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    Resistance to permethrin alters the gut microbiota of Aedes aegypti

    1.WHO Pesticides and their application for the control of vectors and pests of public health importance. In WHO/CDS/NTD/WHOPES/GCDPP/2006.1 (2006).2.Corbel, V. et al. Multiple insecticide resistance mechanisms in Anopheles gambiae and Culex quinquefasciatus from Benin, West Africa. Acta Trop. 101, 207–216 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.N’Guessan, R., Corbel, V., Akogbeto, M. & Rowland, M. Reduced efficacy of insecticide-treated nets and indoor residual spraying for malaria control in pyrethroid resistance area, Benin. Emerg. Infect. Dis. 13, 199–206 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Ranson, H. et al. Pyrethroid resistance in African anopheline mosquitoes: What are the implications for malaria control?. Trends Parasitol. 27, 91–98 (2010).PubMed 
    Article 

    Google Scholar 
    5.Chareonviriyaphap, T. et al. Review of insecticide resistance and behavioral avoidance of vectors of human diseases in Thailand. Parasit. Vectors 6, 280 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Liu, N. insecticide resistance in mosquitoes: Impact, mechanisms and research directions. Annu. Rev. Entomol. 60, 537–559 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Dada, N. et al. Pyrethroid exposure alters internal and cuticle surface bacterial communities in Anopheles albimanus. ISME J. 10, 2447–2464 (2019).Article 

    Google Scholar 
    8.Dada, N., Sheth, M., Liebman, K., Pinto, J. & Lenhart, A. Whole metagenome sequencing reveals links between mosquito microbiota and insecticide resistance in malaria vectors. Sci. Rep. 8, 2084 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Soltani, A., Vatandoost, H., Oshaghi, M. A., Enayati, A. A. & Chavshin, A. R. The role of midgut symbiotic bacteria in resistance of Anopheles stephensi (Diptera: Culicidae) to organophosphate insecticides. Pathog. Glob. Health 111, 289–296 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Pietri, J.E., Tiffany, C. & Liang, D. Disruption of the microbiota affects physiological and evolutionary aspects of insecticide resistance in the German cockroach, an important urban pest. PLoS One 13, e0207985 (2018).11.Cheng, D. et al. Gut symbiont enhances insecticide resistance in a significant pest, the oriental fruit fly Bactrocera dorsalis (Hendel). Microbiome 5, 13 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Xia, X. et al. DNA sequencing reveals the midgut microbiota of diamondback moth, Plutella xylostella (L.) and a possible relationship with insecticide resistance. PLoS ONE 8, e68852 (2013).13.Xia, X. et al. Gut microbiota mediate insecticide resistance in the diamondback moth, Plutella xylostella (L.). Front Microbiol. 9, 25 (2018).14.Kontsedalov, S. et al. The presence of Rickettsia is associated with increased susceptibility of Bemisia tabaci (Homoptera: Aleyrodidae) to insecticides. Pest Manag. Sci. 64, 789–792 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Ghanim, M. & Kontsedalov, S. Susceptibility to insecticides in the Q biotype of Bemisia tabaci is correlated with bacterial symbiont densities. Pest Manag. Sci. 65, 939–942 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Kikuchi, Y. et al. Symbiont-mediated insecticide resistance. Proc. Natl. Acad. Sci. USA 109, 8618–8622 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Badolo, A. et al. Insecticide resistance levels and mechanisms in Aedes aegypti populations in and around Ouagadougou, Burkina Faso. PLoS Negl. Trop. Dis. 13, e0007439 (2019).18.Kandel, Y. et al. Widespread insecticide resistance in Aedes aegypti L. from New Mexico, U.S.A. PLoS One 14, e0212693 (2019).19.Amelia-Yap, Z. H., Chen, C. D., Sofian-Azirun, M. & Low, V. L. Pyrethroid resistance in the dengue vector Aedes aegypti in Southeast Asia: Present situation and prospects for management. Parasit. Vectors 11, 332 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Li, W., Jin, D., Shi, C. & Li, F. Midgut bacteria in deltamethrin-resistant, deltamethrin-susceptible, and field-caught populations of Plutella xylostella, and phenomics of the predominant midgut bacterium Enterococcus mundtii. Sci. Rep. 7, 1947 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Barnard, K., Jeanrenaud, A., Brooke, B. D. & Oliver, S. V. The contribution of gut bacteria to insecticide resistance and the life histories of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). Sci. Rep. 9, 9117 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Tetreau, G. et al. Bacterial microbiota of Aedes aegypti mosquito larvae is altered by intoxication with Bacillus thuringiensis israelensis. Parasit. Vectors 11, 121 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Aislabie, J. & Lloyd-Jones, G. A review of bacterial degradation of pesticides. Aust. J. Soil Res. 33, 925–942 (1995).CAS 
    Article 

    Google Scholar 
    24.Lien, N. T. K. et al. Transcriptome sequencing and analysis of changes associated with insecticide resistance in the dengue mosquito (Aedes aegypti) in Vietnam. Am. J. Trop. Med. Hyg. 100, 1240–1248 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Berticat, C., Rousset, F., Raymond, M., Berthomieu, A. & Weill, M. High Wolbachia density in insecticide-resistant mosquitoes. Proc. R. Soc. Lond. Ser. B-Biol.l Sci. 269, 1413–1416 (2002).26.Hamada, M., Matar, A. & Bashir, A. Carbaryl degradation by bacterial isolates from a soil ecosystem of the Gaza Strip. Braz. J. Microbiol. 46, 1087–1091 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Akbar, S., Sultan, S. & Kertesz, M. Determination of cypermethrin degradation potential of soil bacteria along with plant growth-promoting characteristics. Curr. Microbiol. 70, 75–84 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Durand, C., Ruban, V., Ambles, A., Clozel, B. & Achard, L. Characterisation of road sediments near Bordeaux with emphasis on phosphorus. J. Environ. Monit. 5, 463–467 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Zehetner, F., Rosenfellner, U., Mentler, A. & Gerzabek, M. H. Distribution of road salt residues, heavy metals and polycyclic aromatic hydrocarbons across a highway-forest interface. Water Air Soil Pollut. 198, 125–132 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    30.Fuchs, G., Boll, M. & Heider, J. Microbial degradation of aromatic compounds—From one strategy to four. Nat. Rev. Microbiol. 9, 803–816 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Zhu, K. Y., Merzendorfer, H., Zhang, W., Zhang, J. & Muthukrishnan, S. Biosynthesis, turnover, and functions of chitin in insects. Annu. Rev. Entomol. 61, 177–196 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Czaplicka, M. Sources and transformations of chlorophenols in the natural environment. Sci. Total Environ. 322, 21–39 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Igbinosa, E.O. et al. Toxicological profile of chlorophenols and their derivatives in the environment: The public health perspective. Sci. World J. 2013, 460215 (2013).34.Li, N., Chen, J. M., Zhang, Y. F., He, Y. P. & Chen, L. Z. Comparison for activities of detoxifying enzymes between in resistant-strains and susceptible-imidacloprid endosymbiotic strains of rice brown planthopper, Nilaparvata lugens. Acta Agric. Univ. Zhejiangensis 22, 653–659 (2010).
    Google Scholar 
    35.Dowd, P. F. & Shen, S. K. The contribution of symbiotic yeast to toxin resistance of the cigarette beetle (Lasioderma serricorne). Entomol. Exp. Appl. 56, 241–248 (1990).CAS 
    Article 

    Google Scholar 
    36.Brogdon, W. G. & McAllister, J. C. Simplification of adult mosquito bioassays through use of time-mortality determinations in glass bottles. J. Am. Mosq. Control Assoc. 14, 159–164 (1998).CAS 
    PubMed 

    Google Scholar 
    37.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Muyzer, G., de Waal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Muturi, E. J., Njoroge, T. M., Dunlap, C. & Caceres, C. E. Blood meal source and mixed blood-feeding influence gut bacterial community composition in Aedes aegypti. Parasit. Vectors 14, 83 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Arndt, D. et al. METAGENassist: A comprehensive web server for comparative metagenomics. Nucleic Acids Res. 40, W88–W95 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Hammer, O., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Paleontol. Electron. 4, 4–9 (2001).
    Google Scholar 
    43.Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.3–5. https://CRAN.R-project.org/package=vegan (2016).45.Quinn, G. & Keough, M. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).Book 

    Google Scholar  More

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    Signatures of mitonuclear coevolution in a warbler species complex

    1.Calvo, S. E. & Mootha, V. K. The mitochondrial proteome and human disease. Annu. Rev. Genomics Hum. Genet. 11, 25–44 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Lane, N. Mitonuclear match: optimizing fitness and fertility over generations drives ageing within generations. BioEssays 33, 860–869 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Bar-Yaacov, D. et al. Mitochondrial involvement in vertebrate speciation? The case of mito-nuclear genetic divergence in chameleons. Genome Biol. Evol. 7, 3322–3336 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Hill, G. E. Mitonuclear Ecology (Oxford Univ. Press, 2019).5.Ballard, J. W. O. & Whitlock, M. C. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744 (2004).PubMed 
    Article 

    Google Scholar 
    6.Morales, H. E. et al. Concordant divergence of mitogenomes and a mitonuclear gene cluster in bird lineages inhabiting different climates. Nat. Ecol. Evol. 2, 1258–1267 (2018).PubMed 
    Article 

    Google Scholar 
    7.Hill, G. E. et al. Assessing the fitness consequences of mitonuclear interactions in natural populations. Biol. Rev. 94, 1089–1104 (2019).PubMed 
    Article 

    Google Scholar 
    8.Barreto, F. S. & Burton, R. S. Elevated oxidative damage is correlated with reduced fitness in interpopulation hybrids of a marine copepod. Proc. R. Soc. B Biol. Sci. 280, 20131521 (2013).Article 

