Philippa Kaur

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, Trédarzec, France) to the Ifremer rearing facility (Plouzané, 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 (Tricaïne; 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

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

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. & Węgleńska, T. Disturbances in zooplankton seasonality in Lake Gosławskie (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 Søbygaard, 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 Müggelsee (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. & Hämmerli, 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 
Štrumbelj, 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., Kołba, P., Biernat, H. & Noga, B. The results of the hydrogeological research leading to know and develop the resources of thermal water in the Kleszczów district. Modelowanie Inżynierskie 45, 14 (2012).
Google Scholar 
Rybak, J. I. & Błędzki, 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. & Bielańska-Grajner, I. Rotifers. Monogononta–atlas of species. Polish freshwater fauna (University of Łódź, Łódź, 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 Søbyga˚ 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. & Špoljar, 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 
Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, 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 
Goździejewska, A. M., Skrzypczak, A. R., Koszałka, 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 
Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, 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, Fischläuse, Branchiura (VEB Gustav Fischer Verlag, Jena, 1972).
Google Scholar 
Koste, W. Rotatoria. Die Rädertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband, 52–570, (Gebrüder Borntraeger, Berlin, 1978).Streble H. & Krauter D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des Süβwassers. (Kosmos Gesellschaft der Naturfreunde Franckh’sche Verlagshandlung, Stuttgart, 1978).Błędzki, 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

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

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, Ōmura 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

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. & Côté, 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 
Korpimäki, 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., Péron, 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 
Sæther, 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).González, L. M., Margalida, A., Sánchez, 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).García, F. & Marín, C. Doñana: water and biosphere. (Spanish Ministry of the Environment, 2006).Díaz-Paniagua, C. & Aragonés, 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., Díaz-Delgado, R. & Aragonés, 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 Adélie 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

Nansen, C., Phillips, T. W., Parajuleeb, M. N. & Franqui, R. A. Comparison of direct and indirect sampling procedures for Plodia interpunctella in a maize storage facility. J. Stored Prod. Res. 40, 151–168 (2004).Article 

Google Scholar 
Gerken, A. R. & Campbell, J. F. Using long-term capture data to predict Trogoderma variabile Ballion and Plodia interpunctella (Hübner) population patterns. Insects 10, 93. https://doi.org/10.3390/insects10040093 (2019).Article 
PubMed Central 

Google Scholar 
Athanassiou, C. G. & Buchelos, C. T. Grain properties and insect distribution trends in silos of wheat. J. Stored Prod Res. 88, 101632 (2020).Article 

Google Scholar 
Campbell, J., Mullen, M. & Dowdy, A. Monitoring stored-product pests in food processing plants with pheromone trapping, contour mapping, and mark-recapture. J. Econ. Entomol. 95, 1089–1101 (2002).CAS 
PubMed 
Article 

Google Scholar 
Arbogast, R. T., Weaver, D. K., Kendra, P. E. & Brenner, R. J. Implications of spatial distribution of insect populations in storage ecosystems. Environ. Entomol. 27, 202–216 (1998).Article 

Google Scholar 
Brenner, R. J., Focks, D. A., Arbogast, R. T., Weaver, D. K. & Shuman, D. Practical use of spatial analysis in precision targeting for integrated pest management. Am. Entomol. 44, 79–102 (1998).Article 

Google Scholar 
Arbogast, R. T., Kendra, P. E., Mankin, R. W. & McGovern, J. E. Monitoring insect pests in retail stores by trapping and spatial analysis. J. Econ. Entomol. 93, 1531–1542 (2000).CAS 
PubMed 
Article 

Google Scholar 
Arthur, F. & Phillips, T.W. Stored-product insect pest management and control. In Food Plant Sanitation; Hui, Y.H., Bruinsma, B.L., Gorham, J.R., Nip, W.-K., Tong, P.S., Ventresca, P., Eds.; Marcel Dekker, Inc, pp. 341–348(2003).Campbell, J. F., Toews, M. D., Arthur, F. H. & Arbogast, R. T. Long-term monitoring of Tribolium castaneum in two flour mills: Seasonal patterns and impact of fumigation. J. Econ. Entomol. 103, 991–1001 (2010).PubMed 
Article 

