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.
Bunn AG. 2010. Statistical and visual crossdating in R using the dplR library. Dendrochronologia 28: 251–258.
Japan Meteorological Agency 2022. Mesh data of climate normals 2020.
R Core Team 2021. R: A language and environment for statistical computing. Vienna, R Foundation for Statistical Computing.
History
Corresponding author email address
terazawa-kazuhiko@hro.or.jpTitle (in Japanese)
北海道東部のミズナラの中・大径木の1663~2019年の年輪幅Description (in Japanese)
この公開データは,北海道東部の山地に生育するミズナラ(Quercus crispula Blume)の大径木15本(DBH: 89–157cm)と中径木6本(DBH: 33–58cm)について,成長錘コアの年輪幅の時系列データを提供するものであり,つぎの5個のファイルから構成される. 1.大径木の年輪幅データ:trw_l.csv 2.中径木の年輪幅データ:trw_m.csv 3.サンプル木の属性情報:info_tree.csv 4.コアの属性情報:info_core.csv 5.サンプル木の一例の写真:photo_qm239.jpg 各コアの年輪幅は,年輪計測システム(Velmex社製)と実体顕微鏡を用いて0.01mm単位で計測した.年輪幅計測値は,R(R Core Team 2021)のパッケージdplR(Bunn 2010)を用いてクロスデーティングを行った.Mean series intercorrelation(各コアとマスタークロノロジーとの相関係数の平均)は,大径木で0.545,中径木で0.505である.調査地と調査方法の詳細については,RELATED MATERIALS 1を参照していただきたい. 各ファイルの内容は,以下のとおりである. 1.大径木の年輪幅データ(trw_l.csv) 西暦年(第1列)に対応する年輪幅(mm)をコアごとに示す.コアID(第1行)は,サンプル木の個体ID(5文字)とコア番号(1文字)で構成される. 年輪幅データ中に介在する”0”値は欠損輪(missing ring)を,また”NA”はコアの損傷などによって年輪幅を計測できなかったことを示す.いずれもクロスデーティングによって判断した. 2.中径木の年輪幅データ(trw_m.csv) 大径木と同様に,西暦年(第1列)に対応する年輪幅(mm)をコアごとに示す.コアID(第1行)は,サンプル木の個体ID(6文字)とコア番号(1文字)で構成される.中径木の年輪幅データには,”0”値や”NA”はない. 3.サンプル木の属性情報(info_tree.csv) 成長錘コアを採取したサンプル木の個体ID,樹種,位置(緯度,経度),標高(m),胸高直径(cm),樹高(m),推定樹齢(年),推定樹齢の範囲(下限,上限)を示す.このうち推定樹齢とその範囲については,コアの年輪計測と確率論的な手法を用いて求めた樹齢推定値の分布の平均値と95%包含区間を示した.樹齢推定の方法の詳細についてはRELATED MATERIALS 1を参照していただきたい. 4.コアの属性情報(info_core.csv) 成長錘コアのコアID(個体ID+コア番号),樹種,個体のサイズ区分,コア採取位置の幹直径(Dcore: cm),コア採取高(Hcore: m),コアの採取年月日,コアのタイプ区分,年輪幅時系列の開始年と終了年,年輪数(Ncore),年輪幅の平均値と中央値,標準偏差,一次の自己相関係数,マスタークロノロジーとの相関(series intercorrelation)における相関係数とp値,欠損輪(missing ring)の数,コアの損傷などによって年輪幅を計測できなかった年輪の数,を示す.このうち,コアのタイプ区分は,サンプル木の髄とコアの位置関係によって3つのタイプ(Pith型,Arc型,Short型)に区分した(RELATED MATERIALS 1の図-1(A)を参照).マスタークロノロジーとの相関は,当該コアの年輪幅時系列とマスタークロノロジーとの相関のことを指す.ここでのマスタークロノロジーは,大径木グループと中径木グループのそれぞれについて,すべてのコア(当該コアを除く)の年輪幅時系列の平均として得られた時系列であり,RパッケージdplR(Bunn 2008)のinterseries.cor関数(prewhiten=TRUE, biweight=TRUE, method=pearson)によって求めた. 5.サンプル木の一例の写真(photo_qm239.jpg) 大径木(Qm239)の調査風景(2018年5月12日撮影) 引用文献 Bunn AG. 2008. A dendrochronology program library in R (dplR). Denderochronologia 26: 115–124. Bunn AG. 2010. Statistical and visual crossdating in R using the dplR library. Dendrochronologia 28: 251–258. R Core Team 2021. R: A language and environment for statistical computing. Vienna (AT): R Foundation for Statistical Computing.Manuscript title (in Japanese)
髄を含まない成長錐コアによるミズナラ大径木の樹齢推定Authors (in Japanese)
寺澤和彦,青木菜々花,時田勝広,酒井賢一,新井田利光,大野泰之Copyright
© 2025 Kazuhiko Terazawa, Nanaka Aoki, Katsuhiro Tokita, Ken-ichi, Sakai, Toshimitsu Niida, Yasuyuki OhnoCommon Metadata Elements (Only for the items supported by Japanese public funds)
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