Philippa Kaur

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

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

Overall frameworkCells in our model are arranged along independent radial files, with each cell in one of either the proliferation, enlargement-only, wall thickening, or mature zones, depending on the distance of the cell’s centre from the inside edge of the phloem and the time of year. Only cells that contribute to the formation of xylem tracheids are treated explicitly. A daily timestep is used, on which cells in the proliferation and enlargement-only zones can enlarge in the radial direction if these zones are non-dormant, and on which secondary-wall thickening can occur in the wall thickening zone. Cells in the proliferation zone divide periclinally if they reach a threshold radial length. Cell-size control at division is intermediate between a critical size and a critical increment22. Mother cells divide asymmetrically, with the subsequent relative growth rates of the daughters inversely proportional to their relative sizes. Size at division and asymmetry of division are computed with added statistical noise22, and therefore the model is run for an ensemble of independent radial files with perturbed initial conditions.Equations and parametersCell enlargement and divisionCells in the proliferation and enlargement-only zones, when not dormant, enlarge in the radial direction at a rate dependent on temperature and relative sibling birth size. A Boltzmann-Arrhenius approach is used for the temperature dependence30:$$mu={mu }_{0}{e}^{frac{{E}_{a}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(1)
where μ is the relative rate of radial cell growth at temperature T (μm μm−1 day−1), μ0 is μ at temperature T0 (=283.15 K), Ea is the effective activation energy for cell enlargement, k is the Boltzmann constant (i.e. 8.617 x 10−5 eV K−1), and T is temperature (K). μ0 was calibrated to an observed mean radial file length at the end of the elongation period dataset23 (Table 1; see “Observations”), and Ea was calibrated to an observed temperature dependence of annual ring width dataset31 (Table 1; Supplementary Fig. 4; see “Observations”).Table 1 Model parameters calibrated to observationsFull size tableRadial length of an individual cell then increases according to:$${{Delta }}{L}_{r}={L}_{r}({e}^{epsilon mu }-1)$$
(2)
where ΔLr is the radial increment of the cell (μm day−1), Lr is the radial length of the cell (μm), and ϵ is the cell’s growth dependence on relative birth size, given by22:$$epsilon=1-{g}_{asym}{alpha }_{b}$$
(3)
where gasym is the strength of the dependence of relative growth rate on asymmetric division (Table 2; unitless), and αb is the degree of asymmetry relative to the cell’s sister22 (scalar):$${alpha }_{b}=frac{{L}_{r}{,}_{b}-{L}_{r}{,}_{b}^{sis}}{{L}_{r}{,}_{b}+{L}_{r}{,}_{b}^{sis}}$$
(4)
where Lr,b is the radial length of the cell at birth (μm) and ({L}_{r}{,}_{b}^{sis}) is the radial length of its sister at birth (μm), which are calculated stochastically22:$${L}_{r}{,}_{b}={L}_{r}{,}_{d}(0.5-{Z}_{a})$$
(5)
$${L}_{r}{,}_{b}^{sis}={L}_{r}{,}_{d}(0.5+{Z}_{a})$$
(6)
where Lr,d is the length of the mother cell when it divides (μm) and Za is Gaussian noise with mean zero and standard deviation σa (Table 2; −0.49 ≤Za≤ 0.49 for numerical stability).Table 2 Parameters used in the model that are taken directly from literatureFull size tableLength at division is derived as22:$${L}_{r}{,}_{d}=f{L}_{r}{,}_{b}+{chi }_{b}(2-f+Z)$$
(7)
where f is the mode of cell-size regulation (Table 2; unitless), χb is the mean cell birth size (Table 3; μm), and Z is Gaussian noise with mean zero and standard deviation σ (Table 2).Table 3 Parameters used in the model that are calculated from observationsFull size tableThe first cell in each radial file is an initial, which produces phloem mother cells outwards and xylem mother cells inwards. It grows and divides as other cells in the proliferation zone, but on division one of the daughters is stochastically assigned to phloem or xylem, the other remaining as the initial. The probability of the daughter being on the phloem side is fphloem (Table 3).Cell-wall growthBoth primary and secondary cell-wall growth are influenced by temperature, carbohydrate concentration, and lumen volume. A Michaelis-Menten equation is used to relate the rate of wall growth to the concentration of carbohydrates in the cytoplasm:$${{Delta }}M=frac{{{Delta }}{M}_{max}theta }{theta+{K}_{m}}$$
(8)
where ΔM is the rate of cell-wall growth (mg cell−1 day−1), ΔMmax is the carbohydrate-saturated rate of wall growth (mg cell−1 day−1), θ is the concentration of carbohydrates in the cell’s cytoplasm (mg ml−1), and Km is the effective Michaelis constant (mg ml−1; Table 1).The maximum rate of cell-wall growth, ΔMmax, is assumed to depend linearly on lumen volume (a proxy for the amount of machinery for wall growth), and on temperature as in Eq. (1):$${{Delta }}{M}_{max}=omega {V}_{l}{e}^{frac{{E}_{aw}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(9)
