A signal of competitive dominance in mid-latitude herbaceous plant communities

Author:

Capitán José A.12ORCID,Cuenda Sara3,Ordóñez Alejandro4,Alonso David2ORCID

Affiliation:

1. Complex Systems Group, Department of Applied Mathematics, Universidad Politécnica de Madrid, Av. Juan de Herrera, 6, 28040 Madrid, Spain

2. Theoretical and Computational Ecology, Center for Advanced Studies (CEAB-CSIC), C. Accés Cala St. Francesc 14, 17300 Blanes, Catalonia, Spain

3. Facultad de Ciencias Económicas y Empresariales, Depto. Análisis Económico: Economía Cuantitativa, C. Francisco Tomás y Valiente 5, Universidad Autónoma de Madrid, 28049 Madrid, Spain

4. Department of Bioscience, Aarhus University, Aarhus, Ny Munkegade 114, 8000 Aarhus C, Denmark

Abstract

Understanding the main determinants of species coexistence across space and time is a central question in ecology. However, ecologists still know little about the scales and conditions at which biotic interactions matter and how these interact with the environment to structure species assemblages. Here we use recent theoretical developments to analyse plant distribution and trait data across Europe and find that plant height clustering is related to both evapotranspiration (ET) and gross primary productivity. This clustering is a signal of interspecies competition between plants, which is most evident in mid-latitude ecoregions, where conditions for growth (reflected in actual ET rates and gross primary productivities) are optimal. Away from this optimum, climate severity probably overrides the effect of competition, or other interactions become increasingly important. Our approach bridges the gap between species-rich competition theories and large-scale species distribution data analysis.

Funder

Universidad Politécnica de Madrid

Ministerio de Economía y Competitividad

Ministerio de Ciencia e Innovación

Banco Santander

Publisher

The Royal Society

Subject

Multidisciplinary

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3. Mechanisms of Maintenance of Species Diversity

4. An expanded modern coexistence theory for empirical applications

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