Causal networks of phytoplankton diversity and biomass are modulated by environmental context.

Biodiversity Biomass Ecosystem Phytoplankton Temperature

Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
03 03 2022
Historique:
received: 29 12 2020
accepted: 11 02 2022
entrez: 4 3 2022
pubmed: 5 3 2022
medline: 13 4 2022
Statut: epublish

Résumé

Untangling causal links and feedbacks among biodiversity, ecosystem functioning, and environmental factors is challenging due to their complex and context-dependent interactions (e.g., a nutrient-dependent relationship between diversity and biomass). Consequently, studies that only consider separable, unidirectional effects can produce divergent conclusions and equivocal ecological implications. To address this complexity, we use empirical dynamic modeling to assemble causal networks for 19 natural aquatic ecosystems (N24

Identifiants

DOI: 10.1038/s41467-022-28761-3 PMID: 35241667 PMC: PMC8894464
pubmed: 35241667
doi: 10.1038/s41467-022-28761-3
pii: 10.1038/s41467-022-28761-3
pmc: PMC8894464
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1140

Commentaires et corrections

Type : ErratumIn

Informations de copyright

© 2022. The Author(s).

Références

Tansley, A. G. The use and abuse of vegetational concepts and terms. Ecology 16, 284–307 (1935).
doi: 10.2307/1930070
Lovelock, J. E. & Margulis, L. Atmospheric homeostasis by and for the biosphere: the Gaia hypothesis. Tellus 26, 2–10 (1974).
doi: 10.3402/tellusa.v26i1-2.9731
Berendse, F. Litter decomposability—a neglected component of plant fitness. J. Ecol. 82, 187–190 (1994).
doi: 10.2307/2261398
Watson, A. J. & Lovelock, J. E. Biological homeostasis of the global environment: the parable of Daisyworld. Tellus B: Chem. Phys. Meteorol. 35, 284–289 (1983).
doi: 10.3402/tellusb.v35i4.14616
Miki, T., Ushio, M., Fukui, S. & Kondoh, M. Functional diversity of microbial decomposers facilitates plant coexistence in a plant–microbe–soil feedback model. Proc. Natl Acad. Sci. USA 107, 14251–14256 (2010).
pubmed: 20663953 pmcid: 2922608 doi: 10.1073/pnas.0914281107
Charlson, R. J., Lovelock, J. E., Andreae, M. O. & Warren, S. G. Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature 326, 655–661 (1987).
doi: 10.1038/326655a0
Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).
pubmed: 20581819 doi: 10.1038/nature09273
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
doi: 10.1146/annurev-ecolsys-120213-091917
Chang, C.-W. et al. Long-term warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks. Glob. Change Biol. 26, 6413–6423 (2020).
doi: 10.1111/gcb.15323
Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).
pubmed: 10821284 doi: 10.1038/35012241
Dee, L. E. et al. Operationalizing network theory for ecosystem service assessments. Trends Ecol. Evolution 32, 118–130 (2017).
doi: 10.1016/j.tree.2016.10.011
Snelgrove, P. V. R., Thrush, S. F., Wall, D. H. & Norkko, A. Real world biodiversity—ecosystem functioning: a seafloor perspective. Trends Ecol. Evolution 29, 398–405 (2014).
doi: 10.1016/j.tree.2014.05.002
Martin, J. H. & Fitzwater, S. E. Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic. Nature 331, 341–343 (1988).
doi: 10.1038/331341a0
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752 (2006).
pubmed: 17151666 doi: 10.1038/nature05317
Sunda, W. Feedback interactions between trace metal nutrients and phytoplankton in the ocean. Front. Microbiol. 3, 204 (2012).
Cardinale, B. J., Weis, J. J., Forbes, A. E., Tilmon, K. J. & Ives, A. R. Biodiversity as both a cause and consequence of resource availability: a study of reciprocal causality in a predator–prey system. J. Anim. Ecol. 75, 497–505 (2006).
