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    South African Lagerstätte reveals middle Permian Gondwanan lakeshore ecosystem in exquisite detail

    Lucas, S. G. Permian tetrapod extinction events. Earth Sci. Rev. 170, 31–60 (2017).
    Google Scholar 
    Rampino, M. R. & Shen, S.-Z. The end-Guadalupian (259.8 Ma) biodiversity crisis: the sixth major mass extinction? Hist. Biol. 33, 716–722 (2019).
    Google Scholar 
    Day, M. O. & Rubidge, B. S. The late capitanian mass extinction of terrestrial vertebrates in the Karoo Basin of South Africa. Front. Earth Sci. 9, 631198 (2021).
    Google Scholar 
    Bordy, E. M. & Paiva, F. Stratigraphic architecture of the karoo river channels at the end-capitanian. Front. Earth Sci. 8, 521766 (2021).
    Google Scholar 
    Erwin, D. H., Bowring, S. A. & Yugan, J. In Catastrophic events and mass extinctions: impacts and beyond (eds. Koeberl, C. & MacLeod, K. G.) 363–383 (Geological Society of America, 2002).Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Viglietti, P. A. et al. Evidence from South Africa for a protracted end-Permian extinction on land. Proc. Natl Acad. Sci. USA 118, e2017045118 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rubidge, B. S. Did mammals originate in Africa? South African fossils and the Russian connection. Syd. Haughton Meml. Lect. 4, 1–14 (1995).
    Google Scholar 
    Day, M. O. & Rubidge, B. S. A brief lithostratigraphic review of the Abrahamskraal and Koonap formations of the Beaufort Group, South Africa: towards a basin-wide stratigraphic scheme for the Middle Permian Karoo. J. Afr. Earth Sci. 100, 227–242 (2014).
    Google Scholar 
    Day, M., Ramezani, J., Frazer, R. & Rubidge, B. U-Pb zircon age constraints on the vertebrate assemblages and palaeomagnetic record of the Guadalupian Abrahamskraal Formation, Karoo Basin, South Africa. J. Afr. Earth Sci. 186, 104435 (2022).CAS 

    Google Scholar 
    Koch, N. M., Garwood, R. & Parry, L. Fossils improve phylogenetic analyses of morphological characters. Proc. R. Soc. B Biol. Sci. 288, 1–8 (2021).
    Google Scholar 
    McLoughlin, S. Glossopteris: insights into the architecture and relationships of an iconic Permian Gondwanan plant. J. Bot. Soc. Bengal 65, 93–106 (2011).
    Google Scholar 
    Slater, B. J., McLoughlin, S. & Hilton, J. A high-latitude Gondwanan lagerstätte: the Permian permineralised peat biota of the Prince Charles Mountains, Antarctica. Gondwana Res. 27, 1446–1473 (2015).
    Google Scholar 
    Plumstead, E. P. Three thousand million years of plant life in Africa. (Geological Society of South Africa, 1969).Lacey, W. S., van Dijk, D. E. & Gordon-Gray, K. D. Fossil plants from the Upper Permian in the Mooi River district of Natal, South Africa. Ann. Natal. Mus. 22, 349–420 (1975).
    Google Scholar 
    Anderson, J. M. & Anderson, H. M. Palaeoflora of Southern Africa. Prodomus of South African megafloras. Devonian to Lower Cretaceous. (Balkema, 1985).Bordy, E. M. & Prevec, R. Sedimentology, palaeontology and palaeo-environments of the Middle (?) to Upper Permian Emakwezini Formation (Karoo Supergroup, South Africa). South Afr. J. Geol. 111, 429–458 (2008).Prevec, R. et al. Portrait of a Gondwanan ecosystem: a new late Permian fossil locality from KwaZulu-Natal, South Africa. Rev. Palaeobot. Palynol. 156, 454–493 (2009).
    Google Scholar 
    Mcloughlin, S. & Prevec, R. The architecture of Permian glossopterid ovuliferous reproductive organs. Alcheringa Australas. J. Palaeontol. 43, 480–510 (2019).
    Google Scholar 
    McLoughlin, S. & Prevec, R. The reproductive biology of glossopterid gymnosperms—a review. Rev. Palaeobot. Palynol. 295, 104527 (2021).
    Google Scholar 
    Riek, E. F. New Upper Permian insects from Natal, South Africa. Ann. Natal. Mus. 22, 755–789 (1976).
    Google Scholar 
    Riek, E. F. Fossil insects from the Middle Ecca (Lower Permian) of southern Africa. Palaeontol. Afr. 19, 145–148 (1976).
    Google Scholar 
    Riek, E. F. An entomobryid collembolan (Hexapoda: Collembola) from the Lower Permian of Southern Africa. Palaeontol. Afr. 19, 141–143 (1976).
    Google Scholar 
    McLachlan, I. R. & Anderson, A. M. Fossil insect wings from the Early Permian White Band Formation, South Africa. Palaeontol. Afr. 20, 83–86 (1977).
    Google Scholar 
    Pinto, I. D. & Pinto De Ornellas, L. New fossil insects from the White Band Formation (Permian), South Africa. Pesqui. Zool. 10, 96–104 (1978).
    Google Scholar 
    van Dijk, D. E. & Geertsema, H. Permian insects from the Beaufort Group of Natal, South Africa. Ann. Natal. Mus. 40, 137–171 (1999).
    Google Scholar 
    Geertsema, H., van Dijk, D. E. & van den Heever, A. J. Palaeozoic insects of southern Africa: a review. Palaeontol. Afr. 38, 19–25 (2002).
    Google Scholar 
    Rubidge, B. S., Erwin, D. H., Ramezani, J., Bowring, S. A. & de Klerk, W. J. High-precision temporal calibration of Late Permian vertebrate biostratigraphy: U-Pb zircon constraints from the Karoo Supergroup, South Africa. Geology 41, 363–366 (2013).CAS 

    Google Scholar 
    Mcloughlin, S., Prevec, R. & Slater, B. J. Arthropod interactions with the Permian Glossopteris flora. J. Palaeosciences 70, 43–133 (2021).
    Google Scholar 
    Shcherbakov, D. E. On Permian and Triassic insect faunas in relation to biogeography and the Permian-Triassic crisis. Paleontol. J. 42, 15–31 (2008).
    Google Scholar 
    Nel, A. et al. The earliest known holometabolous insects. Nature 503, 257–261 (2013).CAS 
    PubMed 

    Google Scholar 
    Nicholson, D. B., Mayhew, P. J. & Ross, A. J. Changes to the fossil record of insects through fifteen years of discovery. PLoS ONE 10, 1421–1435 (2015).
    Google Scholar 
    Glenister, B. F., Wardlaw, B. R., Lambert, L. L., Spinosa, C. & Bowring, S. A. Proposal of Guadalupian and component Roadian. Wordian Capitanian Stages Int. Stand. middle Permian Ser. Permophiles 34, 3–11 (1999).
    Google Scholar 
    Allison, P. A. Konservat-Lagerstätten: cause and classification. Paleobiology 14, 331–344 (1988).
    Google Scholar 
    Grimaldi, D. & Engel, M. S. Evolution of the Insects. (Cambridge University Press, 2005).Tian, Q. et al. Experimental investigation of insect deposition in lentic environments and implications for formation of Konservat Lagerstätten. Palaeontology 63, 565–578 (2020).
    Google Scholar 
    McCurry, M. R. et al. A Lagerstätte from Australia provides insight into the nature of Miocene mesic ecosystems. Sci. Adv. 8, 1–11 (2022).
    Google Scholar 
    Beckemeyer, R. J. & Hall, J. D. The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA. Afr. Invertebr. 48, 17 (2007).
    Google Scholar 
    Jell, P. A. The fossil insects of Australia. Mem. Qld. Mus. 50, 1–124 (2004).
    Google Scholar 
    Wickens, H., de, V. & Cole, D. I. Lithostratigraphy of the Skoorsteenberg Formation (Ecca Group, Karoo Supergroup), South Africa. South Afr. J. Geol. 120, 433–446 (2017).
    Google Scholar 
    Rubidge, B. S., Hancox, P. J. & Catuneaunu, O. Sequence analysis of the Ecca–Beaufort contact in the southern Karoo of South Africa. South Afr. J. Geol. 103, 81–96 (2000).
    Google Scholar 
    Lanci, L., Tohver, E., Wilson, A. & Flint, S. Upper Permian magnetic stratigraphy of the lower Beaufort Group, Karoo Basin. Earth Planet. Sci. Lett. 375, 123–134 (2013).CAS 

    Google Scholar 
    Belica, M. E. et al. Refining the chronostratigraphy of the Karoo Basin, South Africa: magnetostratigraphic constraints support an early Permian age for the Ecca Group. Geophys. J. Int. 211, 1354–1374 (2017).CAS 

    Google Scholar 
    Rubidge, B. S. & Day, M. O. Biostratigraphy of the Eodicynodon Assemblage Zone (Beaufort Group, Karoo Supergroup), South Africa. South Afr. J. Geol. 123, 141–148 (2020).
    Google Scholar 
    Nel, A., Garrouste, R. & Prevec, R. The first Permian Gondwanan damselfly-like Protozygoptera (Insecta, Odonatoptera). Hist. Biol. https://doi.org/10.1080/08912963.2022.2067996 (2022).Cawood, R. et al. The first ‘Grylloblattida’ of the family Liomopteridae from the Middle Permian in the Onder Karoo, South Africa (Insecta: Polyneoptera). Comptes Rendus Palevol. https://doi.org/10.5852/cr-palevol2022v21a22 (2022).Surange, K. R. & Chandra, S. Morphology of the gymnospermous fructifications of the Glossopteris flora and their relationships. Palaeontogr. B 149, 153–180 (1975).
    Google Scholar 
    White, M. E. Reproductive structures of the Glossopteridales in the plant fossil collection of the Australian Museum. Rec. Aust. Mus. 31, 473–504 (1978).
    Google Scholar 
    Nishida, H., Pigg, K. B. & DeVore, M. L. In Transformative Paleobotany, Ch. 8 (eds. Krings, M., Harper, C. J., Cúneo, N. R. & Rothwell, G. W.) 145–154 (Academic Press, 2018).McLoughlin, S. New records of Bergiopteris and glossopterid fructifications from the Permian of Western Australia and Queensland. Alcheringa Australas. J. Palaeontol. 19, 175–192 (1995).
    Google Scholar 
    McLoughlin, S. In Gondwana Eight (eds. Findlay, R. H., Unrug, R., Banks, M. R. & Veevers, J. J.) 253–264 (Balkema, 1993).Nishida, H., Pigg, K. B., Kudo, K. & Rigby, J. F. New evidence of the reproductive organs of Glossopteris based on permineralized fossils from Queensland, Australia. II: pollen-bearing organ Ediea gen. nov. J. Plant Res. 127, 233–240 (2014).PubMed 

    Google Scholar 
    Tomescu, A. M. F., Bomfleur, B., Bippus, A. C. & Savoretti, A. In Transformative Paleobotany (eds. Krings, M., Harper, C. J., Cuneo, N. R. & Rothwell, G. W.) 375–416 (Elsevier Academic Press, 2018).Bomfleur, B. et al. Diverse bryophyte mesofossils from the Triassic of Antarctica. Lethaia 47, 120–132 (2014).
    Google Scholar 
    Nel, A., Bechly, G., Prokop, J., Béthoux, O. & Fleck, G. Systematics and evolution of Paleozoic and Mesozoic damselfly-like Odonatoptera of the ‘protozygopteran’ grade. J. Paleontol. 86, 81–104 (2012).
    Google Scholar 
    Riek, E. F. Fossil insects from the Upper Permian of Natal, South Africa. Ann. Natal. Mus. 21, 513–532 (1973).
    Google Scholar 
    Gallego, O. F. et al. The most ancient Platyperlidae (Insecta, Perlida= Plecoptera) from early Late Triassic deposits in southern South America. Ameghiniana 48, 447–461 (2011).
    Google Scholar 
    Martins-Neto, R. G., Gallego, O. F. & Melchor, R. N. The Triassic insect fauna from South America (Argentina, Brazil and Chile): a checklist (except Blattoptera and Coleoptera) and descriptions of new taxa. Acta Zool. Cracoviensia 46, 229–256 (2003).
    Google Scholar 
    van Dijk, D. E. & Geertsema, H. A new genus of Permian Plecoptera (Afroperla) from KwaZulu-Natal, South Africa. Palaeontogr. B 12, 268–270 (2004).
    Google Scholar 
    Béthoux, O., Cui, Y., Kondratieff, B., Stark, B. & Ren, D. At last, a Pennsylvanian stem-stonefly (Plecoptera) discovered. BMC Evol. Biol. 11, 248 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Schubnel, T., Perdu, L., Roques, P., Garrouste, R. & Nel, A. Two new stem-stoneflies discovered in the Pennsylvanian Avion locality, Pas-de-Calais, France (Insecta: ‘Exopterygota’). Alcheringa Australas. J. Palaeontol. 43, 1–6 (2019).
    Google Scholar 
    Sharov, A. G. In Fundamentals of Paleontology: Arthropoda, Tracheata, Chelicerata. (eds. Rohdendorf, B. B. & Davis, D. R.) vol. 9 173–179 (Smithsonian Institution Libraries and NSCF, 1991).Sinitshenkova, N. D. In History of insects. (eds. Rasnitsyn, A. P. & Quicke, D. L. J.) Ch. 3.3, 388–426 (Kluwer Academic Publishers, 2002).Hayes, P. A. & Collinson, M. E. The Flora of the insect limestone (latest Eocene) from the Isle of Wight, southern England. Earth Environ. Sci. Trans. R. Soc. Edinb. 104, 245–261 (2014).
    Google Scholar 
    Zhang, Q. et al. Mayflies as resource pulses in Jurassic lacustrine ecosystems. Geology 50, 1043–1047 (2022).CAS 

    Google Scholar 
    Prokop, J. et al. Ecomorphological diversification of the Late Palaeozoic Palaeodictyopterida reveals different larval strategies and amphibious lifestyle in adults. R. Soc. Open Sci. 6, 190460 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Prokop, J., Nel, A., Engel, M. S., Pecharová, M. & Hörnschemeyer, T. New Carboniferous fossils of Spilapteridae enlighten postembryonic wing development in Palaeodictyoptera. Syst. Entomol. 41, 178–190 (2016).
    Google Scholar 
    Dos Santos, T. B., de Souza Pinheiro, E. R. & Iannuzzi, R. First evidence of seed predation by arthropods from Gondwana and its early Paleozoic history (Rio Bonito Formation, Paraná Basin, Brazil). PALAIOS 35, 292–301 (2020).
    Google Scholar 
    Nel, A., Garrouste, R. & Prokop, J. The first African Anthracoptilidae (Insecta: Paoliida) near the Permian—Triassic boundary in Kenya. Zootaxa 3925, 145 (2015).PubMed 

    Google Scholar 
    Riek, E. F. An unusual immature insect from the Upper Permian of Natal. Ann. Natal. Mus. 22, 271–274 (1974).
    Google Scholar 
    Dunlop, J. A., Penney, D., Tetlie, O. E. & Anderson, L. I. How many species of fossil arachnids are there? J. Arachnol. 36, 267–272 (2008).
    Google Scholar 
    Rasnitsyn, A. P. et al. Sequence and scale of changes in the terrestrial biota during the Cretaceous (based on materials from fossil resins). Cretac. Res. 61, 234–255 (2016).
    Google Scholar 
    Manum, S. B., Bose, M. N. & Sawyer, R. T. Clitellate cocoons in freshwater deposits since the Triassic. Zool. Scr. 20, 347–366 (1991).
    Google Scholar 
    Struck, T. H. et al. Phylogenomic analyses unravel annelid evolution. Nature 471, 95–98 (2011).CAS 
    PubMed 

    Google Scholar 
    Parry, L., Tanner, A. & Vinther, J. The origin of annelids. Palaeontology 57, 1091–1103 (2014).
    Google Scholar 
    Mikulic, D. G., Briggs, D. E. G. & Kluessendorf, J. A Silurian soft-bodied biota. Science 228, 715–717 (1985).CAS 
    PubMed 

