Title

Plant trait database for Cryptomeria japonica and Chamaecyparis obtusa (SugiHinoki DB)—their physiology, morphology, anatomy and biochemistry

Authors

Yoko Osone^1*, Shoji Hashimoto^1,2, Tanaka Kenzo¹, Masatake, G. Araki¹, Yuta Inoue¹, Koji Shichi³, Jumpei Toriyama⁴, Naoyuki Yamashita¹, Kenji Tsuruta¹, Shigehiro Ishizuka¹, Junko Nagakura¹, Kyotaro Noguchi⁵, Kenji Ono⁵, Hisao Sakai¹, Yoshimi Sakai⁴, Tetsuya Sano⁶, Hidetoshi Shigenaga¹, Yoshinori Shinohara⁷, Kenichi Yazaki¹

¹Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan

²Isotope Facility for Agricultural Education and Research, Graduate School of Agricultural and Life Sciences, The University of Tokyo

³Shikoku Research Center, Forestry and Forest Products Research Institute, Asakura, Kochi, 780-8077, Japan

⁴Kyushu Research Center, Forestry and Forest Products Research Institute, Kumamoto 860-0862, Japan

⁵Tohoku Research Center, Forestry and Forest Products Research Institute, Morioka, Iwate 020-0123, Japan

⁶Faculty of Engineering, Department of Environment and Energy, Tohoku Institute of Technology, Sendai, Miyagi, 982-8577, Japan

⁷Faculty of Agriculture, Department of Forest and Environmental Science, University of Miyazaki, Miyazaki, 889-2192, Japan

*Corresponding author: Yoko Osone

Email:osone@affrc.go.jp, Tel.: +81(29)829-8227

Abstract

Cryptomeria japonica (sugi) and Chamaecyparis obtusa (hinoki) are major Japanese timber species whose plantation area accounts for 44% and 25%, respectively, of the plantation forests in Japan. Physiology, anatomy and ecology of the species have been intensively studied for this half century, which now form a huge stock of information. These data, however, were scattered in diverse sources, including papers, bulletins of research institutes, reports of other kinds and books, and were presented in non-standardized, diverse styles in each source. This paper provides a database (SugiHinoki DB) that compiles 177 plant traits of sugi and hinoki from 364 primary sources published since 1950. The compiled traits include physiological, morphological, anatomical and biochemical features that are recognized as relevant to life-history strategies, vegetation modeling and global change responses. Collected data had been obtained under different environmental conditions, for plants with different age, and for organs with different age or different position, which provide information of within species variation for a given trait. Each data entry is accompanied by detailed ancillary information describing the site of measurement, stand of measurement and detailed measurement conditions, which help users to account for data variations as a consequence of phenotypic plasticity and genetic variations and to filter data. To provide data in a consistent format, the data and the ancillary information were standardized, and units were converted. After data compilation, outliers were detected by calculating interquartile rage (IQR) for each trait per each species. In Aug 2019, SugiHinokiDB contains 24683 data entries (16410 for sugi, 8273 for hinoki). As sugi and hinoki are major plantation species in Japan, the data were mostly come from study sites in Japan (from Hokkaido in the north to Yakushima island in Kyushu, to the south), but were also from arboretums or plantation forests in Taiwan, Korea and China. Data were largely obtained for plants in plantation forests but also plants in natural forests and under experimental conditions. The improved availability of trait data offered by SugiHinokiDB provide new research opportunities such as intensive parameterization of vegetation models for more accurate prediction of climate change impacts.

