Philippa Kaur

1.Klupsaite, D. & Gražina, J. Legume: Composition, protein extraction and functional properties. A review. Chem. Technol. 66 (2015).2.Tassoni, A., et al. State-of-the-art production chains for peas, beans and chickpeas-valorization of agro-industrial residues and applications of derived extracts. Molecules (Basel, Switzerland) 25 (2020).3.Vilariño, M. V., Franco, C. & Quarrington, C. Food loss and waste reduction as an integral part of a circular economy. Front. Environ. Sci. 5 (2017).4.Malenica, D. & Bhat, R. Review article: Current research trends in fruit and vegetables wastes and by-products management-scope and opportunities in the estonian context. Agron. Res. 18, 1760–1795 (2020).
Google Scholar 
5.Tharanathan, R. N. & Mahadevamma, S. Grain legumes—a boon to human nutrition. Trends Food Sci. Technol. 14, 507–518 (2003).CAS 

Google Scholar 
6.Nguyen, T. M., Phoukham, K. & Ngo, T. V. Formulation and quality evaluation of pearl oyster mushroom soup powder supplement with some kinds of legumes and vegetables. Acta Sci. Polonorum Technol. Aliment. 19, 435–443 (2020).CAS 

Google Scholar 
7.Apprich, S. et al. Wheat bran-based biorefinery 2: Valorization of products. LWT Food Sci. Technol. 56, 222–231 (2014).CAS 

Google Scholar 
8.Xia, N. et al. Characterization and in vitro digestibility of rice protein prepared by enzyme-assisted microfluidization: Comparison to alkaline extraction. J. Cereal Sci. 56, 482–489 (2012).CAS 

Google Scholar 
9.Zhu, K.-X., Zhou, H.-M. & Qian, H.-F. Proteins extracted from defatted wheat germ: Nutritional and structural properties. Cereal Chem. 83, 69–75 (2006).CAS 

Google Scholar 
10.Tanongkankit, Y., Chiewchan, N. & Devahastin, S. Evolution of antioxidants in dietary fiber powder produced from white cabbage outer leaves: Effects of blanching and drying methods. J. Food Sci. Technol. 52, 2280–2287 (2015).CAS 
PubMed 

Google Scholar 
11.Stojceska, V., Ainsworth, P., Plunkett, A., İbanoğlu, E. & İbanoğlu, Ş. Cauliflower by-products as a new source of dietary fibre, antioxidants and proteins in cereal based ready-to-eat expanded snacks. J. Food Eng. 87, 554–563 (2008).CAS 

Google Scholar 
12.Babbar, N., Oberoi, H. S., Uppal, D. S. & Patil, R. T. Total phenolic content and antioxidant capacity of extracts obtained from six important fruit residues. Food Res. Int. 44, 391–396 (2011).CAS 

Google Scholar 
13.Elbadrawy, E. & Sello, A. Evaluation of nutritional value and antioxidant activity of tomato peel extracts. Arab. J. Chem. 9, S1010–S1018 (2016).CAS 

Google Scholar 
14.Wadhwa, M., Kaushal, S. & Bakshi, M. P. S. Nutritive evaluation of vegetable wastes as complete feed for goat bucks. Small Rumin. Res. 64, 279–284 (2006).
Google Scholar 
15.Wadhwa, M. & Bakshi, M. Vegetable wastes-a potential source of nutrients for ruminants. Indian J. Anim. Nutr. 22, 70–76 (2005).
Google Scholar 
16.Garg, M. Nutritional evaluation and utilization of pea pod powder for preparation of jaggery biscuits. J. Food Process. Technol. 6, 522–528 (2015).
Google Scholar 
17.Belghith Fendri, L. et al. Wheat bread enrichment by pea and broad bean pods fibers: Effect on dough rheology and bread quality. LWT 73, 584–591 (2016).CAS 

Google Scholar 
18.Hanan, E., Rudra, S. G., Sharma, V., Sagar, V. R. & Sehgal, S. Pea pod powder to enhance the storage quality of buckwheat bread. Vegetos (2021).19.Hanan, E., Rudra, G. S, Sagar, V. R. & Sharma, V. Utilization of pea pod powder for formulation of instant pea soup powder. J. Food Process. Preserv. (2020).20.Upasana, V. D. Nutritional evaluation of pea peel and pea peel extracted byproducts. Int. J. Food Sci. Nutr. 3, 65–67 (2018).
Google Scholar 
21.Abd-Allah, I., Rabie, M., Mostfa, D. M., Sulieman, A. & El-Badawi, A. Nutritional evaluation, chemical composition and antioxidant activity of some food processing wastes. Zag. J. Agric. Res. 43, 2115–2132 (2016).
Google Scholar 
22.El-Gohery, S. S. Quality aspects for high nutritional value pretzel. Curr. Sci. Int. 9, 583–593 (2020).
Google Scholar 
23.Hassanien, M. Impact of adding chickpea (Cicer arietinum L.) flour to wheat flour on the rheological properties of toast bread. Int. Food Res. J. 19, 521–525 (2012).
Google Scholar 
24.Sharoba, P. A., El-Desouky, A., Mahmoud, M. & Youssef, M. K. Quality attributes of some breads made from wheat flour substituted by different levels of whole amaranth meal. J. Agric. Sci. Mansoura Univ. 34, 6601–6617 (2009).
Google Scholar 
25.El-Sharnouby, G. Nutritional quality of biscuit supplemented with wheat bran and date palm fruits (Phoenix dactylifera L.). Food Nutr. Sci. 03, 322–328 (2012).CAS 

Google Scholar 
26.Abou El-Ez, A., Rania, W. Y., Shalaby, H. S., Abu El-Maaty, S. M. & Guirguis, A. H. Utlization of fruit and vegetable waste powders for fortification of some food products. Zag. J. Agric. Res. 6, 2189–2201 (2017).
Google Scholar 
27.Abd El-Salam, A. M., Morsy, O. M. & Abd El Mawla, E. M. Production and evaluation crackers and instant noodles supplement with spirulina algae. Curr. Sci. Int. 6, 908–919 (2017).
Google Scholar 
28.DRI. Dietary Reference Intakes, Dietary Reference Intakes for Energy, Carbohydrate, fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients) (The National Academies Press, Washington, 2005).
Google Scholar 
29.FAO/WHO. World health organization, food and agriculture organization of the united nations, United Nations University, 2007. Protein and amino acid requirements in human nutrition: Report of a joint who/fao/unu expert consultation. In: Joint expert consultation on protein and amino acid requirements in human nutrition; who technical report series. WHO, Geneva, Switzerland (2007).30.Shah, A. M., Wang, Z. & Ma, J. Glutamine metabolism and its role in immunity, a comprehensive review. Anim. Open Access J. MDPI 10, 326–332 (2020).
Google Scholar 
31.Rowayshed, G., Salama, A., Abul-Fadl, M., Akila-Hamza, S. & Emad, A. M. Nutritional and chemical evaluation for pomegranate (Punica granatum L.) fruit peel and seeds powders by products. Middle East J. Appl. Sci. 3, 169–179 (2013).
Google Scholar 
32.Hussein, A. M. S., Amal, S. A., Amany, M. H., Abeer, A. A. & Gamal, H. R. Physiochemical sensory and nutritional properties of corn-fenugreek flour composite biscuits. Aust. J. Basic Appl. Sci. 5, 84–95 (2011).CAS 

Google Scholar 
33.Mihiranie, S., Jayasundera, M. & Perera, N. Development of snack crackers incorporated with defatted coconut flour. J. Microbiol. Biotechnol. Food Sci. 7, 153–159 (2019).
Google Scholar 
34.Abdel-Haleem, A. M. & Omran, A. A. Preparation of dried vegetarian soup supplemented with some legumes. J. Food Nutr. Sci. 5, 2274–2282 (2014).
Google Scholar 
35.Holbrook, J. T. et al. Sodium and potassium intake and balance in adults consuming self-selected diets. Am. J. Clin. Nutr. 40, 786–793 (1984).CAS 
PubMed 

Google Scholar 
36.Schwalfenberg, G. K. & Genuis, S. J. The importance of magnesium in clinical healthcare. Scientifica 2017, 4179326 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Hanif, R., Iqbal, Z., Iqbal, M., Hanif, S. & Rasheed, M. Use of vegetables as nutritional food role in human health. J. Agric. Biol. Sci. 1, 18–22 (2006).
Google Scholar 
38.Ibidapo, O. et al. Some functional properties of flours from commonly consumed selected nigerian food crops. Int. Res. J. Agric. Food Sci. 1, 92–98 (2016).
Google Scholar 
39.Fellows, P. Processing Technology Principles and Practice 2nd edn. (Woodhead Publishing Limited and Crc Press LlC, Washington, 2000).
Google Scholar 
40.Monteiro, M. et al. Flours and instant soup from tilapia wastes as healthy alternatives to the food industry. Food Sci. Technol. Res. 20, 571–581 (2014).CAS 

Google Scholar 
41.Hanan, E., Rudra, S., Sagar, V. R. & Sharma, V. Utilization of pea pod powder for formulation of instant pea soup powder short running title: Formulation of instant pea soup powder. J. Food Process. Preserv. 44, e14888 (2020).CAS 

Google Scholar 
42.Belghith-Fendri, L. et al. Pea and broad bean pods as a natural source of dietary fiber: The impact on texture and sensory properties of cake. J. Food Sci. 81, C2360-c2366 (2016).CAS 
PubMed 

Google Scholar 
43.Ravindran, G. & Matia-Merino, L. Starch–fenugreek (Trigonella foenum-graecum L.) polysaccharide interactions in pure and soup systems. Food Hydrocoll. 23, 1047–1053 (2009).CAS 

Google Scholar 
44.Verma, A. Process for the preparation of value added instant tomato-mushroom soup mix incorporated with psyllium husk and its quality evaluation. Int. J. Pure Appl. Biosci. 5, 1502–1507 (2017).
Google Scholar 
45.Bose, D. & Shams-Ud-Din, M. The effect of chickpea (cicer arietinim) husk on the properties of cracker biscuits. J. Bangladesh Agric. Univ. 8, 147–152 (2010).
Google Scholar 
46.Yadav, A. R., Guha, M., Tharanathan, R. N. & Ramteke, R. S. Influence of drying conditions on functional properties of potato flour. Eur. Food Res. Technol. 223, 553–560 (2006).CAS 

Google Scholar 
47.Knezevic, D., Djukic, N., Paunovic, A. & Madic, M. Amino acid contents in grains of different winter wheat (Triticum aestivum L.) varieties. Cereal Res. Commun. 37, 647–650 (2009).CAS 

Google Scholar 
48.Gaines C. Associations among quality attributes of red and white soft wheat cultivars across locations and crop years. Cereal Chem. 68 (1991).49.Chitomarat S. Effects of drying on characteristic of powdered corn milk yoghurt (in thai). B.Sc. Thesis, Chiang Mai University, Thailand. (2002).50.Krokida, M. K. & Marinos-Kouris, D. Rehydration kinetics of dehydrated products. J. Food Eng. 57 (2003).51.Malomo, O., Ogunmoyela, O. O. A., Jimoh, M. & Oluwajoba, S. O. S. Rheological and functional properties of soy-poundo yam flour. Int. J. Food Sci. Nutr. Eng. 2, 101–107 (2013).
Google Scholar 
52.Piga, A. et al. Texture evolution of “amaretti” cookies during storage. Eur. Food Res. Technol. 221, 387–391 (2005).CAS 

