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]]>Bees and their floral visits in Sapporo in 1979 and 1989
Takeshi Matsumura1,Shoko Nakamura2, Hisatomo Taki2*
1658-72 Sankucho, Nasushiobara, Tochigi 329-2745, Japan
1Department of Forest Entomology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
Hisatomo Taki
Department of Forest Entomology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
E-mail: htaki@affrc.go.jp
Tel.: +81 29 829-8250
Bees are an important faunal group and function as pollinators for various flowering plants. The diversity and abundance of bees have decreased in some parts of the world, which has led to fears about habitat degradation and a pollination crisis worldwide. Therefore, establishing long-term monitoring systems and making past data available widely are important for understanding bees and their ecosystem function, and to enable evidence-based action for the conservation and restoration of bees and their ecosystem function. Decades ago, a group led by Shoichi F Sakagami established a systematic method of bee surveys and conducted surveys at multiple places in Japan. In Sapporo, intensive bee surveys involving sampling bees and recording the flowers that were visited by the bees from April to September were conducted systematically at the campus and Botanic Garden of Hokkaido University in 1979 and 1989. These involved sampling bees and recording the flowers that were visited by the bees from April to September. A total of 12,900 bee specimens were represented by Andrenidae, Apidae, Colletidae, Halictidae, Megachilidae and Melittidae. Here, we have converted the information to digital data by archiving the data on the bee species and the plant species that the bees visited in 1979 and 1989 at two sites at Hokkaido University, in Sapporo, Japan.
Andrenidae, Anthophila, Apidae, Apiformes, Apoidea, Colletidae, Halictidae, Hymenoptera, Megachilidae, Melittidae
Bees are a fundamental faunal group that perform an important ecosystem function as pollinators for various flowering plants (Michener 2007). The diversity and abundance of bees have decreased in some parts of the world, which has raised fears about habitat degradation and a worldwide pollination crisis (Potts et al. 2016). Therefore, establishing long-term monitoring systems and making past data available widely are important for understanding bees and their ecosystem function, and to enable evidence-based action for the conservation and restoration of bees and their ecosystem function.
Decades ago, a group led by Shoichi F Sakagami established a systematic method of bee surveys and conducted surveys at multiple places in Japan (Sakagami et al. 1974). In Sapporo, intensive bee surveys involving sampling bees and recording the flowers that were visited by the bees from April to September were conducted systematically at the campus and Botanic Garden of Hokkaido University in 1959, 1969, 1979, and 1989. The primary aim of these surveys was to get information on the annual cycles of the species of local bees as a basis for their comparative bionomics; an additional aim was expanded to analyze the relative abundance and phenology of the bee fauna quantitatively at Hokkaido University (Sakagami & Fukuda 1973). Although the results from 1959 have been already reported and published (Sakagami & Fukuda 1973), the rest of the bee samples remained unidentified and unpublished and kept in the laboratory. It would be a major scientific loss to lose the precious bee specimens and flower records collected with such great effort.
Here, we have tried to identify the bee species and digitize the data. This paper archives the data on the bee species and the plant species that the bees visited in 1979 and 1989 at two places at Hokkaido University, Sapporo, Japan. All of the specimens from 1979 and 1989 had been pinned, labeled, and kept in the laboratory, unlike samples form 1969 which remain in ethanol.
ERDP-2020-01
Takeshi Matsumura
Address: 658-72 Sankucho, Nasushiobara, Tochigi 329-2745, Japan
Tel.: +81 287 38-1503
E-mail: abuhachi86@gmail.com
Hisatomo Taki
Department of Forest Entomology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
E-mail: htaki@affrc.go.jp
Tel.: +81 29 829-8250
Takeshi Matsumura
Address: 658-72 Sankucho, Nasushiobara, Tochigi 329-2745, Japan
Tel.: +81 287 38-1503
E-mail: abuhachi86@gmail.com
Shoko Nakamura
Department of Forest Entomology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
E-mail: nakamuras1220@affrc.go.jp
Tel.: +81 29 829-8250
Hisatomo Taki
Department of Forest Entomology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
E-mail: htaki@affrc.go.jp
Tel.: +81 29 829-8250
1 Joint Research Program of the Institute of Low Temperature Science (10G042)
2 Global Environment Research Fund (S-9)
3 Monitoring and enhancement of pollinators for crop production
1
Takeshi Matsumura
Address: 658-72 Sankucho, Nasushiobara, Tochigi 329-2745, Japan
Tel.: +81 287 38-1503
E-mail: abuhachi86@gmail.com
2 and 3
Hisatomo Taki
Department of Forest Entomology, Forestry and Forest Products Research Institute
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
Tel.: +81 29 829-8250
Fax: +81 29 873-1543
E-mail: htaki@affrc.go.jp
1 Joint Research Program of the Institute of Low Temperature Science, Hokkaido University
2 Global Environment Research Fund, Ministry of Environment of Japan
3 Ministry of Agriculture, Forestry and Fisheries of Japan
Hokkaido University, Sapporo, Hokkaido, Japan
43゚03´N - 43゚06´N, 141゚19´E - 141゚21´E
1 April 1979
2 April 1989
1 September 1979
2 September 1989
Bees were sampled periodically at the university campus (UC) and Botanic Garden (BG) of Hokkaido University in Sapporo (43°03´N – 43°06´N, 141°19´E – 141°21´E). UC is located northwest of the Sapporo station and covers about 1.0 × 1.5 km square. University buildings have been added continuously since 1959. BG occupies 0.3 × 0.3 km square and is southwest of Sapporo station and south of UC. The garden opened in 1886 and includes remnant native plants, patches of non-native plants, and a collection of plants from Hokkaido and neighboring areas.
Sapporo has a population of nearly 2 million and is the largest city on the northern Japanese island of Hokkaido. With urbanization, Sapporo has been growing and expanding continuously. The original vegetation of the Sapporo area principally consisted of deciduous broad leaf forests. It has a humid continental climate, and the adult bee season typically runs from April to October.