Google Scholar 
Doud, C. W. & Phillips, T. W. Activity of Plodia interpunctella (Lepidoptera: Pyralidae) in and around flour mills. J. Econ. Entomol. 93, 1842–1847 (2000).CAS 
PubMed 
Article 

Google Scholar 
Campbell, J. & Mullen, M. Distribution and dispersal behavior of Trogoderma variabile and Plodia interpunctella outside a food processing plant. J. Econ. Entomol. 97, 1455–1464 (2004).CAS 
PubMed 
Article 

Google Scholar 
Larson, Z., Subramanyam, B. & Herrman, T. Stored-product insects associated with eight feed mills in the Midwestern United States. J. Econ. Entomol. 101, 998–1005 (2008).PubMed 
Article 

Google Scholar 
Trematerra, P., Paula, M. C., Sciarretta, A. & Lazzari, S. Spatio-temporal analysis of insect pests infesting a paddy rice storage facility. Neotrop. Entomol. 33, 469–479 (2004).Article 

Google Scholar 
Arthur, F. H., Campbell, J. F. & Toews, M. D. Distribution, abundance, and seasonal patterns of Plodia interpunctella (Hübner) in a commercial food storage facility. J. Stored Prod. Res. 53, 7–14 (2013).Article 

Google Scholar 
McKay, T., White, A. L., Starkus, L. A., Arthur, F. H. & Campbell, J. F. Seasonal patterns of stored-product insects at a rice mill. J. Econ. Entomol. 110, 1366–1376 (2017).PubMed 
Article 

Google Scholar 
Roesli, R., Subramanyam, B., Fairchild, F. J. & Behnke, K. C. Trap catches of stored-product insects before and after heat treatment in a pilot feed mill. J. Stored Prod. Res. 39, 521–540 (2003).Article 

Google Scholar 
Campbell, J., Ching’oma, G.M., Toews, M.D. & Ramaswamy, S. Spatial distribution and movement patterns of stored-product insects. In Proceedings of the 9th International Working Conference on Stored Product Protection, Campinas, Sao Paulo, Brazil, 15–18 October 2006; Lorini, I., Bacaltchuk, B., Beckel, H., Deckers, D., Sundfeld, E., Santos, J.P.D., Biagi, J.D., Celaro, J.C., Faroni, L.R.D., Bortolini, L.D.F., Eds.; Brazilian Post-harvest Association—ABRAPOS: Passo Fundo, RS, Brazil, p. 18 (2006).Trematerra, P., Gentile, P., Brunetti, A., Collins, L. & Chambers, J. Spatio-temporal analysis of trap catches of Tribolium confusum du Val in a semolina-mill, with a comparison of female and male distributions. J. Stored Prod. Res. 43, 315–322 (2007).Article 

Google Scholar 
Semeao, A. A., Campbell, J. F., Whitworth, R. J. & Sloderbeck, P. E. Influence of environmental and physical factors on capture of Tribolium castaneum (Coleoptera: Tenebrionidae) in a flour mill. J. Econ. Entomol. 105, 686–702 (2012).PubMed 
Article 

Google Scholar 
Campbell, J.F., Perez-Mendoza, J. &Weier, J. Insect Pest Management Decisions in Food Processing Facilities. In Stored Product Protection; Hagstrum, D.W., Phillips, T.W., Cuperus, G., Eds.; Kansas State University, pp. 219–232 (2012).Mohandass, S., Arthur, F. H., Zhu, K. & Throne, J. E. Biology and management of Plodia interpunctella (Lepidoptera:Pyralidae) in stored products. J. Stored Prod. Res. 43, 302–311 (2007).Article 

