Mouillot, D., Loiseau, N., Grenié, M., Algar, A. C., Allegra, M., Cadotte, M. W., Casajus, N., Denelle, P., Guéguen, M., Maire, A., Maitner, B., McGill, B. J., McLean, M., Mouquet, N., Munoz, F., Thuiller, W., Villéger, S., Violle, C., & Auber, A. (2021). The dimensionality and structure of species trait spaces. Ecology Letters, 24(9), 1988-2009. https://doi.org/10.1111/ele.13778
The dimensionality and structure of species trait spaces. / Mouillot, David; Loiseau, Nicolas; Grenié, Matthias et al.
In:
Ecology Letters, Vol. 24, No. 9, 09.2021, p. 1988-2009.
Research output: Contribution to journal › Review article › peer-review
Mouillot, D, Loiseau, N, Grenié, M, Algar, AC, Allegra, M, Cadotte, MW, Casajus, N, Denelle, P, Guéguen, M, Maire, A, Maitner, B, McGill, BJ, McLean, M, Mouquet, N, Munoz, F, Thuiller, W, Villéger, S, Violle, C & Auber, A 2021, 'The dimensionality and structure of species trait spaces', Ecology Letters, vol. 24, no. 9, pp. 1988-2009. https://doi.org/10.1111/ele.13778
Mouillot D, Loiseau N, Grenié M, Algar AC, Allegra M, Cadotte MW et al. The dimensionality and structure of species trait spaces. Ecology Letters. 2021 Sept;24(9):1988-2009. doi: 10.1111/ele.13778
Mouillot, David ; Loiseau, Nicolas ; Grenié, Matthias et al. / The dimensionality and structure of species trait spaces. In: Ecology Letters. 2021 ; Vol. 24, No. 9. pp. 1988-2009.
@article{f25b6c1a325a4fd59d4489bb342fc0b4,
title = "The dimensionality and structure of species trait spaces",
abstract = "Trait-based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade-off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.",
author = "David Mouillot and Nicolas Loiseau and Matthias Greni{\'e} and Algar, {Adam C.} and Michele Allegra and Cadotte, {Marc W.} and Nicolas Casajus and Pierre Denelle and Maya Gu{\'e}guen and Anthony Maire and Brian Maitner and McGill, {Brian J.} and Matthew McLean and Nicolas Mouquet and Fran{\c c}ois Munoz and Wilfried Thuiller and S{\'e}bastien Vill{\'e}ger and Cyrille Violle and Arnaud Auber",
note = "Funding Information: This research is supported by the Fondation pour la Recherche sur la Biodiversit{\'e} (FRB) and Electricit{\'e} de France (EDF) in the context of the CESAB project {\textquoteleft}Causes and consequences of functional rarity from local to global scales{\textquoteright} (FREE). Publisher Copyright: {\textcopyright} 2021 John Wiley & Sons Ltd.",
year = "2021",
month = sep,
doi = "10.1111/ele.13778",
language = "English",
volume = "24",
pages = "1988--2009",
journal = "Ecology Letters",
issn = "1461-023X",
publisher = "Wiley-Blackwell",
number = "9",
}
TY - JOUR
T1 - The dimensionality and structure of species trait spaces
AU - Mouillot, David
AU - Loiseau, Nicolas
AU - Grenié, Matthias
AU - Algar, Adam C.
AU - Allegra, Michele
AU - Cadotte, Marc W.
AU - Casajus, Nicolas
AU - Denelle, Pierre
AU - Guéguen, Maya
AU - Maire, Anthony
AU - Maitner, Brian
AU - McGill, Brian J.
AU - McLean, Matthew
AU - Mouquet, Nicolas
AU - Munoz, François
AU - Thuiller, Wilfried
AU - Villéger, Sébastien
AU - Violle, Cyrille
AU - Auber, Arnaud
N1 - Funding Information:
This research is supported by the Fondation pour la Recherche sur la Biodiversité (FRB) and Electricité de France (EDF) in the context of the CESAB project ‘Causes and consequences of functional rarity from local to global scales’ (FREE).
Publisher Copyright:
© 2021 John Wiley & Sons Ltd.
PY - 2021/9
Y1 - 2021/9
N2 - Trait-based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade-off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.
AB - Trait-based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade-off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.
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U2 - 10.1111/ele.13778
DO - 10.1111/ele.13778
M3 - Review article
C2 - 34015168
AN - SCOPUS:85106311193
SN - 1461-023X
VL - 24
SP - 1988
EP - 2009
JO - Ecology Letters
JF - Ecology Letters
IS - 9
ER -