A functional assessment of the response of grassland vegetation to reduced grazing and abandonment

Pakeman, Robin J.; Marriott, Carol A.

doi:10.1111/j.1654-1103.2010.01176.x

Cited by 17 publications

(28 citation statements)

References 28 publications

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“…However, when management variables were considered alone, then the traits shifted to include other measures of structure and leaf morphology. These were expected patterns, for instance rhizomatous species proliferate at lower grazing densities and early flowering is an indication of disturbance or high grazing densities (Pakeman 2004, Pakeman andMarriott 2010). The presence of leaf traits in the selected groups, at least partly, supports the conclusion of Ordonez et al (2009) that continuous leaf traits are a better predictor of plant responses to nutrient supply than growth form.…”

Section: Linking Response and Effect Traitssupporting

confidence: 75%

Multivariate identification of plant functional response and effect traits in an agricultural landscape

Pakeman¹

2011

Ecology

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Plant functional traits have been proposed as a linkage between the environmental control of vegetation and ecosystem function. Identification of traits that mediate the response of plant species to the environment is well established, but the identification of effect traits and the linkage between the two sets is less developed. This was attempted for a study of eight contrasting land uses in a marginal agricultural landscape where data on vegetation, management controls of the disturbance regime, and soil characteristics, including nitrogen release, were measured simultaneously with measures of ecosystem function such as litter decomposition rates and primary productivity on 30 sites. Trait data were assembled from databases, and an iterative multivariate approach using the three table (species, trait, environment) method RLQ was employed to identify a parsimonious set of traits that predict plant species responses to the environment and a parsimonious set of traits that link vegetation to ecosystem function. The lists of response and effect traits were similar, and where differences were observed, traits were usually highly correlated with at least one trait in the other list. This approach identified a small number of traits (canopy height, leaf dry matter content, leaf size, and specific leaf area) that provide a means of linking vegetation responses to environmental change with changes in ecosystem function. Other response traits included vegetative spread strategy, start of flowering, and seed terminal velocity, but within the system studied these traits were all significantly correlated to the traits shared between the response and effect lists.

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Section: Linking Response and Effect Traitssupporting

confidence: 75%

Multivariate identification of plant functional response and effect traits in an agricultural landscape

Pakeman¹

2011

Ecology

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“…Several approaches have been developed to examine the link among these tables. Some authors (e.g., Pakeman and Marriott 2010) combined Q and L to build a sample-by-trait table that contains for each sample the (weighted by the species abundances) averages of numerical traits over all species present or the (weighted) frequencies of categorical traits. The link Manuscript received 31 January 2013; revised 25 July 2013; accepted 4 September 2013.…”

Section: Introductionmentioning

confidence: 99%

Combining the fourth‐corner and the RLQ methods for assessing trait responses to environmental variation

Dray

Choler

Dolédec

et al. 2014

Ecology

461

507

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Assessing trait responses to environmental gradients requires the simultaneous analysis of the information contained in three tables: L (species distribution across samples), R (environmental characteristics of samples), and Q (species traits). Among the available methods, the so‐called fourth‐corner and RLQ methods are two appealing alternatives that provide a direct way to test and estimate trait–environment relationships. Both methods are based on the analysis of the fourth‐corner matrix, which crosses traits and environmental variables weighted by species abundances. However, they differ greatly in their outputs: RLQ is a multivariate technique that provides ordination scores to summarize the joint structure among the three tables, whereas the fourth‐corner method mainly tests for individual trait–environment relationships (i.e., one trait and one environmental variable at a time). Here, we illustrate how the complementarity between these two methods can be exploited to promote new ecological knowledge and to improve the study of trait–environment relationships. After a short description of each method, we apply them to real ecological data to present their different outputs and provide hints about the gain resulting from their combined use.

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“…; Lacourse ; Römermann et al. ; Pakeman & Marriott ): RLQ – a three table ordination developed by Dolédec et al. (), and the fourth‐corner analysis (Legendre et al.…”

Section: Introductionmentioning

confidence: 99%

Disentangling plant trait responses to livestock grazing from spatio‐temporal variation: the partial RLQ approach

Wesuls

Oldeland

Dray

2011

J Vegetation Science

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Questions Which plant traits consistently respond to grazing in different years and across habitat‐related environmental heterogeneity? Does the proposed partial RLQ approach allow partitioning of grazing‐related environmental parameters from other environmental and temporal variations? Location Semi‐arid savannas of central Namibia. Methods We recorded nine quantitative and 12 categorical traits from 87 plant species along grazing gradients in semi‐arid Namibian rangelands. We sampled from gradients in different habitat settings in 2 yr with differing total rainfall amounts. We first examined trait–environment relations with RLQ analysis. To remove confounding effects of temporal and habitat‐related environmental variation on trait performance, we introduced a novel partial RLQ analysis approach. Furthermore, we used the fourth‐corner statistic to quantify and test relations between traits, environmental factors and RLQ axes. Results Habitats and years had strong influences on trait patterns. After removing environmental variation caused by habitats and years, grazing became the most influential factor on trait responses. Traits negatively correlated with increasing grazing pressure were common to perennial grasses, such as long and entire leaves, anemochorous dispersal and rhizomatous growth. Positively correlated traits were those common to herbaceous, annual plants with a prostrate–creeping habit, compound leaves, high specific leaf area (SLA) and exo‐ or endozoochorous dispersal. Some previously acknowledged grazing response traits, like growth form and plant height, were strongly influenced by variations in habitats and years and showed no significant correlation with grazing pressure. Conclusion We emphasize that some traits that respond to grazing may also vary under different habitat conditions and among years, especially in highly variable environments like semi‐arid savannas. When analysing trait–environment relations we recommend using approaches that partition environmental variation, particularly when applying broad sampling schemes at larger geographical scales.

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A functional assessment of the response of grassland vegetation to reduced grazing and abandonment

Cited by 17 publications

References 28 publications

Multivariate identification of plant functional response and effect traits in an agricultural landscape

Multivariate identification of plant functional response and effect traits in an agricultural landscape

Combining the fourth‐corner and the RLQ methods for assessing trait responses to environmental variation

Disentangling plant trait responses to livestock grazing from spatio‐temporal variation: the partial RLQ approach

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