionidae). Am. Museum Novit. 1–52 (2016).Bonaccorso, F. J. & McGuire, L. P. Modeling the colonization of Hawaii by hoary bats (Lasiurus cinereus). In Bat Evolution, Ecology, and Conservation (eds Adams, R. A. & Pedersen, S. C.) 187–205 (Springer, 2013).Chapter 

Google Scholar 
Baird, A. B. et al. Molecular systematic revision of tree bats (Lasiurini): Doubling the native mammals of the Hawaiian Islands. J. Mammal. 96, 1255–1274 (2015).Article 

Google Scholar 
Jacobs, D. S. Morphological divergence in an insular bat, Lasiurus cinereus semotus. Funct. Ecol. 10, 622–630 (1996).Article 

Google Scholar 
Baird, A. B. et al. Nuclear and mtDNA phylogenetic analyses clarify the evolutionary history of two species of native Hawaiian bats and the taxonomy of Lasiurini (Mammalia: Chiroptera). PLoS One 12, e0186085 (2017).Article 

Google Scholar 
Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808 (2002).Article 
ADS 
CAS 

Google Scholar 
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).Article 

Google Scholar 
Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).Article 

Google Scholar 
Espíndola, A. et al. Identifying cryptic diversity with predictive phylogeography. Proc. R. Soc. B Biol. Sci. 283, 20161529 (2016).Article 

Google Scholar 
Padial, J. M., Miralles, A., De la Riva, I. & Vences, M. The integrative future of taxonomy. Front. Zool. 7, 1–14 (2010).Article 

Google Scholar 
Fujita, M. K., Leaché, A. D., Burbrink, F. T., McGuire, J. A. & Moritz, C. Coalescent-based species delimitation in an integrative taxonomy. Trends Ecol. Evol. 27, 480–488 (2012).Article 

Google Scholar 
Solari, S., Sotero-Caio, C. G. & Baker, R. J. Advances in systematics of bats: Towards a consensus on species delimitation and classifications through integrative taxonomy. J. Mammal. 100, 838–851 (2018).Article 

Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).
Google Scholar 
Morales, A. E., De-la-Mora, M. & Piñero, D. Spatial and environmental factors predict skull variation and genetic structure in the cosmopolitan bat Tadarida brasiliensis. J. Biogeogr. 45, 1529–1540 (2018).Article 

Google Scholar 
Pavan, A. C. & Marroig, G. Integrating multiple evidences in taxonomy: Species diversity and phylogeny of mustached bats (Mormoopidae: Pteronotus). Mol. Phylogenet. Evol. 103, 184–198 (2016).Article 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
CAS 

Google Scholar 
Robinson, D. & Foulds, L. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).Article 
MathSciNet 
CAS 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).Article 
ADS 
CAS 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).Article 
CAS 

Google Scholar 
Kapli, P. et al. Multi-rate Poisson Tree Processes for single-locus species delimitation under Maximum Likelihood and Markov Chain Monte Carlo. Bioinformatics 33, 1630–1638 (2017).CAS 

Google Scholar 
Yang, Z. & Rannala, B. Unguided species delimitation using DNA sequence data from multiple loci. Mol. Biol. Evol. 31, 3125–3135 (2014).Article 
CAS 

Google Scholar 
Flouri, T., Jiao, X., Rannala, B. & Yang, Z. Species tree inference with BPP using genomic sequences and the multispecies coalescent. Mol. Biol. Evol. 35, 2585–2593 (2018).Article 
CAS 

Google Scholar 
Van Buuren, S. & Groothuis-Oudshoorn, K. Multivariate imputation by chained equations. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Penone, C. et al. Imputation of missing data in life-history trait datasets: Which approach performs the best?. Methods Ecol. Evol. 5, 961–970 (2014).Article 

Google Scholar 
Berner, D. Size correction in biology: How reliable are approaches based on (common) principal component analysis?. Oecologia 166, 961–971 (2011).Article 
ADS 

Google Scholar 
Simmons, N. B. Order Chiroptera. In Mammal Species of the World: A Taxonomic and Geographic Reference (eds Wilson, D. E. & Reeder, D. M.) 312–529 (The John Hopkins University Press, 2005).
Google Scholar 
Wilson, D. E. & Mittermeier, R. A. Handbook of the Mammals of the World. Vol. 9. Bats (Lynx Editions, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (2022).Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Book 
MATH 

Google Scholar 
Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).Book 
MATH 

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

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling (2022).Barker, B. S., Rodríguez-Robles, J. A. & Cook, J. A. Climate as a driver of tropical insular diversity: Comparative phylogeography of two ecologically distinctive frogs in Puerto Rico. Ecography 38, 769–781 (2015).Article 

Google Scholar 
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).Article 

Google Scholar 
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).Article 

Google Scholar 
Izenman, A. J. Linear discriminant analysis. in Modern Multivariate Statistical Techniques 237–280 (2013).Lever, J., Krzywinski, M. & Altman, N. Points of significance: Principal component analysis. Nat. Methods 14, 641–642 (2017).Article 
CAS 

Google Scholar 
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: Insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).Article 

Google Scholar 
Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
Liu, C., Wolter, C., Xian, W. & Jeschke, J. M. Most invasive species largely conserve their climatic niche. Proc. Natl. Acad. Sci. 117, 23643–23651 (2020).Article 
ADS 
CAS 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar  More