where ω is the normalised rate of cell-wall mass growth (i.e. the rate at T0; Table 1; mg ml−1 day−1), Vl is the cell lumen volume (ml cell−1), and Eaw is the effective activation energy for wall building (eV; Table 1). ω and Km were calibrated to an observed distribution of carbohydrates23 (see next section). Eaw was calibrated to an observed temperature dependence of maximum density31 (Table 1; see “Observations”).Lumen volume is given by:$${V}_{l}={V}_{c}-{V}_{w}$$
(10)
where Vc is total cell volume (ml cell−1) and Vw is total wall volume (ml cell−1). Cells are assumed cuboid and therefore Vc is given by:$${V}_{c}={L}_{a}{L}_{t}{L}_{r}/1{0}^{12}$$
(11)
where La is axial length (μm; Table 2) and Lt is tangential length (μm; Table 3). Vw is given by:$${V}_{w}=M/rho$$
(12)
where M is wall mass (mg cell−1) and ρ is wall-mass density (Table 2; mg[DM] ml−1).Cells in the proliferation and enlargement-only zones only have primary cell walls. ΔMmax (Eq. (9)) is therefore given the following limit:$${{Delta }}{M}_{max}=min ({{Delta }}{M}_{max},rho {V}_{{w}_{p}}-M)$$
(13)
where ({V}_{{w}_{p}}) is the required primary wall volume:$${V}_{{w}_{p}}={V}_{c}-({L}_{a}-2{W}_{p})({L}_{t}-2{W}_{p})({L}_{r}-2{W}_{p})/1{0}^{12}$$
(14)
where Wp is primary cell-wall thickness (Table 3; μm).Carbohydrate distributionThe distribution of carbohydrates across each radial file is calculated independently from the balance of diffusion from the phloem and the uptake into primary and secondary cell walls. The carbohydrate concentration in the phloem is prescribed at the mean value observed across the three observational dates in23, as described below in “Observations”, and the resulting concentration in the cytoplasm of the furthest living cell from the phloem is solved numerically. The inside wall of this cell is assumed to be impermeable to carbohydrates and therefore provides the inner boundary to the solution. It is assumed that the rate of diffusion across each file is rapid relative to the rate of cell-wall building, and therefore concentrations are assumed to be in equilibrium on each day. Carbohydrate diffusion between living cells is assumed to be proportional to the concentration gradient:$${q}_{i}=({theta }_{i-1}-{theta }_{i})/eta$$
(15)
where qi is the rate of carbohydrate diffusion from cell i − 1 to cell i (mg day−1) and η is the resistance to flow between cells (calibrated to the observed distribution of carbohydrates23, see next section; Table 1; day ml−1).As it is assumed that carbohydrates cannot diffuse between radial files, at equilibrium the flux into a given cell must equal the rate of wall growth in that cell plus the wall growth in all cells further along the radial file away from the phloem. From this it can be shown that the equilibrium carbohydrate concentration in the furthest living cell from the phloem in each radial file is given by:$${theta }_{n}={theta }_{p}-eta mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{j=i}^{n}{{Delta }}{M}_{j}$$
(16)
where θp is the concentration of carbohydrates in the phloem (Table 3; mg ml−1) and n is the number of living cells in the file (phloem mother cells are ignored for simplicity). The rate of wall growth in each cell depends on the concentration of carbohydrates (Eq. (8)), and therefore θn must be found that results in an equilibrium flow across the radial file. This is done using Brent’s method41 as implemented in the “ZBRENT” function42.Zone widthsThe widths of the proliferation, enlargement-only, and secondary wall thickening zones vary through the year, and are fitted to observations on three dates23 (see Supplementary Fig. 2 and “Observations”). Linear responses to daylength were found, which are therefore used to determine widths for the observational period and later days:$${z}_{k}={a}_{k}+{b}_{k}{{{{{{{rm{dl}}}}}}}};{{mathrm{DOY}}}ge 185$$
(17)
where zk is the distance of the inner edge of the zone from the inner edge of the phloem (μm), k is proliferation (p), secondary wall thickening (t), or enlargement-only (e), ak and bk are constants (Table 3), dl is daylength (s), and DOY is day-of-year. The proliferation zone width on earlier days when non-dormant was fixed at its DOY 185 width (assuming this to be its maximum, and that it would reach its maximum very soon after cambial dormancy is broken in the spring). During dormancy, the proliferation zone width is fixed at its value on DOY 231 (the first day of dormancy23). The enlargement-only zone width prior to DOY 185, the first observational day, is assumed to be a linear extension of the rate of change after DOY 185. The wall-thickening zone width plays little role prior to DOY 185 at the focal site, and so was set to its Eq. (17) value each earlier day. On all days the condition zt≥ze≥zp is imposed, and zone widths cannot exceed