Trait correlation network analysis identifies biomass allocation traits and stem specific length as hub traits in herbaceous perennial plants

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Michael Kleyer
  • Juliane Trinogga
  • Miguel A. Cebrián-Piqueras
  • Anastasia Trenkamp
  • Camilla Fløjgaard
  • Rasmus Ejrnæs
  • Tjeerd J. Bouma
  • Vanessa Minden
  • Martin Maier
  • Jasmin Mantilla-Contreras
  • Dirk C. Albach
  • Bernd Blasius

Correlations among plant traits often reflect important trade-offs or allometric relationships in biological functions like carbon gain, support, water uptake, and reproduction that are associated with different plant organs. Whether trait correlations can be aggregated to “spectra” or “leading dimensions,” whether these dimensions are consistent across plant organs, spatial scale, and growth forms are still open questions. To illustrate the current state of knowledge, we constructed a network of published trait correlations associated with the “leaf economics spectrum,” “biomass allocation dimension,” “seed dimension,” and carbon and nitrogen concentrations. This literature-based network was compared to a network based on a dataset of 23 traits from 2,530 individuals of 126 plant species from 381 plots in Northwest Europe. The observed network comprised more significant correlations than the literature-based network. Network centrality measures showed that size traits such as the mass of leaf, stem, below-ground, and reproductive tissues and plant height were the most central traits in the network, confirming the importance of allometric relationships in herbaceous plants. Stem mass and stem-specific length were “hub” traits correlated with most traits. Environmental selection of hub traits may affect the whole phenotype. In contrast to the literature-based network, SLA and leaf N were of minor importance. Based on cluster analysis and subsequent PCAs of the resulting trait clusters, we found a “size” module, a “seed” module, two modules representing C and N concentrations in plant organs, and a “partitioning” module representing organ mass fractions. A module representing the plant economics spectrum did not emerge. Synthesis. Although we found support for several trait dimensions, the observed trait network deviated significantly from current knowledge, suggesting that previous studies have overlooked trait coordination at the whole-plant level. Furthermore, network analysis suggests that stem traits have a stronger regulatory role in herbaceous plants than leaf traits.

Original languageEnglish
JournalJournal of Ecology
Volume107
Issue number2
Pages (from-to)829-842
Number of pages14
ISSN0022-0477
DOIs
Publication statusPublished - 01.03.2019
Externally publishedYes

Bibliographical note

Funding Information:
We thank Silke Eilers for contributing to the field work in Denmark, as well as Regine Kayser, Katrin Bahloul, Helga Hots, Natali KD?nitz, Daniela Meißner, and many student assistants for laboratory work. We also thank the Associate Editor and two anonymous reviewers for many helpful suggestions to improve earlier drafts of the manuscript. This project was part of the collaborative research project “Sustainable coastal land management: Trade-offs in ecosystem services” (COMTESS), supported by the German Federal Ministry of Education and Research (grant number 01LL0911). English language services provided by stels-ol.de.

Publisher Copyright:
© 2018 The Authors. Journal of Ecology © 2018 British Ecological Society

    Research areas

  • allometry, biomass allocation, leaf economics spectrum, network centrality, plant development and life-history traits, stoichiometry, trait dimensions

DOI

Recently viewed

Publications

  1. Robust Control of Mobile Transportation Object with 3D Technical Vision System
  2. Supporting the Decision of the Order Processing Strategy by Using Logistic Models
  3. Optimizing price levels in e-commerce applications with respect to customer lifetime values
  4. Data based analysis of order processing strategies to support the positioning between conflicting economic and logistic objectives
  5. Mathematics in Robot Control for Theoretical and Applied Problems
  6. Advances in Dynamics, Optimization and Computation
  7. Top-down contingent attentional capture during feed-forward visual processing
  8. Unity and diversity in the law of state responsibility
  9. Hypertext
  10. Control of the inverse pendulum based on sliding mode and model predictive control
  11. Continuous 3D scanning mode using servomotors instead of stepping motors in dynamic laser triangulation
  12. A decoupled MPC using a geometric approach and feedforward action for motion control in robotino
  13. Model predictive control for switching gain adaptation in a sliding mode controller of a DC drive with nonlinear friction
  14. Loss systems in a random environment: steady state analysis
  15. Differences Between Classical and Bayesian Estimates for Mixed Logit Models
  16. A general structural property in wavelet packets for detecting oscillation and noise components in signal analysis
  17. A denoising procedure using wavelet packets for instantaneous detection of pantograph oscillations
  18. A simple fuzzy controller for robot manipulators with bounded inputs
  19. Perfect anti-windup in output tracking scheme with preaction
  20. Advantages and Disadvanteges of Different Text Coding Procedures for Research and Practice in a School Context
  21. Simulation based comparison of safety-stock calculation methods
  22. Semantic Parsing for Knowledge Graph Question Answering with Large Language Models
  23. Correlation between mechanical behaviour and microstructure in the Mg-Ca-Si-Sr system for degradable biomaterials based on thermodynamic calculations
  24. Trajectory-based computational study of coherent behavior in flows
  25. Enhancing the Building Information Modeling Lifecycle of Complex Structures with IoT
  26. Soft Optimal Computing Methods to Identify Surface Roughness in Manufacturing Using a Monotonic Regressor
  27. A geometric approach for controlling an electromagnetic actuator with the help of a linear Model Predictive Control