Following the methods used in 1959 (Sakagami & Fukuda 1973), bees were sampled weekly from April to September, 1979 and 1989 for 4 or, rarely, 5 hours each week at both UC and BG. These hourly samplings (indicated as A, B, C, D, and E in the data file) were performed either continuously within 1 day or over 2 or, rarely, 3 days, mostly from 9:30 to 14:30 and, rarely, until 15:30, when the weather was fine and calm. During each of the hourly samplings, any wild bees discovered on any flower or in flight were captured without particular choice. The captured individuals were preserved separately according to the flower species visited. Care was taken to not stay too long at any particular spot. When one spot attracted numerous bees due to the presence of many flowers in bloom, as many specimens as possible were collected at one time, and then other places were checked, without waiting for further arrivals of other individuals. No samples were collected at the orchard within UC or at places where certain bee species have formed nest aggregations.
In this study, we tried to identify as many bee species as possible. The study using the data from 1959 named only 51 species, although 102 species were reported (Sakagami & Fukuda 1973). We confirmed most of the unidentified species from 1979 and 1989 this time. All bee species names followed Tadauchi and Murao (2014).
In addition to the bee information, we digitally recorded the names of the flowers visited by each bee specimen. The flower names were written on the labels of the specimens. Mostly Japanese or local names were used for flowering plants, and then these plant names were changed into scientific names following the YList (Yonekura & Kajita 2003) and the literature on plants published by BG (The Botanic Garden Faculty of Agriculture Hokkaido University 1979, 1981; The Hokkaido University Museum 2010). However, some specimens lacked flower names. In these cases, we entered “NA” for not available.
The data file is named “Sakagami_bee_1979_1989.csv”.
The data file is formatted in comma-separated values (csv) format with the UTF-8 encoding.
Line 1 describes the dataset.
In the data file “Sakagami_bee_1979_1989.csv”, the columns include the following data:
Variable name | Variable definition |
---|---|
Specimen_id | Initiation numbers on bee specimens |
Site | Sampling places: university campus (UC) or botanic garden (BG) |
Year | Sampling year |
Family | Family names of the bee species |
Bee | Species names of the bees |
Sex | Bee sex (male or female) |
Date | Sampling date: month_day |
Period | One-hour sampling period within a day. Bees were sampled weekly for 4 or rarely 5 hours each week, and these hourly samplings are indicated as A, B, C, D and E. B2 complemented B1 for one-hour sampling. |
Flower | Name of the flowers that the bees were visiting |
Note: “NA” means not available; “free” means not on flowers; “sp.” is an unidentified species named at the genus level.
This dataset is provided under a Creative Commons Attribution 4.0 International license (CC-BY 4.0) (https://creativecommons.org/licenses/by/4.0/).
http://db.cger.nies.go.jp/JaLTER/ER_DataPapers/archives/2020/ERDP-2020-01
We greatly acknowledge the outstanding mentor Shoichi F Sakagami. We thank Masanori J Toda for his continuous support of our study, and Ryuki Murao, Shuichi Ikudome, Katsushi Mitai, and Hirohiko Nagase for their assistance identifying bee species in Evylaeus, Hylaeus, Nomada, Sphecodes, and Coelioxys. We also thank Mifuyu Ogawa and Keiko Takano for caring for the specimens in the laboratory, Botanic Garden of Hokkaido University for the assistance identifying plants, and Teruyoshi Nagamitsu and Naoki Inari for encouraging this and future studies. This study was partly funded by the Joint Research Program of the Institute of Low Temperature Science, Hokkaido University, the Global Environment Research Fund of the Ministry of Environment of Japan, and the Ministry of Agriculture Forestry and Fisheries of Japan.
Michener, C. D. (2007). The bees of the world (2nd ed.). Baltimore, MD: The Johns Hopkins University Press.
Potts, S. G., Imperatriz‐Fonseca, V., Ngo, H. T., Aizen, M.A., Biesmeijer, J. C., Breeze, T. D., … Vanbergen, A. J. (2016). Safeguarding pollinators and their values to human well‐being. Nature, 540, 220–229. https://doi.org/10.1038/nature20588.
Sakagami, S. F., & Fukuda, H. (1973). Wild bee survey at the campus of Hokkaido University. Journal of the Faculty of Science, Hokkado University, Series VI Zoology, 19, 190–250.
Sakagami, S. F., Fukuda, H., & Kawano, H. (1974). Biofaunistic surveys of wild bees. Problems and methods, with results taken at Mt. Moiwa, Sapporo. Seibutsu Kyozai, 9, 1–60.
Tadauchi, O., & Murao, R. (2014). An illustrated guide to Japanese bees. Tokyo, Japan: Bunichi Sougou Shuppan.
The Botanic Garden Faculty of Agriculture Hokkaido University. (1979). List of herbaceous plants in the botanic garden. Sapporo, Japan: The Botanic Garden, Faculty of Agriculture, Hokkaido University.
The Botanic Garden Faculty of Agriculture Hokkaido University. (1981). List of trees and shrubs in the botanic garden. Sapporo, Japan: The Botanic Garden, Faculty of Agriculture, Hokkaido University.
The Hokkaido University Museum. (2010). Hokudai eco‐campuss dokuhon: Shokubutuen‐hen. Sapporo, Japan: The Hokkaido University Museum.
Yonekura, K., & Kajita, T. (2003). BG Plants Wamei‐Gakumei Index (YList). Retrieved from http://www.ylist.info/index.html.
]]>Authors: Takeshi Matsumura, Shoko Nakamura, Hisatomo Taki
Accession Number: ERDP-2020-01
Accepted: TBA
Published on: TBA
DOI: TBA
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The Data file is stored in JaLTER Database.
]]>You can see the metadata to click this link.
]]>Plant trait database for Cryptomeria japonica and Chamaecyparis obtusa (SugiHinoki DB)—their physiology, morphology, anatomy and biochemistry
Yoko Osone1*, Shoji Hashimoto1,2, 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
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.
functional traits; literature survey; sugi; hinoki; Japanese cypress; Japanese cedar; allocation; photosynthesis; respiration; water relation
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.
