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paleocar

Build Status

cran version

paleocar is an R package implementing functions to perform spatio-temporal paleoclimate reconstruction from tree-rings using the CAR (Correlation Adjusted corRelation) approach of Zuber and Strimmer as implemented in the care package for R. It is optimized for speed and memory use.

This is based on the approach used in Bocinsky and Kohler (2014):

Bocinsky, R. K. and Kohler, T. A. (2014). A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest. Nature Communications, 5:5618. doi: 10.1038/ncomms6618.

The primary difference between the latest version of paleocar and that presented in Bocinsky and Kohler (2014) is, here, model selection is performed by minimizing the corrected Akaike’s Information Criterion.

A more recent reference would be Bocinsky et al. (2016):

Bocinsky, R. K., Rush, J., Kintigh, K. W., and Kohler, T. A. (2016). Exploration and exploitation in the macrohistory of the pre-Hispanic Pueblo Southwest. Science Advances, 2:e1501532.

This package has been built and tested on a source (Homebrew) install of R on macOS 10.12 (Sierra), and has been successfully run on Ubuntu 14.04.5 LTS (Trusty), Ubuntu 16.04.1 LTS (Xenial) and binary installs of R on Mac OS 10.12 and Windows 10.

Development

Install paleocar

  • Development version from GitHub:
install.packages("devtools")
devtools::install_github("bocinsky/paleocar")
library(paleocar)
  • Linux (Ubuntu 14.04.5 or 16.04.1):

First, in terminal:

sudo add-apt-repository ppa:ubuntugis/ppa -y
sudo apt-get update -q
sudo apt-get install libssl-dev libcurl4-openssl-dev netcdf-bin libnetcdf-dev gdal-bin libgdal-dev

Then, in R:

update.packages("survival")
install.packages("devtools")
devtools::install_github("bocinsky/paleocar")
library(paleocar)

Demonstration

This demo script is available in the /inst folder at the location of the installed package.

Load paleocar and set a working directory

library(paleocar)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(magrittr) # The magrittr package enables piping in R.
library(ggplot2)

# Set a directory for testing
testDir <- "./paleocar_test/"
# and create it if necessary
dir.create(testDir, showWarnings=F, recursive=T)

Load test datasets

paleocar ships with test files defining a study area (Mesa Verde National Park), and pre-extracted data from the International Tree Ring Databank using the FedData package. See the data-raw/data.R script (or the documentation for FedData) to learn how to download these data.

# Load spatial polygon for the boundary of Mesa Verde National Park (MVNP) in southwestern Colorado:
data(mvnp)

# Get Tree-ring data from the ITRDB for 10-degree buffer around MVNP
data(itrdb)

# Get 1/3 arc-second PRISM gridded data for the MVNP north study area (water-year [October--September] precipitation, in millimeters)
data(mvnp_prism)

Run paleocar

paleocar can be run for either single location given by a vector of annualized climate data, a matrix of locations, or over gridded climate data such as PRISM in raster format. There are three primary functions:

  • paleocar_models() calculates the CAR-ranked linear models for all reconstructions
  • predict_paleocar_models() generates climate predictions over a specified prediction period, and
  • uncertainty_paleocar_models() generates an estimate of model uncertainty over a specified prediction period.

Finally, the paleocar() method is a convenience wrapper that runs all three of these functions and returns a list with their output. See the documentation for each function for details.

paleocar reconstruction for a single location

paleocar may be run for a single location by providing a vector of annualized values to be reconstructed. Simply provide a numeric vector the same length as your calibration years as the predictands parameter.

# Extract a vector of annualized climate data (the first cell in the raster)
mvnp_prism.vector <- mvnp_prism[1][1,]

test.vector <- paleocar_models(predictands = mvnp_prism.vector,
                               chronologies = itrdb,
                               calibration.years = 1924:1983,
                               prediction.years = 1:2000,
                               verbose = T)
## Calculating PaleoCAR models
## 
## Prepare data and calculate CAR scores: 0.01 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.03 minutes
## Calculate 5 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 123 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.03 minutes
## Calculate 7 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 115 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.03 minutes
## Calculate 7 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 41 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.02 minutes
## Calculate 6 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 13 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.02 minutes
## Calculate 2 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 3 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.02 minutes
## Calculate 1 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 
## Total Modeling Time: 0.200505 minutes
## 
## Optimizing models: 0.01 minutes

# Generate predictions and uncertainty (and plot timeseries of each)                             
test.prediction <- predict_paleocar_models(models = test.vector,
                                           prediction.years = 600:1299)

test.prediction %>%
  ggplot(aes(x = year,
             y = Prediction)) +
  geom_ribbon(aes(ymin = Prediction - `PI Deviation`,
                  ymax = Prediction + `PI Deviation`),
              color = NA,
              fill = "dodgerblue") +
  geom_line(size = 0.2)

paleocar reconstruction for multiple locations using the same set of predictors (in this case, tree-ring chronologies)

Running paleocar on a matrix of locations (predictands) will generate reconstructions that select from the same set of predictors (chronologies). The matrix must be formatted such that each location is in a column, and each row is a year of data. Note that the number of rows of the matrix must be the same as the number of years provided to calibration.years.

