More stories

  • in

    The ecology and epidemiology of malaria parasitism in wild chimpanzee reservoirs

    Liu, W. et al. African origin of the malaria parasite Plasmodium vivax. Nat. Commun. 5, 3346 (2014).PubMed 

    Google Scholar 
    Liu, W. et al. Multigenomic delineation of Plasmodium species of the Laverania subgenus infecting wild-living chimpanzees and gorillas. Genome Biol. Evolution 8, 1929–1939 (2016).CAS 

    Google Scholar 
    Liu, W. et al. Single genome amplification and direct amplicon sequencing of Plasmodium spp. DNA from ape fecal specimens. Protocol Exchange 1–14 (2010).Liu, W. et al. Wild bonobos host geographically restricted malaria parasites including a putative new Laverania species. Nat. Commun. 8, 1635 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Prugnolle, F. et al. African great apes are natural hosts of multiple related malaria species, including Plasmodium falciparum. Proc. Natl Acad. Sci. USA 107, 1458–1463 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sharp, P. M., Plenderleith, L. J. & Hahn, B. H. Ape origins of human malaria. Annu. Rev. Microbiol. 74, 39–63 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, W. et al. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature 467, 420–425 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Otto, T. D. et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat. Microbiol. 3, 687–697 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boundenga, L. et al. Diversity of malaria parasites in great apes in Gabon. Malar. J. 14, 1–8 (2015).CAS 

    Google Scholar 
    Délicat-Loembet, L. et al. No evidence for ape Plasmodium infections in humans in gabon. Plos One 10, e0126933 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Sundararaman, S. A. et al. Plasmodium falciparum-like parasites infecting wild apes in southern Cameroon do not represent a recurrent source of human malaria. Proc. Natl Acad. Sci. USA 110, 7020–7025 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Junker, J. et al. Recent decline in suitable environmental conditions for African great apes. Diversity Distrib. 18, 1077–1091 (2012).
    Google Scholar 
    de Nys, H. M. et al. Age-related effects on malaria parasite infection in wild chimpanzees. Biol. Lett. 9, 20121160 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    de Nys, H. M. et al. Malaria parasite detection increases during pregnancy in wild chimpanzees. Malar. J. 13, 413 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Kaiser, M. et al. Wild chimpanzees infected with 5 Plasmodium species. Emerg. Infect. Dis. 16, 1956–1959 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Paupy, C. et al. Anopheles moucheti and Anopheles vinckei are candidate vectors of ape Plasmodium parasites, including Plasmodium praefalciparum in Gabon. PLoS ONE 8, e57294 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl Acad. Sci. USA 113, 5329–5334 (2016).Loy, D. E. et al. Investigating zoonotic infection barriers to ape Plasmodium parasites using faecal DNA analysis. Int. J. Parasitol. 48, 531–542 (2018).Martin, M., Rayner, J., Gagneux, P., Barnwell, J. & Varki, A. Evolution of human–chimpanzee differences in malaria susceptibility: Relationship to human genetic loss of N-glycolylneuraminic acid. Proc. Natl Acad. Sci. USA 102, 12819–12824 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scully, E. J., Kanjee, U. & Duraisingh, M. T. Molecular interactions governing host-specificity of blood stage malaria parasites. Curr. Opin. Microbiol. 40, 21–31 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sundararaman, S. A. et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat. Commun. 7, 11078 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wanaguru, M., Liu, W., Hahn, B. H., Rayner, J. C. & Wright, G. J. RH5-Basigin interaction plays a major role in the host tropism of Plasmodium falciparum. Proc. Natl Acad. Sci. USA 110, 20735–20740 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ngoubangoye, B. et al. The host specificity of ape malaria parasites can be broken in confined environments. Int. J. Parasitol. 46, 737–744 (2016).PubMed 

    Google Scholar 
    Mapua, M. I. et al. A comparative molecular survey of malaria prevalence among Eastern chimpanzee populations in Issa Valley (Tanzania) and Kalinzu (Uganda). Malar. J. 15, 423 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Wu, D. F. et al. Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees. Malar. J. 17, 1–5 (2018).CAS 

    Google Scholar 
    Craig, M., le Sueur, D. & Snow, B. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol. Today 15, 105–111 (1999).CAS 
    PubMed 

    Google Scholar 
    Mordecai, E. A. et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 16, 22–30 (2013).PubMed 

    Google Scholar 
    Paaijmans, K. P. et al. Influence of climate on malaria transmission depends on daily temperature variation. Proc. Natl Acad. Sci. USA 107, 15135–15139 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parham, P. E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, 620–626 (2010).PubMed 

    Google Scholar 
    LaPointe, D. A., Goff, M. L. & Atkinson, C. T. Thermal constraints to the sporogonic development and altitudinal distribution of avian malaria Plasmodium relictum in Hawai’i. J. Parasitol. 96, 318–324 (2010).PubMed 

    Google Scholar 
    Vanderberg, J. P. & Yoeli, M. Effects of temperature on sporogonic development of Plasmodium berghei. J. Parasitol. 52, 559–564 (1966).Macdonald, G. The Epidemiology and Control of Malaria (Oxford University Press, 1957).Ryan, S. J. et al. Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne Zoonotic Dis. 15, 718–725 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Gemperli, A. et al. Mapping malaria transmission in West and Central Africa. Tropical Med. Int. Health 11, 1032–1046 (2006).
    Google Scholar 
    Gething, P. W. et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasites Vectors 4, 92 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Weiss, D. J. et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000–2012: a high-resolution spatiotemporal prediction. Malar. J. 13, 171 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Lyons, C. L., Coetzee, M. & Chown, S. L. Stable and fluctuating temperature effects on the development rate and survival of two malaria vectors, Anopheles arabiensis and Anopheles funestus. Parasites Vectors 6, 104 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Paaijmans, K. P., Wandago, M. O., Githeko, A. K. & Takken, W. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One 2, e1146 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Faust, C. & Dobson, A. P. Primate malarias: diversity, distribution and insights for zoonotic Plasmodium. One Health 1, 66–75 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Tucker Lima, J. M., Vittor, A., Rifai, S. & Valle, D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 372, 20160125 (2017).
    Google Scholar 
    Borner, J. et al. Phylogeny of haemosporidian blood parasites revealed by a multi-gene approach. Mol. Phylogenetics Evolution 94, 221–231 (2016).CAS 

    Google Scholar 
    Emery Thompson, M., Muller, M. N., Machanda, Z. P., Otali, E. & Wrangham, R. W. The Kibale Chimpanzee Project: over thirty years of research, conservation, and change. Biol. Conserv. 252, 108857 (2020).
    Google Scholar 
    Langergraber, K. E., Mitani, J. C. & Vigilant, L. The limited impact of kinship on cooperation in wild chimpanzees. Proc. Natl Acad. Sci. USA 104, 7786–7790 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Arandjelovic, M. et al. Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol. Ecol. Resour. 9, 28–36 (2009).CAS 
    PubMed 

    Google Scholar 
    Herbert, A. et al. Malaria-like symptoms associated with a natural Plasmodium reichenowi infection in a chimpanzee. Malar. J. 14, 220 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Torres, J. R. Therapy of Infectious Diseases 597–613 (2003).Trampuz, A., Jereb, M., Muzlovic, I. & Prabhu, R. M. Clinical review: severe malaria. Crit. Care 7, 315 (2003).PubMed 
    PubMed Central 

    Google Scholar 
    Akim, N. I. et al. Dynamics of P. falciparum gametocytemia in symptomatic patients in an area of intense perennial transmission in Tanzania. Am. J. Tropical Med. Hyg. 63, 199–203 (2000).CAS 

    Google Scholar 
    Mackinnon, M. J. & Read, A. F. Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi. Evolution 53, 689–703 (1999).PubMed 

    Google Scholar 
    Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
    PubMed 

    Google Scholar 
    Prugnolle, F. et al. African monkeys are infected by Plasmodium falciparum nonhuman primate-specific strains. Proc. Natl Acad. Sci. USA 108, 11948–11953 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ayouba, A. et al. Ubiquitous Hepatocystis infections, but no evidence of Plasmodium falciparum-like malaria parasites in wild greater spot-nosed monkeys (Cercopithecus nictitans). Int. J. Parasitol. 42, 709–713 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogenetics Evolution 47, 261–273 (2008).CAS 

    Google Scholar 
    Thurber, M. I. et al. Co-infection and cross-species transmission of divergent Hepatocystis lineages in a wild African primate community. Int. J. Parasitol. 43, 613–619 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics (Cambridge University Press, 2008).Stanisic, D. I. et al. Acquisition of antibodies against Plasmodium falciparum merozoites and malaria immunity in young children and the influence of age, force of infection, and magnitude of response. Infect. Immun. 83, 646–660 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Taylor, R. R., Allen, S. J., Greenwood, B. M. & Riley, E. M. IgG3 antibodies to Plasmodium falciparum merozoite surface protein 2 (MSP2): increasing prevalence with age and association with clinical immunity to malaria. Am. J. Tropical Med. Hyg. 58, 406–413 (1998).CAS 