    Google Scholar 
    9.Healy, T. M. & Burton, R. S. Strong selective effects of mitochondrial DNA on the nuclear genome. Proc. Natl Acad. Sci. U.S.A. 117, 6616–6621 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Burton, R. S., Pereira, R. J. & Barreto, F. S. Cytonuclear genomic interactions and hybrid breakdown. Annu. Rev. Ecol. Evol. Syst. 44, 281–302 (2013).Article 

    Google Scholar 
    11.Hill, G. E. The mitonuclear compatibility species concept. Auk 134, 393–409 (2017).Article 

    Google Scholar 
    12.Burton, R. S. & Barreto, F. S. A disproportionate role for mtDNA in Dobzhansky-Muller incompatibilities? Mol. Ecol. 21, 4942–4957 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Weir, J. T. & Schluter, D. Ice sheets promote speciation in boreal birds. Proc. R. Soc. B Biol. Sci. 271, 1881–1887 (2004).Article 

    Google Scholar 
    14.Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).Article 

    Google Scholar 
    15.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Innocenti, P., Morrow, E. H. & Dowling, D. K. Experimental evidence supports a sex-specific selective sieve in mitochondrial genome evolution. Science 332, 845–848 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Harada, A. E., Healy, T. M. & Burton, R. S. Variation in thermal tolerance and its relationship to mitochondrial function across populations of Tigriopus californicus. Front. Physiol. 10, 213 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Acevedo, P. et al. Range dynamics driven by quaternary climate oscillations explain the distribution of introgressed mtDNA of Lepus timidus origin in hares from the Iberian Peninsula. J. Biogeogr. 42, 1727–1735 (2015).Article 

    Google Scholar 
    19.Elgvin, T. O. et al. The genomic mosaicism of hybrid speciation. Sci. Adv. 3, e1602996 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Schumer, M., Cui, R., Powell, D. L., Rosenthal, G. G. & Andolfatto, P. Ancient hybridization and genomic stabilization in a swordtail fish. Mol. Ecol. 25, 2661–2679 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Rieseberg, L. H. Hybrid origins of plant species. Annu. Rev. Ecol. Syst. 28, 359–389 (2002).Article 

    Google Scholar 
    22.Barton, N. H. The role of hybridization in evolution. Mol. Ecol. 10, 551–568 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Gagnaire, P. A., Normandeau, E. & Bernatchez, L. Comparative genomics reveals adaptive protein evolution and a possible cytonuclear incompatibility between European and American Eels. Mol. Biol. Evol. 29, 2909–2919 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Sambatti, J. B. M., Ortiz-Barrientos, D., Baack, E. J. & Rieseberg, L. H. Ecological selection maintains cytonuclear incompatibilities in hybridizing sunflowers. Ecol. Lett. 11, 1082–1091 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Baris, T. Z. et al. Evolved genetic and phenotypic differences due to mitochondrial-nuclear interactions. PLoS Genet. 13, e1006517 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Boratyński, Z., Ketola, T., Koskela, E. & Mappes, T. The sex specific genetic variation of energetics in bank voles, consequences of introgression? Evol. Biol. 43, 37–47 (2016).Article 

    Google Scholar 
    27.Rohwer, S. & Wood, C. Three hybrid zones between Hermit and Townsend’s Warblers in Washington and Oregon. Auk 115, 284–310 (1998).Article 

    Google Scholar 
    28.Rohwer, S., Bermingham, E. & Wood, C. Plumage and mitochondrial DNA haplotype variation across a moving hybrid zone. Evolution 55, 405–422 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Krosby, M. & Rohwer, S. A 2000 km genetic wake yields evidence for northern glacial refugia and hybrid zone movement in a pair of songbirds. Proc. R. Soc. B Biol. Sci. 276, 615–621 (2009).CAS 
    Article 

    Google Scholar 
    30.Krosby, M. & Rohwer, S. Ongoing movement of the hermit warbler X Townsend’s Warbler Hybrid Zone. PLoS One 5, e14164 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Wang, S. et al. Selection on a small genomic region underpins differentiation in multiple color traits between two warbler species. Evol. Lett. 4–6, 502–515 (2020).Article 

    Google Scholar 
    32.Choi, Y. & Chan, A. P. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–2747 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Choi, Y., Sims, G. E., Murphy, S., Miller, J. R. & Chan, A. P. Predicting the functional effect of amino acid substitutions and indels. PLoS ONE (2012).34.Murrell, B. et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Michaud, E. J. et al. A molecular model for the genetic and phenotypic characteristics of the mouse lethal yellow (Ay) mutation. Proc. Natl Acad. Sci. USA 91, 2562–2566 (1994).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Nadeau, N. J. et al. Characterization of Japanese quail yellow as a genomic deletion upstream of the avian homolog of the mammalian ASIP (agouti) gene. Genetics 178, 777–786 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Wang, S., Rohwer, S., Delmore, K. E. & Irwin, D. E. Cross-decades stability of an avian hybrid zone. J. Evol. Biol. 32, 1242–1251 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Console, L. et al. The link between the mitochondrial fatty acid oxidation derangement and kidney injury. Front. Physiol. 11, 1–7 (2020).Article 

    Google Scholar 
    39.Houten, S. M. & Wanders, R. J. A. A general introduction to the biochemistry of mitochondrial fatty acid β-oxidation. J. Inherit. Metab. Dis. 33, 469–477 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Clemente, F. J. et al. A selective sweep on a deleterious mutation in CPT1A in arctic populations. Am. J. Hum. Genet. 95, 584–589 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science 349, 1343–1347 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Zoladz, J. A. et al. Effect of temperature on fatty acid metabolism in skeletal muscle mitochondria of untrained and endurance-trained rats. PLoS One 12, e0189456 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.Atkin, O. K. & Macherel, D. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann. Bot. 103, 581–597 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Wu, C. I. The genic view of the process of speciation. J. Evolut. Biol. 14, 851–865 (2001).Article 

    Google Scholar 
    45.Via, S. Natural selection in action during speciation. Proc. Natl Acad. Sci. USA 106, 9939–9946 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Nosil, P. A. Ecological Speciation (Oxford Univ. Press, 2012).47.Feder, J. L., Flaxman, S. M., Egan, S. P., Comeault, A. A. & Nosil, P. Geographic mode of speciation and genomic divergence. Annu. Rev. Ecol. Evol. Syst. 44, 73–97 (2013).Article 

    Google Scholar 
    48.Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Fisher, R. A. The Genetical Theory of Natural Selection (Oxford Univ. Press, 1930).50.Hartl, D. L. & Clark, A. Principles of Population Genetics (Sinauer Associates, 2007).51.Irwin, D. E. et al. A comparison of genomic islands of differentiation across three young avian species pairs. Mol. Ecol. 27, 4839–4855 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Nam, K., Mugal, C., Nabholz, C., Schielzeth, H. & Wolf, J. B. Molecular evolution of genes in avian genomes. Genome Biol. 11, R68 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Shafer, A. B. A., Cullingham, C. I., Côté, S. D. & Coltman, D. W. Of glaciers and refugia: a decade of study sheds new light on the phylogeography of northwestern North America. Mol. Ecol. 19, 4589–4621 (2010).PubMed 
    Article 

    Google Scholar 
    54.Rohwer, S., Bermingham, E. & Wood, C. Plumage and mitochondrial DNA haplotype variation across a moving hybrid zone. Evolution 55, 405 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    55.Pielou, E. C. After the Ice Age (University of Chicago Press, 1991).56.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    58.Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6, e19379 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Baiz, M. D., Wood, A. W., Brelsford, A., Lovette, I. J. & Toews, D. P. L. Pigmentation genes show evidence of repeated divergence and multiple bouts of introgression in Setophaga Warblers. Curr. Biol. 31, 1–7 (2021).Article 
    CAS 

    Google Scholar 
    61.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    62.McKenna, Aaron et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. (2010).63.Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.R Core Team (2017). R: a language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria. R Foundation for Statistical Computing (2017). S0103-6440200400030001566.Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational inference of population structure in large SNP datasets. Genetics 197, 573–589 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
    PubMed 

    Google Scholar 
    68.Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Bateman, A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).Article 
    CAS 

    Google Scholar 
    71.Legendre, P. Numerical Ecology 2nd edn (Elsevier Science, 1998). https://doi.org/10.1017/CBO9781107415324.00472.Korunes, L. K. & Samuk, K. pixy: unbiased estimation of nucleotide diversity and divergence in the presence of missing data. Mol. Ecol. Resour. 21, 1359–1368 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Gompert, Z. & Buerkle, C. A. Bayesian estimation of genomic clines. Mol. Ecol. 20, 2111–2127 (2011).PubMed 
    Article 

    Google Scholar 
    74.Bates, D. M., Maechler, M., Bolker, B. & Walker, S. lme4: linear mixed-effects models using S4 classes. J. Stat. Softw. 67, 48 (2015).Article 

    Google Scholar 
    75.Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
    Article 

    Google Scholar 
    76.Kearse, M. et al. Geneious. Bioinformatics (Oxford, 2012).77.Woolley, S., Johnson, J., Smith, M. J., Crandall, K. A. & McClellan, D. A. TreeSAAP: selection on amino acid properties using phylogenetic trees. Bioinformatics 19, 671–672 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    78.McClellan, D. A. & Ellison, D. D. Assessing and improving the accuracy of detecting protein adaptation with the TreeSAAP analytical software. Int. J. Bioinform. Res. Appl. 6, 120–133 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    79.Wang, T., Hamann, A., Spittlehouse, D. L. & Murdock, T. Q. Climate WNA-high-resolution spatial climate data for western North America. J. Appl. Meteorol. Climatol. 51, 16–29 (2012).ADS 
    Article 

    Google Scholar 
    80.Legendre, P. & Legendre, L. Multidimensional quantitative data. in Numerical Ecology 143–194 (Elsevier UK, 2012). More

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    Italy: Forest harvesting is the opposite of green growth

    CORRESPONDENCE
    13 July 2021

    Italy: Forest harvesting is the opposite of green growth

    Roberto Cazzolla Gatti

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    Gianluca Piovesan

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    Alessandro Chiarucci

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    Roberto Cazzolla Gatti

    Tomsk State University, Russia.