# Extract a matrix of annualized climate data (all cells in the raster)
mvnp_prism.matrix <- mvnp_prism %>%
  raster::as.matrix() %>% 
  t()

test.matrix <- paleocar_models(predictands = mvnp_prism.matrix,
                               chronologies = itrdb,
                               calibration.years = 1924:1983,
                               prediction.years = 1:1985,
                               verbose = T)
## Calculating PaleoCAR models

## Warning in if (class(predictands) %in% c("RasterBrick", "RasterStack")) {: the
## condition has length > 1 and only the first element will be used

## 
## Prepare data and calculate CAR scores: 0.11 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.04 minutes
## Calculate 9 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.08 minutes
## 69264 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.04 minutes
## Calculate 24 linear models: 0.05 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.1 minutes
## 64246 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.04 minutes
## Calculate 34 linear models: 0.06 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.11 minutes
## 47452 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.03 minutes
## Calculate 36 linear models: 0.05 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.09 minutes
## 24085 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.03 minutes
## Calculate 27 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.07 minutes
## 10839 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.03 minutes
## Calculate 12 linear models: 0.01 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 
## Total Modeling Time: 0.4900358 minutes
## 
## Optimizing models: 0.04 minutes

# Generate predictions and uncertainty (and plot location means in uncertainty)
test.prediction <- predict_paleocar_models(models = test.matrix,
                                           prediction.years = 600:1299)

test.prediction %>%
  dplyr::mutate(cell = as.factor(cell)) %>%
  dplyr::filter(cell %in% c(1,200,400,600)) %>%
  ggplot(aes(x = year,
             y = `Prediction (scaled)`)) +
  geom_ribbon(aes(ymin = `Prediction (scaled)` - `PI Deviation (scaled)`,
                  ymax = `Prediction (scaled)` + `PI Deviation (scaled)`,
                  fill = cell),
              color = NA) +
  geom_line(size = 0.2) +
  facet_wrap(~cell, nrow = 2) +
  xlab("Year CE")

paleocar reconstruction over a grid

Paleocar can also be performed over a gridded climate dataset such as PRISM, so long as it is a RasterStack or RasterBrick as defined in the raster package for R. Results will be returned in RasterBrick format.

# Print to show format
mvnp_prism
## class      : RasterStack 
## dimensions : 24, 26, 624, 60  (nrow, ncol, ncell, nlayers)
## resolution : 0.008333333, 0.008333333  (x, y)
## extent     : -108.5542, -108.3375, 37.15417, 37.35417  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs 
## names      : X1924, X1925, X1926, X1927, X1928, X1929, X1930, X1931, X1932, X1933, X1934, X1935, X1936, X1937, X1938, ... 
## min values :   286,   360,   387,   499,   248,   434,   259,   289,   417,   239,   231,   324,   304,   377,   368, ... 
## max values :   498,   602,   615,   745,   417,   739,   437,   420,   690,   434,   364,   628,   588,   612,   720, ...

test.raster <- paleocar_models(predictands = mvnp_prism,
                               chronologies = itrdb,
                               calibration.years = 1924:1983,
                               prediction.years = 600:1299,
                               verbose = T)
## Calculating PaleoCAR models
## 
## Prepare data and calculate CAR scores: 0.11 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.02 minutes
## Calculate 5 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 11856 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.02 minutes
## Calculate 12 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 6838 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.02 minutes
## Calculate 16 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 2361 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.02 minutes
## Calculate 14 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 615 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.02 minutes
## Calculate 7 linear models: 0.01 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 105 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.02 minutes
## Calculate 3 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 
## Total Modeling Time: 0.286265 minutes
## 
## Optimizing models: 0.01 minutes

# Generate predictions and errors
test.raster.predictions <- predict_paleocar_models(models = test.raster,
                                                   prediction.years = 600:1299)
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

test.raster.predictions$`Prediction (scaled)` %>%
  raster::mean() %>%
  raster::plot()

# test.raster.predictions$`PI Deviation (scaled)` %>%
#   raster::mean() %>%
#   raster::plot()
paleocar() convenience wrapper

The paleocar() convenience wrapper returns a list containing the models, reconstructions, and uncertainty. The paleocar() method also automatically saves the output of predict_paleocar_models() and errors_paleocar_models(). Pass variables through this function to other ones (e.g., meanVar = "chained").