Google Scholar 
53.Salem E. Nutritional quality of purslane and its crackers (2016).54.Wang, R., Zhang, M., Mujumdar, A. S. & Sun, J.-C. Microwave freeze–drying characteristics and sensory quality of instant vegetable soup. Drying Technol. 27, 962–968 (2009).
Google Scholar  More

AffiliationsDepartment of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Umeå, SwedenHyungwoo Lim, Torgny Näsholm, Tomas Lundmark & Harald GripNicholas School of the Environment, Duke University, Durham, NC, USARam OrenDepartment of Forest Sciences, University of Helsinki, Helsinki, FinlandRam OrenDepartment of Soil and Environment, SLU, Uppsala, SwedenMonika StrömgrenSouthern Swedish Forest Research Centre, SLU, Alnarp, SwedenSune LinderAuthorsHyungwoo LimRam OrenTorgny NäsholmMonika StrömgrenTomas LundmarkHarald GripSune LinderCorresponding authorsCorrespondence to
Hyungwoo Lim or Ram Oren. More

1.Landman, N. H. et al. (eds) Ammonoid Paleobiology (Plenum, 1996). https://doi.org/10.1007/978-1-4757-9153-2_16.Book 

Google Scholar 
2.Klug, C. et al. (eds) Ammonoid Paleobiology: From Anatomy to Ecology (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_18.Book 

Google Scholar 
3.Klug, C. et al. (eds) Ammonoid Paleobiology: From Macroevolution to Paleogeography (Springer, 2015). https://doi.org/10.1007/978-94-017-9633-0.Book 

Google Scholar 
4.Ritterbush, K. A., Hoffmann, R., Lukeneder, A. & De Baets, K. Pelagic palaeoecology: The importance of recent constraints on ammonoid palaeobiology and life history. J. Zool. 292(4), 229–241. https://doi.org/10.1111/jzo.12118 (2014).Article 

Google Scholar 
5.Westermann, G. E. G. Ammonoid life and habitat. In Ammonoid Paleobiology (eds Landman, N. H. et al.) 607–707 (Plenum, 1996). https://doi.org/10.1007/978-1-4757-9153-2_16.Chapter 

Google Scholar 
6.Lukeneder, A. Ammonoid habitats and life history. In Ammonoid Paleobiology: From Anatomy to Ecology (eds Klug, C. et al.) 689–791 (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_18.Chapter 

Google Scholar 
7.Hoffmann, R. et al. A novel multiproxy approach to reconstruct the paleoecology of extinct cephalopods. Gondwana Res. 67, 64–81. https://doi.org/10.1016/j.gr.2018.10.011 (2018).ADS 
CAS 
Article 

Google Scholar 
8.Hoffmann, R. et al. Recent advances in heteromorph ammonoid palaeobiology. Biol. Rev. Cambr. Philos. Soc. 96, 576–610. https://doi.org/10.1111/brv.12669 (2021).Article 

Google Scholar 
9.Moriya, K., Nishi, H., Kawahata, H., Tanabe, K. & Takayanagi, Y. Demersal habitat of Late Cretaceous ammonoids: Evidence from oxygen isotopes for the Campanian (Late Cretaceous) northwestern Pacific thermal structure. Geology 31, 167–170 (2003).ADS 
CAS 
Article 

Google Scholar 
10.Moriya, K. Isotope signature of ammonoid shells. In Ammonoid Paleobiology: From Anatomy to Ecology (eds Klug, C. et al.) 793–836 (Springer, 2015). https://doi.org/10.1007/978-94-017-9630-9_19.Chapter 

Google Scholar 
11.Sessa, J. A. et al. Ammonite habitat revealed via isotopic composition and comparisons with co-occurring benthic and planktonic organisms. PNAS 112, 15562–15567. https://doi.org/10.1073/pnas.1507554112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Stevens, K., Mutterlose, J. & Wiedenroth, K. Stable isotope data (δ18O, δ13C) of the ammonite genus Simbirskites—Implications for habitat reconstructions of extinct cephalopods. Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 164–175. https://doi.org/10.1016/j.palaeo.2014.10.031 (2015).Article 

Google Scholar 
13.Surlyk, F., Dons, T., Clausen, C. K. & Higham, J. Upper Cretaceous. In The Millennium Atlas: Petroleum Geology of the Central and Northern North Sea (eds Copestake, P. et al.) 213–233 (Geological Society of London, 2003).
Google Scholar 
14.Thibault, N., Harlou, R., Schovsbo, N. H., Stemmerik, L. & Surlyk, F. Late Cretaceous (late Campanian–Maastrichtian) sea surface temperature record of the Boreal Chalk Sea. Clim. Past 12, 429–438. https://doi.org/10.5194/cp-12-429-2016 (2016).Article 

Google Scholar 
15.Wilmsen, M. & Niebuhr, B. High-resolution Campanian-Maastrichtian carbon and oxygen stable isotopes of bulk-rock and skeletal component: Palaeoceanographic and palaeoenvironmental implications for the Boreal shelf sea. Acta Geol. Pol. 67, 47–74. https://doi.org/10.1515/agp-2017-0004 (2017).ADS 
CAS 
Article 

Google Scholar 
16.Birkelund, T. Ammonites from the Maastrichtian White Chalk of Denmark. Bull. Geol. Soc. Denmark 40, 33–81 (1993).Article 

Google Scholar 
17.Niebuhr, B. Late Campanian and Early Maastrichtian ammonites from the white chalk of Kronsmoor (northern Germany)—Taxonomy and stratigraphy. Acta Geol. Pol. 53, 257–281 (2003).
Google Scholar 
18.Kruta, I. & Landman, N. H. Injuries on Nautilus jaws: Implications for the function of ammonite aptychi. Veliger 50, 241–247 (2008).
Google Scholar 
19.Tanabe, K., Kruta, I. & Landman, N. H. Ammonoid buccal mass and jaw apparatus. In Ammonoid Paleobiology: From Macroevolution to Paleogeography (eds Klug, C. et al.) 439–494 (Springer, 2015).
Google Scholar 
20.Kruta, I., Landman, N. H. & Cochran, J. K. A new approach for the determination of ammonite and nautilid habitats. PLoS ONE 9, e87479. https://doi.org/10.1371/journal.pone.0087479 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Machalski, M. Late Maastrichtian and earliest Danian scaphitid ammonites from central Europe: Taxonomy, evolution, and extinction. Acta Palaeontol. Pol. 50(4), 653–696 (2005).
Google Scholar 
22.Machalski, M. Correlation of shell and aptychus growth provides insights into the palaeobiology of a scaphitid ammonite. Palaeontology 64, 225–247. https://doi.org/10.1111/pala.12519 (2021).Article 

Google Scholar 
23.Dubicka, Z. & Peryt, D. Integrated biostratigraphy of Upper Maastrichtian chalk at Chełm (SE Poland). Ann. Soc. Geol. Pol. 81, 185–197 (2011).
Google Scholar 
24.Dubicka, Z. & Peryt, D. Latest Campanian and Maastrichtian palaeoenvironmental changes: Implications from an epicontinental sea (SE Poland and western Ukraine). Cret. Res. 37, 272–284. https://doi.org/10.1016/j.cretres.2012.04.009 (2012).Article 

Google Scholar 
25.Machalski, M. & Malchyk, O. Durophagous predation on late Maastrichtian (Cretaceous) scaphitid ammonites from Poland. In 10th International Symposium “Cephalopods—Present and Past”, Program and Abstracts. Münstersche Forschungen zur Geologie und Paläontologie 110, 77–78 (2018).
Google Scholar 
26.Keupp, H. Sublethal punctures in body chambers of Mesozoic ammonites (forma Aegra fenestra n. f.), a tool to interpret synecological relationships, particularly predator–prey interactions. Paläontol. Z. 80, 112–123. https://doi.org/10.1007/BF02988971 (2006).Article 

Google Scholar 
27.Mironenko, A. Sublethal injuries on the shells of Jurassic ammonites from Central Russia. In Jurassic Deposits of the Southern Part of the Moscow Syneclise and Their Fauna (eds Rogov, M. A. & Zakharov, V. A.) 183–208 (Transactions of the Geological Institute, GEOS, 2017) (in Russian).
Google Scholar 
28.Moriya, K. Evolution of habitat depth in the Jurassic-Cretaceous ammonoids. PNAS 112, 15540–15541. https://doi.org/10.1073/pnas.1520961112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Leszczyński, K. The internal geometry and lithofacies pattern of the Upper Cretaceous-Danian sequence in the Polish Lowlands. Geol. Q. 56, 363–386. https://doi.org/10.7306/gq.1028 (2012).Article 

Google Scholar 
30.Jurkowska, A. & Świerczewska-Gładysz, E. New model of Si balance in the Late Cretaceous epicontinental European Basin. Global Planet. Change 186, 103108. https://doi.org/10.1016/j.gloplacha.2019.103108 (2020).Article 

Google Scholar 
31.Müller, R. D. et al. GPlates: Building a virtual Earth through deep time. Geochem. Geophys. Geosyst. 19, 2243–2261. https://doi.org/10.1029/2018GC007584 (2018).ADS 
Article 

Google Scholar 
32.Walaszczyk, I., Dubicka, Z., Olszewska-Nejbert, D. & Remin, Z. Integrated biostratigraphy of the Santonian through Maastrichtian (Upper Cretaceous) of extra-Carpathian Poland. Acta Geol. Pol. 66, 321–358. https://doi.org/10.1515/agp-2016-0016 (2016).ADS 
CAS 
Article 

Google Scholar 
33.Surlyk, F. et al. Upper Campanian-Maastrichtian holostratigraphy of the eastern Danish Basin. Cret. Res. 46, 232–256. https://doi.org/10.1016/j.cretres.2013.08.006 (2013).Article 

Google Scholar 
34.Tagliavento, M., Lauridsen, B. W. & Stemmerik, L. Episodic dysoxia during Late Cretaceous cyclic chalk-marl deposition—Evidence from framboidal pyrite distribution in the upper Maastrichtian Rørdal Mb., Danish Basin. Cret. Res. 106, 104223. https://doi.org/10.1016/j.cretres.2019.104223 (2020).Article 

Google Scholar 
35.Dubicka, Z., Wierzbowski, H. & Wierny, W. Oxygen and carbon isotope records of Upper Cretaceous foraminifera from Poland: Vital and microhabitat effects. Palaeogeogr. Palaeoclimatol. Palaeoecol. 500, 33–51. https://doi.org/10.1016/j.palaeo.2018.03.029 (2018).Article 

Google Scholar 
36.Klompmaker, A. A., Waljaard, N. A. & Fraaije, R. H. B. Ventral bite marks in Mesozoic ammonoids. Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 245–257. https://doi.org/10.1016/j.palaeo.2009.06.013 (2009).Article 

Google Scholar 
37.Fraaye, R. H. B. Late Cretaceous swimming crabs: Radiation, migration, competition, and extinction. Acta Geol. Pol. 46, 269–278 (1996).
Google Scholar 
38.Caldwell, R. L. & Dingle, H. Stomatopods. Sci. Am. 234, 80–89 (1976).ADS 
Article 

Google Scholar 
39.Dunstan, A. J., Ward, P. D. & Marshall, N. J. Vertical distribution and migration patterns of Nautilus pompilius. PLoS ONE 6, e16311. https://doi.org/10.1371/journal.pone.0016311 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Ward, P., Dooley, F. & Barord, G. J. Nautilus: Biology, systematics, and paleobiology as viewed from 2015. Swiss J. Palaeontol. 135, 169–185. https://doi.org/10.1007/s13358-016-0112-7 (2016).Article 