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    Gianluca Piovesan

    University of Tuscia, Viterbo, Italy.

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    Alessandro Chiarucci

    University of Bologna, Italy.

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    We question plans to step up the harvesting of forest biomass, as set out in Italy’s Fourth Report on the State of Natural Capital. Rather than supporting a transition to a green economy, this could translate into more logging and perturbation of forest ecosystems.The loss of trees in Italy’s forests in recent years (go.nature.com/3yzvdp9) is only partly explained by disturbances such as Storm Vaia in 2018, and salvage logging thereafter. The dominant driver is the production of wood fuel (D. Pettenella et al. Forest@ 18, 1–4; 2021), mainly from coppice. This probably removes about 50% of estimated annual growth (see go.nature.com/3xr1mzc).The new biomass policy could threaten the functionality of forest ecosystems unless it includes measurable targets and a reliable monitoring system for tracking the impacts of removing wood. In a geographically complex country, rich in biodiversity, this could undermine progress towards the European Union’s 2030 biodiversity strategy.For Italy’s forests to contribute to the economy, provide ecosystem services, halt biodiversity loss and mitigate climate change, the country needs ecological planning, data monitoring, forest protection, restoration and rewilding.

    Nature 595, 353 (2021)
    doi: https://doi.org/10.1038/d41586-021-01923-x

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    The authors declare no competing interests.

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    Sustainability

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    Optimization of the flow conditions in the spawning ground of the Chinese sturgeon (Acipenser sinensis) through Gezhouba Dam generating units

    Flow velocity thresholdThere were 92 Chinese sturgeon signals from 2016 to 2019, which were identified with the DIDSON dual-frequency video sonar system. The distribution map of Chinese sturgeon signals was shown in Fig. 1. The number of monitored signals in 2016 was significantly higher than in 2017–2019. The latest wild reproduction of the Chinese sturgeon occurred in 2016. Overall, most Chinese sturgeon signals were distributed within 500 m downstream from Gezhouba Dam, and there were more in the right side(facing downstream) than in the left side. The flow field of each sturgeon signal was simulated by the model, and the velocity of each signal location was obtained. According to the statistical analysis of the flow velocity values, the frequency of the sturgeon signal at different flow velocity values was shown in Fig. 2. The results show that most signals were concentrated in areas with flow velocities of 0.6–1.5 m/s, which accounted for 88.1% of the signals; areas with flow velocities below 0.6 m/s accounted for 4.3% of the signals, and areas with flow velocities above 1.5 m/s accounted for 7.6%. Therefore, 0.6–1.5 m/s was selected as the preferred flow velocity range of the Chinese sturgeon for spawning activity. This result was approximately consistent with the ranges proposed by most other researchers. The low limit of the velocity range was lower than that of other researchers. There may be two reasons for this result: the first was that the bottom velocity we analysed was lower than the surface velocity and vertical average velocity under the same conditions; the second was that our research time was after 2016, and the discharge during the spawning period was relatively low, so the velocity of the Chinese sturgeon signal was also relatively low.Figure 1Distribution map of Chinese sturgeon signals, where ○ indicates Chinese sturgeon signals monitored in 2016, ∆ indicates those in 2017, □ indicates those in 2018, and ✩ indicates those in 2019. Map generated in ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).Full size imageFigure 2Plots of the frequency for the different flow velocity ranges of Chinese sturgeon signals.Full size imageDifferent opening modes with identical dischargeThe discharge of 6150 m3/s on November 24, 2016, when the latest wild reproduction of Chinese sturgeon occurred, was used to study the flow velocity distribution with different opening modes. The specific opening mode cases are shown in Table 1. Case 1 was the actual situation, and the Dajiang Plant featured 7 open units: #8, #11, #13, #14, #16, #19, and #21. According to the amounts of electricity generated by Dajiang Plant and Erjiang Plant on that day, the proportion of the Dajiang River flow was 58.8%, and the average discharge of each unit was 516.6 m3/s. Case 2 and case 3 featured 7 open units with the same discharge, but in case 2, units #15–21 were continuously open on the right-side (facing downstream), and in case 3, units #8–14 were continuously open near the left side. Case 4 and case 5 were the most concentrated conditions with the discharge of 6150 m3/s because the maximum through-discharge for each unit in the Dajiang Plant is 825 m3/s19. In these cases, at least 5 units were open with an average discharge of 723 m3/s per unit. Case 4 involved continuously opening units #8–12 on the left side, and case 5 involved continuously opening units #17–21 on the right side. Case 6 involved simultaneously opening 14 units on Dajiang River, and the average discharge of each unit was 258.3 m3/s.Table 1 Calculation cases with different opening modes of units under the identical discharge.Full size tableFigure 3 shows the flow fields of the spawning ground under different opening modes with identical discharge. By comparing the areas with a velocity threshold range of 0.6–1.5 m/s in different cases, the most favourable opening mode was determined. In case 1, the velocity at the outlet of the units was higher than the 1.5 m/s velocity threshold, but the discharge of each unit was only 516.6 m3/s, so the high-velocity range was limited, and most areas were suitable. In case 2 and case 3, there was a large difference in proportions of suitable area. Because the left side was deeper than the right side, the flow velocity on the right side was higher under the same discharge, and case 3 more easily exceeded the flow threshold, which resulted in a larger unsuitable area. Case 2 was more suitable than case 1, which also demonstrated that opening the left-side units was more favourable. In case 4 and case 5, the proportions of suitable area were small. Because the units were concentrated, the discharge of each unit was too high, and the outlet velocity was more than 2 m/s, so a large area of high velocity appeared downstream of the units with backflow under the shut-down units. The proportion of suitable area in case 5 was larger than those in case 4 and case 3, which further indicates that opening the left-side units was more favourable than opening the right-side units. Case 6 was greater than that of any other case. Because the discharge of each unit was only 258.3 m3/s, the velocity of the unit outlet was less than 1.5 m/s, and almost all areas were suitable except for the small areas on both sides. The suitable-velocity area was the largest when all units of the Dajiang Plant of Gezhouba Dam were open; therefore, for a given discharge, it was best to open all units.Figure 3Flow field of the spawning ground in different opening modes with identical discharge, where the numbers at the top of each picture are the numbers of units to open, and the arrows indicate the direction of the water flow. Maps generated in Tecplot360 EX 2020 R1 (https://www.tecplot.com/products/tecplot-360/).Full size imageDifferent discharges under identical opening modeThe velocity distribution of the spawning field is affected by the opening mode of the units and discharge of Gezhouba Dam. To study the effect of different discharges, 14 cases were simulated, as shown in Table 2. All units of the Dajiang Plant were considered open because the proportion of suitable area was expected to be maximal under such circumstances. From 1982 to the present, the discharge during the spawning day of Chinese sturgeon under Gezhouba Dam has a wide range: the highest discharge was 27,290 m3/s in 1990, and the lowest discharge was 5590 m3/s in 2012. However, the highest design discharge of the Gezhouba Dam units is 17,930 m3/s20. Once the design discharge is exceeded, the spillway on Erjiang River discharges water, and the velocity distribution of the study area is not affected. Therefore, case 1 represents the lowest discharge of 5590 m3/s, and case 2 represents a discharge of 6000 m3/s. For each subsequent case, the discharge was increased by 1000 m3/s to case 13 with the highest flow of 17,930 m 3/s. In case 14, all units reached the design discharge, and the discharge of each unit was 825 m3/s19.Table 2 Calculation cases with the same opening mode under different discharges.Full size tableFigure 4 shows the proportion of suitable-velocity area with all units open under different discharges. According to the calculation results, the proportion of suitable area slightly fluctuated at approximately 96.2% for discharges of 5590–11,000 m3/s. Because the discharge of each unit was low, the velocity of the unit outlet was low, and most areas were within the velocity threshold. Therefore, it is advantageous to open all units when the discharge is low. After the discharge reached 12,000 m3/s, the proportion of suitable area rapidly decreased. Because the discharge of each unit was high, on the right side of Dajiang River, the velocity of the unit outlet exceeded the velocity threshold and increased with increases in discharge, and the range of effect gradually increased. In the last case, the proportion of suitable area was only 6% when the units reached the designed discharge of 825 m3/s. Because the discharge of each unit was too high, almost all areas exceeded the velocity threshold except for small areas on both sides. Therefore, at discharges below 12,000 m3/s, opening all units is favourable, and at discharge above 12,000 m3/s, a higher discharge corresponds to more unfavourable conditions.Figure 4Proportions of the suitable-velocity area with all units opened under different discharges.Full size imageOptimal scheme under high-flow conditionsHigh-flow conditions at Gezhouba Dam are considered those that exceed 12,000 m3/s because of the substantive decline in suitable habitat area at higher discharges. Because opening the units on the left side of the Dajiang Plant provides a more uniform, suitable habitat, we evaluated 20 cases with a left-side opening mode under different discharge, as shown in Table 3. Because the highest discharge of each unit in the Dajiang Plant is 825 m3/s, at least 9 units must be open when the discharge is 12,000 m3/s. Case 1 was designed to open 9 units on the left, i.e., units #13–21, and the discharge of each unit was 784 m3/s. Cases 2–5 increased by 1 unit from left to right until 13 units were opened. For discharges of 13,000 m3/s, 14,000 m3/s, 15,000 m3/s, and 16,000 m3/s, at least 10, 10, 11, and 12 units were opened. When the discharge was 17,000 m3/s and 17,930 m3/s, at least 13 units were open.Table 3 Calculation cases with different opening modes under high-flow conditions.Full size tableFigure 5 shows the proportions of suitable area for different opening modes under high-flow conditions. The calculation results show that when the discharge was 12,000 m3/s, 13,000 m3/s, and 14,000 m3/s, the proportion of suitable area showed a parabolic trend with the increase in number of units. When the discharge was 12,000 m3/s, the proportion of suitable area with 11 open units on the left was the largest, which was 8.7% larger than the value for all open units and 15% larger than the value for the lowest number of open units. When the discharge was 13,000 m3/s, 12 open units on the left had the largest proportion of suitable-flow-velocity area. When the discharge was 14,000 m3/s, the proportions of suitable area produced by opening 12 and 13 units on the left were the largest. The proportion of suitable area of the lowest number of open units was usually minimal because the discharge of each unit was too high, which resulted in a large area of high velocity that was not suitable for Chinese sturgeon to spawn. Because of the underwater topography, opening the left-side units was more favourable than opening the right-side units, so for all open units, the proportions of suitable area will be lower, and the number of units opened in the middle will be the most advantageous. For a discharge of 15,000 m3/s, with the increase in number of units, the proportion of suitable area increased, and there was no parabolic trend because the discharge of each unit exceeded 678 m3/s; thus, on the left side, there was a large area of high velocity, and the effect extended very far, which was not suitable for Chinese sturgeon.Figure 5Proportions of the suitable area for different opening modes under high-flow conditions, where 12,000–09 on the x-axis indicates that the discharge is 12,000 m3/s, and 9 units are open on the left.Full size image More

