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 Asse...
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2025-05-01
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author | Jelyn Gerkema Daniel E. Bunker Andrew M. Cunliffe Erika Bazzato Michela Marignani Tommaso Sitzia Isabelle Aubin Stefano Chelli Julieta A. Rosell Peter Poschlod Josep Penuelas Arildo S. Dias Christian Rossi Tanvir A. Shovon Juan A. Campos Mark C. Vanderwel Sharif A. Mukul Bruno E.L. Cerabolini Thomas Sibret Bruno Hérault Sylvain Schmitt Pedro Higuchi James L. Tsakalos Decky I. Junaedi Yun-Peng Zhao Vanessa Minden Ana Carolina da Silva Tereza Mašková Roberto Canullo Ning Dong Edwin T. Pos |
author_facet | Jelyn Gerkema Daniel E. Bunker Andrew M. Cunliffe Erika Bazzato Michela Marignani Tommaso Sitzia Isabelle Aubin Stefano Chelli Julieta A. Rosell Peter Poschlod Josep Penuelas Arildo S. Dias Christian Rossi Tanvir A. Shovon Juan A. Campos Mark C. Vanderwel Sharif A. Mukul Bruno E.L. Cerabolini Thomas Sibret Bruno Hérault Sylvain Schmitt Pedro Higuchi James L. Tsakalos Decky I. Junaedi Yun-Peng Zhao Vanessa Minden Ana Carolina da Silva Tereza Mašková Roberto Canullo Ning Dong Edwin T. Pos |
author_sort | Jelyn Gerkema |
collection | DOAJ |
description | 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. |
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spelling | doaj-art-23f5a21311034aec8499aea776a4ad522025-02-08T05:00:01ZengElsevierEcological Informatics1574-95412025-05-0186103031Robustness and limitations of maximum entropy in plant community assemblyJelyn Gerkema0Daniel E. Bunker1Andrew M. Cunliffe2Erika Bazzato3Michela Marignani4Tommaso Sitzia5Isabelle Aubin6Stefano Chelli7Julieta A. Rosell8Peter Poschlod9Josep Penuelas10Arildo S. Dias11Christian Rossi12Tanvir A. Shovon13Juan A. Campos14Mark C. Vanderwel15Sharif A. Mukul16Bruno E.L. Cerabolini17Thomas Sibret18Bruno Hérault19Sylvain Schmitt20Pedro Higuchi21James L. Tsakalos22Decky I. Junaedi23Yun-Peng Zhao24Vanessa Minden25Ana Carolina da Silva26Tereza Mašková27Roberto Canullo28Ning Dong29Edwin T. Pos30Quantitative Biodiversity Dynamics, Ecology and Biodiversity, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands; Corresponding author.Department of Biological Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ, USADepartment of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UKDepartment of Life and Environmental Sciences, University of Cagliari, Via Sant’Ignazio da Laconi, 13, 09123, Cagliari, ItalyDepartment of Life and Environmental Sciences, University of Cagliari, Via Sant’Ignazio da Laconi, 13, 09123, Cagliari, ItalyUniversità degli Studi di Padova, Department of Land, Environment, Agriculture and Forestry, Viale dell’Università 16, 35020, Legnaro (PD), Italy; National Biodiversity Future Center, Palermo, ItalyGreat Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, CanadaSchool of Biosciences and Veterinary Medicine, University of Camerino, ItalyLaboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de Mexico, A.P. 70-275, Ciudad Universitaria, Coyoacán, 04510, Mexico City, MexicoEcology and Conservation Biology, Institute of Plant Sciences, University of Regensburg, GermanyCSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain; CREAF, Cerdanyola del Vallès, SpainGoethe University, Department of Physical Geography, Altenhöferallee 1, 60438, Frankfurt am Main, GermanySwiss National Park, Runatsch 124-Chastè Planta-Wildenberg, 7530, Zernez, SwitzerlandCenter for Boreal Research, Northern Alberta Institute of Technology, 8102 99 Ave, Peace River, AB T8S 1R2, CanadaDepartment of Plant Biology and Ecology, Faculty of Science and Technology, University of the