    Google Scholar 
    Prokop, J., Szwedo, J., Lapeyrie, J., Garrouste, R. & Nel, A. New Middle Permian insects from Salagou Formation of the Lodève Basin in southern France (Insecta: Pterygota). Ann. Soci.été Entomol. Fr. NS 51, 14–51 (2015).
    Google Scholar 
    Cai, C. et al. Integrated phylogenomics and fossil data illuminate the evolution of beetles. R. Soc. Open Sci. 9, 211771 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Srivastava, A. K. & Agnihotri, D. Dilemma of late Palaeozoic mixed floras in Gondwana. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 54–69 (2010).
    Google Scholar 
    Raff, R. A. Written in stone: fossils, genes and evo–devo. Nat. Rev. Genet. 8, 911–920 (2007).CAS 
    PubMed 

    Google Scholar 
    Cunningham, J. A., Liu, A. G., Bengtson, S. & Donoghue, P. C. J. The origin of animals: can molecular clocks and the fossil record be reconciled? BioEssays 39, 1–12 (2017).PubMed 

    Google Scholar 
    McCulloch, G. A., Wallis, G. P. & Waters, J. M. A time-calibrated phylogeny of southern hemisphere stoneflies: Testing for Gondwanan origins. Mol. Phylogenet. Evol. 96, 150–160 (2016).PubMed 

    Google Scholar 
    Cui, Y. et al. Rhythms of Insect Evolution. (John Wiley & Sons, Ltd, 2019).Letsch, H. et al. Combining molecular datasets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera). Syst. Entomol. 46, 952–967 (2021).
    Google Scholar 
    Raja, N. B. et al. Colonial history and global economics distort our understanding of deep-time biodiversity. Nat. Ecol. Evol. 6, 145–154 (2022).PubMed 

    Google Scholar 
    Beattie, R. The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia. Afr. Invertebr. 48, 18 (2007).
    Google Scholar 
    Bernardi, M. et al. Late Permian (Lopingian) terrestrial ecosystems: a global comparison with new data from the low-latitude Bletterbach Biota. Earth Sci. Rev. 175, 18–43 (2017).
    Google Scholar 
    Jackson, S. E., Pearson, N. J., Griffin, W. L. & Belousova, E. A. The application of laser ablation-inductively coupled plasma-mass spectrometry to in situ U–Pb zircon geochronology. Chem. Geol. 211, 47–69 (2004).CAS 

    Google Scholar 
    Sláma, J. et al. Plešovice zircon—a new natural reference material for U–Pb and Hf isotopic microanalysis. Chem. Geol. 249, 1–35 (2008).
    Google Scholar 
    Wiedenbeck, M. et al. Three natural zircon standards for U‐Th‐Pb, Lu‐Hf, trace element and REE analyses. Geostand. Newsl. 19, 1–23 (2007).
    Google Scholar 
    Horstwood, M. S. A. et al. Community‐derived standards for LA ‐ ICP ‐ MS U‐(Th‐)Pb geochronology—uncertainty propagation, age interpretation and data reporting. Geostand. Geoanal. Res. 40, 311–332 (2016).CAS 

    Google Scholar 
    Paton, C., Hellstrom, J., Paul, B., Woodhead, J. & Hergt, J. Iolite: freeware for the visualisation and processing of mass spectrometric data. J. Anal. Spectrom. 26, 2508–2518 (2011).CAS 

    Google Scholar 
    Petrus, J. A. & Kamber, B. S. VizualAge: a novel approach to laser ablation ICP-MS U-Pb geochronology data reduction. Geostand. Geoanal. Res. 36, 247–280 (2012).CAS 

    Google Scholar 
    Rees, P. Mc. A., Gibbs, M. T., Ziegler, A. M., Kutzbach, J. E. & Behling, P. J. Permian climates: evaluating model predictions using global paleobotanical data. Geology 27, 891 (1999).
    Google Scholar 
    Walter, H. Vegetation of the Earth and ecological systems of the geo-biosphere. (Springer-Verlag, 1985).Lucas, S. G., Schneider, J. W. & Cassinis, G. Non-marine Permian biostratigraphy and biochronology: an introduction. Geol. Soc. Lond. Spec. Publ. 265, 1–14 (2006).
    Google Scholar 
    Scotese, C. In Atlas of Permo-Triassic Paleogeographic Maps (Mollweide Projection), Maps 43–52, Volumes 3 & 4 of the PALEOMAP Atlas for ArcGIS. (PALEOMAP Project, 2014). More

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    Origin, structure and functional transition of sex pheromone components in a false widow spider

    Experimental spidersExperimental spiders were maintained as previously reported37. Briefly, spiders were the F1 to F4 offspring of mated females collected from hallways of the Burnaby campus of Simon Fraser University (Burnaby, BC, CA). Upon hatching, juvenile spiders were housed individually in petri dishes (100 mm × 20 mm) and provisioned with the vinegar flies Drosophila melanogaster. Subadult spiders were fed with larvae of the mealworm beetle Tenebrio molitor. Each adult female spider was kept in a separate translucent 300-mL plastic cup (Western Family, CA) maintained at 22 °C under a reversed light cycle (12:12 h). Adult males and females were fed with black blow flies, Phormia regina. All spiders had access to water in cotton wicks. Water and food were provided once per week. Laboratory experiments were run during a reversed scotophase (0900 to 1700).Identification of contact pheromone components: Preparation of web extracts (summer 2017; spring and summer 2018)Each of the 100 spiders was allowed to build her web for three days on a wooden triangular prism scaffold (30 cm × 25 cm × 22 cm)44 of bamboo skewers (GoodCook, CA, USA) (Fig. 1b). After the spiders were removed from the scaffold, their webs were reeled up with a glass rod (10 cm × 0.5 cm) and deposited in a 1.5-mL glass vial. Per web, 50 µL of methanol (99.9% HPLC grade, Fisher Chemical, ON, Canada) were added and the silk was extracted for 24 h at room temperature. Prior to analysis, the silk was removed and the sample was concentrated under a steady nitrogen stream to the desired concentration.Identification of contact pheromone components: analyses of web extracts by gas chromatography–mass spectrometry (GC-MS)Aliquots (2 µL) of pooled and concentrated web extract (100 webs in 400 µL of solvent) were analysed by GC–MS, using a Varian Saturn Ion trap 2000 (Varian Inc., now Agilent Technologies Inc., Santa Clara, CA 95051, USA) and an Agilent 7890B GC coupled to a 5977 A MSD, both fitted with a DB-5 GC-MS column (30 m × 0.25 mm ID, film thickness 0.25 µm). The injector port was set to 250 °C, the MS source to 230 °C, and the MS quadrupole to 150 °C. Helium was used as a carrier gas at a flow rate of 35 cm s−1, with the following temperature programme: 50 °C held for 5 min, 10 °C min−1 to 280 °C (held for 10 min). Compounds were identified by comparing their mass spectra and retention indices (relative to aliphatic alkanes67) with those of authentic standards that were purchased or synthesised in our laboratory (Supplementary Table 1).Identification of contact pheromone components: high-performance liquid chromatography (HPLC) of web extractsWeb extract of virgin adult female S. grossa was fractionated by high-performance liquid chromatography (HPLC), using a Waters HPLC system (Waters Corporation, Milford, MA, USA; 600 Controller, 2487 Dual Absorbance Detector, Delta 600 pump) fitted with a Synergy Hydro Reverse Phase C18 column (250 mm × 4.6 mm, 4 µ; Phenomenex, Torrance, CA, USA). The column was eluted with a 1-mL/min flow of a solvent gradient, starting with 80% water (HPLC grade, EMD Millipore Corp., Burlington, MA, USA) and 20% acetonitrile (99.9% HPLC grade, Fisher Chemical, Ottawa, CA) and ending with acetonitrile after 10 min. A 60-web-equivalent extract was injected and 20 1-min fractions were collected. Each HPLC fraction (containing 20 web-equivalents) was tested in T-rod bioassays (Fig. 1c) for the presence of contact pheromone components. All eight fractions that elicited courtship responses by males (Supplementary Fig. 1) were analysed by HPLC-tandem MS/MS.Identification of contact pheromone components: HPLC-tandem MS/MS of bioactive HPLC fractionsThe bioactive HPLC fractions were analysed on a Bruker maXis Impact Quadrupole Time-of-Flight HPLC/MS System. The system consists of an Agilent 1200 HPLC fitted with a spursil C18 column (30 mm × 3.0 mm, 3 µ; Dikma Technologies, Foothill Ranch, CA, USA) and a Bruker maXis Impact Ultra-High Resolution tandem TOF (UHR-Qq-TOF) mass spectrometer. The LC-MS conditions were as follows: The mass spectrometer was set to positive electrospray ionisation (+ESI) with a gas temperature of 200 °C and a gas flow of 9 L/min. The nebuliser was set to 4 bar and the capillary voltage to 4200 V. The column was eluted with a 0.4-mL/min flow of a solvent gradient, starting with 80% water and 20% acetonitrile and ending with 100% acetonitrile after 4 min. The solvent contained 0.1% formic acid to improve peak shape.Identification of contact pheromone components: 1H NMR analyses of a bioactive fractionIn HPLC-MS analyses, a single bioactive fraction (9–10 min) appeared to contain only a single compound. This fraction was then further investigated using 1H NMR spectroscopy. The 1H NMR spectrum was recorded on a Bruker Advance 600 equipped with a QNP (600 MHz) using CDCl3. Signal positions (δ) are given in parts per million from tetramethylsilane (δ 0) and were measured relative to the signal of the solvent (1H NMR: CDCl3: δ 7.26).Identification of contact pheromone components: syntheses of candidate pheromone componentsThe syntheses of candidate pheromone components and synthetic intermediates are reported in the SI.Identification of contact pheromone components: T-rod bioassays (general procedures)The T-rod apparatus37 (Fig. 1c) consisted of a horizontal beam (25 cm × 0.4 cm) and a vertical beam (30 cm × 0.4 cm) held together by labelling tape (3 cm × 1.9 cm, Fisher Scientific, Ottawa, ON, CA). A piece of filter paper (2 cm2) was attached to each distal end of the horizontal beam. For each bioassay, an aliquot of web extract (in methanol), or a blend of synthetic candidate pheromone components, was applied to the randomly assigned treatment filter paper, whereas methanol was applied to the control filter paper. The solvent was allowed to evaporate for 1 min before the onset of a 15-min bioassay. A randomly selected naïve male spider was placed at the base of the vertical beam and the time he spent courting on each filter paper was recorded. In response to the presence of female-produced or synthetic pheromone on a filter paper, the male engaged in courtship, pulling silk with his hindlegs from his spinnerets and adding it to the paper. Sensing contact pheromone, the male essentially behaves as if he were courting on the web of a female. On a web, the male engages in web reduction prior to copulation, a behaviour that entails cutting sections of the female’s web with his chelicerae and wrapping the dismantled web bundle with his own silk pulled from his spinnerets41,56. Each T-rod apparatus was used only once. Replicates of experiments as part of specific research objectives were run in parallel to eliminate day effects on the responses of spiders. The sample size for each experiment was set to 20 unless otherwise stated.Identification of contact pheromone components: T-rod bioassays (specific experiments) (fall 2017; spring and summer 2018)Experiment 1 (fall 2017) tested a synthetic blend of volatile compounds 5–11 unique to mature S. grossa females (Fig. 1c and Supplementary Table 1) vs a solvent control. Parallel experiment 2 tested one web equivalent of virgin female web extract, followed by testing each of the 20 HPLC fractions in six replicates for the occurrence of courtship (spring 2018).Parallel experiments 3–6 (summer 2018) tested web extract at one female web equivalent (1 FWE) (Exp. 3), a ternary blend of the candidate contact pheromone components 12, 16 and 17 (Fig. 2d, Exp. 4), the same ternary blend (12, 16 and 17) in combination with the volatile compounds 5–11 (Exp. 5), and 5–11 on their own (Exp. 6).Parallel dose-response experiments 7–11 (summer 2018) tested the ternary blend of 12, 16 and 17 at five FWEs: 0.001 (Exp. 7); 0.01 (Exp. 8); 0.1 (Exp. 9); 1.0 (Exp. 10); and 10 (Exp. 11).Parallel experiments 12–15 tested the ternary blend, and all possible binary blends, of 12, 16 and 17. Parallel experiments 16–18 tested 12 and 16 in binary combination (Exp. 16) and singly (Exps. 17, 18).Origin of contact pheromone components (fall 2020)To trace the origin of contact pheromone component 12 (and coeluting 16), cold-euthanized female spiders were dissected in saline solution55 (25 mL of water and 25 mL of methanol, 160 mM NaCl, 7.5 mM KCl, 1 mM MgCl2, 4 mM NaHCO3, 4 mM CaCl2, 20 mM glucose, pH 7.4). Samples were homogenised (Kimble Pellet Pestle Motor, Kimble Kontes, USA) in methanol for 1 min, kept 24 h at room temperature for pheromone extraction, and then centrifuged (12,500 rpm, 3 °C for 20 min; Hermle Z 360 K refrigerated centrifuge; B. Hermle AG, Wehingen, DE) to obtain the supernatant for HPLC-MS analyses (see above) for the presence of 12 and 16. Three sequential sets of dissections aimed to determine (1) the pheromone-containing body tagma, (2) the pheromone-containing tissues or glands in that tagma and (3) the specific gland or tissue producing 12 & 16.To identify the pheromone-containing tagma, 11 spiders were severed at the pedicel, generating two tagmata: the cephalothorax with four pairs of legs and the abdomen. Each tagma was then extracted separately in 100 µL of methanol. Eight of 11 abdomen samples contained 12 and 16, whereas only one of 11 thorax samples contained 12 and 16 (Exp. 19), albeit at only trace amounts. With 12 and 16 being present in the abdomen, 20 additional abdomens were dissected68 to obtain separate samples of (i) haemolymph (25 µL), (ii) ventral cuticle (~0.5 cm2 near the pedicel, (iii) the ovaries, (iv) all silk glands combined, and (v) the gut (with anus, cloaca and Malpighian tubules). The remaining spider tissues (vi) were pooled as one sample, and 20 µL of the dissection buffer solution (vii) was obtained to detect potential pheromone bleeding. To each tissue sample, 50 µL of methanol were added. Only silk gland samples contained 12 and 16 (Exp. 20). Having established that only silk gland samples contained 12 and 16, the silk glands of 30 additional spiders were excised in the following order: (i) major ampullate gland, (ii) minor ampullate gland, (iii) anterior aggregate gland, (iv) posterior aggregate gland, (v) tubuliform, (vi) aciniform and flagelliform glands combined and (vii) pyriform gland. The glands from three spiders were combined in each sample and extracted in 30 µL methanol. Seven of ten posterior aggregate gland samples contained 12 and 16, with other silk gland samples not containing 12 and 16 or in only trace amounts (Exp. 21).Transition of contact pheromone components to mate attractant pheromone components: evidence for hydrolysis of contact pheromone components (12, 16 and 17) (spring 2021)To test for the hydrolysis of the contact pheromone components 12, 16 and 17, we compared their breakdown ratio (18/(12 + 16 + 17 + 18) on independent webs aged 0 days and 14 days (Exp. 24). Each of 140 spiders was allowed to spin a web on bamboo scaffolds for three days. Then, the spiders were removed and webs—by random assignment—were extracted immediately (0-day-old webs) or after 14 days of aging (14-day-old webs). On each web, the amount of contact pheromone components 12, 16 and 17, and of amide 18 as a breakdown product, was quantified using HPLC–MS, with 12 and 18 at 25 and 50 ng/µL as external standards.Transition of contact pheromone components to mate attractant pheromone components: Y-tube olfactometer bioassays (general procedures)The attraction of male spiders to web extracts and to candidate mate attractant pheromone components was tested in Y-tube olfactometers56 (Fig. 4a) lined with bamboo sticks to provide grip for the bioassay spider. Test stimuli were presented in translucent oven bags (30 cm × 31 cm; Toppits, Mengen, DE) secured to the orifice of side-arms. Test stimuli consisted of a triangular bamboo prism scaffold (each side 8.5 cm long) bearing a spider’s web, or bearing artificial webbing30 (40 ± 2 mg; Bling Star, CN) that was treated with web extract or synthetic chemicals in methanol (100 µL) as the treatment stimulus or with methanol (100 µL) as the control stimulus. For each experimental replicate, a male spider was introduced into a glass holding tube and allowed 2 min to acclimatise. Then, the holding tube was attached via a glass joint to the Y-tube olfactometer and an air pump was connected to the holding tube, drawing air at 100 mL/min through the olfactometer. Air entered the olfactometer through a glass tube secured to the oven bags’ second opening. A male that entered the olfactometer within the 5-min bioassay period was classed a responder and his first choice of oven bag (the oven bag he reached first) was recorded. Whenever a set of 30 replicates was completed by the same observer, using 30 separate Y-tubes, the Y-tubes were cleaned with hot water and soap (Sparkleen, Thermo Fisher Scientific, MA, United States) and dried in an oven at 100 °C for 3 h, whereas the bamboo sticks and the oven bags were discarded.Transition of contact pheromone components to mate attractant pheromone components: Y-tube olfactometer bioassays (specific experiments) (summer 2018)In experiments 22, 23 and 25–27, males were offered a choice between a solvent control stimulus and a treatment stimulus. The treatment stimulus consisted of (i) virgin female web-extract (1 web-equivalent) (Exp. 22, N = 24), (ii) the volatile compounds 5–11 unique to sexually mature females (Fig. 1d) (Exp. 23, N = 24), (iii) all breakdown products of the contact pheromone components 12, 16 and 17, consisting of the amide N-4-methylvaleroyl-l-serine (18) and the corresponding carboxylic acids 19, 20 and 21 (Exp. 25, N = 30), (iv) a blend of the acids 19, 20 and 21 (Exp. 26, N = 30) and (v) the amide 18 as a single compound (Exp. 27, N = 30). Compounds were tested at quantities as determined in virgin female web extract (50 webs in 150 μL of dichloromethane), following silyl-ester derivatization69 of acids in the extract, with valeric acid (200 ng; ≥99%, Sigma Aldrich, St. Louis, USA) added as an internal standard. Per web equivalent, there were 103 ng of 19, 3 ng of 20 and 54 ng of 21. The amide 18 was present at 200 ng per web equivalent, as determined using N-3-methylbutnaoyl-l-serine methyl ester as an external standard.Transition of contact pheromone components to mate attractant pheromone components: hallway of buildings experiment (fall 2018)As the ternary blend of the carboxylic acids 19, 20 and 21 attracted male spiders in Y-tube olfactometers (see Results), we aimed to confirm their functional role as mate attractant pheromone components also in ‘field’ settings (Exp. 28). To this end, we set up ten replicates of paired traps in building hallways on the Burnaby campus of Simon Fraser University. Adhesive-coated traps (Bell Laboratories Inc., Madison, WI, USA) were spaced 0.5 m within pairs and 20 m between pairs. By random assignment, one trap in each pair was baited with the carboxylic acids 19, 20 and 21 formulated in 200 µL of mineral oil (Anachemia, Montreal, CA; 2.8 mg of 19, 0.112 mg of 20 and 1.52 mg of 21), whereas the control trap received mineral oil only. Test stimuli were disseminated from a 400-μL microcentrifuge tube (Evergreen Scientific, Ranco Dominguez, CA, USA) with a hole in its lid punctured by a No. 3 insect pin (Hamilton Bell, Montvale, NJ, USA). Every week for 4 months (September to December 2018), traps were checked, lures were replaced, and the position of the treatment and the control trap within each trap pair was re-randomised.Communication function of amide breakdown product 18 (fall 2018)As the amide 18 did not attract males in Y-tube olfactometer experiments (see Results), we tested its alternate potential function as a contact pheromone component which, if active, would induce courtship by males. Using the T-rod apparatus (Fig. 1c), we treated one piece of filter paper with a solvent control and the other with a blend comprising both the contact pheromone components 12, 16 and 17 and the amide 18 (Exp. 29), a blend of 12, 16 and 17 (Exp. 30), and 18 alone (Exp. 31).Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: relationship between web pH and breakdown rates of contact pheromone components (summer 2020)We allowed each of the 70 spiders to spin two webs, using one web to quantify the amide breakdown product (18) of the contact pheromone components (see above), and the other web to determine its pH according to the slurry method57 (Exp. 32). To this end, we first measured the pH of 50 µL water (HPLC grade, EMD Millipore Corp., Burlington, MA, USA) and then of a web with the water functioning as a conductor for the pH metre (LAQUAtwin pH 22 (Horiba, Kyoto, JP). Between web measurements, the pH metre was rinsed with water and regularly re-calibrated using a pH 7 and a pH 4 buffer (Horiba, Kyoto, JP).Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: testing for pH-dependent saponification of contact pheromone components (12, 16 and 17) (summer 2021)To test whether pH alone catalyses saponification of the ester bond of contact pheromone components (12, 16 and 17), synthetic 12 was added to a 40% aqueous pH 7 buffer solution (Exp. 34), a pH 4 buffer solution (Exp. 34), and to acetonitrile (Exp. 35) as a polar aprotic solvent control (N = 12; 100 ng/µL each). pH-Dependent breakdown of 12 over time was assessed by analysing (HPLC-MS) diluted aqueous aliquots (2.5 ng/µl) of each sample at day 0 and after 14 days of storage at room temperature.Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: testing for the presence of a carboxylesterhydrolase (CEH) (summer 2021)To test for the presence of a carboxylesterhydrolase (CEH), for each of three replicates we extracted (i) five webs of adult virgin female L. hesperus (positive control, known to have a CEH45), (ii) 20 webs of subadult S. grossa (deemed to have not yet produced a CEH) and (iii) ten webs of adult virgin female S. grossa, accounting for the different amounts of silk produced by these three groups of spiders. For each replicate, webs were extracted in 200 µL 0.05 M Sørensen buffer58 and analysed by Bioinformatics Solutions (Waterloo, ON, CA). After web samples were incubated for 20 min at 60 °C in 2× sample volumes of 10% SDS (lauryl sulfate; protein-denaturing anionic detergent), they were sonicated for 20 min. Then, the supernatant was withdrawn, reduced with dithiothreitol (DTT), and alkylated with iodoacetamide (IAA). Alkylated samples were treated further with a protein solvent (S-Trap kit; Protifi, Farmingdale, NY, USA). Briefly, samples were acidified by phosphoric acid to pH ≤1. Then 6× of sample volume S-trap buffer was mixed in. The mixture was loaded by centrifugation onto an S-Trap Micro Spin Column and washed 3× with S-trap buffer. Using the serine protease trypsin, protein digestions were carried out at 47 °C for 1 h in 50 mM triethylamonium bicarbonate (TEAB) buffer within the S-Trap Micro Spin column. Digestion products were eluted sequentially with 40 µL 50 mM TEAB and 0.2% formic acid. Eluates were dried and re-suspended in 0.1% formic acid.Eluates were analysed by HPLC-MS/MS in positive ion mode on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher, San Jose, CA, USA), equipped with a nanospray ionisation source and a Thermo Fisher Ultimate 3000 RSLCnano HPLC System (Thermo Fisher). Peptide mixtures were loaded onto a PEPMAP100 C18 trap column (75 µm × 20 mm, 5 µm particle size; Thermo Fisher) at a constant flow of 30 μL/min and 60 °C isothermal. Peptides were eluted at a rate of 0.2 μL/min and separated using a Reprosil C18 analytical column (75 μm × 15 mm, 1.9 μm particle size; PepSep, DK) with a 60-min solvent gradient: 0–45 min: 4–35% acetonitrile + 0.1% formic acid; 45–55 min: 90% acetonitrile + 0.1% formic acid; 55–60 min: 4% acetonitrile + 0.1% formic acid.MS data were acquired in data-dependent mode with a cycle time of 3 s. MS1 scan data were acquired with the Orbitrap mass analyser, using a mass range of 400–1600 m/z, with the resolution set to 120,000. The automatic gain control (AGC) was set to 4e5, with a maximum ion injection time of 50 ms, and the radio frequency (RF) lens was set to 30%. Isolations for MS2 scans were run using a quadrupole mass analyser, with an isolation window of 0.7. MS2 scan data were acquired with the Orbitrap mass analyser at a resolution of 15,000 m/z, with a maximum ion injection time of 22 ms, and the AGC target set to 5e4. Higher energy collisional dissociation (HCD; fixed normalised collision energy: 30%) was used for generating MS2 spectra, with the number of microscans set to 1.Statistics and reproducibilityData (Supplementary Table 2) were analysed statistically using R70. Data of experiments 1–18 and 29–31 (testing courtship by male spiders in response to contact pheromone components) were analysed with a Wilcoxon test or Kruskal–Wallis two-tailed rank-sum test with Benjamini–Hochberg correction to adjust for multiple comparison. Data of experiments 19–21 (revealing the presence of contact pheromone components in the abdomen, silk glands, and posterior aggregate silk gland) were analysed with two-tailed, rather than one-tailed, Wilcoxon test or Kruskal–Wallis rank tests because we had no strong assumption as to whether or not pheromone would be present in any of these potential pheromone sources. The p values were adjusted for multiple comparison using the Benjamini–Hochberg method. Y-tube olfactometer data of experiments 22, 23 and 25–27, as well as the hallway experiment 28 (revealing attraction of male spiders to volatile pheromone components) were analysed using an one-tailed71 binomial test, anticipating attraction of spiders to volatile mate attractant pheromone components rather than to solvent control stimuli. Data of experiment 32 (revealing a correlation between web pH and breakdown of web-borne contact pheromone components) were analysed using generalised linear models. Data of experiments 33–35 (showing pH-dependent breakdown of synthetic contact pheromone) were compared using a two-tailed Kruskal–Wallis test with Benjamini–Hochberg correction.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Contributions of distemper control and habitat expansion to the Amur leopard viability

    Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).CAS 
    PubMed 

    Google Scholar 
    Morrison, J. C., Sechrest, W., Dinerstein, E., Wilcove, D. S. & Lamoreux, J. F. Persistence of large mammal faunas as indicators of global human impacts. J. Mammal. 88, 1363–1380 (2007).
    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

    Google Scholar 
    Finnegan, S. P. et al. Reserve size, dispersal and population viability in wide ranging carnivores: the case of jaguars in Emas National Park, Brazil. Anim. Conserv. 24, 3–14 (2021).
    Google Scholar 
    Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc. Ecol. 31, 491–503 (2016).
    Google Scholar 
    Gilbert, M. et al. Distemper, extinction, and vaccination of the Amur tiger. Proc. Natl. Acad. Sci. 117, 31954–31962 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smith, K. F., Acevedo-Whitehouse, K. & Pedersen, A. B. The role of infectious diseases in biological conservation. Anim. Conserv. 12, 1–12 (2009).
    Google Scholar 
    Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Courchamp, F. et al. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).CAS 
    PubMed 

    Google Scholar 
    Wittmann, M. J., Stuis, H. & Metzler, D. Genetic Allee effects and their interaction with ecological Allee effects. J. Anim. Ecol. 87, 11–23 (2018).PubMed 

    Google Scholar 
    Estes, J. A. et al. Trophic Downgrading of Planet Earth. Science 333, 301–306 (2011).CAS 
    PubMed 

    Google Scholar 
    Stein, A. B. et al. IUCN Red List of Threatened Species: Panthera pardus. IUCN Red List Threat. Species (2020).Vitkalova, A. V. et al. Transboundary cooperation improves endangered species monitoring and conservation actions: A case study of the global population of Amur leopards. Conserv. Lett. 11, e12574 (2018).
    Google Scholar 
    Wang, T. et al. A science-based approach to guide Amur leopard recovery in China. Biol. Conserv. 210, 47–55 (2017).
    Google Scholar 
    Lewis, J. et al. Assessing the health risks of reintroduction: The example of the Amur leopard, Panthera pardus orientalis. Transbound. Emerg. Dis. 67, 1177–1188 (2020).PubMed 

    Google Scholar 
    Terio, K. A. & Craft, M. E. Canine distemper virus (CDV) in another big cat: should CDV be renamed carnivore distemper virus? mBio 4, e00702–e00713 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Adhikari, R. B., Shrestha, M., Puri, G., Regmi, G. R. & Ghimire, T. R. Canine Distemper Virus (CDV): an emerging threat to Nepal’s wildlife. Appl. Sci. Technol. Ann. 1, 149–154 (2020).
    Google Scholar 
    Roelke-Parker, M. E. et al. A canine distemper virus epidemic in Serengeti lions (Panthera leo). Nature 379, 441–445 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mulia, B. H. et al. Exposure of Wild Sumatran Tiger (Panthera tigris sumatrae) to Canine Distemper Virus. J. Wildl. Dis. 57, 464–466 (2021).PubMed 

    Google Scholar 
    Gordon, C. H. et al. Canine distemper in endangered Ethiopian wolves. Emerg. Infect. Dis. 824–832 (2015) https://doi.org/10.3201/eid2105.141920.Timm, S. F. et al. A suspected canine distemper epidemic as the cause of a catastrophic decline in Santa Catalina Island foxes (Urocyon littoralis catalinae). J. Wildl. Dis. 45, 333–343 (2009).PubMed 

    Google Scholar 
    Sulikhan, N. S. et al. Canine distemper virus in a wild Far Eastern leopard (Panthera pardus orientalis). J. Wildl. Dis. 54, 170–174 (2018).PubMed 

    Google Scholar 
    Gilbert, M. et al. Canine distemper virus as a threat to wild tigers in Russia and across their range. Integr. Zool. 10, 329–343 (2015).PubMed 

    Google Scholar 
    Almberg, E. S., Cross, P. C. & Smith, D. W. Persistence of canine distemper virus in the Greater Yellowstone Ecosystem’s carnivore community. Ecol. Appl. 20, 2058–2074 (2010).PubMed 

    Google Scholar 
    Cleaveland, S. et al. The conservation relevance of epidemiological research into carnivore viral diseases in the Serengeti. Conserv. Biol. 21, 612–622 (2007).PubMed 

    Google Scholar 
    Haydon, D. T. et al. Low-coverage vaccination strategies for the conservation of endangered species. Nature 443, 692–695 (2006).CAS 
    PubMed 

    Google Scholar 
    Hebblewhite, M., Miquelle, D. G., Murzin, A. A., Aramilev, V. V. & Pikunov, D. G. Predicting potential habitat and population size for reintroduction of the Far Eastern leopards in the Russian Far East. Biol. Conserv. 144, 2403–2413 (2011).
    Google Scholar 
    Jiang, G. et al. New hope for the survival of the Amur leopard in China. Sci. Rep. 5, 15475 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Licht, D. S., Moen, R. A. & Romanski, M. Modeling viability of a potential Canada lynx reintroduction to Isle Royale national park. Nat. Areas J. 37, 170–177 (2017).
    Google Scholar 
    Menges, E. S. Population viability analysis for an endangered plant. Conserv. Biol. 4, 52–62 (1990).
    Google Scholar 
    Beissinger, S. R. & McCullough, D. R. Population viability analysis. J. Wildl. Manag. 67, 481–506 (2003).
    Google Scholar 
    Aresu, M. et al. Assessing the effects of different management scenarios on the conservation of small island vulture populations. Bird. Conserv. Int. 31, 111–128 (2021).
    Google Scholar 
    Benson, J. F. et al. Extinction vortex dynamics of top predators isolated by urbanization. Ecol. Appl. 29, e01868 (2019).PubMed 

    Google Scholar 
    Franklin, A. D., Lacy, R. C., Bauman, K. L., Traylor-Holzer, K. & Powell, D. M. Incorporating drivers of reproductive success improves population viability analysis. Anim. Conserv. 24, 386–400 (2021).
    Google Scholar 
    McCallum, H. Models for managing wildlife disease. Parasitology 143, 805–820 (2016).PubMed 