their values at 24 h daylength (necessary for sites north of the Arctic circle). Supplementary Figure 2 shows the resulting progression of zone widths through the year, together with the observed values.DormancyProliferation was observed to be finished by DOY 23123, and so the proliferation and enlargement-only zones are assumed to enter dormancy then. Release from dormancy in the spring is calculated using an empirical thermal time/chilling model33. It was necessary to adjust the asymptote and temperature threshold of the published model because the heat sum on the day of release calculated from observations in Sweden (see “Observations”) was much lower than reported for Sitka spruce buds in Britain in the original work:$${{{{{{{{rm{dd}}}}}}}}}_{req}=15+4401.8{e}^{-0.042{{{{{{{rm{cd}}}}}}}}}$$
(18)
where ddreq is the required sum of degree-days (°C) from DOY 32 for dormancy release and cd is the chill-day sum from DOY 306. The degree-day sum is the sum of daily mean temperatures above 0 °C, and the chill-day sum is the number of days with mean temperatures below 0 °C. Dormancy can only be released during the first half of the year.Simulation protocolsEach simulation consisted of an ensemble of 100 independent radial files. Each radial file was initialised by producing a file of 100 cells with radial lengths χb(1+Za), allowing these to divide once, ignoring the second daughter from each division, and then limiting the remaining daughters to only those falling inside the proliferation zone on DOY 1. Values for ϵ (the relative growth of daughter cells) and Lr,d (the radial length at division) were derived for each cell. The main simulations were conducted at the observation site in boreal Sweden (64.35°N, 19.77°E) over 1951–1995 to capture the growth period of the observed trees23. Results are mostly presented for 1995 when the observations were made. Simulations for calibration of the effective activation energies (i.e. Ea and Eaw) were performed at 68.26°N, 19.63°E in Arctic Sweden over 1901–200431. Daily mean temperatures for both sites were derived from the appropriate gridbox in a 6 h 1/2 degree global-gridded dataset43.ObservationsObservations of cellular characteristics and carbohydrate concentrations23 were used to derive a number of model parameters, and to test model output (model calibration and testing were performed using different outputs). According to the published study we used, samples were cut from three 44 yr old Scots pine trees growing in Sweden (64°21’ N; 19°46’ E) at different times during the growing season. 30 μm thick longitudinal tangential sections of the cambial region were made, and the radial distributions of soluble carbohydrates measured using microanalytical techniques23. Cell sizes, wall thicknesses, and positions in their Fig. 123, an image of transverse sections on three sampling dates, were digitised using “WebPlotDigitizer”44. These, together with the numbers of cells in each zone and their sizes given in the text of that paper, were used to estimate zone widths, which were then regressed against daylength to give the parameters for Eq. (17) (Table 3), mean cell size in the proliferation zone on the first sampling date (used to derive χb; Table 3), mean cell tangential length (Table 3), and final ring width (used to calibrate μ0; Table 1). The thickness of the primary cell wall (Table 3) was derived by plotting cell-wall thickness against time and taking the low asymptote.The distributions of carbohydrates along the radial files on the last sampling date for “Tree 1” and “Tree 3” (results for “Tree 2” were not shown for this date) shown in Fig. 2 of the observational paper23 were calculated. The masses for each of sucrose, glucose, and fructose in each 30 μm section were digitised using the same method as for cell properties and then summed and converted to concentrations, with the results shown in Supplementary Figure 5. Mean observed carbohydrate concentrations and cell masses at four points were used to calibrate values for the η, ω, and Km parameters in Table 1. Calibration was performed by minimising the summed relative error across the observations.The calibration target for the effective activation energy for wall deposition (i.e. Eaw) was the observed relationship between maximum density and mean June-July-August temperature over 1901-2004 in northern Sweden31 (Supplementary Fig. 3), and for the effective activation energy for cell enlargement the relationship between ring width and temperature (i.e. Ea) target was the same study (Supplementary Fig. 4). These observations were made on living and subfossil Scots pine sample material from the Lake Tornesträsk area (68.21–68.31°N; 19.45–19.80°E; 350–450 m a.s.l.) using X-ray densitometry for maximum density, and standardised to remove non-climatic information31.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. 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

