Vik JO, Brinch CN, Boutin S, Stenseth NC. Interlinking hare and lynx dynamics using a century’s worth of annual data. Popul Ecol. 2008;50:267–74.
Google Scholar
Luo G, Han Q, Zhou D, Li L, Chen X, Li Y, et al. Moderate grazing can promote aboveground primary production of grassland under water stress. Ecol Complex. 2012;11:126–36.
Google Scholar
McNaughton S. Grazing as an optimization process: grass-ungulate relationships in the Serengeti. Am Nat. 1979;113:691–703.
Google Scholar
Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.
Google Scholar
Zhang Z, Yan C, Krebs CJ, Stenseth NC. Ecological non-monotonicity and its effects on complexity and stability of populations, communities and ecosystems. Ecol Modell. 2015;312:374–84.
Google Scholar
Devin S, Giamberini L, Pain-Devin S. Variation in variance means more than mean variations: what does variability tell us about population health status? Environ Int. 2014;73:282–7.
Google Scholar
Steudel B, Hector A, Friedl T, Löfke C, Lorenz M, Wesche M, et al. Biodiversity effects on ecosystem functioning change along environmental stress gradients. Ecol Lett. 2012;15:1397–405.
Google Scholar
Christin S, Hervet É, Lecomte N. Applications for deep learning in ecology. Methods Ecol Evol. 2019;10:1632–44.
Google Scholar
De’ath G, Fabricius KE. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology. 2000;81:3178–92.
Google Scholar
Larsen PE, Field D, Gilbert JA. Predicting bacterial community assemblages using an artificial neural network approach. Nat Methods. 2012;9:621–5.
Google Scholar
Sperlea T, Kreuder N, Beisser D, Hattab G, Boenigk J, Heider D. Quantification of the covariation of lake microbiomes and environmental variables using a machine learning‐based framework. Mol Ecol. 2021;30:2131–44.
Google Scholar
Schiel DR, Lilley SA, South PM. Ecological tipping points for an invasive kelp in rocky reef algal communities. Mar Ecol Prog Ser. 2018;587:93–104.
Google Scholar
Robinson B, Cohen JS, Herman JD. Detecting early warning signals of long-term water supply vulnerability using machine learning. Environ Model Softw. 2020;131:104781.
Google Scholar
Hessen DO, Andersen T, Lyehe A. Carbon metabolism in a humic lake: pool sizes and cycling through zooplankton. Limnol Oceanogr. 1990;35:84–99.
Google Scholar
Nebbioso A, Piccolo A. Molecular characterization of dissolved organic matter (DOM): a critical review. Anal Bioanal Chem. 2013;405:109–24.
Google Scholar
Coble PG, Lead J, Baker A, Reynolds DM, Spencer RG (eds). Aquatic organic matter fluorescence. Cambridge: Cambridge University Press; 2014.
Finstad AG, Andersen T, Larsen S, Tominaga K, Blumentrath S, de Wit HA, et al. From greening to browning: catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes. Sci Rep. 2016;6:1–8.
Google Scholar
Monteith DT, Stoddard JL, Evans CD, de Wit HA, Forsius M, Høgåsen T, et al. Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature. 2007;450:537–40.
Google Scholar
de Wit HA, Valinia S, Weyhenmeyer GA, Futter MN, Kortelainen P, Austnes K, et al. Current browning of surface waters will be further promoted by wetter climate. Environ Sci Technol Lett. 2016;3:430–5.
Google Scholar
Meyer-Jacob C, Tolu J, Bigler C, Yang H, Bindler R. Early land use and centennial scale changes in lake-water organic carbon prior to contemporary monitoring. Proc Natl Acad Sci USA. 2015;112:6579–84.
Google Scholar
Nelson DM, Smith W Jr. Sverdrup revisited: critical depths, maximum chlorophyll levels, and the control of Southern Ocean productivity by the irradiance‐mixing regime. Limnol Oceanogr. 1991;36:1650–61.
Google Scholar
Thrane J-E, Hessen DO, Andersen T. The absorption of light in lakes: negative impact of dissolved organic carbon on primary productivity. Ecosystems. 2014;17:1040–52.
Google Scholar
Tranvik LJ. Allochthonous dissolved organic matter as an energy source for pelagic bacteria and the concept of the microbial loop. In: Salonen KKT, Jones RI, editors. Dissolved organic matter in lacustrine ecosystems. Vol. 73. Dordrecht: Springer; 1992. p. 107–14.