    Google Scholar 
    World Malaria Report (World Health Organization, 2015).Shaman, J. Letter to the Editor: Caution needed when using gridded meteorological data products for analyses in Africa. Eur. Surveill. 19, 20930 (2014).
    Google Scholar 
    Tatem, A. J., Goetz, S. J. & Hay, S. I. Terra and Aqua: new data for epidemiology and public health. Int. J. Appl. Earth Observation Geoinf. 6, 33–46 (2004).
    Google Scholar 
    Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
    PubMed 

    Google Scholar 
    Carter, R. & Mendis, K. N. Evolutionary and historical aspects of the burden of malaria. Clin. Microbiol. Rev. 15, 564–594 (2002).PubMed 
    PubMed Central 

    Google Scholar 
    Kwiatkowski, D. P. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77, 171–192 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tarello, W. A fatal Plasmodium reichenowi infection in a chimpanzee? Rev. de. Med. Veterinaire 156, 503–505 (2005).
    Google Scholar 
    Taylor, D. W. et al. Parasitologic and immunologic studies of experimental Plasmodium falciparum infection in nonsplenectomized chimpanzees (Pan troglodytes). Am. J. Tropical Med. Hyg. 34, 36–44 (1985).CAS 

    Google Scholar 
    Krief, S., Martin, M., Grellier, P., Kasenene, J. & Sevenet, T. Novel antimalarial compounds isolated in a survey of self-medicative behavior of wild chimpanzees in Uganda. Antimicrobial Agents Chemother. 48, 3196–3199 (2004).CAS 

    Google Scholar 
    Cox-Singh, J. et al. Plasmodium knowlesi malaria in humans is widely distributed and potentially life threatening. Clin. Infect. Dis. 46, 165–171 (2008).CAS 
    PubMed 

    Google Scholar 
    Singh, B. & Daneshvar, C. Human infections and detection of Plasmodium knowlesi. Clin. Microbiol. Rev. 26, 165–184 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brasil, P. et al. Outbreak of human malaria caused by Plasmodium simium in the Atlantic Forest in Rio de Janeiro: a molecular epidemiological investigation. Lancet Global Health 5, e1038–e1046 (2017).Krief, S. et al. On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from bonobos. PLoS Pathog. 6, e1000765 (2010).Pacheco, M. A., Cranfield, M., Cameron, K. & Escalante, A. A. Malarial parasite diversity in chimpanzees: the value of comparative approaches to ascertain the evolution of Plasmodium falciparum antigens. Malar. J. 12, 328 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Etienne, L. et al. Noninvasive follow-up of simian immunodeficiency virus infection in wild-living nonhabituated western lowland gorillas in Cameroon. J. Virol. 86, 9760–9772 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keele, B. F. et al. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science 313, 523–526 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keele, B. F. et al. Increased mortality and AIDS-like immunopathology in wild chimpanzees infected with SIVcpz. Nature 460, 515–519 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, Y. et al. Eastern chimpanzees, but not bonobos, represent a simian immunodeficiency virus reservoir. J. Virol. 86, 10776–10791 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Neel, C. et al. Molecular epidemiology of simian immunodeficiency virus infection in wild-living gorillas. J. Virol. 84, 1464–1476 (2010).CAS 
    PubMed 

    Google Scholar 
    Rudicell, R. S. et al. Impact of simian immunodeficiency virus infection on chimpanzee population dynamics. PLoS Pathog. 6, 1–17 (2010).
    Google Scholar 
    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bates, D. & Maechler, M. Lme4: linear mixed-effects models using s4 classes. Cran R Project Website (2010). More

  • in

    From the archive: ancient poisonous honey, and museum photography

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    The relationships between growth rate and mitochondrial metabolism varies over time

    The experiments were approved by the French Ethics Committee in charge of Animal Experimentation (no.2019072411491441) and were in accordance with institutional and ARRIVE guidelines.Animal collection and husbandryIn May 2019, juvenile European sea bass, Dicentrarchus labrax (Linnaeus 1758) (6 months old, mass 5 g), were transferred from a fish farm (Turbot Ichtus, TrĂ©darzec, France) to the Ifremer rearing facility (PlouzanĂ©, France). Fish were kept in a common tank for 5 months, maintained under a 12 L: 12 D photoperiod, and fed at satiety three times a week using commercial pellets (Neo Start, Le Gouessant, Lamballe, France).In October 2019, fish (n = 40) were anaesthetized (TricaĂŻne; 125 mg L−1), weighed (41.5 ± 1.8 g, MCE11201S-2S00-0, Sartorius, Göttingen, Germany), and implanted subcutaneously with an identification tag (RFID; Biolog-id, Bernay, France). The fish were then randomly allocated to ten replicate 400 L tanks supplied with open-flow, fully aerated seawater (oxygen saturation  > 95%, salinity 32 ppt), thermo-regulated during winter to avoid falling below 13 °C, and fed at satiety three times a week. Temperature was recorded weekly. To account for the potential effect of temperature variation over the duration of the trial (15.5 ± 0.5 °C, range: 13.1–17.9 °C) on growth, a correlations analysis was performed between temperature and specific growth rate (SGR). No statistical relationship was found between SGR and temperature (Spearman R2 = 0.060, P = 0.596). Additional fish (n = 40) were present in the tanks (final density: n = 8 per tank) for the need of another project.Growth measurementsBody mass (BM) was measured about every four weeks from October 2019 to June 2020. The fish were fasted for 48 h and anesthetized before each BM measurement (± 0.1 g). The specific growth rate (% day-1) was estimated as follows:$${text{Specific~Growth~Rate}} = ~frac{{ln left( {final~BM} right) – ln left( {initial~BM} right)}}{{{text{days~elapsed}}}} times 100$$In March 2020, a red muscle biopsy sample was collected from fish to measure the mitochondrial metabolic traits. Past growth was defined as specific growth rates before the analysis of mitochondrial metabolic traits (past specific growth rate, SGRpast). SGRpast were calculated using the BM at the muscle biopsy as the final BM and the BM at 7, 11, 16, and 20 weeks before the muscle biopsy as the initial BM (Fig. 1). Future growth was defined as specific growth rates after analysis of mitochondrial metabolic traits (future specific growth rates, SGRfuture). SGRfuture were calculated using the BM at 4, 8, and 12 weeks after the muscle biopsy as the final BM and the BM at the muscle biopsy as the initial BM. In European sea bass, most of the somatic growth occur within the first 3 to 5 years of life, so several months of growth measurement at the juvenile stage might be representative of the overall growth of the animal.Figure 1Experimental design. Juvenile European sea bass (n = 40) were weighted about every four weeks over a 32-week period. At week 20, a biopsy of red muscle was used for mitochondrial assay. Specific growth rates (SGR) were calculated relative to the time of the biopsy. Past growth rate corresponds to SGR calculated before the biopsy, and future growth rate corresponds to SGR calculated after the biopsy.Full size imageMuscle biopsy procedureMuscle biopsy was performed as a non-lethal means of sampling tissue for the mitochondrial assay while allowing us to determine future growth rate. Fish were anaesthetized with tricaine (as above), weighed (76.7 ± 3.6 g), and biopsied. A skin incision ( More

  • in

    Microbacterium kunmingensis sp. nov., an attached bacterium of Microcystis aeruginosa

    Liu LP. Characteristics of blue algal bloom in Dianchi Lake and analysis on its cause. Res Environ Sci. 1999;12:36–37.
    Google Scholar 
    Liu YM, Chen W, Li DH, Shen YW, Liu YD, Song LR. Analysis of paralytic shellfish toxins in Aphanizomenon DC-1 from Lake Dianchi, China. Environ Toxicol. 2006;21:289–95.CAS 
    PubMed 
    Article 

    Google Scholar 
    Dziallas C, Grossart HP. Temperature and biotic factors influence bacterial communities associated with the cyanobacterium Microcystis sp. Environ Microbiol. 2011;13:1632–41.PubMed 
    Article 

    Google Scholar 
    Parveen B, Ravet V, Djediat C, Mary I, Quiblier C, Debroas D, Humbert JF. Bacterial communities associated with Microcystis colonies differ from free-living communities living in the same ecosystem. Environ Microbiol Rep. 2013;5:716–24.CAS 
    PubMed 

    Google Scholar 
    Shi LM, Cai YF, Kong FX, Yu Y. Specific association between bacteria and buoyant Microcystis colonies compared with other bulk bacterial communities in the eutrophic Lake Taihu, China. Environ Microbiol Rep. 2012;4:669–78.CAS 
    PubMed 