Plant trait database for Cryptomeria japonica and Chamaecyparis obtusa (SugiHinoki DB)—their physiology, morphology, anatomy and biochemistry
ERDP-2019-05
Forestry and Forest Products Research Institute
Address: 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
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
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.
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)
Literature published from 1950 to 2018*
*The latest literature will be added accordingly.
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’).
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.
Category | Trait | Unit | Number of data | |
---|---|---|---|---|
Cryptomeria japonica | Chamaecyparis obtusa | |||
Photosynthesis | Maximum photosynthesis per leaf area (Amaxa) |
µmol m-2 s-1 |
216 |
215 |
Maximum photosynthesis rate per leaf dry mass (Amaxm) |
µmol kg-1 s-1 |
844 |
141 |
|
Maximum gross photosynthetic rae per leaf area |
µmol m-2 s-1 |
56 |
10 |
|
Maximum gross photosynthetic rae per dary mass |
µmol kg-1 s-1 |
128 |
5 |
|
Maximum rate of electron transport per leaf area (Jmaxa) |
µmol m-2 s-1 |
66 |
18 |
|
Maximum rate of electron transport per leaf dry mass (Jmaxm) |
µmol kg-1 s-1 |
62 |
0 |
|
Maximum rate of carboxylation per leaf area (Vcmaxa) |
µmol m-2 s-1 |
228 |
34 |
|
Maximum rate of carboxylation per leaf dry mass (Vcmaxm) |
µmol kg-1 s-1 |
62 |
0 |
|
Electron transport rate per leaf area at light saturation (ETR) |
µmol m-2 s-1 |
25 |
23 |
|
Initial slope of light response curve |
molCO2 mol[e]-1 |
48 |
33 |
|
Convexity of light response curve |
NA |
45 |
30 |
|
Light compensation point of photosynthesis |
µmol[e] m-2 s-1 |
7 |
17 |
|
Molar content of chlorophyll per leaf area |
µmol m-2 |
9 |
0 |
|
Molar content of chlorophyll per leaf dry mass |
µmol g-1 |
3 |
0 |
|
Mass content of chlorophyll per leaf dry mass |
mg g-1 |
243 |
0 |
|
Mass content of chlorophyll per leaf fresh mass |
mg g-1 |
211 |
53 |
|
Ratio of chlorophyll a to chlorophyll b |
NA |
45 |
42 |
|
Molar content of Rubisco per leaf area |
g m-2 |
9 |
0 |
|
Mass content of Rubisco per leaf dry mass |
mg g-1 |
3 |
0 |
|
Rubisco activity per leaf area |
U m-2 |
5 |
0 |
|
Respiration | Leaf dark respiration rate per area (R) |
µmol m-2 s-1 |
128 |
223 |
Leaf dark respiration rate per area per a day |
mmol m-2 d-1 |
0 |
29 |
|
Leaf dark respiration rate per leaf dry mass |
µmol kg-1 s-1 |
155 |
3 |
|
Leaf dark respiration rate per leaf fresh mass |
µmol kg-1 s-1 |
0 |
20 |
|
Day respiration rate per leaf area |
µmol m-2 s-1 |
39 |
0 |
|
Branch dark respiration rate per branch dry mass |
µmol kg-1 s-1 |
20 |
0 |
|
Branch dark respiration rate per branch fresh mass |
µmol kg-1 s-1 |
0 |
69 |
|
Branch dark respiration rate per branch volume |
µmol m-3 s-1 |
46 |
26 |
|
Stem dark respiration rate per stem surface area |
µmol m-2 s-1 |
207 |
89 |
|
Stem dark respiration rate per stem surface area per a day |
mmol m-2 d-1 |
0 |
333 |
|
Stem dark respiration rate per stem fresh mass |
µmol kg-1 s-1 |
0 |
95 |
|
Stem dark respiration rate per stem volume |
µmol m-3 s-1 |
61 |
42 |
|
Aboveground dark respiration rate per dry mass |
µmol kg-1 s-1 |
16 |
16 |
|
Aboveground dark respiration rate per dry mass per a day |
mmol kg-1 d-1 |
0 |
72 |
|
Root dark respiration rate per dry mass |
µmol kg-1 s-1 |
26 |
23 |
|
Root dark respiration rate per dry mass per a day |
mmol kg-1 d-1 |
0 |
13 |
|
Root dark respiration rate per fresh mass |
µmol kg-1 s-1 |
0 |
45 |
|
Fineroot dark respiration rate per dry mass |
µmol kg-1 s-1 |
0 |
107 |
|
Fineroot dark respiration rate per fresh mass |
µmol kg-1 s-1 |
0 |
7 |
|
Q10 measured for leaf |
NA |
0 |
30 |
|
Q10 measured for stem |
NA |
18 |
0 |
|
Q10 measured for aboveground part |
NA |
0 |
163 |
|
Water relation | Stomatal conductance for CO2 per leaf area |
mol m-2 s-1 |
59 |
40 |
Stomatal conductance for CO2 per leaf dry mass |