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    Climate change drives mountain butterflies towards the summits

    1.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: The ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P. & Moomaw, W. R. World scientists’ warning of a climate emergency. Bioscience https://doi.org/10.1093/biosci/biz088 (2019).Article 

    Google Scholar 
    3.Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Liu, H. et al. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl. Acad. Sci. 115, 4051–4056 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 
    Article 

    Google Scholar 
    6.Parmesan, C. et al. Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399, 579–583 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Dieker, P., Drees, C. & Assmann, T. Two high-mountain burnet moth species (Lepidoptera, Zygaenidae) react differently to the global change drivers climate and land-use. Biol. Conserv. 144, 2810–2818 (2011).Article 

    Google Scholar 
    8.Habel, J. C., Rödder, D., Schmitt, T. & Nève, G. Global warming will affect the genetic diversity and uniqueness of Lycaena helle populations. Glob. Change Biol. 17, 194–205 (2011).ADS 
    Article 

    Google Scholar 
    9.Grabherr, G., Gottfried, M. & Pauli, H. Climate change impacts in alpine environments. Geogr. Compass 4, 1133–1153 (2010).Article 

    Google Scholar 
    10.Alexander, J. M. et al. Lags in the response of mountain plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).11.Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).Article 

    Google Scholar 
    12.Fleishman, E. & Murphy, D. D. A realistic assessment of the indicator potential of butterflies and other charismatic taxonomic groups. Conserv. Biol. 23, 1109–1116 (2009).PubMed 
    Article 

    Google Scholar 
    13.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).Article 

    Google Scholar 
    14.Herrera, J. M., Ploquin, E. F., Rasmont, P. & Obeso, J. R. Climatic niche breadth determines the response of bumblebees (Bombus spp.) to climate warming in mountain areas of the Northern Iberian Peninsula. J. Insect Conserv. 22, 771–779 (2018).Article 

    Google Scholar 
    15.Habel, J. C. et al. Butterfly community shifts over two centuries. Conserv. Biol. 30, 754–762 (2016).PubMed 
    Article 

    Google Scholar 
    16.Descombes, P., Pradervand, J. N., Golay, J., Guisan, A. & Pellissier, L. Simulated shifts in trophic niche breadth modulate range loss of alpine butterflies under climate change. Ecography 39, 796–804 (2016).Article 

    Google Scholar 
    17.Kerr, J. T. Racing against change: Understanding dispersal and persistence to improve species’ conservation prospects. Proc. R. Soc. B 287, 20202061 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Dapporto, L., Cini, A., Voda, R., Dinca, V., Wiemers, M., Menchetti, M., Magini, G., Talavera, G., Shreeve, T., Bonelli, S., Casacci, L. P., Balletto, E., Scalercio, S. & Vila, R. Data from: Integrating three comprehensive datasets shows that mitochondrial DNA variation is linked to species traits and paleogeographic events in European butterflies. (Version 2, p. 4647103 bytes). Dryad (2019).19.Wiemers, M. et al. An updated checklist of the European butterflies (Lepidoptera, Papilionoidea). ZooKeys 811, 9–45 (2018).Article 

    Google Scholar 
    20.Wiemers, M., Chazot, N., Wheat, C., Schweiger, O. & Wahlberg, N. A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938, 97–124 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Middleton-Welling, J. et al. A new comprehensive trait database of European and Maghreb butterflies, Papilionoidea. Sci. Data 7, 351 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Weckström, K. et al. Impacts of climate warming on alpine lake biota over the past decade. Arct. Antarct. Alp. Res. 48, 361–376 (2016).Article 

    Google Scholar 
    23.Steinbauer, K., Lamprecht, A., Winkler, M., Bardy-Curchhalter, M., Kreiner, D., Suen, M. & Pauli, H. Shifting composition and functioning in alpine plant communities—Evidence of climate warming effects from 14 years biodiversity observation in the Northeastern Alps. In Conference Vol. 621–622 (2017).24.Bräu, M., Arbeitsgemeinschaft Bayerischer Entomologen & Bayerisches Landesamt für Umwelt (Eds.). Tagfalter in Bayern: 26 Tabellen. (Ulmer, 2013).25.Weidemann, H.-J. Tagfalter Vol. 1 (Neumann-Neudamm, 1986).
    Google Scholar 
    26.Weidemann, H.-J. Tagfalter: Biologie-Ökologie-Biotopschutz Vol. 2 (Neumann-Neudamm, 1988).
    Google Scholar 
    27.Konvicka, M., Maradova, M., Benes, J., Fric, Z. & Kepka, P. Uphill shifts in distribution of butterflies in the Czech Republic: Effects of changing climate detected on a regional scale. Glob. Ecol. Biogeogr. 12, 403–410 (2003).Article 

    Google Scholar 
    28.Wilson, R. J., Gutiérrez, D., Gutiérrez, J. & Monserrat, V. J. An elevational shift in butterfly species richness and composition accompanying recent climate change. Glob. Change Biol. 13, 1873–1887 (2007).ADS 
    Article 

    Google Scholar 
    29.Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change: Elevational shifts accompany climate change. Ecol. Lett. 8, 1138–1146 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl. Acad. Sci. 107, 2088–2092 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Hill, J. K. et al. Responses of butterflies to twentieth century climate warming: Implications for future ranges. Proc. R. Soc. Lond. Ser. B Biol. Sci. 269, 2163–2171 (2002).CAS 
    Article 

    Google Scholar 
    33.Essens, T., van Langevelde, F., Vos, R. A., Van Swaay, C. A. M. & WallisDeVries, M. F. Ecological determinants of butterfly vulnerability across the European continent. J. Insect Conserv. 21, 439–450 (2017).Article 

    Google Scholar 
    34.van Swaay, C., Warren, M. & Loïs, G. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209 (2006).Article 

    Google Scholar 
    35.Pyke, G. H., Thomson, J. D., Inouye, D. W. & Miller, T. J. Effects of climate change on phenologies and distributions of bumble bees and the plants they visit. Ecosphere 7, e01267 (2016).Article 

    Google Scholar 
    36.Biella, P. et al. Distribution patterns of the cold adapted bumblebee Bombus alpinus in the Alps and hints of an uphill shift (Insecta: Hymenoptera: Apidae). J. Insect Conserv. 21, 357–366 (2017).Article 

    Google Scholar 
    37.Parolo, G. & Rossi, G. Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl. Ecol. 9, 100–107 (2008).Article 

    Google Scholar 
    38.Filazzola, A., Matter, S. F. & Roland, J. Inclusion of trophic interactions increases the vulnerability of an alpine butterfly species to climate change. Glob. Change Biol. 26, 2867–2877 (2020).ADS 
    Article 

    Google Scholar 
    39.Schweiger, O. et al. Multiple stressors on biotic interactions: How climate change and alien species interact to affect pollination. Biol. Rev. 85, 777–795 (2010).PubMed 

    Google Scholar 
    40.Inouye, B. D., Ehrlén, J. & Underwood, N. Phenology as a process rather than an event: From individual reaction norms to community metrics. Ecol. Monogr. 89, e01352 (2019).Article 

    Google Scholar 
    41.Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 
    Article 

    Google Scholar 
    42.Dapporto, L. & Dennis, R. L. H. The generalist–specialist continuum: Testing predictions for distribution and trends in British butterflies. Biol. Conserv. 157, 229–236 (2013).Article 

    Google Scholar 
    43.Bartoňová, A., Benes, J. & Konvicka, M. Generalist–specialist continuum and life history traits of Central European butterflies (Lepidoptera)—Are we missing a part of the picture?. Eur. J. Entomol. 111, 543–553 (2014).Article 

    Google Scholar 
    44.Bartoňová, A. et al. Isolated Asian steppe element in the Balkans: Habitats of Proterebia afra (Lepidoptera: Nymphalidae: Satyrinae) and associated butterfly communities. J. Insect Conserv. 21, 559–571 (2017).Article 