Google Scholar 
Hamlin, J.C., Reed, W.D. & Phillips, M.E. Biology of the Indianmeal Moth on Dried Fruits in California. USDA Technical Bulletin No. 242, (1931)Hagstrum, D.W. & Subramanyam, B. Review of Stored-Product Insect Resource. AACC International (2009).Soderstrom, T., Stoica, P. & Trulsson, E. Instrumental variable methods for closed loop systems. IFAC 10th Triennial World Congress, Munich, FRG. pp. 363–368(1987).Johnson, J. A., Valero, K. A., Hannel, M. M. & Gill, R. F. Seasonal occurrence of post harvest dried fruit insects and their parasitoids in a culled fig warehouse. J. Econ. Entomol. 93, 1380–1390 (2000).CAS 
PubMed 
Article 

Google Scholar 
Nansen, C., Subramanyam, B. & Roesli, R. Characterizing spatial distribution of trap captures of beetles in retail pet stores using SADIE® software. J. Stored Prod. Res. 40, 471–483 (2004).Article 

Google Scholar 
Phillips, T.W., Berbert, R.C. &Cuperus, G.W. Post-harvest integrated pest management. In: Francis, F.J. (Ed.), Encyclopedia of Food Science and Technology. 2nd ed. Wiley Inc., pp. 2690–2701(2000).Phillips,T.W., Cogan, P.M. & Fadamiro, H.Y. Pheromones. In: Subramanyam, B., Hagstrum, D.W. (Eds.), Alternatives to Pesticides in Stored-product IPM. Kluwer Academic Publishers, pp. 273–302 (2000).Mullen, M. A. & Dowdy, A. K. A pheromone-baited trap for monitoring the Indian meal moth, Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae). J. Stored Prod. Res. 37, 231–235 (2001).PubMed 
Article 

Google Scholar 
Nansen, C. & Phillips, T. W. Ovipositional responses of the Indian meal moth, Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) to oils. Ann. Entomol. Soc. Am. 96, 524–531 (2003).Article 

Google Scholar 
Hagstrum, D. W. Using five sampling methods to measure insect distribution and abundance in bins storing wheat. J. Stored Prod. Res. 36, 253–262 (2000).CAS 
PubMed 
Article 

Google Scholar 
Athanassiou, C. G., Kavallieratos, N. G., Sciarretta, A. & Trematerra, P. Mating disruption of Ephestia kuehniella (Zeller) (Lepidoptera: Pyralidae) in a storage facility: spatio-temporal distribution changed after long-term application. J. Stored Prod. Res. 67, 1–12 (2016).Article 

Google Scholar 
Lee, W. H., Jung, J. M., Kim, J., Lee, H. & Jung, S. Analysis of the spatial distribution and dispersion of Plodia interpunctella (Lepidoptera: Pyralidae) in South Korea. J. Stored Prod. Res. 86, 101577 (2020).Article 

Google Scholar 
Gerken, A.R. & Campbell, J.F. Spatial and temporal variation in stored-product insect pest distributions and implications for pest management in processing and storage facilities. Ann. Entomol. Soc. Am. saab049(2021).Athanassiou, C. G. & Buchelos, CTh. Detection of stored-wheat beetle species and estimation of population density using unbaited probe traps and grain trier samples. Ent. Exp. et Applic. 98, 67–78 (2001).Article 

Google Scholar 
Subramanyam, B. & Hagstrum, D.W. Sampling. In: Subramanyam B. & Hagstrum D.W. (eds), Integrated Management of Insects in Stored Products. Marcel Dekker Inc., pp. 135–193 (1995).Morrison, W. R. et al. Aeration to manage insects in wheat stored in the Balkan peninsula: Computer simulations using historical weather data. Agronomy 10, 1927 (2020).Article 

Google Scholar 
Toews, M. D., Campbell, J. F. & Arthur, F. H. Temporal dynamics and response to fogging or fumigation of stored-product Coleoptera in a grain processing facility. J. Stored Prod. Res. 42, 480–498 (2006).Article 