Key words

functional traits; literature survey; sugi; hinoki; Japanese cypress; Japanese cedar; allocation; photosynthesis; respiration; water relation

Introduction

Plant traits, that is, any physiological, morphological or phenological features measurable at the individual level (Violle et al., 2007), determine how plants respond to abiotic and biotic environmental factors and affect ecosystem processes and services (Aerts & Chapin, 2000; Diaz et al., 2004; Garnier & Navas, 2011; Grime, 2001, 2006). Therefore, information on a set of traits is a powerful predictor of ecosystem dynamics and function. Trait-based approaches are now widely used in ecological and evolutionary studies including physiological ecology, functional ecology, quantitative genetics, conservation ecology and plant geography (e.g., Osone, Ishida & Tateno, 2008; Ozinga et al., 2009; Poorter et al., 2009; Swenson & Weiser, 2010; Wright et al., 2004). Plant trait data have also been used for the estimation of parameter values and for the validation of global vegetation models (Kattge et al., 2009; Sato, Itoh & Kohyama, 2007; White et al., 2000; Zaehle & Friend, 2010).

In recent decades, there have been initiatives to compile datasets for a range of traits and species (e.g., LEDA (Life history traits of the northern European flora: Kleyer et al., 2008), GlopNet (Global Plant Trait Network: Wright et al., 2004), and the Global Wood Density Database (Chave et al., 2009)). TRY (Plant Trait Database: http://www.try-db.org: Kattge et al., 2011a) has thus far integrated more than 300 of these datasets to accomplish unprecedented coverage of trait data at the global scale. However, even the TRY still has phylogenetic biases; some species (mostly European/north American timber species) are rich both in trait number and in data entries, while others have many fewer data entries or lack key ecological traits. Therefore, researchers themselves make great efforts to collect data from the literature if they cannot find the trait data for a species of interest or its close relatives in the existing databases. Plant trait databases still need to be complemented by data about species from broader regions and phylogenies.

Cryptomeria japonica (L.f.) D. Don (sugi) and Chamaecyparis obtusa (Sieb. et Zucc.) Endl. (hinoki) are major Japanese timber species whose plantation area accounts for 44% and 25%, respectively, of the plantation forests in Japan (Japan Forest Agency, 2018). Despite their commercial importance and the ecological services they provide, there has been no dataset made that compiles a range of trait data for these species. On the other hand, Japanese researchers and foresters have intensively studied the physiology, morphology, stand structure and carbon cycle for these timber species for this half century, which now form a huge stock of information. These data, however, were scattered in diverse sources, including papers, bulletins of research institutes, reports of other kinds and books, and presented in non-standardized, diverse styles in each source, partly due to divergent interest (scientific, commercial or political) in the species.

In this paper, we presented a database (SugiHinoki DB) that compiles 177 plant traits of sugi and hinoki from 364 primary sources published since 1950. In Aug 2019, SugiHinokiDB contains 24683 data entries (16410 for sugi, 8273 for hinoki). The compiled traits include physiological, morphological, anatomical and biochemical features that are relevant to life-history strategies, vegetation modeling and global change responses (Diaz et al., 2004; Grime, 1997; Kattge et al., 2011a; Kleyer et al., 2008). Data for each trait are accompanied by detailed ancillary information describing measurement conditions that help users to account for variations as a consequence of phenotypic plasticity and genetic variations and to filter data. The data and the ancillary information have been standardized for consistency.

Metadata

1. TITLE

Plant trait database for Cryptomeria japonica and Chamaecyparis obtusa (SugiHinoki DB)—their physiology, morphology, anatomy and biochemistry

2. IDENTIFIER

ERDP-2019-05

3. CONTRIBUTER

A. Dataset owner

Forestry and Forest Products Research Institute
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan

B. Contact person

Yoko Osone
Department of Forest Soil, Forestry and Forest Products Research Institute
Tel.: +81(29)829-8227
E-mail: osone@ffpri.affrc.go.jp

Shoji Hashimoto
Department of Forest Soil, Forestry and Forest Products Research Institute
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
Tel.: +81(29)829-8227
E-mail: shojih@ffpri.affrc.go.jp

Tanaka Kenzo
Department of Plant Ecology, Forestry and Forest Products Research Institute
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
Tel.: +81(29)829-8220
E-mail: mona@ffpri.affrc.go.jp

