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).
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.
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
#> site morphotype n petiole_metric lower value upper
#> 1 McAbee H1 M1 10 0.0018166221 81.37167 105.44818 136.64852
#> 2 McAbee H1 M5 3 0.0011067149 54.54670 87.26130 139.59661
#> 3 McAbee H1 M8 8 0.0011510539 66.35987 88.58058 118.24193
#> 4 McAbee H1 M12 1 0.0010808383 38.37080 86.47620 194.89128
#> 5 McAbee H1 M13 1 0.0006070381 30.77894 69.37268 156.35916
#> 6 McAbee H1 M18 1 0.0011637972 39.46986 88.95393 200.47708
#> 7 McAbee H1 M19 1 0.0008073257 34.32356 77.35576 174.33838
#> 8 McAbee H1 M24 5 0.0002309535 33.24559 47.95859 69.18289
#> 9 McAbee H1 M28 1 0.0002175761 20.78085 46.87783 105.74787
#> 10 McAbee H1 M41 1 0.0002994371 23.48554 52.96009 119.42543
#> 11 McAbee H1 M44 1 0.0005108600 28.81385 64.94883 146.40011
#> 12 McAbee H1 M73 1 0.0003778943 25.67425 57.88359 130.50081
#> 13 McAbee H1 M79 2 0.0003960090 33.14315 58.92822 104.77386
#> 14 McAbee H1 M91 1 0.0001524066 18.12949 40.91735 92.34838
#> 15 McAbee H2 M1 3 0.0008877491 50.14134 80.21339 128.32102
#> 16 McAbee H2 M8 2 0.0010796906 48.64042 86.44111 153.61845
#> 17 McAbee H2 M18 1 0.0002132426 20.62123 46.51895 104.94097
#> 18 McAbee H2 M19 4 0.0006803596 48.21879 72.46130 108.89200
#> 19 McAbee H2 M22 3 0.0008971039 50.34260 80.53524 128.83571
#> 20 McAbee H2 M24 2 0.0007082087 41.40139 73.58031 130.77005
#> 21 McAbee H2 M28 1 0.0004553366 27.57296 62.15600 140.11437
#> 22 McAbee H2 M29 1 0.0001254609 16.82558 37.98658 85.76111
#> 23 McAbee H2 M31 2 0.0003283234 30.84620 54.85640 97.55577
#> 24 McAbee H2 M47 2 0.0009868057 46.99765 83.52115 148.42836
#> 25 McAbee H2 M76 1 0.0001534697 18.17789 41.02614 92.59292
#> 26 McAbee H2 M94 1 0.0001232820 16.71284 37.73320 85.19165
#> 27 McAbee H2 M97 1 0.0005314955 29.25363 65.93877 148.62843
# 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
#> site n lower value upper
#> 1 McAbee H1 14 61.03750 73.68178 88.94539
#> 2 McAbee H2 13 53.87969 67.58568 84.77822