Bastviken D, Tranvik LJ, Downing JA, Crill PM, Enrich-Prast A. Freshwater methane emissions offset the continental carbon sink. Science. 2011;331:50.
Google Scholar
Cole JJ, Caraco NF, Kling GW, Kratz TK. Carbon dioxide supersaturation in the surface waters of lakes. Science. 1994;265:1568–70.
Google Scholar
Yang H, Andersen T, Dörsch P, Tominaga K, Thrane JE, Hessen DO. Greenhouse gas metabolism in Nordic boreal lakes. Biogeochemistry. 2015;126:211–25.
Google Scholar
Cottrell MT, Kirchman DL. Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low-and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.
Google Scholar
Crump BC, Kling GW, Bahr M, Hobbie JE. Bacterioplankton community shifts in an arctic lake correlate with seasonal changes in organic matter source. Appl Environ Microbiol. 2003;69:2253–68.
Google Scholar
Jones SE, Newton RJ, McMahon KD. Evidence for structuring of bacterial community composition by organic carbon source in temperate lakes. Environ Microbiol. 2009;11:2463–72.
Google Scholar
Kritzberg ES, Langenheder S, Lindström ES. Influence of dissolved organic matter source on lake bacterioplankton structure and function–implications for seasonal dynamics of community composition. FEMS Microbiol Ecol. 2006;56:406–17.
Google Scholar
Lindström ES. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb Ecol. 2000;40:104–13.
Google Scholar
D’Andrilli J, Junker JR, Smith HJ, Scholl EA, Foreman CM. DOM composition alters ecosystem function during microbial processing of isolated sources. Biogeochemistry. 2019;142:281–98.
Google Scholar
Eiler A, Langenheder S, Bertilsson S, Tranvik LJ. Heterotrophic bacterial growth efficiency and community structure at different natural organic carbon concentrations. Appl Environ Microbiol. 2003;69:3701–9.
Google Scholar
Guillemette F, del Giorgio PA. Reconstructing the various facets of dissolved organic carbon bioavailability in freshwater ecosystems. Limnol Oceanogr. 2011;56:734–48.
Google Scholar
Judd KE, Crump BC, Kling GW. Variation in dissolved organic matter controls bacterial production and community composition. Ecology. 2006;87:2068–79.
Google Scholar
Romera-Castillo C, Sarmento H, Alvarez-Salgado XA, Gasol JM, Marrasé C. Net production and consumption of fluorescent colored dissolved organic matter by natural bacterial assemblages growing on marine phytoplankton exudates. Appl Environ Microbiol. 2011;77:7490–8.
Google Scholar
Kawasaki N, Benner R. Bacterial release of dissolved organic matter during cell growth and decline: molecular origin and composition. Limnol Oceanogr. 2006;51:2170–80.
Google Scholar
Battin TJ, Luyssaert S, Kaplan LA, Aufdenkampe AK, Richter A, Tranvik LJ. The boundless carbon cycle. Nat Geosci. 2009;2:598–600.
Google Scholar
Osterholz H, Singer G, Wemheuer B, Daniel R, Simon M, Niggemann J, et al. Deciphering associations between dissolved organic molecules and bacterial communities in a pelagic marine system. ISME J. 2016;10:1717–30.
Google Scholar
Cory RM, Kling GW. Interactions between sunlight and microorganisms influence dissolved organic matter degradation along the aquatic continuum. Limnol Oceanogr Lett. 2018;3:102–16.
Google Scholar
Khomich M, Kauserud H, Logares R, Rasconi S, Andersen T. Planktonic protistan communities in lakes along a large-scale environmental gradient. FEMS Microbiol Ecol. 2017;93:fiw231.
Khomich M, Davey ML, Kauserud H, Rasconi S, Andersen T. Fungal communities in Scandinavian lakes along a longitudinal gradient. Fungal Ecol. 2017;27:36–46.
Google Scholar
Andersen T, Hessen DO, Håll JP, Khomich M, Kyle M, Lindholm M, et al. Congruence, but no cascade-pelagic biodiversity across 3 trophic levels in Nordic lakes. Ecol Evol. 2020;10:8153–65.
Google Scholar
Lyche Solheim A, Rekolainen S, Moe SJ, Carvalho L, Phillips G, Ptacnik R, et al. Ecological threshold responses in European lakes and their applicability for the Water Framework Directive (WFD) implementation: synthesis of lakes results from the REBECCA project. Aquatic Ecol. 2008;42:317–34.