pubmed: 16638002 doi: 10.1111/j.1365-2656.2006.01070.x
Cardinale, B. J. Biodiversity improves water quality through niche partitioning. Nature 472, 86–89 (2011).
pubmed: 21475199 doi: 10.1038/nature09904
Fetzer, I. et al. The extent of functional redundancy changes as species’ roles shift in different environments. Proc. Natl Acad. Sci. USA 112, 14888–14893 (2015).
pubmed: 26578806 pmcid: 4672811 doi: 10.1073/pnas.1505587112
Adler, P. B. et al. Productivity is a poor predictor of plant species richness. Science 333, 1750–1753 (2011).
pubmed: 21940895 doi: 10.1126/science.1204498
Waide, R. B. et al. The relationship between productivity and species richness. Annu. Rev. Ecol. Syst. 30, 257–300 (1999).
doi: 10.1146/annurev.ecolsys.30.1.257
Moran, P. A. P. The statistical analysis of the Canadian lynx cycle. Aust. J. Zool. 1, 291–298 (1953).
doi: 10.1071/ZO9530291
Deyle, E. R., May, R. M., Munch, S. B. & Sugihara, G. Tracking and forecasting ecosystem interactions in real time. Proc. R. Soc. B 283, 20152258 (2016).
pubmed: 26763700 pmcid: 4721089 doi: 10.1098/rspb.2015.2258
Ushio, M. et al. Fluctuating interaction network and time-varying stability of anatural fish community. Nature 554, 360–363 (2018).
pubmed: 29414940 doi: 10.1038/nature25504
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).
pubmed: 22678289 doi: 10.1038/nature11118
Mittelbach, G. G. et al. What is the observed relationship between species richness and productivity? Ecology 82, 2381–2396 (2001).
doi: 10.1890/0012-9658(2001)082[2381:WITORB]2.0.CO;2
Enoki, T. et al. Progress in the 21st century: a roadmap for the Ecological Society of Japan. Ecol. Res. 29, 357–368 (2014).
doi: 10.1007/s11284-014-1140-7
Harley, C. D. G. Climate change, keystone predation, and biodiversity loss. Science 334, 1124–1127 (2011).
pubmed: 22116885 doi: 10.1126/science.1210199
Hsu, S. B., Hubbell, S. & Waltman, P. A mathematical theory for single-nutrient competition in continuous cultures of micro-organisms. SIAM J. Appl. Math. 32, 366–383 (1977).
doi: 10.1137/0132030
Huisman, J., Jonker, R. R., Zonneveld, C. & Weissing, F. J. Competition for light between phytoplankton species: experimental tests of mechanistic theory. Ecology 80, 211–222 (1999).
doi: 10.1890/0012-9658(1999)080[0211:CFLBPS]2.0.CO;2
Hughes, R. A., Byrnes, J. E., Kimbro, D. L. & Stachowicz, J. J. Reciprocal relationships and potential feedbacks between biodiversity and disturbance. Ecol. Lett. 10, 849–864 (2007).
pubmed: 17663718 doi: 10.1111/j.1461-0248.2007.01075.x
Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).
doi: 10.1890/12-1399.1
Pikitch, E. K. et al. Ecosystem-based fishery management. Science 305, 346–347 (2004).
pubmed: 15256658 doi: 10.1126/science.1098222
Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).
pubmed: 22997134 doi: 10.1126/science.1227079
Takens, F. Dynamical systems and turbulence. Lect. Notes Math. 898, 366–381 (1981).
doi: 10.1007/BFb0091924
Ye, L. et al. Functional diversity promotes phytoplankton resource use efficiency. J. Ecol. 107, 2353–2363 (2019).
doi: 10.1111/1365-2745.13192
Behrenfeld, M. J. et al. Biospheric primary production during an ENSO transition. Science 291, 2594–2597 (2001).
pubmed: 11283369 doi: 10.1126/science.1055071
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
pubmed: 28869964 doi: 10.1038/nature23886
Anneville, O., Ginot, V. & Angeli, N. Restoration of Lake Geneva: expected versus observed responses of phytoplankton to decreases in phosphorus. Lakes Reserv. Res. Manag 7, 67–80 (2002).
doi: 10.1046/j.1440-169X.2002.00179.x
