More stories

  • in

    Global phylogeography of a pantropical mangrove genus Rhizophora

    1.Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves. (Earthscan, 2010).2.Duke, N. et al. A world without mangroves?. Science 317, 41–42 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Friess, D. et al. The state of the world’s mangrove forests: past, present, and future. Annu. Rev. Env. Resour. 44, 89–115 (2019).Article 

    Google Scholar 
    4.Wee, et al. The integration and application of genomic information in mangrove conservation. Conserv. Biol. 33, 206–209 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Duke, N., Lo, E. & Sun, M. Global distribution and genetic discontinuities of mangroves—emerging patterns in the evolution of Rhizophora. Trees-Struct. Funct. 16, 65–79 (2002).Article 

    Google Scholar 
    6.Ellison, A. M., Farnsworth, E. J. & Merkt, R. E. Origins of mangrove ecosystems and the mangrove biodiversity anomaly. Global Ecol. Biogeogr. 8, 95–115 (1999).
    Google Scholar 
    7.Plaziat, J.-C., Cavagnetto, C., Koeniguer, J.-C. & Baltzer, F. History and biogeography of the mangrove ecosystem, based on a critical reassessment of the paleontological record. Wetl. Ecol. Manag. 9, 161–180 (2001).Article 

    Google Scholar 
    8.Duke, N., Ball, M. & Ellison, J. Factors influencing biodiversity and distributional gradients in mangroves. Global Ecol. Biogeogr. Lett. 7, 27–47 (1998).Article 

    Google Scholar 
    9.Duke, N. Genetic diversity, distributional barriers and rafting continents—more thoughts on the evolution of mangroves. Hydrobiologia 295, 167–181 (1995).Article 

    Google Scholar 
    10.Tomlinson, P. B. The botany of mangroves. (Cambridge University press, 1986).11.Schwarzbach, A. E. & Ricklefs, R. E. Systematic affinities of Rhizophoraceae and Anisophylleaceae, and intergeneric relationships within Rhizophoraceae, based on chloroplast DNA, nuclear ribosomal DNA, and morphology. Am. J. Bot. 87, 547–564 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Lo, E. Y. Y. Testing hybridization hypotheses and evaluating the evolutionary potential of hybrids in mangrove plant species. J. Evol. Biol. 23, 2249–2261 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Takayama, K., Tamura, M., Tateishi, Y., Webb, E. L. & Kajita, T. Strong genetic structure over the American continents and transoceanic dispersal in the mangrove genus Rhizophora (Rhizophoraceae) revealed by broad-scale nuclear and chloroplast DNA analysis. Am. J. Bot. 100, 1191–1201 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Lo, E., Duke, N. & Sun, M. Phylogeographic pattern of Rhizophora (Rhizophoraceae) reveals the importance of both vicariance and long-distance oceanic dispersal to modern mangrove distribution. BMC Evol. Biol. 14, 83 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Chen, Y. et al. Applications of multiple nuclear genes to the molecular phylogeny, population genetics and hybrid identification in the mangrove genus Rhizophora. PLoS ONE 10, e0145058 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Xu, S. H. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl. Sci. Rev. 4, 721–734 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Tyagi, A. P. Cytogenetics and reproductive biology of mangroves in Rhizophoraceae. Aust. J. Bot. 50, 601–605 (2002).Article 

    Google Scholar 
    18.Tyagi, A. P. Chromosomal Pairing and Pollen Viability in Rhizophora mangle and Rhizophora stylosa Hybrids. S. Pac. J. Nat. Sci. 20, 1–3 (2002).Article 

    Google Scholar 
    19.Tyagi, A. P. & Singh, E. V. V. Pollen fertility and intraspecific and interspecific compatibility in mangroves of Fiji. Sex. Plant Reprod. 11, 60–63 (1998).Article 

    Google Scholar 
    20.Steininger, F. F. & Rögl, F. Paleogeography and palinspastic reconstruction of the Neogene of the Mediterranean and Paratethys. Geol. Soc. Spec. Publ. 17, 659–668 (1984).ADS 
    Article 

    Google Scholar 
    21.Harzhauser, M. et al. Biogeographic responses to geodynamics: a key study all around the Oligo-Miocene Tethyan Seaway. Zoo. Anz. 246, 241–256 (2007).Article 

    Google Scholar 
    22.Vrielynck, B., Odin, G. & Dercourt, J. Miocene palaeogeography of the Tethys Ocean; potential global correlations in the Mediterranean. Miocene stratigraphy: an integrated approach. Elsevier Science, (1997).23.Harzhauser, M., Piller, W. E. & Steininger, F. F. Circum-Mediterranean Oligo-Miocene biogeographic evolution—the gastropods’ point of view. Palaeogeogr. Palaeoclimatol. Palaeoecol. 183, 103–133 (2002).Article 

    Google Scholar 
    24.Dercourt, J. et al. Geological evolution of the Tethys belt from the Atlantic to the Pamirs since the LIAS. Tectonophysics 123, 241–315 (1986).ADS 
    Article 

    Google Scholar 
    25.Marko, P. B. Fossil calibration of molecular clocks and the divergence times of geminate species pairs separated by the Isthmus of Panama. Mol. Biol. Evol. 19, 2005–2021 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    26.Saenger, P. Mangrove vegetation: an evolutionary perspective. Mar. Freshw. Res. 49, 277–286 (1998).CAS 
    Article 

    Google Scholar 
    27.Muller, J. & Caratini, C. Pollen of Rhizophora (Rhizophoraceae) as a guide fossil. Pollen Spores 19, 361–390 (1977).
    Google Scholar 
    28.Muller, J. Fossil pollen records of extant angiosperms. Bot. Rev. 47, 1–142 (1981).Article 

    Google Scholar 
    29.Germeraad, J. H., Hopping, C. A. & Muller, J. Palynology of tertiary sediments from tropical areas. Rev. Palaeobot. Palyno. 6, 189–348 (1968).Article 

    Google Scholar 
    30.Zachos, J., Pagani, H., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    31.Pole, M. S. & Macphail, M. K. Eocene Nypa from Regatta Point, Tasmania. Rev. Palaeobot. Palyno. 92, 55–67 (1996).Article 

    Google Scholar 
    32.Hornibrook, N. D. B. New Zealand Cenozoic marine paleoclimates: a review based on the distribution of some shallow water and terrestrial biota. Pacific Neogene: environment, evolution, and events, 83–106 University of Tokyo Press, (1992).33.Hou, Z. & Li, S. Tethyan changes shaped aquatic diversification. Biol. Rev. 93, 874–896 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Wee, A. K. S. et al. Genetic differentiation and phylogeography of partially sympatric species complex Rhizophora mucronata Lam. and R. stylosa Griff. using SSR markers. BMC Evol. Biol. 15, 57 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Ng, W. L. et al. Closely related and sympatric but not all the same: genetic variation of Indo-West Pacific Rhizophora mangroves across the Malay Peninsula. Conserv. Genet. 16, 137–150 (2015).Article 

    Google Scholar 
    36.Doyle, J. & Doyle, J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 9, 11–15 (1987).
    Google Scholar 
    37.Strand, A. E., Leebens-Mack, J. & Milligan, B. G. Nuclear DNA-based markers for plant evolutionary biology. Mol. Ecol. 6, 113–118 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Cronn, R. C., Small, R. L. & Wendel, J. F. Duplicated genes evolve independently after polyploid formation in cotton. Proc. Natl. Acad. Sci. USA 96, 14406–14411 (1999).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Hayashi, K. PCR-SSCP: a simple and sensitive method for detection of mutations in the genomic DNA. Genome Res. 1, 34–38 (1991).CAS 
    Article 

    Google Scholar 
    40.Rozas, J., Sanchez-DelBarrio, J. C., Messeguer, X. & Rozas, R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19, 2496–2497 (2003).CAS 
    Article 

    Google Scholar 
    41.Swofford, D.L. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Sinauer Associates, Sunderland, Massachusetts, (2002).42.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

    Google Scholar 
    44.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, I37-48 (1999).Article 

    Google Scholar 
    45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Heled, J. & Drummond, A. J. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27, 570–580 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    47.Graham, A. Paleobotanical evidence and molecular data in reconstructing the historical phytogeography of Rhizophoraceae. Ann. Mo. Bot. Gard. 93, 325–334 (2006).Article 

    Google Scholar 
    48.Rambaut, A. Fig Tree v1.4. (2012). Available at http://tree.bio.ed.ac.uk/software/figtree/49.Matzke, N. J. Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 242–248 (2013).Article 

    Google Scholar 
    50.Blair, C. & He, X. J. RASP 4: ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).PubMed 
    Article 
    CAS 

    Google Scholar 
    51.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in a mangrove species, Rhizophora stylosa (Rhizophoraceae). Conserv. Genet. Resour. 1, 175–178 (2009).Article 

    Google Scholar 
    52.Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in the red mangrove Rhizophora mangle (Rhizophoraceae) and its related species. Conserv. Genet. 9, 1323–1325 (2008).CAS 
    Article 

    Google Scholar 
    53.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes. 7, 574–578 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
    Article 

    Google Scholar  More

  • in

    Insights into the role of deep-sea squids of the genus Histioteuthis (Histioteuthidae) in the life cycle of ascaridoid parasites in the Central Mediterranean Sea waters

    1.Voss, N. A., Nesis, K. N. & Rodhouse, P. G. The cephalopod family Histioteuthidae (Oegopsida): systematics, biology, and biogeography in Systematics and Biogeography of Cephalopods (eds. Voss, N. A., Vecchione, M., Toll, R. B. & Sweeney M. J.) 277–291 (Smithsonian Contributions to Zoology, 1998).2.Crocetta, F. et al. Biogeographical homogeneity in the eastern Mediterranean Sea – III: new records and a state of the art of Polyplacophora, Scaphopoda and Cephalopoda (Mollusca) from Lebanon. Spixiana 37(2), 183–206 (2014).
    Google Scholar 
    3.Guerra, A. Mollusca, cephalopoda in Fauna Iberica (eds. Ramos, M. A. et al.) 327 (Museo Nacional de Ciencias Naturales, 1992).4.Cuccu, D., Mereu, M., Loi, B., Sanna, I. & Cau, A. The squid family Histioteuthidae in the Sardinian waters. Biol. Mar. Mediterr. 13, 262–263 (2007).
    Google Scholar 
    5.Jereb, P. & Roper, C. F. E. Cephalopods of the world. An annotated and illustrated catalogue of cephalopod species known to date. Myopsid and Oegopsid Squids in FAO species catalogue for fishery purposes (ed. FAO) 649 (FAO, 2010).6.Quetglas, A., de Mesa, A., Ordines, F. & Grau, A. Life history of the deep-sea cephalopod family Histioteuthidae in the western Mediterranean. Deep Sea Res. Part I 57, 999–1008. https://doi.org/10.1016/j.dsr.2010.04.008 (2010).Article 

    Google Scholar 
    7.Oshima, T., Shimazu, T., Koyama, H. & Akahane, H. J. J. On the larvae of the genus Anisakis (Nematoda: Anisakidaae) from euphausiids. Jpn. J. Parasitol. 18, 241–248 (1969).
    Google Scholar 
    8.Hochberg, F. G. The parasites of cephalopods: a review. Mem. Nat. Mus. Vict. 44, 109–145. https://doi.org/10.24199/j.mmv.1983.44.10 (1983).Article 

    Google Scholar 
    9.Bello, G. Role of cephalopods in the diet of the swordfish, Xiphias gladius, from the eastern Mediterranean Sea. Bull. Mar. Sci. 49, 312–324 (1991).
    Google Scholar 
    10.Bello, G. Teuthophagous predators as collectors of oceanic cephalopods: the case of the Adriatic Sea. Boll. Malacol. 32, 71–78 (1996).
    Google Scholar 
    11.Santos, M. et al. Stomach contents of sperm whales Physeter macrocephalus stranded in the North Sea 1990–1996. Mar. Ecol. Prog. Ser. 183, 281–294 (1999).ADS 
    Article 

    Google Scholar 
    12.Xavier, J. et al. Current status of using beaks to identify cephalopods: III International Workshop and training course on Cephalopod beaks, Faial island, Azores, April 2007. Arquipélago-Life Mar. Sci. 24, 41–48 (2007).
    Google Scholar 
    13.Marcogliese, D. J. & Cone, D. K. Food webs: a plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Abollo, E. et al. Squid as trophic bridges for parasite flow within marine ecosystems: the case of Anisakis simplex (Nematoda: Anisakidae), or when the wrong way can be right. Afr. J. Mar. Sci. 20, 223–232. https://doi.org/10.2989/025776198784126575 (1998).Article 

    Google Scholar 
    15.Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H. Deep-water metazoan fish parasites of the world. (eds. Klimpel, S., Seehagen, A., Palm, H. W. & Rosenthal, H.) (Logos Verlag, 2001).16.Parker, G. A., Chubb, J. C., Ball, M. A. & Roberts, G. N. Evolution of complex life cycles in helminth parasites. Nature 425, 480–484 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Santoro, M., Iaccarino, D. & Bellisario, B. Host biological factors and geographic locality influence predictors of parasite communities in sympatric sparid fishes off the southern Italian coast. Sci. Rep. 10(1), 13283. https://doi.org/10.1038/s41598-020-69628-1 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Pascual, S., González, A., Arias, C. & Guerra, A. Helminth infection in the short-finned squid Illex coindetii (Cephalopoda, Ommastrephidae) off NW Spain. Dis. Aquat. Org. 23, 71–75. https://doi.org/10.3354/dao023071 (1995).Article 

    Google Scholar 
    19.Petrić, M., Mladineo, I. & Šifner, S. Insight into the short-finned squid Illex coindetii (Cephalopoda: Ommastrephidae) feeding ecology: is there a link between helminth parasites and food composition? J. Parasitol. 97, 55–62. https://doi.org/10.1645/GE-2562.1 (2011).Article 
    PubMed 

    Google Scholar 
    20.Klimpel, S. & Rückert, S. Life cycle strategy of Hysterothylacium aduncum to become the most abundant anisakid fish nematode in the North Sea. Parasitol. Res. 97, 141–149. https://doi.org/10.1007/s00436-005-1407-6 (2005).Article 
    PubMed 