Scheffers, B. R., Yong, D. L., Harris, J. B. C., Giam, X. & Sodhi, N. S. The world’s rediscovered species: back from the brink? PloS ONE 6, e22531 (2011).Article 
CAS 

Google Scholar 
Abeli, T., Albani Rocchetti, G., Barina, Z., Bazos, I. & Draper, D. et al. Seventeen ‘extinct’ plant species back to conservation attention in Europe. Nat. Plants 7, 282–286 (2021).Article 

Google Scholar 
Guidelines for Using the IUCN Red List Categories and Criteria Version 14 (IUCN Standards and Petitions Committee, 2019); http://www.iucnredlist.org/documents/RedListGuidelines.pdfDalrymple, S. E. & Abeli, T. Ex situ seed banks and the IUCN Red List. Nat. Plants 5, 122–123 (2019).Article 

Google Scholar 
Albani Rocchetti, G. et al. Selecting the best candidates for resurrecting extinct-in-the-wild plants from herbaria. Nat. Plants. https://doi.org/10.1038/s41477-022-01296-7 (2022).The IUCN Red List of Threatened Species Version 2022-1 (IUCN, accessed 264 October 2022); https://www.iucnredlist.orgHumphreys, A. M., Govaerts, R., Ficinski, S. Z., Lughadha, E. N. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).Article 

Google Scholar 
Knapp, W. M., Frances, A., Noss, R., Naczi, R. F. & Weakley, A. et al. Vascular plant extinction in the continental United States and Canada. Conserv. Biol. 35, 360–368 (2021).Article 