    Google Scholar 
    Kouzuma A, Watanabe K. Exploring the potential of algae/bacteria interactions. Curr Opin Biotech. 2015;33:125–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Cooper MB, Smith AG. Exploring mutualistic interactions between microalgae and bacteria in the omics age. Curr Opin Plant Biol. 2015;26:147–53.PubMed 
    Article 

    Google Scholar 
    Yang L, Xiao L. Outburst, jeopardize and control of cyanobacterial bloom in lakes. Beijing: Science Press; 2011. p. 71–212.
    Google Scholar 
    de-Bashan LE, Antoun H, Bashan Y. Involvement of indole-3-acetic-acid produced by the growth-promoting bacterium Azospirillum spp. in promoting growth of Chlorella vulgaris. J Phycol. 2008;44:938–47.CAS 
    PubMed 
    Article 

    Google Scholar 
    Xiao Y, Wang L, Wang X, Chen M, Chen J, Tian BY, Zhang BH. Nocardioides lacusdianchii sp. nov., an attached bacterium of Microcystis aeruginosa. Antonie van Leeuwenhoek. 2022;115:141–53.PubMed 
    Article 

    Google Scholar 
    Shirling EB, Gottlieb D. Methods for characterization of Streptomyces species. Int J Syst Bacteriol. 1966;16:313–40.Article 

    Google Scholar 
    Zhang BH, Chen W, Li HQ, Zhou EM, Hu WY, Duan YQ, Mohamad OA, Gao R, Li WJ. An antialgal compound produced by Streptomyces jiujiangensis JXJ 0074T. Appl Microbiol Biotechnol. 2015;99:7673–83.CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang BH, Salam N, Cheng J, Xiao M, Li HQ, Yang JY, Zha DM, Li WJ. Citricoccus lacusdiani sp. nov., an actinobacterium promoting Microcystis growth with limited soluble phosphorus. Antonie Van Leeuwenhoek. 2016;109:1457–65.CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang BH, Salam N, Cheng J, Li HQ, Yang JY, Zha DM, Guo QG, Li WJ. Microbacterium lacusdiani sp. nov., a phosphate–solubilizing novel actinobacterium isolated from mucilaginous sheath of Microcystis. J Antibiot. 2017;70:147–51.Article 

    Google Scholar 
    Smibert RM, Krieg NR. Phenotypic characterization. In: Gerhardt P, Murray RGE, Wood WA, Krieg NR, editors. Methods for general and molecular bacteriology. Washington, DC: American Society for Microbiology; 1994. p. 607–54.Dong XZ, Cai MY. Manual of systematic identification of common bacteria. Beijing: Science Press; 2001. p. p349–89.
    Google Scholar 
    Minnikin DE, Collins MD, Goodfellow M. Fatty acid and polar lipid composition in the classification of Cellulomonas, Oerskovia and related taxa. J Appl Bacteriol. 1979;47:87–95.CAS 
    Article 

    Google Scholar 
    Tamaoka J, Katayama-Fujimura Y, Kuraishi H. Analysis of bacterial menaquinone mixtures by high performance liquid chromatography. J Appl Bacteriol. 1983;54:31–36.CAS 
    Article 

    Google Scholar 
    Schleifer KH, Kandler O. Peptidoglycan types of bacterial cell walls and their taxonomic implications. Bacteriol Rev. 1972;36:407–77.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tang SK, Wang Y, Chen Y, Lou K, Cao LL, Xu LH, Li WJ. Zhihengliuella alba sp. nov., and emended description of the genus Zhihengliuella. Int J Syst Evol Microbiol. 2009;59:2025–32.CAS 
    PubMed 
    Article 

    Google Scholar 
    Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, Chun J. Introducing EzBiocloud: a taxonomically united database of 16S rRNA gene sequences and whole–genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Saitou N, Nei M. The neighbor–joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–42.CAS 
    PubMed 

    Google Scholar 
    Fitch WM. Toward defining the course of evolution: minimum change for a specific tree topology. Syst Zool. 1971;20:406–16.Article 

    Google Scholar 
    Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981;17:368–76.CAS 
    PubMed 
    Article 

    Google Scholar 
    Felsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985;39:783–91.PubMed 
    Article 

    Google Scholar 
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Massouras A, Hens K, Gubelmann C, Uplekar S, Decouttere F, Rougemont J, Cole ST, Deplancke B. Primer-initiated sequence synthesis to detect and assemble structural variants. Nat Methods. 2010;7:485–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, Hugenholtz P. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinforma. 2007;8:209.Article 

    Google Scholar 
    Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence–based species delimitation with confidence intervals and improved distance functions. BMC Bioinforma. 2013;14:60.Article 

    Google Scholar 
    Xiao Y, Chen J, Chen M, Deng SJ, Xiong ZQ, Tian BY, Zhang BH. Mycolicibacterium lacusdiani sp. nov., an attached bacterium of Microcystis aeruginosa. Front Microbiol. 2022;13:861291.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vaz-Moreira I, Lopes AR, Faria C, Spröer C, Schumann P, Nunes OC, Manaia CM. Microbacterium invictum sp. nov., isolated from homemade compost. Int J Syst Evol Microbiol. 2009;59:2036–41.PubMed 
    Article 

    Google Scholar 
    Ohta Y, Ito T, Mori K, Nishi S, Shimane Y, Mikuni K, Hatada Y. Microbacterium saccharophilum sp. nov., isolated from a sucrose-refining factory. Int J Syst Evol Microbiol. 2013;63:2765–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kageyama A, Takahashi Y, ƌmura S. Microbacterium deminutum sp. nov., Microbacterium pumilum sp. nov. and Microbacterium aoyamense sp. nov. Int J Syst Evol Microbiol. 2006;56:2113–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Stackebrandt E, Ebers J. Taxonomic parameters revisited: tarnished gold standards. Microbiol Today. 2006;33:152–5.
    Google Scholar 
    Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinforma. 2013;14:60.Article 

    Google Scholar 
    Kim M, Oh HS, Park SC, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol. 2014;64:346–51.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chun J, Oren A, Ventosa A, Christensen H, Arahal DR, da Costa MS, Rooney AP, Yi H, Xu XW, De Meyer S, Trujillo ME. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol. 2018;68:461–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hoke AK, Reynoso G, Smith MR, Gardner MI, Lockwood DJ, Gilbert NE, Wilhelm SW, Becker IR, Brennan GJ, Crider KE, Farnan SR, Mendoza V, Poole AC, Zimmerman ZP, Utz LK, Wurch LL, Steffen MM. Genomic signatures of Lake Erie bacteria suggest interaction in the Microcystis phycosphere. PLoS ONE. 2021;16:e0257017.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang BH, Salam N, Cheng J, Li HQ, Yang JY, Zha DM, Zhang YQ, Ai MJ, Hozzein WN, Li WJ. Modestobacter lacusdianchii sp. nov., a phosphate-solubilizing actinobacterium with ability to promote Microcystis growth. PLoS ONE. 2016;11:e0161069.PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Save the world’s forest giants from infernos

    Gigantic trees occur in only a few regions on Earth. Some of the world’s largest eucalypts, for example, are on the island of Tasmania, off southeastern Australia. As wildfires increase in severity and frequency as a result of climate change, we urge the authorities to protect these trees by adopting measures similar to those applied to safeguard California’s redwood forests.
    Competing Interests
    The authors declare no competing interests. More

  • in

    Quantifying wood decomposition by insects and fungi using computed tomography scanning and machine learning

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

    Google Scholar 
    Seibold, S. et al. The contribution of insects to global forest deadwood decomposition. Nature 597, 77–81 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Filipiak, M. Nutrient dynamics in decomposing dead wood in the context of wood eater requirements: The ecological stoichiometry of saproxylophagous insects. In Saproxylic Insects (ed. Ulyshen, M. D.) 429–470 (Springer, 2018).
    Google Scholar 
    Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: A role for trait variation among tree species?. Ecol. Lett. 12, 45–56 (2009).PubMed 

    Google Scholar 
    Oberle, B. et al. Accurate forest projections require long-term wood decay experiments because plant trait effects change through time. Glob. Change Biol. 26, 864–875 (2020).ADS 

    Google Scholar 
    Guo, C., Yan, E. & Cornelissen, J. H. C. Size matters for linking traits to ecosystem multifunctionality. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.06.003 (2022).Article 
    PubMed 

    Google Scholar 
    Ulyshen, M. D. Wood decomposition as influenced by invertebrates. Biol. Rev. 91, 70–85 (2016).PubMed 