Empirical dataWe applied our theory to two datasets (Table 1): the plant survey dataset and the biodiversity-manipulated experiment dataset. The plant survey dataset contains nine sites of long-term grassland experiments across the United States (see also Hallett et al.10, and Zhao et al.23). Five of nine sites are from the Long Term Ecological Research (LTER) network (see Table 1). Plant abundances were measured either as biomass or as percent cover. In percent-cover cases, summed values can exceed 100% due to vertically overlapping canopies. All sites were sampled annually and were spatially replicated. We only used data of the plant survey dataset from unmanipulated control plots. Methods for data collection were constant over time.The biodiversity-manipulated experimental dataset comprises two long-term grassland experiments, BigBio and BioCON, at the Cedar Creek Ecosystem Science Reserve. Both experiments directly manipulated plant species number (1, 2, 4, 8, 16 for BigBio; and 1, 4, 9, 16 for BioCON). BioCON also contains different treatment levels for nitrogen and atmospheric CO2, but here only data from the ambient CO2 and ambient N treatments were used. We excluded plots with only one species. BigBio comprises 125 plots over 17 years, and BioCON comprises 59 plots over 22 years (Table 1).TheoryLet xi(t) denote the biomass of species i = 1, …, S at time t = 1, …, t and let μi = mean (xi (t)), σi = ({{mbox{sd}}})(xi (t)), and ({v}_{i}={sigma }_{i}^{2}) be the mean, standard deviation and variance of species i, computed through time. Let vij = cov (({x}_{i}left(tright),, {x}_{j}left(tright))) be the covariance, through time, of the dynamics of species i and j. Let xtot (left(tright)={sum }_{i}{x}_{i}(t)), ({mu }_{{{mbox{tot}}}}={sum }_{i}{mu }_{i}), ({v}_{{{mbox{tot}}}}={sum }_{i,j}{v}_{{ij}}), and ({{{{{{rm{sigma }}}}}}}_{{{{{{rm{tot}}}}}}}=sqrt{{v}_{{{{{{rm{tot}}}}}}}}). When population time series are uncorrelated, ({v}_{{{{{{rm{tot}}}}}}}={sum }_{i}{v}_{i}).As defined previously10,15, community stability is the inverse coefficient of variation of ({x}_{{{mbox{tot}}}}left(tright)), ({S}_{{{{{{rm{com}}}}}}}={mu }_{{{{{{rm{tot}}}}}}}/{sigma }_{{{{{{rm{tot}}}}}}}). Population stability is the inverse of weighted-average population variability9, ({sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}{{CV}}_{i}={sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}frac{{sigma }_{i}}{{mu }_{i}}={sum }_{i}frac{{sigma }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}), i.e, ({S}_{{pop}}={mu }_{{{{{{rm{tot}}}}}}}/{sum }_{i}{sigma }_{i}). The ratio of community stability over population stability is the Loreau-de Mazancourt asynchrony index14, Φ = ({sum }_{i}{sigma }_{i}/{sigma }_{{{{{{rm{tot}}}}}}}), so that$${S}_{{{{{{rm{com}}}}}}}=varPhi {S}_{{{{{{rm{pop}}}}}}}.$$
(1)
Now we suppose a hypothetical community with the same species-level variances and means as the original community but with species covariances equal to zero. Then, (1) becomes Scom_ip = (SAE)Spop, where ({S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{v}_{i}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}) is the value of community stability in the case of uncorrelated or independent populations and SAE is the component of Φ due to statistical averaging (here, “ip” stands for “independent populations”). The equation Scom_ip = (SAE)Spop can be interpreted as a definition of SAE. We then have$$SAE=frac{{S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}}=frac{{sum }_{i}{sigma }_{i}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}.$$
(2)
The compensatory effect is then the rest of Φ, i.e.,$$CPE=frac{{S}_{{{{{{rm{com}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}times SAE}=frac{{sum }_{i}{sigma }_{i}}{{sigma }_{{{{{{rm{tot}}}}}}}left({sum }_{i}{sigma }_{i}/sqrt{{sum }_{i}{sigma }_{i}^{2}}right)}=frac{sqrt{{sum }_{i}{sigma }_{i}^{2}}}{{sigma }_{{{{{{rm{tot}}}}}}}}.$$
(3)
Considering the classic variance ratio ({{{{{rm{varphi }}}}}}=frac{{V}_{{{{{{rm{tot}}}}}}}}{{sum }_{i}{V}_{i}}=frac{{sigma }_{{{{{{rm{tot}}}}}}}^{2}}{{sum }_{i}{sigma }_{i}^{2}}), our CPE is (1/sqrt{varphi }). Values CPE  > 1 (respectively, More

Evans, K., Franco, J. & De Scurrah, M. M. Distribution of species of potato cyst-nematodes in South America. Nematologica 21, 365–369. https://doi.org/10.1163/187529275×00103 (1975).Article 

Google Scholar 
Plantard, O. et al. Origin and genetic diversity of Western European populations of the potato cyst nematode (Globodera pallida) inferred from mitochondrial sequences and microsatellite loci. Mol. Ecol. 17, 2208–2218. https://doi.org/10.1111/j.1365-294X.2008.03718.x (2008).Article 
CAS 

Google Scholar 
Price, J. A., Coyne, D., Blok, V. C. & Jones, J. T. Potato cyst nematodes Globodera rostochiensis and G. pallida. Mol. Plant Pathol. 22, 495–507. https://doi.org/10.1111/mpp.13047 (2021).Article 
CAS 

Google Scholar 
CABI. Globodera rostochiensis (yellow potato cyst nematode). https://www.cabi.org/isc/datasheet/27034 (2021).CABI. Globodera pallida (white potato cyst nematode). https://www.cabi.org/isc/datasheet/27033 (2021).Ruthes, A. C. & Dahlin, P. The impact of management strategies on the development and status of potato cyst nematode populations in Switzerland: An overview from 1958 to present. Plant Dis. 106, 1096–1104. https://doi.org/10.1094/pdis-04-21-0800-sr (2021).Article 

Google Scholar 
Minnis, S. T. et al. Potato cyst nematodes in England and Wales—Occurrence and distribution. Ann. Appl. Biol. 140, 187–195. https://doi.org/10.1111/j.1744-7348.2002.tb00172.x (2002).Article 

