Package 'dilp'

Title: Reconstruct Paleoclimate and Paleoecology with Leaf Physiognomy
Description: Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (2024).
Authors: Matthew J. Butrim [aut, cre, cph], Alexander J. Lowe [aut], Andrew G. Flynn [aut], Aly Baumgartner [aut], Daniel J. Peppe [aut], Dana L. Royer [aut]
Maintainer: Matthew J. Butrim <[email protected]>
License: GPL (>= 3)
Version: 1.3.0
Built: 2026-06-03 08:28:02 UTC
Source: https://github.com/mjbutrim/dilp

Help Index

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

Climate Calibration Data

Description

Temperature and precipitation data associated with the modern localities used to calibrate the DiLP model

Usage

climate_calibration_data

Format

climate_calibration_data

A data frame with 92 rows and 5 columns:

Site

Locality name

MAT

Mean Annual Temperature (celsius)

MAP

Mean Annual Precipitation (mm)

Koppen

Koppen climate classifcations

Whittaker

Whittaker climate classifications

Source

Peppe et al. 2011

References

  • 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

Generate DiLP results

Description

dilp() processes raw leaf physiognomic data, checks for common errors/outliers, and returns the processed data, keys to finding potential errors or outliers, and paleoclimate reconstructions.

Usage

dilp(specimen_data, params = "PeppeGlobal", subsite_cols = NULL)

Arguments

specimen_data

A data frame containing specimen level leaf physiognomic data. See Lowe et al. 2024 for more information on how to collect this data. A good reference for how to put together the data: McAbeeExample

Required columns:

  • site

  • specimen_number

  • morphotype

  • margin

  • feret

  • blade_area

  • raw_blade_perimeter

  • internal_raw_blade_perimeter

  • length_of_cut_perimeter

  • no_of_primary_teeth

  • no_of_subsidiary_teeth

Recommended columns:

  • petiole_width

  • petiole_area

  • blade_perimeter

  • minimum_feret

  • raw_blade_area

  • internal_raw_blade_area

params

Either a string referring to one of two preloaded parameter sets of a list of custom parameters (same format as the list below).

Preloaded parameter sets are "PeppeGlobal" and "PeppeNH" which are calibrated based on global and northern hemisphere data respectively. Allen et al. (2020) illustrates a situation in which the northern hemisphere parameters may be preferable. The "PeppeNH" parameters only estimate MAT. Use "PeppeGlobal" for all MAP estimates. Defaults to "PeppeGlobal" as follows (Peppe et al. 2011):

  • MAT.MLR.M = 0.21,

  • MAT.MLR.FDR = 42.296,

  • MAT.MLR.TC.IP = -2.609,

  • MAT.MLR.constant = -16.004,

  • MAT.MLR.error = 4,

  • MAT.SLR.M = 0.204,

  • MAT.SLR.constant = 4.6,

  • MAT.SLR.error = 4.9,

  • MAP.MLR.LA = 0.298,

  • MAP.MLR.TC.IP = 0.279,

  • MAP.MLR.PR = -2.717,

  • MAP.MLR.constant = 3.033,

  • MAP.MLR.SE = 0.6,

  • MAP.SLR.LA = 0.283,

  • MAP.SLR.constant = 2.92,

  • MAP.SLR.SE = 0.61

subsite_cols

A vector or list of columns present in specimen_data to calculate paleoclimate estimates for. A completely optional parameter - allows different groupings of specimens to be tested, or comparisons of paleoclimate estimates at different levels of grouping. Adds additional estimates to $results.

Value

A list of tables that includes all pertinent DiLP information:

  • processed_leaf_data: the full set of cleaned and newly calculated leaf physiognomic data that is necessary for DiLP analysis. See dilp_processing() for more information.

  • processed_morphotype_data: morphospecies-site pair means for all leaf physiognomic data.

  • processed_site_data: site means for all leaf physiognomic data.

  • errors: lists any specimens that may be causing common errors in DiLP calculations. See dilp_errors() for more information.

  • outliers: flags outliers in variables used for DiLP analysis that may represent incorrect data. See dilp_outliers() for more information.

  • results: climate reconstructions of MAT and MAP using single and multi-linear regressions.

