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Estimate precipitation with simple linear regression

Source: R/slr.R
precip_slr.Rd

precip_slr() will produce estimates of mean annual precipitation and standard error using leaf area analysis.

Usage

precip_slr(
  data,
  regression = "Peppe2018",
  slope = NULL,
  constant = NULL,
  error = NULL
)

Arguments

data

A data frame that must include the columns "morphotype", "leaf_area", and "specimen_number". Must be species level data.

regression

A string representing one of the following pre-loaded regressions:

  • "Peppe2018" - for global precipitation estimates

  • "Peppe2011" - The Americas, Japan, and Oceania

  • "Jacobs2002" - Africa

  • "Wilf1998" - The Americas and Africa

slope

Slope, if using a custom regression

constant

Constant, if using a custom regression

error

Standard error, if using a custom regression

Value

A table with MAP estimates for each site

References

  • Peppe, D. J., Baumgartner, A., Flynn, A., & Blonder, B. (2018). Reconstructing paleoclimate and paleoecology using fossil leaves. Methods in paleoecology: Reconstructing Cenozoic terrestrial environments and ecological communities, 289-317.

  • Peppe, D.J., Royer, D.L., Cariglino, B., Oliver, S.Y., Newman, S., Leight, E., Enikolopov, G., Fernandez-Burgos, M., Herrera, F., Adams, J.M., Correa, E., Currano, E.D., Erickson, J.M., Hinojosa, L.F., Hoganson, J.W., Iglesias, A., Jaramillo, C.A., Johnson, K.R., Jordan, G.J., Kraft, N.J.B., Lovelock, E.C., Lusk, C.H., Niinemets, Ü., Peñuelas, J., Rapson, G., Wing, S.L. and Wright, I.J. (2011), Sensitivity of leaf size and shape to climate: global patterns and paleoclimatic applications. New Phytologist, 190: 724-739. https://doi.org/10.1111/j.1469-8137.2010.03615.x

  • Jacobs, B. F. (2002). Estimation of low-latitude paleoclimates using fossil angiosperm leaves: examples from the Miocene Tugen Hills, Kenya. Paleobiology, 28, 399–421.

  • Wilf, P. (2008). Fossil angiosperm leaves: paleobotany’s difficult children prove themselves. Paleontological Society Papers, 14, 319–333.

Examples

precip_slr(McAbeeExample, regression = "Peppe2011")
#>        site  n    lower      MAP    upper
#> 1 McAbee H1 17 57.88926 126.7697 360.0805
#> 2 McAbee H2 13 62.04646 135.8734 385.9390