Google Scholar 
Djebroune, A. et al. Integrative morphometric and molecular approach to update the impact and distribution of potato cyst nematodes Globodera rostochiensis and Globodera pallida (Tylenchida: Heteroderidae) in Algeria. Pathogens 10, 216. https://doi.org/10.3390/pathogens10020216 (2021).Article 

Google Scholar 
Vallejo, D. et al. Occurrence and molecular characterization of cyst nematode species (Globodera spp.) associated with potato crops in Colombia. PLoS One 16, e0241256. https://doi.org/10.1371/journal.pone.0241256 (2021).Article 
CAS 

Google Scholar 
Hajjaji, A., Mhand, R. A., Rhallabi, N. & Mellouki, F. First report of morphological and molecular characterization of Moroccan populations of Globodera pallida. J. Nematol. 53, e2021-07. https://doi.org/10.21307/jofnem-2021-007 (2021).Article 
CAS 

Google Scholar 
Camacho, M. J. et al. Potato cyst nematodes: Geographical distribution, phylogenetic relationships and integrated pest management outcomes in Portugal. Front. Plant Sci. 11, 9. https://doi.org/10.3389/fpls.2020.606178 (2020).Article 

Google Scholar 
Handayani, N. D. et al. Distribution, DNA barcoding and genetic diversity of potato cyst nematodes in Indonesia. Eur. J. Plant Pathol. 158, 363–380. https://doi.org/10.1007/s10658-020-02078-7 (2020).Article 
CAS 

Google Scholar 
Bairwa, A. et al. Morphological and molecular characterization of potato cyst nematode populations from the Nilgiris. Indian J. Agric. Sci. 90, 273–278 (2020).Article 
CAS 

Google Scholar 
Mburu, H. et al. Potato cyst nematodes: A new threat to potato production in East Africa. Front. Plant Sci. 11, 13. https://doi.org/10.3389/fpls.2020.00670 (2020).Article 

Google Scholar 
Altas, A., Evlice, E., Ozer, G., Dababat, A. & Imren, M. Identification, distribution and genetic diversity of Globodera rostochiensis (Wollenweber, 1923) Skarbilovich, 1959 (Tylenchida: Heteroderidae) populations in Turkey. Turk. Entomol. Derg. Turk. J. Entomol. 44, 385–397. https://doi.org/10.16970/entoted.740223 (2020).Article 

Google Scholar 
Dandurand, L.-M., Zasada, I. A., Wang, X. & Mimee, B. Current status of potato cyst nematodes in North America. Annu. Rev. Phytopathol. 57, 117–133. https://doi.org/10.1146/annurev-phyto-082718-100254 (2019).Article 
CAS 

Google Scholar 
Blacket, M. J. et al. Molecular assessment of the introduction and spread of potato cyst nematode, Globodera rostochiensis, in Victoria, Australia. Phytopathology 109, 659–669. https://doi.org/10.1094/phyto-06-18-0206-r (2019).Article 
CAS 

Google Scholar 
Sullivan, M. J., Inserra, R. N., Franco, J., Moreno-Leheude, I. & Greco, N. Potato cyst nematodes: Plant host status and their regulatory impact. Nematropica 37, 193–201 (2007).
Google Scholar 
Hodda, M. & Cook, D. C. Economic impact from unrestricted spread of potato cyst nematodes in Australia. Phytopathology 99, 1387–1393. https://doi.org/10.1094/phyto-99-12-1387 (2009).Article 
CAS 

Google Scholar 
Koirala, S., Watson, P., McIntosh, C. S. & Dandurand, L. M. Economic impact of Globodera pallida on the Idaho economy. Am. J. Potato Res. 97, 214–220. https://doi.org/10.1007/s12230-020-09768-2 (2020).Article 

Google Scholar 
Trudgill, D. L., Elliott, M. J., Evans, K. & Phillips, M. S. The white potato cyst nematode (Globodera pallida)—A critical analysis of the threat in Britain. Ann. Appl. Biol. 143, 73–80. https://doi.org/10.1111/j.1744-7348.2003.tb00271.x (2003).Article 

Google Scholar 
Duan, Y. X. Plant Nematology (Science Press, 2011).
Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644. https://doi.org/10.5194/hess-11-1633-2007 (2007).Article 
ADS 

Google Scholar 
Li, J. Suitable Risk Assessment for Six Potential Invasive Nematodes in China, Master thesis (Jilin Agriculture University, 2008).
Google Scholar 
Contina, J. B., Dandurand, L. M. & Knudsen, G. R. A spatiotemporal analysis and dispersal patterns of the potato cyst nematode Globodera pallida in Idaho. Phytopathology 110, 379–392. https://doi.org/10.1094/phyto-04-19-0113-r (2020).Article 
CAS 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models with Applications in R (Cambridge University Press, 2017). https://doi.org/10.1017/9781139028271.Book 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning 83. https://doi.org/10.1145/1015330.1015412 (Association for Computing Machinery, 2004).Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008).Article 

Google Scholar 
Wan, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19, 2072–2082. https://doi.org/10.1016/S2095-3119(19)62840-6 (2020).Article 
CAS 

Google Scholar 
Midmore, D. J. Potato production in the tropics. In The Potato Crop: The Scientific Basis for Improvement 728–793 (Springer Netherlands, 1992).Chapter 

Google Scholar 
Naika, S., de Jeude, J. V. L., de Goffau, M., Hilmi, M. & van Dam, B. Cultivation of Tomato: Production, Processing and Marketing (Digigrafi, 2005).
Google Scholar 
Chapman, M. A. Eggplant breeding and improvement for future climates. In Genomic Designing of Climate-Smart Vegetable Crops 257–276 (Springer, 2020).Chapter 

Google Scholar 
Management, D. o. C. List of National Agricultural Plant Quarantine Pests Distribution Administrative Areas. https://www.moa.gov.cn/nybgb/2019/201906/201907/t20190701_6320036.htm (Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2021).Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2011).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).Article 

