This is a 'read me' file for data and code associated with "Forecasting climate change impacts on plant populations over large spatial extents" by Andrew T. Tredennick, Mevin B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew Kleinhesselink, and Peter B. Adler (2016, Ecosphere). Read the paper here.
Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely-sensed, species-specific estimates of plant cover and statistical models developed for spatio-temporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28 year time series of sagebrush (Artemisia spp.) percent cover over a
km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broad-scale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.
Funding for this work was provided by the National Science Foundation through a Postdoctoral Research Fellowship in Biology to Andrew (DBI-1400370) and a CAREER award to Peter (DEB-1054040).
Send questions to: Andrew Tredennick (atredenn@gmail.com)
This directory contains all of the data and R code necessary to reproduce the analysis and figures from Tredennick et al. 201x. The scripts/ and data/ directories holds all code and data, while the docs/ directory contains a R Markdown file (sageAbundance_ms.Rmd) with paper text. Note that model fitting is computationally demanding and was performed on the Utah State University High Performance Computing System. I would not attempt to fit the model on a PC. All non-essential code is in cache subdirectories and the devel directory.
The reproduce our results, see the sourcing_all_scripts.R R script in the scripts/ directory.