mol kg-1 s-1 |
139 |
24 |
|
Transpiration rate per leaf area |
mmol m-2 s-1 |
76 |
95 |
|
Transpiration rate per leaf dry mass |
mmol kg-1 s-1 |
395 |
207 |
|
Transpiration rate per leaf dry mass per a day |
g g-1 d-1 |
141 |
83 |
|
Cuticle transpiration as %loss of leaf water per hour |
% h-1 |
48 |
0 |
|
Sap flow velocity measured |
cm h-1 |
26 |
104 |
|
Sap flow density/velocity measured by Granier method. |
cm3 m-2 s-1 |
88 |
19 |
|
Sap frow density averaged for a day |
cm3 m-2 s-1 |
29 |
24 |
|
Sap flow density per a day |
cm d-1 |
35 |
59 |
|
Heat pulse velocity |
cm h-1 |
35 |
46 |
|
Heat pulse velocity per a day |
cm d-1 |
12 |
14 |
|
Sap flow (or transpiration) per a tree |
g s-1 |
3 |
28 |
|
Sap flow (or transpiration) per a tree per a day |
kg d-1 |
112 |
176 |
|
Soil to leaf hydraulic conductance per leaf area |
mmol m-2 s-1 Mpa-1 |
37 |
9 |
|
Bulk leaf hydraulic conductance |
mmol m-2 s-1 Mpa-1 |
0 |
25 |
|
Stem (sapwood) specific conductivity |
kg m-1 s-1 MPa-1 |
18 |
9 |
|
Soil to leaf hydlauric resistance per leaf area |
Mpa m2 s mmol-1 |
21 |
15 |
|
Soil to leaf Hydlauric resistance per leaf mass |
Mpa kg s mmol-1 |
64 |
60 |
|
Soil to leaf hydlauric resistance |
103 Mpa s kg-1 |
9 |
9 |
|
Predawn leaf water potential |
MPa |
51 |
132 |
|
Midday leaf water potential |
MPa |
370 |
189 |
|
Osmotic potential at water saturation |
MPa |
258 |
93 |
|
Leaf water potential at turgor loss point |
MPa |
262 |
161 |
|
Water content at turgor loss point |
g g-1 |
101 |
93 |
|
Bulk elastic modulus |
MPa |
101 |
53 |
|
Branch water potential at 50% conductivity loss |
MPa |
2 |
2 |
|
Root water potential at 50% conductivity loss |
MPa |
1 |
2 |
|
Leaf hydraulic capacitance |
mol m-2 MPa-1 |
41 |
0 |
|
Satulated water content per leaf area |
g m-2 |
44 |
0 |
|
Water content in stem as (FM-DM)/DM*100 |
% |
51 |
0 |
|
Water use efficiency |
mg g-1 |
8 |
8 |
|
13C:12C ratio in leaves |
‰ |
74 |
44 |
|
13C:12C ratio in stem |
‰ |
39 |
0 |
|
Ci to Ca ratio |
NA |
78 |
0 |
|
Leaf morphology | Specific leaf area (SLA) |
m2 kg-1 |
379 |
244 |
Shoot silhouette area to projected needle area ratio (SPAR) |
NA |
39 |
0 |
|
Root morphology | Specific root length |
m g-1 |
132 |
196 |
Specific root tips |
mg-1 |
7 |
3 |
|
Specific root surface area |
cm2 mg-1 |
44 |
3 |
|
Anatomy | Number of stomata per leaf area |
mm-2 |
173 |
7 |
Width of stomata |
µm |
3 |
2 |
|
Length of stomata |
µm |
21 |
2 |
|
Wax amount per leaf dry weight |
mg g-1 |
61 |
0 |
|
Wax amount per leaf leaf weight |
mg g-1 |
35 |
0 |
|
Cuticle thickness of leaves |
µm |
6 |
0 |
|
Cross sectional area of leaf xylem |
µm2 |
43 |
33 |
|
Cross sectional area of leaf transfusion cell |
µm2 |
46 |
34 |
|
Tracheid length of early wood |
mm |
8 |
99 |
|
Tracheid length of late wood |
mm |
495 |
7 |
|
Tracheid diameter (tangent) of early wood |
µm |
47 |
3 |
|
Tracheid diameter (radial) of early wood |
µm |
56 |
3 |
|
Tracheid diameter (tangent) of late wood |
µm |
32 |
3 |
|
Tracheid diameter (radial) of late wood |
µm |
56 |
3 |
|
Wood density | Basic density of stem |
g cm3 |
640 |
44 |
Air-dry stem density |
g cm3 |
148 |
4 |
|
Oven-dry stem density |
g cm3 |
63 |
0 |
|
Basic density of branch |
g cm3 |
31 |
0 |
|
Basic density of root |
g cm3 |
4 |
4 |
|
Density of fine root as dry weight divided by fresh volume |
g cm3 |
44 |
105 |
|
Fresh density of root |
g cm3 |
0 |
67 |
|
Resource use | Photosynthetic nitrogen use efficiency |
µmol mol-1 s-1 |
45 |
0 |
Nitrogen resorption efficiency of leaves |
% |
3 |
19 |
|
Mean residence time of leaf nitrogen |
year |
1 |
0 |
|
Nitrogen use efficiency |
NA |
1 |
1 |
|
N content of leaf litter fall |
mg g-1 |
6 |
52 |
|
15N:14N ratio in leaves |
‰ |
4 |
4 |
|
Leaf longevity |
year |
24 |
47 |
|
Fineroot longevity |
year |
1 |
7 |
|
Nitrogen content | Nitrogen content per leaf area |
g m-2 |
381 |
112 |
Nitrogen content per leaf dry mass |
mg g-1 |
2116 |
759 |
|