Google Scholar 
41.Landman, N. H., Cobban, W. A. & Larson, N. L. Mode of life and habitat of scaphitid ammonites. Geobios 45, 87–98. https://doi.org/10.1016/j.geobios.2011.11.006 (2012).Article 

Google Scholar 
42.Peterman, D. J. et al. Syn vivo hydrostatic and hydrodynamic properties of scaphitid ammonoids from the U.S. Western Interior. Geobios 60, 79–98. https://doi.org/10.1016/j.geobios.2020.04.004 (2021).Article 

Google Scholar 
43.Tsujita, C. J. & Westermann, G. Ammonoid habitats and habits in the Western Interior Seaway: A case study from the Upper Cretaceous Bearpaw Formation of southern Alberta, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 135–160. https://doi.org/10.1016/S0031-0182(98)00090-X (1998).Article 

Google Scholar 
44.Fraaije, R. H. B., Van Bakel, B. W. M., Jagt, J. W. M. & Viegas, P. A. The rise of a novel, plankton-based marine ecosystem during the Mesozoic: A bottom-up model to explain new higher-tier invertebrate morphotypes. Boletín de la Sociedad Geol. Mexicana 70, 187–200. https://doi.org/10.18268/bsgm2018v70n1a11 (2018).Article 

Google Scholar 
45.Alldredge, A. L. & King, J. M. The distance demersal zooplankton migrate above the benthos: Implications for predation. Marine Biol. 84, 253–260. https://doi.org/10.1007/BF00392494 (1985).Article 

Google Scholar 
46.Hammer, O., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Pal. Electron. 4, 1–9 (2001).
Google Scholar 
47.Anderson, T. F. & Arthur, M. A. Stable isotopes of oxygen and carbon and their application to sedimentologic and paleonvironmental problems. In Stable Isotopes in Sedimentary Geology, The Society of Economic Paleontologists and Mineralogists Short Course Vol. 10 (eds Arthur, M. A. et al.) 1–151 (SEPM, 1983). https://doi.org/10.2110/scn.83.01.0000.Chapter 

Google Scholar 
48.Coplen, T. B., Kendall, C. & Hopple, J. Comparison of stable isotope reference samples. Nature 302, 236–238. https://doi.org/10.1038/302236a0 (1983).ADS 
CAS 
Article 

Google Scholar 
49.McLennan, S. M. Rare earth elements in sedimentary rocks: Influence of provenance and sedimentary process. Rev. Mineral. 21, 169–200 (1989).CAS 

Google Scholar 
50.Webb, G. E. & Kamber, B. S. Rare earth elements in Holocene reefal microbialites: a new shallow seawater proxy. Geochim. Cosmochim. Acta 64, 1557–1565. https://doi.org/10.1016/S0016-7037(99)00400-7 (2000).ADS 
CAS 
Article 

Google Scholar  More

Calculating landscape, attractor, and restrictorIn this work, we considered communities with commensal, mutualistic, and exploitative interactions. Below, we describe the differential equations for each type of interaction, and how we calculate the corresponding community function landscape, species-composition attractor, and Newborn restrictor.Commensal H–M community: The model community for most simulations is the same commensal H–M community used in our previous work15. The community function landscape plots P(T) as a function of ϕM(0) and ({overline{f}}_{P}(0)). Assume that a Newborn community has 100 biomass units, that all cells have the same genotype (all M cells have the same ({f}_{P}={overline{f}}_{P}(0))), that death and birth processes are deterministic, and that there is no mutation. P(T) can then be numerically integrated from the following set of scaled differential equations for any given pair of ϕM(0) and ({overline{f}}_{P}(0))15:$$frac{dR}{dt}=-{c}_{{RM}}{g}_{M}M-{c}_{{RH}}{g}_{H}H$$
(1)
$$frac{dB}{dt}={g}_{H}H-{c}_{{BM}}{g}_{M}M$$
(2)
$$frac{dP}{dt}={f}_{P}{g}_{M}M$$
(3)
$$frac{dH}{dt}={g}_{H}H-{delta }_{H}H$$
(4)
$$frac{dM}{dt}={g}_{M}left(1-{f}_{P}right)M-{delta }_{M}M$$
(5)
where$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}$$
(6)
$${g}_{M}(R, B)={g}_{{Mmax}}frac{{R}_{M}{B}_{M}}{{R}_{M}+{B}_{M}}left(frac{1}{{R}_{M}+1}+frac{1}{{B}_{M}+1}right)$$
(7)
and RM = R/KMR and BM = B/KMB. Unless otherwise specified, landscapes in this paper are obtained by integrating Equations (1–5) from t = 0 to t = 17.Equation (1) states that Resource R is depleted by biomass growth of M and H, where cRM and cRH represent the amount of R consumed per unit of M and H biomass, respectively. Equation (2) states that Byproduct B is released as H grows, and is decreased by biomass growth of M due to consumption (cBM amount of B per unit of M biomass). Equation (3) states that Product P is produced as fP fraction of potential M growth. Equation (4) states that H biomass increases at a rate dependent on Resource R in a Monod fashion (Equation (6)) and decreases at the death rate δH. Note that Agricultural waste is not a state variable here as it is present in excess. Equation (5) states that M biomass increases at a rate dependent on Resource R and Byproduct B according to the Mankad and Bungay model (Equation (7)51) discounted by (1 − fP) due to the fitness cost of making Product, and decreases at the death rate δM. In the Monod growth model (Equation (6)), gHmax is the maximal growth rate of H and KHR is the R at which gHmax/2 is achieved. In the Mankad and Bungay model (Equation (7)), KMR is the R at which gMmax/2 is achieved when B is in excess; KMB is the B at which gMmax/2 is achieved when R is in excess.Mutualistic H–M community: If Byproduct is harmful for H, then the community is mutualistic: H and M promote the growth of each other. Such a mutualistic community can still be described by Equations (1–5) and (7), but Equation (6) is replaced with$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}exp left(-frac{B}{{B}_{0}}right)$$
(8)
where larger B0 indicates lower sensitivity, or higher resistance of H to its Byproduct B.Exploitative H–M community: If M releases an antagonistic byproduct A that inhibits the growth of H, then the interaction is exploitative: H promotes the growth of M, but M inhibits the growth of H. Besides Eqs (1–5) and (7), we then need to add an equation that describes the dynamics of A$$frac{dwidetilde{A}}{dt}={r}_{A}{g}_{M}left(1-{f}_{P}right)M$$where rA is the amount of A released when M’s biomass grows by 1 unit. We can then normalize (widetilde{A}) with rA$$A=widetilde{A}/{r}_{A}$$so that$$frac{dA}{dt}={g}_{M}left(1-{f}_{P}right)M.$$
(9)
We also need to modify the growth rates for H:$${g}_{H}={g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}frac{{A}_{0}}{A+{A}_{0}}$$
(10)
where larger A0 indicates lower sensitivity, or higher resistance of H to M’s Antagonistic by product A.To calculate the community function landscape, species attractor, and Newborn restrictor, all phenotype parameters, except ({overline{f}}_{P}(0)) take the value from the Bounds column in Table 1. To construct the landscape such as in Fig. 2c, we calculated P(T) for every grid point on a 2D quadrilateral mesh of 10−2 ≤ ϕM(0) ≤ 0.99 and (1{0}^{-2} le {overline{f}}_{P}(0) le 0.99) with a mesh size of ΔϕM(0) = 10−2 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-2}). To construct the landscapes in Fig. 5b(ii) and b(iii), P(T) was similarly calculated on a 2D grid with a finer mesh of ΔϕM(0) = 5 × 10−3 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-4}).To calculate the species composition attractor, we integrated Equations (1–5) to obtain ϕM(T) − ϕM(0) for each grid point on the 2D mesh of ϕM(0) and ({overline{f}}_{P}(0)). The contour of ϕM(T) − ϕM(0) = 0 is then the species attractor (blue dashed curve in Fig. 2b).The attractor-induced Newborn restrictor at a given ({overline{f}}_{P}(0)) is calculated from its definition: if ϕM(0) of a parent Newborn is on the restrictor, then so is the average ϕM(0) among its offspring Newborns. Under no spiking, since the average ϕM(0) among offspring Newborn is the same as ϕM(T) of their parent Adult, the Newborn restrictor coincides with the species attractor (Fig. 3b and Fig. 5b ii). Under x% H spiking, x% of the biomass in Newborns is replaced with H cells. Thus if the parent Adult’s fraction of M biomass is ϕM(T), the average ϕM(0) among its offspring Newborns is (1 − x%)ϕM(T) under x% H spiking. The Newborn restrictor therefore is the contour of (1 − x%)ϕM(T) − ϕM(0) = 0 (teal curve in Fig. 5a ii and b iii, Fig. 2d ii). Compared with the orange restrictor under no spiking, the teal restrictor is shifted down.Parameter choicesDetails justifying our parameter choices are given in the Methods section of our previous work15. Briefly, our parameter choices are based on experimental measurements of microorganisms (e.g., S. cerevisiae and E. coli). To ensure the coexistence of H and M, M must grow faster than H for part of the maturation cycle since M has to wait for H’s Byproduct at the beginning of a cycle. Because we have assumed M and H to have similar affinities for Resource (Table 1), the maximal growth rate of M (gMmax) must exceed the maximal growth rate of H (gHmax), and M’s affinity for Byproduct (1/KMB) must be sufficiently large. Moreover, metabolite release and consumption need to be balanced to avoid extreme species ratios. We assume that H and M consume the same amount of Resource per new cell (cRH = cRM) since the biomass of various microbes shares similar elemental (e.g., carbon or nitrogen) compositions. We set consumption value so that the input Resource can support a maximum of 104 total biomass. The evolutionary bounds are set, such that evolved H and M could coexist for fp  0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}). Because integer number of cells is assigned to each Newborn, the total biomass might not be exactly BMtarget but within a small deviation of ~2 biomass units.To mimic reproducing through pipetting, each M and H cell in an Adult community is assigned a random integer between 1 and dilution factor nD (Equation (12)). All cells assigned with the same random integer are then dealt to the same Newborn, generating nD Newborn communities. If φS  > 0, the number of H cells supplemented into each Newborn is a random number drawn from a Poisson distribution of a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}).To mimic reproducing through cell sorting, each Newborn receives a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult. Suppose that the fraction of M biomass in the parent Adult is ϕM(T), then M cells from the parent Adult are randomly assigned to the Newborn, until the total biomass of M comes closest to (B{M}_{{{{{{{{rm{target}}}}}}}}}{phi }_{M}(T)left(1-{varphi }_{S}right)) without exceeding it. H cells with a total biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{phi }_{M}(T)right)left(1-{varphi }_{S}right)) are assigned similarly. If φS  > 0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}) where LH is the biomass of individual H cell in the parent Adult. Because each of M and H cells had a length between 1 and 2, the actual biomass of M and H assigned to a Newborn could vary from the target by up to 2 biomass units. Consequently, deviations of BM(0) from BMtarget and of ϕM(0) from parent Adult’s ϕM(T) are only a few percent.Simulating species spiking when both H and M cells evolveIn the more complex scenario, both H and M evolve. We thus need to spike with evolved H and M clones. Additionally, Newborns are spiked with H or M clones from their own lineage as demonstrated in Supplementary Fig. 11a. Below, we describe the simulation code for the experimental procedure (Supplementary Fig. 11a) we simulated.In all simulations where 6 or 7 phenotypes are modified by mutations, chosen Adults are reproduced through pipetting in a similar fashion as described above. After Newborns are reproduced from a chosen Adult in Cycle C − 1, a preset number of H or M cells are randomly picked from the remaining of this Adult to form H or M-spiking mix for Cycle C. At the end of Cycle C, we choose 10 Adults with the highest functions. Assuming that each chosen Adult is reproduced through pipetting with φS-H-spiking strategy, a Newborn receives on average a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult community and on average a biomass of BMtargetφS from H spiking mix generated at the end of Cycle C − 1. Since each chosen Adult usually gives rise to 10 Newborns, the number of cells distributed from the chosen Adult to each Newborn is drawn from a multinomial distribution. Specifically, denote the integer random numbers of cells that would be assigned to 10 Newborns to be {x1, x2,…, x10}. If the chosen Adult has a total biomass of BM(T) composed of IM M cells and IH H cells (both IM and IH are integers), the probability that {x1, x2,…, x10} cells are assigned to 10 Newborns, respectively, and x11 cells remain, is$$Pr left({{x}_{1},{x}_{2},…,{x}_{10},{x}_{11}}right)=frac{({I}_{H}+{I}_{M})!}{{x}_{1}!cdots {x}_{10}!{x}_{11}!},{p}_{0}{{,}^{{x}_{1}+cdots +{x}_{10}}},{p}_{11}^{{x}_{11}}.$$Here, ({p}_{0}=B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)/BM(T)) is the probability that a cell is assigned to one of 10 Newborns, p11 = 1 − 10p0 is the probability that a cell is not assigned to Newborns. Thus, ({x}_{11}={I}_{H}+{I}_{M}-mathop{sum }nolimits_{i = 1}^{10}{x}_{i}) is the number of cells remaining after reproduction, from which H and M cells are randomly picked to generate the spiking mix for Cycle C + 1.Suppose that the current spiking strategy is φS-H, then these 10 Newborns are spiked with H-spiking mix generated in Cycle C − 1. An average of BMtargetφS of H biomass is spiked into each Newborn so that the total biomass of Newborns is on average BMtarget. Suppose that five H cells from the parent Adult’s lineage are randomly picked at the end of Cycle C − 1, and that they have biomass {LH1, LH2, LH3, LH4, LH5}, respectively. The total number of H cells assigned to each Newborn, xH, is then randomly drawn from a Poisson distribution with a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}/{overline{L}}_{H}), where ({overline{L}}_{H}=frac{1}{5}mathop{sum }nolimits_{j = 1}^{5}{L}_{Hj}) is the average biomass of the five H cells. Each spiked H cell has an equal chance of being one of the five cells.Updating spiking percentage based on heritability checksWhen the community function landscape is unknown, we can estimate heritability of community function under different spiking percentages through parent–offspring regression. In most simulations (e.g., Fig. 7), heritability evaluation is carried out about every 100 cycles (“periodic heritability check”). In the simulations demonstrated in Supplementary Fig. 17, the average improvement rate in community function is estimated from the chosen Adults over the last 50 cycles. Heritability evaluation is carried out when this average improvement rate becomes negative (“adaptive heritability check”). For both periodic and adaptive checks, heritability evaluation can be postponed until within-community selection improves cell growth sufficiently to provide sufficient biomass for heritability check.During one round of heritability evaluation, heritability of community function is estimated through parent–offspring community function regression under all candidate spiking strategies (Supplementary Fig. 11b). The current spiking strategy is updated if an alternative spiking strategy confers significantly higher community function heritability.To evaluate heritability under one spiking strategy, up to 100 Newborn communities are generated under this spiking strategy. After these mature into Adults, their functions are the parent functions. Each Adult parent then gives rise to six Newborn offspring under the same spiking strategy. When the six Newborn offspring mature into Adults, the median of their functions is the average offspring function. When offspring functions are plotted against their parent functions, the slope of the least-squares linear regression (green dashed line in Supplementary Fig. 11b) quantifies the heritability of community function. Heritability of a community function is thus similar to heritability of an individual trait, except that we use median instead of mean of offspring functions, because median is less sensitive to outliers. The 95% confidence interval of heritability is then estimated by nonparametric bootstrap58,59. More specifically, first, 100 pairs of parent–offspring community functions are resampled with replacement. Second, heritability is calculated with the resampled data. Third, 1000 heritabilities are calculated from 1000 independent resamplings, from which the 95% confidence interval is estimated from the 5th and 95th percentile.An alternative spiking strategy is considered significantly more advantageous than the current spiking strategy if heritability of the alternative spiking strategy is higher than the right endpoint of the 95% confidence interval of the heritability of the current spiking strategy. If more than one alternative spiking strategies are more advantageous, the one with the highest heritability is implemented to replace the current strategy. Similarly, an alternative spiking strategy is considered more disadvantageous if heritability of the alternative spiking strategy is lower than the left endpoint of the 95% confidence interval of the heritability of the current spiking strategy. When implementing random spiking strategy, the current spiking strategy is updated with a strategy randomly picked from candidate spiking strategies.Simulating community selection with large population sizeWhen the population size of each community is scaled up by 10 or 100 times (Supplementary Figs. 2 and 18b), the simulation codes described above become inefficient. Instead of tracking the biomass and phenotype of each cell in a large population, we divide the cells into categories and track the number of cells from different categories, where a category is defined by a unique combination of cell biomass and phenotype ranges. In our simulations, the biomass of each cell ranges between 1 and 2, fP of each M cell ranges between 0 and 1. Since H cells do not mutate, H cells are divided into 100 categories. H cells that belong to category i have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] where ΔL = 10−2. Since only fP of M cells are modified by mutations, M cells are divided into 100 × 105 categories. M cells that belong to category (i, j) have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] and fP between [(j − 1) × ΔfP, j × ΔfP] where ΔfP = 10−5. Every time fP of a M cell is modified by mutations, this cell jumps from the current category to a new category determined by its new fP value.Similar to simulations with small population sizes, each selection cycle starts with ntot = 100 Newborn communities. Maturation time T is divided into time steps of length Δτ = 0.05. Over each time step, the growth in cell biomass and the changes in metabolites are simulated in a similar fashion as described above. At the end of each time step, the number of cells to die or to mutate in each category is drawn from a bionomial distribution. If fP of a M cell is modified by mutation, the mutation effect is drawn from the same distribution as described above: (frac{1}{2}) of mutations reduce fP to 0 and the other (frac{1}{2}) is randomly drawn from the distribution in Equation (11).At the end of a maturation cycle, top 10 Adults with the highest functions are chosen. Each then reproduces 10 Newborns via pipetting for the next cycle. The fold of dilution is similarly adjusted, so that the average of Newborn total biomass is BMtarget over all selection cycles. From each category of a chosen Adult, the number of cells assigned to a Newborn community is randomly drawn from a multinomial distribution.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Catchpole, C. K. Variation in the song of the great reed warbler Acrocephalus arundinaceus in relation to mate attraction and territorial defence. Anim. Behav. 31, 1217–1225 (1983).
Google Scholar 
2.Andersson, M. Sexual selection (University Press, 1994).
Google Scholar 
3.Searcy, W. A. & Yasukawa, K. Song and female choice. In Ecology and evolution of acoustic communication in birds (eds Kroodsma, D. E. & Miller, E. H.) 454–473 (Cornell University Press, 1996).
Google Scholar 
4.O’Loghlen, A. L. & Beecher, M. D. Mate, neighbour and stranger song: A female song sparrow perspective. Anim. Behav. 58, 13–20 (1999).
Google Scholar 
5.Gentner, T. Q. & Hulse, S. H. Female European starling preference and choice for variation in conspecific male song. Anim. Behav. 59, 443–458 (2000).CAS 
PubMed 