Google Scholar 
Foot, M. A. The Ecology of Globodera pallida (Stone) Mulvey & Stone (Nematoda, Heteroderidae) at Pukekohe, New Zealand, Doctoral thesis (The University of Auckland, 1978).
Google Scholar 
Phillips, S. J., Dudík, M., & Schapire, R. E. Maxent software for modeling species niches and distributions (Version 3.4.1) [Internet]. http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 24-10-2020.Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Wan, J., Wang, R., Ren, Y. & McKirdy, S. Potential distribution and the risks of Bactericera cockerelli and its associated plant pathogen Candidatus Liberibacter solanacearum for global potato production. Insects 11, 298. https://doi.org/10.3390/insects11050298 (2020).Article 

Google Scholar 
Cobos, M. E., Peterson, A. T., Barve, N. & Osorio-Olvera, L. kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7, e6281. https://doi.org/10.7717/peerj.6281 (2019).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. Accessed 30-10-2020.Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Model. 213, 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008 (2008).Article 

Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. https://doi.org/10.1890/10-1171.1 (2011).Article 

Google Scholar 
ESRI. ArcGIS Desktop: Release 10 (Version 10.4.1) (Environmental Systems Research Institute). Accessed 24-10-2020.Kong, W., Li, X. & Zou, H. Optimizing MaxEnt model in the prediction of species distribution. J. Appl. Ecol. 30, 2116–2128. https://doi.org/10.13287/j.1001-9332.201906.029 (2019).Article 

Google Scholar 
Fischer, G. et al. Global Agro-ecological Zones (GAEZ v3.0). http://pure.iiasa.ac.at/id/eprint/13290/ (2012).Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
da Silva, J. C. P., de Medeiros, F. H. V. & Campos, V. P. Building soil suppressiveness against plant-parasitic nematodes. Biocontrol Sci. Technol. 28, 423–445. https://doi.org/10.1080/09583157.2018.1460316 (2018).Article 

Google Scholar 
Kim, E., Seo, Y., Kim, Y. S., Park, Y. & Kim, Y. H. Effects of soil textures on infectivity of root-knot nematodes on carrot. Plant Pathol. J. 33, 66–74. https://doi.org/10.5423/ppj.Oa.07.2016.0155 (2017).Article 
CAS 

Google Scholar 
Duyck, P.-F. et al. Niche partitioning based on soil type and climate at the landscape scale in a community of plant-feeding nematodes. Soil Biol. Biochem. 44, 49–55. https://doi.org/10.1016/j.soilbio.2011.09.014 (2012).Article 
CAS 

Google Scholar 
Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. & ReşitAkçakaya, H. Combining static and dynamic variables in species distribution models under climate change. Methods Ecol. Evol. 3, 349–357. https://doi.org/10.1111/j.2041-210X.2011.00157.x (2012).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Multimodel inference understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).Article 
MathSciNet 

Google Scholar 
Din, A. U. et al. The impact of COVID-19 on the food supply chain and the role of e-commerce for food purchasing. Sustainability 14, 3074. https://doi.org/10.3390/su14053074 (2022).Article 
CAS 

Google Scholar 
Lang, T. & McKee, M. The reinvasion of Ukraine threatens global food supplies. BMJ https://doi.org/10.1136/bmj.o676,o676,10.1136/bmj.o676 (2022).Article 

Google Scholar 
Hijmans, R. J. Global distribution of the potato crop. Am. J. Potato Res. 78, 403–412. https://doi.org/10.1007/bf02896371 (2001).Article 

Google Scholar 
Motti, R. The Solanaceae family: Botanical features and diversity. In The Wild Solanums Genomes 1–9 (Springer International Publishing, 2021).
Google Scholar 
Chytrý, M. et al. Projecting trends in plant invasions in Europe under different scenarios of future land-use change. Glob. Ecol. Biogeogr. 21, 75–87. https://doi.org/10.1111/j.1466-8238.2010.00573.x (2012).Article 

Google Scholar 
Lin, W., Cheng, X. & Xu, R. Impact of different economic factors on biological invasions on the global scale. PLoS One 6, e18797. https://doi.org/10.1371/journal.pone.0018797 (2011).Article 
ADS 
CAS 

Google Scholar 
Jones, L. M. et al. Climate change is predicted to alter the current pest status of Globodera pallida and G. rostochiensis in the United Kingdom. Glob. Change Biol. 23, 4497–4507. https://doi.org/10.1111/gcb.13676 (2017).Article 
ADS 
MathSciNet 

Google Scholar 
Kaczmarek, A., MacKenzie, K., Kettle, H. & Blok, V. C. Influence of soil temperature on Globodera rostochiensis and Globodera pallida. Phytopathol. Mediterr. 53, 396–405. https://doi.org/10.14601/Phytopathol_Mediterr-13512 (2014).Article 
CAS 

Google Scholar 
Hearne, R., Lettice, E. P. & Jones, P. W. Interspecific and intraspecific competition in the potato cyst nematodes Globodera pallida and G. rostochiensis. Nematology 19, 463–475. https://doi.org/10.1163/15685411-00003061 (2017).Article 
CAS 

Google Scholar 
Skelsey, P., Kettle, H., Mackenzie, K. & Blok, V. Potential impacts of climate change on the threat of potato cyst nematode species in Great Britain. Plant. Pathol. 67, 909–919. https://doi.org/10.1111/ppa.12807 (2018).Article 

Google Scholar 
Carlton, J. & Ruiz, G. M. Invasive Species: Vectors and Management Strategies (Island Press, 2003).
Google Scholar 
Singh, S. K., Paini, D. R., Ash, G. J. & Hodda, M. Prioritising plant-parasitic nematode species biosecurity risks using self organising maps. Biol. Invasions 16, 1515–1530. https://doi.org/10.1007/s10530-013-0588-7 (2014).Article 

