paleocar

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

Kyle Bocinsky - Crow Canyon Archaeological Center, Cortez, CO

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 line

Paleocar

Install / Use

README

paleocar

Development

Install `paleocar`

Demonstration

Load `paleocar` and set a working directory

Load test datasets

Run `paleocar`

`paleocar` reconstruction for a single location

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

Paleocar

Install / Use

README

paleocar

Development

Install paleocar

Demonstration

Load paleocar and set a working directory

Load test datasets

Run paleocar

paleocar reconstruction for a single location

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

Install `paleocar`

Load `paleocar` and set a working directory

Run `paleocar`

`paleocar` reconstruction for a single location

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