Plants are multifaceted organisms that have evolved ecological strategies for sustaining populations in resource-limited environments (Grime 1979; Craine 2009). Plant strategies can be quantified by measuring functional traits (Grime et al. 1997; Reich et al. 2003), which are the properties of plants that impact plant fitness (Violle et al. 2008) and ecosystem processes (Lavorel & Garnier 2002). Comparisons of functional traits across taxa have provided insight into the primary functional gradients among plants (e.g. Grime et al. 1997; Reich et al. 1999; Craine et al. 2001; Díaz et al. 2004). One important gradient describes differences in resource acquisition (Reich, Walters & Ellsworth 1997), known as the ‘leaf economics spectrum’ (sensuWright et al. 2004a), which runs from plants with quick returns on investment in nutrients and dry matter [i.e. plants with leaves that have high photosynthetic rates, short life spans, high SLA, and high leaf nitrogen (N) concentrations] to plants with slower returns on their investments. This multi-trait spectrum (or strategy axis) is only one out of potentially many spectra important to plant growth, reproduction, and survival (Reich et al. 2003; Craine 2009).
Westoby (1998) proposed a simple ‘Leaf-Height-Seed’ (LHS) scheme that operationally quantifies the strategy of a plant species by its location in a three-dimensional space defined by three functional traits: specific leaf area, height, and seed mass. Specific leaf area (SLA, leaf area per unit dry mass) represents variation along the leaf economics spectrum and is indicative of a species’ ability to respond to opportunities for rapid growth (Reich et al. 1999). Plant height at maturity has been related to competitive ability and fecundity (Keddy & Shipley 1989; Aarssen & Jordan 2001). Seed mass reflects variation in dispersal capability and cotyledon-stage seedling survivorship (Westoby, Leishman & Lord 1996; Jakobsson & Eriksson 2000). This LHS plant ecology strategy scheme is potentially useful since it requires the measurement of only three easy-to-measure traits. However, plant strategies are thought to be gradients in multiple correlated traits rather than gradients in single traits (Reich et al. 2003). Moreover, any plant strategy scheme based entirely on aboveground traits may neglect strategies of belowground resource capture if root functioning is not mirrored in any of the three axes. How then do fine roots fit into the LHS scheme?
Root traits are harder to measure and have received far less attention than aboveground traits, despite the fact that most of the biomass and production in perennial-dominated ecosystems is belowground, and that many important ecosystem processes are tightly coupled with plant roots and rhizospheres (Aerts & Chapin 2000). There is some evidence for a ‘root economics spectrum’ analogous to tradeoffs seen in leaves (Eissenstat & Yanai 1997). For example, fine root N concentration scales positively with leaf N concentration (Craine & Lee 2003; Tjoelker et al. 2005; Kerkhoff et al. 2006). A recent analysis by Kembel et al. (2008) indicates that there are at least two gradients of root function. The strongest gradient suggests that species with fast relative growth rates have high leaf and root N concentrations, shorter-lived roots, and high SLA, indicating that roots and leaves are functionally coordinated. The weaker orthogonal axis described variation in specific root length. Specific root length (SRL, root length per unit root dry mass) of fine absorptive roots has been suggested to be the belowground analogue to SLA (Cornelissen et al. 2003). SRL is indicative of the potential rate of water and nutrient uptake and is considered to be a morphological index of belowground competitive ability (Lambers, Chapin & Pons 1998). SLA and fine root SRL were uncorrelated among a set of grassland species despite the positive association between leaf and fine root tissue chemistry (Tjoelker et al. 2005). Some studies have illustrated positive relationships between relative growth rate (which scales positively with SLA; Lambers & Poorter 1992) and SRL (Reich et al. 1998; Wright & Westoby 1999), whereas others have reported opposite trends (Boot 1989; Lambers & Poorter 1992) or even no relationship (Poorter & Remkes 1990; Huante, Rincon & Gavito 1992). If an independent root economics spectrum exists, then the LHS scheme may need additional dimensions (Westoby et al. 2002; Westoby & Wright 2006), but if leaf and root traits are functionally coordinated, then the LHS scheme will be supported because variation in root traits will be mirrored by aboveground traits.
Functional traits not only define plant strategies for survival, they are thought to influence important ecosystem processes (Chapin et al. 2000). Decomposition of leaf litter is one critical step in the internal recycling of limiting nutrients. Decomposition rates are partly controlled by tissue nutrient concentration and the density of structural material in the leaf (Cornwell et al. 2008), suggesting that the leaf axis in the LHS scheme controls leaf litter decomposition rates.
We quantified 10 functional traits on 133 plant species that commonly occur in southwestern USA Pinus ponderosa var. scopulorum P. & C. Lawson (ponderosa pine) forests. In addition to SLA, height, seed mass, SRL, leaf N and fine root N, we measured four additional traits that can influence plant fitness: leaf phosphorus concentration, which has been shown to be an important component of the leaf economics spectrum (Wright et al. 2004a); flowering date and duration, which summarize phonological aspects of a species’ life history (Grime et al. 1997); and leaf dry matter content (LDMC, ratio of leaf dry mass to fresh mass), which is indicative of the amount of structural material in a leaf (Garnier et al. 2001; Kazakou et al. 2006). We asked the following questions: (1) Is the LHS scheme supported when multiple traits, including root traits, are assessed simultaneously in a multivariate framework? (2) Are root traits correlated with leaf traits? (3) Do the LHS axes explain variation in leaf litter decomposition rates?