Nitrogen content per green branch dry mass |
mg g-1 |
58 |
1 |
|
Nitrogen content per branch dry mass |
mg g-1 |
173 |
20 |
|
Nitrogen content per stem dry mass |
mg g-1 |
163 |
32 |
|
Nitrogen content per avoveground dry mass |
mg g-1 |
27 |
14 |
|
Nitrogen content per root dry mass |
mg g-1 |
152 |
41 |
|
Nitrogen content per bark dry mass |
mg g-1 |
28 |
3 |
|
Nitrogen content per sapwood dry mass |
mg g-1 |
27 |
2 |
|
Nitrogen content per heartwood dry mass |
mg g-1 |
27 |
2 |
|
Nitrogen content per thick root (>20mm) dry mass |
mg g-1 |
28 |
4 |
|
Nitrogen content per thin root (<20mm, >2mm) dry mass |
mg g-1 |
28 |
4 |
|
Nitrogen content per fine root (<2mm) dry mass |
mg g-1 |
63 |
107 |
|
Phosphorous content | Phosphorus content per leaf dry mass |
mg g-1 |
692 |
357 |
Phosphorus content per green branch dry mass |
mg g-1 |
58 |
1 |
|
Phosphorus content per branch dry mass |
mg g-1 |
120 |
18 |
|
Phosphorus content per stem dry mass |
mg g-1 |
163 |
32 |
|
Phosphorus content per avoveground dry mass |
mg g-1 |
16 |
7 |
|
Phosphorus content per root dry mass |
mg g-1 |
145 |
34 |
|
Phosphorus content per bark dry mass |
mg g-1 |
28 |
3 |
|
Phosphorus content per sapwood dry mass |
mg g-1 |
27 |
2 |
|
Phosphorus content per heartwood dry mass |
mg g-1 |
27 |
2 |
|
Phosphorus content of thick root (>20mm) dry mass |
mg g-1 |
28 |
4 |
|
Phosphorus content of thin root (<20mm, >2mm) dry mass |
mg g-1 |
28 |
4 |
|
Phosphorus content of fine root (<2mm) dry mass |
mg g-1 |
26 |
4 |
|
Pottasium content | Pottasium content per leaf mass |
mg g-1 |
722 |
354 |
Pottasium content per green branch dry mass |
mg g-1 |
58 |
1 |
|
Pottasium content per branch dry mass |
mg g-1 |
120 |
18 |
|
Pottasium content per stem dry mass |
mg g-1 |
163 |
32 |
|
Pottasium content per avoveground dry mass |
mg g-1 |
28 |
7 |
|
Pottasium content per root dry mass |
mg g-1 |
157 |
34 |
|
Pottasium content per bark dry mass |
mg g-1 |
28 |
3 |
|
Pottasium content per sapwood dry mass |
mg g-1 |
27 |
2 |
|
Pottasium content per heartwood dry mass |
mg g-1 |
27 |
2 |
|
Pottasium content per thick root (>20mm) dry mass |
mg g-1 |
28 |
4 |
|
Pottasium content per thin root (<20mm, >2mm) dry mass |
mg g-1 |
28 |
4 |
|
Pottasium content per fine root (<2mm) dry mass |
mg g-1 |
26 |
4 |
|
Chemical composition | Lignin content of leaf |
% |
3 |
4 |
Lignin content of leaf litter fall |
% |
2 |
1 |
|
Lignin content of deadwood |
% |
69 |
67 |
|
Holocellulose content of leaf |
% |
3 |
4 |
|
Holocellulose content of leaf litter fall |
% |
2 |
1 |
|
Holocellulose content of deadwood |
% |
69 |
67 |
|
Nitrogen content of deadwood |
g kg-1 |
69 |
67 |
|
Carbon content of deadwood |
g kg-1 |
69 |
67 |
|
Biomass | Leaf biomass |
kg |
150 |
77 |
Stem+branch biomass |
kg |
150 |
77 |
|
Aboveground biomass |
kg |
45 |
67 |
|
Root biomass |
kg |
195 |
100 |
|
Total biomass |
kg |
195 |
100 |
|
Branch biomass |
kg |
72 |
77 |
|
Thick root biomass (>20mm) |
kg |
29 |
16 |
|
Thin root biomass (<20mm, >2mm) |
kg |
29 |
16 |
|
Fine root biomass (<2mm) |
kg |
39 |
16 |
|
Allocation | Annual allocation of net primary production to leaf |
t ha-1 y-1 |
94 |
22 |
Annual allocation of net primary production to stem + branch |
t ha-1 y-1 |
94 |
22 |
|
Annual allocation of net primary production to root |
t ha-1 y-1 |
94 |
22 |
|
Total net primary production |
t ha-1 y-1 |
94 |
22 |
|
Annual allocation of net primary production to branch |
t ha-1 y-1 |
80 |
21 |
|
Annual leaf litterfall |
t ha-1 y-1 |
46 |
130 |
|
Annual branch + bark litterfall |
t ha-1 y-1 |
26 |
83 |
|
Annual litterfall other than leaf, branch and bark |
t ha-1 y-1 |
23 |
87 |
|
Total annual litterfall |
t ha-1 y-1 |
47 |
96 |
|
Stand characteristics | Leaf area index (LAI) |
NA |
16 |
62 |
LAI-based light absorption coefficient |
NA |
6 |
1 |
|
Leaf mass-based Light absorption coefficient |
NA |
2 |
2 |
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.