Google Scholar
Henriksen A, Skjelvåle BL, Mannio J, Wilander A, Harriman R, Curtis C, et al. Northern European lake survey, 1995: Finland, Norway, Sweden, Denmark, Russian Kola, Russian Karelia, Scotland and Wales. Ambio. 1998;27:80–91.
Ptacnik R, Andersen T, Brettum P, Lepistö L, Willén E. Regional species pools control community saturation in lake phytoplankton. Proc Royal Soc B. 2010;277:3755–64.
Google Scholar
Mitchell BG, Kahru M, Wieland J, Stramska M. Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples. In: Mueller JL, Fargion GS, McClain CR, editors. Ocean optics protocols for satellite ocean color sensor validation, Revision IV. Vol. 4. Greenbelt, Maryland: Goddard Space Flight Center; 2003. p. 39–64.
Weishaar JL, Aiken GR, Bergamaschi BA, Fram MS, Fujii R, Mopper K. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ Sci Technol. 2003;37:4702–8.
Google Scholar
Bricaud A, Stramski D. Spectral absorption coefficients of living phytoplankton and nonalgal biogenous matter: a comparison between the Peru upwelling areaand the Sargasso Sea. Limnol Oceanogr. 1990;35:562–82.
Google Scholar
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
Google Scholar
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.
Google Scholar
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
Google Scholar
R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [Internet]. 2017.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara RB et al. vegan: community ecology package. R package version 2.5-6. 2019. https://CRAN.R-project.org/package=vegan.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
Google Scholar
Venables W, Ripley B. Modern applied statistics with S. New York, NY: Springer; 2002.
van Buuren S. MICE: multivariate imputation by chained equations. R package version 2.22. 2015. https://mran.microsoft.com/snapshot/2014-11-17/web/packages/mice/mice.pdf. Accessed 12 Aug 2019.
Minchin PR. An evaluation of the relative robustness of techniques for ecological ordination. In: Prentice IC, van der Maarel E, editors. Theory and models in vegetation science. Dordrecht: Springer; 1987. p. 89–107.
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–6.
Google Scholar
He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Malik J, Wang X, Yan S, Tang X, Jia J, editors. Proceedings of the IEEE international conference on computer vision. IEEE; 1730 Massachusetts Ave., NW Washington, DC, United States. 2015. p. 1026–34.
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, et al. Detecting novel associations in large data sets. Science. 2011;334:1518–24.
Google Scholar
Newton RJ, Jones SE, Eiler A, McMahon KD, Bertilsson S. A guide to the natural history of freshwater lake bacteria. Microbiol Mol Biol Rev. 2011;75:14–49.
Google Scholar
Zwart G, Crump BC, Kamst-van Agterveld MP, Hagen F, Han S-K. Typical freshwater bacteria: an analysis of available 16S rRNA gene sequences from plankton of lakes and rivers. Aquat Microb Ecol. 2002;28:141–55.
Google Scholar
Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.
Google Scholar
Qiu Z, Coleman MA, Provost E, Campbell AH, Kelaher BP, Dalton SJ, et al. Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp. Proc Royal Soc B. 2019;286:20181887.
Google Scholar
Kovárová-Kovar K, Egli T. Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics. Microbiol Mol Biol Rev. 1998;62:646–66.
Google Scholar
Scheffer M, Carpenter S, Foley JA, Folke C, Walker B. Catastrophic shifts in ecosystems. Nature. 2001;413:591–6.
Google Scholar
Borchani H, Varando G, Bielza C, Larranaga P. A survey on multi‐output regression. Wiley Interdiscip Rev Data Min Knowl Discov. 2015;5:216–33.
Google Scholar
del Giorgio PA, Cole JJ. Bacterial growth efficiency in natural aquatic systems. Annu Rev Ecol Evol Syst. 1998;29:503–41.
Google Scholar
Tranvik LJ. Bacterioplankton growth on fractions of dissolved organic carbon of different molecular weights from humic and clear waters. Appl Environ Microbiol. 1990;56:1672–7.
Google Scholar
Hilbe C, Nowak MA, Sigmund K. Evolution of extortion in iterated prisoner’s dilemma games. Proc Natl Acad Sci USA. 2013;110:6913–8.
Google Scholar
Palen WJ, Schindler DE, Adams MJ, Pearl CA, Bury RB, Diamond SA. Optical characteristics of natural waters protect amphibians from UV‐B in the US Pacific Northwest. Ecology. 2002;83:2951–7.
Google Scholar
Source: Ecology - nature.com