Check out this running list of links to dendrochronology software, hardware, climate data sources, and analysis techniques.
Here is some helpful code to run some of the main dendro packages in R. I use this to teach at the Dendrochronology Field School - https://github.com/treeringer.
Here's an R markdown walkthrough of the code above: https://rpubs.com/treeringer.Â
Check out the OpenDendro Github with development for dplR by Andy Bunn, Kevin Anchukaitis, Tyson Swetnam, and many others: https://opendendro.org/.Â
I've developed a few videos to help those new to tree-ring analysis. The CooRecorder videos were recently published as part of a research note. Please cite this publication if you use CooRecorder in your research.
Maxwell, R.S. and Larsson, L.A. 2021. Measuring tree-ring widths using the CooRecorder software application. Dendrochronologia 67, 125841. https://doi.org/10.1016/j.dendro.2021.125841Â
You can download an mp4 file of the Coorecorder Tutorial 1 here.
You can download an mp4 file of the Coorecorder Tutorial 2 here.
You can download an mp4 file of the Cdendro Tutorial here.
Here are the data for the "Creating a tree-ring chronology in dplR", "Climate-Growth Response Analysis in Dendrochronology", and "Disturbance Analysis in Dendrochronology". Download and save to your working directory.
This was a collaboration with Dr. Jeremy Wojdak in the Department of Biology at Radford University. Dr. Wodjak was awarded a grant from the NSF to use image analysis to teach biology and statistics to undergraduate students. More info here.
Summary: In this module, students analyze images of tree rings already collected from the field - eastern hemlock trees from Northeastern US forests. Then, students will be asked to think about the relationship between annual growth in trees and different aspects of climate. They will make one or more novel hypotheses and test those hypotheses with the data they generate from the tree ring analysis, and publicly available long-term climate records. The data and hypotheses most students will generate will be amenable to analysis by linear regression. The image analysis part of the module can easily be supplemented with local fieldwork, where possible, enriching the experience and offering really interesting opportunities to talk about data sources, sharing, and reuse.
Download the module here: https://qubeshub.org/publications/544/2