    Google Scholar 
    45.Hodkinson, I. D. Terrestrial insects along elevation gradients: Species and community responses to altitude. Biol. Rev. 80, 489 (2005).PubMed 
    Article 

    Google Scholar 
    46.Roth, T., Plattner, M. & Amrhein, V. Plants, birds and butterflies: Short-term responses of species communities to climate warming vary by taxon and with altitude. PLoS ONE 9, e82490 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    47.Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Filz, K. J., Engler, J. O., Stoffels, J., Weitzel, M. & Schmitt, T. Missing the target? A critical view on butterfly conservation efforts on calcareous grasslands in south-western Germany. Biodivers. Conserv. 22, 2223–2241 (2013).Article 

    Google Scholar 
    49.Hiebl, J. & Frei, C. Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theor. Appl. Climatol. 124, 161–178 (2016).ADS 
    Article 

    Google Scholar 
    50.Hiebl, J. & Frei, C. Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Climatol. 132, 327–345 (2018).ADS 
    Article 

    Google Scholar 
    51.Bivand, R. & Yu, D. spgwr: Geographically Weighted Regression (R Package Version 0.6-34) [Computer Software]. https://CRAN.R-project.org/package=spgwr (2019).52.Hijmans, R. J. raster: Geographic Data Analysis and Modeling (R Package Version 3.3-13) [Computer Software]. https://CRAN.R-project.org/package=raster (2019).53.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling (R Package Version 1.1-4) [Computer Software]. https://CRAN.R-project.org/package=dismo (2017)54.Höttinger, H. & Pennerstorfer, J. Rote Liste der Tagschmetterlinge Österreichs (Lepidoptera: Papilionoidea & Hesperioidea). In Rote Listen gefährdeter Tiere Österreichs. Checklisten, Gefährdungsanalysen, Handlungsbedarf. Teil 1: Säugetiere, Vögel, Heuschrecken, Wasserkäfer, Netzflügler, Schnabelfliegen, Tagfalter. Grüne Reihe des Bundesministeriums für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft (Gesamtherausgeberin Ruth Wallner) Band 14/1 (ed. Zulka, K. P.) 313–354 (Böhlau, 2005).55.Blonder, B. & Harris, D. J. hypervolume: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls (R Package Version 2.0.12) [Computer Software]. https://CRAN.R-project.org/package=hypervolume (2019).56.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    57.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

    Google Scholar 
    58.Phillips, S. J., Dudík, M. & Schapire, R. E. Maxent Software for Modeling Species Niches and Distributions (Version 3.4.1) [Computer Software]. http://biodiversityinformatics.amnh.org/open_source/maxent/ (2017).59.Swets, J. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    60.Weiss, M. & Banko, G. Ecosystem Type Map v3.1—Terrestrial and Marine Ecosystems. ETC/BD report to the EEA (2018). More

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    The impact of large-scale afforestation on ecological environment in the Gobi region

    The gobi region ecosystem has low stability because of its single species composition and simple structure (Fig. 8a). Large-scale shrub planting destroyed the original stable state (Fig. 8b) and resulted in another stable state via self-adjustment. In this process, the planted shrubs deteriorated the original ecosystem by competing for water and a chain reaction may ensue, leading to greater ecological problems. The original intention of the large-scale planting of shrubs was to maintain regional ecological balance, protect biodiversity, and fix sand, thus improving the environment (Fig. 8c). However, given the poor choice of the planting location, the expected results were not achieved. In fact, the opposite results of the original good intentions were achieved (Fig. 8d).Figure 8Diagram of different development stages of large-scale afforestation in the gobi region (a: original ground surface; b: holes dug for afforestation; c the living trees planted; d: ground surface when the trees are dead).Full size imageChina has a large expanse of arid areas, and has suffered from droughts for a long time. Land afforestation has been at the forefront of China’s policy principles, and there are government departments specializing in this field. In recent years, the Chinese Government has recommended a series of major strategies, for example, the “construction of ecological civilization” and “lucid waters and lush mountains are invaluable assets”, and also promoted greening projects, including “Three North Shelterbelt Project”, “Beijing-Tianjin Sandstorm Source Control Project”, and the “Natural Forest Protection Project”. More recently, desert greening has been conducted by people and enterprises, for example, the Ant Forest and Society of Entrepreneurs & Ecology (SEE). As a result of these projects and initiatives, China’s greening has contributed to global greening totals15,16. For afforestation, China’s policy departments have recommended the principles of “sticking to local conditions, suitable land for green, suitable trees for trees, suitable shrub for shrub, suitable grass for grass” and promoting the overall protection of “Mountain-River-Forest-Farmland-Lake-Grass-Desert system”, with particular references to desert. Their goal is to scientifically promote afforestation of the land and to clarify “where to afforest, what to afforest, how to afforest, how to manage”. However, problems arise very easily when grassroots executors are involved.The total area of the gobi region in China is approximately 56.95 × 104 km2, accounting for 13.36% of the national area, and is primarily distributed in the northwest extreme arid regions17. As mentioned above, gobi refers to a special arid landform that has a notably low water supply and is unsuitable for growing trees and shrubs. As an important natural landform, the gobi plays a key role in ecological protection; hence, its reference as “black vegetation”. However, there is a lack of understanding of the gobi, and it is often regarded as an area that needs to be greened or reformed. However, gobi, as an extremely arid region, is a fragile ecosystem. Once the gravel on the gobi surface is destroyed, it could lead to a series of ecological and environmental problems. Therefore, afforestation in arid areas is both a scientific and technical issue which must be conducted according to different regional characteristics, rather than by blindly planting trees in unsuitable areas. This study aims to attract more attention from the government forestry department and implementation personnel involved in afforestation activities so as to revise relevant policies. In response to the findings of this study, we have several recommendations: (1) it is necessary to popularize the understanding of scientific greening within the general public; (2) scientific understanding of the gobi needs to be increased, and awareness must be raised to promote its protection; (3) afforestation projects and management must be scientifically and systematically improved to ensure long-term effectiveness, and; (4) restoration and protection measures should be taken immediately in the gobi regions that have been afforested or destroyed.One of the most important causes of all these problems is the implementation of national policies on subsidies for greening and planting trees in desert areas. According to our survey, personnel who specifically plant trees and engage in afforestation are businessmen, farmers, or others, with most of them being businessmen from abroad, and only a few being local people. All the personnel are more concerned about the subsidies than greening and planting trees itself. According to the policy, they will receive majority of the subsidy if the planted trees live for three years, irrespective of whether the trees survive after that. Therefore, to guarantee the survival of the planted trees for three years, they even use water tankers to carry water to the trees from a great distance. However, after three years, the people stop watering the trees planted in the Gobi region, thereby leading to the death of trees after a few years as they cannot survive only on natural precipitation and groundwater. In pursuit of maximum profits, these businessmen will pursue larger areas for planting trees, which will cause further damage to the ecological environment in the Gobi region. Based on the current situation, we propose the following suggestions: (1) Trees that are planted must be monitored over a long time period, which will greatly reduce the short-term profit motive of the people engaged in planting trees. (2) We must plan greening and planting trees according to local conditions, respecting the laws of nature. Not all areas should be greened; moreover, we should not plant trees, especially in the gobi region, where planting trees can possibly destroy the gobi ecological environment, which is a very fragile desert ecosystem. (4) Personnel responsible for the destruction of the gobi ecological environment by unscientific greening and planting of trees must be obligated to restore the surface conditions of the gobi to prevent the aggravation of wind erosion and desertification, which will increase their awareness of environmental protection and receive punishment for environmental damage. More

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    Year-round high abundances of the world’s smallest marine vertebrate (Schindleria) in the Red Sea and worldwide associations with lunar phases

    1.Giltay, L. Les larves de Schindler sont-elles des Hemirhamphidae?. Notes Ichthyol. Mus. Roy. d’Hist. Nat Belgique 10, 1–10 (1934).
    Google Scholar 
    2.Johnson, G. D. & Brothers, E. B. Schindleria: a paedomorphic goby (Teleostei: Gobioidei). Bull. Mar. Sci. 52, 441–471 (1993).
    Google Scholar 
    3.Kon, T. & Yoshino, T. Diversity and evolution of life histories of gobioid fishes from the viewpoint of heterochrony. Mar. Freshw. Res. 53, 377–402 (2002).Article 

    Google Scholar 
    4.Randall, J. E. & Cea, A. Shore fishes of Easter Island. (University of Hawaii Press, 2011).5.Kon, T., Yoshino, T., Mukai, T. & Nishida, M. DNA sequences identify numerous cryptic species of the vertebrate: a lesson from the gobioid fish Schindleria. Mol. Phylogenet. Evol. 44, 53–62 (2007).CAS 
    Article 

    Google Scholar 
    6.Robitzch, V., Schröder, M. & Ahnelt, H. Morphometrics reveal inter- and intraspecific sexual dimorphisms in two Hawaiian Schindleria, the long dorsal finned S. praematura and the short dorsal finned S. pietschmanni. Zool. Anz. 292, 197–206 (2021).Article 

    Google Scholar 
    7.Schindler, O. Ein neuer Hemirhamphus aus dem Pazifischen Ozean. Anzeiger der Akad. der Wissenschaften Wien 67, 79–80 (1930).
    Google Scholar 
    8.Schindler, O. Sexually mature larval Hemiramphidae from the Hawaiian Islands. Bull. Bernice P. Bish. Museum 1–28 (1932).9.Landaeta, M. F., Veas, R. & Castro, L. R. First record of the paedomorphic goby Schindleria praematura, Easter Island, South Pacific. J. Fish Biol. 61, 289–292 (2002).Article 