Google Scholar 
6.Molles, L. E. & Vehrencamp, S. L. Neighbour recognition by resident males in the banded wren Thryothorus pleurostictus a tropical songbird with high song type sharing. Anim. Behav. 61, 119–127 (2001).PubMed 

Google Scholar 
7.Ballentine, B., Hyman, J. & Nowicki, S. Vocal performance influences female response to male bird song: An experimental test. Behav. Ecol. 15, 163–168 (2004).
Google Scholar 
8.Forstmeier, W., Kempenaers, B., Meyer, A. & Leisler, B. A novel song parameter correlates with extra-pair paternity and reflects male longevity. Proc. R. Soc. Lond. B 269, 1479–1485 (2002).
Google Scholar 
9.de Kort, S. R., Eldermire, E. R. B., Valderrama, S., Botero, C. A. & Vehrencamp, S. L. Trill consistency is an age-related assessment signal in banded wrens. Proc. R. Soc. Lond. B 276, 2315–2321 (2009).
Google Scholar 
10.Węgrzyn, E., Leniowski, K. & Osiejuk, T. Whistle duration and consistency reflect philopatry and harem size in great reed warblers. Anim. Behav. 79, 1363–1372 (2010).
Google Scholar 
11.Węgrzyn, E., Leniowski, K. & Osiejuk, T. Introduce yourself at the beginning – Possibile identification function of the initial part of the song in the great reed warbler Acrocephalus arundinaceus. Ornis Fennica 86, 61–70 (2009).
Google Scholar 
12.Węgrzyn, E. & Leniowski, K. Middle Spotted Woodpecker territory owners distinguish between stranger and familiar foaters based on their vocal characteristics. Eur. Zool. J. 87, 58–72 (2020).
Google Scholar 
13.Podos, J. Motor constraints on vocal development in a songbird. Anim. Behav. 51, 1061–1070 (1996).
Google Scholar 
14.Podos, J., Southall, J. A. & Rossi-Santos, M. R. Vocal mechanics in Darwin’s finches: Correlation of beak gape and song frequency. J. Exp. Biol. 207, 607–619 (2004).PubMed 

Google Scholar 
15.Nelson, B. S., Deckers, G. J. L. & Suthers, R. A. Vocal tract filtering and sound radiation in a songbird. J. Exp. Biol. 208, 297–308 (2005).PubMed 

Google Scholar 
16.Falls, J. B. Individual recognition by sounds in birds. In Acoustic communication in birds Vol. 2 (eds Kroodsma, D. E. & Miller, E. H.) 237–278 (Academic Press, 1982).
Google Scholar 
17.Wiley, R. H., Hatchwell, B. J. & Davies, N. B. Recognition of individual males songs by female dunnocks: A mechanism increasing the number of copulatory partners and reproductive success. Ethology 88, 145–153 (1991).
Google Scholar 
18.Lind, H., Dabelsteen, T. & McGregor, P. K. Female great tits can identify mates by song. Anim. Behav. 52, 667–671 (1996).
Google Scholar 
19.Aubin, T., Jouventin, P. & Hildebrand, C. Penguins use the two-voice system to recognize each other. Proc. R. Soc. Lond. B 267, 1081–1087 (2000).CAS 

Google Scholar 
20.Charrier, I., Jouventin, P., Mathevon, N. & Aubin, T. Individual identity coding depends on call type in the South Polar Skua Catharacta maccormicki. Polar Biol. 24, 378–382 (2001).
Google Scholar 
21.Stoddard, P. K., Beecher, M. D., Horning, C. L. & Willis, M. S. Strong neighbor– stranger discrimination in song sparrows. Condor 92, 1051–1056 (1990).
Google Scholar 
22.Stoddard, P. K., Beecher, M. D., Horning, C. L. & Campbell, S. E. Recognition of individual neighbors by song in the song sparrow, a species with song repertoires. Behav. Ecol. Sociobiol. 29, 211–215 (1991).
Google Scholar 
23.Godard, R. Long–term memory of individual neighbours in a migratory songbird. Nature 350, 228–229 (1991).ADS 