# Generate models and perform the reconstruction and error predictions.

mvnp_models <- paleocar_models(predictands = mvnp_prism,
                       label = "mvnp_prism",
                       chronologies = itrdb,
                       calibration.years = 1924:1983,
                       prediction.years = 600:1299,
                       out.dir = testDir,
                       force.redo = T,
                       verbose = T)
## Calculating PaleoCAR models
## 
## Prepare data and calculate CAR scores: 0.1 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.02 minutes
## Calculate 5 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 11856 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.02 minutes
## Calculate 12 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 6838 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.02 minutes
## Calculate 16 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 2361 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.02 minutes
## Calculate 14 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 615 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.02 minutes
## Calculate 7 linear models: 0.01 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 105 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.02 minutes
## Calculate 3 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 
## Total Modeling Time: 0.2881436 minutes
## 
## Optimizing models: 0.01 minutes

mvnp_recon <- paleocar(models = mvnp_models,
                       predictands = mvnp_prism,
                       label = "mvnp_prism",
                       chronologies = itrdb,
                       calibration.years = 1924:1983,
                       prediction.years = 600:1299,
                       out.dir = testDir,
                       force.redo = T,
                       verbose = T)
## 
## Calculating all models
## Calculating PaleoCAR models
## 
## Prepare data and calculate CAR scores: 0.1 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.02 minutes
## Calculate 5 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 11856 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.02 minutes
## Calculate 12 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 6838 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.02 minutes
## Calculate 16 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 2361 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.02 minutes
## Calculate 14 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 615 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.02 minutes
## Calculate 7 linear models: 0.01 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 105 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.02 minutes
## Calculate 3 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 
## Total Modeling Time: 0.2952359 minutes
## 
## Optimizing models: 0.01 minutes
## 
## Generating prediction

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## 
## The entire reconstruction took 0.68 minutes

mvnp_recon <- paleocar(predictands = mvnp_prism,
                       label = "mvnp_prism",
                       chronologies = itrdb,
                       calibration.years = 1924:1983,
                       prediction.years = 600:1299,
                       out.dir = testDir,
                       force.redo = T,
                       verbose = T)
## 
## Calculating all models
## Calculating PaleoCAR models
## 
## Prepare data and calculate CAR scores: 0.1 minutes
## 
## Calculating models of with 1 input vectors.
## Define models: 0.02 minutes
## Calculate 5 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 11856 cell-years remaining
## 
## Calculating models of with 2 input vectors.
## Define models: 0.02 minutes
## Calculate 12 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.07 minutes
## 6838 cell-years remaining
## 
## Calculating models of with 3 input vectors.
## Define models: 0.02 minutes
## Calculate 16 linear models: 0.03 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.06 minutes
## 2361 cell-years remaining
## 
## Calculating models of with 4 input vectors.
## Define models: 0.02 minutes
## Calculate 14 linear models: 0.02 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.05 minutes
## 615 cell-years remaining
## 
## Calculating models of with 5 input vectors.
## Define models: 0.02 minutes
## Calculate 7 linear models: 0.01 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.04 minutes
## 105 cell-years remaining
## 
## Calculating models of with 6 input vectors.
## Define models: 0.02 minutes
## Calculate 3 linear models: 0 minutes
## Clean linear models: 0 minutes
## Total modeling time: 0.03 minutes
## 
## Total Modeling Time: 0.2987443 minutes
## 
## Optimizing models: 0.01 minutes
## 
## Generating prediction

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in Proj4
## definition

## 
## The entire reconstruction took 0.67 minutes

# Examine the structure of the output
str(mvnp_recon, 
    max.level = 2)
## List of 2
##  $ models     :List of 5
##   ..$ models               : tibble [3,115 × 7] (S3: tbl_df/tbl/data.frame)
##   .. ..- attr(*, ".internal.selfref")=<externalptr> 
##   ..$ predictands          :Formal class 'RasterStack' [package "raster"] with 11 slots
##   ..$ predictor.matrix     : num [1:60, 1:120] 1.315 0.883 1.354 1.011 1.354 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   ..$ reconstruction.matrix: num [1:700, 1:120] NA NA NA NA NA NA NA NA NA NA ...
##   .. ..- attr(*, "dimnames")=List of 2
##   ..$ carscores            : tibble [74,880 × 3] (S3: tbl_df/tbl/data.frame)
##  $ predictions:List of 4
##   ..$ Prediction           :Formal class 'RasterBrick' [package "raster"] with 12 slots
##   ..$ PI Deviation         :Formal class 'RasterBrick' [package "raster"] with 12 slots
##   ..$ Prediction (scaled)  :Formal class 'RasterBrick' [package "raster"] with 12 slots
##   ..$ PI Deviation (scaled):Formal class 'RasterBrick' [package "raster"] with 12 slots

You can quickly load a prior reconstruction by setting force.redo = FALSE:

# Generate models and perform the reconstruction and error predictions.
mvnp_recon <- paleocar(predictands = mvnp_prism,
                       label = "mvnp_prism",
                       chronologies = itrdb,
                       calibration.years = 1924:1983,
                       prediction.years = 600:1299,
                       out.dir = testDir,
                       force.redo = F,
                       verbose = T)
## 
## Calculating all models
## 
## Generating prediction

## 
## The entire reconstruction took 0 minutes

Plot results

mvnp_recon$predictions$Prediction %>%
  raster::mean() %>%
  raster::plot()

mvnp_recon$predictions$`PI Deviation` %>%
  raster::mean() %>%
  raster::plot()

mvnp_recon$predictions$`Prediction (scaled)` %>%
  raster::mean() %>%
  raster::plot()

mvnp_recon$predictions$`PI Deviation (scaled)` %>%
  raster::mean() %>%
  raster::plot()