    Google Scholar 
    21.Tursi, A., D’Onghia, A., Matarrese, A., Panetta, P. & Maiorano, P. Finding of uncommon cephalopods (Ancistroteuthis lichtensteinii, Histioteuthis bonnellii, Histioteuthis reversa) and first record of Chiroteuthis veranyi in the Ionian Sea. Cah. Biol. Mar. 35, 339–346 (1994).
    Google Scholar 
    22.Koutsoubas, D. & Boyle, P. Histioteuthis bonnelli (Férussac, 1835) (Cephalopoda) in the Eastern Mediterranean: new record and biological considerations. J. Mollus. Stud. 65, 380–383. https://doi.org/10.1093/mollus/65.3.380 (1999).Article 

    Google Scholar 
    23.Bello, G. How rare is Histioeuthis bonnellii (Cephalopoda: Histioteuthidae) in the eastern Mediterranean Sea? J. Mollus. Stud. 66, 575–576. https://doi.org/10.1093/mollus/66.4.575 (2000).Article 

    Google Scholar 
    24.Belcari, P. & Sartor, P. Bottom trawling teuthofauna of the northern Tyrrhenian Sea. Sci. Mar. 57, 145–152 (1993).
    Google Scholar 
    25.Quetglas, A., Carbonell, A. & Sánchez, P. Demersal continental shelf and upper slope cephalopod assemblages from the Balearic Sea (North-Western Mediterranean). Biological aspects of some deep-sea species. Estuar. Coast. Shelf Sci. 50, 739–749. https://doi.org/10.1006/ecss.1999.0603 (2000).ADS 
    Article 

    Google Scholar 
    26.Culurgioni, J., Cuccu, D., Mereu, M. & Figus, V. Larval anisakid nematodes of Histioteuthis reversa (Verril, 1880) and H. bonnellii (Férussac, 1835) (Cephalopoda: Teuthoidea) from Sardinian Channel (western Mediterranean). Bull. Eur. Ass. Fish Pathol. 30, 217 (2010).
    Google Scholar 
    27.Capua, D. I cefalopodi delle coste e dell’Arcipelago Toscano: sistematica, anatomia, fisiologia e sfruttamento delle specie presenti nel Mediterraneo. 446 (Evolver, 2004).28.Crocetta, F. et al. Bottom-trawl catch composition in a highly polluted coastal area reveals multifaceted native biodiversity and complex communities of fouling organisms on litter discharge. Mar. Environ. Res. 155, 104875. https://doi.org/10.1016/j.marenvres.2020.104875 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
    PubMed 

    Google Scholar 
    30.Meyer, C. P. Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification patterns in the tropics. Biol. J. Linn. Soc. 79, 401–459. https://doi.org/10.1046/j.1095-8312.2003.00197.x (2003).Article 

    Google Scholar 
    31.Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24, 1757–1764. https://doi.org/10.1093/bioinformatics/btn322 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Berland, B. Nematodes from some Norwegian marine fishes. Sarsia 2, 1–50. https://doi.org/10.1080/00364827.1961.10410245 (1961).Article 

    Google Scholar 
    33.Nagasawa, K. & Moravec, F. Larval anisakid nematodes of Japanese common squid (Todarodes pacificus) from the Sea of Japan. J. Parasitol. 81, 69–75. https://doi.org/10.2307/3284008 (1995).CAS 
    Article 
    PubMed 

    Google Scholar 
    34.Nagasawa, K. & Moravec, F. Larval anisakid nematodes from four species of squid (Cephalopoda: Teuthoidea) from the central and western North Pacific Ocean. J. Nat. Hist. 36, 8. https://doi.org/10.1080/00222930110051752 (2002).Article 

    Google Scholar 
    35.Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83(4), 575–583 (1997).CAS 
    Article 

    Google Scholar 
    36.Zhu, X., Gasser, R. B., Podolska, M. & Chilton, N. Characterisation of anisakid nematodes with zoonotic potential by nuclear ribosomal DNA sequences. Int. J. Parasitol. 28, 1911–1921. https://doi.org/10.1016/S0020-7519(98)00150-7 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    37.Nadler, S. A. & Hudspeth, D. S. Phylogeny of the Ascaridoidea (Nematoda: Ascaridida) based on three genes and morphology: hypotheses of structural and sequence evolution. J. Parasitol. 86, 380–393. https://doi.org/10.1645/0022-3395(2000)086[0380:POTANA]2.0.CO;2 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Valentini, A. et al. Genetic relationships among Anisakis species (Nematoda: Anisakidae) inferred from mitochondrial cox2 sequences, and comparison with allozyme data. J. Parasitol. 92, 156–166. https://doi.org/10.1645/GE-3504.1 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180. https://doi.org/10.1111/j.1096-0031.2010.00329.x (2011).Article 

    Google Scholar 
    40.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772. https://doi.org/10.1038/nmeth.2109 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Guindon, S. & Gascuel, O. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Syst. Biol. 52, 696–704. https://doi.org/10.1080/10635150390235520 (2003).Article 
    PubMed 

    Google Scholar 
    42.Akaike, H. Information theory and an extension of the maximum likelihood principle in Proceeding of the second international symposium on information theory (eds. Petrov, T. & Caski, F.) 267–281 (Akademiai Kiado, 1973).43.Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256. https://doi.org/10.1093/molbev/msn083 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Posada, D. & Buckley, T. R. Model selection and model averaging in phylogenetics: advantages of Akaike Information Criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53, 793–808. https://doi.org/10.1080/10635150490522304 (2004).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Ronquist, F. & Huelsenbeck, J. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).CAS 
    Article 

    Google Scholar 
    46.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
    CAS 
    Article 

    Google Scholar 
    47.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Lindgren, A. R. Molecular inference of phylogenetic relationships among Decapodiformes (Mollusca: Cephalopoda) with special focus on the squid Order Oegopsida. Mol. Phylogenet. 56(1), 77–90. https://doi.org/10.1016/j.ympev.2010.03.025 (2010).Article 

    Google Scholar 
    49.Taite, M., Vecchione, M., Fennell, S. & Allcock, L. A. Paralarval and juvenile cephalopods within warm-core eddies in the North Atlantic. Bul. Mar. Sci. 96(2), 235–262. https://doi.org/10.5343/bms.2019.0042 (2020).Article 

    Google Scholar 
    50.Guardone, L. et al. Larval ascaridoid nematodes in horned and musky octopus (Eledone cirrhosa and E. moschata) and longfin inshore squid (Doryteuthis pealeii): safety and quality implications for cephalopod products sold as fresh on the Italian market. Int. J. Food Microbiol. 333, 108812 (2020).CAS 
    Article 

    Google Scholar 
    51.Pascual, S., Abollo, E., Mladineo, I. & Gestal, C. Metazoa and Related Diseases in Handbook of Pathogens and Diseases in Cephalopods (eds. Gestal, C., Pascual S., Guerra A., Fiorito G. & Vieites, J. M.) 169–179 (2019).52.Mattiucci, S. & Nascetti, G. Advances and trends in the molecular systematics of anisakid nematodes, with implications for their evolutionary ecology and host—parasite co-evolutionary processes. Adv. Parasitol. 66, 47–148. https://doi.org/10.1016/S0065-308X(08)00202-9 (2008).Article 
    PubMed 

    Google Scholar 
    53.Mattiucci, S., Cipriani, P., Levsen, A., Paoletti, M. & Nascetti, G. Molecular epidemiology of Anisakis and Anisakiasis: an ecological and evolutionary road map. Adv. Parasitol. 99, 93–263. https://doi.org/10.1016/bs.apar.2017.12.001 (2018).Article 
    PubMed 

    Google Scholar 
    54.Kie, M. Aspects of the life cycle and morphology of Hysterothylacium aduncum (Rudolphi, 1802) (Nematoda, Ascaridoidea, Anisakidae). Can. J. Zool. 71, 1289–1296. https://doi.org/10.1139/z93-178 (1993).Article 

    Google Scholar 
    55.Santoro, M. et al. Helminth parasites of the dwarf sperm whale Kogia sima (Cetacea: Kogiidae) from the Mediterranean Sea, with implications on host ecology. Dis. Aquat. Organ. 14, 175–182. https://doi.org/10.3354/dao03251 (2018).CAS 
    Article 

    Google Scholar 
    56.Kawakami, T. A review of sperm whale food. Sci. Rep. Whales Res. Inst. 32, 199–218 (1980).
    Google Scholar 
    57.Garibaldi, F. & Podestà, M. Stomach contents of a sperm whale (Physeter macrocephalus) stranded in Italy (Ligurian Sea, northwestern Mediterranean). JMBA 94(6), 1087–1091. https://doi.org/10.1017/S0025315413000428 (2014).Article 

    Google Scholar 
    58.Mattiucci, S., Nascetti, G., Bullini, L., Orecchia, P. & Paggi, L. Genetic structure of Anisakis physeteris and its differentiation from the Anisakis simplex complex (Ascaridida: Anisakidae). Parasitology 93, 383–387. https://doi.org/10.1017/S0031182000051544 (1986).CAS 
    Article 
    PubMed 

    Google Scholar 
    59.Mattiucci, S. et al. Genetic divergence and reproductive isolation between Anisakis brevispiculata and Anisakis physeteris (Nematoda: Anisakidae). Int. J. Parasitol. 31, 9–14. https://doi.org/10.1016/S0020-7519(00)00125-9 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Gupta, P. C. & Masoodi, B. A. Three new and one known nematode (Family: Anisakidae) from marine fishes of India. Indian J. Parasitol. 14(2), 157–164 (1990).
    Google Scholar 
    61.Vicente, J. J., Mincarone, M. M. & Pint, R. M. First report of Lappetascaris lutjani Rasheed, 1965 (Nematoda, Ascaridoidea, Anisakidae) parasitizing Trachipterus arawatae (Pisces, Lampridiformes) on the Atlantic coast of Brazil. Mem. Inst. Oswaldo Cruz. 97, 93–94. https://doi.org/10.1590/s0074-02762002000100015(2002) (2002).Article 
    PubMed 

    Google Scholar 
    62.Bruce, N. L. & Cannon, L. R. G. Hysterothylacium, Iheringascaris and Maricostula new genus, nematodes (Ascaridoidea) from Australian pelagic marine fishes. J. Nat. Hist. 23(6), 1397–1441. https://doi.org/10.1080/00222938900770771 (1989).Article 

    Google Scholar 
    63.Shamsi, S. Morphometric and molecular descriptions of three new species of Hysterothylacium (Nematoda: Raphidascarididae) from Australian marine fish. J. Helminthol. 91, 1–12. https://doi.org/10.1017/S0022149X16000596 (2016).CAS 
    Article 

    Google Scholar 
    64.Li, L. et al. Molecular phylogeny and dating reveal a terrestrial origin in the early carboniferous for ascaridoid nematodes. Syst. Biol. 67(5), 888–900. https://doi.org/10.1093/sysbio/syy018 (2018).Article 
    PubMed 

    Google Scholar 
    65.Garcia, A., Mattiucci, S., Santos, M. N., Damiano, S. & Nascetti, G. Metazoan parasites of Xiphias gladius (L. 1758) (Pisces: Xiphiidae) from the Atlantic Ocean: implications for host stock identification. ICES J. Mar. Sci. 68, 175–182 (2010).Article 

    Google Scholar 
    66.Klimpel, S. & Palm, H. W. Anisakid Nematode (Ascaridoidea) Life Cycles and Distribution: Increasing Zoonotic Potential in the Time of Climate Change? in Progress in Parasitology. Parasitology Research Monographs (ed. Mehlhorn, H.) https://doi.org/10.1007/978-3-642-21396-0_11 (Springer, 2011).67.Kuhn, T., Cunze, S., Kochmann, J. & Klimpel, S. Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm. Sci. Rep. 6, 30246. https://doi.org/10.1038/srep30246 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Cipriani, P. et al. Occurrence of larval ascaridoid nematodes in the Argentinean short-finned squid Illex argentinus from the Southwest Atlantic Ocean (off Falkland Islands). Int. J. Food Microbiol. 297, 27–31. https://doi.org/10.1016/j.ijfoodmicro.2019.02.019 (2019).Article 
    PubMed 

    Google Scholar 
    69.Cipriani, P. et al. Anisakis simplex (s.s.) larvae (Nematoda: Anisakidae) hidden in the mantle of European flying squid Todarodes sagittatus (Cephalopoda: Ommastrephidae) in NE Atlantic Ocean: food safety implications. Int. J. Food Microbiol. 339, 109021 (2021).CAS 
    Article 

    Google Scholar 
    70.Klimpel, S., Kellermanns, E. & Palm, H. W. The role of pelagic swarm fish (Myctophidae: Teleostei) in the oceanic life cycle of Anisakis sibling species at the Mid-Atlantic Ridge, Central Atlantic. Parasitol. Res. 104, 43–53. https://doi.org/10.1007/s00436-008-1157-3 (2008).Article 
    PubMed 

    Google Scholar 
    71.Mattiucci, S., Paoletti, M. & Webb, S. C. Anisakis nascettii n. sp. (Nematoda: Anisakidae) from beaked whales of the southern hemisphere: morphological description, genetic relationships between congeners and ecological data. Syst. Parasitol. 74, 199–217. https://doi.org/10.1007/s11230-009-9212-8 (2009).Article 
    PubMed 

    Google Scholar 
    72.Pico-Duran, G., Pulleiro-Potel, L., Abollo, E., Pascual, S. & Munoz, P. Molecular identification of Anisakis and Hysterothylacium larvae in commercial cephalopods from the Spanish Mediterranean coast. Vet. Parasitol. 220, 47–53. https://doi.org/10.1016/j.vetpar.2016.02.020 (2016).Article 
    PubMed 

    Google Scholar 
    73.Menconi, V. et al. Occurrence of ascaridoid nematodes in Illex coindetii, a commercially relevant cephalopod species from the Ligurian Sea (Northwest Mediterranean Sea). Food Control https://doi.org/10.1016/j.foodcont.2020.107311 (2020).Article 