Google Scholar 
24.Stoddard, P. K. Vocal recognition of neighbors by territorial passerines. In Ecology and evolution of acoustic communication in birds (eds Kroodsma, D. E. & Miller, E. H.) 56–374 (Cornell University Press, 1996).
Google Scholar 
25.Hyman, J. Seasonal variation in response to neighbors and strangers by a territorial songbird. Ethology 111, 951–961 (2010).
Google Scholar 
26.Mackin, W. A. Neighbor–stranger discrimination in Audubon’s Shearwater Puffinus l. lherminieri explained by a “real enemy” effect. Behav. Ecol. Sociobiol. 59(2), 326–332 (2005).
Google Scholar 
27.Charrier, I., Mathevon, N., Jouventin, P. & Aubin, T. Acoustic communication in a Black-headed Gull colony: How do chicks identify their parents?. Ethology 107, 961–974 (2001).
Google Scholar 
28.Lengagne, T., Lauga, J. & Aubin, T. Intra–syllabic acoustic signatures used by the King Penguin in parent–chick recognition: An experimental approach. J. Exp. Biol. 204, 663–672 (2001).CAS 
PubMed 

Google Scholar 
29.Jouventin, P. & Aubin, T. Acoustic systems are adapted to breeding ecologies: Individual recognition in nesting penguins. Anim. Behav. 64, 747–757 (2002).
Google Scholar 
30.Cucco, M. & Malacarne, G. Is the song of black restart males an honest signal of status?. Condor 101, 689–694 (1999).
Google Scholar 
31.Christie, P. J., Mennill, D. J. & Ratcliffe, L. M. Chickadee song structure is individually distinctive over long broadcast distances. Behaviour 141, 101–124 (2004).
Google Scholar 
32.Sherman, P. W., Reeve, H. K. & Pfennig D. W. Recognition systems. In: Krebs JR,DaviesNB, editors. Behavioural ecology: An evolutionary approach. Oxford: Blackwell Scientific. pp. 69–96 (1997).33.Kilham, L. Behavior and methods of communication of Pileated woodpeckers. Condor 61, 377–387 (1959).
Google Scholar 
34.Lawrence, L. & de Kort, S. R. A comparative life–history study of four species of woodpeckers. Ornithol. Monogr. 5, 1–155 (1967).
Google Scholar 
35.Winkler, H. & Short, L. A comparative analysis of acoustical signals in Pied woodpeckers (Aves, Picoides). Bull. Am. Mus. Nat. Hist. 160, 1–110 (1978).
Google Scholar 
36.Crusoe, D. A. Acoustic behavior and its role in the social relations of the red-headed wood-pecker: Picidae, Melanerpes erythrocephalus. Doctoral dissertation, University of Illinois at Chicago Circle (1980).37.Pardo, M. A. et al. Wild acorn woodpeckers recognize associations between individuals in other groups. Proc. R. Soc. B 285, 20181017 (2018).PubMed 
PubMed Central 

Google Scholar 
38.Leniowski, K. & Węgrzyn, E. The carotenoid-based red cap of the middle spotted woodpecker Dendrocopos medius reflects individual quality and territory size. Ibis 155(4), 804–813 (2013).
Google Scholar 
39.Podos, J., Lahti, D. C. & Moseley, D. L. Vocal performance and sensorimotor learning in songbirds. Adv. Study Behav. 40, 159–195 (2009).
Google Scholar 
40.Tremain, S. B., Swiston, K. A. & Mennill, D. J. Seasonal variation in acoustic signals of Pileated Woodpeckers. Wilson J. Ornithol. 120(3), 499–504 (2008).
Google Scholar 
41.Kilham, L. Reproductive behavior of red–bellied woodpeckers. Wilson Bull. 73, 237–254 (1961).
Google Scholar 
42.Catchpole, C. K. & Slater, P. J. B. Bird Song. Biological themes and variation 2nd edn. (Cambridge University Press, Cambridge, 2008).
Google Scholar 
43.Falls, J. B. & McNicholl, M. K. Neighbor–stranger discrimination by song in male blue grouse. Can. J. Zool. 57(2), 457–462 (1979).
Google Scholar 
44.Galeotti, P. & Pavan, G. Individual recognition of male tawny owls Strix aluco using spectrograms of their territorial calls. Ethol. Ecol. Evol. 3(2), 113–126 (1991).
Google Scholar 
45.Prum, R. O. Sexual selection and the evolution of mechanical sound production in manakins Aves: Pipridae. Anim. Behav. 55(4), 977–994 (1998).CAS 
PubMed 

Google Scholar 
46.Rebbeck, M., Corrick, R., Eaglestone, B. & Stainton, C. Recognition of individual European Nightjars Caprimulgus europaeus from their song. Ibis 143, 468–475 (2001).
Google Scholar 
47.Dodenhoff, D. J. An analysis of acoustic communication within the social system of downy woodpeckers Picoides pubescens. Doctoral dissertation, The Ohio State University. (2002).48.Budka, M., Deoniziak, K., Tumiel, T. & Białas, J. T. Vocal individuality in drumming in great spotted woodpecker—A biological perspective and implications for conservation. PLoS ONE 13(2), e0191716 (2018).PubMed 
PubMed Central 

Google Scholar 
49.Ydenberg, R. C., Giraldeau, L. A. & Falls, J. B. Neighbours, strangers, and the asymmetric war of attrition. Anim. Behav. 36(2), 343–347 (1988).
Google Scholar 
50.Delport, W., Kemp, A. C. & Ferguson, W. H. Vocal identification of individual African Wood Owls Strix woodfordii: a technique to monitor long-term adult turnover and residency. Ibis 144, 30–39 (2002).
Google Scholar 
51.Peake, T. M. et al. Individuality in Corncrake Crex crex vocalisations. Ibis 140, 120–217 (1998).
Google Scholar 
52.Hoodless, A. N., Inglis, J. G., Doucet, J.-P. & Aebischer, N. J. Vocal individuality in the roding calls of Woodcock Scolopax rusticola and their use to validate a survey method. Ibis 150, 80–89 (2008).
Google Scholar 
53.Grava, T., Mathevon, N., Place, E. & Balluet, P. Individual acoustic monitoring of the European Eagle Owl Bubo bubo. Ibis 150, 279–287 (2008).
Google Scholar 
54.Odom, K. J., Slaght, J. C. & Gutierrez, R. J. Distinctiveness in the territorial calls of Great horned owls within and among years. J. Raptor Res. 47, 21–30 (2013).
Google Scholar 
55.Aubin, T., Mathevon, N., Staszewski, V. & Boulinier, T. Acoustic communication in the Kittiwake Rissa tridactyla: potential cues for sexual and individual signatures in ling calls. Polar Biol. 30, 1027–1033 (2007).
Google Scholar 
56.Bretagnolle, V. & Laquette, B. Structural variation in the call of the Cory’s Shearwater (Colonectris diodemea, Aves, Procellaridae). Ethology 85, 313–323 (1990).
Google Scholar 
57.de Broke, M. L. Sexual differences in the voice and individual vocal recognition in the Manx Shearwater (Puffinus Puffinus). Anim. Behav. 26, 622–629 (1978).
Google Scholar 
58.Dreiss, A. N., Ruppli, C. A. & Roulin, A. Individual vocal signatures in barn owl nestling: does individual recognition have an adaptive role in sibling vocal competition?. J. Evol. Biol. 27, 63–75 (2014).CAS 
PubMed 

Google Scholar 
59.Volodin, I. A., Volodina, E. V., Klenova, A. V. & Filatova, O. A. Individual and sexual differences in the calls of the monomorphic White-faced Whistling Duck Dendrocygna viduata. Acta Ornithol. 40, 43–52 (2005).
Google Scholar 
60.Bragina, E. & Beme, J. sexual and individual features in the long range and short range calls of the White-naped crane. Condor 115, 501–507 (2013).
Google Scholar 
61.Terry, A. M. R., Peake, T. M. & McGregor, P. K. The role of vocal individuality in conservation. Front. Zool. 2, 10 (2005).PubMed 
PubMed Central 

Google Scholar 
62.Pasinelli, G. Oaks (Quercus sp.) and only oaks? Relations between habitat structure and home range size of the middle spotted woodpecker (Dendrocopos medius). Biol. Conserv. 93(2), 227–235 (2000).
Google Scholar 
63.Pasinelli, G. Dendrocopos medius middle spotted woodpecker. BWP Update 5(1), 49–99 (2003).
Google Scholar 
64.Michalek, K. G. & Winkler, H. Parental care and parentage in monogamous great spotted woodpeckers Picoides major and middle spotted woodpeckers Picoides medius. Behaviour 138(10), 1259–1285 (2001).
Google Scholar 
65.Pasinelli, G., Hegelbach, J. & Reyer, H.-U. Spacing behavior of the Middle Spotted Woodpecker in central Europe. J. Wildl. Manag. 65, 432–441 (2001).
Google Scholar 
66.Pasinelli, G. Breeding performance of the middle spotted woodpecker Dendrocopos medius in relation to weather and territory quality. Ardea 89, 353–361 (2001).
Google Scholar 
67.Kosiński, Z. & Winiecki, A. Ocena liczebności dzięcioła średniego Dendrocopos medius – Porównanie metody kartograficznej z użyciem stymulacji magnetofonowej z metodą wyszukiwania gniazd. Notatki Ornitologiczne 44, 43–55 (2003).
Google Scholar 
68.Specht, R. Avisoft-SASLab Pro: sound analysis and synthesis laboratory (Avisoft Bioacoustics, 2002).
Google Scholar 
69.Mundry, R. & Sommer, C. Discriminant function analysis with nonindependent data: consequences and an alternative. Anim. Behav. 74, 965–976 (2007).
Google Scholar 
70.Tabachnick, B. G. & Fidell, L. S. Using multivariate statistics 4th edn. (Allyn and Bacon, 2001).
Google Scholar 
71.Leniowski, K. Signaling quality in the Middle Spotted Woodpecker Dendrocopos medius: home ranges, colour ornaments and calls. (PhD thesis) Adam Mickiewicz University, Poznań, Poland (2011). More

1.Bergmann, C. About the relationships between heat conservation and body size of animals. Goett. Stud. (original in German) 1, 595–708 (1847).
Google Scholar 
2.Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming?. Trends Ecol. Evol. 26, 285–291 (2011).PubMed 

Google Scholar 
3.Ashton, K. G., Tracy, M. C. & de Queiroz, A. Is Bergmann’s rule valid for mammals?. Am. Nat. 156, 390–415 (2000).PubMed 

Google Scholar 
4.Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).
Google Scholar 
5.Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. Elife 7, e27166 (2018).PubMed 
PubMed Central 

Google Scholar 
6.Mousseau, T. A. Ectotherms follow the converse to Bergmann’s rule. Evolution 51, 630–632 (1997).PubMed 

Google Scholar 
7.Ashton, K. G. & Feldman, C. R. Bergmann’s rule in nonavian reptiles: Turtles follow it, lizards and snakes reverse it. Evolution 57, 1151–1163 (2003).PubMed 

Google Scholar 
8.Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16, 606–617 (2007).
Google Scholar 
9.Adams, D. C. & Church, J. O. Amphibians do not follow Bergmann’s rule. Evolution 62, 413–420 (2008).PubMed 

Google Scholar 
10.Angilletta, M. J. Jr. & Dunham, A. E. The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general. Am. Nat. 162, 333–342 (2003).
Google Scholar 
11.Peralta-Maraver, I. & Rezende, E. L. Heat tolerance in ectotherms scales predictably with body size. Nat. Clim. Change 11, 58–63 (2021).ADS 