C. Author Contribution

S.H. and Y. O. conceived the study. Y.O., S.H., T.K., M.G-A. planned and designed the research. Y.O., T.K., and Y.I. surveyed the literature. Y.O. constructed the database. N.Y., K.S., J.T. and S.H. categorized soil types and parent materials. K.T. verified the water relation data. K. N. provided information of root studies. K.Y. provided stomata data. S.I., K.O., H.S. and T.S. provided litter data. S.I. and Y.S. provided data on the chemical composition of deadwood. J.N. and H.S. provided leaf nitrogen data. Y.O. wrote the manuscript.

4. GEOGRAPHICAL COVERAGE

Japan (30°20’N, 130°32’E - 41°25’N, 140°6’E)
Taiwan (Xitou Nature Education Area of the Experimental Forest of National Taiwan University)
Korea (Jangseong)
China (Nanjing)

5. TEMPORAL COVERAGE

Literature published from 1950 to 2018*
*The latest literature will be added accordingly.

6. Methods

Data sources

The sugi-hinoki database (SugiHinokiDB) compiled physiological and morphological features of Cryptomeria japonica (L.f.) D. Don (sugi) and Chamaecyparis obtusa (Sieb. et Zucc.) Endl. (hinoki) from primary sources published after 1950, which include journal papers, bulletins published by universities and research institutes, theses and books. Abstracts from the annual meeting of the Japanese Forest Society where there were many reports on sugi and hinoki were also included. Sources were found by search engines such as Google Scholar, J-STAGE (https://www.jstage.jst.go.jp/browse/-char/ja/), the Japanese Forestry Literature Information System (FOLIS, http://www2.ffpri.affrc.go.jp/folis21/folis-hp-j.html), Agriknowledge (https:// agriknowledge.affrc.go.jp/) and the research information repositories of universities and research institutes, with keywords: ‘sugi’, ‘hinoki’, ‘Cryptomeria japonica’, ‘Chamaecyparis obtusa’, ‘Japanese cedar’, ‘Japanese cypress’. Owing to the recent intensive effort to digitize scientific literature, the metadata for the literature sources could often be obtained by these search engines. However, there are still sources whose metadata have not been digitized and are only available in hard copies in libraries. For these sources, we examined each of the hard copies, searching for the keywords. These searches hit more than 900 primary sources, and 364 of them, which include target traits described below, were used for our database. One-thirds of the 364 sources were written in English (marked as ‘E’ in reference list, D4_reference), and the rest were written in Japanese with (marked as ‘J*’) or without English abstract (marked as ‘J’).

Compiled traits

SugiHinokiDB focuses on 177 traits in 15 groups (Table 1, see also D2_dictionary) which were agreed on as relevant to plant life-history strategies, vegetation modeling and global change responses (Diaz et al. 2004; Grime 1997; Kattage et al. 2011a; Kleyer et al. 2008). By definition, ‘plant traits’ are morphological, physiological, and biochemical features measurable at the individual level (Violle et al., 2007). Accordingly, we basically compiled individual-based features. For example, biomass data are generally available both in individual-base and area-base, but we only collected individual-based biomass data. However, there are features that are rarely available on single individuals, such as leaf longevity, allocation to organs and leaf area index (LAI) but are essential to understanding plant growth or are often used as model parameters. These features were specifically included in the database. As sugi and hinoki are major plantation species in Japan, the data were mostly from study sites in Japan, but were also from arboretums or plantation forests in Taiwan, Korea and China. The largest part of the data came from plants in plantation forests but also from plants in natural forests and under experimental conditions.

