Tree-ring widths of large- and medium-sized Quercus crispula trees spanning 1663 – 2019 in eastern Hokkaido, Japan

This open data provides the time-series data of tree-ring widths measured on the increment cores of fifteen large trees (DBH:89–157cm) and six medium-sized trees (DBH:33–58cm) of Quercus crispula growing on a hillside of eastern Hokkaido, Japan. The data consists of the following five files. 1. Tree-ring widths of large trees: trw_l.csv 2. Tree-ring widths of medium-sized trees: trw_m.csv 3. Attribute information of sample trees: info_tree.csv 4. Attribute information of cores: info_core.csv 5. Photo of a large sample tree: photo_qm239.jpg Study site: This tree-ring research was conducted at an oak-dominated deciduous forest (43.49N, 144.14E) near Lake Akan in eastern Hokkaido, northern Japan. The stand covered approximately 200 ha in area and ranged from 480 m to 570 m a.s.l. This stand was mostly dominated by an even-aged medium-sized tree population of Quercus crispula Blume. In this stand, more than one hundred large-diameter (DBH > 80 cm) trees of the same oak species have been found. Annual mean temperature and annual precipitation at the study site is 3 .6 °C and 1,109 mm, respectively (Japan Meteorological Agency 2022). Methods: Tree-ring widths were measured to the nearest 0.01 mm using a Velmex tree-ring measurement system and a stereoscopic microscope. The time-series of tree-ring widths were crossdated statistically using the package dplR (Bunn 2010) of R (R Core Team 2021). Mean series intercorrelations (the average of correlation coefficients between each series and the master site chronology) were 0.545 and 0.505 for large- and medium-sized trees, respectively. 1. Tree-ring widths of large trees (trw_l.csv) This csv file contains the time-series of tree-ring widths (mm) of each core for the large trees. The 1st column represents the calendar year (CE). The 1st row represents the core IDs, which are the combinations of tree ID (5 letters) and core number (1 letter). Zero values “0” in a time series indicate missing rings. Internal NA values indicate unmeasured rings within a broken part of core. 2. Tree-ring widths of medium-sized trees (trw_m.csv) This csv file contains the time-series of tree-ring widths (mm) of each core for the medium-sized trees. The 1st column represents the calendar year (CE). The 1st row represents the core IDs, which are the combinations of tree ID (6 letters) and core number (1 letter). There is no zero value and no internal NA value in the time-series for the medium-sized trees. 3. Attribute information of sample trees (info_tree.csv) This csv file contains the following attributes of the trees from which core samples were taken: treeID, species, location (latitude, longitude), elevation (m), diameter at breast height (cm), tree height (m), estimated age (year), and range of estimated age (lower and upper limits). To estimate the tree ages along with their uncertainties, we adopted stochastic approaches for a part of estimation processes. Using a Monte Calro method, we obtained the means and 95 % coverage intervals of the distribution of estimated ages. Please see the RELATED MATERIALS 1 for the details of tree age estimation. 4. Attribute information of cores (info_core.csv) This csv file contains the following attributes of the cores: coreID, species, size class, stem diameter at coring height (Dcore: cm), coring height (Hcore: m), date of sampling, core type, first and last years of tree-ring time series, total number of tree rings (Ncore), mean and median of tree-ring widths, standard deviation, first-order autocorrelation, series intercorrelation (correlation coefficient, p-value), number of missing rings and number of unmeasured rings. Core types were defined based on the positional relationships of the core with pith (Pith-, Arc-, and Short-type); please see the Fig.1(A) in RELATED MATERIALS 1. Series intercorrelations were calculated, separately for large- and medium-sized tree groups, using the “interseries.cor” function in the R-package dplR (Bunn 2008) with the following arguments: prewhiten=TRUE, biweight=TRUE, method=pearson. 5. Photo of a large sample tree (photo_qm239.jpg) A picture of our fieldwork ; we were measuring the size of a large tree (Qm239) on May 12, 2018. References Bunn AG. 2008. A dendrochronology program library in R (dplR). Denderochronologia 26: 115–124. https://doi.org/10.1016/j.dendro.2008.01.002 Bunn AG. 2010. Statistical and visual crossdating in R using the dplR library. Dendrochronologia 28: 251–258. https://doi.org/10.1016/j.dendro.2009.12.001 Japan Meteorological Agency 2022. Mesh data of climate normals 2020. https://www.data.jma.go.jp/stats/etrn/view/atlas.html R Core Team 2021. R: A language and environment for statistical computing. Vienna, R Foundation for Statistical Computing.