Improving plant functional groups for dynamic models of biodiversity: at the crossroads between functional and community ecology

Abstract

The pace of on-going climate change calls for reliable plant biodiversity scenarios. Traditional dynamic vegetation models use plant functional types that are summarized to such an extent that they become meaningless for biodiversity scenarios. Hybrid dynamic vegetation models of intermediate complexity (hybrid-DVMs) have recently been developed to address this issue. These models, at the crossroads between phenomenological and process-based models, are able to involve an intermediate number of well-chosen plant functional groups (PFGs). The challenge is to build meaningful PFGs that are representative of plant biodiversity, and consistent with the parameters and processes of hybrid-DVMs. Here, we propose and test a framework based on few selected traits to define a limited number of PFGs, which are both representative of the diversity (functional and taxonomic) of the flora in the Ecrins National Park, and adapted to hybrid-DVMs. This new classification scheme, together with recent advances in vegetation modeling, constitutes a step forward for mechanistic biodiversity modeling.

Keywords: biodiversity scenarios; dynamic vegetation model; emergent groups; functional diversity; functional traits; hybrid model; plant functional groups; plant functional types.

Fig. 1. Iterative steps to build Plant Functional Groups from a regional flora

The first step is the selection of a subset of the flora which represents the dominant species, relevant to the modeling the vegetation dynamics. The second step is the selection of a limited number of key traits in order to represent the vegetation structure and ecosystem functions but also biodiversity. The third step is a classification to determine emergent groups. The fourth step aims to attribute the groups’ trait values and producing diversity indices for the final evaluation.

Fig. 2. The six types of mechanisms for the selection of classification features

Two theoretical frameworks are presented on the sides and are related to the six categories. Left: theoretical background from functional ecology; Right: theoretical background from community ecology; Middle: examples of traits or species characteristics are given for each category.

Fig. 3. Study area

The study area is located in the southeast of France in the French part of the Alpine Arc. Grey strips in the inlay indicate the Alpine Convention area. The Ecrins National Park, delimited with a bold line, is situated along the Italian border, in the southeast of France, close to the Mediterranean Sea. Community plots that have been surveyed in the region are represented by triangles. The hillshade background represents the elevation.

Fig. 4. Comparisons of species-based and PFG-based measurements of diversity

The following graphs show the relationship between species-based and PFG-based measurements of diversity. Results for all 1,902 plots are shown as grey dots and results for the 1,128 well-represented plots are shown as black dots. Four different indices are presented. (a) Taxonomic diversity. (b) Functional diversity of classification traits, including plant height, Raunkiaer life form, and dispersal distance class. (c) Functional diversity of independent traits, including mowing tolerance, woodiness, dispersal vector, and seed mass. (d) Functional diversity of Leaf-Height-Seed traits, including plant height, seed mass and three leaf traits (Leaf area, Specific Leaf Area, Leaf Dry Matter Content).

Fig. 4. Comparisons of species-based and PFG-based measurements of diversity

The following graphs show the relationship between species-based and PFG-based measurements of diversity. Results for all 1,902 plots are shown as grey dots and results for the 1,128 well-represented plots are shown as black dots. Four different indices are presented. (a) Taxonomic diversity. (b) Functional diversity of classification traits, including plant height, Raunkiaer life form, and dispersal distance class. (c) Functional diversity of independent traits, including mowing tolerance, woodiness, dispersal vector, and seed mass. (d) Functional diversity of Leaf-Height-Seed traits, including plant height, seed mass and three leaf traits (Leaf area, Specific Leaf Area, Leaf Dry Matter Content).

Fig. 4. Comparisons of species-based and PFG-based measurements of diversity

The following graphs show the relationship between species-based and PFG-based measurements of diversity. Results for all 1,902 plots are shown as grey dots and results for the 1,128 well-represented plots are shown as black dots. Four different indices are presented. (a) Taxonomic diversity. (b) Functional diversity of classification traits, including plant height, Raunkiaer life form, and dispersal distance class. (c) Functional diversity of independent traits, including mowing tolerance, woodiness, dispersal vector, and seed mass. (d) Functional diversity of Leaf-Height-Seed traits, including plant height, seed mass and three leaf traits (Leaf area, Specific Leaf Area, Leaf Dry Matter Content).

Fig. 4. Comparisons of species-based and PFG-based measurements of diversity

The following graphs show the relationship between species-based and PFG-based measurements of diversity. Results for all 1,902 plots are shown as grey dots and results for the 1,128 well-represented plots are shown as black dots. Four different indices are presented. (a) Taxonomic diversity. (b) Functional diversity of classification traits, including plant height, Raunkiaer life form, and dispersal distance class. (c) Functional diversity of independent traits, including mowing tolerance, woodiness, dispersal vector, and seed mass. (d) Functional diversity of Leaf-Height-Seed traits, including plant height, seed mass and three leaf traits (Leaf area, Specific Leaf Area, Leaf Dry Matter Content).