References

  • Allen, S. E., Lowe, A. J., Peppe, D. J., & Meyer, H. W. (2020). Paleoclimate and paleoecology of the latest Eocene Florissant flora of central Colorado, USA. Palaeogeography, Palaeoclimatology, Palaeoecology, 551, 109678.

  • 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

  • Lowe. A.J., Flynn, A.G., Butrim, M.J., Baumgartner, A., Peppe, D.J., and Royer, D.L. (2024), Reconstructing terrestrial paleoclimate and paleoecology with fossil leaves using Digital Leaf Physiognomy and leaf mass per area. J. Vis. Exp. (212), e66838, doi:10.3791/66838 (2024).

Examples

dilp_results <- dilp(McAbeeExample)
dilp_results$processed_leaf_data
dilp_results$processed_morphotype_data
dilp_results$processed_site_data
dilp_results$errors
dilp_results$outliers
dilp_results$results

Test if site leaf physiognomy falls within the physiognomic space of the DiLP calibration dataset

Description

dilp_cca plots a canonical correspondence analysis (CCA) ordination of the leaf physiognomic space represented in the calibration dataset of Peppe et al. (2011). The fossil sites being tested are placed along the CCA axes. If a fossil site falls outside of the plotted calibration space, paleoclimate reconstructions for that fossil site should be treated with caution.

Usage

dilp_cca(
  dilp_table,
  physiognomy_calibration = physiognomy_calibration_data,
  climate_calibration = climate_calibration_data,
  colorby = "data"
)

Arguments

dilp_table

The results of a call to dilp()
physiognomy_calibration

A physiognomic calibration dataset. Defaults to an internal version of physiognomy_calibration_data.
climate_calibration

A climate calibration dataset. Defaults to an internal version of climate_calibration_data.
colorby

One of "data", "koppen", "whittaker". Defaults to data, which colors points by whether they are from the calibration data or not. Koppen and Whittaker are works in progress.

Value

A ggplot2 plot

References

  • 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

Examples

results <- dilp(McAbeeExample)
dilp_cca(results)

Check for common errors in DiLP measurements

Description

dilp_errors() will typically only be called internally by dilp(). However, it can be used on its own to evaluate errors that commonly occur during the data collection and processing steps. A dilp_errors() call will nearly always follow a dilp_processing() call. Returns a data frame.

Usage

dilp_errors(specimen_data)

Arguments

specimen_data

Processed specimen level leaf physiognomic data. The structure should match the structure of the output from dilp_processing()

Value

A 7 by X data frame. Each row shows a common error, and which specimens from the input dataset are tripping it.

Examples

# Check for errors in the provided McAbeeExample dataset.
dilp_dataset <- dilp_processing(McAbeeExample)
dilp_errors <- dilp_errors(dilp_dataset)
dilp_errors

Identify outlier specimens

Description

dilp_outliers() is called internally by dilp(). However, it can be used on its own to flag specimens that may have been reported, measured, or prepared incorrectly. dilp_outliers() returns a data frame listing specimens that have unusually high or low values for the four key parameters used in DiLP analyses. This includes whether a specimen is an outlier in the entire dataset, or among other specimens in the same morphotype. If flagged, we suggest looking at the raw measurements and prepped specimen and evaluating if the data is in error or is correct. If in error, the specimen will need to be reprepared and/or remeasured, and the updated datasheet re-read back into R.

Usage

dilp_outliers(specimen_data)

Arguments

specimen_data

Processed specimen level leaf physiognomic data. The structure should match the structure of the output from dilp_processing()

Value

A 4 by X data frame. Each row represents one of the DiLP parameters, and the specimens that are outliers for that parameter.

Examples

# Check for outliers in the provided McAbeeExample dataset. Each
# of these outliers has been manually re-examined and was found acceptable.
dilp_dataset <- dilp_processing(McAbeeExample)
dilp_outliers <- dilp_outliers(dilp_dataset)
dilp_outliers

Process raw leaf physiognomic data

Description

dilp_processing() will typically only be called internally by dilp(). However, it can be used on its own to generate and view a processed DiLP dataset that includes raw and derived physiognomic values useful for DiLP and other physiognomic analyses. Returns a data frame.