    Google Scholar 
    74.Blazekovic, K. et al. Three Anisakis spp. isolated from toothed whales stranded along the eastern Adriatic Sea coast. Int. J. Parasitol. 45(1), 17–31. https://doi.org/10.1016/j.ijpara.2014.07.012 (2015).Article 
    PubMed 

    Google Scholar 
    75.Santoro, M. et al. Epidemiology of Sulcascaris sulcata (Nematoda: Anisakidae) ulcerous gastritis in the Mediterranean loggerhead sea turtle (Caretta caretta). Parasitol. Res. 118, 1457–1463. https://doi.org/10.1007/s00436-019-06283-0 (2019).Article 
    PubMed 

    Google Scholar 
    76.Bao, M., Cipriani, P., Giulietti, L., Drivenes, N. & Levsen, A. Quality issues related to the presence of the fish parasitic nematode Hysterothylacium aduncum in export shipments of fresh Northeast Arctic cod (Gadus morhua). Food Control 121, 107724. https://doi.org/10.1016/j.foodcont.2020.107724 (2020).CAS 
    Article 

    Google Scholar 
    77.Zhang, K., Xu, Z., Chen, H. X., Guo, N. & Li, L. Anisakid and raphidascaridid nematodes (Ascaridoidea) infection in the important marine food-fish Lophius litulon (Jordan) (Lophiiformes: Lophiidae). Int. J. Food Microbiol. 284, 105–111. https://doi.org/10.1016/j.ijfoodmicro.2018.08.002 (2018).Article 
    PubMed 

    Google Scholar 
    78.Szostakowska, B., Myjak, P., Kur, J. & Sywula, T. Molecular evaluation of Hysterothylacium auctum (Nematoda, Ascaridida, Raphidascarididae) taxonomy from fish of the southern Baltic. Acta Parasitol. 46(3), 194–201 (2001).CAS 

    Google Scholar 
    79.Andres, M. J., Peterson, M. S. & Overstreet, R. M. Endohelminth parasites of some midwater and benthopelagic stomiiform fishes from the northern Gulf of Mexico. Gulf Caribb. Res. 27, 11–19. https://doi.org/10.18785/gcr.2701.02 (2016).Article 

    Google Scholar 
    80.Li, L., Liu, Y. Y. & Zhang, L. P. Morphological and molecular identification of Hysterothylacium longilabrum sp. Nov. (Nematoda: Anisakidae) and larvae of different stages from marine fishes in the South China Sea. Parasitol. Res. 111(2), 767–777 (2012).Article 

    Google Scholar 
    81.Shamsi, S. et al. Occurrence of ascaridoid nematodes in selected edible fish from the Persian Gulf and description of Hysterothylacium larval type XV and Hysterothylacium persicum n. sp. (Nematoda: Raphidascarididae). Int. J. Food Microbiol. 236, 65–67. https://doi.org/10.1016/j.ijfoodmicro.2016.07.006 (2016).Article 
    PubMed 

    Google Scholar 
    82.Chen, H. X. et al. Detection of ascaridoid nematode parasites in the important marine food-fish Conger myriaster (Brevoort) (Anguilliformes: Congridae) from the Zhoushan fishery, China. Parasit. Vectors 11, 274. https://doi.org/10.1186/s13071-018-2850-4 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Liu, Y. Y., Xu, Z., Zhang, L. P. & Li, L. Redescription and genetic characterization of Hysterothylacium thalassini Bruce, 1990 (Nematoda: Anisakidae) from marine fishes in the South China Sea. J. Parasitol. 99, 655–661. https://doi.org/10.1645/12-136.1 (2013).Article 
    PubMed 

    Google Scholar 
    84.Shamsi, S., Gasser, R. & Beveridge, I. Description and genetic characterisation of Hysterothylacium (Nematoda: Raphidascarididae) larvae parasitic in Australian marine fishes. Parasitol. Int. 62, 320–328. https://doi.org/10.1016/j.parint.2012.10.001 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    85.Li, L., Zhao, W. T., Guo, Y. N. & Zhang, L. P. Nematode parasites infecting the starry batfish Halieutaea stellata (Vahl) (Lophiiformes: Ogcocephalidae) from the East and South China Sea. J. Fish. Dis. 39(5), 515–529. https://doi.org/10.1111/jfd.12374 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    86.Zhao, W. T. et al. Ascaridoid parasites infecting in the frequently consumed marine fishes in the coastal area of China: a preliminary investigation. Parasitol. Int. 65(2), 87–98. https://doi.org/10.1016/j.parint.2015.11.002 (2016).Article 
    PubMed 

    Google Scholar 
    87.Hossen, M. S. & Shamsi, S. Zoonotic nematode parasites infecting selected edible fish in New South Wales, Australia. Int. J. Food Microbiol. 308, 108306. https://doi.org/10.1016/j.ijfoodmicro.2019.108306 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    88.Jabbar, A. et al. Mutation scanning-based analysis of anisakid larvae from Sillago flindersi from Bass Strait, Australia. Electrophoresis 33, 499–505. https://doi.org/10.1002/elps.201100438 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    89.Jabbar, A. et al. Molecular characterization of anisakid nematode larvae from 13 species of fish from Western Australia. Int. J. Food Microbiol. 161(3), 247–253. https://doi.org/10.1016/j.ijfoodmicro.2012.12.012 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    90.Shamsi, S., Stellar, E. & Chen, Y. New and known zoonotic nematode larvae within selected fish species from Queensland waters in Australia. Int. J. Food Microbiol. 272, 73–82. https://doi.org/10.1016/j.ijfoodmicro.2018.03.007 (2018).Article 
    PubMed 

    Google Scholar 
    91.Arizono, N. et al. Ascariasis in Japan: is pig-derived ascaris infecting humans? Jpn. J. Infect. Dis. 63(6), 447–448 (2010).PubMed 

    Google Scholar 
    92.Mattiucci, S. et al. Metazoan parasitic infections of swordfish (Xiphias gladius) from the Mediterranean Sea and Atlantic Gibraltar waters: implications for stock assessment. Col. Vol. Sci. Pap. ICCAT 58(4), 1470–1482 (2005).
    Google Scholar 
    93.Di Azevedo, M. I. N. & Iñiguez, A. M. Nematode parasites of commercially important fish from the southeast coast of Brazil: morphological and genetic insight. Int. J. Food Microbiol. 267, 29–41. https://doi.org/10.1016/j.ijfoodmicro.2017.12.014 (2018).Article 
    PubMed 

    Google Scholar 
    94.Pekmezci, G. Z., Yardimci, B., Onuk, E. E. & Umur, S. Molecular characterization of Hysterothylacium fabri (Nematoda: Anisakidae) from Zeus faber (Pisces: Zeidae) caught off the Mediterranean coasts of Turkey based on nuclear ribosomal and mitochondrial DNA sequences. Parasitol. Int. 63(1), 127–131. https://doi.org/10.1016/j.parint.2013.10.006 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    95.Zhao, J. Y., Zhao, W. T., Ali, A. H., Chen, H. X. & Li, L. Morphological variability, ultrastructure and molecular characterisation of Hysterothylacium reliquens (Norris & Overstreet, 1975) (Nematoda: Raphidascarididae) from the oriental sole Brachirus orientalis (Bloch & Schneider) (Pleuronectiformes: Soleidae). Parasitol. Int. 66(1), 831–838. https://doi.org/10.1016/j.parint.2016.09.012 (2016).Article 
    PubMed 