    Google Scholar 
    Lustenhouwer, N. et al. A trait-based understanding of wood decomposition by fungi. Proc. Natl. Acad. Sci. U.S.A. 117, 1–8 (2020).
    Google Scholar 
    Tlåskal, V. et al. Complementary roles of wood-Inhabiting fungi and bacteria facilitate deadwood decomposition. mSystems 6, e01078-20 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Schmidt, O. Wood and Tree Fungi: Biology, Damage, Protection and Use (Springer, 2006).
    Google Scholar 
    Arantes, V. & Goodell, B. Current understanding of brown-rot fungal biodegradation mechanisms: A review. ACS Symp. Ser. 1158, 3–21 (2014).CAS 

    Google Scholar 
    Jacobsen, R. M., Sverdrup-Thygeson, A., Kauserud, H., Mundra, S. & Birkemoe, T. Exclusion of invertebrates influences saprotrophic fungal community and wood decay rate in an experimental field study. Funct. Ecol. 32, 2571–2582 (2018).
    Google Scholar 
    Fukami, T. et al. Assembly history dictates ecosystem functioning: Evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).PubMed 

    Google Scholar 
    Wang, J. Y. et al. Durability of mass timber structures: A review of the biological risks. Wood Fiber Sci. 50, 110–127 (2018).CAS 

    Google Scholar 
    Venugopal, P., Junninen, K., Linnakoski, R., Edman, M. & Kouki, J. Climate and wood quality have decayer-specific effects on fungal wood decomposition. For. Ecol. Manag. 360, 341–351 (2016).
    Google Scholar 
    Ulyshen, M. D. & Wagner, T. L. Quantifying arthropod contributions to wood decay. Methods Ecol. Evol. 4, 345–352 (2013).
    Google Scholar 
    Freschet, G. T., Weedon, J. T., Aerts, R., van Hal, J. R. & Cornelissen, J. H. C. Interspecific differences in wood decay rates: Insights from a new short-term method to study long-term wood decomposition. J. Ecol. 100, 161–170 (2012).
    Google Scholar 
    Chang, C. et al. Methodology matters for comparing coarse wood and bark decay rates across tree species. Methods Ecol. Evol. 11, 828–838 (2020).
    Google Scholar 
    HervĂ©, V., Mothe, F., Freyburger, C., Gelhaye, E. & Frey-Klett, P. Density mapping of decaying wood using X-ray computed tomography. Int. Biodeterior. Biodegrad. 86, 358–363 (2014).
    Google Scholar 
    Williamson, G. B. & Wiemann, M. C. Measuring wood specific gravity…Correctly. Am. J. Bot. 97, 519–524 (2010).PubMed 

    Google Scholar 
    Van Der Wal, A., Gunnewiek, P. J. A. K., Cornelissen, J. H. C., Crowther, T. W. & De Boer, W. Patterns of natural fungal community assembly during initial decay of coniferous and broadleaf tree logs. Ecosphere 7, e01393 (2016).
    Google Scholar 
    Saint-Germain, M., Buddle, C. M. & Drapeau, P. Substrate selection by saprophagous wood-borer larvae within highly variable hosts. Entomol. Exp. Appl. 134, 227–233 (2010).
    Google Scholar 
    Lettenmaier, L. et al. Beetle diversity is higher in sunny forests due to higher microclimatic heterogeneity in deadwood. Oecologia https://doi.org/10.1007/s00442-022-05141-8 (2022).Article 
    PubMed 

    Google Scholar 
    Gao, S. et al. A critical analysis of methods for rapid and nondestructive determination of wood density in standing trees. Ann. For. Sci. 74, 1–13 (2017).
    Google Scholar 
    Arnstadt, T. et al. Dynamics of fungal community composition, decomposition and resulting deadwood properties in logs of Fagus sylvatica, Picea abies and Pinus sylvestris. For. Ecol. Manag. 382, 129–142 (2016).
    Google Scholar 
    Gessner, M. O. Ergosterol as a measure of fungal biomass. In Methods to Study Litter Decomposition (eds BĂ€rlocher, F. et al.) 247–255 (Springer, 2020). https://doi.org/10.1007/978-3-030-30515-4_27.Chapter 

    Google Scholar 
    Baldrian, P. et al. Responses of the extracellular enzyme activities in hardwood forest to soil temperature and seasonality and the potential effects of climate change. Soil Biol. Biochem. 56, 60–68 (2013).CAS 

    Google Scholar 
    Strid, Y., Schroeder, M., Lindahl, B., Ihrmark, K. & Stenlid, J. Bark beetles have a decisive impact on fungal communities in Norway spruce stem sections. Fungal Ecol. 7, 47–58 (2014).
    Google Scholar 
    Hagge, J. et al. Bark coverage shifts assembly processes of microbial decomposer communities in dead wood. Proc. R. Soc. B Biol. Sci. 286, 20191744 (2019).
    Google Scholar 
    Birkemoe, T., Jacobsen, R. M., Sverdrup-Thygeson, A. & Biedermann, P. H. W. Insect–fungus interactions in dead wood. In Saproxylic Insects (ed. Ulyshen, M. D.) 377–427 (Springer, 2018).
    Google Scholar 
    Leach, J. G., Ork, L. W. & Christensen, C. Further studies on the interrelationship of insects and fungi in the deterioration of felled Norway pine logs. J. Agric. Res. 55, 129–140 (1937).
    Google Scholar 
    Ulyshen, M. D., Wagner, T. L. & Mulrooney, J. E. Contrasting effects of insect exclusion on wood loss in a temperate forest. Ecosphere 5, art47 (2014).
    Google Scholar 
    Shigo, A. L. & Marx, H. G. Compartmentalization of decay in trees (1977).De Ligne, L. et al. Studying the spatio-temporal dynamics of wood decay with X-ray CT scanning. Holzforschung 76, 408–420 (2022).
    Google Scholar 
    Freyburger, C., Longuetaud, F., Mothe, F., Constant, T. & Leban, J. M. Measuring wood density by means of X-ray computer tomography. Ann. For. Sci. 66, 804 (2009).
    Google Scholar 
    Wei, Q., Leblon, B. & La Rocque, A. On the use of X-ray computed tomography for determining wood properties: A review. Can. J. For. Res. 41, 2120–2140 (2011).
    Google Scholar 
    Fuchs, A., Schreyer, A., Feuerbach, S. & Korb, J. A new technique for termite monitoring using computer tomography and endoscopy. Int. J. Pest Manag. 50, 63–66 (2004).
    Google Scholar 
    Choi, B., Himmi, S. K. & Yoshimura, T. Quantitative observation of the foraging tunnels in Sitka spruce and Japanese cypress caused by the drywood termite Incisitermes minor (Hagen) by 2D and 3D X-ray computer tomography (CT). Holzforschung 71, 535–542 (2017).CAS 

    Google Scholar 
    BĂ©langer, S. et al. Effect of temperature and tree species on damage progression caused by whitespotted sawyer (Coleoptera: Cerambycidae) larvae in recently burned logs. J. Econ. Entomol. 106, 1331–1338 (2013).PubMed 

    Google Scholar 
    Pereira Junior, A. & Garcia de Carvalho, M. An initial study in wood tomographic image classification using the SVM and CNN techniques. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) Vol. 4 575–581 (2022).Kautz, M., Peter, F. J., Harms, L., Kammen, S. & Delb, H. Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle. J. Pest Sci. https://doi.org/10.1007/s10340-022-01490-8 (2022).Article 

    Google Scholar 
    Ehnström, B. & Axelsson, R. Insektsgnag i bark och ved (ArtDatabanken SLU, 2002).
    Google Scholar 
    Philpott, T. J., Prescott, C. E., Chapman, W. K. & Grayston, S. J. Nitrogen translocation and accumulation by a cord-forming fungus (Hypholoma fasciculare) into simulated woody debris. For. Ecol. Manag. 315, 121–128 (2014).
    Google Scholar 
    Kahl, T. et al. Wood decay rates of 13 temperate tree species in relation to wood properties, enzyme activities and organismic diversities. For. Ecol. Manag. 391, 86–95 (2017).
    Google Scholar 
    Deflorio, G., Johnson, C., Fink, S. & Schwarze, F. W. M. R. Decay development in living sapwood of coniferous and deciduous trees inoculated with six wood decay fungi. For. Ecol. Manag. 255, 2373–2383 (2008).
    Google Scholar 
    Fuhr, M. J., Schubert, M., Schwarze, F. W. M. R. & Herrmann, H. J. Modelling the hyphal growth of the wood-decay fungus Physisporinus vitreus. Fungal Biol. 115, 919–932 (2011).CAS 
    PubMed 

