Robustness and limitations of maximum entropy in plant community assembly

作者：Jelyn Gerkema, Daniel E. Bunker, Andrew M. Cunliffe, Erika Bazzato, Michela Marignani, Tommaso Sitzia, Isabelle Aubin, Stefano Chelli, Julieta A. Rosell, Peter Poschlod, Josep Peñuelas, Arildo S. Dias, Christian Rossi, Tanvir Ahmed Shovon, Juan Antonio Campos, Mark C. Vanderwel, Sharif A. Mukul, Bruno Enrico Leone Cerabolini, Thomas Sibret, Bruno Hérault, Sylvain Schmitt, Pedro Higuchi, James L. Tsakalos, Decky Indrawan Junaedi, Yunpeng Zhao, Vanessa Minden, Ana Carolina da Silva, Tereza Mašková, Roberto Canullo, Ning Dong, Edwin Pos · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103031 · 被引用次数：1 · 研究领域：Complex Systems and Decision Making、Ecosystem dynamics and resilience、Slime Mold and Myxomycetes Research

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 tha...