    Google Scholar 
    Bradshaw, C. J. A. et al. Novel coupling of individual-based epidemiological and demographic models predicts realistic dynamics of tuberculosis in alien buffalo. J. Appl. Ecol. 49, 268–277 (2012).
    Google Scholar 
    Shoemaker, K. T. et al. Effects of prey metapopulation structure on the viability of black-footed ferrets in plague-impacted landscapes: a metamodelling approach. J. Appl. Ecol. 51, 735–745 (2014).
    Google Scholar 
    Shaffer, M. L. Minimum population sizes for species conservation. BioScience 31, 131–134 (1981).
    Google Scholar 
    Seimon, T. A. et al. Canine distemper virus: an emerging disease in wild endangered Amur tigers (Panthera tigris altaica). mBio 4, e00410–e00413 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, T. et al. An introduction to Long-term Tiger-Leopard Observation Network based on camera traps in Northeast China. Biodivers. Sci. 28, 1059 (2020).
    Google Scholar 
    Gilbert, M. et al. Estimating the potential impact of canine distemper virus on the Amur tiger population (Panthera tigris altaica) in Russia. PLOS ONE 9, e110811 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Fahrig, L. How much habitat is enough? Biol. Conserv. 100, 65–74 (2001).
    Google Scholar 
    Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E. & Ramakrishnan, U. Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol. Conserv. 218, 181–191 (2018).
    Google Scholar 
    Hostetler, J. A., Onorato, D. P., Jansen, D. & Oli, M. K. A cat’s tale: the impact of genetic restoration on Florida panther population dynamics and persistence. J. Anim. Ecol. 82, 608–620 (2013).PubMed 

    Google Scholar 
    Johnson, W. E. et al. Genetic restoration of the Florida panther. Science 329, 1641–1645 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sankar, K. et al. Monitoring of reintroduced tigers in Sariska Tiger Reserve, Western India: preliminary findings on home range, prey selection and food habits. Trop. Conserv. Sci. 3, 301–318 (2010).
    Google Scholar 
    Kelly, P., Stack, D. & Harley, J. A review of the proposed reintroduction program for the Far Eastern leopard (Panthera pardus orientalis) and the role of conservation organizations, veterinarians, and zoos. Top. Companion Anim. Med. 28, 163–166 (2013).PubMed 

    Google Scholar 
    Hayward, M. W. & Somers, M. J. Reintroduction of top-order predators: using science to restore one of the drivers of biodiversity. in Reintroduction of Top-Order Predators 1–9 (John Wiley & Sons, Ltd, 2009). https://doi.org/10.1002/9781444312034.ch1.Pujol, B., Zhou, S.-R., Sanchez Vilas, J. & Pannell, J. R. Reduced inbreeding depression after species range expansion. Proc. Natl Acad. Sci. 106, 15379–15383 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    He, C., Du, J., Zhu, D. & Zhang, L. Population viability analysis of small population: a case study for Asian elephant in China. Integr. Zool. 15, 350–362 (2020).PubMed 

    Google Scholar 
    Sugimoto, T., Aramilev, V. V., Nagata, J. & McCullough, D. R. Winter food habits of sympatric carnivores, Amur tigers and Far Eastern leopards, in the Russian Far East. Mamm. Biol. 81, 214–218 (2016).
    Google Scholar 
    Athreya, V., Odden, M., Linnell, J. D. C., Krishnaswamy, J. & Karanth, K. U. A cat among the dogs: leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50, 156–162 (2016).
    Google Scholar 
    Steinmetz, R., Seuaturien, N., Intanajitjuy, P., Inrueang, P. & Prempree, K. The effects of prey depletion on dietary niches of sympatric apex predators in Southeast Asia. Integr. Zool. 16, 19–32 (2021).CAS 
    PubMed 

    Google Scholar 
    Appel, M. J. G. et al. Canine distemper epizootic in lions, tigers, and leopards in North America. J. Vet. Diagn. Invest. 6, 277–288 (1994).CAS 
    PubMed 

    Google Scholar 
    Coltman, D. W., Pilkington, J. G., Smith, J. A. & Pemberton, J. M. Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution 53, 1259–1267 (1999).PubMed 

    Google Scholar 
    Fox, C. W. & Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. Evolution 65, 246–258 (2011).PubMed 

    Google Scholar 
    Feng, L. et al. Collaboration brings hope for the last Amur leopards. Cat. N. 65, 20 (2017).
    Google Scholar 
    Lacy, R. C., Pollak, J. P., Miller, P. S., Hungerford, L. & Bright, P. Outbreak. Version 2.10. (2020).Lacy, R. C. & Pollak, J. P. Vortex: A stochastic simulation of the extinction process. Version 10.4. (2021).Pollak, J. P. & Lacy, R. C. Metamodel manager. Version 1.0.6. (2020).Pacioni, C., Sullivan, S., Lees, C. M., Miller, P. S. & Lacy, R. C. Outbreak user’s manual. Version 1.1. (2020).Roscoe, D. E. Epizootiology of canine-distemper in new-jersey raccoons. J. Wildl. Dis. 29, 390–395 (1993).CAS 
    PubMed 

    Google Scholar 
    Odden, M. & Wegge, P. Spacing and activity patterns of leopards Panthera pardus in the Royal Bardia National Park, Nepal. Wildl. Biol. 11, 145–152 (2005).
    Google Scholar 
    Stander, P. E., Haden, P. J., Kaqece, I. & Ghau, I. The ecology of asociality in Namibian leopards. J. Zool. 242, 343–364 (1997).
    Google Scholar 
    Huisman, J., Kruuk, L. E. B., Ellis, P. A., Clutton-Brock, T. & Pemberton, J. M. Inbreeding depression across the lifespan in a wild mammal population. Proc. Natl Acad. Sci. 113, 3585–3590 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morton, N. E., Crow, J. F. & Muller, H. J. An estimate of the mutational damage in man from data on consanguineous marriages. Proc. Natl Acad. Sci. 42, 855–863 (1956).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Balme, G. A., Slotow, R. & Hunter, L. T. B. Edge effects and the impact of non-protected areas in carnivore conservation: leopards in the Phinda–Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
    Google Scholar 
    Kumbhojkar, S., Yosef, R., Mehta, A. & Rakholia, S. A camera-trap home-range analysis of the Indian leopard (Panthera pardus fusca) in Jaipur, India. Animals 10, 1600 (2020).PubMed Central 

    Google Scholar 
    Rozhnov, V. V. et al. Home range structure and space use of a female Amur leopard, Panthera pardus orientalis (Carnivora, Felidae). Biol. Bull. 42, 821–830 (2015).
    Google Scholar 
    Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2, 185–193 (1988).
    Google Scholar 
    Hammersley, J. M. & Handscomb, D. C. General principles of the Monte Carlo method. in Monte Carlo Methods (eds. Hammersley, J. M. & Handscomb, D. C.) 50–75 (Springer Netherlands, 1964). https://doi.org/10.1007/978-94-009-5819-7_5.Kenney, J. S., Allendorf, F. W., McDougal, C. & Smith, J. L. D. How much gene flow is needed to avoid inbreeding depression in wild tiger populations? Proc. R. Soc. B Biol. Sci. 281, 20133337 (2014).
    Google Scholar 
    O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).
    Google Scholar 
    Miller, P. S., Lacy, R. C., Medina-Miranda, R., López-Ortiz, R. & Díaz-Soltero, H. Confronting the invasive species crisis with metamodel analysis: An explicit, two-species demographic assessment of an endangered bird and its brood parasite in Puerto Rico. Biol. Conserv. 196, 124–132 (2016).
    Google Scholar  More

  • in

    Increases in multiple resources promote competitive ability of naturalized non-native plants

    Study speciesTo increase our ability to generalize the results, we conducted two multispecies experiments34. The experiments were designed independently, but, as they used similar treatments, we analyzed them jointly to further increase generalizability. For the experiment in China, we selected eight species that are either native or non-native in China (Supplementary Table 1). For the experiment in Germany, we selected 16 species that are either native or non-native in Germany (Supplementary Table 1). All 24 species, representing seven families, are herbaceous, mainly occur in grasslands, and are common in the respective regions. To control for phylogenetic non-independence of species, we selected at least one non-native and one native species in each of the seven families. All non-native species are fully established (i.e. naturalized sensu Richardson et al.35) in the country where the respective experiment was conducted, and, as they are common, most of them could be considered invasive36,37. We classified the species as naturalized non-native or native to China or Germany based on the following databases: (1) “The Checklist of the Alien Invasive Plants in China”38, (2) the Flora of China (www.efloras.org), and (3) BiolFlor (www.ufz.de/biolflor). Seeds or stem fragments of the study species were obtained from local botanical gardens, local commercial seed companies, or from wild populations (Supplementary Table 1).The experiment in ChinaFrom 21 May to 27 June 2020, we planted or sowed the eight study species into plastic trays filled with potting soil (Pindstrup Plus, Pindstrup Mosebrug A/S, Denmark). We sowed the species at different times (Supplementary Table 1) because they were known to require different times until germination. Three species were grown from stem fragments because they mainly rely on clonal propagation, and the others were propagated from seeds (Supplementary Table 1).On 13 July 2020, we transplanted the cuttings or seedlings into 2.5-L circular plastic pots filled with a mixture of sand and vermiculite (1:1 v/v). Three competition treatments were imposed: (1) competition-free, in which plants were grown alone; (2) intraspecific competition, in which two individuals of the same species were grown together; (3) interspecific competition, in which two individuals, each from a different species were grown together. We grew all eight species without competition, in intraspecific competition, and in all 28 possible pairs of interspecific competition. For the competition-free and intraspecific-competition treatments, we replicated each species seven times (i.e. we had seven technical replicates). For the interspecific-competition treatment, for which we had many pairs of species (i.e. biological replicates), we replicated each pair two times.The experiment took place in a greenhouse at the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (Changchun, China). The greenhouse had a transparent plastic film on the top, which reduced the ambient light intensity by 12%. It was open on the sides so that insects and other organisms could enter. To vary nutrient availability, we applied to each pot either 5 g (low-nutrient treatment) or 10 g (high-nutrient treatment) of a slow-release fertilizer (Osmocote® Exact Standard, Everris International B.V., Geldermalsen, The Netherlands; 15% N + 9% P2O5 + 12% K2O + 2% MgO + trace elements). To vary light availability, we used two cages (size: 9 m × 4.05 m × 1.8 m). One of them was covered with two layers of black netting material, which reduced the light intensity by 71% (low light-intensity treatment, where the light intensity was on average 233.5 μmol m−2 s−1, measured on a sunny day). The other was left uncovered (high light-intensity treatment, where the light intensity was on average 826.7 μmol m−2 s−1).The experiment included a total of 672 pots ([8 no-competition × 7 replicates + 8 intraspecific-competition × 7 replicates + 28 interspecific-competition × 2 replicates]×2 nutrient treatments × 2 light treatments). The pots were randomly assigned to positions and were randomized once on 15 August within each block (i.e. the low or high light-intensity treatment). The initial height of each plant was measured on 15 July 2020, two days after the transplanting. We watered the plants daily to avoid water limitations. On 1 September 2020, we harvested the aboveground biomass of all plants. The biomass was dried at 65°C for 72 h to constant weight and then weighed to the nearest mg.The experiment in GermanyOn 15 June 2020, we sowed seeds of the 16 species into plastic trays filled with potting soil (Topferde, Einheitserde Co). On 6 July 2020, we transplanted the seedlings into 1.5-L pots filled with a mixture of potting soil and sand (1:1 v/v). Like the experiment in China, we imposed three competition treatments: competition-free, intraspecific competition, and interspecific competition. However, in this experiment, which had two times more species than the experiment in China, we only included 24 randomly chosen species pairs for the interspecific-competition treatment, and all of these pairs consisted of one naturalized non-native and one native species. For the competition-free treatment, we replicated each species two times (i.e. we had two technical replicates). For the competition treatments, we did not use technical replicates for any of the species combinations for logistic reasons. However, as we had a large number of species pairs in the inter-specific competition treatment, we had many biological replicates.The experiment took place outdoors in the Botanical Garden of the University of Konstanz (Konstanz, Germany). To vary nutrient availability, we applied to each pot once a week either 100 ml of a low-concentration liquid fertilizer (low-nutrient treatment; 0.5‰ Universol ® Blue oxide fertilizer, 18% N + 11% P + 18% K + 2.5% MgO + trace elements) or 100 ml of a high-concentration of the same liquid fertilizer (high-nutrient treatment; 1‰). In total, pots in the low- and high-nutrient treatment received 0.4 and 0.8 g fertilizer, respectively. To vary light availability, we used eight metal wire cages (size: 2 m × 2 m × 2 m). Four of the cages were covered with one layer of white and one layer of green netting material, which reduced the ambient light intensity by 84% (low light-intensity treatment; where the light intensity was on average 219.0 μmol m−2 s−1, measured on a sunny day). The remaining four cages were covered only with one layer of the white netting material, which served as a positive control for the effect of netting and reduced light intensity by 53% (high light-intensity treatment; where the light intensity was on average 678.4 μmol m−2 s−1). In other words, the low light-intensity treatment received 34% (66% reduction) of the light intensity in the high light-intensity treatment.The experiment included a total of 320 pots ([16 no-competition × 2 replicates + 16 intraspecific-competition + 32 interspecific-competition]×2 nutrient treatments × 2 light treatments). The eight cages were randomly assigned to fixed positions in the botanical garden. The pots were randomly assigned to the eight cages (40 pots in each cage) and were re-randomized once within and across cages of the same light treatment on 3 August 2020. Besides the weekly fertilization, we watered the plants two or three times a week to avoid water limitations. On 7 and 8 September 2020, we harvested the aboveground biomass of all plants. The biomass was dried at 70 °C for 96 h to constant weight and then weighed to the nearest 0.1 mg.Statistical analysesAll analyses were performed using R version 3.6.139. To test whether resource availability affected competitive outcomes between native and non-native species, we applied linear mixed-effects models to analyze the biomass of the plants in the two experiments jointly and separately, using the nlme package40. For the model used to analyze the two experiments jointly, we excluded interspecific competition between two non-natives and interspecific competition between two natives from the experiment in China, because non-native-non-native and native-non-native combinations were not included in the experiment in Germany. When we analyzed each experiment separately, the results were overall similar to the results of the joint analysis. Therefore, we focus in the manuscript on the joint analysis and present the results of the separate analyses in Supplementary Note 2.Because plant mortality was low and mainly happened after transplanting, we excluded pots in which plants had died. The final dataset contained 1180 individuals from 871 pots. In the model, we included the aboveground biomass of individuals as the response variable. We included the origin of the species (non-native or native), competition treatment (see below for details), nutrient treatment, light treatment and their interactions as fixed effects; study site (China or Germany), and identity and family of the species as random effects. In addition, we allowed each species to respond differently to the nutrient and light treatments (i.e. we included random slopes). To account for pseudoreplication41, we also included pots as random effects and cages (ten cages, eight from Germany and two from China) as random block effects. In the competition treatment, we had three levels: (1) no competition, (2) intraspecific competition, and (3) interspecific competition between native and non-native species. To split them into two contrasts, we created two variables42 testings (1) the effect of the presence of competitors, and (2) the difference between intra- and interspecific competition (see Supplementary Note 3 for details). To improve the normality of the residuals, we natural-log-transformed aboveground biomass. To improve the homoscedasticity of the residuals, we allowed the species and competition treatment to have different variances by using the varComb and varIdent functions43. Significances of the fixed effects were assessed with likelihood-ratio tests (type II) with the car package44.To determine the ‘competitive outcome’, i.e. which species will exclude or dominate over the other species at the endpoint for the community45,46, one should ideally conduct a long-term study. Alternatively, one could vary the density of each species, which mimics the dynamics of species populations across time (see refs. 47,48 for examples). However, applying this space-for-time-substitution method would have largely increased the size of the experiment, especially when combined with the light and nutrient treatments. Still, by growing plants alone, in intraspecific competition and in interspecific competition, our experiments meet the minimal requirement for measuring competitive outcome, at least in terms of short-term biomass production46,49.In the linear mixed-effects model of individual biomass, a significant effect of origin would indicate that native and naturalized non-native species differed in their biomass production, across all competition and resource-availability (light and nutrients) treatments. This would tell us the competitive outcome between non-natives and natives across different resource availabilities. For example, an overall higher level of biomass production of non-native species would indicate that non-natives would dominate when competing with natives. A significant interaction between a resource-availability treatment and the origin of the species would indicate that resource availability affects the biomass production of native and non-native species differently, averaged across all competition treatments. In other words, it would indicate that resource availability affects the competitive outcome between natives and non-natives. A significant interaction between a resource-availability treatment and the competition treatment would indicate that resource availabilities modify the effect of competition (e.g. no competition vs. competition). Other studies frequently have inferred competitive outcomes from the effect of competition by calculating the relative interaction intensity50. However, while the competitive outcome and effect of competition are often related, they are not equivalent45. This is because the competitive outcome is both determined by the effect of competition and intrinsic growth rate48,49. For example, a plant species that strongly suppress other species but has a low intrinsic growth rate still cannot dominate the community.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Coupling phenotypic changes to extinction and survival in an endemic prey community threatened by an invasive snake

    Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl. Acad. Sci. 113, 11261–11265 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salo, P., Korpimäki, E., Banks, P. B., Nordström, M. & Dickman, C. R. Alien predators are more dangerous than native predators to prey populations. Proc. R. Soc. B Biol. Sci. 274, 1237–1243 (2007).
    Google Scholar 
    Losos, J. B., Schoener, T. W. & Spiller, D. A. Predator-induced behaviour shifts and natural selection in field-experimental lizard populations. Nature 432, 505–508 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Komine, H., Iwai, N. & Kaji, K. Rapid responses in morphology and performance of native frogs induced by predation pressure from invasive mongooses. Biol. Invasions 23, 1293–1305 (2021).
    Google Scholar 
    Nunes, A. L. et al. A global meta-analysis of the ecological impacts of alien species on native amphibians. Proc. R. Soc. B Biol. Sci. 286, 20182528 (2019).
    Google Scholar 
    Berthon, K. How do native species respond to invaders? Mechanistic and trait-based perspectives. Biol. Invasions 17, 2199–2211 (2015).
    Google Scholar 
    Strauss, S. Y., Lau, J. A. & Carroll, S. P. Evolutionary responses of natives to introduced species: What do introductions tell us about natural communities?. Ecol. Lett. 9, 354–371 (2006).
    Google Scholar 
    Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Sih, A. et al. Predator–prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 (2010).
    Google Scholar 
    Gould, S. J. & Vrba, E. Exaptation—A missing term in the science of form. Paleobiology 8, 4–15 (1982).
    Google Scholar 
    Komine, H. et al. Rapid behavioural responses of native frogs caused by past predation pressure from invasive mongooses. J. Zool. 310, 126–134 (2020).
    Google Scholar 
    Hoare, J. M., Pledger, S., Nelson, N. J. & Daugherty, C. H. Avoiding aliens: Behavioural plasticity in habitat use enables large, nocturnal geckos to survive Pacific rat invasions. Biol. Conserv. 136, 510–519 (2007).
    Google Scholar 
    Trompeter, W. P. & Langkilde, T. Invader danger: Lizards faced with novel predators exhibit an altered behavioral response to stress. Horm. Behav. 60, 152–158 (2011).CAS 
    PubMed 

    Google Scholar 
    Thawley, C. J., Goldy-Brown, M., McCormick, G. L., Graham, S. P. & Langkilde, T. Presence of an invasive species reverses latitudinal clines of multiple traits in a native species. Glob. Change Biol. 25, 620–628 (2019).ADS 

    Google Scholar 
    Melotto, A., Manenti, R. & Ficetola, G. F. Rapid adaptation to invasive predators overwhelms natural gradients of intraspecific variation. Nat. Commun. 11, 3608 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Langkilde, T. Invasive fire ants alter behavior and morphology of native lizards. Ecology 90, 208–217 (2009).PubMed 

    Google Scholar 
    Moore, R. D., Griffiths, R. A., O’Brien, C. M., Murphy, A. & Jay, D. Induced defences in an endangered amphibian in response to an introduced snake predator. Oecologia 141, 139–147 (2004).ADS 
    PubMed 

    Google Scholar 
    Fritts, T. H. & Rodda, G. H. The role of introduced species in the degradation of island ecosystems: A case history of Guam. Annu. Rev. Ecol. Syst. 29, 113–140 (1998).
    Google Scholar 
    Caut, S., Angulo, E. & Courchamp, F. Dietary shift of an invasive predator: Rats, seabirds and sea turtles. J. Appl. Ecol. 45, 428–437 (2008).PubMed 

    Google Scholar 
    Bonnaud, E. et al. The diet of feral cats on islands: A review and a call for more studies. Biol. Invasions 13, 581–603 (2011).
    Google Scholar 
    Guiden, P. W., Bartel, S. L., Byer, N. W., Shipley, A. A. & Orrock, J. L. Predator–prey interactions in the Anthropocene: Reconciling multiple aspects of novelty. Trends Ecol. Evol. 34, 616–627 (2019).PubMed 

    Google Scholar 
    Savidge, J. A. Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668 (1987).
    Google Scholar 
    Wu, N. C., Alton, L. A., Clemente, C. J., Kearney, M. R. & White, C. R. Morphology and burrowing energetics of semi-fossorial skinks (Liopholis spp.). J. Exp. Biol. 218, 2416–2426 (2015).PubMed 

    Google Scholar 
    Losos, J. B. The evolution of form and function: Morphology and locomotor performance in West Indian lizards. Evolution 44, 1189–1203 (1990).PubMed 

    Google Scholar 
    Irschick, D. J. et al. A comparative analysis of clinging ability among pad-bearing lizards. Biol. J. Linn. Soc. 59, 21–35 (1996).
    Google Scholar 
    Winchell, K. M., Maayan, I., Fredette, J. R. & Revell, L. J. Linking locomotor performance to morphological shifts in urban lizards. Proc. R. Soc. B Biol. Sci. 285, 20180229 (2018).
    Google Scholar 
    Tan, W. C., Vanhooydonck, B., Measey, J. & Herrel, A. Morphology, locomotor performance and habitat use in southern African agamids. Biol. J. Linn. Soc. 130, 166–177 (2020).
    Google Scholar 
    Snyder, R. C. The anatomy and function of the pelvic girdle and hindlimb in lizard locomotion. Am. J. Anat. 95, 1–45 (1954).CAS 
    PubMed 

    Google Scholar 
    Losos, J. B., Schoener, T. W., Langerhans, R. B. & Spiller, D. A. Rapid temporal reversal in predator-driven natural selection. Science 314, 1111 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Anson, J. R., Dickman, C. R., Boonstra, R. & Jessop, T. S. Stress triangle: Do introduced predators exert indirect costs on native predators and prey?. PLoS One 8, e60916 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sheriff, M. J., Peacor, S. D., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: A dearth of evidence. J. Anim. Ecol. 89, 1302–1316 (2020).PubMed 

    Google Scholar 
    Cabrera-Pérez, M. Á., Gallo-Barneto, R., Esteve, I., Patiño-Martínez, C. & López-Jurado, L. F. The management and control of the California kingsnake in Gran Canaria (Canary Islands): Project LIFE+ Lampropeltis. Aliens Invasive Species Bull. 32, 20–28 (2012).
    Google Scholar 
    Hubbs, B. Common Kingsnakes: A Natural History of Lampropeltis getula (Tricolor Books, 2009).
    Google Scholar 
    Pyron, R. A. & Burbrink, F. T. Systematics of the common kingsnake (Lampropeltis getula; Serpentes: Colubridae) and the burden of heritage in taxonomy. Zootaxa 2241, 22–32 (2009).
    Google Scholar 
    Monzón-Argüello, C. et al. Snakes on an island: Independent introductions have different potentials for invasion. Conserv. Genet. 16, 1225–1241 (2015).
    Google Scholar 
    Piquet, J. C. & López-Darias, M. Invasive snake causes massive reduction of all endemic herpetofauna on Gran Canaria. Proc. R. Soc. B Biol. Sci. 288, 20211939 (2021).
    Google Scholar 
    Martín-González, E. & Sánchez-Pinto, L. Nuevos hallazgos de vertebrados fósiles de Fuerteventura: Identificación de una especie de serpiente utilizando técnicas de micro-escáner. Boletín la Asoc. Amigos del Mus. la Nat. y el Hombre Tenerife 15, 76–85 (2013).
    Google Scholar 
    García-Talavera, F., Rage, J.-C. & Barahona, F. The first record of snakes on the Canary Islands: A vertebra from the Upper Miocene of Lanzarote. Amphibia-Reptilia 19, 419–425 (1998).
    Google Scholar 
    Martín, A. & Lorenzo, J. Aves del archipiélago Canario (Francisco Lemus Editor S.L., 2001).
    Google Scholar 
    Nogales, M. & Medina, F. M. Trophic ecology of feral cats (Felis silvestris f. catus) in the main environments of an oceanic archipelago (Canary Islands): An updated approach. Mamm. Biol. 74, 169–181 (2009).
    Google Scholar 
    Salvador, A. & Pleguezuelos, J. Reptiles españoles: Identificación, historia natural y distribución (Esfagnos, 2002).
    Google Scholar 
    Vernet, R., Castanet, J. & Baez, M. Comparative water fux and daily energy expenditure of lizards of the genus Gallotia (Lacertidae) from the Canary Islands. Amphibia-Reptilia 16, 55–66 (1995).
    Google Scholar 
    Brown, R. P. Microevolution and Ecophysiology of Canary Island Skinks (Chalcides) (Thesis from the University of Aberdeen, 1990).
    Google Scholar 
    Penado, A. et al. Where to “rock”? Choice of retreat sites by a gecko in a semi-arid habitat. Acta Herpetol. 10, 47–54 (2015).
    Google Scholar 
    Brown, R. P. Thermal biology of the gecko Tarentola boettgeri: Comparisons among populations from different elevation within Gran Canaria. Herpetologica 52, 396–405 (1996).
    Google Scholar 
    Wiseman, K. D., Greene, H. W., Koo, M. S. & Long, D. J. Feeding ecology of generalist predator, the California kingsnake (Lampropeltis californiae): Why rare prey matter. Herpetol. Conserv. Biol. 14, 1–30 (2019).
    Google Scholar 
    King, R. B. Predicted and observed maximum prey size—Snake size allometry. Funct. Ecol. 16, 766–772 (2002).
    Google Scholar 
    Crystal-Ornelas, R. & Lockwood, J. L. The ‘known unknowns’ of invasive species impact measurement. Biol. Invasions 22, 1513–1525 (2020).
    Google Scholar 
    del Arco Aguilar, M. J. & Rodríguez Delgado, O. Vegetation of the Canary Islands (Springer, 2018).
    Google Scholar 
    AEMET. Standard climate values. AEMET (2022). http://www.aemet.es/en/serviciosclimaticos/datosclimatologicos/valoresclimatologicos?k=mur#tab2 (Accessed 9th February 2021)GESPLAN. Action A.1: Desarrollo de protocolos para la sistematización de las labores de captura y la recolección de datos. Official report (2015).Atzori, A. et al. Advances in methodologies of sexing and marking less dimorphic gekkonid lizards: The study case of the Moorish gecko, Tarentola mauritanica. Amphibia-Reptilia 28, 449–454 (2007).
    Google Scholar 
    Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).
    Google Scholar 
    Peig, J. & Green, A. J. The paradigm of body condition: A critical reappraisal of current methods based on mass and length. Funct. Ecol. 24, 1323–1332 (2010).
    Google Scholar 
    Molina-Borja, M. & Rodríguez-Domínguez, M. A. Evolution of biometric and life-history traits in lizards (Gallotia) from the Canary Islands. J. Zool. Syst. Evol. Res. 42, 44–53 (2004).
    Google Scholar 
    Suárez, C. R., Rodríguez-Domínguez, M. A. & Molina-Borja, M. Sexual dimorphism in morphological traits and scaling relationships in two populations of Gallotia stehlini (Fam. Lacertidae: Squamata) from Gran Canaria. Afr. J. Herpetol. 65, 1–20 (2016).
    Google Scholar 
    Tejangkura, T. Hybrid Zone Genetics and Within-Island Diversity of the Gecko Tarentola boettgeri (Liverpool John Moores University, 2012).
    Google Scholar 
    Rózsa, L., Reiczigel, J. & Majoros, G. Quantifying parasites in samples of hosts. J. Parasitol. 86, 228–232 (2000).PubMed 

    Google Scholar 
    Zapatero-Ramos, L. M., Gonzalez-Santiago, P. M., Solera-Puertas, M. A. & Carvajal-Gallardo, M. M. Estudio de nuevas especies de Pterigosomidae (Acari: Actinedida) sobre geckónidos de las Islas Canarias. Descripción de Geckobia canariensis n. sp. y Geckobia tinerfensis n. sp. Rev. Ibérica Parasitol. 49, 51–64 (1989).
    Google Scholar 
    Fain, A. & Bannert, B. Two new species of Ophionyssus Mégnin (Acari: Macronyssidae) parasitic on lizards of the genus Gallotia Boulenger (Reptilia: Lacertidae) from the Canary Islands. Int. J. Acarol. 26, 41–50 (2000).
    Google Scholar 
    Rosner, B. On the detection of many outliers. Technometrics 17, 221–227 (1975).MathSciNet 
    MATH 

    Google Scholar 
    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 (2010).
    Google Scholar 
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
    Google Scholar 
    Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.1 (2021).Langsrud, Ø. ANOVA for unbalanced data: Use type II instead of type III sums of squares. Stat. Comput. 13, 163–167 (2003).MathSciNet 

    Google Scholar 
    Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.5-1 (2021).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
    Google Scholar 
    Halliday, T. R. & Verrell, P. A. Body size and age in amphibians and reptiles. J. Herpetol. 22, 253–265 (1988).
    Google Scholar 
    Lopez-Darias, M., Vanhooydonck, B., Cornette, R. & Herrel, A. Sex-specific differences in ecomorphological relationships in lizards of the genus Gallotia. Funct. Ecol. 29, 506–514 (2015).
    Google Scholar 
    Márquez, R. & Cejudo, D. Defensive behavior as an escape strategy in four species of Gallotia (Sauria, Lacertidae) from the Canary Islands. Copeia 2000, 601–605 (2000).
    Google Scholar 
    Moon, B. R., Penning, D. A., Segall, M. & Herrel, A. Feeding in snakes: Form, function, and evolution of the feeding system. In Feeding in Vertebrates: Evolution, Morphology, Behavior, Biomechanics (eds Bels, V. & Whishaw, I. Q.) 527–574 (Springer, 2019).
    Google Scholar 
    Castanet, J. & Baéz, M. Adaptation and evolution in Gallotia lizards from the Canary Islands: Age, growth, maturity and longevity. Amphibia-Reptilia 12, 81–102 (1991).
    Google Scholar 
    Zamora-Camacho, F. J., Reguera, S., Rubiño-Hispán, M. V. & Moreno-Rueda, G. Effects of limb length, body mass, gender, gravidity, and elevation on escape speed in the lizard Psammodromus algirus. Evol. Biol. 41, 509–517 (2014).
    Google Scholar 
    Glossip, D. & Losos, J. B. Ecological correlates of number of subdigital lamellae in anoles. Herpetologica 53, 192–199 (1997).
    Google Scholar 
    Crandell, K. E., Herrel, A., Sasa, M., Losos, J. B. & Autumn, K. Stick or grip? Co-evolution of adhesive toepads and claws in Anolis lizards. Zoology 117, 363–369 (2014).PubMed 

    Google Scholar 
    Landová, E., Jančúchová-Lásková, J., Musilová, V., Kadochová, Š & Frynta, D. Ontogenetic switch between alternative antipredatory strategies in the leopard gecko (Eublepharis macularius): Defensive threat versus escape. Behav. Ecol. Sociobiol. 67, 1113–1122 (2013).
    Google Scholar 
    Eifler, M. A., Marchand, R., Eifler, D. A. & Malela, K. Habitat use and activity patterns in the nocturnal gecko, Chondrodactylus turneri. Herpetologica 73, 43–47 (2017).
    Google Scholar 
    Hielen, B. Unterschiedliche Fortpflanzungsstrategien bei Geckos der Gattung Tarentola Gray, 1825. Salamandra 28, 179–194 (1993).
    Google Scholar 
    Magnhagen, C. Predation risk as a cost of reproduction. Trends Ecol. Evol. 6, 183–186 (1991).CAS 
    PubMed 

    Google Scholar 
    Shine, R. ‘Costs’ of reproduction in reptiles. Oecologia 46, 92–100 (1980).ADS 
    PubMed 

    Google Scholar 
    Moran, E. V. & Alexander, J. M. Evolutionary responses to global change: Lessons from invasive species. Ecol. Lett. 17, 637–649 (2014).PubMed 