Google Scholar 
Jiang, D., Chen, S., Hao, M. M., Fu, J. Y. & Ding, F. Y. Mapping the Potential global codling moth (Cydia pomonella L.) distribution based on a machine learning method. Sci. Rep. 8, 8. https://doi.org/10.1038/s41598-018-31478-3 (2018).Article 
CAS 

Google Scholar 
Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66. https://doi.org/10.1016/j.tree.2012.07.013 (2013).Article 

Google Scholar 
Ravichandra, N. Nematodes of quarantine importance. In Horticultural Nematology 369–385 (Springer, 2014).
Google Scholar  More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).Article 

Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang. 2, 121–124 (2012).Article 
ADS 

Google Scholar 
Princé, K. & Zuckerberg, B. Climate change in our backyards: The reshuffling of North America’s winter bird communities. Glob. Change Biol. 21, 572–585 (2015).Article 
ADS 

Google Scholar 
Brotons, L., Jiguet, F., Herando, S. & Lehikoinen, A. Bird communities and climate change. In Effects of Climate Change on Birds (eds Dunn, P. O. & Møller, A. P.) 221–235 (Oxford University Press, 2019).Chapter 

Google Scholar 
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).Article 

Google Scholar 
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. Birds are tracking climate warming, but not fast enough. Proc. R. Soc. B Biol. Sci. 275, 2743–2748 (2008).Article 

Google Scholar 
Lehikoinen, A. et al. Wintering bird communities are tracking climate change faster than breeding communities. J. Anim. Ecol. 90, 1085–1095 (2021).Article 

Google Scholar 
McNaughton, S. J. Diversity and stability of ecological communities: A comment on the role of empiricism in ecology. Am. Nat. 111, 515–525 (1977).Article 

Google Scholar 
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science (80-. ) 294, 804–808 (2001).Article 
ADS 
CAS 

Google Scholar 
Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).Article 

Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).Article 

Google Scholar 
Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’?. Trends Ecol. Evol. 30, 503–506 (2015).Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).Article 
CAS 

Google Scholar 
Oksanen, J. et al. Community ecology package vegan, R package version 2.0-7 (2013).Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package (2014).García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl. Acad. Sci. U. S. A. 115, 8400–8405 (2018).Article 
ADS 

Google Scholar 
De Boeck, H. J. et al. Patterns and drivers of biodiversity-stability relationships under climate extremes. J. Ecol. 106, 890–902 (2018).Article 

Google Scholar 
Fridley, J. D. et al. The invasion paradox: Reconciling pattern and process in species invasions. Ecology 88, 3–17 (2007).Article 
CAS 

Google Scholar 
Elton, C. S. The Ecology of Invasions by Plants and Animals (Methuen, 1958).Book 

Google Scholar 
Pigot, A. L., Trisos, C. H. & Tobias, J. A. Functional traits reveal the expansion and packing of ecological niche space underlying an elevational diversity gradient in passerine birds. Proc. R. Soc. B Biol. Sci. 283, 20152013 (2016).Article 

Google Scholar 
Pellissier, V., Barnagaud, J. Y., Kissling, W. D., Şekercioǧlu, Ç. H. & Svenning, J. C. Niche packing and expansion account for species richness–productivity relationships in global bird assemblages. Glob. Ecol. Biogeogr. 27, 604–615 (2018).Article 

Google Scholar 
Schipper, A. M. et al. Contrasting changes in the abundance and diversity of North American bird assemblages from 1971 to 2010. Glob. Change Biol. 22, 3948–3959 (2016).Article 
ADS 

Google Scholar 
Jarzyna, M. A. & Jetz, W. A near half-century of temporal change in different facets of avian diversity. Glob. Change Biol. 23, 2999–3011 (2017).Article 
ADS 

Google Scholar 
Catano, C. P., Fristoe, T. S., LaManna, J. A. & Myers, J. A. Local species diversity, β-diversity and climate influence the regional stability of bird biomass across North America. Proc. R. Soc. B Biol. Sci. 287, 20192520 (2020).Article 

Google Scholar 
Wang, S. et al. An invariability-area relationship sheds new light on the spatial scaling of ecological stability. Nat. Commun. 8, 1–8 (2017).ADS 

Google Scholar 
Pimm, S. L. & Redfearn, A. The variability of population densities. Nature 334, 613–614 (1988).Article 
ADS 

Google Scholar 
Santangeli, A. & Lehikoinen, A. Are winter and breeding bird communities able to track rapid climate change? Lessons from the high North. Divers. Distrib. 23, 308–316 (2017).Article 

Google Scholar 
Sauer, J. R. et al. The first 50 years of the North American Breeding Bird Survey. Condor 119, 576–593 (2017).Article 

Google Scholar 
Meehan, T. D., Michel, N. L. & Rue, H. Spatial modeling of Audubon Christmas Bird Counts reveals fine-scale patterns and drivers of relative abundance trends. Ecosphere 10, e020707 (2019).Article 
ADS 

Google Scholar 
Meller, K., Piha, M., Vähätalo, A. V. & Lehikoinen, A. A positive relationship between spring temperature and productivity in 20 songbird species in the boreal zone. Oecologia 186, 883–893 (2018).Article 
ADS 

Google Scholar 
Lefcheck, J. S. & Duffy, J. E. Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers. Ecology 96, 2973–2983 (2015).Article 

Google Scholar 
Alerstam, T. & Högstedt, G. Bird migration and reproduction in relation to habitats for survival and breeding. Scand. J. Ornithol. 13, 25–37 (1982).
Google Scholar 
Dingle, H. Migration: The Biology of Life on the Move (Oxford University Press, 1996).
Google Scholar 
Jones, P. D. et al. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res. Atmos. https://doi.org/10.1029/2011JD017139 (2012).Article 
ADS 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).Article 

Google Scholar 
Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).Article 

Google Scholar 
Thébault, E. & Loreau, M. Trophic interactions and the relationship between species diversity and ecosystem stability. Am. Nat. 166, E95–E114 (2005).Article 