Plant traits usually vary within a species as a consequence of genetic variation (among cultivars or genotypes within a species) and phenotypic plasticity. Phenotypic plasticity was expressed both among individuals grown in different environments and among different parts of the organ within an individual, such as leaf traits obtained from different crown positions. Ancillary information is necessary to account for the variation and to filter data. In the database, each datum is accompanied by detailed information about the location, environmental conditions, experimental treatment, measurement methods, status of measured individuals in the stand and the position of measured parts (e.g., upper or lower crown for photosynthesis measurements) (D2_dictionary).

Plant traits also show temporal variation at different time scales: ontogenetic changes as individuals grow, changes as organs age and seasonal changes. To cover these temporal variations, the database compiled traits for organs/individuals of any age measured in any month. However, compiling each point of the daily time course of physiology is meaningless and is not relevant in this kind of database. Instead, daily maximum (minimum in the case of water potential), daily mean or daily integral were compiled.

Shoots of sugi have complex structure with needles being attached densely and helicoidally to a stalk, while shoots of hinoki are planar with scale like leaves being arranged in a flat surface. As a result, leaf area and thus leaf area based leaf traits could vary largely in sugi depending on how the area had been determined (Hashimoto & Suzaki, 1979; Tobita et al., 2014). The leaf area measurements can be largely grouped to ‘needle area’ measurements where needle area is determined by flat-bed scanner or any other method and ‘shoot silhouette area’ measurements where shadow area of a whole shoot is measured. Similarly, ‘leaf mass’ could be the mass of the needles or a shoot (needles and the stalk). In the database, leaf trait entries of sugi are associated with ancillary information about whether the area and the mass were that of the needles or the shoot.

Table 1 Summary of SugiHinokiDB for the 15 groups of 177 traits.

Data treatment

The curation process was shown in Fig. 1. First, data in graphs, tables and text in the literature sources were collected. In the case of graphs, data were digitized using WebPlotDigitizer ver4.1 (https://automeris.io/WebPlotDigitizer). To combine data from different sources and provide them in a consistent format, trait entries and ancillary data were standardized. Units of data were converted to those conventionally used at present so that data expressed in different units but representing an identical trait would be uniform (Table 2, see also Pearcy et al. 1989). In the case that several units were conventionally used, the units were conformed with the priority of molecular weight > mass > volume. Due to their dependency on temperature, volume-based units were avoided as much as possible. When volume-based units were converted to other units, the temperature at the time of data measurement and the standard atmosphere (101.325 kPa) were used (Jones, 1992).

Character data, most of which were ancillary data, were categorized into several groups when possible. For example, for the experimental condition in ancillary data (shown as ‘Treatment_cond’ in the database), the types of experimental treatment were presented as any of seven categories: D=dry/wet treatment, F=fertilization, L=light manipulation, WC=water cut, T=thinning, O=other treatment, NT=not treated. Soil types (‘Soil_WRD’, ‘Soil_jp’) and growth environment (‘Growth_env’) are also categorized into several groups. Such categorization makes data filtering easy for users by explicitly presenting preexisting choices. For example, when filtering data measured under near-field conditions, which is often the case, users only need to select the ‘Natural’ and ‘Plantation’ categories in ‘Growth_env’ and ‘NT’ in ‘Treatment_cond’. On the other hand, there are ancillary data that are difficult to categorize. These data, including the geographical location of study sites and light environment of sample trees (presented as crown openness, light intensity, status in the stand, etc.), were compiled just as they appeared in the original text. Further details of the data treatment policy for each trait and ancillary information are described in the dictionary table (D2_dictionary).

The data are arranged to ensure that the relationship between different traits that were measured on the same plant is maintained. This is a prerequisite for plant trait databases because correlations between traits are important as they often reflect evolutionary restrictions, such as leaf trait syndrome (Kattge et al., 2011b; Reich et al., 2003; Wright et al., 2004). In a two-dimensional spreadsheet where traits and additional information appear in separate columns and observations in rows, this means that trait entries obtained from the same plant at the same measurement and their ancillary data should be in the same row. Lining up data on 177 traits and the additional information in columns may cause some difficulty in glancing over the sheet. However, two-dimensional spreadsheets are straightforward and easy to understand and still have advantages over other formats for this size of dataset.