Usage

dilp_processing(specimen_data)

Arguments

specimen_data

A data frame containing specimen level leaf physiognomic data. A good reference for how to put together the data: McAbeeExample

Value

A data frame containing cleaned and processed specimen level leaf physiognomic data. New variables calculated are:

  • Leaf area

  • Feret diameter

  • Feret diameter ratio (FDR)

  • Raw blade perimeter corrected (Raw blade perimeter - length of cut perimeter)

  • Internal raw blade perimeter corrected (Internal raw blade perimeter - length of cut perimeter)

  • Total tooth count

  • Total tooth count : internal perimeter (TC:IP)

  • Perimeter ratio

  • Petiole metric

  • Aspect ratio

  • Shape factor

  • Compactness

  • Tooth area

  • Tooth area : perimeter (TA:P)

  • Tooth area: internal perimeter (TA:IP)

  • Tooth area : blade area (TA:BA)

  • Average primary tooth area (Avg TA)

  • Tooth count : blade area (TC:BA)

  • Tooth count : perimeter (TC:P)

Examples

dilp_dataset <- dilp_processing(McAbeeExample)
dilp_dataset

Plot climate reconstructions on a Whittaker Biome plot

Description

dilp_whittaker() plots dilp() outputs onto a Whittaker Biome plot. Base Whittaker Plot from the plotbiomes package by Ștefan Valentin and Sam Levin.

Usage

dilp_whittaker(climate_data)

Arguments

climate_data

A data frame containing either the direct output of a dilp() call, or the $results tab from that output. Can also be a data frame with the following required columns:

  • site

  • MAT.MLR

  • MAT.MLR.error

  • MAP.MLR

  • MAP.MLR.error.minus

  • MAP.MLR.error.plus

Value

A modifiable ggplot with dilp climate-reconstructed sites plotted onto a Whittaker diagram.

References

Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245

Examples

results <- dilp(McAbeeExample)
dilp_whittaker(results)

Generate a suite of leaf mass per area results

Description

lma() takes either raw or processed leaf physiognomic data and returns leaf mass per area (LMA) reconstructions of species-mean, site-mean, and site- variance.

lma() calls calc_lma() multiple times with different sets of parameters. See calc_lma() for more control over LMA reconstructions.

Usage

lma(specimen_data)

Arguments

specimen_data

A table that must include "Site", "Morphotype", and either "Petiole Metric", or "Blade Area", "Petiole Area", and "Petiole Width".

Value

A list of tables containing leaf mass per area reconstructions.

  • species_mean_lma contains the average LMA for each morphospecies-site pair. Values calculated using the regression from Royer et al. (2007).

  • royer_site_mean_lma contains the average LMA for each site. Values calculated using the regression from Royer et al. (2007)

  • lowe_site_lma contains the average LMA for each site. Values calculated using the regression from Lowe et al. (2024)

  • lowe_variance contains the variance in LMA for each site. Values calculated using the regression from Lowe et al. (2024)

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

results <- lma(McAbeeExample)
results

McAbee Example Data

Description

Leaf physiognomic data of specimens collected from the McAbee Fossil Beds in British Columbia, Canada (Lowe et al. 2018).

Usage

McAbeeExample

Format

McAbeeExample

A data frame with 192 rows and 18 columns:

Site

Stratigraphic layer or locality

Specimen Number

Repository number for individual specimen

Morphotype

Morphotype the specimen belongs to

Measurer comments

Additional notes about the specimen or its measurements

Margin

Whether the margin is toothed (0) or entire (1)

Petiole Width

The width of the petiole at the basalmost point of insertion into the leaf lamina

Blade area

The reconstructed area of the leaf lamina, not including the petiole

Blade perimeter

The length of the perimeter of the leaf lamina, not including the petiole

Feret

The diameter of a circle with the same area as the leaf lamina, not including the petiole

Minimum Feret

The longest line that can be drawn between two points on the perimeter of a selection that is perpendicular to Feret length. Approximates blade width.

Raw blade area

The area of a leaf prepared for tooth measurements that still has its teeth.

Raw blade perimeter

The perimeter of a leaf prepared for tooth measurements that still has its teeth.

Internal raw blade area

The area of a leaf prepared for tooth measurements with teeth digitally removed.

Internal raw blade perimeter

The perimeter of a leaf prepared for tooth measurements with teeth digitally removed.