North, R. P., North, R. L., Livingstone, D. M., Köster, O. & Kipfer, R. Long-term changes in hypoxia and soluble reactive phosphorus in the hypolimnion of a large temperate lake: consequences of a climate regime shift. Glob. Change Biol. 20, 811–823 (2014).
doi: 10.1111/gcb.12371
Smith, V. H. & Schindler, D. W. Eutrophication science: where do we go from here? Trends Ecol. Evolution 24, 201–207 (2009).
doi: 10.1016/j.tree.2008.11.009
Matsuzaki, S.-iS., Suzuki, K., Kadoya, T., Nakagawa, M. & Takamura, N. Bottom-up linkages between primary production, zooplankton, and fish in a shallow, hypereutrophic lake. Ecology 99, 2025–2036 (2018).
pubmed: 29884987 doi: 10.1002/ecy.2414
Anneville, O. et al. Temporal mapping of phytoplankton assemblages in Lake Geneva: annual and interannual changes in their patterns of succession. Limnol. Oceanogr. 47, 1355–1366 (2002).
doi: 10.4319/lo.2002.47.5.1355
Shatwell, T. & Köhler, J. Decreased nitrogen loading controls summer cyanobacterial blooms without promoting nitrogen-fixing taxa: Long-term response of a shallow lake. Limnol. Oceanogr. 64, S166–S178 (2019).
doi: 10.1002/lno.11002
Paerl, H. W. et al. It takes two to tango: when and where dual nutrient (N & P) reductions are needed to protect lakes and downstream ecosystems. Environ. Sci. Technol. 50, 10805–10813 (2016).
pubmed: 27667268 doi: 10.1021/acs.est.6b02575
Schindler, D. W. The dilemma of controlling cultural eutrophication of lakes. Proc. R. Soc. B 279, 4322–4333 (2012).
pubmed: 22915669 pmcid: 3479793 doi: 10.1098/rspb.2012.1032
Lewandowska, A. M., Hillebrand, H., Lengfellner, K. & Sommer, U. Temperature effects on phytoplankton diversity—The zooplankton link. J. Sea Res. 85, 359–364 (2014).
doi: 10.1016/j.seares.2013.07.003
Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).
pubmed: 20189677 doi: 10.1016/j.tree.2010.01.010
Kosten, S. et al. Lake and watershed characteristics rather than climate influence nutrient limitation in shallow lakes. Ecol. Appl. 19, 1791–1804 (2009).
pubmed: 19831070 doi: 10.1890/08-0906.1
Seitzinger, S. P. & Nixon, S. W. Eutrophication and the rate of denitrification and N
doi: 10.4319/lo.1985.30.6.1332
Hsieh, C.-h et al. Phytoplankton community reorganization driven by eutrophication and warming in Lake Biwa. Aquat. Sci. 72, 467–483 (2010).
doi: 10.1007/s00027-010-0149-4
Parain, E. C., Rohr, R. P., Gray, S. M. & Bersier, L.-F. Increased temperature disrupts the biodiversity–ecosystem functioning relationship. Am. Naturalist 193, 227–239 (2019).
doi: 10.1086/701432
Holt, R. D. Spatial heterogeneity, indirect interactions, and the coexistence of prey species. Am. Naturalist 124, 377–406 (1984).
doi: 10.1086/284280
Lewis, W. M. Tropical limnology. Annu. Rev. Ecol. Syst. 18, 159–184 (1987).
doi: 10.1146/annurev.es.18.110187.001111
Allesina, S. & Levine, J. M. A competitive network theory of species diversity. Proc. Natl Acad. Sci. USA 108, 5638–5642 (2011).
pubmed: 21415368 pmcid: 3078357 doi: 10.1073/pnas.1014428108
Neutel, A.-M., Heesterbeek, J. A. P. & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120–1123 (2002).
pubmed: 12004131 doi: 10.1126/science.1068326
Cuvelier, M. L. et al. Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton. Proc. Natl Acad. Sci. 107, 14679–14684 (2010).
pubmed: 20668244 pmcid: 2930470 doi: 10.1073/pnas.1001665107
Chang, C.-W. et al. Reconstructing large interaction networks from empirical time series data. Ecol. Lett. 00, 1–12 (2021).
Yu, Z., Gan, Z., Huang, H., Zhu, Y. & Meng, F. Regularized S-Map reveals varying bacterial interactions. Appl. Environ. Microbiol. 86, e01615-20 (2020).
pubmed: 32801185 pmcid: 7531961 doi: 10.1128/AEM.01615-20