Types of variables | Variables in database | Original units | Units used in the DB | Notes |
---|---|---|---|---|
Photosynthetic rate per leaf area | Amax_a, Amax_gr_a, Jmax_a, Vcmax_a | 1 mgCO2 dm-2 h-1 | 0.631 µmolCO2 m-2 s-1 | |
Photosynthetic rate per leaf dry weight | Amax_m, Amax_gr_m, Jmax_m, Vcmax_m | 1 mgCO2 g-1 h-1 | 6.31 µmolCO2 kg[DW]-1 s-1 | |
Respiration rate per organ dry/fresh weight | R_leaf_m, R_leaf_fw,R_branch_dw, R_branch_fw, R_stem_fw, R_top, R_root_dw, R_root_fw, R_froot_dw, R_froot_fw | 1 gCO2 kg-1 h-1 | 6.31 µmolCO2 kg[DW/FW]-1 s-1 | |
1 mgCO2 g-1 h-1 | 6.31 µmolCO2 kg[DW/FW]-1 s-1 | |||
1 mgCO2 kg-1 h-1 | 6.31 x 10-3 µmolCO2 kg[DW/FW]-1 s-1 | |||
1 nmol g-1 s-1 | 1 µmolCO2 kg[DW/FW]-1 s-1 | |||
Leaf respiration rate per area | R_leaf_a | 1 mgCO2 dm-2 h-1 | 0.631 µmolCO2 m-2 s-1 | |
Branch/Stem respiration per volume | R_branch_dw, R_branch_fw | 1 mg dm-3 h-1 | 6.31 µmolCO2 m-3 s-1 | |
1 g m-3 h-1 | 6.31 µmolCO2 m-3 s-1 | |||
Stem respiration per surface area | R_stem_sa | 1 mgCO2 m-2 h-1 | 6.31 x 10-3 µmolCO2 m-2 s-1 | |
1 mgCO2 dm-2 h-1 | 0.631 µmolCO2 m-2 s-1 | |||
Stomatal conductance per leaf area | gs_a | 1 cmCO2 s-1 | 0.409 molCO2 m-2 s-1 | at the temperature of 25℃ calculated by 1/100x1000x101325/(8314x(273+25)) |
1 cmH2O s-1 | 0.256 molCO2 m-2 s-1 | at the temperature of 25℃ calculated by 1/100*1000*101325/(8314*(273+25))/1.6 | ||
1 molH2O m-2 s-1 | 0.630 molCO2 m-2 s-1 | |||
Stomatal conductance per leaf dry weight | gs_m | 1 cm3CO2 g-1 s-1 | 0.041 molCO2 kg-1 s-1 | at the temperature of 25℃ calculated by 1/1000*(101325)/(8314*(273+25))*1000 |
1 molH2O kg-1 s-1 | 0.630 molCO2 kg-1 s-1 | |||
Transpiration per leaf area | E_a | 1 gH2O m-2 s-1 | 55.6 mmolH2O m-2 s-1 | |
1 μgH2O cm-2 s-1 | 0.556 mmolH2O m-2 s-1 | |||
1 cm3H2O m-2 s-1 | 0.041 mmolH2O m-2 s-1 | at the temperature of 25℃ calculated by 1/1000*(101325)/(8314*(273+25))*1000 | ||
Transpiration per leaf dry weight | E_m | 1 mgH2O g-1 min-1 | 0.926 mmolH2O kg-1 s-1 | |
1 kgH2O kg-1 h-1 | 15.4 mmolH2O kg-1 s-1 | |||
Sap flow velocity/Sap flux density | SFV, SFD | 1 cm-3 m-2 s-1 | 0.360 cm h-1 | |
Daily sap flow density/Daily sap flow velocity | SFD_day | 1 cc cm-2 d-1 | 1 cm d-1 | |
1 m3 m-2 d-1 | 100 cm d-1 | |||
Sap flow per a tree | SF | 1 g h-1 | 278 x 10-6 g s-1 | |
1 cm3 s-1 | 1 g s-1 | |||
1 ℓ h-1 | 0.278g s-1 | |||
Daily sap flow (transpilation) per a tree | SF_day | 1 ℓ d-1 | 1 kg d-1 | |
1 cm3 d-1 | 0.001 kg d-1 | |||
1 cc d-1 | 0.001 kg d-1 | |||
Soil to leaf hydraulic resistance per leaf area | Ω_a | 1 Mpa m2 s gH2O-1 | 18 Mpa m2 s mmolH2O-1 | |
1 Mpa cm2 s μgH2O-1 | 1.8 Mpa m2 s mmolH2O-1 | |||
Soil to leaf hydraulic resistance per leaf mass | Ω_m | 1 Mpa kg s kgH2O-1 | 18 x 10-6 Mpa kg s mmolH2O-1 | |
1 Mpa g s μgH2O-1 | 18 Mpa kg s mmolH2O-1 | |||
1 Mpa kg h kgH2O-1 | 64.8 x 10-3 Mpa kg s mmolH2O-1 | |||
Soil to leaf hydraulic resistance per tree | Ω_SF | 1 Mpa h g-1 | 3.6 x 106 MPa s kg-1 | |
1 Mpa h cm-3 | 3.6 x 106 MPa s kg-1 | |||
1 Mpa s m-3 | 103 MPa s kg-1 |
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.
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.
The first row of the files presents the variable names.
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.
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/).
Research on evaluation of influence of climate change on plantation in Japan
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/
Agriculture, Forestry and Fisheries Research Council, Japan
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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]]>Authors: Yoko Osone, Shoji Hashimoto, Tanaka Kenzo, Masatake, G. Araki, Yuta Inoue, Koji Shichi, Jumpei Toriyama, Naoyuki Yamashita, Kenji Tsuruta, Shigehiro Ishizuka, Junko Nagakura, Kyotaro Noguchi, Kenji Ono, Hisao Sakai, Yoshimi Sakai, Tetsuya Sano, Hidetoshi Shigenaga, Yoshinori Shinohara, Kenichi Yazaki
Accession Number: ERDP-2019-05
Accepted: Sep. 2019
Published on: TBA
DOI: TBA
Click to see Metadata.
The Data file is stored in JaLTER Database.
]]>Accession Number | ERDP-2017-05 |
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Title | Acoustic monitoring data of avian species inside and outside the evacuation zone of the Fukushima Daiichi power plant accident |
Publication | Ecological Research 32(6): 769. doi: 10.1007/s11284-017-1491-y |
Authors | Fukasawa, K.; Mishima, Y.; Yoshioka, A.; Kumada, N.; Totsu, K. |
ecomoni_fukushima@nies.go.jp, k.fukasawa37@gmail.com |
The data files have been updated.
Nitrate reductase activities in plants from different ecological and taxonomic groups grown in Japan
Lina A. Koyama1,2, Masakazu Terai1,3, Naoko Tokuchi4
1: Laboratory of Forest Ecology, Graduate School of Agriculture, Kyoto University
2: Laboratory of Biosphere Informatics, Department of Social Informatics, Graduate School of Informatics, Kyoto University
3: FUJICCO Co., Ltd.