Basque Country UPV/EHU, Bilbao, SpainDepartment of Biology, University of Regina, Regina, CanadaDepartment of Environment and Development Studies, United International University, Dhaka, 1212, Bangladesh; Tropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, Queensland, 4556, AustraliaDepartment of Biotechnology and Life Science, University of Insubria, via J.H. Dunant, 3, 21100, Varese, ItalyQ-forestlab, Department of Environment, Ghent University, Ghent, BelgiumCIRAD, Forêts et Sociétés, Montpellier, F-34398, France; Forêts et Sociétés, Univ Montpellier, CIRAD, Montpellier, FranceCIRAD, Forêts et Sociétés, Montpellier, F-34398, France; Forêts et Sociétés, Univ Montpellier, CIRAD, Montpellier, FranceDepartamento de Engenharia Florestal, Universidade do Estado de Santa Catarina, Lages, SC, BrazilHarry Butler Institute, Murdoch University, Murdoch, Perth, WA, AustraliaResearch Centre for Ecology and Ethnobiology, National Research and Innovation Agency (BRIN), IndonesiaLaboratory of Systematic & Evolutionary Botany and Biodiversity, College of Life Sciences, Zhejiang University, 310058, Hangzhou, ChinaDepartment of Biology, Research group WILD, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, BelgiumDepartamento de Engenharia Florestal, Universidade do Estado de Santa Catarina, Lages, SC, BrazilInstitute of Ecology and Evolution with Herbarium Haussknecht and Botanical Garden, Plant Biodiversity Group, Friedrich Schiller University Jena, D-07743, Jena, Germany; Senckenberg Institute for Plant Form and Function Jena (SIP), D-07743, Jena, GermanySchool of Biosciences and Veterinary Medicine, University of Camerino, ItalyGeorgina Mace Centre for the Living Planet, Imperial College London, Department of Life Sciences, Silwood Park Campus, Ascot SL5 7PY, UKQuantitative Biodiversity Dynamics, Ecology and Biodiversity, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands; Utrecht University Botanic Gardens, P.O. Box 80162, 3508 TD, Utrecht, The NetherlandsAn 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.http://www.sciencedirect.com/science/article/pii/S1574954125000408Plant ecologyCommunity assemblyMaximum entropyTrait-based filteringDispersal mass effects |
spellingShingle | Jelyn Gerkema Daniel E. Bunker Andrew M. Cunliffe Erika Bazzato Michela Marignani Tommaso Sitzia Isabelle Aubin Stefano Chelli Julieta A. Rosell Peter Poschlod Josep Penuelas Arildo S. Dias Christian Rossi Tanvir A. Shovon Juan A. Campos Mark C. Vanderwel Sharif A. Mukul Bruno E.L. Cerabolini Thomas Sibret Bruno Hérault Sylvain Schmitt Pedro Higuchi James L. Tsakalos Decky I. Junaedi Yun-Peng Zhao Vanessa Minden Ana Carolina da Silva Tereza Mašková Roberto Canullo Ning Dong Edwin T. Pos Robustness and limitations of maximum entropy in plant community assembly Ecological Informatics Plant ecology Community assembly Maximum entropy Trait-based filtering Dispersal mass effects |
title | Robustness and limitations of maximum entropy in plant community assembly |
title_full | Robustness and limitations of maximum entropy in plant community assembly |
title_fullStr | Robustness and limitations of maximum entropy in plant community assembly |
title_full_unstemmed | Robustness and limitations of maximum entropy in plant community assembly |
title_short | Robustness and limitations of maximum entropy in plant community assembly |
title_sort | robustness and limitations of maximum entropy in plant community assembly |
topic | Plant ecology Community assembly Maximum entropy Trait-based filtering Dispersal mass effects |
url | http://www.sciencedirect.com/science/article/pii/S1574954125000408 |
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