Google Scholar 
12.Huey, R. B., Kearney, M. R., Krockenberger, A., Holtum, J. M. & Williams, S. E. Predicting organismal vulnerability to climate warming: Roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).
Google Scholar 
13.Ohlberger, J. Climate warming and ectotherm body size—from individual physiology to community ecology. Funct. Ecol. 27, 991–1001 (2013).
Google Scholar 
14.Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 89, 329–338 (2002).CAS 
PubMed 

Google Scholar 
15.West-Eberhard, M. J. Alternative adaptations, speciation, and phylogeny (A Review). Proc. Natl. Acad. Sci. USA 83, 1388–1392 (1986).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Forsman, A., Ahnesjö, J., Caesar, S. & Karisson, M. A model of ecological and evolutionary consequences of color polymorphism. Ecology 89, 34–40 (2008).PubMed 

Google Scholar 
17.McLean, C. A. & Stuart-Fox, D. Geographic variation in animal colour polymorphisms and its role in speciation. Biol. Rev. 89, 860–873 (2014).PubMed 

Google Scholar 
18.Spotila, J. R. Role of temperature and water in the ecology of lungless salamanders. Ecol. Monogr. 42, 95–125 (1972).
Google Scholar 
19.Cabe, P. R. et al. Fine-scale population differentiation and gene flow in a terrestrial salamander (Plethodon cinereus) living in continuous habitat. Heredity 98, 53–60 (2007).CAS 
PubMed 

Google Scholar 
20.Peterman, W. E. & Semlitsch, R. D. Fine-scale habitat associations of a terrestrial salamander: The role of environmental gradients and implications for population dynamics. PLoS ONE 8, e62184 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Farallo, V. R. & Miles, D. B. The importance of microhabitat: A comparison of two microendemic species of Plethodon to the widespread P. cinereus. Copeia 104, 67–77 (2016).
Google Scholar 
22.Burton, T. M. & Likens, G. E. Salamander populations and biomass in the Hubbard Brook experimental forest, New Hampshire. Copeia 1975, 541–546 (1975).
Google Scholar 
23.Mathis, A. Territories of male and female terrestrial salamanders: Costs, benefits, and intersexual spatial associations. Oecologia 86, 433–440 (1991).ADS 
PubMed 

Google Scholar 
24.Anthony, C. D. & Pfingsten, R. A. Eastern red-backed salamander. Plethodon cinereus (Green 1818). In Amphibians of Ohio. Ohio Biological Survey (eds Pfingsten, R. A. et al.) 335–360 (2013).
25.Moore, J.-D. & Ouellet, M. Questioning the use of an amphibian colour morph as an indicator of climate change. Glob. Change Biol. 21, 566–571 (2015).ADS 

Google Scholar 
26.Highton, R. Revision of North American salamanders of the genus Plethodon. Bull. Fla. State Mus. 6, 236–367 (1962).
Google Scholar 
27.Acord, M. A., Anthony, C. D. & Hickerson, C. M. Assortative mating in a polymorphic salamander. Copeia 2013, 676–683 (2013).
Google Scholar 
28.Reiter, M. K., Anthony, C. D. & Hickerson, C. A. M. Territorial behavior and ecological divergence in a polymorphic salamander. Copeia 2014, 481–488 (2014).
Google Scholar 
29.Paluh, D. J., Eddy, C., Ivanov, K., Hickerson, C. M. & Anthony, C. D. Selective foraging on ants by a terrestrial polymorphic salamander. Am. Midl. Nat. 174, 265–277 (2015).
Google Scholar 
30.Stuczka, A., Hickerson, C. M. & Anthony, C. D. Niche partitioning along the diet axis in a colour polymorphic population of Eastern Red-backed Salamanders, Plethodon cinereus. Amphibia-Reptilia 37, 283–290 (2016).
Google Scholar 
31.Otaibi, B. W., Johnson, Q. K. & Cosentino, B. J. Postautotomy tail movement differs between colour morphs of the red-backed salamander (Plethodon cinereus). Amphibia-Reptilia 38, 395–399 (2017).
Google Scholar 
32.Hantak, M. M., Brooks, K. M., Hickerson, C. M., Anthony, C. D. & Kuchta, S. R. A spatiotemporal assessment of dietary partitioning between color morphs of a terrestrial salamander. Copeia 108, 727–736 (2020).
Google Scholar 
33.Moreno, G. Behavioral and physiological differentiation between the color morphs of the salamander, Plethodon cinereus. J. Herpetol. 23, 335–341 (1989).
Google Scholar 
34.Anthony, C. D., Venesky, M. D. & Hickerson, C. A. M. Ecological separation in a polymorphic terrestrial salamander. J. Anim. Ecol. 77, 646–653 (2008).PubMed 

Google Scholar 
35.Evans, A. E., Urban, M. C. & Jockusck, E. L. Developmental temperature alters color polymorphism but not hatchling size in a woodland salamander. Oecoloiga 192, 909–918 (2020).ADS 

Google Scholar 
36.Petruzzi, E. E., Niewiarowski, P. H. & Moore, F. B. G. The role of thermal niche selection in maintenance of a colour polymorphism in redback salamanders (Plethodon cinereus). Front. Zool. 5, 3–10 (2006).
Google Scholar 
37.Muñoz, D. J., Hesed, K. M., Grant, E. H. C. & Miller, D. A. W. Evaluating within-population variability in behavior and demography for the adaptive potential of a dispersal-limited species to climate change. Ecol. Evol. 6, 8740–8755 (2016).PubMed 
PubMed Central 

Google Scholar 
38.Lotter, F. & Scott, N. J. Jr. Correlation between climate and distribution of the color morphs of the salamander Plethodon cinereus. Copeia 1977, 681–690 (1977).
Google Scholar 
39.Gibbs, J. P. & Karraker, N. E. Effects of warming conditions in eastern North American forests on Red-Backed Salamander morphology. Conserv. Biol. 20, 913–917 (2006).PubMed 

Google Scholar 
40.Cosentino, B. J., Moore, J.-D., Karraker, N. E., Ouellet, M. & Gibbs, J. P. Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecol. Evol. 7, 5426–5434 (2017).PubMed 
PubMed Central 

Google Scholar 
41.Evans, A. E., Forester, B. R., Jockusch, E. L. & Urban, M. C. Salamander morph frequencies do not evolve as predicted in response to 40 years of climate change. Ecography 41, 1687–1697 (2018).
Google Scholar 
42.Vose, R., Easterling, D., Kunkel, K., LeGrande, A. & Wehner, M. Temperature changes in the United States. In (eds Wuebbles, D. J. et al.). Climate Science Special Report: Fourth National Climate Assessment, Vol. 1, 185–206 (2017).43.Highton, R. Correlating costal grooves with trunk vertebrae in salamanders. Copeia 1957, 107–109 (1957).
Google Scholar 
44.Fisher-Reid, C. M. & Wiens, J. J. Is geographic variation within species related to macroevolutionary patterns between species?. J. Evol. Biol. 28, 1502–1515 (2015).CAS 
PubMed 

Google Scholar 
45.Wake, D. B. Comparative osteology and evolution of the lungless salamanders, family Plethodontidae. Mem. South. Calif. Acad. Sci. 4, 1–111 (1966).
Google Scholar 
46.Jockush, E. L. Geographic variation and phenotypic plasticity of number of trunk vertebrae in Slender Salamanders, Batrachoseps (Caudata: Plethodontidae). Evolution 51, 1966–1982 (1997).
Google Scholar 
47.Parra-Olea, G. & Wake, D. B. Extreme morphological and ecological homoplasy in tropical salamanders. Proc. Natl. Acad. Sci. USA 98, 7888–7891 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Pike, D. A. & Mitchell, J. C. Burrow-dwelling ecosystem engineers provide thermal refugia throughout the landscape. Anim. Conserv. 16, 694–703 (2013).
Google Scholar 
49.Caruso, N. M., Sears, M. W., Adams, D. C. & Lips, K. R. Widespread rapid reductions in body size of adult salamanders in response to climate change. Glob. Change Biol. 20, 1751–1759 (2014).ADS 

Google Scholar 
50.Radomski, T., Hantak, M. M., Brown, A. D. & Kuchta, S. R. Multilocus phylogeography of the Eastern Red-backed Salamander (Plethodon cinereus): Cryptic Appalachian diversity and post-glacial range expansion. Herpetologica 76, 61–73 (2020).
Google Scholar 
51.Abramoff, M. D., Magalhaes, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
52.Hill, A. W. et al. The Notes from Nature tool for unlocking biodiversity records from museum records through citizen science. ZooKeys 209, 219–223 (2012).
Google Scholar 
53.Constable, H., Guralnick, R., Wieczorek, J., Spencer, C. & Peterson, A. T. VertNet: A new model for biodiversity data sharing. PLoS Biol. 8, e1000309 (2010).PubMed 
PubMed Central 

Google Scholar 
54.Guralnick, R. & Constable, H. VertNet: Creating a data-sharing community. Bioscience 60, 258–259 (2010).
Google Scholar 
55.Guralnick, R. P. et al. The importance of digitized biocollections as a source of trait data and a new VertNet resource. Database 2016, baw158 (2016).PubMed 
PubMed Central 

Google Scholar 
56.Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720 (2016).PubMed 
PubMed Central 

Google Scholar 
57.Hollister, J., Shah, T., Robitaille, A., Beck, M. & Johnson, M. elevatr: Access elevation data from various APIs. R package version 0.3.1. https://doi.org/10.5281/zenodo.4282962 (2020).58.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2019).59.Barton, K. Package ‘MuMIn’. Model Selection and Model Averaging Based on Information Criteria. R package version 3.2.4. http://cran.r-project.org/web/packages/MuMIn/index.html (2012).60.Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
PubMed 

Google Scholar 
61.Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).ADS 
CAS 
PubMed 

Google Scholar 
62.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).ADS 

Google Scholar 
63.Weeks, B. C. et al. Shared morphological consequences of global warming in North American migratory birds. Ecol. Lett. 23, 316–325 (2020).PubMed 

Google Scholar 
64.Fisher-Reid, M. C., Engstrom, T. N., Kuczynski, C. A., Stephens, P. R. & Wiens, J. J. Parapatric divergence of sympatric morphs in a salamander: Incipient speciation on Long Island?. Mol. Ecol. 22, 4681–4694 (2013).PubMed 

Google Scholar 
65.Brodie, E. D. III. & Brodie, E. D. Jr. Tetrodotoxin resistance in garter snakes: An evolutionary response of predators to dangerous prey. Evolution 44, 651–659 (1990).PubMed 

Google Scholar 
66.Brodie, E. D. Jr., Ridenhour, B. J. & Brodie, E. D. III. The evolutionary response of predators to dangerous prey: Hotspots and coldspots in the geographic mosaic of coevolution between garter snakes and newts. Evolution 56, 2067–2082 (2002).PubMed 

Google Scholar 
67.Siepielski, A. M., DiBattista, J. D. & Carlson, S. M. It’s about time: The temporal dynamics of phenotypic selection in the wild. Ecol. Lett. 12, 1261–1276 (2009).PubMed 

Google Scholar 
68.Siepielski, A. M. et al. Spatial patterns of directional phenotypic selection. Ecol. Lett. 16, 1382–1392 (2013).PubMed 

Google Scholar 
69.Thompson, J. N. Coevolution: The geographic mosaic of coevolutionary arms races. Curr. Biol. 15, 992–994 (2005).
Google Scholar 
70.Corl, A., Davis, A. R., Kuchta, S. R. & Sinervo, B. Selective loss of polymorphic mating types is associated with rapid phenotype evolution during morphic speciation. Proc. Natl. Acad. Sci. 107, 4254–4259 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Roulin, A. Melanin-based colour polymorphism responding to climate change. Glob. Change Biol. 20, 3344–3350 (2014).ADS 