4: Field Science Education and Research Center, Kyoto University
Lina A. Koyama
Laboratory of Biosphere Informatics, Department of Social Informatics, Graduate School of Informatics, Kyoto University
Kyoto 606-8501, Japan
Phone: +81-75-753-3297
E-mail: linak@bre.soc.i.kyoto-u.ac.jp
Plants generally use soil inorganic nitrogen, ammonium (NH4+-N), and nitrate (NO3--N) as sources of nitrogen, an essential nutrient. The assimilation processes after uptake differ considerably from ammonium to nitrate. Nitrate must be reduced to ammonium in plant tissue before it is synthesized to amino acids, while ammonium is directly and immediately synthesized to amino acids after its uptake. Nitrate reductase is an enzyme that catalyzes the first and rate-limiting step of nitrate assimilation, reducing nitrate to nitrite. It is a substrate-inducible enzyme, and the capacity to induce nitrate reductase varies greatly among plant species. In vivo nitrate reductase activity (NRA) is generally measured as a nitrite production rate during incubation using fine cut pieces of plant tissue, and it is applicable as an indicator on plant nitrate use. Here we present in vivo NRA of leaves from a total of 108 species including arboreal trees, small trees, shrubs, herbs, a vine and a moss. For 75 of the species, NRA in fine roots was also determined. At least 20 species in sampled plants were imported and planted for scientific or industrial purposes, but most sampled species were native to Japan. Several inventory studies of plant NRA have been conducted, mainly in Europe, and they provided information on the species-specific capacity of nitrate use by plants in Europe. However, to our best knowledge, there has been hitherto no published inventory of the NRA of plants in Japan where many endemic species are distributed. Our dataset contains plant NRA with species, family name, life form, leaf lifespan (evergreen or deciduous), growth stage, the season of sample collection, growth conditions (natural or cultivated) and other treatments/conditions when applicable. The data provided by this study may contribute to future works that require information regarding the plant species characteristics for nitrate use capacity or nitrate preference.
Nitrate reductase activities in plants from different ecological and taxonomic groups grown in Japan
ERDP-2019-06
Name: Lina A. Koyama
Affiliation: Laboratory of Biosphere Informatics, Department of Social Informatics, Graduate School of Informatics, Kyoto University
Address: Kyoto 606-8501 Japan
Tel: +81-75-753-3297
Fax: +81-75-753-3133
E-mail: linak@bre.soc.kyoto-u.ac.jp
Name: Lina A. Koyama
Affiliation: Laboratory of Biosphere Informatics, Department of Social Informatics, Graduate School of Informatics, Kyoto University
Address: Kyoto 606-8501 Japan
Tel: +81-75-753-3297
Fax: +81-75-753-3133
E-mail: linak@bre.soc.kyoto-u.ac.jp
Samples were collected from six locations in Japan, including a growth chamber, a greenhouse, an arboretum, and two plantation sites, in addition to the collection of natural vegetation. Table 1 shows the positions, environmental factors, and descriptions of the sampling sites.
Table 1 Description for sampling sites.
Site | Location | Elevation(m a.s.l.) | Mean annua temperature(°C) | Mean annual precipitation(mm) | Vegetation type | Dominant species | |
LAT | LON | ||||||
Ashiu | 35°18′ N | 135°43′ E | about 600-700 | 12.2 | 2548 | Cool temperate deciduous forest | Fagus crenata,Quercus crispula |
Daisen | 35°21′ N | 133°32′ E | about 1000 | 10.5 | 2736 | Cool temperate deciduous forest | Fagus crenata |
Isaki | 35°12′ N | 136°05′ E | about 200 | 15.1 | 1475 | Warm temperate coniferous plantation | Chamaecyparis obtusa,Camellia japonica |
Kamigamo | 35°04′ N | 135°46′ E | about 200 | 15 | 1584 | Warm temperate deciduous forest/plantation | Quercus serrata,Quercus glauca |
Kitashirakawa | 35°02' N | 135°47'E | about 100 | 15.1 | 1465 | Arboretum | - |
Tottori | 35°32' N | 134°13' E | 0 - 100 | - | - | Green house | - |
Tottori | 35°32' N | 134°13' E | 0 - 100 | - | - | Growth chamber | - |
Tottori | 35°32' N | 134°13' E | 0 - 100 | 14.9 | 1914 | Coastal sand dunes | Elaeagnus umbellata,Vitex rotundifolia,Artemisia capillaris |
Aug, 1997 - August, 2017
Samples were collected from the six locations shown in Table 1. The following are additional descriptions for each site.
In Ashiu, the dominant inorganic N form on the upper slope was NH4+-N, while it was NO3--N on the lower slope of this site (Tateno and Takeda 2003). In total, 16 species were sampled, and samples of Fagus crenata were collected from both the upper slope and lower slope.
In Daisen, fertilization was conducted from 1987 to 2002, with an annual supply of 5,250 kg yr-1 ha (N:P:K = 20:10:10). Samples were collected from the fertilization plot and the control plot in this site.
Isaki was located on a peninsula in Lake Biwa and had been colonized by great cormorants (Phalacrocorax carbo) since 1980-90s (Kameda 2012), and the cormorant-derived N influenced N dynamics (Hobara et al. 2005). Three sampling locations were selected to represent different levels of cormorant colonization effects at this site; a currently occupied area, a previously occupied but now abandoned area, and a control area never colonized by cormorants. In the currently occupied area and the previously occupied area, both the N mineralization rate and the nitrification rate were high, and the ratio of nitrification to N mineralization was close to 100 %, suggesting high NO3--N availability in these areas (Hobara et al. 2001).
In Kamigamo, a very low nitrification rate in comparison with the N mineralization rate was reported for both the organic layer and the mineral soil layer (Tokuchi et al. 2002) Accordingly, the dominant inorganic N form in the soil was NH4+-N. Samples from 11 species were collected at this site.
Kitashirakawa is an arboretum of the Field Science Education and Research Center, Kyoto University, where about 500 species are planted in a 1.3 ha area. Forty-eight species were sampled from this site, including coniferous and broad-leaved species of trees, shrubs, and herbaceous species, including native and imported species.
In Tottori, samplings were conducted under two artificial conditions (greenhouse and growth chamber) and a natural condition (coastal sand dunes).
