Plant traits, species pools and the prediction of relative abundance in plant communities: a maximum entropy approach

Authors

  • Grégory Sonnier,

    1. Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada J1K2R1
    2. Montpellier SupAgro, Centre d'Ecologie Fonctionnelle et Evolutive (UMR 5175), 1919 Route de Mende, 34293 Montpellier Cedex 5, France.
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  • Bill Shipley,

    1. Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada J1K2R1
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  • Marie-Laure Navas

    1. Montpellier SupAgro, Centre d'Ecologie Fonctionnelle et Evolutive (UMR 5175), 1919 Route de Mende, 34293 Montpellier Cedex 5, France.
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  • Co-ordinating Editor: Dr. Ingolf Kühn.

Sonnier, G. (Gregory.Sonnier@USherbrooke.ca) & Shipley, B. (corresponding author, Bill.Shipley@USherbrooke.ca): Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada J1K2R1
Sonnier, G. & Navas, M.-L. (navas@supagro.inra.fr): Montpellier SupAgro, Centre d'Ecologie Fonctionnelle et Evolutive (UMR 5175), 1919 Route de Mende, 34293 Montpellier Cedex 5, France.

Abstract

Questions: To what extent can Shipley et al.'s original maximum entropy model of trait-based community assembly predict relative abundances of species over a large (3000 km2) landscape? How does variation in the species pool affect predictive ability of the model? How might the effects of missing traits be detected? How can non-trait-based processes be incorporated into the model?

Location: Central England.

Material and Methods: Using 10 traits measured on 506 plant species from 1308 1-m2 plots collected over 3000 km2 in central England, we tested one aspect of Shipley et al.'s original maximum entropy model of “pure” trait-based community assembly (S1), and modified it to represent both a neutral (S2) and a hybrid (S3) scenario of community assembly at the local level. Predictive ability of the three corresponding models was determined with different species pool sizes (30, 60, 100 and 506 species). Statistical significance was tested using a distribution-free permutation test.

Results: Predictive ability was high and significantly different from random expectations in S1. Predictive ability was low but significant in S2. Highest predictive ability occurred when both neutral and trait-based processes were included in the model (S3). Increasing the pool size decreased predictive ability, but less so in S3. Incorporating habitat affinity (to indicate missing traits) increased predictive ability.

Conclusions: The measured functional traits were significantly related to species relative abundance. Our results both confirm the generality of the original model but also highlight the importance of (i) taking into account neutral processes during assembly of a plant community, and (ii) properly defining the species pool.

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