Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness

Simone Fontana, Mridul K. Thomas, Mirela Moldoveanu, Piet Spaak, Francesco Pomati

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits[mdash]which determine responses to the abiotic and biotic environment[mdash]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[mdash]the regularity in distribution of individual cells/colonies within the trait space[mdash]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.
Original languageEnglish
JournalI S M E Journal
Pages (from-to)1-11
ISSN1751-7362
DOIs
Publication statusPublished - 2018
Externally publishedYes

Cite this

@article{d743576d6a3342f8bc1fd420a027bca9,
title = "Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness",
abstract = "Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits[mdash]which determine responses to the abiotic and biotic environment[mdash]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[mdash]the regularity in distribution of individual cells/colonies within the trait space[mdash]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.",
author = "Simone Fontana and Thomas, {Mridul K.} and Mirela Moldoveanu and Piet Spaak and Francesco Pomati",
year = "2018",
doi = "10.1038/ismej.2017.160",
language = "English",
pages = "1--11",
journal = "I S M E Journal",
issn = "1751-7362",
publisher = "Nature Publishing Group",

}

Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness. / Fontana, Simone; Thomas, Mridul K.; Moldoveanu, Mirela; Spaak, Piet; Pomati, Francesco.

In: I S M E Journal, 2018, p. 1-11.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness

AU - Fontana, Simone

AU - Thomas, Mridul K.

AU - Moldoveanu, Mirela

AU - Spaak, Piet

AU - Pomati, Francesco

PY - 2018

Y1 - 2018

N2 - Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits[mdash]which determine responses to the abiotic and biotic environment[mdash]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[mdash]the regularity in distribution of individual cells/colonies within the trait space[mdash]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.

AB - Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits[mdash]which determine responses to the abiotic and biotic environment[mdash]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[mdash]the regularity in distribution of individual cells/colonies within the trait space[mdash]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.

U2 - 10.1038/ismej.2017.160

DO - 10.1038/ismej.2017.160

M3 - Journal article

SP - 1

EP - 11

JO - I S M E Journal

JF - I S M E Journal

SN - 1751-7362

ER -