    Google Scholar 
    Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 2007).
    Google Scholar 
    Pinya, S., Tejada, S., Capó, X. & Sureda, A. Invasive predator snake induces oxidative stress responses in insular amphibian species. Sci. Total Environ. 566–567, 57–62 (2016).ADS 
    PubMed 

    Google Scholar 
    Genovart, M. et al. The young, the weak and the sick: Evidence of natural selection by predation. PLoS One 5, e9774 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sand, H., Wikenros, C., Ahlqvist, P., Strømseth, T. H. & Wabakken, P. Comparing body condition of moose (Alces alces) selected by wolves (Canis lupus) and human hunters: Consequences for the extent of compensatory mortality. Can. J. Zool. 90, 403–412 (2012).
    Google Scholar 
    Vedder, O., Bouwhuis, S. & Sheldon, B. C. The contribution of an avian top predator to selection in prey species. J. Anim. Ecol. 83, 99–106 (2014).PubMed 

    Google Scholar 
    Lopez, L. K. & Duffy, M. A. Mechanisms by which predators mediate host–parasite interactions in aquatic systems. Trends Parasitol. 37, 890–906 (2021).CAS 
    PubMed 

    Google Scholar 
    Garrido, M. & Pérez-Mellado, V. Human pressure, parasitism and body condition in an insular population of a Mediterranean lizard. Eur. J. Wildl. Res. 61, 617–621 (2015).
    Google Scholar 
    Amo, L., López, P. & Martín, J. Habitat deterioration affects body condition of lizards: A behavioral approach with Iberolacerta cyreni lizards inhabiting ski resorts. Biol. Conserv. 135, 77–85 (2007).
    Google Scholar 
    Kindinger, T. L. & Albins, M. A. Consumptive and non-consumptive effects of an invasive marine predator on native coral-reef herbivores. Biol. Invasions 19, 131–146 (2017).
    Google Scholar 
    Main, A. R. & Bull, C. M. The impact of tick parasites on the behaviour of the lizard Tiliqua rugosa. Oecologia 122, 574–581 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Garrido, M. & Pérez-Mellado, V. Sprint speed is related to blood parasites, but not to ectoparasites, in an insular population of lacertid lizards. Can. J. Zool. 92, 67–72 (2014).
    Google Scholar 
    Wirsing, A. J., Heithaus, M. R., Brown, J. S., Kotler, B. P. & Schmitz, O. J. The context dependence of non-consumptive predator effects. Ecol. Lett. 24, 113–129 (2021).PubMed 

    Google Scholar 
    Civantos, E., López, P. & Martín, J. Non-lethal effects of predators on body growth and health state of juvenile lizards, Psammdromus algirus. Physiol. Behav. 100, 332–339 (2010).CAS 
    PubMed 

    Google Scholar 
    Graham, S. P., Freidenfelds, N. A., McCormick, G. L. & Langkilde, T. The impacts of invaders: Basal and acute stress glucocorticoid profiles and immune function in native lizards threatened by invasive ants. Gen. Comp. Endocrinol. 176, 400–408 (2012).CAS 
    PubMed 

    Google Scholar 
    Donihue, C. M. et al. Hurricane-induced selection on the morphology of an island lizard. Nature 560, 88–91 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Donihue, C. M. et al. Hurricane effects on Neotropical lizards span geographic and phylogenetic scales. Proc. Natl. Acad. Sci. 117, 10429–10434 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goodman, B. A., Miles, D. B. & Schwarzkopf, L. Life on the rocks: Habitat use drives morphological and performance evolution in lizards. Ecology 89, 3462–3471 (2008).PubMed 

    Google Scholar 
    Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human influences on evolution, and the ecological and societal consequences. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160028 (2017).
    Google Scholar  More

  • in

    The trophic niche of subterranean populations of Speleomantes italicus

    Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427. https://doi.org/10.1101/sqb.1957.022.01.039 (1957).Article 

    Google Scholar 
    Manenti, R., Melotto, A., Guillaume, O., Ficetola, G. F. & Lunghi, E. Switching from mesopredator to apex predator: How do responses vary in amphibians adapted to cave living?. Behav. Ecol. Sociobiol. 74, 126. https://doi.org/10.1007/s00265-020-02909-x (2020).Article 

    Google Scholar 
    Pekár, S., García, L. F. & Viera, C. Behaviour and Ecology of Spiders (Springer, 2017).
    Google Scholar 
    Nawrocki, B., Colborne, S. F., Yurkowski, D. J. & Fisk, A. T. Foraging ecology of Bowfin (Amia calva), in the Lake Huron-Erie Corridor of the Laurentian Great Lakes: Individual specialists in generalist populations. J. Great Lakes Res. 42, 1452–1460. https://doi.org/10.1016/j.jglr.2016.08.002 (2016).Article 

    Google Scholar 
    Nifong, J. C. Living on the edge: Trophic ecology of Alligator mississippiensis (American alligator) with access to a shallow estuarine impoundment. Bull. Fla. Mus. Nat. Hist. 54, 13–49 (2016).
    Google Scholar 
    Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786. https://doi.org/10.1126/science.1103538 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jaeger, A. et al. Age, sex, and breeding status shape a complex foraging pattern in an extremely long-lived seabird. Ecology 95, 2324–2333 (2014).Article 
    PubMed 

    Google Scholar 
    Salwiczek, L. H. et al. Adult cleaner wrasse outperform capuchin monkeys, chimpanzees and orang-utans in a complex foraging task derived from cleaner: Client reef fish cooperation. PLoS ONE 7, e49068. https://doi.org/10.1371/journal.pone.0049068 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Juáres, M. A., Santos, M., Mennucci, J. A., Coria, N. R. & Mariano-Jelicich, R. Diet composition and foraging habitats of Adélie and gentoo penguins in three different stages of their annual cycle. Mar. Biol. 163, 105. https://doi.org/10.1007/s00227-016-2886-y (2016).Article 
    CAS 

    Google Scholar 
    Rosenblatt, A. E. et al. Factors affecting individual foraging specialization and temporal diet stability across the range of a large “generalist” apex predator. Oecologia 178, 5–16. https://doi.org/10.1007/s00442-014-3201-6 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Casper, R. M. et al. The influence of diet on foraging habitat models: A case study using nursing Antarctic fur seals. Ecography 33, 748–759. https://doi.org/10.1111/j.1600-0587.2009.06155.x (2010).Article 

    Google Scholar 
    Pagani-Núñez, E., Barnett, C. A., Gu, H. & Goodale, E. The need for new categorizations of dietary specialism incorporating spatio-temporal variability of individual diet specialization. J. Zool. 300, 1–7. https://doi.org/10.1111/jzo.12364 (2016).Article 

    Google Scholar 
    Quevedo, M., Svanbäck, R. & Eklöv, P. Intrapopulation niche partitioning in a generalist predator limits food web connectivity. Ecology 90, 2263–2274. https://doi.org/10.1890/07-1580.1 (2009).Article 
    PubMed 

    Google Scholar 
    Ćirović, D., Penezić, A., Milenković, M. & Paunović, M. Winter diet composition of the Golden jackal (Canis aureus L. 1758) in Serbia. Mamm. Biol. 79, 132–137. https://doi.org/10.1016/j.mambio.2013.11.003 (2014).Article 

    Google Scholar 
    Moser, C. F., de Avila, F. R., de Oliveira, M. & Tozetti, A. M. Diet composition and trophic niche overlap between two sympatric species of Physalaemus (Anura, Leptodactylidae, Leiuperinae) in a subtemperate forest of southern Brazil. Herpeto. Notes 10, 9–15 (2017).
    Google Scholar 
    Lunghi, E. et al. What shapes the trophic niche of European plethodontid salamanders?. PLoS ONE 13, e0205672. https://doi.org/10.1371/journal.pone.0205672 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evangelista, C., Boiche, A., Lecerf, A. & Cucherousset, J. Ecological opportunities and intraspecific competition alter trophic niche specialization in an opportunistic stream predator. J. Anim. Ecol. 83, 1025–1034. https://doi.org/10.1111/1365-2656.12208 (2014).Article 
    PubMed 

    Google Scholar 
    Cloyed, C. S. & Eason, P. K. Niche partitioning and the role of intraspecific niche variation in structuring a guild of generalist anurans. R. Soc. Open Sci. 4, 170060. https://doi.org/10.1098/rsos.170060 (2017).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dehnhard, N. et al. Is individual consistency in body mass and reproductive decisions linked to individual specialization in foraging behavior in a long-lived seabird?. Ecol. Evol. 6, 4488–4501. https://doi.org/10.1002/ece3.2213 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jirka, K. J. & Kraft, C. E. Diet niche width and individual specialization of Brook trout in Adirondack lakes. Trans. Am. Fish Soc. 146, 716–731. https://doi.org/10.1080/00028487.2017.1290680 (2017).Article 

    Google Scholar 
    Reading, C. & Jofré, G. Diet composition changes correlated with body size in the Smooth snake, Coronella austriaca, inhabiting lowland heath in southern England. Amphib. Reptil. 34, 463–470. https://doi.org/10.1163/15685381-00002899 (2013).Article 

    Google Scholar 
    Novak, T. et al. Niche partitioning in orbweaving spiders Meta menardi and Metellina merianae (Tetragnathidae). Acta Oecol. 36, 522–529. https://doi.org/10.1016/j.actao.2010.07.005 (2010).Article 
    ADS 

    Google Scholar 
    Stamp, N. E. & Bowers, M. D. Presence of predatory wasps and stinkbugs alters foraging behavior of cryptic and non-cryptic caterpillars on plantain (Plantago lanceolata). Oncologic 95, 376–384 (1993).ADS 

    Google Scholar 
    Magnusson, W. E. & Lima, A. P. The ecology of a cryptic predator, Paleosuchus tigonatus, in a tropical rainforest. J. Herpetol. 25, 41–48 (1991).Article 

    Google Scholar 
    Riesch, R., Tobler, M. & Plath, M. Extremophile Fishes Ecology, Evolution, and Physiology of Teleosts in Extreme Environments (Springer, 2015).
    Google Scholar 
    Horikoshi, K. Barophiles: Deep-sea microorganisms adapted to an extreme environment. Curr. Opin. Microbiol. 1, 291–295 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mammola, S. et al. Collecting eco-evolutionary data in the dark: Impediments to subterranean research and how to overcome them. Ecol. Evol. 11, 5911–5926. https://doi.org/10.1002/ece3.7556 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Crovetto, F., Romano, A. & Salvidio, S. Comparison of two non-lethal methods for dietary studies in terrestrial salamanders. Wildl. Res. 39, 266–270. https://doi.org/10.1071/WR11103 (2012).Article 

    Google Scholar 
    Wake, D. B. The enigmatic history of the European. Asian and American plethodontid salamanders. Amphib-reptile 34, 323–336 (2013).Article 

    Google Scholar 
    Lanza, B., Pastorelli, C., Laghi, P. & Cimmaruta, R. A review of systematics, taxonomy, genetics, biogeography and natural history of the genus Speleomantes Dubois, 1984 (Amphibia Caudata Plethodontidae). Atti Mus. Civ. Stor. Nat. Trieste 52, 5–135 (2006).
    Google Scholar 
    Rondinini, C., Battistoni, A., Peronace, V. & Teofili, C. Lista Rossa IUCN dei Vertebrati Italiani (Comitato Italiano IUCN e Ministero dell’Ambiente e della Tutela del Territorio e del Mare, 2013).European Community. Council directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. J. Eur. Union 206(7), 1–44 (1992).
    Google Scholar 
    Salvidio, S., Palumbi, G., Romano, A. & Costa, A. Safe caves and dangerous forests? Predation risk may contribute to salamander colonization of subterranean habitats. Sci. Nat. 104, 20. https://doi.org/10.1007/s00114-017-1443-y (2017).Article 
    CAS 

    Google Scholar 
    Lunghi, E., Manenti, R. & Ficetola, G. F. Do cave features affect underground habitat exploitation by non-troglobite species?. Acta Oecol. 55, 29–35. https://doi.org/10.1016/j.actao.2013.11.003 (2014).Article 
    ADS 

    Google Scholar 
    Salvidio, S., Oneto, F., Ottonello, D., Costa, A. & Romano, A. Trophic specialization at the individual level in a terrestrial generalist salamander. Can. J. Zool. 93, 79–83. https://doi.org/10.1139/cjz-2014-0204 (2015).Article 

    Google Scholar 
    Lunghi, E. et al. Environmental suitability models predict population density, performance and body condition for microendemic salamanders. Sci. Rep. 8, 7527. https://doi.org/10.1038/s41598-018-25704-1 (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ficetola, G. F. et al. Differences between microhabitat and broad-scale patterns of niche evolution in terrestrial salamanders. Sci. Rep. 8, 10575. https://doi.org/10.1038/s41598-018-28796-x (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oneto, F., Ottonello, D., Pastorino, M. V. & Salvidio, S. in Scripta Herpetologica. Studies on Amphibians and Reptiles in honour of Benedetto Lanza (eds M. Capula & C. Corti) (Edizioni Belvedere, 2014).Lunghi, E., Mascia, C., Mulargia, M. & Corti, C. Is the Sardinian grass snake (Natrix natrix cetti) an active hunter in underground environments?. Spixiana 41, 160 (2018).
    Google Scholar 
    Ficetola, G. F., Canedoli, C. & Stock, F. The Racovitzan impediment and the hidden biodiversity of unexplored environments. Conserv. Biol. 33, 214–216. https://doi.org/10.1111/cobi.13179 (2019).Article 
    PubMed 

    Google Scholar 
    Lunghi, E. et al. Field-recorded data on the diet of six species of European Hydromantes cave salamanders. Sci. Data 5, 180083. https://doi.org/10.1038/sdata.2018.83 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E. et al. Updating salamander datasets with phenotypic and stomach content information for two mainland Speleomantes. Sci. Data 8, 150. https://doi.org/10.1038/s41597-021-00931-w (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Deban, S. M. & Dicke, U. Activation patterns of the tongue-projector muscle during feeding in the imperial cave salamander Hydromantes imperialis. J. Exp. Biol. 207, 2071–2081. https://doi.org/10.1242/jeb.00978 (2004).Article 
    PubMed 

    Google Scholar 
    Deban, S. M., O’Reilly, J. C., Dicke, U. & van Leeuwen, J. L. Extremely high-power tongue projection in plethodontid salamanders. J. Exp. Biol. 210, 655–667. https://doi.org/10.1242/jeb.02664 (2007).Article 
    PubMed 

    Google Scholar 
    Vignoli, L., Caldera, F. & Bologna, M. A. Trophic niche of cave populations of Speleomantes italicus. J. Nat. Hist. 40, 1841–1850 (2006).Article 

    Google Scholar 
    Salvidio, S. et al. Consistency in trophic strategies between populations of the Sardinian endemic salamander Speleomantes imperialis. Anim. Biol. 67, 1–16. https://doi.org/10.1163/15707563-00002517 (2017).Article 

    Google Scholar 
    Lunghi, E., Manenti, R. & Ficetola, G. F. Seasonal variation in microhabitat of salamanders: Environmental variation or shift of habitat selection?. PeerJ 3, e1122. https://doi.org/10.7717/peerj.1122 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E. et al. Thermal equilibrium and temperature differences among body regions in European plethodontid salamanders. J. Therm. Biol. 60, 79–85. https://doi.org/10.1016/j.jtherbio.2016.06.010 (2016).Article 
    PubMed 

    Google Scholar 
    Spotila, J. R. Role of temperature and water in the ecology of lungless salamanders. Ecol. Monogr. 42, 95–125 (1972).Article 

    Google Scholar 
    Manenti, R., Lunghi, E. & Ficetola, G. F. Distribution of spiders in cave twilight zone depends on microclimatic features and trophic supply. Invertebr. Biol. 134, 242–251. https://doi.org/10.1111/ivb.12092 (2015).Article 

    Google Scholar 
    Yurkowski, D. J. et al. Latitudinal variation in ecological opportunity and intraspecific competition indicates differences in niche variability and diet specialization of Arctic marine predators. Ecol. Evol. 6, 1666–1678. https://doi.org/10.1002/ece3.1980 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28. https://doi.org/10.1086/343878 (2003).Article 
    MathSciNet 
    PubMed 