Google Scholar 
Buckman, K. A., Campbell, J. F. & Subramanyam, B. Tribolium castaneum (Coleoptera: Tenebrionidae) associated with rice mills: Fumigation efficacy and population rebound. J. Econ. Entomol. 106, 499–512 (2013).PubMed 
Article 

Google Scholar 
Campbell, J. F., Buckman, K. A., Fields, P. G. & Subramanyam, Bh. Evaluation of structural treatment efficacy against Tribolium castaneum and Tribolium confusum (Coleoptera: Tenebrionidae) using meta-analysis of multiple studies conducted in food facilities. J. Econ. Entomol. 108, 2125–2140 (2015).CAS 
PubMed 
Article 

Google Scholar 
Levene, H. Robust tests for equality of variances. In Ingram Olkin; Harold Hotelling; et al. (eds.). Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press. pp. 278–292(1960).SAS Institute. SAS/STAT 9.2 User’s guide. SAS Institute (2008).Taylor, L. R. Aggregation, variance and mean. Nature 189, 732–735 (1961).ADS 
Article 

Google Scholar 
Iwao, S. A new method of sequential sampling to classify populations according to a critical density. Res. Popln. Ecol. 16, 281–288 (1975).
Google Scholar 
Green, R. H. Measurement of non-randomness in spatial distribution. Res. Popln. Ecol. 8, 1–17 (1966).
Google Scholar 
Hillhouse, T. L. & Pitre, H. N. Comparison of sampling techniques to obtain measurements of insect populations on soybeans. J. Econ. Entomol. 67, 411–414 (1974).Article 

Google Scholar 
Cassie, R. M. Frequency distribution models in the ecology of plankton and other organisms. J. Anim. Ecol. 31, 65–92 (1962).Article 

Google Scholar 
Southwood, T. R. E. Ecological Methods, with Particular Reference to the Study of Insect Population (Chapman and Hall, 1995).
Google Scholar 
Costa, M. G., Barbosa, J. C., Yamamoto, P. T. & Leal, R. M. Spatial distribution of Diaphorina citri Kuwayama (Hemiptera: Psyllidae) in citrus orchards. Scientia Agric 67, 546–554 (2010).Article 

Google Scholar 
Patil, G. P. & Stiteler, W. M. Concepts of aggregation and their quantification: A critical review with some new result and applications. Pop. Ecol. 15, 238–254 (1974).Article 

Google Scholar 
David, F. N. & Moor, P. G. Notes on contagious distribution in plant populations. Ann. Bot. 18, 47–53 (1954).Article 

Google Scholar 
Lloyd, M. Mean crowding. J. Anim. Ecol. 36, 1–30 (1967).Article 

Google Scholar 
Southwood, T. R. E. & Henderson, P. A. Ecological Methods 3rd edn. (Blackwell Sciences, 2000).
Google Scholar 
Feng, M. G. & Nowierski, R. M. Spatial distribution and sampling plans for four species of cereal aphids (Homoptera: Aphididae) infesting spring wheat in southwestern Idaho. J. Econ. Entomol. 85, 830–837 (1992).Article 