Google Scholar 
72.Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: Definitions, interpretations and evidence. Biol. Rev. 94, 1294–1316 (2019).PubMed 

Google Scholar 
73.Delhey, K. Gloger’s rule. Curr. Biol. 27, R689–R691 (2017).CAS 
PubMed 

Google Scholar 
74.Delhey, K. Darker where cold and wet: Australian birds follow their own version of Gloger’s rule. Ecography 41, 673–683 (2018).
Google Scholar 
75.Hantak, M. M. & Kuchta, S. R. Predator perception across space and time: Relative camouflage in a colour polymorphic salamander. Biol. J. Linn. Soc. 123, 21–33 (2018).
Google Scholar 
76.Atkinson, D. Temperature and organism size—A biological law for ectotherms?. Adv. Ecol. Res. 25, 1–58 (1994).
Google Scholar 
77.Angilletta, M. J. Jr., Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: Fitting pieces of a life-history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).PubMed 

Google Scholar 
78.Martof, B. S. & Rose, F. L. Geographic variation in southern populations of Desmognathus ochrophaeus. Am. Midl. Nat. 69, 376–425 (1963).
Google Scholar 
79.Tilley, S. G. Life histories and comparative demography of two salamander populations. Copeia 1980, 806–821 (1980).
Google Scholar 
80.Peterman, W. E., Crawford, J. A. & Hocking, D. J. Effects of elevation on plethodontid salamander body size. Copeia 104, 202–208 (2016).
Google Scholar 
81.Williams, E. E., Highton, R. & Cooper, D. M. Breakdown of polymorphism of the red-backed salamander on Long Island. Evolution 22, 76–86 (1968).PubMed 

Google Scholar 
82.Wake, D. B. & Lynch, J. F. The distribution, ecology and evolutionary history of plethodontid salamanders in tropical America. Sci. Bull. Nat. Hist. Mus. Los Angel Cty. 25, 1–65 (1976).
Google Scholar 
83.Baken, E. K., Mellenthin, L. E. & Adams, D. C. Macroevolution of desiccation-related morphology in plethodontid salamanders as inferred from a novel surface area to volume ratio estimation approach. Evolution 74, 476–486 (2020).PubMed 

Google Scholar 
84.Wake, D. B. Homoplasy: The result of natural selection, or evidence of design limitations?. Am. Nat. 138, 543–567 (1991).
Google Scholar 
85.Farallo, V. R., Wier, R. & Miles, D. B. The bogert effect revisited: Salamander regulatory behaviors are differently constrained by time and space. Ecol. Evol. 8, 11522–11532 (2018).PubMed 
PubMed Central 

Google Scholar 
86.Connette, G. M., Crawford, J. A. & Peterman, W. E. Climate change and shrinking salamanders: Alternative mechanisms for changes in plethodontid salamander body size. Glob. Change Biol. 21, 2834–2843 (2015).ADS 

Google Scholar 
87.Karell, P., Ahola, K., Karstinen, T., Valkama, J. & Brommer, J. E. Climate change drives microevolution in a wild bird. Nat. Commun. 2, 208 (2011).ADS 
PubMed 

Google Scholar 
88.Lepetz, V., Massot, M., Chaine, A. S. & Clobert, J. Climate warming and the evolution of morphotypes in a reptile. Glob. Change Biol. 15, 454–466 (2009).ADS 

Google Scholar 
89.Panayotova, I. N. & Horth, L. Modeling the impact of climate change on a rare color morph in fish. Ecol. Model. 387, 10–16 (2018).
Google Scholar 
90.Clusella-Trullas, S. & Nielsen, M. The evolution of insect body coloration under changing climates. Curr. Opin. Insect Sci 41, 25–32 (2020).PubMed 

Google Scholar 
91.Sullivan, C. N. & Koski, M. H. The effects of climate change on floral anthocyanin polymorphisms. Proc. R. Soc. B Biol. Sci. 288, 20202693 (2021).
Google Scholar 
92.Hugall, A. F. & Stuart-Fox, D. Accelerated speciation in colour-polymorphic birds. Nature 485, 631–634 (2012).ADS 
CAS 
PubMed 

Google Scholar 
93.Gray, S. M. & Mckinnon, J. S. Linking color polymorphism maintenance and speciation. Trends Ecol. Evol. 22, 71–79 (2007).PubMed 

Google Scholar 
94.Mckinnon, J. S. & Pierotti, M. R. Colour polymorphism and correlated characters: Genetic mechanisms and evolution. Mol. Ecol. 19, 5101–5125 (2010).PubMed 

Google Scholar 
95.Hantak, M. M. et al. Do genetic structure and landscape heterogeneity impact color morph frequency in a polymorphic salamander?. Ecography 42, 1383–1394 (2019).
Google Scholar 
96.U. S. Geological Survey – Gap Analysis Project. Eastern Red-backed Salamander (Plethodon cinereus) aERBSx_CONUS_2001v1 Range Map. https://doi.org/10.5066/F7P26X90 (2017). More

1.Krebs, P., Pezzatti, G. B., Mazzoleni, S., Talbot, L. M. & Conedera, M. Fire regime: History and definition of a key concept in disturbance ecology. Theory Biosci. 129, 53–69 (2010).PubMed 

Google Scholar 
2.Harvey, B. J., Donato, D. C. & Turner, M. G. Burn me twice, shame on who? Interactions between successive forest fires across a temperate mountain region. Ecology 97, 2272–2282 (2016).PubMed 

Google Scholar 
3.Prichard, S. J., Stevens-Rumann, C. S. & Hessburg, P. F. Tamm Review: Shifting global fire regimes: Lessons from reburns and research needs. For. Ecol. Manag. 396, 217–233 (2017).
Google Scholar 
4.González, M. E., Lara, A., Urrutia, R. & Bosnich, J. Cambio climático y su impacto potencial en la ocurrencia de incendios forestales en la zona centro-sur de Chile (33°–42° S). Bosque 32, 215–219 (2011).
Google Scholar 
5.Perfetti-Bolaño, A., González-acuña, D., Barrientos, C. & Moreno, L. Efectos del fuego sobre la avifauna del cerro Cayumanque, región del Bío-bío, Chile. Boletín Chil. Ornitol. 19, 1–11 (2013).
Google Scholar 
6.Engstrom, R. T. First-order fire effects on animals: Review and recommendations. Fire Ecol. 6, 115–130 (2010).
Google Scholar 
7.Doherty, T. S. et al. Ecosystem responses to fire: Identifying cross-taxa contrasts and complementarities to inform management strategies. Ecosystems 20, 872–884 (2017).
Google Scholar 
8.Kowaljow, E. et al. A 55-year-old natural experiment gives evidence of the effects of changes in fire frequency on ecosystem properties in a seasonal subtropical dry forest. Land Degrad. Dev. 30, 266–277 (2019).
Google Scholar 
9.Ferreira, C. C., Santos, X. & Carretero, M. A. Does ecophysiology mediate reptile responses to fire regimes? Evidence from Iberian lizards. PeerJ 4, e2107 (2016).PubMed 
PubMed Central 

Google Scholar 
10.Russell, K. R., Van Lear, D. H. & Guynn, D. C. Prescribed fire effects on herpetofauna: Review and management implications. Wildl. Soc. Bull. 27, 374–384 (1999).
Google Scholar 
11.Shine, R., Brown, G. P. & Elphick, M. J. Effects of intense wildfires on the nesting ecology of oviparous montane lizards. Austral. Ecol. 41, 756–767 (2016).
Google Scholar 
12.Driscoll, D. A., Smith, A. L., Blight, S. & Maindonald, J. Reptile responses to fire and the risk of post-disturbance sampling bias. Biodivers. Conserv. 21, 1607–1625 (2012).
Google Scholar 
13.Hu, Y., Kelly, L. T., Gillespie, G. R. & Jessop, T. S. Lizard responses to forest fire and timber harvesting: Complementary insights from species and community approaches. For. Ecol. Manag. 379, 206–215 (2016).
Google Scholar 
14.Hromada, S. J. et al. Response of reptile and amphibian communities to the reintroduction of fire in an oak/hickory forest. For. Ecol. Manag. 428, 1–13 (2018).
Google Scholar 
15.Chergui, B., Pleguezuelos, J. M., Fahd, S. & Santos, X. Modelling functional response of reptiles to fire in two Mediterranean forest types. Sci. Total Environ. 732, 139205 (2020).ADS 
CAS 
PubMed 

Google Scholar 
16.Costa, B. M., Pantoja, D. L., Sousa, H. C., de Queiroz, T. A. & Colli, G. R. Long-term, fire-induced changes in habitat structure and microclimate affect Cerrado lizard communities. Biodivers. Conserv. 29, 1659–1681 (2020).
Google Scholar 
17.Gómez-González, S., Ojeda, F. & Fernandes, P. M. Portugal and Chile: Longing for sustainable forestry while rising from the ashes. Environ. Sci. Policy 81, 104–107 (2018).
Google Scholar 
18.Arroyo, M. T. K., Cavieres, L., Peñaloza, A., Riveros, M. & Faggi, A. Relaciones fitogeográficas y patrones regionales de riqueza de especies en la flora del bosque lluvioso templado de Sudamérica. In: Ecología de los Bosques Nativos de Chile (eds Armesto, J. et al.) 71–100 (1995).19.González, M. E., Veblen, T. T. & Sibold, J. S. Fire history of Araucaria-Nothofagus forests in Villarrica National Park, Chile. J. Biogeogr. 32, 1187–1202 (2005).
Google Scholar 
20.Veblen, T. T. Regeneration patterns in Araucaria araucana forests in Chile. J. Biogeogr. 9, 11 (1982).
Google Scholar 
21.Aagesen, D. L. Indigenous resource rights and conservation of the monkey-puzzle tree (Araucaria araucana, Araucariaceae): A case study from southern Chile. Econ. Bot. 52, 146–160 (1998).
Google Scholar 
22.Aagesen, D. Burning monkey-puzzle: Native fire ecology and forest management in northern Patagonia. Agric. Human Values 21, 233–242 (2004).
Google Scholar 
23.Pollmann, W. & Veblen, T. T. Nothofagus regeneration dynamics in south-central Chile: A test of a general model. Ecol. Monogr. 74, 615–634 (2004).
Google Scholar 
24.Ortega, M., Ponce, X. & Tamarín, R. Manual con medidas para la prevención de incendios forestales, IX Región (Corporación Nacional Forestal (CONAF), 2006).
Google Scholar 
25.Ferreira, D., Pinho, C., Brito, J. C. & Santos, X. Increase of genetic diversity indicates ecological opportunities in recurrent-fire landscapes for wall lizards. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
26.Nimmo, D. G. et al. Predicting the century-long post-fire responses of reptiles. Glob. Ecol. Biogeogr. 21, 1062–1073 (2012).
Google Scholar 
27.Smith, A. L., Michael Bull, C. & Driscoll, D. A. Successional specialization in a reptile community cautions against widespread planned burning and complete fire suppression. J. Appl. Ecol. 50, 1178–1186 (2013).
Google Scholar 
28.Kelly, L. T., Bennett, A. F., Clarke, M. F. & Mccarthy, M. A. Optimal fire histories for biodiversity conservation. Conserv. Biol. 29, 473–481 (2015).PubMed 