    Google Scholar  More

  • in

    Multiple impacts of microplastics can threaten marine habitat-forming species

    Collection of marine organismsMarine invertebrates such as Corallium rubrum are ideal organisms to perform controlled experiments and to gather useful information on a variety of environmental conditions74. This species, whose diet is based on small zooplankton captured with the polyp tentacles, has been already used in long-term experiments74,75,76. Coral specimens were collected in March 2017 at ca. 35-m depth in the Marine Protected Area of Portofino (Punta del Faro, 44°17′41.02 N; 9°13′31.30 E) in the Ligurian Sea (North-Western Mediterranean Sea) by scuba divers (using TRIMIX blending).Experimental designAfter recovery, the coral specimens were brought to the laboratory and maintained in a tank (30 L) at in situ temperature (13 ± 0.8 °C) and subjected to the continuous flux of natural seawater filtered onto 0.7-µm pore-size membranes (micro-glass fibre paper, Munktell) by using two submersible pumps (Euronatale, 203 V, 50 Hz, 4 Watt).Sixty coral branches obtained from different colonies, with similar morphology, and a surface of ~2 cm2 each, were distributed among 12 experimental tanks, in order to have 5 coral branches per tank (12 L glass tanks, containing on average, 274 ± 26.4 coral polyps each). The corals were acclimatised for 20 days in a temperature-controlled room, and dim light conditions, before starting experiments. Each tank, filled with natural seawater, was equipped with a prefiltered (0.2 µm) channelled aeration system combined with motor-driven paddles in order to create convective currents, which allowed the resuspension of the microplastic mixture, thus ensuring as much as possible a homogeneous distribution of the polymers. This experimental system was designed and set up according to Sutherland et al.77. To assess the potential effects of increasing microplastic  microparticles L−1 (here defined as low, medium and high concentrations of microplastic particles). We also quantified the exact amount of particles actually interacting with the corals, by discounting the fractions loss due to experimental manipulations (see details in Supplementary Methods). According to the results reported in the Supplementary Results, the systems were responsible for the loss of ca. 40% of the microplastic particles, thus the corals in experimental systems were actually exposed to 60, 300 and 600 microplastic particles per litre (to which we referred the low, medium and high concentrations).The highest concentrations of microplastics (up to 600 microplastic particles per litre) can reflect future contamination on the basis of estimates obtained by numerical models9, whereas the low and medium concentrations have been selected to represent highly-contaminated marine habitats, including the areas where the corals were collected (Ligurian Sea)78,79. In particular, for the Ligurian Sea, we estimated an average value of 94 microplastic particles L−1, based on the concentrations of microplastic particles ( >200 µm) determined by Fossi et al.78,79, and the most cautionary correction factor (105) calculated by Brandon et al.10 for the unaccounted smaller fraction of microplastics (25–75% of the fragments falling approximately in the 20–100 µm dimensional class with median range: 59–116).Microplastic mixtures were also prepared considering the concentration and composition of dominant polymers in different coastal marine environments, especially in hot spots of microplastic contamination5,7,8.The microplastic mixture added to the tanks was composed of 76.6% polyethylene, 10.9% polypropylene, 7.3% polystyrene, 3.3% polyvinylchloride and 1.8% polyethylene terephthalate particles. These particles were obtained by milling plastic objects from everyday life (i.e., containers, bottles, cups, pipes) according to Paul-Pont et al.8 (Supplementary Table 5). Plastic milling was carried out under a laminar flow hood in chilled sterilized and 0.02 µm prefiltered milliQ water. All the tools used for handling plastics were pre-treated with 1% sodium hypochlorite in water and rinsed 10 times with sterilized and 0.02 µm prefiltered milliQ water, and then dried under laminar flow hood. Details on the preparation of microplastic mixtures are reported in the Supplementary Methods.The low, medium and high concentrations of microplastics were added in triplicate tanks (n = 3 for each concentration). Additional systems containing seawater and coral branches without microplastics (n = 3, here defined Controls), and seawater added with microplastics (at the highest concentration) without red corals (n = 3, here define CTRL MPs) were used as controls. Overall, the experimental setup comprised 15 tanks.The experiments for assessing the impact of microplastics on red corals started immediately after the microplastic mixture addition (time 0). During the experiment, seawater temperature (range: 13.10 ± 0.01–13.13 ± 0.05 °C), salinity (range: 38.35 ± 0.18–38.65 ± 0.18) and oxygen levels (7.10 ± 0.08–7.36 ± 0.2 mg L−1) were monitored daily in all tanks using a probe (YSI Professional Plus, USA) and corals were fed three times a week with 103 Artemia salina nauplii L−1.After ten days the condition of corals that were exposed to microplastics was deteriorating, so we collected one coral branch from each tank for molecular analyses (i.e., associated microbiome, gene expression and DNA damage). After 14 days, the experiment was stopped because the coral branches that were exposed to the medium and high microplastic particle concentrations were completely wrapped in mucus, with a large portion of damaged tissue and without polyp activity, therefore corals were defined dead (overall 12 branches, see ‘Results’ section for details). Coral branches in the controls showed no visible signs of necrosis or other macroscopic stress. The tissue remained intact, and the colour unchanged until the end of the experiment.Effects of microplastic ingestionCoral feeding activityTo assess the impact of microplastics on feeding activity of C. rubrum, analyses based on the use of Artemia salina were performed after 2 and 10 days from the start of the experiment (t0) in replicate systems (n = 3 for each treatment, n = 3 for the controls) according to standard international protocols80. The nauplii of Artemia salina were reared in the laboratory, incubating 0.5 g of cysts (Ocean Nutrition) in 1 L of seawater filtered onto 0.2-µm filter in a separatory funnel, 2 days before the analysis of feeding rate. At hatching, nauplii were counted and maintained in vials to obtain the concentration of 1000 nauplii L−1. To avoid stress, corals (one branch for each tank) were transferred underwater to beakers along with 1 L seawater of each tank. After addition of live A. salina nauplii (1000 nauplii L−1) to the 1 L beakers containing the coral branches and to the controls, three aliquots of 10 ml seawater were collected after ~30 s from the start of the experiment (t0) and after 2 and 4 h. The remaining nauplii present in each seawater aliquot were counted under a stereomicroscope at ×3.2 magnification (Zeiss Stami 2000). Mean ingestion rates (nauplii removed h−1) were determined by linear regression analysis.Accumulation of microplastics by C. rubrum
    To investigate the accumulation of plastic polymers by C. rubrum polyps, the number of microplastic particles ingested by coral polyps was evaluated after 14 days of exposure to microplastic mixture, by dissolving polyps and skeleton of the corals (one for each tank at the concentration of 1000 microplastic particles L−1) using an acid/base digestion protocol36 with some modifications.To exclude biases on the estimate of the number of microplastic particles actually accumulated within the polyps, coral branches were accurately rinsed with milliQ water and checked under stereomicroscope (at ×50 magnification) for the potential presence of microplastic particles adherent to the coral tissue. Coral branches were then soaked in 5 ml of 4.5% sodium hypochlorite (NaClO) for 24 h and dissolved in 5 ml of 37% HCl for 30 min. Particulate material was retained on a 0.2-μm filter in a vacuum filtration system, and microplastic particles were counted under a stereomicroscope at ×50 magnification. The chemical composition of the polymers ingested by corals was confirmed by FT-IR analyses (Perkin Elmer, software Packages Spectrum 5.3.1). To evaluate possible damage to plastic polymers due to the use of acid/base solutions, we exposed polypropylene, polyethylene, polystyrene, polyvinylchloride and polyethylene terephthalate at the same volume and concentration of NaClO and HCl during digestion of the coral.Potential transfer of microplastics by zooplanktonWhile testing the exposure of the red corals to microplastics, we also determined the rate of microplastic ingestion by A. salina nauplii used to feed the red corals, in order to assess their role as potential vectors of microplastics. To do this, additional tanks (n = 3) were added with 0.2 µm prefiltered 12 L natural seawater, 1000 nauplii L−1 of A. salina and the same microplastic mixture used for the experiment on the red corals (at the highest concentration). Three other tanks were used as controls containing 0.2 µm prefiltered 12 L natural seawater and 1000 nauplii L−1 of A. salina.Microplastic ingestion by A. salina was determined after 2 and 10 days of experiment following the enzymatic digestion protocol previously developed81 with some modifications. Such a procedure degrades biological tissues without affecting shape, colour and composition of plastic fragments. Gut contents of 100 individuals of A. salina (n = 5) were assessed under a stereomicroscope (Leica MZ125) and light microscope (Zeiss Axiovert 200) and photographed with a Zeiss Axiocam digital camera. Afterwards, nauplii were processed immediately according to the modified enzymatic digestion protocol. Nauplii were dried in an oven for 3 h at 60 °C, transferred to glass jars containing a buffer homogenizing solution (400 mM Tris-HCl pH 8, 60 mM EDTA pH 8, 5 M NaCl, SDS 1%) incubated at 50 °C for 15 min and exposed to Proteinase K (1 mg ml−1). Then, samples were dried for 2 h at 50 °C, homogenized and re-incubated at 60 °C for 20 min and sonicated on ice (1–2 min) three times. After digestion, the microplastic-containing suspensions were placed in Utermöhl chambers and the microplastics were examined at the inverted light microscope (Leica DMI3000-Bat ×200 magnification) and counted. Microplastics obtained from nauplii digested after 10 days of incubation were also measured and categorized by colours and shape to evaluate the numbers and the size spectra of microplastics ingested by A. salina during the experiment.Physical impact on coral coenenchymaScanning electron microscopy (SEM) analysesTo investigate the physical damage of the microplastic mixture on the coral tissues, samples (one branch from each tank including the control) were collected before the start of the experiment (t0), after 7 days and at the end of the experiment and prepared for SEM analyses according to standard protocols82 with some modifications. Coral branches were stored in 0.7 µm prefiltered seawater with 4% buffered formalin. After 24 h, samples were washed with 0.7 µm prefiltered seawater and dehydrated for 3 h in 20% ethanol. After 3 h they were washed in the same way and dehydrated in ethanol 50%. After 3 h, samples were stored in 70% ethanol. Samples were stored at +4 °C. We dehydrated samples using different gradients of ethanol solutions (70–80%, 80–90%, 90–95%, 95–99% in 2 days)82. Then, samples were dried using HMDS (Hexamethyldisilazane, Aldrich 440191)83. Dried samples were mounted on aluminium stubs using Leit-C glue (conductive carbon cement, Neubauer Chemikalien) and sputter-coated with gold. Samples were examined with a Scanning Electron Microscope (Zeiss SUPRA 40). In addition, the tissue damage percentage was assessed on SEM micrographs at ×200 of magnification by using PhotoQuad v1.4 software84. Such a software for advanced image processing of 2D photographic quadrat samples, dedicated to ecological applications, was used for the analysis of three randomly selected areas from the apex to the base of each coral rotating it on three sides (n = 9). Additional analyses through random SEM observations (n = 20) at 3.00KX to 17.00KX of magnification were carried out to determine prokaryotic cell abundances around lesions of corals (n = 3) exposed to high concentrations of microplastic particles. Data were standardised to the coral surface analysed.Mucus release and trapped microplastics and prokaryotic cellsTo evaluate the first symptoms of coral stress, a photographic report was conducted daily. The abundance of microplastic particles trapped in coral mucus was estimated using an enzymatic digestion protocol81 with some modifications. Mucus produced by corals exposed to higher microplastics concentrations was dried in oven at 60 °C for 12 h. After 12 h, five ml of homogenizing solution was added to the samples and incubated at 50 °C for 15 min. Proteinase K (1 mg mL−1) was added to the samples, which subsequently were incubated at 50 °C for 2 h. Then, samples were homogenized and incubated again at 60 °C for 20 min, after that samples were sonicated three times (three 1-min treatments using a Branson Sonifier 2200; 60 W). After digestion, microplastics-containing suspension was filtered on 0.2-μm filters in a vacuum filtration system (Whatman, Nuclepore). Filters were analysed at stereomicroscope at ×50 magnification (Zeiss Stemi 2000).Stress signals at the molecular levelRNA extraction, cDNA synthesis and gene expression level by qPCRTo assess potential changes in the gene expression pattern of C. rubrum due to microplastics, total RNA was extracted from ca. 20 mg of tissue (wet weight) from one coral branch randomly collected from each treatment (n = 3) and control (n = 3) after 10 days of experiment by using Quick-RNA™ MiniPrep (Zymo Research, Freiburg, Germany) according to the manufacturer’s instructions. Total RNA was also extracted from additional samples of coral branches collected randomly at the beginning of the experiment. Once scraped by surgical disposable scalpels (Braun), coral tissues were placed in new 2 ml sterile tubes and washed three times with phosphate-buffered saline (PBS 1×). Samples were centrifuged at 1800 rpm for 10 min in an Eppendorf® 5810r refrigerated centrifuge using a swing-out rotor at 4 °C and, after removing the supernatant, were homogenized for 5 min with a RNase-free sterile glass stick in RNA lysis buffer. Contaminating DNA was degraded by treating each sample with DNase dissolved in RNase-free water included in the kit. For each sample, 250 ng of total RNA extracted was retrotranscribed with an iScript™ cDNA Synthesis kit (Bio-Rad, Milan, Italy), following the manufacturer’s instructions. The reaction was performed on the Veriti™ 96-Well Thermal Cycler (Applied Biosystem, Monza, Italy). To evaluate the efficiency of cDNA synthesis, a PCR was performed with primers of the reference gene, cytochrome oxidase I (COI, Supplementary Table 6). The reaction was carried out using MyTaq™ HS DNA Polymerase (Bioline, Luckenwalde, Germany) on the Veriti™ 96-Well Thermal Cycler (Applied Biosystem, Monza, Italy). The PCR programme consisted of a denaturation step at 95 °C for 1 min, 35 cycles at 95 °C for 45 s, 60 °C for 45 s, and 72 °C for 45 s and a final extension step at 72 °C for 10 min.The expression levels of the six genes of hsp70, hsp60, MnSOD, mtMutS, EF1 and cytb, involved in a broad range of functional responses, such as stress, detoxification processes, and DNA repair, were followed by real-time qPCR to identify potential stress of corals exposed to microplastics61. For the cytb, target-specific primer pairs were designed with the Primer 3 software (http://primer3.ut.ee85) using nucleotide sequences retrieved from the GenBank database for C. rubrum as template (https://www.ncbi.nlm.nih.gov/genbank/; Supplementary Table 6). SensiFAST™ SYBR® & Fluorescein mix (Bioline, Luckenwalde, Germany) were used for measuring the levels of mRNAs on CFX Connect™ Real-Time PCR detection system (Biorad, Milan, Italy). Fluorescence was measured using CFX Manager™ software (Biorad, Milan, Italy). All genes tested by qPCR in this study were amplified with primers purchased from Life Technologies/Thermo Fisher Scientific (Milan, Italy). The fold change in target gene mRNA expression of corals exposed to microplastics compared with the control was calculated using the comparative CT method using the 2−ΔΔCt equation86. COI was used as reference gene for normalising the gene expression analyses.DNA oxidative damageFor evaluating oxidative DNA damage potentially due to microplastic exposure on C. rubrum, the content of 8-hydroxydeoxyguanosine (8-OHdG) was analysed. DNA was extracted from 20 mg (wet weight) of tissue randomly collected from one coral branch for each treatment (n = 3) and control (n = 3) after 10 days of experiment using DNeasy Blood & Tissue Kits (Qiagen, Valencia, CA) and following the manufacturer’s protocol. Finally, samples were kept at −20 °C before subsequent analyses. Nucleic acids extracted (2 μg) were transferred into new 2-ml tubes and incubated for 5 min at 95 °C, then rapidly chilled on ice. Samples were digested to nucleosides by incubating the denatured DNA in sodium acetate 20 mM, pH 5.2 with 2 μl of nuclease P1 (6 U/μl; Merck KGaA, Darmstadt, Germany) for 2 h at 37 °C. Each sample was then incubated with 5 μl alkaline phosphatase (1 U/μl; Roche, Mannheim, Germany) in Tris-HCl 100 mM, pH 7.5 for 1 h at 37 °C. The reaction mixtures were then centrifuged for 5 min at 6000 × g and the supernatants tested for DNA oxidation with an OxiSelect™ Oxidative DNA Damage ELISA Kit (8-OHdG Quantitation; Cell Biolabs, CA, USA). As positive control, Escherichia coli genomic DNA (2 μg) was incubated in a final concentration of 50 and 100 mM H2O2 overnight at 37 °C, and subsequently tested.Prokaryotic abundance in coral mucus and surrounding seawaterTo highlight possible effects in terms of prokaryotic contamination associated with the exposure of the corals to microplastics, we determined prokaryotic abundances in the mucus released by C. rubrum and the surrounding seawater.Prokaryotic abundances in the coral mucus collected from each tank (except for the control where coral mucus was not released) after 14 days of the experiment, were analysed by epifluorescence microscopy. The extraction of prokaryotic cells from the mucus (ca. 1 mL for each tank) was performed using pyrophosphate (final concentration, 5 mM) and ultrasound treatment (three 1-min treatments using a Branson Sonifier 2200; 60 W)87. Then, samples were diluted from 50- to 100-fold with sterile water filtered onto 0.2-μm pore-size filters (Anodisc filters; black-stained polycarbonate). The filters were stained using SYBR Green I (10,000× in anhydrous dimethyl sulfoxide, Molecular Probes-Invitrogen) diluted 1:20 in prefiltered TE buffer (pH 7.5) and incubated in the dark for 20 min; a drop (20 µl) of antifade solution (composed of 50% 6.7 mmol L−1 phosphate buffer at pH 7.8 and 50% glycerol with the addition of 0.5% ascorbic acid) was laid both on a glass slide and on the filter mounted on it. Prokaryotic counts were performed under epifluorescence microscopy (magnification, ×1000; Zeiss filter set #09, 488009-9901-000, excitation BP 450–490 nm, beam splitter FT 515, emission LP 520), by examining at least 20 fields per slide and counting at least 400 cells per filter.For the determination of prokaryote abundance in seawater surrounding corals, three replicates of 10 ml of seawater were collected from each tank. Total prokaryotic abundance was determined according to Danovaro87. Samples were filtered onto 0.2-μm pore-size filters (Anodisc black-stained polycarbonate filters, Whatman) into a funnel with vacuum pressure no greater than 20 kPa (or 150 mmHg) to avoid cell damage. When the sample had passed through, filters were stained with 20 µl of SYBR Green I (10,000× in anhydrous dimethyl sulfoxide, Molecular Probes-Invitrogen) diluted 1:20 in prefiltered TE buffer (pH 7.5) and incubated in the dark for 20 min. Then, to remove the excess stain, filters were washed three times using 3 ml of Milli-Q water; a drop (20 µl) of antifading solution (composed of 50% 6.7 mmol L−1 phosphate buffer at pH 7.8 and 50% glycerol with the addition of 0.5% ascorbic acid) was laid both on a glass slide and on the filter mounted on it. Prokaryotic counts were carried out as described above.Microbiome of corals exposed to microplasticsThe coral microbiome was analysed immediately before the start of the experiment (before the addition of microplastics) and after 10 days of the experiment, both in replicated coral branches exposed to microplastics and in unexposed corals (Control t10). For the analysis of the microbiome, ca. 20 mg of tissue (wet weight) from one coral branch randomly collected from two tanks of each treatment and control was scraped from the skeleton by using surgical disposable scalpels (Braun) and DNA extraction was performed using the QIAGEN DNeasy Blood & Tissue Kit. Briefly, samples were digested with proteinase K at 56 °C overnight or until the tissue was completely lysed, then samples were processed following the manufacturer’s protocol. Finally, samples were held at −20 °C before PCR amplification and sequencing. The molecular size of the DNA extracts was analysed by agarose gel electrophoresis (1%) and the amount and purity of DNA were determined by Nanodrop spectrophotometer (ND-1000). For PCR amplification of the 16S V3 region, the Bacteria-specific primer pair 805R/341F was chosen with Illumina-specific adapters and barcodes. Sequencing was performed on an Illumina MiSeq platform by LGC Genomics GmbH (Berlin, Germany).Raw sequencing paired-end reads were first joined using the bbmerge tool from the BBMap suite88 in a two-step process: reads that did not merge in a first step were quality-trimmed to remove low-quality bases (Q  More

  • in

    Assessing multiple threats to seabird populations using flesh-footed shearwaters Ardenna carneipes on Lord Howe Island, Australia as case study

    1.Dias, M. P. et al. Threats to seabirds: A global assessment. Biol. Cons. 237, 525–537 (2019).Article 

    Google Scholar 
    2.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 
    Article 

    Google Scholar 
    3.Michael, P. E. et al. Illegal fishing bycatch overshadows climate as a driver of albatross population decline. Mar. Ecol. Prog. Ser. 579, 185–199 (2017).ADS 
    Article 