Google Scholar 
Kokkoris, G. D., Jansen, V. A. A., Loreau, M. & Troumbis, A. Y. Variability in interaction strength and implications for biodiversity. J. Anim. Ecol. 71, 362–371 (2002).Article 

Google Scholar 
Vázquez, D. P. & Simberloff, D. Ecological specialization and susceptibility to disturbance: Conjectures and refutations. Am. Nat. 159, 606–623 (2002).Article 

Google Scholar 
Carvalheiro, L. G. et al. The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecol. Lett. 17, 1389–1399 (2014).Article 

Google Scholar 
Morris, R. J., Lewis, O. T. & Godfray, H. C. J. Experimental evidence for apparent competition in a tropical forest food web. Nature 428, 310–313 (2004).Article 
ADS 
CAS 

Google Scholar 
Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14, 483–488 (1999).Article 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science (80-. ) 345, 401–406 (2014).Article 
ADS 
CAS 

Google Scholar 
Lindström, Å., Green, M., Paulson, G., Smith, H. G. & Devictor, V. Rapid changes in bird community composition at multiple temporal and spatial scales in response to recent climate change. Ecography (Cop.) 36, 313–322 (2013).Article 

Google Scholar 
Pearce-Higgins, J. W., Eglington, S. M., Martay, B. & Chamberlain, D. E. Drivers of climate change impacts on bird communities. J. Anim. Ecol. 84, 943–954 (2015).Article 

Google Scholar 
Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl. Acad. Sci. 114, 12976–12981 (2017).Article 
ADS 
CAS 

Google Scholar 
Pollock, H. S., Brawn, J. D. & Cheviron, Z. A. Heat tolerances of temperate and tropical birds and their implications for susceptibility to climate warming. Funct. Ecol. https://doi.org/10.1111/1365-2435.13693 (2020).Article 

Google Scholar 
Wu, J. X., Wilsey, C. B., Taylor, L. & Schuurman, G. W. Projected avifaunal responses to climate change across the U.S. National Park System. PLoS ONE 13, 1–18 (2018).
Google Scholar 
Root, T. Environmental factors associated with avian distributional boundaries. J. Biogeogr. 15, 489 (1988).Article 

Google Scholar 
Zuckerberg, B. et al. Climatic constraints on wintering bird distributions are modified by urbanization and weather. J. Anim. Ecol. 80, 403–413 (2011).Article 

Google Scholar 
Newton, I. The Migration Ecology of Birds (Academic Press Inc., 2008).
Google Scholar 
La Sorte, F. A., Johnston, A. & Ault, T. R. Global trends in the frequency and duration of temperature extremes. Clim. Change 166, 1–14 (2021).Article 
ADS 

Google Scholar 
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).Article 
ADS 

Google Scholar 
Jiguet, F., Brotons, L. & Devictor, V. Community responses to extreme climatic conditions. Curr. Zool. 57, 406–413 (2011).Article 

Google Scholar 
Tayleur, C. et al. Swedish birds are tracking temperature but not rainfall: Evidence from a decade of abundance changes. Glob. Ecol. Biogeogr. 24, 859–872 (2015).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Indicators of transitions in biological systems. Ecol. Lett. 21, 905–919 (2018).Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
ADS 
CAS 

Google Scholar 
Fischer, J. et al. Functional richness and relative resilience of bird communities in regions with different land use intensities. Ecosystems 10, 964–974 (2007).Article 

Google Scholar 
Olivier, T., Thébault, E., Elias, M., Fontaine, B. & Fontaine, C. Urbanization and agricultural intensification destabilize animal communities differently than diversity loss. Nat. Commun. 11, 1–9 (2020).Article 
ADS 

Google Scholar 
Michel, N. L. et al. Metrics for conservation success: Using the “Bird‐Friendliness Index” to evaluate grassland and aridland bird community resilience across the Northern Great Plains ecosystem. Divers. Distrib. 26, 1687–1702 (2020).National Audubon Society. The Christmas bird count historical results [online]. http://www.christmasbirdcount.org (2019).BirdLife International & NatureServe. Bird species distribution maps of the world. Version 4.0 (2015).Billerman, S. M., Keeney, B. K., Rodewald, P. G. & Schulenberg, T. S. Birds of the world (2020).BirdLife International. IUCN red list for birds. http://www.birdlife.org (2020).De Magalhães, J. P. & Costa, J. A database of vertebrate longevity records and their relation to other life-history traits. J. Evol. Biol. 22, 1770–1774 (2009).Article 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2019-2. http://www.iucnredlist.org (2019).Morelli, F., Benedetti, Y., Møller, A. P. & Fuller, R. A. Measuring avian specialization. Ecol. Evol. 9, 8378–8386 (2019).Article 

Google Scholar 
MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: A review and meta-analysis. Glob. Change Biol. 23, 4094–4105 (2017).Article 
ADS 

Google Scholar 
Jiguet, F., Gadot, A. S., Julliard, R., Newson, S. E. & Couvet, D. Climate envelope, life history traits and the resilience of birds facing global change. Glob. Change Biol. 13, 1672–1684 (2007).Article 
ADS 

Google Scholar 
Julliard, R., Jiguet, F. & Couvet, D. Common birds facing global changes: What makes a species at risk?. Glob. Change Biol. 10, 148–154 (2004).Article 
ADS 

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

Google Scholar 
Grimm, A. et al. Earlier breeding, lower success: Does the spatial scale of climatic conditions matter in a migratory passerine bird?. Ecol. Evol. 5, 5722–5734 (2015).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).Article 

