Traditional plant functional groups explain variation in economic but not size-related traits across the tundra biome

Abstract

Aim: Plant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits.

Location: Tundra biome.

Time period: Data collected between 1964 and 2016.

Major taxa studied: 295 tundra vascular plant species.

Methods: We compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species-level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species-level traits.

Results: Traditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species-level trait expression.

Main conclusions: Traditional functional groups only coarsely represent variation in well-measured traits within tundra plant communities, and better explain resource economic traits than size-related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling.

Keywords: cluster analysis; community composition; ecosystem function; plant functional groups; plant functional types; plant traits; tundra biome; vegetation change.

Figure 1

Studies employing an “evergreen shrub ‐ deciduous shrub ‐ graminoid – forb” functional group classification (or close variant) to examine the response of tundra communities to environmental change over the past two decades. Studies were identified based on a literature search on Web of Science using the search terms “tundra" and “plant functional group” or “plant functional type”. For a list of studies see Appendix A. Studies are grouped by whether they found clear differences in functional group response (Yes: clear differences were found between some (but not necessarily all) functional groups; Not clear: differences between groups were inconsistent amongst sites or over time; No: No significant differences in functional group response). Studies vary in duration from 2–30 years and incorporate a range of bioclimatic contexts and experimental types. For full meta‐analyses of functional group response see Dormann and Woodin (2002) and Dorrepaal (2007)

Figure 2

Smoothed distribution of species‐level traits represented by the four traditional tundra plant functional groups. Distributions are based on species‐level mean traits for the 295 tundra species for which data are available for all six plant traits of interest. Trait values are presented on the x axis in untransformed units on a log scale. Significance of distributions is indicated by symbols (pairwise Wilcoxon rank sum test; * = p < 0.05; ** = p < 0.01, *** = p < 0.001). Pairs of traits that are significantly different from each other, but not different from other functional groups, are indicated by black bars connecting the centre of those two distributions

Figure 3

Distribution of tundra species in trait space. Inset plots indicate principal components analysis (PCA) multivariate distribution of six plant traits for three tundra sites, (a) Qikiqtaruk, (b) Abisko (c) Davos, and for (d) the whole tundra biome. Trait space was defined based on plant height (PH), seed mass (SM), leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and leaf nitrogen content (LN). Individual species are represented by points and functional groups by point colour (blue = evergreen shrub, green = deciduous shrub, yellow = graminoid, purple = forb). Ellipses represent 95% confidence interval of functional group distributions. Arrows indicate direction and weighting of each trait. Georeferenced trait collection locations are indicated on the map by grey circles and modelled site locations by red circles

Figure 4

Trait variation explained by functional groups for all possible trait combinations. Functional groups best explained combinations of (a) only economic traits, or (b) those containing leaf dry matter content (LDMC), and worst explained combinations of only morphological traits or (c) those containing plant height or seed mass. Points indicate the mean variance explained (PERMANOVA R ²) by functional groups and coloured to visualize the importance of different trait combinations

Figure 5

Comparison of group structure, trait variation explained, and group composition between traditional functional groups and post hoc classifications. (a–c) principal components analysis (PCA) visualization of species clusters as defined by (a) traditional functional groups, (b) k‐means clustering, and (c) hierarchical‐agglomerative clustering (HCA). Species are indicated by points and group distribution by ellipses. Colours indicate groups (dark blue = evergreen shrub, green = deciduous shrub, yellow = graminoid, purple = forb). Post hoc classifications are matched with functional groups based on maximum species correspondence between grouping methods, such that each post hoc classification corresponds with a traditional functional group. Post hoc groups approximately represent (i) tall species with large leaves and seeds (purple), (ii) mid‐sized species with economically acquisitive strategies (yellow), (iii) small species with economically acquisitive strategies (green) and (iv) small species with economically conservative strategies (blue). (d–f) Trait variation explained by (d) traditional functional groups, (e) k‐means, and (f) hierarchical agglomerative clustering (HCA) for multivariate combinations of all six plant traits (white), size‐related traits only (red) and economic traits only (light blue). (g) Comparison of group composition across clustering methods. The stacked bars represent individual species and are ordered by traditional functional group (species order remains consistent across columns). The colour of each stacked bar represents the group to which species were assigned by each classification method (classification can change across columns). For example, a species categorized as a graminoid by traditional functional groups can be categorized in the group most corresponding to forbs by post hoc classifications

Figure 6

Functional groups and post hoc trait‐based classifications capture different characteristics of tundra plant communities. Solid circles enclose characteristics represented by functional groups, post hoc classifications, and by both approaches, according to the findings of this study. The dotted circle encloses the data gaps for traits that are not well represented in tundra trait databases or trait‐based analysis yet are suggested to be important in the literature (Bardgett, Mommer, & Vries, 2014; Chave et al., 2009; Cleland et al., 2012; Eckstein et al., 1999)