    Google Scholar 
    Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F. A. Ilastik: Interactive learning and segmentation toolkit. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233. https://doi.org/10.1109/ISBI.2011.5872394 (2011).Dodds, K. J., Graber, C. & Stephen, F. M. Facultative intraguild predation by larval Cerambycidae (Coleoptera) on bark beetle larvae (Coleoptera: Scolytidae). Environ. Entomol. 30, 17–22 (2001).
    Google Scholar 
    Graham, S. A. Temperature as a limiting factor in the life of subcortical insects. J. Econ. Entomol. 17, 377–383 (1924).
    Google Scholar 
    Baldrian, P. et al. Estimation of fungal biomass in forest litter and soil. Fungal Ecol. 6, 1–11 (2013).
    Google Scholar 
    Ơnajdr, J. et al. Spatial variability of enzyme activities and microbial biomass in the upper layers of Quercus petraea forest soil. Soil Biol. Biochem. 40, 2068–2075 (2008).
    Google Scholar 
    Möller, G. Struktur- und Substratbindung holzbewohnender Insekten, Schwerpunkt Coleoptera—KĂ€fer. Dissertation at Freien UniversitĂ€t Berlin (Freie UniversitĂ€t Berlin, 2009).
    Google Scholar 
    Baldrian, P. Forest microbiome: Diversity, complexity and dynamics. FEMS Microbiol. Rev. 41, 109–130 (2017).CAS 
    PubMed 

    Google Scholar 
    Steger, C., Ulrich, M. & Wiedemann, C. Machine Vision Algorithms and Applications (Wiley, 2008).
    Google Scholar 
    Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional Networks for Biomedical Image Segmentation (Springer, 2015).
    Google Scholar 
    Jansche, M. Maximum expected F-measure training of logistic regression models. In Proceedings of the conference on human language technology and empirical meth-ods in natural language processing 692–699 (Association for Computational Linguistics, 2005).Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).
    Google Scholar 
    Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chollet, F. Keras. https://github.com/fchollet/keras (2015).Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems. Tensorflow.org. (2015).R Core Team. R: A language and environment for statistical computing (2020). More

  • in

    Hardship at birth alters the impact of climate change on a long-lived predator

    Seneviratne, S. I. et al. Changes in climate extremes and their impacts on the natural physical environment. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Field, C.B. et al. eds) vol. 9781107025 109–230 (Cambridge University Press, 2012).Tan, X., Gan, T. Y. & Horton, D. E. Projected timing of perceivable changes in climate extremes for terrestrial and marine ecosystems. Glob. Chang. Biol. 24, 4696–4708 (2018).ADS 
    PubMed 
    Article 

    Google Scholar 
    Parmesan, C., Root, T. L. & Willig, M. R. Impacts of extreme weather and climate on terrestrial biota. Bull. Am. Meteorol. Soc. 81, 443–450 (2000).ADS 
    Article 

    Google Scholar 
    Van de Pol, M., Jenouvrier, S., Cornelissen, J. H. C. & Visser, M. E. Behavioural, ecological and evolutionary responses to extreme climatic events: challenges and directions. Philos. Trans. R. Soc. B Biol. Sci. 372, 1–16 (2017).Smith, M. D. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).Article 

    Google Scholar 
    Wingfield, J. C. et al. How birds cope physiologically and behaviourally with extreme climatic events. Philos. Trans. R. Soc. B Biol. Sci. 372, 1–10 (2017).Sergio, F., Blas, J. & Hiraldo, F. Animal responses to natural disturbance and climate extremes: a review. Glob. Planet. Change. 161, 28–40 (2018).ADS 
    Article 

    Google Scholar 
    Aghakouchak, A. et al. Climate Extremes and Compound Hazards in a Warming World. Annu. Rev. Earth Planet. Sci. 48, 519–548 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1–14 (2019).CAS 
    Article 

    Google Scholar 
    Boyce, M. S. et al. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 
    Article 

    Google Scholar 
    Lindström, J. Early development and fitness in birds and mammals. Trends Ecol. Evol. 14, 343–348 (1999).PubMed 
    Article 

    Google Scholar 
    Monaghan, P. Early growth conditions, phenotypic development and environmental change. Philos. Trans. R. Soc. B Biol. Sci. 363, 1635–1645 (2008).Article 

    Google Scholar 
    Nussey, D. H., Kruuk, L. E. B., Morris, A. & Clutton-Brock, T. H. Environmental conditions in early life influence ageing rates in a wild population of red deer. Curr. Biol. 17, 1000–1001 (2007).Article 
    CAS 

    Google Scholar 
    Van De Pol, M., Bruinzeel, L. W., Heg, D., Van Der Jeugd, H. P. & Verhulst, S. A silver spoon for a golden future: long-term effects of natal origin on fitness prospects of oystercatchers (Haematopus ostralegus). J. Anim. Ecol. 75, 616–626 (2006).PubMed 
    Article 

    Google Scholar 
    Reid, J. M., Bignal, E. M., Bignal, S., McCracken, D. I. & Monaghan, P. Environmental variability, life-history covariation and cohort effects in the red-billed chough Pyrrhocorax pyrrhocorax. J. Anim. Ecol. 72, 36–46 (2003).Article 

    Google Scholar 
    Hamel, S., Gaillard, J. M., Festa-Bianchet, M. & CĂŽtĂ©, S. D. Individual quality, early-life conditions, and reproductive success in contrasted populations of large herbivores. Ecology 90, 1981–1995 (2009).PubMed 
    Article 

    Google Scholar 
    Kordosky, J. R. et al. Landscape of stress: tree mortality influences physiological stress and survival in a native mesocarnivore. PLoS One. 16, 1–22 (2021).Article 
    CAS 

    Google Scholar 
    Millon, A., Petty, S. J., Little, B. & Lambin, X. Natal conditions alter age-specific reproduction but not survival or senescence in a long-lived bird of prey. J. Anim. Ecol. 80, 968–975 (2011).PubMed 
    Article 

    Google Scholar 
    Mugabo, M., Marquis, O., Perret, S. & Le Galliard, J. F. Immediate and delayed life history effects caused by food deprivation early in life in a short-lived lizard. J. Evol. Biol. 23, 1886–1898 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Taborsky, B. The influence of juvenile and adult environments on life-history trajectories. Proc. R. Soc. B Biol. Sci. 273, 741–750 (2006).Article 

    Google Scholar 
    Hayward, A. D., Rickard, I. J. & Lummaa, V. Influence of early-life nutrition on mortality and reproductive success during a subsequent famine in a preindustrial population. Proc. Natl Acad. Sci. 110, 13886–13891 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    KorpimĂ€ki, E. & Lagerström, M. Survival and natal dispersal of fledglings of Tengmalm’s owl in relation to fluctuating food conditions and hatching date. J. Anim. Ecol. 57, 433–441 (1988).Article 

    Google Scholar 
    Gluckman, P. D., Hanson, M. A. & Spencer, H. G. Predictive adaptive responses and human evolution. Trends Ecol. Evol. 20, 527–533 (2005).PubMed 
    Article 

    Google Scholar 
    Gluckman, P. D., Hanson, M. A., Spencer, H. G. & Bateson, P. Environmental influences during development and their later consequences for health and disease: implications for the interpretation of empirical studies. Proc. R. Soc. B Biol. Sci. 272, 671–677 (2005).Article 

    Google Scholar 
    Grafen, A. On the uses of data on lifetime reproductive success. in Reproductive Success (ed. T. H. Clutton-Brock) 454–471 (Chicago University Press, 1988).Jenouvrier, S., PĂ©ron, C. & Weimerskirch, H. Extreme climate events and individual heterogeneity shape lifehistory traits and population dynamics. Ecol. Monogr. 85, 605–623 (2015).Article 

    Google Scholar 
    McNamara, J. M. & Houston, A. I. State-dependent life histories. Nature 380, 215–221 (1996).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Douhard, M. et al. Fitness consequences of environmental conditions at different life stages in a long-lived vertebrate. Proc. R. Soc. B Biol. Sci. 281, 1–8 (2014).Monaghan, P. Organismal stress, telomeres and life histories. J. Exp. Biol. 217, 57–66 (2014).PubMed 
    Article 

    Google Scholar 
    Zimmer, C., Larriva, M., Boogert, N. J. & Spencer, K. A. Transgenerational transmission of a stress-coping phenotype programmed by early-life stress in the Japanese quail. Sci. Rep. 7, 1–19 (2017).Article 
    CAS 

    Google Scholar 
    Krause, E. T., Honarmand, M., Wetzel, J. & Naguib, M. Early fasting is long lasting: differences in early nutritional conditions reappear under stressful conditions in adult female zebra finches. PLoS One. 4, 1–6 (2009).Article 
    CAS 

    Google Scholar 
    Martin, T. G. et al. Acting fast helps avoid extinction. Conserv. Lett. 5, 274–280 (2012).Article 

    Google Scholar 
    Lewontin, R. C. & Cohen, D. On population growth in a randomly varying environment. Proc. Natl Acad. Sci. 62, 1056–1060 (1969).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Séther, B. E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).Article 

