Dendrochronologia

This video is attached to Dendrochronologia's technical note on how to take samples for small basic dendroecological studies. The aim of this video is to show how to prepare and sample trees.

A copy of the Superposed Epoch Analysis (SEA) code developed in this paper to reproduce the results. Also includes the datasets used.

Measurements of tree-ring widths from seven mountain hemlock (Tsuga mertensiana [Bong.] Carr.) sites in Crater Lake National Park, Oregon. Ring-width data are stored as text files in Tucson decadal format (http://www.cybis.se/wiki/index.php?title=Tucson_format). Measurements are total ring-widths in microns. The first three lines of each file contain metadata describing: *Three-letter site code Site name Tree species code Country Elevation (meters above sea level) Latitude and longitude (decimal degrees) First year of chronology Final year Contributors

Chaco Tree ring data was provided by Guiterman et al. 2017. Historical tree ring data from the San Juan Basin is available on the International Tree Ring Data Bank (ITRDB): https://data.noaa.gov/dataset/international-tree-ring-data-bank-itrdb. R code is provided to replicate these results. References Guiterman, C.H., Swetnam, T.W., Dean, J.S. 2016. Eleventh-century shift in timber procurement areas for the great houses of Chaco Canyon. PNAS 113(5): 1186-1190 10.1073/pnas.1514272112

The contained files provide coding scripts in the R statistical programming environment that can be used to develop hierarchical Bayesian models for dendroclimatic standardization and climate reconstruction.

-------------------------- METHODOLOGY -------------------------- We aim to identify the most likely climate variables driving the growth and wood anatomy of the species using climwin package. We used the weekly resolved climate data and a randomization technique to find, for each climate variable, the most relevant period of the year in which climate was most related to growth according to climwin. To identify the most likely climate predictors of the growth and wood anatomy features and the most relevant time window (the most influential period of the year for individual climate variables), we fitted simple linear regressions with the growth/anatomy variables as the response variables and the climate variables as predictors. The mean of each factor in each time window considered was used as the aggregate statistics. For each factor all possible window lengths (periods of year) at weekly resolution (but monthly resolution for the flow river) was calculated and the one with the lowest ΔAICc compared to the null model (i.e., including the intercept only) was selected. Finally, randomization tests were calculated using 1000 repetitions to calculate pΔAICc (the likelihood that a climatic signal is real). October 1 of the previous year was established as the threshold for the beginning of the windows and November 31 of the year of growth as the limit for the end of the windows. A minimum length of two weeks was pre-defined. A multiple linear regression were fitted using P. sylvestris pine lumen area chronology, without distinguishing between earlywood and latewood, as the response variable and including the climate variables found to be statistically significant. For building the model with climwin we followed this procedure: (i) among the simple linear models calculated with climwin for the response variable, the model with the lowest ∆AICc was selected; (ii) using this model as baseline model, we introduced the rest of climatic variables one by one in order to fit all possible two-factor models, obtaining for each model ∆AICc, climate windows and p∆AICc; and (iii) the models with p∆AICc < 0.05 were selected. Finally, only a model with two climate variables met this condition. If more significant models with different climatic variables had been found, the whole process would have to be repeated including the model with two climatic factors with lower ∆AICc in the baseline model. Multicollinearity was avoided by controlling the variance inflation factor (VIF).