Individual-level Trait Diversity Predicts Phytoplankton Community Properties Better Than Species Richness or Evenness

Overview

Journal ISME J

Publisher Oxford University Press

Specialties Environmental Health
Microbiology

Date 2017 Oct 4

PMID 28972571

Citations 12

Authors

Simone Fontana

Mridul Kanianthara Thomas

Mirela Moldoveanu

Piet Spaak

Francesco Pomati

Affiliations

Soon will be listed here.

Abstract

Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits-which determine responses to the abiotic and biotic environment-may help us rigorously link them. However, our capacity to measure traits in natural communities has thus far been limited. Here we compared the predictive power of trait richness (trait space coverage), evenness (regularity in trait distribution) and divergence (prevalence of extreme phenotypes) derived from individual-based measurements with two species-level metrics (taxonomic richness and evenness) when modelling the productivity of natural phytoplankton communities. Using phytoplankton data obtained from 28 lakes sampled at different spatial and temporal scales, we found that the diversity in individual-level morphophysiological traits strongly improved our ability to predict community resource-use and biomass yield. Trait evenness-the regularity in distribution of individual cells/colonies within the trait space-was the strongest predictor, exhibiting a robust negative relationship across scales. Our study suggests that quantifying individual microbial phenotypes in trait space may help us understand how to link physiology to ecosystem-scale processes. Elucidating the mechanisms scaling individual-level trait variation to microbial community dynamics could there improve our ability to forecast changes in ecosystem properties across environmental gradients.

Citing Articles

Trade-offs between receptor modification and fitness drive host-bacteriophage co-evolution leading to phage extinction or co-existence.

Chen L, Zhao X, Wongso S, Lin Z, Wang S ISME J. 2024; 18(1).

PMID: 39441988 PMC: 11538992. DOI: 10.1093/ismejo/wrae214.

An integrated individual-level trait-based phytoplankton dataset from transitional waters.

Laraib M, Titocci J, Rosati I, Basset A Sci Data. 2023; 10(1):897.

PMID: 38092782 PMC: 10719296. DOI: 10.1038/s41597-023-02785-w.

A species-level trait dataset of bats in Europe and beyond.

Froidevaux J, Toshkova N, Barbaro L, Benitez-Lopez A, Kerbiriou C, Le Viol I Sci Data. 2023; 10(1):253.

PMID: 37137926 PMC: 10156679. DOI: 10.1038/s41597-023-02157-4.

Winners and Losers of Atlantification: The Degree of Ocean Warming Affects the Structure of Arctic Microbial Communities.

Ahme A, von Jackowski A, McPherson R, Wolf K, Hoppmann M, Neuhaus S Genes (Basel). 2023; 14(3).

PMID: 36980894 PMC: 10048660. DOI: 10.3390/genes14030623.

Reproductive trait differences drive offspring production in urban cavity-nesting bees and wasps.

Moretti M, Fontana S, Carscadden K, MacIvor J Ecol Evol. 2021; 11(15):9932-9948.

PMID: 34367550 PMC: 8328425. DOI: 10.1002/ece3.7537.

References

Bila K, Moretti M, Bello F, Dias A, Pezzatti G, Van Oosten A . Disentangling community functional components in a litter-macrodetritivore model system reveals the predominance of the mass ratio hypothesis. Ecol Evol. 2014; 4(4):408-16. PMC: 3936387. DOI: 10.1002/ece3.941. View

Bolnick D, Amarasekare P, Araujo M, Burger R, Levine J, Novak M . Why intraspecific trait variation matters in community ecology. Trends Ecol Evol. 2011; 26(4):183-92. PMC: 3088364. DOI: 10.1016/j.tree.2011.01.009. View

Ptacnik R, Solimini A, Andersen T, Tamminen T, Brettum P, Lepisto L . Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc Natl Acad Sci U S A. 2008; 105(13):5134-8. PMC: 2278227. DOI: 10.1073/pnas.0708328105. View

Shade A, Carey C, Kara E, Bertilsson S, McMahon K, Smith M . Can the black box be cracked? The augmentation of microbial ecology by high-resolution, automated sensing technologies. ISME J. 2009; 3(8):881-8. DOI: 10.1038/ismej.2009.56. View