Google Scholar 
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).Article 

Google Scholar 
Barnagaud, J. Y. et al. Biogeographical, environmental and anthropogenic determinants of global patterns in bird taxonomic and trait turnover. Glob. Ecol. Biogeogr. 26, 1190–1200 (2017).Article 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects model (2019).Barton, K. MuMIn: Multi-model inference. R package (2020).R Core Team. R: A language and environment for statistical computing. Version 3.5.3. http://www.r-project.org/ (2019).Devictor, V. et al. Functional biotic homogenization of bird communities in disturbed landscapes. Glob. Ecol. Biogeogr. 17, 252–261 (2008).Article 

Google Scholar 
Neutel, A. M., Heesterbeek, J. A. P. & De Ruiter, P. C. Stability in real food webs: weak links in long loops. Science (80-. ) 296, 1120–1123 (2002).Article 
ADS 
CAS 

Google Scholar 
Wallach, A. D. et al. Trophic cascades in 3D: Network analysis reveals how apex predators structure ecosystems. Methods Ecol. Evol. 8, 135–142 (2017).Article 

Google Scholar  More

In the autumn (November), L. dispar egg masses were collected at two sites: unpolluted and polluted forest. The first was a mixed oak forest at Kosmaj Mountain, 40 km south-east of Belgrade (coordinates 44°27′56″N 20°33′56″E). These woods are regarded as unpolluted because they are far from direct pollution and are part of the system of protected green areas around Belgrade, where the construction of industrial facilities and traffic infrastructure with potential negative effects on the environment is prohibited by legal regulations. The second site was Lipovica Forest (coordinates 44°38′11″N 20°24′12″E), with mixed Quercus frainetto and Quercus cerris trees, considered a polluted forest since it is located along the border of State Road 22, one of the most frequently used IB-class roads in Serbia.Collected egg masses were kept in a refrigerator at 4 °C until spring (March) when 200 eggs for each experimental group were set for hatching. After hatching in transparent Petri dishes (V = 200 mL), 10 first instar larvae were transferred and reared together at 23 °C with a 12:12 h light: dark photoperiod and relative humidity of 60%, until the third larval instar. Then, five 3rd instar larvae were reared together in the same Petri dish. After molting into the 4th instar, each larva was kept individually until the third day of the 5th instar, when they were sacrificed. Larvae were fed on an artificial diet designed for L. dispar42, and food was replaced every 48 h. Each experimental group contained between 50 and 60 larvae (Fig. 7).Figure 7A schematic figure of the experimental treatments.Full size imageThe optimal temperature for L. dispar larval development is 23 °C, and the control group was reared at this temperature. The highest summer temperature (2007–2010) measured in Serbian Quercus forests at a similar elevation was 28.4 °C, and the lowest 19.6 °C, while the average summer temperature was 26.3 °C43. Thus, we established variable temperature regimens that included brief (24 h) and daily (72 h) exposures to 28 °C. The control group of larvae were reared through the whole experiment on optimal 23 °C. Results of Huey et al.44 indicate that short term (daily) exposure to higher temperatures during development can increase both optimal temperature and maximal growth rate at the optimum, an example of beneficial thermal acclimation. In our previous research we found that induced thermotolerance modifies the activity of detoxifying enzymes in larvae originating from the polluted forest. We exposed L. dispar larvae in several experimental groups to that regime at 4th larval instar, with intention of analyze the effects of induce thermotolerance on observed parameters (ALP, ACP, hsp 70) in 5th instar larvae reared on optimal or elevated temperature28.At sacrifice on the third day of the 5th instar, the caterpillar midguts were dissected out on ice (n = 8–11 larval midguts per group for each enzyme assay). Midgut from single larvae was weighed and homogenized in insect physiological saline, as insect fluids have buffer values similar to vertebrates45. Homogenization was performed in ice-cold 0.15 M NaCl (final tissue concentration was 100 mg/mL in each sample), for 3 intervals of 10 s with a 15 s pause between them, at 5000 rpm, using Ultra Turrax homogenizer (IKA-Werke, Staufen, Germany). The homogenates were centrifuged for 10 min at 10,000 g at 4 ℃, and supernatants were used for enzyme assays and NATIVE gel electrophoresis. This protocol ensured that supernatants would contain cytosol and lysosomes.On the third day of the 5th instar, larval brain tissues were dissected out on ice and weighed. Pooled brain tissue (n = 30 brain tissues per experimental group) was diluted with 0.9% NaCl (1:9/w:V) and homogenized on ice at 5000 rpm during three 10 s intervals, separated by 15 s pauses (MHX/E Xenox homogenizer, Germany). Homogenates were centrifuged at 25,000 g for 10 min at 4 °C in an Eppendorf 5417R centrifuge (Germany). The supernatants were used for Western blotting and indirect non-competitive enzyme-linked immunosorbent assay (ELISA). Protein concentrations samples were determined using BSA as the standard46.A modified method by Nemec and Socha47 was used to determine the activity of ALP. The reaction mixture contained 0.1 M Tris HCl buffer pH 8.6, 5 mM MgCl2, midgut homogenate, and 5 mM p-nitrophenyl phosphate. During 30 min of incubation time at 30 ℃, the hydrolytic release of p-nitrophenol from p-nitrophenyl phosphate (pNPP) occurred under alkaline conditions.The reaction was stopped with 0.5 M NaOH, and the absorbance of p-nitrophenol was measured at 405 nm. Blank and non-catalytic probes were included. One unit of enzyme activity was defined as the amount of enzyme that released 1 mmol of p-nitrophenol per minute under the assay conditions.The same modified method of Nemec and Socha47 was employed to determine ACP activity, but under acidic conditions (0.1 M citrate buffer pH 5.6 was found optimal for L. dispar ACP), with a prolonged incubation time of 60 min. One unit of enzyme activity was defined as