    Google Scholar 
    10.Watson, W. & Walker, H. J. J. The world’s smallest vertebrate, Schindleria brevipinguis, a new paedomorphic species in the family Schindleriidae (Perciformes: Gobioidei). Rec. Aust. Museum 56, 139–142 (2004).Article 

    Google Scholar 
    11.Kon, T., Yoshino, T. & Nishida, M. Cryptic species of the gobioid paedomorphic genus Schindleria from Palau, Western Pacific Ocean. Ichthyol. Res. https://doi.org/10.1007/s10228-010-0178-y (2010).Article 

    Google Scholar 
    12.Ahnelt, H. & Sauberer, M. Deep-water, offshore, and new records of Schindler’s fishes, Schindleria (Teleostei, Gobiidae), from the Indo-west Pacific collected during the Dana-Expedition, 1928–1930. Zootaxa 4731, 451–470 (2020).Article 

    Google Scholar 
    13.Bruun, A. F. A study of a collection of the fish Schindleria from South Pacific waters. Dana Rep. 21, 1–12 (1940).
    Google Scholar 
    14.Jones, S. & Kumaran, M. On the fishes of the genus Schindleria (Giltay) from the Indian Ocean. J. Mar. Biol. 6, 257–264 (1964).
    Google Scholar 
    15.Leis, J. M. Coral Sea atoll lagoons: closed nurseries for the larvae of a few coral reef fishes. Bull. Mar. Sci. 54, 206–227 (1994).ADS 

    Google Scholar 
    16.Belyanina, T. P. Ichthyoplankton in the regions of the Nazca and Salas y Gomez submarine ridges. J. Ichthyol. 29, 84–90 (1989).
    Google Scholar 
    17.Parin, N. V., Mironov, A. N. & Nesis, K. N. Biology of the Nazca and Salas y Gomez submarine ridges, an outpost of the Indo-West Pacific fauna in the Eastern Pacific Ocean: composition and distribution of the fauna, its communities and history. Adv. Mar. Biol. 32, 147–242 (1997).
    Google Scholar 
    18.Ahnelt, H. & Sauberer, M. A new species of Schindler’s fish (Teleostei: Gobiidae: Schindleria) from the Malay archipelago (Southeast Asia), with notes on the caudal fin complex of Schindleria. Zootaxa 4531, 95–108 (2018).Article 

    Google Scholar 
    19.Leis, J. M., Goldman, B. & Read, S. E. Epibenthic fish larvae in the Great Barrier Reef Lagoon near Lizard Island, Australia. Japanese J. Ichthyol. 35, 428–433 (1989).
    Google Scholar 
    20.Thacker, C. & Grier, H. Unusual gonad structure in the paedomorphic teleost Schindleria praematura (Teleostei Gobioidei): a comparison with other gobioid fishes. J. Fish Biol. 66, 378–391 (2005).Article 

    Google Scholar 
    21.Young, S.-S. & Chiu, T.-S. New records of a paedomorphic fish Schindleria praematura (Pisces: Schindleriidae), from Waters of Taiwan. Acta Zool. Taiwanica 11, 127–137 (2000).
    Google Scholar 
    22.Watson, W. & Leis, J. M. Ichthyoplankton of Kaneohe Bay, Hawaii. A one-year study of fish eggs and larvae. 1–178 (University of Hawaiʻi Sea Grant Program, 1974).23.Leis, J. M. & Trnski, T. The larvae of Indo-Pacific shorefishes. (New South Wales Univ. Press, Sydney & Univ. of Hawaii Press, 1989).24.Fricke, R. & Abu El-Regal, M. A. Schindleria nigropunctata, a new species of paedomorphic gobioid fish from the Red Sea (Teleostei: Schindleriidae). Mar. Biodivers. https://doi.org/10.1007/s12526-017-0831-z (2017).Article 

    Google Scholar 
    25.Fricke, R. & Abu El-Regal, M. A. Schindleria elongata, a new species of paedomorphic gobioid from the Red Sea (Teleostei: Schindleriidae). J Fish Biol 2, 1–8. https://doi.org/10.1111/jfb.13280 (2017).Article 

    Google Scholar 
    26.Abu El-Regal, M. A. & Kon, T. First record of the Schindler’s fish, Schindleria praematura (Actinopterygii: Perciformes: Schindleriidae), from the Red Sea. Acta Ichthyol. Piscat. 49, 75–78 (2019).Article 

    Google Scholar 
    27.EAbu El-Regal, M. & Kon, T. First record of the paedomorphic fish Schindleria (Gobioidei, Schindleriidae) from the Red Sea. J. Fish Biol. 72, 1539–1543 (2008).Article 

    Google Scholar 
    28.Ahnelt, H. Redescription of the paedomorphic goby Schindleria nigropunctata Fricke & El-Regal 2017 (Teleostei: Gobiidae) from the Red Sea. Zootaxa 4615, 450–456 (2019).Article 

    Google Scholar 
    29.Contreras, J. E., Landaeta, M. F., Plaza, G., Ojeda, F. P. & Bustos, C. A. The contrasting hatching patterns and larval growth of two sympatric clingfishes inferred by otolith microstructure analysis. Mar. Freshw. Res. 64, 157–167 (2013).Article 

    Google Scholar 
    30.Team, R. C. R: a language and environment for statistical computing (version 3.6). https://www.R-project.org (2020).31.Kleiber, C. & Zeileis, A. Applied econometrics with R. (Springer Science & Business Media, 2008).32.Kleiber, C. & Zeileis, A. AER: applied econometrics with R. R package version 1.1. (2009).33.Batschelet, E. Circular statistics in biology. (Academic Press, New York, 1981).34.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    35.Robitzch, V. & Berumen, M. L. Recruitment of coral reef fishes along a cross-shelf gradient in the Red Sea peaks outside the hottest season. Coral Reefs 39, 1565–1579 (2020).Article 

    Google Scholar 
    36.Whittle, A. G. Ecology, abundance, diversity, and distribution of larval fishes and Schindleriidae (Teleostei: Gobioidei) at two sites on O’ahu, Hawai’i. (University of Hawaiʻi, 2003).37.Depczynski, M. & Bellwood, D. R. Shortest recorded vertebrate lifespan found in a coral reef fish. Curr. Biol. 15, 10 (2005).Article 

    Google Scholar 
    38.Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE 12, 1–24 (2017).Article 

    Google Scholar 
    39.Depczynski, M. & Bellwood, D. R. Extremes, plasticity, and invariance in vertebrate life history traits: insights from coral reef fishes. Ecology 87, 3119–3127 (2006).Article 

    Google Scholar 
    40.Nanninga, G. B., Saenz-Agudelo, P., Zhan, P., Hoteit, I. & Berumen, M. L. Not finding Nemo: limited reef-scale retention in a coral reef fish. Coral Reefs 34, 383–392 (2015).ADS 
    Article 

    Google Scholar 
    41.Hernaman, V. & Munday, P. L. Life-history characteristics of coral reef gobies. I. Growth and life-span. Mar. Ecol. Prog. Ser. 290, 207–221 (2005).ADS 
    Article 

    Google Scholar 
    42.Lefèvre, C. D., Nash, K. L., González-Cabello, A. & Bellwood, D. R. Consequences of extreme life history traits on population persistence: do short-lived gobies face demographic bottlenecks?. Coral Reefs 35, 399–409 (2016).ADS 
    Article 

    Google Scholar  More

  • in

    Tipping point realized in cod fishery

    1.Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl. Acad. Sci. 118, e2008478118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).PubMed 
    Article 

    Google Scholar 
    3.Myers, R., Hutchings, J. & Barrowman, N. Hypotheses for the decline of cod in the North Atlantic. Mar. Ecol. Prog. Ser. 138, 293–308 (1996).ADS 
    Article 

    Google Scholar 
    4.Sguotti, C. et al. Catastrophic dynamics limit Atlantic cod recovery. Proc. R. Soc. B Biol. Sci. 286, 20182877 (2019).Article 

    Google Scholar 
    5.Levin, P. S. & Möllmann, C. Marine ecosystem regime shifts: Challenges and opportunities for ecosystem-based management. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130275 (2015).Article 

    Google Scholar 
    6.King, J. R., Mcfarlane, G. A. & Punt, A. E. Shifts in fisheries management: Adapting to regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130277 (2015).Article 

    Google Scholar 
    7.Döring, R., Berkenhagen, J., Hentsch, S. & Kraus, G. Small-Scale Fisheries in Germany: A Disappearing Profession? In Small-Scale Fisheries in Europe: Status, Resilience and Governance (eds. Pascual-Fernández, J. J., Pita, C. & Bavinck, M.) vol. 23 483–502 (Springer International Publishing, 2020).8.Papaioannou, E. A., Vafeidis, A. T., Quaas, M. F., Schmidt, J. O. & Strehlow, H. V. Using indicators based on primary fisheries’ data for assessing the development of the German Baltic small-scale fishery and reviewing its adaptation potential to changes in resource abundance and management during 2000–09. Ocean Coast. Manag. 98, 38–50 (2014).Article 

    Google Scholar 
    9.EU. Regulation (EU) 2016/1139 of the European Parliament and of the Council of 6 July 2016 establishing a multiannual plan for the stocks of cod, herring and sprat in the Baltic Sea and the fisheries exploiting those stocks, amending Council Regulation (EC) No 2187/2005 and repealing Council Regulation (EC) No 1098/2007. (2016).10.Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Lenton, T. M. Environmental tipping points. Annu. Rev. Environ. Resour. 38, 1–29 (2013).ADS 
    Article 

    Google Scholar 
    12.Möllmann, C., Folke, C., Edwards, M. & Conversi, A. Marine regime shifts around the globe: Theory, drivers and impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130260 (2015).Article 