Gibson, J. F., Doyne Farmer, J., Casdagli, M. & Eubank, S. An analytic approach to practical state space reconstruction. Phys. D: Nonlinear Phenom. 57, 1–30 (1992).
doi: 10.1016/0167-2789(92)90085-2
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U. & Perrings, C. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
pubmed: 22678280 doi: 10.1038/nature11148
Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, 1995).
Hutchinson, G. E. Homage to Santa Rosalia or why are there so many kinds of animals? Am. Naturalist 93, 145–159 (1959).
doi: 10.1086/282070
Tilman, D. Resource Competition and Community Structure, Vol. 17 (Princeton University Press, 1982).
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
pubmed: 11452308 doi: 10.1038/35083573
Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evolution 4, 1485–1494 (2020).
doi: 10.1038/s41559-020-1280-9
Lewandowska, A. M. et al. The influence of balanced and imbalanced resource supply on biodiversity–functioning relationship across ecosystems. Philos. Transact. Royal Soc. B: Biol. Sci. 371, 20150283 (2016).
Utermöhl, H. Zur Vervollkommnung der quantitativen Phytoplankton-Methodik: Mit 1 Tabelle und 15 abbildungen im Text und auf 1 Tafel. Int. Ver. f.ür. theoretische und Angew. Limnologie: Mitteilungen 9, 1–38 (1958).
Ye, H. et al. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proc. Natl Acad. Sci. USA 112, E1569–E1576 (2015).
pubmed: 25733874 pmcid: 4386326 doi: 10.1073/pnas.1417063112
Sauer, T., Yorke, J. & Casdagli, M. Embedology. J. Stat. Phys. 65, 579–616 (1991).
doi: 10.1007/BF01053745
Cummins, B., Gedeon, T. & Spendlove, K. On the efficacy of state space reconstruction methods in determining causality. SIAM J. Appl. Dyn. Syst. 14, 335–381 (2015).
doi: 10.1137/130946344
Clark, A. T. et al. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96, 1174–1181 (2015).
pubmed: 26236832 doi: 10.1890/14-1479.1
Sugihara, G., Deyle, E. R. & Ye, H. Reply to Baskerville and Cobey: misconceptions about causation with synchrony and seasonal drivers. Proc. Natl Acad. Sci. USA 114, E2272–E2274 (2017).
pubmed: 28298533 pmcid: 5373349 doi: 10.1073/pnas.1700998114
Deyle, E. R., Maher, M. C., Hernandez, R. D., Basu, S. & Sugihara, G. Global environmental drivers of influenza. Proc. Natl Acad. Sci. USA 113, 13081–13086 (2016).
pubmed: 27799563 pmcid: 5135382 doi: 10.1073/pnas.1607747113
McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794 (1998).
doi: 10.1038/27427
BozorgMagham, A. E., Motesharrei, S., Penny, S. G. & Kalnay, E. Causality analysis: identifying the leading element in a coupled dynamical system. PLoS ONE 10, e0131226 (2015).
pubmed: 26125157 pmcid: 4488350 doi: 10.1371/journal.pone.0131226
Chang, C.-W., Ushio, M. & Hsieh, C.-H. Empirical dynamic modeling for beginners. Ecol. Res. 32, 785–796 (2017).
doi: 10.1007/s11284-017-1469-9
Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evolution 4, 75–81 (2020).
doi: 10.1038/s41559-019-1052-6
van Nes, E. H. et al. Causal feedbacks in climate change. Nat. Clim. Change 5, 445–448 (2015).
doi: 10.1038/nclimate2568
Legendre, P. & Legendre, L. F. Numerical Ecology. Vol. 24 (Elsevier, 2012).
Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014).
Ye, H. et al. rEDM: Applications of empirical dynamic modeling (EDM) from time series. R package version 1.2.3. https://CRAN.R-project.org/package=rEDM (2020).
Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).
Chang, C.-W. et al. Source data and R code from: causal networks of phytoplankton diversity and biomass are modulated by environmental context. Github online repository https://github.com/biozoo/Chang_etal_2021_SI_CausalFeedback (2022).