Google Scholar 
Sasidharan, N. Cynometra beddomei. The IUCN Red List of Threatened Species 2020 (IUCN, accessed 27 October 2021); https://www.iucnredlist.org/species/31184/115932185Cronk, Q. C. B. A new species and hybrid in the St Helena endemic genus Trochetiopsis. Edinb. J. Bot. 52, 205–213 (1995).Article 

Google Scholar 
Loizeau, P. A. & Jackson, P. W. World Flora Online mid-term update. Ann. Missouri Bot. Gard. 102, 341–346 (2017).Article 

Google Scholar 
Edwards, C., Bassüner, B., Birkinshaw, C., Camara, C. & Lehavana, A. et al. A botanical mystery solved by phylogenetic analysis of botanical garden collections: the rediscovery of the presumed-extinct Dracaena umbraculifera. Oryx 52, 427–436 (2018).Article 

Google Scholar 
MosaChristas, K., Karthick, R., Kowsalya, E. & Jaquline, C. R. I. Musa kattuvazhana (Musaceae): rediscovery and additional notes on a critically endangered species from Western Ghats of Tamil Nadu, India. Feddes Repert. 132, 263–268 (2021).Article 

Google Scholar 
Van Hoi, Q. U. A. C. H., Doudkin, R. V., Cuong, T. Q., Le Van, S. O. N. & Van Dung, L. U. O. N. G. et al. Rediscovery of Camellia langbianensis (Theaceae) in Vietnam. Phytotaxa 480, 85–90 (2021).Article 

Google Scholar 
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G. & Axton, M. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).Costello, M. J. & Wieczorek, J. Best practice for biodiversity data management and publication. Biol. Conserv. 173, 68–73 (2014).Article 

Google Scholar 
Wieczorek, J., Bloom, D., Guralnick, R., Blum, S., Döring, M., Giovanni, R., Robertson, T. & Vieglais, D. Darwin Core: an evolving community-developed biodiversity data standard. PloS ONE 7, e29715 (2012).Article 
CAS 

Google Scholar 
de Lange, P.J. Lepidium obtusatum Fact Sheet (content continuously updated) (New Zealand Plant Conservation Network, accessed 16 December 2021); https://www.nzpcn.org.nz/flora/species/lepidium-obtusatum/Knapp, W.M., Poindexter, D.B. & Weakley, A.S. The true identity of Marshallia grandiflora an extinct species and the description of Marshallia pulchra (Asteraceae Helenieae Marshalliinae). Phytotaxa 447, 1–15 (2020).Article 

Google Scholar  More

National negotiators inked a deal to protect nature in the early hours of 19 December in Montreal.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

Despite earlier signals of possible failure, countries around the world have cemented a deal to safeguard nature — and for the first time, the agreement sets quantitative biodiversity targets akin to the one that nations set seven years ago to limit global warming to 1.5–2 ºC above pre-industrial levels.In the early hours of 19 December, more than 190 countries eked out the deal, known as the Kunming-Montreal Global Biodiversity Framework, during the COP15 international biodiversity summit in Montreal, Canada. A key target it sets is for nations to protect and restore 30% of the world’s land and seas globally by 2030, while also respecting the rights of Indigenous peoples who depend on and steward much of Earth’s remaining biodiversity. Another target is for nations to reduce the extinction rate by 10-fold for all species by 2050.
10 startling images of nature in crisis — and the struggle to save it
Steven Guilbeault, the Canadian environment minister, described COP15 as the most significant biodiversity conference ever held. “We have taken a great step forward in history,” he said at a plenary session where the framework was adopted.At several points during the United Nations summit, which ran from 7–19 December, arguments over details threatened to derail a deal. In the final hours of negotiations, the Democratic Republic of the Congo (DRC) objected to how the framework would be funded. Nonetheless, Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the agreement.Negotiators from several African countries, which are home to biodiversity hotspots but say they need funding to preserve those areas, thought that China’s presidency strong-armed the deal. Uganda called it “fraud”. A source who spoke to Nature from the African delegation, and who asked not to be named to maintain diplomacy, said the negotiating process was not equitable towards developing countries and that the deal will not enable significant progress towards stemming biodiversity loss. “It was a coup d’état,” they say. However, a legal expert for the Convention on Biological Diversity — the treaty within which the framework now sits — told COP15 attendees that the adoption of the framework is legitimate.Concerns and disappointmentsScientists and conservation groups have welcomed the deal, emphasizing that there has never been an international agreement to protect nature on this scale. Kina Murphy, an ecologist and chief scientist at the Campaign for Nature, a conservation group, says, “It’s a historic moment for biodiversity.”

Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the biodiversity deal, despite objections from representatives of the Democratic Republic of the Congo.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

But some concerns and disappointments remain. For one, the deal lacks a mandatory requirement for companies to track and disclose their impact on biodiversity. “Voluntary action is not enough,” says Eva Zabey, executive director of Business for Nature, a global coalition of 330 businesses seeking such a requirement so that firms can compete on a level playing field. Nevertheless, it sends a powerful signal to industry that it will need to reduce negative impacts over time, says Andrew Deutz, an environmental law and finance specialist at the Nature Conservancy, a conservation group in Arlington, Virginia.In addition, the deal is weak on tackling the drivers of biodiversity loss, because it does not specifically call out the most ecologically damaging industries, such as commercial fishing and agriculture, or set precise targets for them to put biodiversity conservation at the centre of their operations, researchers say.
Can the world save a million species from extinction?
“I would have liked more ambition and precision in the targets” to address those drivers, says Sandra Diaz, an ecologist at the National University of Córdoba, in Argentina.The deal is not legally binding, but countries will have to demonstrate progress towards achieving the framework’s goals through national and global reviews. Countries failed to meet the previous Aichi Biodiversity Targets, which were set in 2010 and expired in 2020; scientists have suggested that this failure occurred because of a lack of an accountability mechanism.With the reviews included, the framework “is a very good start, with clear quantitative targets” that will allow us to understand progress and the reasons for success and failure, says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.A long time comingScientists have estimated that one million species are under threat because of habitat loss, mainly through converting land for agriculture. And they have warned that this biodiversity loss could threaten the health of ecosystems on which humans depend for clean water and disease prevention, and called for a new international conservation effort.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
The new agreement took 4 years to resolve, in part because of delays caused by the COVID-19 pandemic (the summit was supposed to take place in Kunming, China, in 2020), but also because of arguments over how to finance conservation efforts. Nations finally agreed that by 2030, funding for biodiversity from all public and private sources must rise to at least US$200 billion per year. This includes at least $30 billion per year, contributed from wealthy to low-income nations. These figures fall short of the approximately $700 billion that researchers say is needed to fully safeguard and restore nature, but represents a tripling of existing donations.Low- and middle-income countries (LMICs), including the DRC, had called for a brand-new, independent fund for biodiversity financing. Lee White, environment minister from Gabon, told Nature that biodiversity-rich LMICs have difficulty accessing the Global Environment Facility (GEF), the current fund held by the World Bank in Washington DC, and that it is slow to distribute funds.But France and the European Union strongly objected to a new fund, arguing it would take too long to set up. The framework instead compromises by establishing a trust fund by next year under the GEF. The final agreement also calls on the GEF to reform its process to address the concerns of LMICs.Progress without drastic changeAnother sticking point during negotiations was how to fairly and equitably share the benefits of ‘digital sequence information’ — genetic data collected from plants, animals and other organisms. Communities in biodiversity-rich regions where genetic material is collected have little control over the commercialization of the data, and no way to recoup financial or other benefits from them. But countries came to an agreement to set up a mechanism to share profits, the details of which will be worked out by the next international biodiversity summit, COP16, in 2024.Overall, the deal marks progress toward tackling biodiversity loss, but it is not the drastic change scientists say they were hoping for. “I am not so sure that it has enough teeth to curb the activities that do most of the harm,” Diaz says. 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