MultiTraits (version 0.6.0)

CSR_hodgson: Classify Plant Strategies using Hodgson et al. (1999) CSR Method

Description

This function calculates C, S, and R scores as percentages based on input plant trait data, following the approach of Hodgson et al. (1999) and its application in Caccianiga et al. (2006). Input is a dataframe with specific trait columns, and the output is a new dataframe containing calculated CSR coordinates, percentages, and assigned CSR type.

Usage

CSR_hodgson(data)

Value

A data.frame with the following columns:

• growth_form, CH, LDMC, FP, LS, LDW, SLA, FS — copied from input;

• C, S, R — calculated CSR percentages;

• type — assigned CSR type label (e.g., "C", "CSR", "S/CSR").

Arguments

data

A data.frame containing the following columns:

growth_form

Character vector: plant growth form, "g" for graminoid, "n" for non-graminoid.

CH

Numeric: Canopy height (mm).

LDMC

Numeric: Leaf dry matter content (percent).

FP

Numeric: Flowering period (# of months).

LS

Numeric: Lateral spread (six-point classification).

LDW

Numeric: Leaf dry weight (mg).

SLA

Numeric: Specific leaf area (mm2/mg).

FS

Numeric: Flowering start (month).

Details

Implements the Hodgson et al. (1999) method for allocating plant species into the CSR (Competitor–Stress-tolerator–Ruderal) triangle based on plant functional traits. Also assigns each species to the nearest CSR type.

This implementation:

• Uses different equations for graminoids and non-graminoids to compute raw CSR dimensions.

• Scales results to coordinate space \([-2.5, 2.5]\), then shifts to positive and converts to percentages.

• Assigns the nearest CSR type based on standard reference CSR percentages from Hodgson's scheme.

References

1. Hodgson, J.G., Wilson, P.J., Hunt, R., Grime, J.P. & Thompson, K. (1999). Allocating CSR plant functional types: a soft approach to a hard problem. Oikos, 85, 282–294.

2. Caccianiga, M., Luzzaro, A., Pierce, S., Ceriani, R.M. & Cerabolini, B. (2006). The functional basis of a primary succession resolved by CSR classification. Oikos, 112, 10–20.

Examples

# Example trait dataset
traits <- data.frame(
  growth_form = c("g", "g", "n", "g", "n"),
  CH = c(45.3, 169.7, 13.7, 132.7, 76.0),
  LDMC = c(33.0, 37.9, 25.9, 28.0, 15.7),
  FP = c(2, 2, 2, 1, 2),
  LS = c(3, 5, 4, 2, 5),
  LDW = c(1.9, 9.9, 2.3, 7.5, 40.2),
  SLA = c(19.0, 20.4, 15.2, 22.6, 21.8),
  FS = c(5, 5, 4, 5, 5)
)

# Run CSR classification
result <- CSR_hodgson(traits)
print(result)

# Plot CSR positions
CSR_plot(data = result)