    Google Scholar 
    Araújo, M. S., Bolnick, D. L. & Layman, C. A. The ecological causes of individual specialisation. Ecol. Lett. 14, 948–958. https://doi.org/10.1111/j.1461-0248.2011.01662.x (2011).Article 
    PubMed 

    Google Scholar 
    Lunghi, E. et al. Same diet, different strategies: Variability of individual feeding habits across three populations of Ambrosi’s cave salamander (Hydromantes ambrosii). Diversity 12, 180. https://doi.org/10.3390/d12050180 (2020).Article 

    Google Scholar 
    Costa, A., Crovetto, F. & Salvidio, S. European plethodontid salamanders on the forest floor: Local abundance is related to fine-scale environmental factors. Herpetol. Conserv. Biol. 11, 344–349 (2016).
    Google Scholar 
    Salvidio, S., Romano, A., Oneto, F., Ottonello, D. & Michelon, R. Different season, different strategies: Feeding ecology of two syntopic forest-dwelling salamanders. Acta Oecol. 43, 42–50 (2012).Article 
    ADS 

    Google Scholar 
    Culver, D. C. & Pipan, T. The Biology of Caves and Other Subterranean Habitats 2nd edn. (Oxford University Press, 2019).Book 

    Google Scholar 
    Lunghi, E., Manenti, R. & Ficetola, G. F. Cave features, seasonality and subterranean distribution of non-obligate cave dwellers. PeerJ 5, e3169. https://doi.org/10.7717/peerj.3169 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E., Ficetola, G. F., Zhao, Y. & Manenti, R. Are the neglected Tipuloidea crane flies (Diptera) an important component for subterranean environments?. Diversity 12, 333. https://doi.org/10.3390/d12090333 (2020).Article 

    Google Scholar 
    Manenti, R. et al. The stenoendemic cave-dwelling planarians (Platyhelminthes, Tricladida) of the Italian Alps and Apennines: Conservation issues. J. Nat. Conserv. 45, 90–97. https://doi.org/10.1016/j.jnc.2018.08.001 (2018).Article 

    Google Scholar 
    Lunghi, E. et al. Ecological observations on hybrid populations of European plethodontid salamanders, genus Speleomantes. Diversity 13, 285. https://doi.org/10.3390/d13070285 (2021).Article 

    Google Scholar 
    Lunghi, E., Guillaume, O., Blaimont, P. & Manenti, R. The first ecological study on the oldest allochthonous population of European cave salamanders (Hydromantes sp.). Amphib-Reptile 39, 113–119. https://doi.org/10.1163/15685381-00003137 (2018).Article 

    Google Scholar 
    Bolnick, D. I. et al. Ecological release from interspecific competition leads to decoupled changes in population and individual niche width. Proc. R. Soc. B 277, 1789–1797. https://doi.org/10.1098/rspb.2010.0018 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E. et al. Comparative reproductive biology of European cave salamanders (genus Hydromantes): Nesting selection and multiple annual breeding. Salamandra 54, 101–108 (2018).
    Google Scholar 
    Ficetola, G. F., Lunghi, E. & Manenti, R. Microhabitat analyses support relationships between niche breadth and range size when spatial autocorrelation is strong. Ecography 43, 724–734. https://doi.org/10.1111/ecog.04798 (2020).Article 

    Google Scholar 
    Lormée, H., Jouventin, P., Trouve, C. & Chastel, O. Sex-specific patterns in baseline corticosterone and body condition changes in breeding Red-footed Boobies Sula sula. Ibis 145, 212–219 (2003).Article 

    Google Scholar 
    Du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: Implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob Change Biol 18, 3063–3070. https://doi.org/10.1111/j.1365-2486.2012.02778.x (2012).Article 
    ADS 

    Google Scholar 
    Lunghi, E. & Corti, C. Predation of European cave salamanders (Speleomantes) by the spider Meta bourneti. Spixiana 44, 54 (2021).
    Google Scholar 
    Lunghi, E. Doubling the lifespan of European plethodontid salamanders. Ecology 103, e03581. https://doi.org/10.1002/ecy.3581 (2022).Article 
    PubMed 

    Google Scholar 
    Ficetola, G. F., Pennati, R. & Manenti, R. Spatial segregation among age classes in cave salamanders: Habitat selection or social interactions?. Popul Ecol 55, 217–226 (2013).Article 

    Google Scholar 
    Lunghi, E. et al. Interspecific and inter-population variation in individual diet specialization: Do environmental factors have a role?. Ecology 101, e03088. https://doi.org/10.1002/ecy.3088 (2020).Article 
    PubMed 

    Google Scholar 
    Blamires, S. J. Plasticity in extended phenotypes: Orb web architectural responses to variations in prey parameters. J. Exp. Biol. 213, 3207–3212. https://doi.org/10.1242/jeb.045583 (2010).Article 
    PubMed 

    Google Scholar 
    Costa, A. et al. Generalisation within specialization: Inter-individual diet variation in the only specialized salamander in the world. Sci. Rep. 5, 13260. https://doi.org/10.1038/srep13260 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E. et al. Capture-mark-recapture data on the strictly protected Speleomantes italicus. Ecology 103, e3641. https://doi.org/10.1002/ecy.3641 (2022).Article 
    PubMed 

    Google Scholar 
    Lunghi, E. & Bruni, G. Long-term reliability of visual implant elastomers in the Italian cave salamander (Hydromantes italicus). Salamandra 54, 283–286 (2018).
    Google Scholar 
    Lunghi, E., Bacci, F. & Zhao, Y. How can we record reliable information on animal colouration in the wild?. Diversity 13, 356. https://doi.org/10.3390/d13080356 (2021).Article 

    Google Scholar 
    Lunghi, E. et al. On the stability of the dorsal pattern of European cave salamanders (genus Hydromantes). Herpetozoa 32, 249–253. https://doi.org/10.3897/herpetozoa.32.e39030 (2019).Article 

    Google Scholar 
    Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.5-7. https://cran.r-project.org, https://github.com/vegandevs/vegan (2020).R Development Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021) http://www.R-project.org/.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (2001).Article 

    Google Scholar 
    Băncilă, R. I., Hartel, T. R. P., Smets, J. & Cogălniceanu, D. Comparing three body condition indices in amphibians: A case study of yellow-bellied toad Bombina variegata. Amphib-Reptile 31, 558–562. https://doi.org/10.1163/017353710X518405 (2010).Article 

    Google Scholar 
    Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119. https://doi.org/10.1111/j.1600-0706.2013.00755.x (2014).Article 

    Google Scholar 
    Lunghi, E. et al. Photographic database of the European cave salamanders, genus Hydromantes. Sci. Data 7, 171. https://doi.org/10.1038/s41597-020-0513-8 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lunghi, E., Corti, C., Manenti, R. & Ficetola, G. F. Consider species specialism when publishing datasets. Nat. Ecol. Evol. 3, 319. https://doi.org/10.1038/s41559-019-0803-8 (2019).Article 
    PubMed 

    Google Scholar  More

  • in

    Evidence of sweet corn yield losses from rising temperatures

    Brown, M. E. et al. In Climate Change, global food security, and the U.S. food system (2015).Masson-Delmotte, V. et al. AR6 Climate Change 2021: The Physical Science Basis—IPCC. In Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).Douris, J. et al. WMO Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019) (WMO-No. 1267). In WMO Statement on the state of the Global Climate vol. 1267 (WMO, 2021).Smith, A. B. U.S. Billion-dollar Weather and Climate Disasters, 1980–present (NCEI Accession 0209268). In National Centers for Environmental Information (2020).Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).Article 
    ADS 

    Google Scholar 
    Mann, M. E. et al. Projected changes in persistent extreme summer weather events: The role of quasi-resonant amplification. Sci. Adv. 4, 5 (2018).Article 

    Google Scholar 
    Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Chang. Biol. 25, 2325–2337 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Daloz, A. S. et al. Direct and indirect impacts of climate change on wheat yield in the Indo-Gangetic plain in India. J. Agric. Food Res. 4, 100–132 (2021).
    Google Scholar 
    Leng, G. Maize yield loss risk under droughts in observations and crop models in the Unites States. Environ. Res. Lett. 16, 24016 (2021).Article 

    Google Scholar 
    Backlund, P., Janetos, A., & Schimel, D. In The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States Synthesis and Assessment Product, vol. 4.3 (2008).Scheelbeek, P. F. D., Tuomisto, H. L., Bird, F. A., Haines, A. & Dangour, A. D. Effect of environmental change on yield and quality of fruits and vegetables: Two systematic reviews and projections of possible health effects. Lancet Glob. Health 5, S21 (2017).Article 

    Google Scholar 
    Drewnowski, A., Dwyer, J., King, J. C. & Weaver, C. M. A proposed nutrient density score that includes food groups and nutrients to better align with dietary guidance. Nutr. Rev. 77, 404–416 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weinberger, K. & Lumpkin, T. A. Diversification into horticulture and poverty reduction: A research agenda. World Dev. 35, 1464–1480 (2007).Article 

    Google Scholar 
    Barnabás, B., Jäger, K. & Fehér, A. The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 31, 11–38 (2008).PubMed 

    Google Scholar 
    Kazan, K. & Lyons, R. The link between flowering time and stress tolerance. J. Exp. Bot. 67, 47–60 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Messina, C. D. et al. On the dynamic determinants of reproductive failure under drought in maize. In Silico Plants 1, 1–14 (2019).Article 

    Google Scholar 
    Yang, X., Wang, B., Chen, L., Li, P. & Cao, C. The different influences of drought stress at the flowering stage on rice physiological traits, grain yield, and quality. Sci. Rep. 9, 3742 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Deryng, D., Conway, D., Ramankutty, N., Price, J. & Warren, R. Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett. 9, 034011 (2014).Article 
    ADS 

    Google Scholar 
    Owen, P. C. Responses of a semi-dwarf wheat to temperatures representing a tropical dry season. II. Extreme temperatures. Exp. Agric. 7, 43–47 (1971).Article 

    Google Scholar 
    Liu, F., Jensen, C. R. & Andersen, M. N. A review of drought adaptation in crop plants: Changes in vegetative and reproductive physiology induced by ABA-based chemical signals. Aust. J. Agric. Res. 56, 1245–1252 (2005).Article 
    CAS 

    Google Scholar 
    Turc, O., Bouteillé, M., Fuad-Hassan, A., Welcker, C. & Tardieu, F. The growth of vegetative and reproductive structures (leaves and silks) respond similarly to hydraulic cues in maize. New Phytol. 212, 377–388 (2016).Article 
    PubMed 

    Google Scholar 
    Fuad-Hassan, A., Tardieu, F. & Turc, O. Drought-induced changes in anthesis-silking interval are related to silk expansion: A spatio-temporal growth analysis in maize plants subjected to soil water deficit. Plant Cell Environ. 31, 1349–1360 (2008).Article 
    PubMed 

    Google Scholar 
    USDA–National Agricultural Statistics Service (2021). https://data.nal.usda.gov/dataset/nass-quick-stats, accessed 29 December 2021.Challinor, A. J., Parkes, B. & Ramirez-Villegas, J. Crop yield response to climate change varies with cropping intensity. Glob. Chang. Biol. 21, 1679–1688 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Kukal, M. S. & Irmak, S. Climate-driven crop yield and yield variability and climate change impacts on the U.S. great plains agricultural production. Sci. Rep. 8, 1–18 (2018).Article 
    ADS 

    Google Scholar 
    Iizumi, T. & Sakai, T. The global dataset of historical yields for major crops 1981–2016. Sci. Data 7, 1–7 (2020).Article 

    Google Scholar 
    Thornton, M. M. et al. In Daymet: Daily surface weather data on a 1-km grid for North America, Version 4. ORNL DAAC (2020).Ritchie, S. W., Hanway, J. J., Benson, G. O., & Herman, J. C. How a corn plant develops: Special report no. 48. In Ames: Iowa State University of Science and Technology Cooperative Extension Service (1986).Nicholls, N. Increased Australian wheat yield due to recent climate trends. Nature 387, 484–485 (1997).Article 
    ADS 
    CAS 

    Google Scholar 
    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl. Acad. Sci. USA 106, 15594–15598 (2009).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dhaliwal, D. S. & Williams, M. M. I. I. Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn. PLoS ONE 15, e0228809 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Siirila-Woodburn, E. R. et al. A low-to-no snow future and its impacts on water resources in the western United States. Nat. Rev. Earth Environ. 2, 800–819 (2021).Article 
    ADS 

    Google Scholar 
    Gilmore, E. C. & Rogers, J. S. heat units as a method of measuring maturity in corn. Agron. J. 50, 611–615 (1958).Article 

    Google Scholar 
    Wang, J. Y. A critique of the heat unit approach to plant response studies. Ecology 41, 785–790 (1960).Article 

    Google Scholar 
    Cross, H. Z. & Zuber, M. S. Prediction of flowering dates in maize based on different methods of estimating thermal units. Agron. J. 64, 351–355 (1972).Article 

    Google Scholar 
    Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).Article 
    ADS 

    Google Scholar 
    Díaz, E. L. et al. In Chapter 20: US Caribbean. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II,(2018).Wang, Y. et al. Flowering dynamics, pollen, and pistil contribution to grain yield in response to high temperature during maize flowering. Environ. Exp. Bot. 158, 80–88 (2019).Article 

    Google Scholar 
    Lohani, N., Singh, M. B. & Bhalla, P. L. High temperature susceptibility of sexual reproduction in crop plants. J. Exp. Bot. 71, 555–568 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jagadish, S. V. K., Craufurd, P. Q. & Wheeler, T. R. High temperature stress and spikelet fertility in rice (Oryza sativa L.). J. Exp. Bot. 58, 1627–1635 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gourdji, S. M., Sibley, A. M. & Lobell, D. B. Global crop exposure to critical high temperatures in the reproductive period: Historical trends and future projections. Environ. Res. Lett. 8, 024041 (2013).Article 
    ADS 

    Google Scholar 
    Hedhly, A., Hormaza, J. I. & Herrero, M. Global warming and sexual plant reproduction. Trends Plant Sci. 14, 30–36 (2008).Article 
    PubMed 

    Google Scholar 
    Zhao, F. et al. Effects of heat stress during grain filling on sugar accumulation and enzyme activity associated with sucrose metabolism in sweet corn. Acta Agron. Sin. 39, 1644–1651 (2013).Article 
    CAS 

    Google Scholar 
    Lobell, D. B., Bonfils, C. J., Kueppers, L. M. & Snyder, M. A. Irrigation cooling effect on temperature and heat index extremes. Geophys. Res. Lett. 35, 9705 (2008).Article 
    ADS 

    Google Scholar 
    Thiery, W. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. Atmos. 122, 1403–1422 (2017).Article 
    ADS 

    Google Scholar 
    Li, Y. et al. Quantifying irrigation cooling benefits to maize yield in the US Midwest. Glob. Chang. Biol. 26, 3065–3078 (2020).Article 
    ADS 
    PubMed 

    Google Scholar  More

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    Optimization of the process of seed extraction from the Larix decidua Mill. cones including evaluation of seed quantity and quality