    Google Scholar 
    4.Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).Article 

    Google Scholar 
    5.Rayner, M. J. et al. Predictive habitat modelling for the population census of a burrowing seabird: A study of the endangered Cook’s petrel. Biol. Cons. 138, 235–247 (2007).Article 

    Google Scholar 
    6.Habeeb, R. L., Trebilco, J., Wotherspoon, S. & Johnson, C. R. Determining natural scales of ecological systems. Ecol. Monogr. 75, 467–487 (2005).Article 

    Google Scholar 
    7.Li, G. D., Sun, S. A. & Fang, C. L. The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landscape Urban Plan. 174, 63–77 (2018).Article 

    Google Scholar 
    8.Ranjeva, S. L. et al. Untangling the dynamics of persistence and colonization in microbial communities. ISME J. 13, 2998–3010 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Thurman, L. L., Barner, A. K., Garcia, T. S. & Chestnut, T. Testing the link between species interactions and species co-occurrence in a trophic network. Ecography 42, 1658–1670 (2019).Article 

    Google Scholar 
    10.Murcia, C. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Jonzen, N., Wilcox, C. & Possingham, H. P. Habitat selection and population regulation in temporally fluctuating environments. Am. Nat. 164, 103–114 (2004).Article 

    Google Scholar 
    12.Coulson, J. C. Difference in the quality of birds nesting in the centre and on the edges of a colony. Nature 217, 478–479 (1968).ADS 
    Article 

    Google Scholar 
    13.Reid, T., Hindell, M., Lavers, J. L. & Wilcox, C. Re-examining mortality sources and population trends in a declining seabird: using Bayesian methods to incorporate existing information and new data. PLoS ONE 8(4), e58230 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Lavers, J. L., Hutton, I. & Bond, A. L. Changes in technology and imperfect detection of nest contents impedes reliable estimates of population trends in burrowing seabirds. Global Ecol. Conserv. 17, e00579 (2019).Article 

    Google Scholar 
    15.Priddel, D., Carlile, N., Fullagar, P., Hutton, I. & O’Neill, L. Decline in the distribution and abundance of flesh-footed shearwaters (Puffinus carneipes) on Lord Howe Island, Australia. Biol. Cons. 128, 412–424 (2006).Article 

    Google Scholar 
    16.Baker, G. B. & Wise, G. S. The impact of pelagic longline fishing on the flesh-footed shearwater Puffinus carneipes in Eastern Australia. Biol. Cons. 126, 306–136 (2005).Article 

    Google Scholar 
    17.Tuck, G. N. & Wilcox, C. Assessing the potential impacts of fishing on the Lord Howe Island population of flesh-footed shearwaters 86 (Australian Fisheries Management Authority and CSIRO Marine and Atmospheric Research, 2010).
    Google Scholar 
    18.Carlile, N., Priddel, D., Reid, T. & Fullager, P. Flesh-footed shearwater decline on Lord Howe Island: rebuttal to Lavers et al 2019. Global Ecol. Conserv. 20, 1–3 (2019).
    Google Scholar 
    19.Lavers, J. L. Population status and threats to flesh-footed shearwater (Puffinus carneipes) in Western and South Australia. ICES J. Mar. Sci. 72, 316–327 (2014).Article 

    Google Scholar 
    20.Carey, M. J. The effects of investigator disturbance on procellariiform seabirds: a review. N. Z. J. Zool. 36, 367–377 (2009).Article 

    Google Scholar 
    21.Carey, M. J. Investigator disturbance reduces reproductive success in Short-tailed Shearwaters Puffinus tenuirostris. Ibis 153, 363–372 (2011).Article 

    Google Scholar 
    22.Orr, J. A. et al. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. B Biol. Sci. 287, 20200421. https://doi.org/10.1098/rspb.2020.0421 (2020).Article 

    Google Scholar 
    23.Piggott, J. J., Townsend, C. R. & Matthael, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5(7), 1538–1547 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Ormerod, S. J., Dobson, M., Hildrew, A. G. & Townsend, C. R. Multiple stressors in freshwater ecosystems. Freshw. Biol. 55(Suppl. 1), 1–4. https://doi.org/10.1111/j.1365-2427.2009.02395.x (2010).Article 

    Google Scholar 
    25.Powell, C. D. L. Foraging movements and the migration trajectory of Flesh-footed Shearwaters Puffinus carneipes from the south coast of Western Australia. Mar. Ornithol. 37, 115–120 (2009).
    Google Scholar 
    26.Rexer-Huber, K., Parker, G. C., Ryan, P. G. & Cuthbert, R. J. Burrow occupancy and population size in the Atlantic Petrel Pterodroma incerta: a comparison of methods. Mar. Ornithol. 42, 137–141 (2014).
    Google Scholar 
    27.Rebstock, G. A., Boersma, P. D. & Garcia-Barbaroglu, P. Changes in habitat use and nesting density in a declining seabird Colony. Popul. Ecol. 58, 105–119 (2016).Article 

    Google Scholar 
    28.Ponchon, A. et al. When things go wrong: intra-season dynamics of breeding failure in a seabird. Ecosphere 5(1), 4. https://doi.org/10.1890/ES13-00233.1 (2014).Article 

    Google Scholar 
    29.Jackson, A. L., Bearhop, S. & Thompson, D. R. Shape can influence the rate of colony fragmentation in ground nesting seabirds. Oikos 111, 473–478 (2005).Article 

    Google Scholar 
    30.Martinez-Abrain, A. Why do ecologists aim to get positive results? Once again, negative results are necessary for better knowledge accumulation. Anim. Biodivers. Conserv. 36, 33–36 (2013).Article 

    Google Scholar 
    31.Gales, R., Brothers, N. & Reid, T. Seabird mortality in the Japanese longline tuna fishery around Australia, 1988–1995. Biol. Cons. 86, 37–56 (1997).Article 

    Google Scholar 
    32.Trebilco, R. et al. Characterizing seabird bycatch in the eastern Australian tuna and billfish pelagic longline fishery in relation to temporal, spatial and biological influences. Aquat. Conserv. Mar. Freshwat. Ecosyst. 20, 531–542 (2010).Article 

    Google Scholar 
    33.Chan, K. M. A. Value and advocacy in conservation biology: crisis discipline or discipline in crisis. Conserv. Biol. 22, 1–3 (2008).PubMed 
    Article 

    Google Scholar 
    34.Hindwood, K. A. The birds of Lord Howe Island. Emu 40, 1–86 (1940).Article 

    Google Scholar 
    35.McDougall, I., Embleton, B. J. J. & Stone, D. B. Origin and evolution of Lord Howe Island, Southwest Pacific Ocean. J. Geol. Soc. Aust. 28, 155–176 (1981).CAS 
    Article 

    Google Scholar 
    36.Pickard, J. Vegetation of Lord Howe Island. Cunninghamia 1, 133–265 (1983).
    Google Scholar 
    37.Marchant, S. & Higgins, P. J. (eds) Handbook of Australian, New Zealand and Antarctic Birds. Ratites to Ducks Vol. 1 (Oxford University Press, 1990).
    Google Scholar 
    38.Serventy, D. L. & Whittell, H. M. A Handbook of the Birds of Western Australia 2nd edn. (Paterson Brokensha Pty., Ltd, 1951).
    Google Scholar 
    39.Powell, C. D. L., Wooller, R. D. & Bradley, J. S. Breeding biology of the flesh-footed shearwater (Puffinus carneipes) on Woody Island, Western Australia. Emu 107, 275–283 (2007).Article 

    Google Scholar 
    40.Reid, T. A. et al. Nonbreeding distribution of flesh-footed shearwaters and the potential for overlap with north Pacific fisheries. Biol. Cons. 166, 3–10 (2013).Article 

    Google Scholar 
    41.Lombal, A. J. et al. Genetic divergence between colonies of flesh-footed shearwaters Ardenna carneipes exhibiting different foraging strategies. Conserv. Genet. 9, 27–41 (2018).Article 

    Google Scholar 
    42.Carlile, N. & Priddel, D. Seabird islands No. 261: Mutton Bird Island, Lord Howe Group, New South Wales. Corella 37(4), 94–96 (2013).
    Google Scholar 
    43.Carlile, N., Priddel, D. & Bower, H. Seabird islands No. 256: Roach Island, Lord Howe Group, New South Wales. Corella 37(4), 82–85 (2013).
    Google Scholar 
    44.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. (2015)45.Wood, S. N. Generalised Additive Models: An Introduction with R (Chapman and Hall/CRC, 2006).
    Google Scholar 
    46.Burnham, K. R. & Anderson, D. R. Model Selection and Multimodal Inference: A Practical Information Theoretic Approach (Springer, 2002).
    Google Scholar 
    47.Barton, K. MuMIn: Multi-Model Inference. R package version 1.15.1. http://CRAN.R-project.org/package=MuMIn (2015).48.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/.(2005).49.Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).
    Google Scholar  More

  • in

    Geographical distribution of the dispersal ability of alien plant species in China and its socio-climatic control factors

    1.Bartz, R. & Kowarik, I. Assessing the environmental impacts of invasive alien plants: a review of assessment approaches. Neobiota https://doi.org/10.3897/neobiota.43.30122 (2019).Article 

    Google Scholar 
    2.Chen, C. et al. Historical introduction, geographical distribution, and biological characteristics of alien plants in China. Biodivers. Conserv. 26, 353–381. https://doi.org/10.1007/s10531-016-1246-z (2017).Article 

    Google Scholar 
    3.Feng, J. & Zhu, Y. Alien invasive plants in China: risk assessment and spatial patterns. Biodivers. Conserv. 19, 3489–3497. https://doi.org/10.1007/s10531-010-9909-7 (2010).Article 

    Google Scholar 
    4.Thapa, S., Chitale, V., Rijal, S. J., Bisht, N. & Shrestha, B. B. Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. Plos One https://doi.org/10.1371/journal.pone.0195752 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Majewska, M. L. et al. Do the impacts of alien invasive plants differ from expansive native ones? An experimental study on arbuscular mycorrhizal fungi communities. Biol. Fertil. Soils 54, 631–643. https://doi.org/10.1007/s00374-018-1283-8 (2018).Article 

    Google Scholar 
    6.Shi, J., Luo, Y.-Q., Zhou, F. & He, P. The relationship between invasive alien species and main climatic zones. Biodivers. Conserv. 19, 2485–2500. https://doi.org/10.1007/s10531-010-9855-4 (2010).Article 

    Google Scholar 
    7.Hulme, P. E. Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18. https://doi.org/10.1111/j.1365-2664.2008.01600.x (2009).Article 

    Google Scholar 
    8.Hulme, P. E. et al. Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J. Appl. Ecol. 45, 403–414. https://doi.org/10.1111/j.1365-2664.2007.01442.x (2008).Article 

    Google Scholar 
    9.Jara-Guerrero, A., De la Cruz, M. & Mendez, M. Seed dispersal spectrum of woody species in south ecuadorian dry forests: environmental correlates and the effect of considering species abundance. Biotropica 43, 722–730. https://doi.org/10.1111/j.1744-7429.2011.00754.x (2011).Article 

    Google Scholar 
    10.van Oudtshoorn, K. v. R. & van Rooyen, M. W. Dispersal biology of desert plants. (Springer 1999).11.Liu, J., Liang, S. C., Liu, F. H., Wang, R. Q. & Dong, M. Invasive alien plant species in China: regional distribution patterns. Divers. Distrib. 11, 341–347. https://doi.org/10.1111/j.1366-9516.2005.00162.x (2005).Article 

    Google Scholar 
    12.Caughlin, T. T., Ferguson, J. M., Lichstein, J. W., Bunyavejchewin, S. & Levey, D. J. The importance of long-distance seed dispersal for the demography and distribution of a canopy tree species. Ecology 95, 952–962. https://doi.org/10.1890/13-0580.1 (2014).Article 
    PubMed 

    Google Scholar 
    13.Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647. https://doi.org/10.1016/j.tree.2008.08.003 (2008).Article 
    PubMed 

    Google Scholar 
    14.Wang, R. et al. Multiple mechanisms underlie rapid expansion of an invasive alien plant. New Phytol. 191, 828–839. https://doi.org/10.1111/j.1469-8137.2011.03720.x (2011).Article 
    PubMed 

    Google Scholar 
    15.Vittoz, P. & Engler, R. Seed dispersal distances: a typology based on dispersal modes and plant traits. Bot. Helv. 117, 109–124. https://doi.org/10.1007/s00035-007-0797-8 (2007).Article 

    Google Scholar 
    16.Willson, M. F., Rice, B. L. & Westoby, M. Seed dispersal spectra – a comparison of temperate plant-communities. J. Veg. Sci. 1, 547–562. https://doi.org/10.2307/3235789 (1990).Article 

    Google Scholar 
    17.Nilsson, C., Brown, R. L., Jansson, R. & Merritt, D. M. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. 85, 837–858. https://doi.org/10.1111/j.1469-185X.2010.00129.x (2010).Article 
    PubMed 

    Google Scholar 
    18.Eminniyaz, A. et al. Dispersal Mechanisms of the Invasive Alien Plant Species Buffalobur (Solanum rostratum) in Cold Desert Sites of Northwest China. Weed Sci. 61, 557–563. https://doi.org/10.1614/ws-d-13-00011.1 (2013).CAS 
    Article 

    Google Scholar 
    19.Soons, M. B., Heil, G. W., Nathan, R. & Katul, G. G. Determinants of long-distance seed dispersal by wind in grasslands. Ecology 85, 3056–3068. https://doi.org/10.1890/03-0522 (2004).Article 

    Google Scholar 
    20.Tackenberg, O. Modeling long-distance dispersal of plant diaspores by wind. Ecol. Monogr. 73, 173–189. https://doi.org/10.1890/0012-9615(2003)073[0173:mldopd]2.0.co;2 (2003).Article 

    Google Scholar 
    21.Wallace, H. M., Howell, M. G. & Lee, D. J. Standard yet unusual mechanisms of long-distance dispersal: seed dispersal of Corymbia torelliana by bees. Divers. Distrib. 14, 87–94. https://doi.org/10.1111/j.1472-4642.2007.00427.x (2008).Article 