Auteurs

Chun-Wei Chang (CW)

National Center for Theoretical Sciences, Taipei, 10617, Taiwan.
Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan.
ORCID: 0000-0002-9817-2956

Takeshi Miki (T)

Faculty of Advanced Science and Technology, Ryukoku University, Otsu, Shiga, 520-2194, Japan.
Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan.
Center for Biodiversity Science, Ryukoku University, Otsu, Shiga, 520-2194, Japan.
ORCID: 0000-0002-2452-8681

Hao Ye (H)

Health Science Center Libraries, University of Florida, Gainesville, FL, 32611, USA.
ORCID: 0000-0002-8630-1458

Sami Souissi (S)

Univ. Lille, CNRS, Univ, Littoral Côte D'Opale, IRD, UMR 8187, LOG- Laboratoire D'Océanologie et de Géosciences, Station Marine de Wimereux, F- 59000, Lille, France.
ORCID: 0000-0002-6720-0096

Rita Adrian (R)

Leibniz Institute of Freshwater Ecology and Inland Fisheries, IGB, 12587, Berlin, Germany.
Freie Universität Berlin, Department of Biology, Chemistry and Pharmacy, 14195, Berlin, Germany.

Orlane Anneville (O)

National Research Institute for Agriculture, Food and Environment (INRAE), CARRTEL, Université Savoie Mont Blanc, 74200, Thonon les Bains, France.

Helen Agasild (H)

Centre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5D, 51014, Tartu, Estonia.

Syuhei Ban (S)

Department of Ecosystem Studies, School of Environmental Science, The University of Shiga Prefecture, Hikone, 522-8533, Shiga, Japan.
ORCID: 0000-0002-7168-5583

Yaron Be'eri-Shlevin (Y)

Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel.

Yin-Ru Chiang (YR)

Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan.

Heidrun Feuchtmayr (H)

UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, Lancashire, LA1 4AP, UK.
ORCID: 0000-0002-2028-4843

Gideon Gal (G)

Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel.
ORCID: 0000-0002-6962-4001

Satoshi Ichise (S)

Lake Biwa Environmental Research Institute, Otsu, 520-0022, Japan.

Maiko Kagami (M)

Faculty of Environment and Information Sciences, Yokohama National University, Yokohama, 240-8502, Kanagawa, Japan.
Department of Environmental Science, Faculty of Science, Toho University, Funabashi, Chiba, 274-8510, Japan.
ORCID: 0000-0003-3086-390X

Michio Kumagai (M)

Lake Biwa Environmental Research Institute, Otsu, 520-0022, Japan.
Research Center for Lake Biwa & Environmental Innovation, Ritsumeikan University, Kusatsu, 525-0058, Shiga, Japan.
ORCID: 0000-0002-0648-3061

Xin Liu (X)

Department of Ecosystem Studies, School of Environmental Science, The University of Shiga Prefecture, Hikone, 522-8533, Shiga, Japan.
ORCID: 0000-0002-6660-4486

Shin-Ichiro S Matsuzaki (SS)

Biodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan.

Marina M Manca (MM)

CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy.
ORCID: 0000-0003-2214-0103

Peeter Nõges (P)

Centre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5D, 51014, Tartu, Estonia.
ORCID: 0000-0003-2919-6038

Roberta Piscia (R)

CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy.
ORCID: 0000-0003-3123-7759

Michela Rogora (M)

CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy.

Fuh-Kwo Shiah (FK)

Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan.
Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan.

Stephen J Thackeray (SJ)

UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, Lancashire, LA1 4AP, UK.
ORCID: 0000-0003-3274-2706

Claire E Widdicombe (CE)

Plymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, PL1 3DH, UK.

Jiunn-Tzong Wu (JT)

Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan.

Tamar Zohary (T)

Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel.

Chih-Hao Hsieh (CH)

National Center for Theoretical Sciences, Taipei, 10617, Taiwan. chsieh@ntu.edu.tw.
Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan. chsieh@ntu.edu.tw.
Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan. chsieh@ntu.edu.tw.
Institute of Ecology and Evolutionary Biology, Department of Life Science, National Taiwan University, Taipei, 10617, Taiwan. chsieh@ntu.edu.tw.
ORCID: 0000-0001-5935-7272

Articles similaires

Environmental impact assessment of Flora and its role in offsetting carbon along National Highway-16, India.

H Ramamohan, S Raminaidu, I Sudhakara Rao et al.
1.00
India Carbon Sequestration Environmental Monitoring Carbon Biomass

SALINITY-Induced Changes in Diversity, Stability, and Functional Profiles of Microbial Communities in Different Saline Lakes in Arid Areas.

Lei Gao, Manik Prabhu Narsing Rao, Yong-Hong Liu et al.
1.00
Lakes Salinity Archaea Bacteria Microbiota

The critical need for long-term biomonitoring: The case study of a major river system in Anatolia.

Irmak Kurtul
1.00
Rivers Turkey Biodiversity Environmental Monitoring Animals

Modeling NPP and NDVI time series in different bioclimatic regions of Iran.

Fahimeh Sayedzadeh, Saied Soltani, Reza Modarres
1.00
Iran Environmental Monitoring Seasons Ecosystem Forests

Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Biodiversité › Causal networks of phytoplankton diversity and...
questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Biomasse › Causal networks of phytoplankton diversity and...
questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Causal networks of phytoplankton diversity and...
questionsmedicales.fr › Stades d'organismes › Plancton › Phytoplancton › Causal networks of phytoplankton diversity and...
questionsmedicales.fr › Environnement et santé publique › Environnement › Concepts météorologiques › Atmosphère › Temps (météorologie) › Température › Causal networks of phytoplankton diversity and...