Google Scholar 
29.Valentine, L. E., Reaveley, A., Johnson, B., Fisher, R. & Wilson, B. A. Burning in banksia woodlands: How does the fire-free period influence reptile communities?. PLoS ONE 7, e34448 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Uribe, S. & Estades, C. F. Reptiles in monterey pine plantations of the coastal range of central Chile. Rev. Chil. Hist. Nat. 87, 1–8 (2014).
Google Scholar 
31.Santos, X., Badiane, A. & Matos, C. Contrasts in short- and long-term responses of Mediterranean reptile species to fire and habitat structure. Oecologia 180, 205–216 (2016).ADS 
PubMed 

Google Scholar 
32.Ferreira, D., Mateus, C. & Santos, X. Responses of reptiles to fire in transition zones are mediated by bioregion affinity of species. Biodivers. Conserv. 25, 1543–1557 (2016).
Google Scholar 
33.Zúñiga, A. H. Changes in the structure of assemblages of three liolaemus lizards (Iguania, liolaemidae) in a protected area of south-central Chile affected by a mixed-severity wildfire. Zoodiversity 54, 265–274 (2020).
Google Scholar 
34.Rubio, A. V. & Simonetti, J. A. Lizard assemblages in a fragmented landscape of central Chile. Eur. J. Wildl. Res. 57, 195–199 (2011).
Google Scholar 
35.Driscoll, D. A. & Henderson, M. K. How many common reptile species are fire specialists? A replicated natural experiment highlights the predictive weakness of a fire succession model. Biol. Conserv. 141, 460–471 (2008).
Google Scholar 
36.Lindenmayer, D. B., Claridge, A. W., Gilmore, A. M., Michael, D. & Lindenmayer, B. D. The ecological roles of logs in Australian forests and the potential impacts of harvesting intensification on log-using biota. Pacific Conserv. Biol. 8, 121–140 (2002).
Google Scholar 
37.Evans, M. J., Newport, J. S. & Manning, A. D. A long-term experiment reveals strategies for the ecological restoration of reptiles in scattered tree landscapes. Biodivers. Conserv. 28, 2825–2843 (2019).
Google Scholar 
38.Mella, J. E. Guía de Campo Reptiles de Chile. Tomo: 1 Zona Central (2017).39.Whitford, K. R. & McCaw, W. L. Coarse woody debris is affected by the frequency and intensity of historical harvesting and fire in an open eucalypt forest. Aust. For. 82, 56–69 (2019).
Google Scholar 
40.Vidal, M. A. & Labra, A. Herpetología de Chile (GráficAndes, 2008).
Google Scholar 
41.Meiri, S. & Chapple, D. G. Biases in the current knowledge of threat status in lizards, and bridging the ‘assessment gap’. Biol. Conserv. 204, 6–15 (2016).
Google Scholar 
42.Tingley, R., Meiri, S. & Chapple, D. G. Addressing knowledge gaps in reptile conservation. Biol. Conserv. 204, 1–5 (2016).
Google Scholar 
43.Watson, J. E. M., Whittaker, R. J. & Dawson, T. P. Habitat structure and proximity to forest edge affect the abundance and distribution of forest-dependent birds in tropical coastal forests of southeastern Madagascar. Biol. Conserv. 120, 311–327 (2004).
Google Scholar 
44.Scott, D. M. et al. The impacts of forest clearance on lizard, small mammal and bird communities in the arid spiny forest, southern Madagascar. Biol. Conserv. 127, 72–87 (2006).
Google Scholar 
45.Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 

Google Scholar 
46.Hu, Y., Urlus, J., Gillespie, G., Letnic, M. & Jessop, T. S. Evaluating the role of fire disturbance in structuring small reptile communities in temperate forests. Biodivers. Conserv. 22, 1949–1963 (2013).
Google Scholar 
47.Gutiérrez, J. A., Krenz, J. D. & Ibargüengoytía, N. R. Effect of altitude on thermal responses of Liolaemus pictus argentinus in Argentina. J. Therm. Biol. 35, 332–337 (2010).
Google Scholar 
48.Artacho, P., Saravia, J., Perret, S., Bartheld, J. L. & Le Galliard, J. F. Geographic variation and acclimation effects on thermoregulation behavior in the widespread lizard Liolaemus pictus. J. Therm. Biol. 63, 78–87 (2017).PubMed 

Google Scholar 
49.Elzer, A. L. et al. Forest-fire regimes affect thermoregulatory opportunities for terrestrial ectotherms. Austral. Ecol. 38, 190–198 (2013).
Google Scholar 
50.Todd, B. D. & Andrews, K. M. Response of a reptile guild to forest harvesting. Conserv. Biol. 22, 753–761 (2008).PubMed 

Google Scholar 
51.Santos, X., Sillero, N., Poitevin, F. & Cheylan, M. Realized niche modelling uncovers contrasting responses to fire according to species-specific biogeographical affinities of amphibian and reptile species. Biol. J. Linn. Soc. 126, 55–67 (2019).
Google Scholar 
52.Farnsworth, L. M., Nimmo, D. G., Kelly, L. T., Bennett, A. F. & Clarke, M. F. Does pyrodiversity beget alpha, beta or gamma diversity? A case study using reptiles from semi-arid Australia. Divers. Distrib. 20, 663–673 (2014).
Google Scholar 
53.Vera-Escalona, I. M., Coronado, T., Muñoz-Mendoza, C. & Victoriano, P. F. Distribución histórica y actual de la lagartija Liolaemus pictus (Dumeril & Bibron 1837) (Liolaemidae) y nuevo límite continental sur de distribución. Gayana 74, 139–146 (2010).
Google Scholar 
54.Gunderson, A. R., Mahler, D. L. & Leal, M. Thermal niche evolution across replicated Anolis lizard adaptive radiations. Proc. R. Soc. B Biol. Sci. 285, 20172241 (2018).
Google Scholar 
55.Bowman, D. M. J. S. & Haberle, S. G. Paradise burnt: How colonizing humans transform landscapes with fire. Proc. Natl. Acad. Sci. USA. 107, 21234–21235 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Maia-Carneiro, T., Dorigo, T. A. & Rocha, C. F. D. Influences of seasonality, thermal environment and wind intensity on the thermal ecology of Brazilian sand lizards in a restinga remnant. South Am. J. Herpetol. 7, 241–251 (2012).
Google Scholar 
57.Nimmo, D. G., Kelly, L. T., Farnsworth, L. M., Watson, S. J. & Bennett, A. F. Why do some species have geographically varying responses to fire history?. Ecography 37, 805–813 (2014).
Google Scholar 
58.Jones, G. M. et al. Megafires: An emerging threat to old-forest species. Front. Ecol. Environ. 14, 300–306 (2016).
Google Scholar 
59.Chergui, B., Fahd, S., Santos, X. & Pausas, J. G. Socioeconomic factors drive fire-regime variability in the mediterranean basin. Ecosystems 21, 619–628 (2018).
Google Scholar 
60.Hellmich, W. & Goetsch, W. Die eidechsen Chiles, insbesondere die gattung Liolaemus, nach den sammlungen Goetsch-Hellmich, Vol. 24 (1934).61.Veblen, T. T., Burns, B. R., Kitzberger, T., Lara, A., Villalba,
A. The ecology of the conifers of southern South America. in Ecology of the Southern Conifers (eds Enright, N. J. & Hill, R. S.) 129–135 (Melbourne University Press, Carlton, Victoria, 1995).
Google Scholar 
62.Donoso, C. Bosques templados de Chile y Argentina. Variación, Estructura y Dinámica (Editorial Universitaria S.A., 1993).
Google Scholar 
63.Fuentes-Ramirez, A., Barrientos, M., Almonacid, L., Arriagada-Escamilla, C. & Salas-Eljatib, C. Short-term response of soil microorganisms, nutrients and plant recovery in fire-affected Araucaria araucana forests. Appl. Soil Ecol. 131, 99–106 (2018).
Google Scholar 
64.Urrutia-Estrada, J., Fuentes-Ramírez, A. & Hauenstein, E. Diferencias en la composición florística en bosques de Araucaria-Nothofagus afectados por distintas severidades de fuego. Gayana Bot. 75, 12–25 (2018).
Google Scholar 
65.González, M. E., Szejner, M., Muñoz, A. A. & Silva, J. Incendios catastróficos en bosques andinos de Araucaria-Nothofagus: Efecto de la severidad y respuesta de la vegetación. Bosque Nativ. 46, 12–17 (2009).
Google Scholar 
66.Luebert, F. & Pliscoff, P. Sinopsis bioclimática y vegetacional de Chile (Editorial Universitaria S.A., 2006).
Google Scholar 
67.(CONAF), C. N. F. Análisis de la afectación y severidad de los incendios forestales (2017).68.Zúñiga, A. H. et al. Rodent assemblage composition as indicator of fire severity in a protected area of south-central Chile. Austral. Ecol. 46, 249–260 (2021).
Google Scholar 
69.Demangel, D. Reptiles en Chile (Fauna Nativa Ediciones, 2016).
Google Scholar 
70.Vera-Escalona, I. et al. Lizards on ice: Evidence for multiple refugia in Liolaemus pictus (Liolaemidae) during the last glacial maximum in the southern Andean beech forests. PLoS ONE 7, e48358 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Ibarra, J. T. & Martin, K. Biotic homogenization: Loss of avian functional richness and habitat specialists in disturbed Andean temperate forests. Biol. Conserv. 192, 418–427 (2015).
Google Scholar 
72.Buckland, S. T., Rexstad, E. A., Marques, T. A. & Oedekoven, C. S. Methods in Statistical Ecology (Springer, 2015).MATH 

Google Scholar 
73.Ibarra, J. T. & Martin, K. Beyond species richness: An empirical test of top predators as surrogates for functional diversity and endemism. Ecosphere 6, 1–15 (2015).
Google Scholar 
74.Royle, J. A., Dawson, D. K. & Bates, S. Modeling abundance effects in distance sampling. Ecology 85, 1591–1597 (2004).
Google Scholar 
75.Marques, T. A., Thomas, L., Fancy, S. G. & Buckland, S. T. Improving estimates of bird density using multiple-covariate distance sampling. Auk 124, 1229–1243 (2007).
Google Scholar 
76.Fiske, I. J. & Chandler, R. B. Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23 (2011).
Google Scholar 
77.R Core Team. R: A Language and Environment for Statistical Computing (2019).78.Furnas, B. J., Newton, D. S., Capehart, G. D. & Barrows, C. W. Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion. Ecol. Evol. 9, 3046–3058 (2019).PubMed 
PubMed Central 

Google Scholar 
79.Burnham, K. P. & Anderson, D. R. Model Selection and Inference. A Practical Information-Theoretical Approach (Springer, 2002).MATH 

Google Scholar 
80.Pinheiro, J. & Bates, D. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models (2020).81.Mazerolle, J. M. Package ‘AICcmodavg’: Model Selection and Multimodel Inference Based on (Q)AIC(c) (2020).82.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50, 346–363 (2008).MathSciNet 
MATH 

Google Scholar  More

Accumulation and distribution of N in flue-cured tobacco growing in different soilsAccumulation dynamics of N in different soilsNitrogen gradually increased in loam soil, clay loam, and sandy loam soils with plant growth (Fig. 1), attaining a maximum at the mature-plant stage(2.10 g/plant, 1.43 g/plant, and 2.90 g/plant, respectively). Nitrogen accumulation was lower in plants grown in clay loam than in plants grown in loam soil and sandy loam during the entire growth period, indicating that the N supply capacity of clay loam was relatively weak, and tobacco plants grown in this soil had the lowest levels of N uptake and utilisation. The N uptake and accumulation in flue-cured tobacco grown in loam soil and sandy loam were basically the same before the ceiling stage, but at the mature stage, N accumulation was significantly higher in plants grown in sandy loam than in plants grown in loam soil and clay loam (P  More