    Google Scholar 
    22.Soons, M. B., Nathan, R. & Katul, G. G. Human effects on long-distance wind dispersal and colonization by grassland plants. Ecology 85, 3069–3079. https://doi.org/10.1890/03-0398 (2004).Article 

    Google Scholar 
    23.Taylor, K., Brummer, T., Taper, M. L., Wing, A. & Rew, L. J. Human-mediated long-distance dispersal: an empirical evaluation of seed dispersal by vehicles. Divers. Distrib. 18, 942–951. https://doi.org/10.1111/j.1472-4642.2012.00926.x (2012).Article 

    Google Scholar 
    24.Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227. https://doi.org/10.2307/2656714 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Thomson, F. J., Moles, A. T., Auld, T. D. & Kingsford, R. T. Seed dispersal distance is more strongly correlated with plant height than with seed mass. J. Ecol. 99, 1299–1307. https://doi.org/10.1111/j.1365-2745.2011.01867.x (2011).Article 

    Google Scholar 
    26.Zhu, J., Liu, M., Xin, Z., Zhao, Y. & Liu, Z. Which factors have stronger explanatory power for primary wind dispersal distance of winged diaspores: the case of Zygophyllum xanthoxylon (Zygophyllaceae)?. J Plant Ecol 9, 346–356. https://doi.org/10.1093/jpe/rtv051 (2016).Article 

    Google Scholar 
    27.Jones, F. A. & Muller-Landau, H. C. Measuring long-distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods. J. Ecol. 96, 642–652. https://doi.org/10.1111/j.1365-2745.2008.01400.x (2008).Article 

    Google Scholar 
    28.Snell, R. S. Simulating long-distance seed dispersal in a dynamic vegetation model. Glob. Ecol. Biogeogr. 23, 89–98. https://doi.org/10.1111/geb.12106 (2014).Article 

    Google Scholar 
    29.Jongejans, E. & Telenius, A. Field experiments on seed dispersal by wind in ten umbelliferous species (Apiaceae). Plant Ecol. 152, 67–78. https://doi.org/10.1023/a:1011467604469 (2001).Article 

    Google Scholar 
    30.Guitian, J. & Sanchez, J. M. Seed dispersal spectra of plant-communities in the Iberian Peninsula. Vegetatio 98, 157–164. https://doi.org/10.1007/bf00045553 (1992).Article 

    Google Scholar 
    31.Ou, H., Lu, C. & O’Toole, D. K. A risk assessment system for alien plant bio-invasion in Xiamen China. J. Environ. Sci. 20, 989–997. https://doi.org/10.1016/s1001-0742(08)62198-1 (2008).Article 

    Google Scholar 
    32.Huang, Q. Q., Wu, J. M., Bai, Y. Y., Zhou, L. & Wang, G. X. Identifying the most noxious invasive plants in China: role of geographical origin, life form and means of introduction. Biodivers. Conserv. 18, 305–316. https://doi.org/10.1007/s10531-008-9485-2 (2009).Article 

    Google Scholar 
    33.Liu, J. et al. Invasive alien plants in China: role of clonality and geographical origin. Biol. Invasions 8, 1461–1470. https://doi.org/10.1007/s10530-005-5838-x (2006).Article 

    Google Scholar 
    34.Xu, H., Wang, J., Qiang, S. & Wang, C. Study of key issues under the convention on biological diversity: alien species invasion, biosafety, genetic resources. (Science Press, 2004).35.Ma, J. & Li, H. The checklist of the alien invasive plants in China. (Higher Education Press, 2018).36.Wang, C., Liu, J., Xiao, H., Zhou, J. & Du, D. Floristic characteristics of alien invasive seed plant species in China. Anais Da Academia Brasileira De Ciencias 88, 1791–1797. https://doi.org/10.1590/0001-3765201620150687 (2016).Article 
    PubMed 

    Google Scholar 
    37.Xie, Y., Li, Z. Y., Gregg, W. P. & Dianmo, L. Invasive species in China – an overview. Biodivers. Conserv. 10, 1317–1341 (2001).Article 

    Google Scholar 
    38.Qi, W., Liu, S. H., Zhao, M. F. & Liu, Z. China’s different spatial patterns of population growth based on the “Hu Line”. J. Geog. Sci. 26, 1611–1625. https://doi.org/10.1007/s11442-016-1347-3 (2016).Article 

    Google Scholar 
    39.Chen, M. X., Gong, Y. H., Li, Y., Lu, D. D. & Zhang, H. Population distribution and urbanization on both sides of the Hu Huanyong Line: answering the Premier’s question. J. Geog. Sci. 26, 1593–1610. https://doi.org/10.1007/s11442-016-1346-4 (2016).Article 

    Google Scholar 
    40.Pan, X. B. et al. Spatial similarity in the distribution of invasive alien plants and animals in China. Nat. Hazards 77, 1751–1764. https://doi.org/10.1007/s11069-015-1672-3 (2015).Article 

    Google Scholar 
    41.Yan, X. et al. The categorization and analysis on the geographic distribution patterns of Chinese alien invasive plants. Biodiv. Sci. 22, 667–676 (2014).Article 

    Google Scholar 
    42.Wang, G., Bai, F. & Sang, W. Spatial distribution of invasive alien animal and plant species and its influencing factors in China. Plant Sci. J. 35, 513–524 (2017).
    Google Scholar 
    43.Weber, E., Sun, S. G. & Li, B. Invasive alien plants in China: diversity and ecological insights. Biol. Invasions 10, 1411–1429. https://doi.org/10.1007/s10530-008-9216-3 (2008).Article 

    Google Scholar 
    44.Zhou, Q. et al. Geographical distribution and determining factors of different invasive ranks of alien species across China. Sci. Total Environ. 722, 137929. https://doi.org/10.1016/j.scitotenv.2020.137929 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Ding, J., Mack, R. N., Lu, P., Ren, M. & Huang, H. China’s booming economy is sparking and accelerating biological invasions. Bioscience 58, 317–324. https://doi.org/10.1641/b580407 (2008).Article 

    Google Scholar 
    46.Pysek, P. et al. Alien plants in checklists and floras: towards better communication between taxonomists and ecologists. Taxon 53, 131–143. https://doi.org/10.2307/4135498 (2004).Article 

    Google Scholar 
    47.Zeng, C. & Chen, W. A forecasting model of urban underground space development intensity. Chin. J. Undergr. Space Eng. 14, 1154–1160 (2018).
    Google Scholar 
    48.Shao, M. N. et al. Outbreak of a new alien invasive plant Salvia reflexa in north-east China. Weed Res. 59, 201–208. https://doi.org/10.1111/wre.12357 (2019).Article 

    Google Scholar 
    49.Wan, F. H. et al. Invasive mechanism and control strategy of Ageratina adenophora (Sprengel). Sci. China-Life Sci. 53, 1291–1298. https://doi.org/10.1007/s11427-010-4080-7 (2010).Article 
    PubMed 

    Google Scholar 
    50.Poudel, A. S., Jha, P. K., Shrestha, B. B. & Muniappan, R. Biology and management of the invasive weed Ageratina adenophora (Asteraceae): current state of knowledge and future research needs. Weed Res. 59, 79–92. https://doi.org/10.1111/wre.12351 (2019).Article 

    Google Scholar 
    51.Datta, A., Schweiger, O. & Kuehn, I. Niche expansion of the invasive plant species Ageratina adenophora despite evolutionary constraints. J. Biogeogr. 46, 1306–1315. https://doi.org/10.1111/jbi.13579 (2019).Article 

    Google Scholar 
    52.Guo, X., Ren, M. & Ding, J. Do the introductions by botanical gardens facilitate the invasion of Solidago canadensis (Asterceae) in China?. Weed Res. 56, 442–451. https://doi.org/10.1111/wre.12227 (2016).Article 

    Google Scholar 
    53.Ganneru, S., Shaik, H., Peddi, K. & Mudiam, M. K. R. Evaluating the metabolic perturbations in Mangifera indica (mango) ripened with various ripening agents/practices through gas chromatography – mass spectrometry based metabolomics. J. Sep. Sci. 42, 3086–3094. https://doi.org/10.1002/jssc.201900291 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    54.Mahandran, V., Murugan, C. M., Marimuthu, G. & Nathan, P. T. Seed dispersal of a tropical deciduous Mahua tree, Madhuca latifolia (Sapotaceae) exhibiting bat-fruit syndrome by pteropodid bats. Glob. Ecol. Conserv. 14, e00396. https://doi.org/10.1016/j.gecco.2018.e00396 (2018).Article 

    Google Scholar 
    55.Weber, E. & Li, B. Plant invasions in China: What is to be expected in the wake of economic development?. Bioscience 58, 437–444. https://doi.org/10.1641/b580511 (2008).Article 

    Google Scholar 
    56.Jian, L., Hua, C., Kowarik, I., Zhang, Y. & Wang, R. Plant invasions in China: an emerging hot topic in invasion science. Neobiota 15, 27–41 (2012).Article 

    Google Scholar  More

  • in

    Native Burmese pythons exhibit site fidelity and preference for aquatic habitats in an agricultural mosaic

    1.Gurarie, E., Andrews, R. D. & Laidre, K. L. A novel method for identifying behavioural changes in animal movement data. Ecol. Lett. 12, 395–408 (2009).PubMed 
    Article 

    Google Scholar 
    2.Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Dulau, V. et al. Continuous movement behavior of humpback whales during the breeding season in the southwest Indian Ocean: on the road again!. Mov. Ecol. 5, 11 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Glaudas, X. & Alexander, G. J. Food supplementation affects the foraging ecology of a low-energy, ambush-foraging snake. Behav. Ecol. Sociobiol. 71, 5 (2017).Article 

    Google Scholar 
    5.Moorter, B. V., Rolandsen, C. M., Basille, M. & Gaillard, J.-M. Movement is the glue connecting home ranges and habitat selection. J. Anim. Ecol. 85, 21–31 (2016).PubMed 
    Article 

    Google Scholar 
    6.Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. L. Southeast Asian biodiversity: an impending disaster. Trends Ecol. Evol. 19, 654–660 (2004).PubMed 
    Article 

    Google Scholar 
    7.Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).PubMed 
    Article 

    Google Scholar 
    8.Schneider, A. et al. A new urban landscape in East-Southeast Asia, 2000–2010. Environ. Res. Lett. 10, 034002 (2015).ADS 
    Article 

    Google Scholar 
    9.Böhm, M. et al. Correlates of extinction risk in squamate reptiles: the relative importance of biology, geography, threat and range size—extinction risk correlates in squamate reptiles. Glob. Ecol. Biogeogr. 25, 391–405 (2016).Article 

    Google Scholar 
    10.Ditchkoff, S. S., Saalfeld, S. T. & Gibson, C. J. Animal behavior in urban ecosystems: Modifications due to human-induced stress. Urban Ecosyst. 9, 5–12 (2006).Article 

    Google Scholar 
    11.Shamoon, H., Maor, R., Saltz, D. & Dayan, T. Increased mammal nocturnality in agricultural landscapes results in fragmentation due to cascading effects. Biol. Conserv. 226, 32–41 (2018).Article 

    Google Scholar 
    12.Shine, R. & Fitzgerald, M. Large snakes in a mosaic rural landscape: the ecology of carpet pythons Morelia spilota (Serpentes: Pythonidae) in coastal eastern Australia. Large Snakes Mosaic Rural Landsc. Ecol. Carpet Pythons Morelia Spilota Serpentes Pythonidae Coast. East. Aust. 76, 113–122 (1996).13.Charles, K. E. & Linklater, W. L. Dietary breadth as a predictor of potential native avian–human conflict in urban landscapes. Wildl. Res. 40, 482 (2013).Article 

    Google Scholar 
    14.Soulsbury, C. D. & White, P. C. L. Human–wildlife interactions in urban areas: a review of conflicts, benefits and opportunities. Wildl. Res. 42, 541 (2015).Article 

    Google Scholar 
    15.Gibbon, J. W. et al. The global decline of reptiles Déjà Vu Amphibians. BioScience 50, 653 (2000).Article 

    Google Scholar 
    16.Todd, B., Willson, J. & Gibbons, J. The Global Status of Reptiles and Causes of Their Decline. in Ecotoxicology of Amphibians and Reptiles, Second Edition (eds. Sparling, D., Linder, G., Bishop, C. & Krest, S.) 47–67 (CRC Press, 2010). https://doi.org/10.1201/EBK1420064162-c3.17.Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).Article 

    Google Scholar 
    18.Barker, D. G. & Barker, T. M. The distribution of the burmese python, python molurus bivittatus. Bull. Chic. Herpetol. Soc. 43, 33–38 (2008).
    Google Scholar 
    19.Rahman, S. C., Jenkins, C. L., Trageser, S. J. & Rashid, S. M. A. Radio-telemetry study of Burmese python (Python molurus bivittatus) and elongated tortoise (Indotestudo elongata) in Lawachara National Park, Bangladesh: a prelimiary observation. Khan MAR Ali MS Feeroz MM Naser MN Ed. Festschr. 50th Anniversary IUCN Red List Threat. Species 54–62 (2014).20.Bhupathy, S., Ramesh, C. & Bahuguna, A. Feeding habits of Indian rock pythons in Keoladeo National Park, Bharatpur India. Herpetol. J. 24, 59–64 (2014).
    Google Scholar 
    21.Shine, R., Harlow, P. S., Keogh, J. S. & Boeadi. The influence of sex and body size on food habits of a giant tropical snake, Python reticulatus. Funct. Ecol. 12, 248–258 (1998).22.Dorcas, M. E. et al. Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park. Proc. Natl. Acad. Sci. 109, 2418–2422 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Dove, C. J., Snow, R. W., Rochford, M. R. & Mazzotti, F. J. Birds Consumed by the Invasive Burmese Python (Python molurus bivittatus) in Everglades National Park, Florida, USA. Wilson J. Ornithol. 123, 126–131 (2011).Article 