    Google Scholar 
    Morris, W. F. & Doak, D. F. Buffering of Life Histories against Environmental Stochasticity: Accounting for a Spurious Correlation between the Variabilities of Vital Rates and Their Contributions to Fitness. Am. Nat. 163, 579–590 (2004).PubMed 
    Article 

    Google Scholar 
    Rodríguez-Caro, R. C. et al. The limits of demographic buffering in coping with environmental variation. Oikos 130, 1346–1358 (2021).Article 

    Google Scholar 
    Bakker, V. J., Doak, D. F. & Ferrara, F. J. Understanding extinction risk and resilience in an extremely small population facing climate and ecosystem change. Ecosphere 12, 1–20 (2021).Beissinger, S. R. Modeling extinction in periodic environments: Everglades water levels and Snail Kite population viability. Ecol. Appl. 5, 618–631 (1995).Article 

    Google Scholar 
    Simberloff, D. Small and declining populations. in Conservation science and action (ed. Sutherland, W. J.) 116–134 (Blackwell, 1998).Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Blake, J. G. & Loiselle, B. A. Enigmatic declines in bird numbers in lowland forest of eastern Ecuador may be a consequence of climate change. PeerJ. 2015, 1–20 (2015).
    Google Scholar 
    Whitfield, S. M. et al. Amphibian and reptile declines over 35 years at La Selva, Costa Rica. Proc. Natl Acad. Sci. 104, 8352–8356 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One 12, (2017).GonzĂĄlez, L. M., Margalida, A., SĂĄnchez, R. & Oria, J. Supplementary feeding as an effective tool for improving breeding success in the Spanish imperial eagle (Aquila adalberti). Biol. Conserv. 129, 477–486 (129AD).GarcĂ­a, F. & MarĂ­n, C. Doñana: water and biosphere. (Spanish Ministry of the Environment, 2006).DĂ­az-Paniagua, C. & AragonĂ©s, D. Permanent and temporary ponds in Doñana National Park (SW Spain) are threatened by desiccation. Limnetica 34, 407–424 (2015).
    Google Scholar 
    Schmidt, G. et al. The state of water in Doñana: an evaluation of the state of the water and of the ecosystems of the protected space. (WWF/Adena, Madrid, 2017).Camacho, C. et al. Groundwater extraction poses extreme threat to Doñana World Heritage Site. Nat. Ecol. Evol. 6, 654–655 (2022).Navedo, J. G., Piersma, T., Figuerola, J. & Vansteelant, W. Spain’s Doñana World Heritage Site in danger. Science 376, 144 (2022).ADS 
    PubMed 
    Article 

    Google Scholar 
    Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change. 63, 90–104 (2008).ADS 
    Article 

    Google Scholar 
    Goubanova, K. & Li, L. Extremes in temperature and precipitation around the Mediterranean basin in an ensemble of future climate scenario simulations. Glob. Planet. Change 57, 27–42 (2007).ADS 
    Article 

    Google Scholar 
    Hertig, E. & Tramblay, Y. Regional downscaling of Mediterranean droughts under past and future climatic conditions. Glob. Planet. Change. 151, 36–48 (2017).ADS 
    Article 

    Google Scholar 
    Bustamante, J., Pacios, F., DĂ­az-Delgado, R. & AragonĂ©s, D. Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images. J. Environ. Manag. 90, 2219–2225 (2009).Article 

    Google Scholar 
    Veiga, J. P. & Hiraldo, F. Food habits and the survival and growth of nestlings in two sympatric kites (Milvus milvus and Milvus migrans). Ecography (Cop.). 13, 62–71 (1990).Article 

    Google Scholar 
    Viñuela, J. & Bustamante, J. Effect of growth and hatching asynchrony on the fledging age of Black and Red Kites. Auk 109, 748–757 (1992).Article 

    Google Scholar 
    Newton, I., Davis, P. E. & Davis, J. E. Age of first breeding, dispersal and survival of Red Kites Milvus milvus in Wales. Ibis (Lond. 1859). 131, 16–21 (1989).Article 

    Google Scholar 
    Katzenberger, J., Gottschalk, E., Balkenhol, N. & Waltert, M. Density-dependent age of first reproduction as a key factor for population dynamics: stable breeding populations mask strong floater declines in a long-lived raptor. Anim. Conserv. 24, 862–875 (2021).Article 

    Google Scholar 
    Sergio, F., Tavecchia, G., Blas, J., Tanferna, A. & Hiraldo, F. Demographic modeling to fine-tune conservation targets: importance of pre-adults for the decline of an endangered raptor. Ecol. Appl. 31, 1–12 (2021).Article 

    Google Scholar 
    Sergio, F. et al. Protected areas under pressure: decline, redistribution, local eradication and projected extinction of a threatened predator, the red kite, in Doñana National Park, Spain. Endanger. Species Res. 38, 189–204 (2019).Article 

    Google Scholar 
    Sergio, F. et al. Preservation of wide-ranging top predators by site-protection: black and red kites in Doñana National Park. Biol. Conserv. 125, 11–21 (2005).Article 

    Google Scholar 
    Sofaer, H. R., Chapman, P. L., Sillett, T. S. & Ghalambor, C. K. Advantages of nonlinear mixed models for fitting avian growth curves. J. Avian Biol. 44, 469–478 (2013).
    Google Scholar 
    Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, New York, 2009).Lebreton, J. D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

    Google Scholar 
    Anderson, D. R. Model based inference in the life sciences: a primer on evidence (Springer, 2008).White, G. C. & Burnham, K. P. Program mark: survival estimation from populations of marked animals. Bird. Study. 46, S120–S139 (1999).Article 

    Google Scholar 
    Grosbois, V. & Tavecchia, G. Modeling dispersal with capture-recapture data: disentangling decisions of leaving and settlement. Ecology 84, 1225–1236 (2003).Article 

    Google Scholar 
    Caswell, H. Matrix population models (Sinauer, 2001).Ballerini, T., Tavecchia, G., Pezzo, F., Jenouvrier, S. & Olmastroni, S. Predicting responses of the AdĂ©lie penguin population of Edmonson Point to future sea ice changes in the Ross Sea. Front. Ecol. Evol. 3, 1–11 (2015).Bateson, P. et al. Developmental plasticity and human health. Nature 430, 419–421 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models

    Murphy, G. E. P., Romanuk, T. N. & Worm, B. Cascading effects of climate change on plankton community structure. Ecol. Evol. 10, 2170–2181. https://doi.org/10.1002/ece3.6055 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Woodward, G., Daniel, M., Perkins, D. M. & Brown, L. E. Climate change and freshwater ecosystems: Impacts across multiple levels of organization. Philos. Trans. R. Soc. B 365, 2093–2106. https://doi.org/10.1098/rstb.2010.0055 (2010).Article 

    Google Scholar 
    Lampert, W. Zooplankton research: The contribution of limnology to general ecological paradigms. Aquat. Ecol. 31, 19–27. https://doi.org/10.1023/A:1009943402621 (1997).Article 

    Google Scholar 
    Gannon, J. E. & Stemberger, R. S. Zooplankton (especially crustaceans and rotifers) as indicators of water quality. Trans. Am. Microsc. Soc. 97, 16–35. https://doi.org/10.2307/3225681 (1978).Article 

    Google Scholar 
    Ferdous, Z. & Muktadir, S. K. M. A review: Potentiality of zooplankton as bioindicator. Am. J. Appl. Sci. 6, 1815–1819 (2009).Article 

    Google Scholar 
    Ejsmont-Karabin, J. The usefulness of zooplankton as lake ecosystem indicators: Rotifer Trophic State Index. Pol. J. Ecol. 60, 339–350 (2012).
    Google Scholar 
    Gillooly, J. F. Effect of body size and temperature on generation time in zooplankton. J. Plankton Res. 22(2), 241–251 (2000).Article 

    Google Scholar 
    Lewandowska, A. M., Hillebrand, H., Lengfellner, K. & Sommer, U. Temperature effects on phytoplankton diversity—The zooplankton link. J. Sea Res. 85, 359–364. https://doi.org/10.1016/j.seares.2013.07.003 (2014).ADS 
    Article 

    Google Scholar 
    Carter, J. L. & Schindler, D. L. Responses of zooplankton populations to four decades of climate warming in Lakes of Southwestern Alaska. Ecosystems 15, 1010–1026. https://doi.org/10.1007/s10021-012-9560-0 (2012).CAS 
    Article 

    Google Scholar 
    Ejsmont-Karabin, J. & WęgleƄska, T. Disturbances in zooplankton seasonality in Lake GosƂawskie (Poland) affected by permanent heating and heavy fish stocking. Ekol. Pol. 36, 245–260 (1988).
    Google Scholar 
    Ejsmont-Karabin, J. et al. Rotifers in Heated Konin Lakes—A review of long-term observations. Water 12, 1660. https://doi.org/10.3390/w12061660 (2020).Article 

