calc_lma {dilp}

Generate leaf mass per area results

Description

calc_lma() will typically only be called internally by lma(). It provides the flexibility to use custom regression parameters to calculate leaf mass per area (LMA).

Usage

calc_lma(data, params, resolution = "species")

Arguments

data

Must include "petiole metric" or some combination of columns to calculate petiole metric such as "Blade Area", "Petiole Area", and "Petiole Width", or "Leaf Area" and "Petiole Width". If calculating morphospecies-mean LMA, must include "Site" and "Morphotype" columns. If calculating species-mean LMA, only needs to include a "Site' column.

params

A list of regression parameters. Must contain "stat" (= "mean" or = "variance"), "regression_slope", "y_intercept", "unexplained_mean_square", "sample_size_calibration" "mean_log_petiole_metric_calibration", "sum_of_squares_calibration", and "critical_value".

Pre-loaded sets of parameters:

"royer_species_mean_ma":

• stat = "mean",

• regression_slope = 0.382,

• y_intercept = 3.070,

• unexplained_mean_square = 0.032237,

• sample_size_calibration = 667,

• mean_log_petiole_metric_calibration = -3.011,

• sum_of_squares_calibration = 182.1,

• critical_value = 1.964

"royer_site_mean_ma":

• stat = "mean",

• regression_slope = 0.429,

• y_intercept = 3.214,

• unexplained_mean_square = 0.005285,

• sample_size_calibration = 25,

• mean_log_petiole_metric_calibration = -2.857,

• sum_of_squares_calibration = 5.331,

• critical_value = 2.069

"lowe_site_mean_ma":

• stat = "mean",

• regression_slope = 0.345,

• y_intercept = 2.954,

• unexplained_mean_square = 0.01212861,

• sample_size_calibration = 70,

• mean_log_petiole_metric_calibration = -2.902972,

• sum_of_squares_calibration = 1.154691,

• critical_value = 1.995469

"lowe_site_variance_ma":

• stat = "variance",

• regression_slope = 0.302,

• y_intercept = 5.028,

• unexplained_mean_square = 0.1713672,

• sample_size_calibration = 70,

• mean_log_petiole_metric_calibration = -5.97104,

• sum_of_squares_calibration = 5.085184,

• critical_value = 1.995469

resolution

Either "species" or "site". Informs whether the function should calculate morphospecies-mean LMA values ("species") or site-mean/site- variance LMA values ("site"). If resolution = "site", data must already be in the form of species-mean LMA.

Value

A table with LMA results

References

• Royer, D. L., L. Sack, P. Wilf, C. H. Lusk, G. J. Jordan, Ulo Niinemets, I. J. Wright, et al. 2007. Fossil Leaf Economics Quantified: Calibration, Eocene Case Study, and Implications. Paleobiology 33: 574–589

• Lowe, A. J., D. L. Royer, D. J. Wieczynski, M. J. Butrim, T. Reichgelt, L. Azevedo-Schmidt, D. J. Peppe, et al. 2024. Global patterns in community-scale leaf mass per area distributions of woody non-monocot angiosperms and their utility in the fossil record. In review.

Examples

# Calculate morphospecies-mean LMA values with the parameters from Royer et al. (2007)
results <- calc_lma(McAbeeExample,
  params = list(
    stat = "mean",
    regression_slope = 0.382,
    y_intercept = 3.070,
    unexplained_mean_square = 0.032237,
    sample_size_calibration = 667,
    mean_log_petiole_metric_calibration = -3.011,
    sum_of_squares_calibration = 182.1,
    critical_value = 1.964
  ),
  resolution = "species"
)
results

# Calculate site-mean LMA values with the parameters from Lowe et al. (2024) entered from scratch
site_results <- calc_lma(results,
  params = list(
    stat = "mean",
    regression_slope = 0.345,
    y_intercept = 2.954,
    unexplained_mean_square = 0.01212861,
    sample_size_calibration = 70,
    mean_log_petiole_metric_calibration = -2.902972,
    sum_of_squares_calibration = 1.154691,
    critical_value = 1.995469
  ),
  resolution = "site"
)
site_results