Length of cut perimeter

The total length of all segments of leaf removed from the leaf blade while removing damage during preparation of the leaf.

no. of primary teeth

The number of primary teeth along the undamaged perimeter

no. of secondary teeth

The number of secondary teeth along the undamaged perimeter

Source

Lowe et al. 2018

References

  • Lowe, A. J., D. R. Greenwood, C. K. West, J. M. Galloway, M. Sudermann, and T. Reichgelt. 2018. Plant community ecology and climate on an upland volcanic landscape during the Early Eocene Climatic Optimum: McAbee Fossil Beds, British Columbia, Canada. Palaeogeography, Palaeoclimatology, Palaeoecology 511: 433–448.

Physiognomy Calibration Data

Description

Leaf physiognomic data taken from the modern localities used to calibrate the DiLP model

Usage

physiognomy_calibration_data

Format

physiognomy_calibration_data

A data frame with 92 rows and 12 columns:

Site

Locality name

Leaf.area

Average leaf area at site

FDR

Feret diameter:Feret length. Describes leaf linearity compared to a circle

Perimeter.ratio

Ratio - Raw blade perimeter:Internal raw blade perimeter

TC.P

Ratio - Tooth count:Perimeter

TC.IP

Ratio - Tooth count:Internal perimeter

Avg.TA

Average area of a primary tooth

TA.BA

Ratio - Tooth area:Blade area

TA.P

Ratio - Tooth area:Perimeter

TA.IP

Ratio - Tooth area:Internal perimeter

TC.BA

Ratio - Tooth count:Blade area

Margin

Percentage of untoothed species at the site

Source

Peppe et al. 2011

References

  • 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

Estimate precipitation with simple linear regression

Description

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 leaf 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")

Estimate temperature with simple linear regression

Description

temp_slr() will produce estimates of mean annual temperature and standard error using leaf margin analysis. There are different ways to represent error. The most simple is using the standard error of the regression. These are listed in the table below. However, this is not the only source of uncertainty and is too simplistic a measure of error. This function instead uses the method outlined in Miller et al. 2006, and reported in Peppe et al 2018 (eq. 4), which also accounts for binomial sampling error and overdispersion, offering what we consider a best practice approach. The standard error of the regression provides a minimum error value. Note, Peppe et al. 2018 suggests that a conservative minimum uncertainty for all leaf margin analysis results is probably +/- 5 degrees Celsius.

Standard error of regression:

Regression SE
Peppe2018 4.5
Peppe2011 4.8
Peppe2011NH 3.4
Miller2006 -
WingGreenwood 0.8
Wilf1997 2.0
KowalskiDilcher 3.6

Usage

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

Arguments

data

A data frame that must include the columns "morphotype" and "margin". Can be leaf or species level data.
regression

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

  • "Peppe2018" - for global temperature estimates

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

  • "Peppe2011NH" - Peppe 2011 (Northern Hemisphere only)

  • "Miller2006" - North and Central America

  • "WingGreenwood" - East Asia - original leaf margin analysis regression

  • "Wilf1997" - The Americas

  • "KowalskiDilcher" - North America

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 MAT estimates for each site

References

  • Kowalski, E.A. & Dilcher, D.L. (2003). Warmer paleotemperatures for terrestrial ecosystems. Proceedings of the National Academy of Sciences, 100, 167–170.

  • Miller, I. M., Brandon, M. T., & Hickey, L. J. (2006). Using leaf margin analysis to estimate mid-Cretaceous (Albian) paleolatitude of the Baja BC block. Earth and Planetary Science Letters, 245, 95–114.

  • 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

  • Wing, S., & Greenwood, D. R. (1993). Fossils and fossil climate: the case for equable continental interiors in the Eocene. Philosophical Transactions of the Royal Society of London Series B, 341, 243–252.

  • Wilf, P. (1997). When are leaves good thermometers? A new case for leaf margin analysis. Paleobiology, 23, 373–390.

Examples

temp_slr(McAbeeExample, regression = "Peppe2011")

View preloaded regressions

Description

View preloaded regressions

Usage

view_regressions(type)

Arguments

type

Must be either "dilp", "lma", temp", or "precip".

Value

A data frame containing the parameters for each available regression of the selected type.

Examples

view_regressions("dilp")

Whittaker Biome Data

Description

Delineations of Whittaker biomes from github.com/valentinitnelav/plotbiomes

Usage

Whittaker_biomes

Format

Whittaker_biomes

A data frame with points mapping out Whittaker biome delineations.

Source

github.com/valentinitnelav/plotbiomes

References

*Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245