    Google Scholar 
    Evans, L. E., Hirst, A. G., Kratina, P. & Beaugrand, G. Temperature-mediated changes in zooplankton body size: Large scale temporal and spatial analysis. Ecography 43, 581–590. https://doi.org/10.1111/ecog.04631 (2020).Article 

    Google Scholar 
    Wang, L. et al. Is zooplankton body size an indicator of water quality in (sub)tropical reservoirs in China?. Ecosystems 25, 656–662. https://doi.org/10.1007/s10021-021-00656-2 (2021).CAS 
    Article 

    Google Scholar 
    Williamson, C. E., Saros, J. E., Vincent, W. F. & Smol, J. P. Lakes and reservoirs as sentinels, integrators, and regulators of climate change. Limnol. Oceanogr. 54(6), 2273–2282 (2009).ADS 
    Article 

    Google Scholar 
    Richardson, A. J. In hot water: Zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295. https://doi.org/10.1093/icesjms/fsn028 (2008).Article 

    Google Scholar 
    Visconti, A., Manca, M. & De Bernardi, R. Eutrophication-like response to climate warming: An analysis of Lago Maggiore (N. Italy) zooplankton in contrasting years. J. Limnol. 67(2), 87–92 (2008).Article 

    Google Scholar 
    Vandysh, O. I. The effect of thermal flow of large power facilities on zooplankton community under subarctic conditions. Water Res. 36(3), 310–318. https://doi.org/10.1134/S0097807809030063 (2009).CAS 
    Article 

    Google Scholar 
    Alric, B. et al. Local forcings affect lake zooplankton vulnerability and response to climate warming. Ecology 94(12), 2767–2780 (2013).Article 

    Google Scholar 
    Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. PNAS 106(31), 12788–12793. https://doi.org/10.1073/pnas.0902080106 (2009).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gutierrez, M. F. et al. Is recovery of large-bodied zooplankton after nutrient loading reduction hampered by climate warming? A long-term study of shallow hypertrophic Lake SÞbygaard, Denmark. Water 8, 341. https://doi.org/10.3390/w8080341 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884. https://doi.org/10.1038/nature02808 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Thackeray, S. J., Jones, I. D. & Maberly, S. C. Long-term change in the phenology of spring phytoplankton: Species-specific responses to nutrient enrichment and climatic change. J. Ecol. 96, 523–535. https://doi.org/10.1111/j.1365-2745.2008.01355.x (2008).Article 

    Google Scholar 
    Adrian, A., Wilhelm, S. & Gerten, D. Life-history traits of lake plankton species may govern their phenological response to climate warming. Life-history traits of lake plankton species may govern their phenological response to climate warming. Glob. Change Biol. 12, 652–661. https://doi.org/10.1111/j.1365-2486.2006.01125.x (2006).ADS 
    Article 

    Google Scholar 
    Costello, J. H., Sullivan, B. K. & Gifford, D. J. A physical–biological interaction underlying variable phenological responses to climate change by coastal zooplankton. J. Plankton Res. 28(11), 1099–1105. https://doi.org/10.1093/plankt/fbl042 (2006).Article 

    Google Scholar 
    Lewandowska, A. M. et al. Effects of sea surface warming on marine plankton. Ecol. Lett. 17, 614–623. https://doi.org/10.1111/ele.12265 (2014).Article 
    PubMed 

    Google Scholar 
    Wagner, C. & Adrian, R. Exploring lake ecosystems: Hierarchy responses to long-term change?. Glob. Change Biol. 15, 1104–1115. https://doi.org/10.1111/j.1365-2486.2008.01833.x (2009).ADS 
    Article 

    Google Scholar 
    Hart, R. C. Zooplankton feeding rates in relation to suspended sediment content: Potential influences on community structure in a turbid reservoir. Fresh. Biol. 19, 123–139. https://doi.org/10.1111/j.1365-2427.1988.tb00334.x (1988).Article 

    Google Scholar 
    Carter, J. L., Schindler, D. E. & Francis, T. B. Effects of climate change on zooplankton community interactions in an Alaskan lake. Climate Change Resp. 4, 3. https://doi.org/10.1186/s40665-017-0031-x (2017).Article 

    Google Scholar 
    Calbet, A. The trophic roles of microzooplankton in marine systems. ICES J. Mar. Sci. 65, 325–331 (2008).Article 

    Google Scholar 
    Wollrab, S. et al. Climate change-driven regime shifts in a planktonic food web. Am. Natur. 197, 281–295. https://doi.org/10.1086/712813 (2021).Article 
    PubMed 

    Google Scholar 
    Recknagel, F., Adrian, R. & Köhler, J. Quantifying phenological asynchrony of phyto- and zooplankton in response to changing temperature and nutrient conditions in Lake MĂŒggelsee (Germany) by means of evolutionary computation. Environ. Model. Softw. 146, 105224. https://doi.org/10.1016/j.envsoft.2021.105224 (2021).Article 

    Google Scholar 
    EEA. Projected changes in annual, summer and winter temperature. European Environmental Agency. https://www.eea.europa.eu/data-and-maps/figures/projected-changes-in-annual-summer-1 (2014).IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021).Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427. https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

    Google Scholar 
    Ferrario, A. & HĂ€mmerli, R. On Boosting: Theory and Applications. SSRN: https://ssrn.com/abstract=3402687 (2019).Meysman, F. J. R. & Bruers, S. Ecosystem functioning and maximum entropy production: A quantitative test of hypotheses. Philos. Trans. R. Soc. B 365, 1405–1416. https://doi.org/10.1098/rstb.2009.0300 (2010).CAS 
    Article 

    Google Scholar 
    Yu, Q., Ji, W., Prihodko, L., Anchang, J. Y. & Hanan, N. P. Study becomes insight: Ecological learning from machine learning. Methods Ecol. Evol. 12, 217–2128. https://doi.org/10.1111/2041-210X.13686 (2021).Article 

    Google Scholar 
    Park, J. et al. Interpretation of ensemble learning to predict water quality using explainable artificial intelligence. Sci. Total Environ. 832, 155070. https://doi.org/10.1016/j.scitotenv.2022.155070 (2022).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Grbčić, L. et al. Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis. Environ. Model. Softw. 155, 105458. https://doi.org/10.1016/j.envsoft.2022.105458 (2022).Article 

    Google Scholar 
    Kruk, M., Artiemjew, P. & Paturej, E. The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters. Ecol. Inf. 66, 101462. https://doi.org/10.1016/j.ecoinf.2021.101462 (2021).Article 

    Google Scholar 
    Hebert, P. D. N. Competition in zooplankton communities. Ann. Zool. Fennici 19, 349–356 (1982).
    Google Scholar 
    Eigen, M. & Winkler, R. Laws of the Game. How the Principles of Nature Govern Chance (Princeton University Press, 1993).
    Google Scholar 
    Tilman, A. R., Plotkin, J. B. & Akçay, E. Evolutionary games with environmental feedbacks. Nat. Commun. 11, 915. https://doi.org/10.1038/s41467-020-14531-6 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shapley, L. S. A Value for n-Person Games. In Contributions to the Theory of Games II (eds Kuhn, H. W. & Tucker, A. W.) 315–317 (Princeton University Press, 1953).
    Google Scholar 
    Lundberg, S. M. & Lee, S. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
    Google Scholar 
    Ơtrumbelj, E. & Kononenko, I. An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 11, 1–18 http://dl.acm.org/citation.cfm?id=1756006.1756007 (2010).Gan, G., Ma, C. & Wu, J. Data clustering: Theory, algorithms, and applications. ASA-SIAM Ser. Stat. Appl. Math. https://doi.org/10.1137/1.9780898718348 (2007).Article 
    MATH 

    Google Scholar 
    Riechert, S. E. & Hammerstein, P. Game theory in the ecological context. Ann. Rev. Ecol. Syst. 14, 377–409. https://doi.org/10.1146/annurev.es.14.110183.002113 (1983).Article 

    Google Scholar 
    Maynard-Smith, J. Evolution and the Theory of Games (Cambridge University Press, 1982).Book 

    Google Scholar 
    Nowak, M. A. & Sigmund, K. Evolutionary dynamics of biological games. Science 303(5659), 793–799. https://doi.org/10.1126/science.1093411 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Maloney, K. O., Schmid, M. & Weller, D. E. Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods Ecol. Evol. 3, 116–128. https://doi.org/10.1111/j.2041-210X.2011.00124.x (2012).Article 

    Google Scholar 
    Cao, H., Recknagel, F. & Orr, P. T. Parameter optimization algorithms for evolving rule models applied to freshwater ecosystems. IEEE Trans. Evol. Comput. 18, 793–806. https://doi.org/10.1109/TEVC.2013.2286404 (2014).Article 