Fig.1 The data curation process in constructing SugiHinokiDB.

Table 2 Unit conversion for traits.

Data verification

Since the majority of data have been obtained from published studies, they have gone through initial quality assurance (Falster et al., 2015; Tavﾅ歛noﾄ殕u & Pausas, 2018). After data compilation, duplicates were identified by checking the date of measurements, location of measurements and the authors of the data sources. In our database, data calculated by different methods were regarded as different data (not duplication) even if they were obtained in the same measurements. For example, photosynthetic rate obtained in a measurement but expressed per shoot silhouette area and per projected needle area were regarded as different. Finally, outliers were detected for each trait per species. Since data were not normally distributed in many traits even after log-transformation (Shapiro-Wilk test), outliers were identified by the method of interquartile range (IQR), which is applicable for data of non-normal distribution (Kattge et al., 2011). In this method, the accepted range is defined as:

Q1 – 1.5 * IQR < accepted range < Q3 + 1.5 * IQR

where Q1 is the first and Q3 is the third quartile of the data, and IQR is given by:

IQR = Q3-Q1.

If a trait datum is out of the range, it is an outlier. Acceptance ranges were not calculated for traits related to biomass and allocation (presented as net primary production allocated to each organ per hectare) because these traits largely depend on tree (stand) size or age and could be very small or very large. Detected outliers have not been excluded from the database because we feel that the calculated accepted ranges are sometimes too strict in traits that tend to vary largely by their nature. Instead, the list of acceptant ranges for each trait per each species (D3_outlier) is provided to leave a decision of whether to exclude or not to exclude outliers up to users. Thus, we recommended users to check the ancillary information thoroughly to determine whether to exclude the outliers, and to filter data according to their purposes.

7. DATA STRUCTURE

A. Dataset Files

• D1_database: a table of trait data and ancillary information, with columns as defined in Table 1 and D2_dictionary
• D2_dictionary: a table of variable definitions
• D3_outlier: a list of acceptance ranges (normal/ln-transformed) for each trait per species
• D4_reference: a list of primary sources

B. Supplementary materials

• S1_reference_org: a list of primary sources in their original language (Japanese if sources were written in Japanese)

C. Data format

The data files are encoded in UTF8, separated by comma with the extension of .txt. Trait data and outlier data are not quoted, but ancillary information and reference information are placed inside double quotation marks.

D. Header information

The first row of the files presents the variable names.

8. DATA UPDATES

The dataset will be updated by the addition of new data or corrections to existing data. Check the latest version when using the database.
Latest update Apr 2019.

9. ACCESSIBILITY

License:
The dataset is provided under a Creative Commons Attribution 4.0 International license (CC-BY 4.0) (https://creativecommons.org/licences/by/4.0/).

10. PROJECT

A. Title

Research on evaluation of influence of climate change on plantation in Japan

B. Personal

Organization: Forestry and Forest Products Research Institute, Japan
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
Tel.: +81(29)873-3211
Web Address:https: //www.ffpri.affrc.go.jp/

C. Funding

Agriculture, Forestry and Fisheries Research Council, Japan

D. Objectives

Climate Change Adaptation Plan of Ministry of Agriculture, Forestry and Fisheries (Aug, 2015) predicted that climate change could decrease forest land suitable for plantation. Although the effects of climate change on natural forests or vegetation had been assessed, the effects on plantation forests has yet to be clarified. The goal of this project is to establish methods for quantitative assessment of climate change effects on plantation forests with scientific basis. Special attention is paid to elucidating physiological responses of trees to long term environmental changes, predicting stand growth by flux data and vegetation modeling, and wide-area mapping of climate change effects. This project would contribute to specify the land area where plantation is suitable/unsuitable under changing climate and to increase profit by reducing afforestation cost.

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