Robustness and limitations of maximum entropy in plant community assembly

An in-depth understanding of local plant community assembly is critical to direct conservation efforts to promising areas and increase the efficiency of management strategies. This, however, remains elusive due to the sheer complexity of ecological processes. The maximum entropy-based Community Assembly via Trait Selection (CATS) model was designed to quantify the relative contributions of trait-based filtering, dispersal mass effects, and stochastic processes on community assembly. As a maximum entropy model, it does so without introducing additional bias or assumptions. Despite its increasing use, questions regarding its robustness and potential limitations remain. Here, we compared model predictions using either local or database-derived trait values, across different levels of species richness and between different taxonomic levels. A total of 19 datasets and 790 plots were analysed, spanning multiple habitat types (n = 18) and biomes (n = 7). Results indicate trait value origin does indeed influence model outcomes, warranting caution in selecting the method for obtaining trait data. We hypothesise that, for example, intraspecific trait variation combined with trait-based filtering or stochastic processes causes local and database trait values to deviate, potentially even further exacerbated by imputing missing trait data. Furthermore, trait-related information obtained from the model decreased with increasing species richness. We further hypothesise this could signal that stochastic processes are more dominant within species-rich systems, for example, due to functional redundancy or the existence of multiple fitness strategies. This general pattern was conserved across biomes, although with varying strength, showing CATS’ robustness despite these challenges. • We analysed data from 19 open-access studies with the maximum entropy model CATS. • Trait value origin (local or from the TRY database) impacted CATS’ predictions. • CATS’ decomposition functions and significance test exacerbated these differences. • The importance of trait-based filtering declined with increasing species richness. • Information content obtained from the model declined with increasing species richness.

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Publication Details

Journal
Ecological Informatics
Published
2025-02-03
DOI
https://doi.org/10.1016/j.ecoinf.2025.103031
Citations
1
Primary Topic
Complex Systems and Decision Making
Type
article
Field-Weighted Citation Impact
2.11
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article

Robustness and limitations of maximum entropy in plant community assembly

Juan Antonio Campos, Michela Marignani, Christian Rossi, Sharif A. Mukul et al.
1 citations
Ecological Informatics
Complex Systems and Decision Making
2.11
article

Robustness and limitations of maximum entropy in plant community assembly

Juan Antonio Campos, Michela Marignani, Christian Rossi, Sharif A. Mukul, Vanessa Minden, Josep Peñuelas, Mark C. Vanderwel, Bruno Hérault, Ning Dong, Stefano Chelli, Thomas Sibret, Erika Bazzato, Roberto Canullo, Andrew M. Cunliffe, Julieta A. Rosell, Sylvain Schmitt, James L. Tsakalos, Bruno Enrico Leone Cerabolini, Peter Poschlod, Tanvir Ahmed Shovon, Arildo S. Dias, Edwin Pos, Tereza Mašková, Tommaso Sitzia, Pedro Higuchi, Isabelle Aubin, Decky Indrawan Junaedi, Yunpeng Zhao, Daniel E. Bunker, Ana Carolina da Silva, Jelyn Gerkema
article en
1 citations

Abstract

An in-depth understanding of local plant community assembly is critical to direct conservation efforts to promising areas and increase the efficiency of management strategies. This, however, remains elusive due to the sheer complexity of ecological processes. The maximum entropy-based Community Assembly via Trait Selection (CATS) model was designed to quantify the relative contributions of trait-based filtering, dispersal mass effects, and stochastic processes on community assembly. As a maximum entropy model, it does so without introducing additional bias or assumptions. Despite its increasing use, questions regarding its robustness and potential limitations remain. Here, we compared model predictions using either local or database-derived trait values, across different levels of species richness and between different taxonomic levels. A total of 19 datasets and 790 plots were analysed, spanning multiple habitat types (n = 18) and biomes (n = 7). Results indicate trait value origin does indeed influence model outcomes, warranting caution in selecting the method for obtaining trait data. We hypothesise that, for example, intraspecific trait variation combined with trait-based filtering or stochastic processes causes local and database trait values to deviate, potentially even further exacerbated by imputing missing trait data. Furthermore, trait-related information obtained from the model decreased with increasing species richness. We further hypothesise this could signal that stochastic processes are more dominant within species-rich systems, for example, due to functional redundancy or the existence of multiple fitness strategies. This general pattern was conserved across biomes, although with varying strength, showing CATS’ robustness despite these challenges. • We analysed data from 19 open-access studies with the maximum entropy model CATS. • Trait value origin (local or from the TRY database) impacted CATS’ predictions. • CATS’ decomposition functions and significance test exacerbated these differences. • The importance of trait-based filtering declined with increasing species richness. • Information content obtained from the model declined with increasing species richness.

Ecological InformaticsVol. 86
Università di Camerino (IT), Goethe University Frankfurt (DE), University of Insubria (IT), New Jersey Institute of Technology (US), Canadian Forest Service (CA), Natural Resources Canada (CA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), University of Padua (IT), University of the Basque Country (ES), University of Cagliari (IT), University of the Sunshine Coast (AU), Utrecht University (NL), University of Regina (CA), Université de Montpellier (FR), Northern Alberta Institute of Technology (CA), University of Exeter (GB), Swiss National Park (CH), Ghent University (BE), Institute for Biodiversity (DE), Forests and Societies (FR), Centre for Research on Ecology and Forestry Applications (ES), Global Ecology Unit CREAF-CSIC-UAB, University of Regensburg (DE), United International University (BD), Universidad Nacional Autónoma de México (MX)
Climate action
Openalex Percentile: Top 17%
Complex Systems and Decision Making
2.11
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