    Google Scholar 
    Naqshbandi, N., Iranmanesh, M. & Askari Hesni, M. Effects of environmental factors on species diversity of rotifers using biodiversity indicators and canonical correlation analysis (CCA). J. Aquat. Ecol. 7, 66–75 https://www.sid.ir/en/journal/ViewPaper.aspx?id=661950 (2017).Weisse, M. & Frahm, A. Species-specific interactions between small planctonic ciliates (Urotricha spp.) and rotifers (Keratella spp.). J. Plank. Res. 23, 1329–1338 (2001).Article 

    Google Scholar 
    Sokal, R. R. & Rohlf, F. J. The comparison of dendrograms by objective methods. Taxon 11, 33–40 (1962).Article 

    Google Scholar 
    Pomerleau, C., Sastri, A. R. & Beisner, B. E. Evaluation of functional trait diversity for marine zooplankton communities in the Northeast subarctic Pacific Ocean. J. Plankton Res. 37, 712–726. https://doi.org/10.1093/plankt/fbv045 (2015).Article 

    Google Scholar 
    Hopcroft, R. R., Kosobokova, K. N. & Pinchuk, A. I. Zooplankton community patterns in the Chukchi Sea during summer 2004. Deep-Sea Res. II(57), 27–39. https://doi.org/10.1016/j.dsr2.2009.08.003 (2010).ADS 
    Article 

    Google Scholar 
    Neumann, L. S. et al. Connectivity between coastal and oceanic zooplankton from Rio Grande do Norte in the Tropical Western Atlantic. Front. Mar. Sci. 6, 00287. https://doi.org/10.3389/fmars.2019.00287 (2019).Article 

    Google Scholar 
    Benedetti, F., Ayata, S.-D., Irisson, J.-O., Adloff, F. & Guilhaumon, F. Climate change may have minor impact on zooplankton functional diversity in the Mediterranean Sea. Divers. Distrib. 25, 568–581. https://doi.org/10.1111/ddi.12857 (2019).Article 

    Google Scholar 
    Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
    Google Scholar 
    O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).CAS 
    Article 

    Google Scholar 
    Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–867 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    Jasnos, K., KoƂba, P., Biernat, H. & Noga, B. The results of the hydrogeological research leading to know and develop the resources of thermal water in the KleszczĂłw district. Modelowanie InĆŒynierskie 45, 14 (2012).
    Google Scholar 
    Rybak, J. I. & BƂędzki, L. A Freshwater Planktonic Crustaceans (Warsaw University Press, 2010).
    Google Scholar 
    Kim, H.-W., Hwang, S.-J. & Joo, G.-J. Zooplankton grazing on bacteria and phytoplankton in a regulated large river (Nakdong River, Korea). J. Plankton Res. 22, 1559–1577 (2000).CAS 
    Article 

    Google Scholar 
    Moreira, F. W. A. et al. Assessing the impacts of mining activities on zooplankton functional diversity. Acta Limn. Bras. 28, e7. https://doi.org/10.1590/S2179-975X0816 (2016).Article 

    Google Scholar 
    Obertegger, U. & Flaim, G. Taxonomic and functional diversity of rotifers, what do they tell us about community assembly?. Hydrobiologia 823, 79–91. https://doi.org/10.1007/s10750-018-3697-6 (2018).Article 

    Google Scholar 
    Ejsmont-Karabin, J., Radwan, S. & BielaƄska-Grajner, I. Rotifers. Monogononta–atlas of species. Polish freshwater fauna (University of ƁódĆș, ƁódĆș, 2004).
    Google Scholar 
    Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol Oceanogr. 52, 886–895. https://doi.org/10.4319/lo.2007.52.2.0886 (2007).ADS 
    Article 

    Google Scholar 
    Huntley, M. E. & Lopez, M. D. Temperature-dependent production of marine copepods: A global synthesis. Am. Nat. 140, 201–242. https://doi.org/10.1086/285410 (1992).CAS 
    Article 
    PubMed 

    Google Scholar 
    Olonscheck, D., Hofmann, M., Worm, B. & Schellnhuber, H. J. Decomposing the effects of ocean warming on chlorophyll a concentrations into physically and biologically driven contributions. Environ. Res. Lett. 8, 014043. https://doi.org/10.1088/1748-9326/8/1/014043 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Hillebrand, H. et al. Goldman revisited: Faster-growing phytoplankton has lower N:P and lower stoichiometric flexibility. Limnol. Oceanogr. 58, 2076–2088. https://doi.org/10.4319/lo.2013.58.6.2076 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Kruk, M., Kobos, J., Nawrocka, L. & Parszuto, K. Positive and negative feedback loops in nutrient phytoplankton interactions related to climate dynamics factors in a shallow temperate estuary (Vistula Lagoon, southern Baltic). J. Mar. Syst. 180, 49–58. https://doi.org/10.1016/j.jmarsys.2018.01.003 (2018).Article 

    Google Scholar 
    Santer, B. & Hansen, A.-M. Diapause of Cyclops vicinus (Uljanin) in Lake Sþbyga˚ rd: Indication of a risk-spreading strategy. Hydrobiologia 560, 217–226. https://doi.org/10.1007/s10750-005-1067-7 (2006).Article 

    Google Scholar 
    Mayer, J. et al. Seasonal successions and trophic relations between phytoplankton, zooplankton, ciliate and bacteria in a hypertrophic shallow lake in Vienna, Austria. Hydrobiologia 342(343), 165–174 (1997).Article 

    Google Scholar 
    Galir Balkić, A., Ternjej, I. & Ơpoljar, M. Hydrology driven changes in the rotifer trophic structure and implications for food web interactions. Ecohydrology 11, 1917. https://doi.org/10.1002/eco.1917 (2018).Article 

    Google Scholar 
    GoĆșdziejewska, A. M., GwoĆșdzik, M., Kulesza, S., Bramowicz, M. & KoszaƂka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9, 16113. https://doi.org/10.1038/s41598-019-52542-6 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    GoĆșdziejewska, A. M., Skrzypczak, A. R., KoszaƂka, J. & Bowszys, M. Effects of recreational fishing on zooplankton communities of drainage system reservoirs at an open-pit mine. Fish. Manag. Ecol. 27, 279–291. https://doi.org/10.1111/fme.12411 (2020).Article 

    Google Scholar 
    GoĆșdziejewska, A. M., Skrzypczak, A. R., Paturej, E. & KoszaƂka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 419, 33. https://doi.org/10.1051/kmae/2018020 (2018).Article 

    Google Scholar 
    von Flössner, D. Krebstiere (Branchiopoda, FischlÀuse, Branchiura (VEB Gustav Fischer Verlag, Jena, 1972).
    Google Scholar 
    Koste, W. Rotatoria. Die RĂ€dertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband, 52–570, (GebrĂŒder Borntraeger, Berlin, 1978).Streble H. & Krauter D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des SĂŒÎČwassers. (Kosmos Gesellschaft der Naturfreunde Franckh’sche Verlagshandlung, Stuttgart, 1978).BƂędzki, L. A. & Rybak, J. I. Freshwater crustacean zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida). Key to species identification with notes on ecology, distribution, methods and introduction to data analysis. (Springer, Switzerland, 2016).Bottrell, H. H. et al. Review of some problems in zooplankton production studies. Norw. J. Zool. 24, 419–456 (1976).
    Google Scholar 
    Ejsmont-Karabin, J. Empirical equations for biomass calculation of planktonic rotifers. Pol. Arch. Hydr. 45, 513–522 (1998).
    Google Scholar 
    APHA. Standard methods for the examination of water and wastewater, 20th ed.. (American Public Health Association, Washington, DC, 1999).Wei, Z.-G. et al. Comparison of methods for picking the operational taxonomic units from amplicon sequences. Front. Microbiol. 24, 644012. https://doi.org/10.3389/fmicb.2021.644012 (2021).Article 

    Google Scholar 
    Sgalella. Kaggle. https://www.kaggle.com/sgalella/correlation-heatmaps-with-hierarchical-clustering (2019).Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
    Article 

    Google Scholar 
    Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. 22 ACM SIGKDD Conference on Knowledge, Discovery and Data mining, 12–17 August, San Francisco. https://doi.org/10.1145/2939672.2939785 (2016).Kirpal, E. Kaggle. https://www.kaggle.com/eshaan90/ensembles-and-model-stacking (2019).Brownlee, J. Github. https://github.com/datamangit/codes_for_articles/blob/master/Explain%20your%20model%20with%20the%20SHAP%20values%20for%20article.ipynb (2021).Rathi, P. Toward Data Science. https://towardsdatascience.com/a-novel-approach-to-feature-importance-shapley-additive-explanations-d18af30fc21 (2020). More