Norberg J, Swaney D, Dushoff J, Lin J, Casagrandi R, Levin S . Phenotypic diversity and ecosystem functioning in changing environments: a theoretical framework. Proc Natl Acad Sci U S A. 2001; 98(20):11376-81. PMC: 58737. DOI: 10.1073/pnas.171315998. View

Pomati F, Kraft N, Posch T, Eugster B, Jokela J, Ibelings B . Individual cell based traits obtained by scanning flow-cytometry show selection by biotic and abiotic environmental factors during a phytoplankton spring bloom. PLoS One. 2013; 8(8):e71677. PMC: 3741118. DOI: 10.1371/journal.pone.0071677. View

Violle C, Enquist B, McGill B, Jiang L, Albert C, Hulshof C . The return of the variance: intraspecific variability in community ecology. Trends Ecol Evol. 2012; 27(4):244-52. DOI: 10.1016/j.tree.2011.11.014. View

Foladori P, Quaranta A, Ziglio G . Use of silica microspheres having refractive index similar to bacteria for conversion of flow cytometric forward light scatter into biovolume. Water Res. 2008; 42(14):3757-66. DOI: 10.1016/j.watres.2008.06.026. View

Hillebrand H, Matthiessen B . Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol Lett. 2009; 12(12):1405-19. DOI: 10.1111/j.1461-0248.2009.01388.x. View

10.

Volf M, Redmond C, Albert A, Le Bagousse-Pinguet Y, Biella P, Gotzenberger L . Effects of long- and short-term management on the functional structure of meadows through species turnover and intraspecific trait variability. Oecologia. 2016; 180(4):941-50. DOI: 10.1007/s00442-016-3548-y. View

11.

Powell J, Welsh A, Hallin S . Microbial functional diversity enhances predictive models linking environmental parameters to ecosystem properties. Ecology. 2015; 96(7):1985-93. DOI: 10.1890/14-1127.1. View

12.

Matthews B, Narwani A, Hausch S, Nonaka E, Peter H, Yamamichi M . Toward an integration of evolutionary biology and ecosystem science. Ecol Lett. 2011; 14(7):690-701. DOI: 10.1111/j.1461-0248.2011.01627.x. View

13.

Hart S, Schreiber S, Levine J . How variation between individuals affects species coexistence. Ecol Lett. 2016; 19(8):825-38. DOI: 10.1111/ele.12618. View

14.

Stomp M, van Dijk M, van Overzee H, Wortel M, Sigon C, Egas M . The timescale of phenotypic plasticity and its impact on competition in fluctuating environments. Am Nat. 2008; 172(5):169-85. DOI: 10.1086/591680. View

15.

Stomp M, Huisman J, Stal L, Matthijs H . Colorful niches of phototrophic microorganisms shaped by vibrations of the water molecule. ISME J. 2007; 1(4):271-82. DOI: 10.1038/ismej.2007.59. View

16.

Cardinale B, Matulich K, Hooper D, Byrnes J, Duffy E, Gamfeldt L . The functional role of producer diversity in ecosystems. Am J Bot. 2011; 98(3):572-92. DOI: 10.3732/ajb.1000364. View

17.

Fontana S, Jokela J, Pomati F . Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry. Front Microbiol. 2014; 5:324. PMC: 4076614. DOI: 10.3389/fmicb.2014.00324. View

18.

Krause S, Le Roux X, Niklaus P, van Bodegom P, Lennon J, Bertilsson S . Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front Microbiol. 2014; 5:251. PMC: 4033906. DOI: 10.3389/fmicb.2014.00251. View

19.

Filstrup C, Hillebrand H, Heathcote A, Harpole W, Downing J . Cyanobacteria dominance influences resource use efficiency and community turnover in phytoplankton and zooplankton communities. Ecol Lett. 2014; 17(4):464-74. DOI: 10.1111/ele.12246. View

20.

Hodapp D, Hillebrand H, Blasius B, Ryabov A . Environmental and trait variability constrain community structure and the biodiversity-productivity relationship. Ecology. 2016; 97(6):1463-74. DOI: 10.1890/15-0730.1. View