The greenhouse was located on the campus of the Arid Land Research Center, Tottori University. Imported arid land plant species and domestic plant species were grown in the greenhouse to supply experiments, and 28 species were sampled. They were planted in locally available sand and regularly watered. Three levels of fertilization were applied to four Salix species; non-fertilized (0 N-kg ha-1), fertilized (7.4 N-kg ha-1) and intensively fertilized (29.4 N-kg ha-1). Yamamoto et al. (2003) described in detail the influence of fertilization on the four Salix species. Eight Fagaceae species grown in the greenhouse were regularly fertilized with 1000-fold diluted Hyponex solution (Hyponex, Japan).
The growth chamber was located on the campus of the Arid Land Research Center, Tottori University. The chamber conditions were set to a temperature of 18 C, humidity at 90 %, illuminance of 0 lux during the dark period, while in the light period the temperature was set at 25 °C, the humidity at 60 %, and illuminance 80,000 lux. During the light treatment, 11 hours of darkness were alternated with 13 hours of light.
Coastal sand dunes were located immediately north of the Arid Land Research Center, Tottori University, facing the Sea of Japan. The dunes were surrounded by a Pinus thunbergii plantation mixed with Robinia pseudoacasia established as a windbreak to protect agricultural and residential areas nearby. Since the establishment of the windbreak plantation, coastal plant species such as Elaeagnus umbellata, Vitex rotundifolia, and Artemisia capillaris began invading the dunes. Soil nitrate pool sizes were spatially and temporally heterogeneous, and minimum and maximum nitrate pool sizes were 0.9 and 240 mg N m-2, respectively (Lina A. Koyama unpublished data). Samples were collected from 11 species at this site.
To avoid the effect of diurnal changes in leaf NRA, samples were collected from 10:00 to 14:00 on sunny days except for growth chamber samples, and were kept on ice until laboratory analysis. Growth chamber samples were collected 5 hours after initiation of the last light period. In the dark treatment, the illuminance was kept to 0 lux after the last dark period for 5 hours, and then the samples were collected.
The in vivo NRA assay was performed based on a modified version of the Jaworski method (Jaworski 1971; Thomas and Hilker 2000; Koyama and Tokuchi 2003). Root samples were washed with tap water, followed by deionized water, to remove soil. About 100 mg (fresh weight) of leaf laminae, needles, or fine roots were cut into small fragments (2.5‑mm‑diameter disks or about 4‑mm2 segments of leaves, and about 2‑mm‑long needles or roots) and transferred to test tubes. The incubation buffer (5 mL) was added to the needles and roots, and the tube contents were vacuum infiltrated. The composition of the incubation buffer was as follows: 0.1 mol L-1 KNO3, 0.1 mol L-1 KH2PO4, and 1.5 % 1‑propanol; the pH was adjusted to ca. 7.5 using a NaOH solution. The samples were incubated for 1 h in darkness, and the NO2-‑N concentration in the incubation buffer was measured as the end-point. Before the measurement, enzyme activity was terminated by placing the sample vials in hot water (>80 °C). The concentration of NO2-‑N in the incubation buffer was measured colorimetrically following diazotization (Keeney and Nelson, 1982). The confounding effects of plant pigments were corrected by subtracting the absorbance of controls to which N‑naphthylethylene diamine dihydrochloride was not added (Gebauer et al. 1998). A fraction of each leaf sample was oven-dried at 105 °C and then weighed to calculate the activity per unit dry weight.
This data set is provided under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0: https://creativecommons.org/licenses/by-nc/4.0/).
Name: Lina A. Koyama
Affiliation: Laboratory of Biosphere Informatics, Department of Social Informatics, Graduate School of Informatics, Kyoto University
Address: Kyoto 606-8501 Japan
Tel: +81-75-753-3297
Fax: +81-75-753-3133
E-mail: linak@bre.soc.kyoto-u.ac.jp
Table 2 List of data tables.
Data file name | Description |
Data.csv | Dataset of plant nitrate reductase activity (NRA) with species taxonomic and growth conditions descriptions |
The data files are presented as UTF-8 text and comma-delimited (csv) files.
Headers corresponding to variable names (see section 9.D) are included in the first row of the data file.
The variables are listed according to their order of appearance in the data file. The variable names are headers included as the first row in the data file.
Table 3 List of variables in the dataset and their descriptions.
Data file name | Variable name | Variable definition |
Data.csv | ID | Identification number of each sample |
Scientific name | Identification of taxa (Species) | |
Family name | Identification of taxa (Family) | |
Life form | Tree*/Small tree*/Shrub*/Herb/Vine *: Trees are 10 m~, Small trees are 5 – 10 m, Shrubs are ~5 m at maturity. | |
Leaf lifespan | Tree, Small tree or Shrub: Evergreen/Deciduous Herb: Perennial/Annual | |
Leaf NRA | Leaf NRA (µmol g dry wt-1 h-1) | |
Root NRA | Root NRA (µmol g dry wt-1 h-1) | |
Year | The year in which the sample was collected | |
Month | The month in which the sample was collected representing the season | |
Site | Sample collection site: Ashiu/Daisen/Isaki/Kamigamo/Kitashirakawa/Tottori | |
Growth stage | Growth stage of the individual sample: Mature/Young tree*/Seedling *: a young tree is defined as a tree that did not reach the canopy height. | |
Growth condition | Growth conditions of the individual samples: Arboretum/Greenhouse/Growth chamber/Natural/Plantation | |
Treatment/Condition | Particular treatments or conditions for the individual samples:
|
|
Remarks | Additional information |
We are grateful for the support of our field and laboratory work provided by Drs. N. Yamanaka, M. Yamamoto, K. Kameda, S. Hobara, K. Fukushima, R. Tateno, and M. Hirobe. The staff of the Arid Land Research Center, Tottori University, and the Field Science, Education and Research Center (former University Forests), Kyoto University also helped us in our field and laboratory work.
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]]>Authors: Lina A. Koyama, Masakazu Terai, Naoko Tokuchi
Accession Number: ERDP-2019-06
Accepted: Sep. 2019
Published on: TBA
DOI: TBA
Click to see Metadata.
The Data file is stored in JaLTER Database.
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