    Google Scholar 
    24.Stuart, B. et al. Python bivittatus (errata version published in 2019). https://doi.org/10.2305/IUCN.UK.2012-1.RLTS.T193451A151341916.en. (2019).25.Goodyear, N. C. Python molurus bivittatus (Burmese python) Movements. Herpetol. Rev. 25, 71–72 (1994).
    Google Scholar 
    26.You, C.-W. et al. Return of the pythons: first formal records, with a special note on recovery of the Burmese python in the demilitarized Kinmen islands. Zool. Stud. 52, 8 (2013).Article 

    Google Scholar 
    27.Miranda, E. B. P., Ribeiro, R. P. & Strüssmann, C. The ecology of human-anaconda conflict: a study using internet videos. Trop. Conserv. Sci. 9, 43–77 (2016).Article 

    Google Scholar 
    28.Nóbrega Alves, R. R. et al. A zoological catalogue of hunted reptiles in the semiarid region of Brazil. J. Ethnobiol. Ethnomed. 8, 27 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Orzechowski, S. C. M., Frederick, P. C., Dorazio, R. M. & Hunter, M. E. Environmental DNA sampling reveals high occupancy rates of invasive Burmese pythons at wading bird breeding aggregations in the central Everglades. PLoS ONE 14, e0213943 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Marshall, B. M. et al. No room to roam: King Cobras reduce movement in agriculture. Mov. Ecol. 8, 33 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Reed, R. N. & Rodda, G. H. Giant constrictors: biological and management profiles and an establishment risk assessment for nine large species of pythons, anacondas, and the boa constrictor: U.S. Geological Survey Open-File Report. (2009).32.Reinert, H. K. & Cundall, D. An Improved Surgical Implantation Method for Radio-Tracking Snakes. Copeia 1982, 702–705 (1982).Article 

    Google Scholar 
    33.R Core Team. R: a language and environment for statistical computing.34.R Studio Team. RStudio: integrated development environment for R.35.Silva, I., Crane, M., Suwanwaree, P., Strine, C. & Goode, M. Using dynamic Brownian Bridge Movement Models to identify home range size and movement patterns in king cobras. PLoS ONE 13, e0203449 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Silva, I., Crane, M., Marshall, B. M. & Strine, C. T. Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. Mov. Ecol. 8, 43 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Kranstauber, B., Kays, R., LaPoint, S. D., Wikelski, M. & Safi, K. A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. J. Anim. Ecol. 81, 738–746 (2012).PubMed 
    Article 

    Google Scholar 
    38.Kranstauber, B., Smolla, M. & Scharf, A. K. move: Visualizing and Analyzing Animal Track Data. (2020).39.Calenge, C. The package adehabitat for the R software: tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 1035 (2006).Article 

    Google Scholar 
    40.Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). (2020).41.Bracis, C., Bildstein, K. L. & Mueller, T. Revisitation analysis uncovers spatio-temporal patterns in animal movement data. Ecography https://doi.org/10.1111/ecog.03618 (2018).Article 

    Google Scholar 
    42.Berger-Tal, O. & Bar-David, S. Recursive movement patterns: review and synthesis across species. Ecosphere 6, art149 (2015).43.Avgar, T., Potts, J. R., Lewis, M. A. & Boyce, M. S. Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods Ecol. Evol. 7, 619–630 (2016).Article 

    Google Scholar 
    44.Signer, J., Fieberg, J. & Avgar, T. Animal movement tools ( amt ): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Marshall, B. M. et al. Data set and code supporting Marshall et al. 2020. No room to roam: King Cobras reduce movement in agriculture. (Version 1.1) . (2020).46.Thurfjell, H., Ciuti, S. & Boyce, M. S. Applications of step-selection functions in ecology and conservation. Mov. Ecol. 2, 4 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Muff, S., Signer, J. & Fieberg, J. Accounting for individual-specific variation in habitat-selection studies: efficient estimation of mixed-effects models using Bayesian or frequentist computation. J. Anim. Ecol. 89, 80–92 (2020).PubMed 
    Article 

    Google Scholar 
    48.Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).49.Hart, K. M. et al. Home range, habitat use, and movement patterns of non-native Burmese pythons in Everglades National Park, Florida, USA. Anim. Biotelemetry 3, 8 (2015).Article 

    Google Scholar 
    50.Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Rettie, W. J. & Messier, F. Range use and movement rates of woodland caribou in Saskatchewan. Can. J. Zool. 79, 1933–1940 (2001).Article 

    Google Scholar 
    52.Doherty, T. S., Fist, C. N. & Driscoll, D. A. Animal movement varies with resource availability, landscape configuration and body size: a conceptual model and empirical example. Landsc. Ecol. 34, 603–614 (2019).Article 

    Google Scholar 
    53.Young, L. I., Dickman, C. R., Addison, J. & Pavey, C. R. Spatial ecology and shelter resources of a threatened desert rodent (Pseudomys australis) in refuge habitat. J. Mammal. 98, 1604–1614 (2017).Article 

    Google Scholar 
    54.Ross, C. T. & Winterhalder, B. Sit-and-wait versus active-search hunting: A behavioral ecological model of optimal search mode. J. Theor. Biol. 387, 76–87 (2015).MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    55.Krysko, K., Nifong, J., Mazzotti, F., Snow, R. & Enge, K. Reproduction of the Burmese python (Python molurus bivittatus) in southern Florida. Appl. Herpetol. 5, 93–95 (2008).Article 

    Google Scholar 
    56.Smith, B. J. et al. Betrayal: radio-tagged Burmese pythons reveal locations of conspecifics in Everglades National Park. Biol. Invasions 18, 3239–3250 (2016).Article 

    Google Scholar 
    57.Hunter, M. E. et al. Environmental DNA (eDNA) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons. PLoS ONE 10, e0121655 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    58.Frishkoff, L. O., Hadly, E. A. & Daily, G. C. Thermal niche predicts tolerance to habitat conversion in tropical amphibians and reptiles. Glob. Change Biol. 21, 3901–3916 (2015).ADS 
    Article 

    Google Scholar 
    59.Fujioka, M., Don Lee, S. & Kurechi, M. Bird use of Rice Fields in Korea and Japan. Waterbirds 33, 8 (2010).Article 

    Google Scholar 
    60.Marshall, B. M. et al. Space fit for a king: spatial ecology of king cobras (Ophiophagus hannah) in Sakaerat Biosphere Reserve, Northeastern Thailand. Amphib.-Reptil. 40, 163–178 (2019).Article 

    Google Scholar 
    61.Barua, M., Bhagwat, S. A. & Jadhav, S. The hidden dimensions of human–wildlife conflict: Health impacts, opportunity and transaction costs. Biol. Conserv. 157, 309–316 (2013).Article 

    Google Scholar 
    62.Crane, M. et al. A report of a Malayan Krait Snake Bungarus Candidus Mortality as By-Catch in a Local Fish Trap from Nakhon Ratchasima Thailand. Trop. Conserv. Sci. 9, 313–320 (2016).Article 

    Google Scholar 
    63.Marshall, B. M. et al. Hits close to home: repeated persecution of King Cobras ( Ophiophagus hannah ) in Northeastern Thailand. Trop. Conserv. Sci. 11, 194008291881840 (2018).Article 

    Google Scholar 
    64.Webster, M. M. & Rutz, C. How strange are your study animals?. Nature 582, 337–340 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Mutascio, H. E., Pittman, S. E., Zollner, P. A. & D’Acunto, L. E. Modeling relative habitat suitability of southern Florida for invasive Burmese pythons (Python molurus bivittatus). Landsc. Ecol. 33, 257–274 (2018).Article 

    Google Scholar 
    66.Steen, D. A. Snakes in the grass: secretive natural histories defy both conventional and progressive statistics. Herpetol. Conserv. Biol. 5, 183 (2010).
    Google Scholar  More

  • in

    Implications of size-dependent tree mortality for tropical forest carbon dynamics

    1.Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Lutz, J. A. et al. Global importance of large-diameter trees. Glob. Ecol. Biogeogr. 27, 849–864 (2018).Article 

    Google Scholar 
    5.Meakem, V. et al. Role of tree size in moist tropical forest carbon cycling and water deficit responses. New Phytol. 219, 947–958 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    6.Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).PubMed 
    Article 

    Google Scholar 
    8.Camac, J. S. et al. Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species. Proc. Natl Acad. Sci. USA 115, 12459 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Condit, R., Hubbell, S. P. & Foster, R. B. Mortality rates of 205 neotropical tree and shrub species and the impact of a severe drought. Ecol. Monogr. 65, 419–439 (1995).Article 

    Google Scholar 
    10.McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Forrester, D. I. Does individual-tree biomass growth increase continuously with tree size? For. Ecol. Manag. 481, 118717 (2021).Article 

    Google Scholar 
    13.Sheil, D. et al. Does biomass growth increase in the largest trees? Flaws, fallacies and alternative analyses. Funct. Ecol. 31, 568–581 (2017).Article 

    Google Scholar 
    14.Condit, R., Pérez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. 4, 17 (2017).Article 

    Google Scholar 
    15.McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).Article 

    Google Scholar 
    16.Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Change 9, 384–388 (2019).Article 

    Google Scholar 
    17.Parlato, B., Gora, E. M. & Yanoviak, S. P. Lightning damage facilitates beetle colonization of tropical trees. Ann. Entomol. Soc. Am. 113, 447–451 (2020).
    Google Scholar 
    18.Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556 (1987).Article 

    Google Scholar 
    19.Gale, N. & Hall, P. Factors determining the modes of tree death in three Bornean rain forests. J. Veg. Sci. 12, 337–348 (2001).Article 

    Google Scholar 
    20.Fontes, C. G., Chambers, J. Q. & Higuchi, N. Revealing the causes and temporal distribution of tree mortality in Central Amazonia. For. Ecol. Manag. 424, 177–183 (2018).Article 

    Google Scholar 
    21.de Toledo, J. J., Magnusson, W. E. & Castilho, C. V. Competition, exogenous disturbances and senescence shape tree size distribution in tropical forest: evidence from tree mode of death in Central Amazonia. J. Veg. Sci. 24, 651–663 (2013).Article 

    Google Scholar 
    22.Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).PubMed 
    Article 

    Google Scholar 
    23.Yanoviak, S. P. et al. Lightning is a major cause of large tropical tree mortality in a lowland neotropical forest. New Phytol. 225, 1936–1944 (2020).PubMed 
    Article 

    Google Scholar 
    24.Rifai, S. W. et al. Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237 (2016).PubMed 
    Article 

    Google Scholar 
    25.McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Change 5, 669–672 (2015).Article 

    Google Scholar 
    26.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2013).Article 

    Google Scholar 
    27.Sperry, J. S. & Love, D. M. What plant hydraulics can tell us about responses to climate-change droughts. New Phytol. 207, 14–27 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Roberts, J., Osvaldo, M. R. C. & De Aguiar, L. F. Stomatal and boundary-layer conductances in an Amazonian terra firme rain forest. J. Appl. Ecol. 27, 336–353 (1990).Article 

    Google Scholar 
    29.Olson, M. E. et al. Plant height and hydraulic vulnerability to drought and cold. Proc. Natl Acad. Sci. USA 115, 7551–7556 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    30.McGregor, I. R. et al. Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest. New Phytol. https://doi.org/10.1111/nph.16996 (2020).31.Mencuccini, M. et al. Size-mediated ageing reduces vigour in trees. Ecol. Lett. 8, 1183–1190 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).PubMed 
    Article 

    Google Scholar 
    33.Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).PubMed 
    Article 

    Google Scholar 
    34.Nepstad, D. C., Tohver, I. M., Ray, D., Moutinho, P. & Cardinot, G. Mortality of large trees and lianas following experimental drought in an Amazon forest. Ecology 88, 2259–2269 (2007).PubMed 
    Article 

    Google Scholar 
    35.da Costa, A. C. L. et al. Effect of 7 yr of experimental drought on vegetation dynamics and biomass storage of an eastern Amazonian rainforest. New Phytol. 187, 579–591 (2010).PubMed 
    Article 

    Google Scholar 
    36.Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Bartholomew, D. C. et al. Small tropical forest trees have a greater capacity to adjust carbon metabolism to long-term drought than large canopy trees. Plant Cell Environ. 43, 2380–2393 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Fauset, S. et al. Drought-induced shifts in the floristic and functional composition of tropical forests in Ghana. Ecol. Lett. 15, 1120–1129 (2012).PubMed 
    Article 

    Google Scholar 
    39.Zuleta, D., Duque, A., Cardenas, D., Muller-Landau, H. C. & Davies, S. J. Drought-induced mortality patterns and rapid biomass recovery in a terra firme forest in the Colombian Amazon. Ecology 98, 2538–2546 (2017).PubMed 
    Article 

    Google Scholar 
    40.van der Meer, P. J. & Bongers, F. Patterns of tree-fall and branch-fall in a tropical rain forest in French Guiana. J. Ecol. 84, 19–29 (1996).Article 

    Google Scholar 
    41.Parker, G. G. in Forest canopies (eds Lowman, M. D. & Nadkarni, N. M.) 73–106 (Academic Press, 1995).42.Terborgh, J., Huanca Nuñez, N., Feeley, K. & Beck, H. Gaps present a trade-off between dispersal and establishment that nourishes species diversity. Ecology 101, e02996 (2020).PubMed 
    Article 

    Google Scholar 
    43.Ribeiro, G. H. P. M. et al. Mechanical vulnerability and resistance to snapping and uprooting for Central Amazon tree species. For. Ecol. Manag. 380, 1–10 (2016).Article 

    Google Scholar 
    44.Peterson, C. J. et al. Critical wind speeds suggest wind could be an important disturbance agent in Amazonian forests. Forestry 92, 444–459 (2019).Article 

    Google Scholar 
    45.Uriarte, M., Thompson, J. & Zimmerman, J. K. Hurricane María tripled stem breaks and doubled tree mortality relative to other major storms. Nat. Commun. 10, 1362 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Silvério, D. V. et al. Fire, fragmentation, and windstorms: a recipe for tropical forest degradation. J. Ecol. 107, 656–667 (2019).Article 