    Google Scholar 
    13.ICES. Advice cod in subdivisions 22–24, western Baltic stock (western Baltic Sea). (2019) https://doi.org/10.17895/ICES.ADVICE.5587.14.Conversi, A. et al. A holistic view of marine regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130279 (2015).Article 

    Google Scholar 
    15.Ratajczak, Z. et al. Abrupt change in ecological systems: Inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).PubMed 
    Article 

    Google Scholar 
    16.Turner, M. G. et al. Climate change, ecosystems and abrupt change: Science priorities. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190105 (2020).Article 

    Google Scholar 
    17.Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).Article 

    Google Scholar 
    18.Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).Article 

    Google Scholar 
    19.Subbey, S., Devine, J. A., Schaarschmidt, U. & Nash, R. D. Modelling and forecasting stock–recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).Article 

    Google Scholar 
    20.Grasman, R. P. P. P., Maas, H. L. J. van der & Wagenmakers, E.-J. Fitting the Cusp Catastrophe in r : A cusp Package Primer. J. Stat. Softw. 32, 1-27 (2009).21.Thom, R. Structural Stability and Morphogenesis—An Outline of a General Theory of Models (Benjamin Inc, 1975).MATH 

    Google Scholar 
    22.Zeeman, E. Catastrophe theory. Sci. Am. 234, 65–83 (1976).Article 

    Google Scholar 
    23.Barunik, J. & Vosvrda, M. Can a stochastic cusp catastrophe model explain stock market crashes?. J. Econ. Dyn. Control 33, 1824–1836 (2009).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    24.Xiaoping, Z., Jiahui, S. & Yuan, C. Analysis of crowd jam in public buildings based on cusp-catastrophe theory. Build. Environ. 45, 1755–1761 (2010).Article 

    Google Scholar 
    25.Guastello, S. J., Boeh, H., Shumaker, C. & Schimmels, M. Catastrophe models for cognitive workload and fatigue. Theor. Issues Ergon. Sci. 13, 586–602 (2012).Article 

    Google Scholar 
    26.Angelis, V., Angelis-Dimakis, A. & Dimaki, K. The Cusp Catastrophe model in describing a bank’s attractiveness as measured by its image. Proc. Econ. Finance 19, 261–277 (2015).Article 

    Google Scholar 
    27.Sideridis, G. D., Simos, P., Mouzaki, A. & Stamovlasis, D. Efficient word reading: Automaticity of print-related skills indexed by rapid automatized naming through cusp-catastrophe modeling. Sci. Stud. Read. 20, 6–19 (2016).Article 

    Google Scholar 
    28.Diks, C. & Wang, J. Can a stochastic cusp catastrophe model explain housing market crashes?. J. Econ. Dyn. Control 69, 68–88 (2016).Article 

    Google Scholar 
    29.Xu, Y. & Chen, X. Protection motivation theory and cigarette smoking among vocational high school students in China: A cusp catastrophe modeling analysis. Glob. Health Res. Policy 1, 3 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Chen, D.-G., Lin, F., Chen, X., Tang, W. & Kitzman, H. Cusp Catastrophe Model: A nonlinear model for health outcomes in nursing research. Nurs. Res. 63, 211–220 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Mostafa, M. M. Catastrophe theory predicts international concern for global warming. J. Quant. Econ. https://doi.org/10.1007/s40953-020-00199-8 (2020).Article 

    Google Scholar 
    32.Sguotti, C. et al. Non-linearity in stock–recruitment relationships of Atlantic cod: Insights from a multi-model approach. ICES J. Mar. Sci. 77, 1492–1502 (2020).Article 

    Google Scholar 
    33.Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).ADS 
    Article 

    Google Scholar 
    34.Gröger, M., Arneborg, L., Dieterich, C., Höglund, A. & Meier, H. E. M. Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model. Clim. Dyn. 53, 5945–5966 (2019).Article 

    Google Scholar 
    35.Litzow, M. A., Mueter, F. J. & Hobday, A. J. Reassessing regime shifts in the North Pacific: Incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability. Glob. Change Biol. 20, 38–50 (2014).ADS 
    Article 

    Google Scholar 
    36.Auber, A., Travers-Trolet, M., Villanueva, M. C. & Ernande, B. Regime shift in an exploited fish community related to natural climate oscillations. PLoS One 10, e0129883 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Karnauskas, M. et al. Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico. Glob. Change Biol. 21, 2554–2568 (2015).ADS 
    Article 

    Google Scholar 
    38.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Kotta, J. et al. Novel crab predator causes marine ecosystem regime shift. Sci. Rep. 8, 4956 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Vert-pre, K. A., Amoroso, R. O., Jensen, O. P. & Hilborn, R. Frequency and intensity of productivity regime shifts in marine fish stocks. Proc. Natl. Acad. Sci. 110, 1779–1784 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Perretti, C. et al. Regime shifts in fish recruitment on the Northeast US Continental Shelf. Mar. Ecol. Prog. Ser. 574, 1–11 (2017).ADS 
    Article 

    Google Scholar 
    42.Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B. & Puerta, P. Nonstationary effects of ocean temperature on Pacific salmon productivity. Can. J. Fish. Aquat. Sci. 76, 1923–1928 (2019).Article 

    Google Scholar 
    43.van der Maas, H. L. J., Kolstein, R. & van der Pligt, J. Sudden transitions in attitudes. Sociol. Methods Res. 32, 125–152 (2003).MathSciNet 
    Article 

    Google Scholar 
    44.Griffith, G. P. Closing the gap between causality, prediction, emergence, and applied marine management. ICES J. Mar. Sci. 77, 1456–1462 (2020).Article 

    Google Scholar 
    45.Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    46.Hilborn, R., Hively, D. J., Jensen, O. P. & Branch, T. A. The dynamics of fish populations at low abundance and prospects for rebuilding and recovery. ICES J. Mar. Sci. 71, 2141–2151 (2014).Article 

    Google Scholar 
    47.Köster, F. Trophodynamic control by clupeid predators on recruitment success in Baltic cod?. ICES J. Mar. Sci. 57, 310–323 (2000).Article 

    Google Scholar 
    48.Rowe, S., Hutchings, J. A., Bekkevold, D. & Rakitin, A. Depensation, probability of fertilization, and the mating system of Atlantic cod (Gadus morhua L.). ICES J. Mar. Sci. 61, 1144–1150 (2004).Article 

    Google Scholar 
    49.Keith, D. M. & Hutchings, J. A. Population dynamics of marine fishes at low abundance. Can. J. Fish. Aquat. Sci. 69, 1150–1163 (2012).Article 

    Google Scholar 
    50.Kuparinen, A., Keith, D. M. & Hutchings, J. A. Allee effect and the uncertainty of population recovery: Allee effect and population recovery. Conserv. Biol. 28, 790–798 (2014).PubMed 
    Article 

    Google Scholar 
    51.Neuenhoff, R. D. et al. Continued decline of a collapsed population of Atlantic cod (Gadus morhua) due to predation-driven Allee effects. Can. J. Fish. Aquat. Sci. 76, 168–184 (2019).Article 

    Google Scholar 
    52.Vergnon, R., Shin, Y.-J. & Cury, P. Cultivation, Allee effect and resilience of large demersal fish populations. Aquat. Living Resour. 21, 287–295 (2008).Article 

    Google Scholar 
    53.Saha, B., Bhowmick, A. R., Chattopadhyay, J. & Bhattacharya, S. On the evidence of an Allee effect in herring populations and consequences for population survival: A model-based study. Ecol. Model. 250, 72–80 (2013).Article 

    Google Scholar 
    54.Perälä, T. & Kuparinen, A. Detection of Allee effects in marine fishes: Analytical biases generated by data availability and model selection. Proc. R. Soc. B Biol. Sci. 284, 20171284 (2017).Article 

    Google Scholar 
    55.Lundquist, C. J. & Botsford, L. W. Estimating larval production of a broadcast spawner: The influence of density, aggregation, and the fertilization Allee effect. Can. J. Fish. Aquat. Sci. 68, 30–42 (2011).Article 

    Google Scholar 
    56.Sæther, B.-E., Engen, S., Lande, R. & Saether, B.-E. Density-dependence and optimal harvesting of fluctuating populations. Oikos 76, 40 (1996).MATH 
    Article 

    Google Scholar 
    57.Rowe, S. & Hutchings, J. A. Mating systems and the conservation of commercially exploited marine fish. Trends Ecol. Evol. 18, 567–572 (2003).Article 

    Google Scholar 
    58.Swain, D. P. & Chouinard, G. A. Predicted extirpation of the dominant demersal fish in a large marine ecosystem: Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 65, 2315–2319 (2008).Article 

    Google Scholar 
    59.Kuparinen, A. & Hutchings, J. A. Increased natural mortality at low abundance can generate an Allee effect in a marine fish. R. Soc. Open Sci. 1, 140075 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Swain, D. & Benoît, H. Extreme increases in natural mortality prevent recovery of collapsed fish populations in a Northwest Atlantic ecosystem. Mar. Ecol. Prog. Ser. 519, 165–182 (2015).ADS 
    Article 

    Google Scholar 
    61.Walters, C. & Kitchell, J. F. Cultivation/depensation effects on juvenile survival and recruitment: Implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58, 39–50 (2001).Article 

    Google Scholar 
    62.Andreasen, H. et al. Diet composition and food consumption rate of harbor porpoises (Phocoena phocoena) in the western Baltic Sea. Mar. Mamm. Sci. 33, 1053–1079 (2017).Article 

    Google Scholar 
    63.Hüssy, K. Review of western Baltic cod (Gadus morhua) recruitment dynamics. ICES J. Mar. Sci. 68, 1459–1471 (2011).Article 

    Google Scholar 
    64.Winter, A., Richter, A. & Eikeset, A. M. Implications of Allee effects for fisheries management in a changing climate: Evidence from Atlantic cod. Ecol. Appl. 30, 1–14 (2020).65.Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