    Cone characteristics: the entire set and individual variantsCones used in all the test variants did not differ from each other in terms of height (coefficient of variance in the Student t-test–F = 1.33 at p = 0.23), diameter (F = 1.77 at p = 0.08), or initial weight (F = 0.86 at p = 0.55). Analysis of variance revealed a significant difference for cone humidity (F = 2.52 at p ˂ 0.05).Cone parameters such as height, diameter and initial weight are factors that can determine the course of the extraction process. Therefore, the relationship between diameter and height for all cones used in the study was described using a linear regression equation ((y=0.2794x+8.3195)), which means that cone diameter increased by 0.28 mm per 1 mm of cone height, ((R=0.778 >0.104-{R}_{kr})).The initial weight of cones may be associated with their harvest time or storage conditions. A linear regression equation was also used to describe the relationship between the height and initial weight of the examined cones (y = 0.238x–3.918), which means that initial weight increased on average by 0.238 g per 1 mm of height, (R = 0.795  > 0.104).Table 2 shows means with standard deviations, the minimum and maximum values of the measured parameters, the range of variance, the coefficient of variation and the standard error for the entire set of studied cones and seeds. The Shapiro–Wilk test showed that the examined characteristics had a normal distribution.Table 2 Cone and seed parameters for the entire study set.Full size tableThe cones used in the study had a height of 21.4–44.1 mm and a diameter of 12.5–24.3 mm. The mean height of a cone was 33.8 (± 3.4) mm and the mean diameter was 17.8 (± 1.6) mm. The initial weight of cones ranged from 2.137 to 9.111 g, with a mean of 4.144 (± 1.019) g. The initial moisture content of cones was from 27.6 to 57.1%, with a mean of 40.4 (± 4.5)%. Analysis was performed for individual extraction variants. The mean values of cone height h, diameter d, initial weight m01, and moisture content W were calculated (Table 3).Table 3 Mean parameter values and standard deviations for the nine process variants.Full size tableThe HSD Tukey test revealed one homogeneous group for cone height encompassing all variants and two homogeneous groups for diameter. The first group consisted of all variants except 7, and the second group included all variants except 2. One homogeneous group was obtained for initial weight. Two homogeneous groups were found for moisture content, one consisting of all variants except 7, and the other one containing variants 1, 4, 5, 6, 7, 8, and 9.Seed extraction results for the studied stepsSeed extraction conditions and timeThe change in cone weight in each step of the extraction process depended on its duration, temperature and humidity conditions in the extraction cabinet, as well as on the initial moisture content of the cones.Humidity inside the drying chamber decreased to an average of 30% after 2 h of the process in each step as a result of increasing temperature. Over the subsequent 4 h of the process, after increasing the temperature, the humidity inside the chamber declined significantly, and then (over 2 and 4 h) it decreased further only slightly, stabilizing at approx. 5% for the 10 h variants, 6% for the 8 h variants, and 8% for 6 h variants on average.Moisture content changes in cones during the seed extraction processThe initial moisture content (u01) of the studied cones was much greater than 0.20 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), which means that special care must be taken during seed extraction, which should be conducted at a temperature of up to 50 °C8.The relatively high moisture content of the cones could be attributed to the absence of preliminary drying in airy storage places prior to seed extraction (which is typically the case in commercial practice) and the early date of cone harvest, at the beginning of the extraction season. The initial (u0x) and final (ukx) moisture content of cones used in each process variant is given with standard deviation in Table 4.Table 4 Initial and final moisture content of cones used in each process variant.Full size tableThe initial moisture content of cones (u0x) in most variants increased with each extraction step due to immersion. In most variants, the final moisture content (ukx) was the highest in the first extraction step and decreased or remained at the same level with each subsequent step.The mean initial moisture content for the three process variants with 10 h of drying was 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 10 h of drying, the mean moisture content decreased to 0.130 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). The mean initial moisture content in the fifth extraction step was 0.437 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.071 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 10 h reached on average 7% moisture content after extraction steps 4 and 5.The mean initial moisture content for the three process variants with 8 h of drying was 0.412 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 8 h of drying, the mean moisture content decreased to 0.128 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.440 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.064 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 8 h reached on average 7.1% moisture content after extraction step IV and 6.4% after step V.The mean initial moisture content for the three process variants with 6 h of drying was 0.389 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 6 h of drying, the mean moisture content decreased to 0.129 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.415 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.084 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 6 h reached on average 8.9% moisture content after extraction step IV and 8.4% moisture content after step V, which means that their final moisture content was higher than that of cones dried for 8 h and 10 h.The cones with the longest immersion time (15 min) were characterized by the highest initial moisture content in each extraction step as compared to the other two variants (immersion of 5 min and 10 min) with the same drying time. The final moisture content in a given extraction step differed between cones with different immersion times. Cones with an immersion time of 15 min were characterized by the highest final moisture content in individual extraction steps, and those with 5 min immersion revealed the lowest final moisture content.The Tukey HSD test revealed homogeneous groups in terms of initial moisture content (u01, u02, u03, u04, u05) and final moisture content (uk1, uk2, uk3, uk4, uk5) in each step, as shown in Table 4. For instance, four homogeneous groups were found for the final moisture content after extraction step V (uk5): the first one consisted of all variants except for 7, 8, and 9, the second one included variants 1, 2, 3, and 7, the third one comprised of variants 7 and 8, while the fourth one was constituted by variant 9 alone.Using Eq. (1), changes in moisture content were described for each of the tested cones over all five steps of each variant. The equation included the initial and final values of moisture content and the b coefficient for individual cones. The average values of the b coefficient and standard deviations for each extraction step are presented in Table 5 for individual extraction variants.Table 5 Mean values of the b coefficient and standard deviations for the five steps of the studied process variants.Full size tableThe lowest value of the b coefficient was recorded for the first step of the 10h_15min variant (b1 = 0.34), while the highest value was obtained for the fifth step of the 8 h_15 min variant (b5 = 0.60). In the process variants involving 10 and 8 h of drying , the b coefficient increased with each extraction step until the third one; in the fourth step it slightly decreased and in the fifth step it remained constant. In the variants with 6 h of drying the b coefficient almost peaked in the second extraction step and remained at a similar level until the fifth step. In the first steps of the variants with 6 h of drying, the mean value of the b coefficient was 0.54 and did not differ significantly from the coefficients obtained during the other steps. It was noted that in the 8 h_15 min variant, the b coefficients increased over successive steps.Figures 2–3 show examples of curves of actual and model changes in moisture content and the rate of extraction for sample cones, one each for variants 10 h_15 min and 8 h_15 min.Figure 2Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 32 in the 10 h_15 min variant throughout effective extraction.Full size imageFigure 3Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 17 in the 8 h_15 min variant throughout effective extraction.Full size imageEquations for changes in moisture content and extraction rate in consecutive extraction steps are given below for the graphically for the cone shown in Fig. 2 (no. 32 in the 10 h_15 min variant):Step I: ({u}_{1}=0.264cdot {mathrm{e }}^{left(-0.38 cdot {tau }_{i}right)}+0.107) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.100cdot {mathrm{e }}^{(-0.38 cdot {tau }_{i})})Step II: ({u}_{2}=0.372cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.095) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.164cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step III: ({u}_{3}=0.397cdot {mathrm{e }}^{left(-0.49 cdot {tau }_{i}right)}+0.086) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.195cdot {mathrm{e }}^{(-0.49 cdot {tau }_{i})})Step IV: ({u}_{4}=0.536cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.080) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.236cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step V: ({u}_{5}=0.485cdot {mathrm{e }}^{left(-0.46 cdot {tau }_{i}right)}+0.076) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.223cdot {mathrm{e }}^{(-0.46 cdot {tau }_{i})})Equations for changes (Fig. 3) in moisture content and extraction rate in consecutive extraction steps are also given for this cone (no. 17 in the 8 h_15 min variant):Step I: ({u}_{1}=0.304cdot {mathrm{e }}^{left(-0.53 cdot {tau }_{i}right)}+0.113) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.53 cdot {tau }_{i})})Step II: ({u}_{2}=0.292cdot {mathrm{e }}^{left(-0.55 cdot {tau }_{i}right)}+0.085) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.55 cdot {tau }_{i})})Step III: ({u}_{3}=0.369cdot {mathrm{e }}^{left(-0.70 cdot {tau }_{i}right)}+0.077) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.258cdot {mathrm{e }}^{(-0.70 cdot {tau }_{i})})Step IV: ({u}_{4}=0.379cdot {mathrm{e }}^{left(-0.71 cdot {tau }_{i}right)}+0.059) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.269cdot {mathrm{e }}^{(-0.71 cdot {tau }_{i})})Step V: ({u}_{5}=0.428cdot {mathrm{e }}^{left(-0.77 cdot {tau }_{i}right)}+0.060) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.330cdot {mathrm{e }}^{(-0.77 cdot {tau }_{i})})Finally, equations for changes in moisture content and extraction rate in consecutive extraction steps are given for cone no. 5 in the 6 h_15 min variant:Step I: ({u}_{1}=0.308cdot {mathrm{e }}^{left(-0.58 cdot {tau }_{i}right)}+0.0904) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.179cdot {mathrm{e }}^{(-0.58 cdot {tau }_{i})})Step II: ({u}_{2}=0.346cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.1070) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.218cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step III: ({u}_{3}=0.368cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.0837) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.232cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step IV: ({u}_{4}=0.387cdot {mathrm{e }}^{left(-0.68 cdot {tau }_{i}right)}+0.0838) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.263cdot {mathrm{e }}^{(-0.68 cdot {tau }_{i})})Step V: ({u}_{5}=0.396cdot {mathrm{e }}^{left(-0.65 cdot {tau }_{i}right)}+0.0743) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.257cdot {mathrm{e }}^{(-0.65 cdot {tau }_{i})})Figures 2a, 3a show the curves of actual changes in the moisture content of three sample cones subjected to different drying times (10 and 8 h) but the same immersion time (15 min); the curves were fitted to a model which is widely used in descriptions of drying at constant temperature (mostly for vegetables). The present study used variable temperature, which may have influenced the fit of the model, in addition to the input variables (drying and immersion times). The best fit was found for the cone subjected to the variant with 8 h of drying (Fig. 3), with a slight deviation in the first three extraction steps, and with a very good fit in the fourth and fifth steps. The lowest fit was found for the cone subjected to 6 h drying, which may be caused by insufficient drying time (the cone was exposed to 35 °C for 2 h, and to 50 °C for only 4 h).Figures 2b, 3b show diagrams for cone extraction rates at different drying times (10 h and 8 h) at the same immersion times (15 min). As can be seen, extraction rates decreased in the very beginning, which is characteristic of the so-called second period of solid drying (Pabis44).Seed extraction dynamicsTable 2 presents data on the number of scales and seeds for the studied cones. There were from 33 to 70 open scales per cone, with an average of 48 (± 6). From 1 to 76 seeds were extracted per cone, with an average of 36 (± 18). Finally, each cone contained from 5 to 97 seeds, with an average of 52 (± 19). The weight of the extracted seeds ranged from 0.001 g to 0.651 g, on average 0.193 (± 0.109) g.Cones obtained from different process variants did not differ in terms of the number of seeds extracted (F = 0.862 at p = 0.55) or their weight (F = 0.720 at p = 0.674). However, ANOVA did reveal significant differences in the number of scales (F = 3.561 at p ˂0.05) and the total number of seeds per cone (F = 2.93601 at p = 0.003645). Table 6 gives mean scale and seed numbers per larch cone (with standard deviations) for the various extraction variants and homogeneous groups.Table 6 Mean numbers of cone scales and seeds for each process variant.Full size tableOn average, 70% of the seeds were extracted from cones used in all nine study variants, with 30% of the seeds remaining in the cones. Table 7 shows the number of seeds extracted in individual variants and the number of seeds remaining in the cones, expressed as a percentage.Table 7 Number of seeds extracted from and remaining in the cones for each process variant.Full size tableThe greatest number of seeds was obtained in process variants 2–73%, closely followed by variants 3, 1, and 7 (72%), and 8 (70%). The lowest seed yield was obtained from variant 4 (65%).In all study variants, some of the seeds were obtained in the process of extraction in the chamber and some in the process of shaking in the drum (Table 7). The highest number of seeds in the chamber was obtained in variant 2 (69%), and the lowest in variant 9 (56%). On average, the largest quantity of seeds was obtained in the chamber in the 10 h variants, and the lowest quantity in the 6 h variants. Comparing different process variants of the same drying duration, the greatest number of seeds in the chamber were obtained in variants 2, 5, and 7 (and also in variant 8—only 1% fewer). The greatest quantity of seeds extracted by shaking in the drum was obtained in variant 9 (44%), and the lowest in variant 2 (31%). On average, 38% of seeds extracted in all variants were obtained by shaking in the drum.It can be seen that in each of the variants and their individual steps, the highest number of seeds was obtained after 6 h of the process. Figure 4a–c shows the percentage of seeds obtained during the effective extraction time, where the number of seeds extracted at a given step was added cumulatively to those from the previous steps.Figure 4Percentage seed yield dynamics for each step of a five-step extraction process: (a) 10 h of drying, (b) 8 h of drying, (c) 6 h of drying.Full size imageThe diagrams in Fig. 4 show the percentage of seeds obtained throughout the entire process. Each step consists of drying, shaking, immersion, and soaking, except for step V, which involved only drying and shaking without immersion or soaking. Analysis of seed yield over 10 h of drying (Fig. 4a) shows that on average 37% of all extracted seeds were obtained in the first step, 26% in the second step, approx. 20% in the third step, 11% in the fourth step, and about 6% in the fifth step.As regards the 8 h process (Fig. 4b), on average 30% of all extracted seeds were obtained in the in the first extraction step in the 8 h_5 min and 8 h_15 min variants, and as much as 53% in the 8 h_10 min variant. An average of 27% of all seeds were extracted in the second step, 15% in the third step, about 11% in the fourth step, and approx. 5% in the fifth step. The 8 h_10 min variant was characterized by the highest seed yield, beginning in the first step of the process (as compared to the 8 h_5 min and 8 h_15 min variants).As far as the variant with 6 h of drying is concerned (Fig. 4c), on average approx. 46% of all extracted seeds were obtained in the first step, 24% in the second step, 15% in the third step, approx. 11% in the fourth step, about 4% in the fifth step.When extracting seeds from larch cones, scale deflection and the number of obtained seeds are not assessed during the process, as is the case with pine and spruce cones due to the difficulties caused by the aforementioned morphology of larch cones (Tyszkiewicz, 1949). The presented diagrams show that a satisfactory seed yield (60%) was obtained in variants with 8 and 6 h of drying already after 10 h of effective extraction time.The seed yield coefficient, α (3), and the cone mass yield coefficient, β (4), for each extraction variant are presented in Table 8.Table 8 Seed yield coefficient and cone mass yield coefficient for each process variants.Full size tableThe seed yield coefficient was the highest for variants 2 (0.73) and 3 (0.72), and the lowest for variants 4 (0.65) and 6 and 9 (0.67). The cone mass yield coefficient was the highest for variant 5, and the lowest for variant 9.Seed viabilityTable 9 presents germination energy (E) and capacity (Z) for the control seeds as well as for seeds obtained from the various steps of the nine process variants, as well as their corresponding quality classes.Table 9 Germination energy and capacity for the control seeds as well as for seeds obtained from the various extraction process variants.Full size tableGermination energy and capacity for the control sample were 45% and 57%, respectively, meaning that naturally released seeds, not subjected to any thermal or mechanical treatments, were classified in quality class I18. Importantly, seeds obtained from all the studied process variants were also placed in the same class; their germination energy ranged from 30 to 59%, and their germination capacity from 35 to 61%. When analyzing each extraction step separately, no correlation was found between decreasing germination energy and successive steps. However, the average germination energy was 46% for seeds obtained in the first extraction step of all nine variants, 45% for those from the second and third steps, 41% for seeds from the fourth step and 40% for those from the fifth one. Thus, in each subsequent step the average germination energy of seeds was equal or lower than in the previous step, which is consistent with literature reports that prolonged drying may reduce the quality of seeds8. This is also corroborated by the fact that the highest germination energy and capacity was revealed by seeds from variants with 6 h of drying while the lowest germination indicators characterized seeds from the 10 h variants. Furthermore, seeds from variant 1 exhibited the lowest germination energy and capacity and seeds from variant 8–the highest.Another reason for the higher quality of seeds from variants with 6 h of drying may be the lower initial moisture content of the cones due to the longer time they were kept at room temperature immediately before the test (u01 = 0.391 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) as compared to u01 = 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) for seeds from variants with 8 and 10 h of drying). These results are in line with the study of Tyszkiewicz8, who noted that under the same temperature and humidity conditions, the quality of seeds from cones with a lower moisture content did not deteriorate, in contrast to the quality of seeds obtained from cones with a higher moisture content.The germination capacity of seeds calculated from the mean capacity of seeds obtained from the same extraction steps of all process variants was similar at 45% for each of the steps.In summary, in the study the authors investigated a five-step process of extracting seeds from larch cones involving immersion and heat treatment to maximize seed yield. It was found that the two-step process widely used in extractories is insufficient, while a four-step process does not lead to a significantly higher number of obtained seeds. Thus, a three-step process appears to be optimal. More