Google Scholar  More

Structural characterization of slag samplesThe FTIR spectra of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) are presented in Fig. 1.Figure 1FTIR spectra of slag samples.Full size imageBy analysing the spectrum (detailed figure) in the range of 700–1100 cm−1, it can be found that there are obvious absorption peaks in the spectrum of all the slag samples. The granulated blast furnace slag shows the characteristic absorption bands at 3640, 1418, 980, 944, 861, 753 and 710 cm−1. The band at 3640 cm−1 is assigned to the stretching vibration of the hydroxyl group originated from the weakly absorbed water molecules on the slag surface24. The characteristic absorption bands at 1418, 861 and 710 cm−1 are ascribed to the asymmetric stretching mode and bending mode of carbonate group, respectively and the band at 980 cm−1 are attributable to the stretching vibrations of Si–O25. The band at 944 and 752 cm−1 represent the internal vibration of [SiO4]4− and [AlO4]5− tetrahedral and comes from Si (Al)–O-antisymmetric stretching vibration26.The different vibration modes for the sample of waste slag can be observed in the FTIR spectrum. The absorption bands shown are at 1418, 873, 712, 667 and 419 cm−1. The peak at 1418 cm−1 is assigned to the asymmetric stretching mode and bending mode of carbonate group. Calcite phase is confirmed by characteristic peaks at 712 cm−1 (ʋ2 out of plane bending vibration of the CO3−2 ion) and 873 cm−1 peak (ʋ2 split in-plane bending vibrations of the CO3−2 ion27. Calcium aluminate phase is identified by characteristic peak at 419 cm−128. Peak around 667 cm−1 is described as absorption band for different M–O (metal oxide) such as Al–O, Fe–O, Mg–O etc.29.In the case of combination of both 50% granulated blast furnace slag and 50% waste slag dumped in landfill the intensity of absorption peaks is smaller in comparison with Sample 1 and Sample 2 of slag. The characteristic absorption peaks (978 and 753 cm−1) which correspond with characteristic peaks of Sample 1 are shifted compared to the Sample 1, assigned to the stretching vibrations of Si–O and to the Si (Al)–O-antisymmetric stretching vibration, respectively, can provide important evidence of chemical interaction between Sample 1 and Sample 2. The decrease of the intensity of the bands appearing at 875 and 709 cm−1 cans be attributed to overlapping the vibrations of the CO3−2 ion from calcite phase.Figure 2 presents the SEM micrographs of the slag samples (Sample 1–3). One can see the characteristic morphology- the sizes and the forms of the slag samples.Figure 2SEM images of slag samples.Full size imageAt larger magnifications it can be observed that the surface is rough and uneven, and one can notice rounded grain-like rugged formations. The slag samples display aggregated particles with average diameter of a few microns. Also, in these rounded formations it can be seen different morphologies like spheres, rods, boards specific each compound/phase from metallurgical slags.Figure 3 illustrates the EDX elemental analysis of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3).Figure 3EDX elemental map of slag samples.Full size imageOne can observe that the predominant elements in the examined area are constated in carbon, oxygen, calcium, and iron, confirming the FTIR spectra.Figure 4 shows EDX spectra of slag samples recorded on different selected punctual area, to obtain more information about the elemental composition of specific areas. For all the tested slag samples have similar elements content.Figure 4EDX spectra analysis of slag samples.Full size imageThe selected punctual areas are highlighted thus: the spheric structure are with yellow line and the structure like boards are with green line for all the analysed slag samples. In the case of Sample 1 for both structures the values of chemical elements present are similar and the silicon has a higher value at spheric structure which can be correlated with the presence of silica (SiO2). The higher content of calcium reveals that the Sample 1 is blast furnace slag dominated by calcium and silicon compositions. In the case of slag dumped in landfill (Sample 2) the content of carbon increase for both structures and some chemical elements like titanium, barium, manganese doesn`t appear in EDX spectra and the explanation for this phenomenon is that the slag was dumped in landfill for more than 30 years. One can observe for combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) that the values of all the chemical elements for both spheric and board-like structure are between the first two samples, confirming the FTIR spectra regarding chemical interaction between Sample 1.XRD patterns of the slag samples with the phases identified are shown in Fig. 5. Sample 1 show minor peaks of free CaO and MgO, which may be deleterious and cause reduction in strength. The phases and amorphous contents of the Sample 1 granulated blast furnace slag are broadly consistent with literature30. Sample 3 of slag consists of crystalline phase – Ca2Mg2SiO7, Ca2Fe2AlO5, CaCO3 and CaO as observed by the XRD analysis. In