    Google Scholar 
    47.van Wilgen, B. W., Biggs, H. C., Mare, N. & O’Regan, S. P. A fire history of the savanna ecosystems in the Kruger National Park, South Africa, between 1941 and 1996. S. Afr. J. Sci. 96, 167–178 (2000).
    Google Scholar 
    48.Tutin, C. E. G., White, L. J. T. & Mackanga-Missandzou, A. Lightning strike burns large forest tree in the Lope Reserve, Gabon. Glob. Ecol. Biogeog. Lett. 5, 36–41 (1996).Article 

    Google Scholar 
    49.Magnusson, W. E., Lima, A. P. & de Lima, O. Group lightning mortality of trees in a Neotropical forest. J. Trop. Ecol. 12, 899–903 (1996).Article 

    Google Scholar 
    50.Anderson, J. A. R. Observations on climatic damage in peat swamp forest in Sarawak. Commonw. Forestry Rev. 43, 145–158 (1964).
    Google Scholar 
    51.Gora, E. M., Burchfield, J. C., Muller-Landau, H. C., Bitzer, P. M. & Yanoviak, S. P. Pantropical geography of lightning-caused disturbance and its implications for tropical forests. Glob. Change Biol. 26, 5017–5026 (2020).Article 

    Google Scholar 
    52.Gora, E. M. et al. A mechanistic and empirically-supported lightning risk model for forest trees. J. Ecol. 108, 1956–1966 (2020).Article 

    Google Scholar 
    53.Alencar, A., Nepstad, D. & Diaz, M. C. V. Forest understory fire in the Brazilian Amazon in ENSO and non-ENSO years: area burned and committed carbon emissions. Earth Interact. 10, 1–17 (2009).Article 

    Google Scholar 
    54.Brando, P. M. et al. Droughts, wildfires, and forest carbon cycling: A pantropical synthesis. Annu. Rev. Earth Planet. Sci. 47, 555–581 (2019).CAS 
    Article 

    Google Scholar 
    55.Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Kauffman, J. B. & Uhl, C. in Fire in the Tropical Biota. Ecological Studies (Analysis and Synthesis) Vol. 84 (ed. Goldammer, J. G.) (Springer, 1990).57.Pfeiffer, M., Spessa, A. & Kaplan, J. O. A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0). Geosci. Model Dev. 6, 643–685 (2013).Article 
    CAS 

    Google Scholar 
    58.Nepstad, D. C. et al. Large-scale impoverishment of Amazonian forests by logging and fire. Nature 398, 505–508 (1999).CAS 
    Article 

    Google Scholar 
    59.Ray, D., Nepstad, D. & Moutinho, P. Micrometeorological and canopy controls of fire susceptibility in a forested Amazon landscape. Ecol. Appl. 15, 1664–1678 (2005).Article 

    Google Scholar 
    60.Brando, P. M. et al. Fire-induced tree mortality in a neotropical forest: the roles of bark traits, tree size, wood density and fire behavior. Glob. Change Biol. 18, 630–641 (2012).Article 

    Google Scholar 
    61.Barlow, J., Peres, C. A., Lagan, B. O. & Haugaasen, T. Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol. Lett. 6, 6–8 (2003).Article 

    Google Scholar 
    62.Liebhold, A. M., MacDonald, W. L., Bergdahl, D. & Mastro, V. C. Invasion by Exotic Forest Pests: A Threat to Forest Ecosystems Forest Science Monographs 30 (Society of American Foresters, 1995).63.McGregor, I. R. et al. Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest. New Phytol. https://doi.org/10.1111/nph.16996 (2020).64.Gilbert, G. S. & Hubbell, S. P. Plant diseases and the conservation of tropical forests. BioScience 46, 98–106 (1996).Article 

    Google Scholar 
    65.Liu, X. et al. Dilution effect of plant diversity on infectious diseases: latitudinal trend and biological context dependence. Oikos 129, 457–465 (2020).Article 

    Google Scholar 
    66.Chen, L. et al. Differential soil fungus accumulation and density dependence of trees in a subtropical forest. Science 366, 124 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Bell, T., Freckleton, R. P. & Lewis, O. T. Plant pathogens drive density-dependent seedling mortality in a tropical tree. Ecol. Lett. 9, 569–574 (2006).PubMed 
    Article 

    Google Scholar 
    68.Peters, H. A. Neighbour-regulated mortality: the influence of positive and negative density dependence on tree populations in species-rich tropical forests. Ecol. Lett. 6, 757–765 (2003).Article 

    Google Scholar 
    69.Gilbert, G. S., Foster, R. B. & Hubbell, S. P. Density and distance-to-adult effects of a canker disease of trees in a moist tropical forest. Oecologia 98, 100–108 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Coley, P. D. & Barone, J. A. Herbivory and plant defenses in tropical forests. Annu. Rev. Ecol. Syst. 27, 305–335 (1996).Article 

    Google Scholar 
    71.Suresh, H. S., Dattaraja, H. S. & Sukumar, R. Relationship between annual rainfall and tree mortality in a tropical dry forest: results of a 19-year study at Mudumalai, southern India. For. Ecol. Manag. 259, 762–769 (2010).Article 

    Google Scholar 
    72.Forrister, D. L., Endara, M.-J., Younkin, G. C., Coley, P. D. & Kursar, T. A. Herbivores as drivers of negative density dependence in tropical forest saplings. Science 363, 1213 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Stephenson, N. L., Das, A. J., Ampersee, N. J., Bulaon, B. M. & Yee, J. L. Which trees die during drought? The key role of insect host-tree selection. J. Ecol. 107, 2383–2401 (2019).Article 

    Google Scholar 
    74.Wing, L. D. & Buss, I. O. Elephants and forests. Wildl. Monogr. 19, 3–92 (1970).75.Berzaghi, F. et al. Carbon stocks in central African forests enhanced by elephant disturbance. Nat. Geosci. 12, 725–729 (2019).CAS 
    Article 

    Google Scholar 
    76.Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).PubMed 
    Article 

    Google Scholar 
    77.Rüger, N. et al. Beyond the fast–slow continuum: demographic dimensions structuring a tropical tree community. Ecol. Lett. 7, 1075–1084 (2018).Article 

    Google Scholar 
    78.Montgomery, R. A. & Chazdon, R. L. Forest structure, canopy architecture, and light transmittance in old-growth and secondgrowth tropical rain forests. Ecology 82, 2707–2718 (2001).Article 

    Google Scholar 
    79.Kobe, R. K. Carbohydrate allocation to storage as a basis of interspecific variation in sapling survivorship and growth. Oikos 80, 226–233 (1997).Article 

    Google Scholar 
    80.Waring, B. G. & Powers, J. S. Overlooking what is underground: root:shoot ratios and coarse root allometric equations for tropical forests. For. Ecol. Manag. 385, 10–15 (2017).Article 

    Google Scholar 
    81.Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Casper, B. B. & Jackson, R. B. Plant competition underground. Annu. Rev. Ecol. Syst. 28, 545–570 (1997).Article 

    Google Scholar 
    83.Coomes, D. A., Duncan, R. P., Allen, R. B. & Truscott, J. Disturbances prevent stem size–density distributions in natural forests from following scaling relationships. Ecol. Lett. 6, 980–989 (2003).Article 

    Google Scholar 
    84.Pillet, M. et al. Disentangling competitive vs. climatic drivers of tropical forest mortality. J. Ecol. 106, 1165–1179 (2018).Article 

    Google Scholar 
    85.Rozendaal, D. M. A. et al. Competition influences tree growth, but not mortality, across environmental gradients in Amazonia and tropical Africa. Ecology 101, e03052 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Rodríguez-Ronderos, M. E., Bohrer, G., Sanchez-Azofeifa, A., Powers, J. S. & Schnitzer, S. A. Contribution of lianas to plant area index and canopy structure in a Panamanian forest. Ecology 97, 3271–3277 (2016).PubMed 
    Article 

    Google Scholar 
    87.Schnitzer, S. A., Kuzee, M. E. & Bongers, F. Disentangling above- and below-ground competition between lianas and trees in a tropical forest. J. Ecol. 93, 1115–1125 (2005).Article 

    Google Scholar 
    88.Putz, F. E. The natural history of lianas on Barro Colorado Island, Panama. Ecology 65, 1713–1724 (1984).Article 

    Google Scholar 
    89.van der Heijden, G. M. F., Powers, J. S. & Schnitzer, S. A. Lianas reduce carbon accumulation and storage in tropical forests. Proc. Natl Acad. Sci. USA 112, 13267–13271 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    90.Visser, M. D. et al. Tree species vary widely in their tolerance for liana infestation: A case study of differential host response to generalist parasites. J. Ecol. 106, 781–794 (2018).CAS 
    Article 

    Google Scholar 
    91.Schnitzer, S. A. & Bongers, F. The ecology of lianas and their role in forests. Trends Ecol. Evol. 17, 223–230 (2002).Article 

    Google Scholar 
    92.García León, M. M., Martínez Izquierdo, L., Mello, F. N. A., Powers, J. S. & Schnitzer, S. A. Lianas reduce community-level canopy tree reproduction in a Panamanian forest. J. Ecol. 106, 737–745 (2018).Article 
    CAS 

    Google Scholar 
    93.Reis, S. M. et al. Causes and consequences of liana infestation in Southern Amazonia. J. Ecol. 108, 2184–2197 (2020).Article 

    Google Scholar 
    94.Sheil, D., Salim, A., Chave, J., Vanclay, J. & Hawthorne, W. D. Illumination–size relationships of 109 coexisting tropical forest tree species. J. Ecol. 94, 494–507 (2006).Article 

    Google Scholar 
    95.Myers, J. A. & Kitajima, K. Carbohydrate storage enhances seedling shade and stress tolerance in a neotropical forest. J. Ecol. 95, 383–395 (2007).CAS 
    Article 

    Google Scholar 
    96.Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees—from what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    97.Enquist, B. J., West, G. B., Charnov, E. L. & Brown, J. H. Allometric scaling of production and life-history variation in vascular plants. Nature 401, 907–911 (1999).CAS 
    Article 

    Google Scholar 
    98.Hubau, W. et al. The persistence of carbon in the African forest understory. Nat. Plants 5, 133–140 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Chambers, J. Q., Higuchi, N. & Schimel, J. P. Ancient trees in Amazonia. Nature 391, 135–136 (1998).CAS 
    Article 

    Google Scholar 
    100.Poorter, L. & Kitajima, K. Carbohydrate storage and light requirements of tropical moist and dry forest tree species. Ecology 88, 1000–1011 (2007).PubMed 
    Article 

    Google Scholar 
    101.Conrath, U. et al. Priming: getting ready for battle. Mol. Plant Microbe Interact. 19, 1062–1071 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    102.Arellano, G., Medina, N. G., Tan, S., Mohamad, M. & Davies, S. J. Crown damage and the mortality of tropical trees. New Phytol. 221, 169–179 (2018).PubMed 
    Article 

    Google Scholar 
    103.Zhang, Y.-J. et al. Size‐dependent mortality in a Neotropical savanna tree: the role of height‐related adjustments in hydraulic architecture and carbon allocation. Plant Cell Environ. 32, 1456–1466 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    104.Feldpausch, T. R. et al. Amazon forest response to repeated droughts. Glob. Biogeochem. Cycles 30, 964–982 (2016).CAS 
    Article 

    Google Scholar 
    105.Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    106.Harel, M. & Price, C. Thunderstorm trends over Africa. J. Clim. 33, 2741–2755 (2020).Article 

    Google Scholar 
    107.IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (Cambridge Univ. Press, 2014).108.Fu, Z. et al. Recovery time and state change of terrestrial carbon cycle after disturbance. Environ. Res. Lett. 12, 104004 (2017).Article 

    Google Scholar 
    109.Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    110.Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    111.Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).Article 

    Google Scholar 
    112.Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    113.Banin, L. et al. What controls tropical forest architecture? Testing environmental, structural and floristic drivers. Glob. Ecol. Biogeogr. 21, 1179–1190 (2012).Article 

    Google Scholar 
    114.Brando, P. et al. Effects of partial throughfall exclusion on the phenology of Coussarea racemosa (Rubiaceae) in an east-central Amazon rainforest. Oecologia 150, 181–189 (2006).PubMed 
    Article 

    Google Scholar 
    115.Lugo, A. E. & Scatena, F. N. Background and catastrophic tree mortality in tropical moist, wet, and rain forests. Biotropica 28, 585–599 (1996).Article 

    Google Scholar 
    116.Feeley, K. J., Bravo-Avila., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Change 10, 965–970 (2020).CAS 
    Article 

    Google Scholar 
    117.Brienen, R. J. W. et al. Forest carbon sink neutralized by pervasive growth–lifespan trade-offs. Nat. Commun. 11, 4241 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    118.Bugmann, H. et al. Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale. Ecosphere 10, e02616 (2019).Article 

    Google Scholar 
    119.Arellano, G., Zuleta, D. & Davies, S. J. Tree death and damage: A standardized protocol for frequent surveys in tropical forests. J. Veg. Sci. 32, e12981 (2021).Article 

    Google Scholar 
    120.Chan, K.-J., Phillips, O. L., Monteagudo, A., Torres-Lezama, A. & Vásquez Martínez, R. How do trees die? Mode of death in northern Amazonia. J. Veg. Sci. 20, 260–268 (2009).Article 

    Google Scholar  More

  • in

    Distant drivers of deforestation

    1.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Science 361, 1108–1111 (2018).CAS 
    Article 

    Google Scholar 
    2.Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Environ. Res. Lett. 14, 055003 (2019).Article 

    Google Scholar 
    3.Zhang, Q. et al. J. Clean. Prod. 250, 119503 (2020).Article 

    Google Scholar 
    4.Hoang, N. T. & Kanemoto, K. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01417-z (2021).5.Hansen, M. C. et al. Science 342, 850–853 (2013).CAS 
    Article 

    Google Scholar 
    6.System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (United Nations, European Commission, Food and Agriculture Organization, Organization for Economic Co-operation and Development & World Bank, 2014).7.Wilting, H. C., Schipper, A. M., Ivanova, O., Ivanova, D. & Huijbregts, M. A. J. J. Ind. Ecol. 25, 79–94 (2021).Article 

    Google Scholar 
    8.Bruckner, M. et al. Environ. Sci. Technol. 53, 11302–11312 (2019).CAS 
    Article 

    Google Scholar 
    9.Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019). More