the amount of enzyme that released 1 μmol of p-nitrophenol per minute per mg of total protein. Total ACP activity determined in the midgut samples came from lysosomal ACP that ended up in the cytosol and non-lysosomal ACP, typically localized in the cytosol.Lysosomal ACP were detected indirectly48, under the same conditions, in a mixture containing the specific enzyme inhibitor NaF (50 mM). The absorbance determined at 405 nm is proportional to the activity of the non-lysosomal fraction of total ACP. The activity of the lysosomal fraction was obtained by subtracting not inhibited non-lysosomal acid phosphatases from the total phosphatase activity. Specific activities of ACP are given in mU per mg of total protein.A modified method by Allen et al.49 was used to detect ALP isoforms after native PAGE. Using 12% polyacrylamide gel, 10 μg protein aliquots per well were separated at 100 V and 4 ℃. The ALP isoform activity was visualized by soaking the gel in an incubation mixture consisting of 0.13% α-naphthyl phosphate, 100 mM Tris–HCl buffer (pH 8.6), and 0.1% Fast Blue B. The gels were incubated at room temperature until bands appeared.For ACP phosphatase detection, the same method of Allen et al.49 was also modified. After electrophoresis, the gel was washed with deionized water and equilibrated in 100 mM acetate buffer (pH 5.2) at 30 ℃. The nitrocellulose membrane was pre-soaked in 0.13% α-naphthyl phosphate dissolved in the same acetate buffer for 50 min at room temperature. The gel was covered with the membrane and incubated in a moist chamber for 60 min at 30 ℃. The membrane was soaked in 0.3% Fast Blue B stain dissolved in acetate buffer until bands became visible.Gels were scanned with a CanoScan LiDE 120 (Japan). The intensities of enzyme bands in the regions of ALP and ACP activities were analyzed using the ImageJ 1.42q software (U. S. National Institutes of Health, Bethesda, Maryland, USA).An indirect non-competitive ELISA was used to quantify the concentration of hsp70 in L. dispar brain tissue. Samples were diluted with carbonate-bicarbonate buffer (pH 9.6) and coated on a microplate (15 μg of tissue/well) (Multiwell immunoplate, NAXISORP, Thermo Scientific, Denmark) overnight at 4 °C, in the dark. The indirect non-competitive ELISA for L. dispar hsp70 was performed according to general practice: samples were first incubated with monoclonal anti-Hsp70 mouse IgG1 (dilution 1:5000) (clone BRM-22, Sigma Aldrich, USA) for 12 h at 4 °C, and then for 2 h at 25 °C with secondary anti-mouse IgG1 (gamma-chain)-HRP conjugate (dilution 1:5000) antibodies (Sigma Aldrich, USA). Chromogenic substrate 3, 3’, 5, 5’-Tetramethylbenzidine (TMB) was used as a visualizing reagent. Absorption was measured on a microplate reader (LKB 5060-006, Austria) at 450 nm. To enable statistically valid comparisons of experimental groups across multiple microplates, each microplate contained serial dilutions of standard hsp70 (recombinant hsp70, 50 ng/mL), used for the hsp70 standard curve, and homogenized brain tissues pulled by each treatment that were loaded on the microplates in a matched design, ensuring that each data point represented the mean of three replicates from each experimental group.Western blots were used to detect the presence of heat-shock protein 70 isoforms. Brain tissue homogenates were separated by SDS PAGE electrophoresis on 12% gels, according to Laemmli50. Protein transfer from the gel to the nitrocellulose membrane (Amersham Prothron, Premium 0.45 mm NC, GE Healthcare Life Sciences, UK) was left overnight at 40 V and 4 °C. Monoclonal anti-hsp70 mouse IgG1 (1:5000 dilution, clone BRM-22, Sigma Aldrich) and secondary mouse anti-mouse Hsp70 horseradish peroxidase conjugate antiserum (1:10,000 dilution, Sigma-Aldrich) were used for detection of hsp70 expression patterns in L. dispar larval brain tissue. Bands were visualized using chemiluminescence (ECL kit, Amersham).This study identified the hsp70 concentration in brain tissue and specific activities of total ACP and ALP in the larval midgut as the most promising biomarkers, which are sensitive and have consistent responses to thermal stress. These three biomarkers were combined into an IBR analysis according to Beliaeff and Burgeot51. The value of each biomarker (Xi) was standardized by the formula Yi = (Xi − mean)/SD, where Yi is the standardized biomarker response, and mean and SD were obtained from all values of the selected parameters. The next step was describing Zi as Zi = Yi or Zi = − Yi, depending on whether the temperature treatment caused induction or inhibition of the selected biomarkers. After finding the minimum value of Zi for each biomarker (min), the scores (Si) were computed as Si = Zi + |min|. Scores for biomarkers were used as the radius coordinates of the studied biomarker in the star plots. Star plot areas for the three-biomarker assembly, positioned in successive clockwise order—Hsp70, total ACP, and ALP, were obtained from the following formulas: ({A}_{i}=frac{{S}_{i}}{2*mathrm{sin}beta }left({S}_{i}*mathrm{cos}beta + {S}_{i+1}*mathrm{sin}beta right)), (beta = {mathrm{tan}}^{-1}left(frac{{S}_{i+1}*mathrm{sin}alpha }{{S}_{i}-{S}_{i+1}*mathrm{cos}alpha }right)),(alpha =2pi /n) radians (n is the number of biomarkers). The IBR values were calculated as follows:(IBR= sum_{i=1}^{n}{A}_{i}), where Ai is the area represented by two consecutive biomarkers on the star plot. Excel software (Microsoft, USA) was used to calculate IBR values and generate star plots.Statistical analyses were conducted in GraphPad Prism 6 (GraphPad Software, Inc., USA). Mean values ± standard errors of mean values (SEM) were calculated for the activity of enzymes, larval midgut mass, and the hsp70 concentration in brain tissue. D’Agostino-Pearson omnibus and Shapiro–Wilk tests were used to check the normality of data distribution. The effects of thermal treatments and their interaction on the variance of analyzed biomarkers in larvae from the polluted and the unpolluted forest were tested using two-way ANOVA with thermal treatments as fixed factors. For all comparisons, the level of significance was set at p  More