    Google Scholar 
    66.Szuwalski, C. S., Vert-Pre, K. A., Punt, A. E., Branch, T. A. & Hilborn, R. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish Fish. 16, 633–648 (2015).Article 

    Google Scholar 
    67.Funk, S., Krumme, U., Temming, A. & Möllmann, C. Gillnet fishers’ knowledge reveals seasonality in depth and habitat use of cod (Gadus morhua) in the Western Baltic Sea. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa071 (2020).Article 

    Google Scholar 
    68.Hüssy, K., Hinrichsen, H.-H. & Huwer, B. Hydrographic influence on the spawning habitat suitability of western Baltic cod (Gadus morhua). ICES J. Mar. Sci. 69, 1736–1743 (2012).Article 

    Google Scholar 
    69.Hinrichsen, H.-H., Hüssy, K. & Huwer, B. Spatio-temporal variability in western Baltic cod early life stage survival mediated by egg buoyancy, hydrography and hydrodynamics. ICES J. Mar. Sci. 69, 1744–1752 (2012).Article 

    Google Scholar 
    70.Petereit, C., Hinrichsen, H.-H., Franke, A. & Köster, F. Floating along buoyancy levels: Dispersal and survival of western Baltic fish eggs. Prog. Oceanogr. 122, 131–152 (2014).ADS 
    Article 

    Google Scholar 
    71.Stiasny, M. H. et al. Ocean acidification effects on Atlantic Cod larval survival and recruitment to the fished population. PLoS One 11, e0155448 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    72.Voss, R. et al. Ecological-economic sustainability of the Baltic cod fisheries under ocean warming and acidification. J. Environ. Manag. 238, 110–118 (2019).Article 

    Google Scholar 
    73.Lindegren, M., Möllmann, C., Nielsen, A. & Stenseth, N. C. Preventing the collapse of the Baltic cod stock through an ecosystem-based management approach. Proc. Natl. Acad. Sci. 106, 14722–14727 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Lindegren, M. et al. Ecological forecasting under climate change: The case of Baltic cod. Proc. R. Soc. B Biol. Sci. 277, 2121–2130 (2010).Article 

    Google Scholar 
    75.Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Levin, P. S. et al. Building effective fishery ecosystem plans. Mar. Policy 92, 48–57 (2018).Article 

    Google Scholar 
    77.Dawson, C. & Levin, P. S. Moving the ecosystem-based fisheries management mountain begins by shifting small stones: A critical analysis of EBFM on the U.S. West Coast. Mar. Policy 100, 58–65 (2019).Article 

    Google Scholar 
    78.Link, J. S. & Marshak, A. R. Characterizing and comparing marine fisheries ecosystems in the United States: Determinants of success in moving toward ecosystem-based fisheries management. Rev. Fish Biol. Fish. 29, 23–70 (2019).Article 

    Google Scholar 
    79.Townsend, H. et al. Progress on implementing ecosystem-based fisheries management in the United States through the use of ecosystem models and analysis. Front. Mar. Sci. 6, 641 (2019).Article 

    Google Scholar 
    80.Koehn, L. E. et al. Case studies demonstrate capacity for a structured planning process for ecosystem-based fisheries management. Can. J. Fish. Aquat. Sci. 77, 1256–1274 (2020).Article 

    Google Scholar 
    81.Skern-Mauritzen, M. et al. Ecosystem processes are rarely included in tactical fisheries management. Fish Fish. 17, 165–175 (2016).Article 

    Google Scholar 
    82.Marshall, K. N., Koehn, L. E., Levin, P. S., Essington, T. E. & Jensen, O. P. Inclusion of ecosystem information in US fish stock assessments suggests progress toward ecosystem-based fisheries management. ICES J. Mar. Sci. 76, 1–9 (2019).Article 

    Google Scholar 
    83.Otto, S. A., Kadin, M., Casini, M., Torres, M. A. & Blenckner, T. A quantitative framework for selecting and validating food web indicators. Ecol. Ind. 84, 619–631 (2018).Article 

    Google Scholar 
    84.Kadin, M. et al. Trophic interactions, management trade-offs and climate change: The need for adaptive thresholds to operationalize ecosystem indicators. Front. Mar. Sci. 6, 249 (2019).ADS 
    Article 

    Google Scholar 
    85.Samhouri, J. F. et al. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8, 1–21 (2017).86.Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4, 289 (2017).Article 

    Google Scholar 
    87.Tommasi, D. et al. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).ADS 
    Article 

    Google Scholar 
    88.Haltuch, M. et al. Unraveling the recruitment problem: A review of environmentally-informed forecasting and management strategy evaluation. Fish. Res. 217, 198–216 (2019).Article 

    Google Scholar 
    89.Hobday, A. J. et al. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Front. Mar. Sci. 5, 137 (2018).Article 

    Google Scholar 
    90.Hobday, A. J. et al. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsy210 (2019).Article 

    Google Scholar 
    91.Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. A. & Haddon, M. Management strategy evaluation: Best practices. Fish Fish. 17, 303–334 (2016).Article 

    Google Scholar 
    92.Grüss, A. et al. Recommendations on the use of ecosystem modeling for informing ecosystem-based fisheries management and restoration outcomes in the Gulf of Mexico. Mar. Coast. Fish. 9, 281–295 (2017).Article 

    Google Scholar 
    93.Hollowed, A. B. et al. Integrated modeling to evaluate climate change impacts on coupled social-ecological systems in Alaska. Front. Mar. Sci. 6, 775 (2020).Article 

    Google Scholar 
    94.Okamoto, D. K. et al. Attending to spatial social–ecological sensitivities to improve trade-off analysis in natural resource management. Fish Fish. 21, 1–12 (2020).Article 

    Google Scholar 
    95.Möllmann, C. et al. Implementing ecosystem-based fisheries management: From single-species to integrated ecosystem assessment and advice for Baltic Sea fish stocks. ICES J. Mar. Sci. 71, 1187–1197 (2014).Article 

    Google Scholar 
    96.Voss, R. et al. Assessing social—ecological trade-offs to advance ecosystem-based fisheries management. PLoS One 9, e107811 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    97.Schmidt, J. O. et al. Future ocean observations to connect climate, fisheries and marine ecosystems. Front. Mar. Sci. 6, 550 (2019).Article 

    Google Scholar 
    98.Hicks, C. C. et al. Engage key social concepts for sustainability. Science 352, 38–40 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Hornborg, S. et al. Ecosystem-based fisheries management requires broader performance indicators for the human dimension. Mar. Policy 108, 103639 (2019).Article 

    Google Scholar 
    100.Levin, P. S. et al. Conceptualization of social-ecological systems of the california current: An examination of interdisciplinary science supporting ecosystem-based management. Coast. Manag. 44, 397–408 (2016).Article 

    Google Scholar 
    101.ICES. Herring (Clupea harengus) in subdivisions 20-24, spring spawners (Skagerrak, Kattegat, and western Baltic). https://doi.org/10.17895/ICES.ADVICE.4715 (2019).102.Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

    Google Scholar 
    103.Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquac. 26, 400–415 (2018).Article 

    Google Scholar 
    104.Holsman, K. K. et al. Towards climate resiliency in fisheries management. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsz031 (2019).Article 

    Google Scholar 
    105.Bell, R. J., Odell, J., Kirchner, G. & Lomonico, S. Actions to promote and achieve climate-ready fisheries: Summary of current practice. Mar. Coast. Fish. 12, 166–190 (2020).Article 

    Google Scholar 
    106.Gaichas, S. K., Link, J. S. & Hare, J. A. A risk-based approach to evaluating northeast US fish community vulnerability to climate change. ICES J. Mar. Sci. 71, 2323–2342 (2014).Article 

    Google Scholar 
    107.Pecl, G. T. et al. Rapid assessment of fisheries species sensitivity to climate change. Clim. Change 127, 505–520 (2014).ADS 
    Article 

    Google Scholar 
    108.Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the Northeast U.S. Continental Shelf. PLoS One 11, e0146756 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    109.Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

    Google Scholar 
    110.Whitney, C. K. et al. Adaptive capacity: From assessment to action in coastal social-ecological systems. Ecol. Soc. 22, art22 (2017).Article 

    Google Scholar 
    111.Johnson, F. A., Eaton, M. J., Mikels-Carrasco, J. & Case, D. Building adaptive capacity in a coastal region experiencing global change. Ecol. Soc. 25, art9 (2020).Article 

    Google Scholar 
    112.ICES. Baltic Fisheries Assessemant Working Group. (2019). https://doi.org/10.17895/ICES.PUB.5949.113.ICES. Baltic Fisheries Assessemant Working Group. ICES CM 2014/ACOM:10 (2014).114.Hüssy, K. et al. Spatio-temporal trends in stock mixing of eastern and western Baltic cod in the Arkona Basin and the implications for recruitment. ICES J. Mar. Sci. J. Conseil 73, 293–303 (2016).Article 

    Google Scholar 
    115.Weist, P. et al. Assessing SNP-markers to study population mixing and ecological adaptation in Baltic cod. PLoS One 14, e0218127 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.R Core Team. R: A Language and Environment for Statistical Computing. (Accessed 2 July 2021); https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).117.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
    Article 

    Google Scholar 
    118.Killick, R. & Eckley, I. A. Changepoint: An R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).119.Zeileis, A., Kleiber, C., Krämer, W. & Hornik, K. Testing and dating of structural changes in practice. Comput. Stat. Data Anal. 44, 109–123 (2003).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    120.Otto, S. A. Comparison of change point detection methods. (Accessed 2 July 2021); https://www.marinedatascience.co/blog/2019/09/28/comparison-of-change-point-detection-methods/. (2019). More