terms of the relations of phase thermal equilibrium, the compounds identified form an isomorphic series of melilites that is specific to basic metallurgical slags.Figure 5X-ray diffraction patterns of slag samples.Full size imageIn Table 1 are presented the values expressed as ppm of chemical element detected in slag samples (Sample 1, 2 and 3).Table 1 XRF analysis of the slag samples.Full size tableThe results show a large quantity of calcium in all three samples of slag. Also, the elements detected such as Fe, Al, Mg and Si are in accordance with XRD spectra.Physical–chemical characterization of soil-slag mixturesThe chemical composition of the major elements that compound the soil, soil- slag and slag samples was determined by XRF. The values expressed as ppm of chemical elements are presented in Table 2. In the case of soil sample the content of the main constituents is iron, titanium, manganese, and potentially toxic elements (PTE) such as arsenic, zinc, copper, and cobalt. For soil-slag 1 with weight ratio soil: slag (1:1) it can be observed the disappearance of the potentially toxic elements (PTE) founded in soil sample and the decrease of concentration value of zinc. When the weight ratio of slag increases at 3 (soil-slag 2 sample) the values of main component increased in accordance with values of slag sample, but in the case of soil-slag 3 sample where the weight ratio of soil is bigger (3) it can be observed the cobalt presence. Based on these XRF results we can say that take place an elimination of potentially toxic elements in contaminated soil by applying slag in a bigger proportion.Table 2 XRF analysis of the soil-slag samples.Full size tableWith the aid of a pH meter, CONSORT C 533 the important parameters of soil and slag solutions were measured as: the pH, conductivity, and the salinity, as shown in Table 3. The data presented in Table 3 suggest that the soil sampled has the pH = 5.2 corresponding to a medium acid soil, which does not sustain a high fertility and is not able to offer proper conditions for crops. Also, the pH of soil has important influence on soil fertility, decreases the availability of essential elements and the activity of soil microorganisms which can determine calcium and magnesium deficiency in plants and decreases phosphorous availability. The pH value of slag solution (12.5) corresponds as strongly basic character which is beneficial in amelioration process of acidic soils and the presence of this type of slag sustain the improving of soil characteristics, too. For the soil-slag samples the pH value increase with the increasing of the weight ratio of slag and the mixtures soil-slag obtained can be framed into the category of weakly alkaline soils.Table 3 The physical–chemical characteristics of soil and slag solutions.Full size tableThe data given in Table 4 show that the humidity of soil is bigger and decreases in soil-slag samples with adding of slag content. The values of total soil-slag porosity are between 40 and 50% and depends on the density and apparent density of the soil being influenced by the mineralogical composition, the content of organic matter and the degree of compaction and loosening of the soil, the crystalline structure of soil minerals.Table 4 The physical–chemical characteristics of soil-slag samples.Full size tableConsidering the structural and morphological characterization of the investigated slag samples we propose a recipe of blast furnace slag and of waste slag dumped in landfill in accordance with the waste directive 2008/98/EC regarding the strategic goal of EU to a complete elimination of the disposal of wastes. The slag dump of Steel Plant of Galati has an enormous quantity of unused waste slag which may be mixed with granulated blast furnace slag, to save the natural resources used as raw materials in the metallurgical technological process.The presence of Ca2+ in the composition of the slag can maintain high alkalinity in the soil for a long time in the natural environment. The alkaline pH of the soil may contribute to a decrease the available concentration of heavy metals by reducing metal mobility and bonding metals into more stable fractions. One of the objectives of this research is improving the quality of the environment by using the mixture between two different slags on agricultural lands and reintroducing them in the agricultural centre, especially in acid soils. Acidic soils are characterized by an acidic pH that has spread in recent years due to excessive fertilizers or far too aggressive work31. The production is significantly influenced, and the treatment of acid soils is usually done using a series of natural materials (lime, dolomite), the consumption being approx. 20 t/hectare depending on the acidity of the soil and the nature of the plants grown on the respective surfaces.Our research consists in improving the characteristics and qualities of the acidic soils and helping to reintroduce it into the agricultural circuit by transforming a waste into a new material friendly-environmental, the mixture of blast furnace slag and waste slag dumped in landfill. More