Data Repository : Assessing insect herbivory on broadleaf canopy trees at 19 natural forest sites across Japan

Data Set Citation:
When using this data, please cite the data package:
Nakamura M. Assessing insect herbivory on broadleaf canopy trees at 19 natural forest sites across Japan ERDP-2021-01.1.6 (https://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2021-01.1.6/jalter-en)

General Information:
Title:	Assessing insect herbivory on broadleaf canopy trees at 19 natural forest sites across Japan
Identifier:	ERDP-2021-01.1.6
Abstract:	We present the largest freely available herbivory dataset for Japan representing data collected from a network of 19 natural forest sites across the country. Sampled network sites were part of the Monitoring Sites 1000 Project organized by the Ministry of the Environment. Sites were located across a range of climate zones, from subarctic to subtropical, and broadleaf trees (both evergreen and deciduous) were targeted at each site. Litterfall traps were used to assess leaf damage caused by leaf-chewing insects in 2014 and 2015. Using a standardized protocol, we assessed herbivory on 117,918 leaves of 39 dominant tree species. Preliminary analyses suggest that insect herbivory increases with increasing latitude for deciduous broadleaf species. In particular, oak (Quercus crispula) and beech (Fagus crenata) were subject to increased insect herbivory with increasing latitude. In contrast, insect herbivory decreased with increasing latitude in evergreen broadleaf species. The latitudinal gradient of herbivory differed according to leaf type (i.e., evergreen or deciduous). This dataset offers excellent opportunities for meta-analysis and comparative studies of herbivory among various forest types. The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2021-01.1/jalter-en.
Keywords:	evergreen species deciduous species insect-plant interactions the Monitoring sites 1000 Project latitudinal gradient

Data Table, Image, and Other Data Details:
Metadata download:	Ecological Metadata Language (EML) File
Data Table:	Herbivory.csv ( View Metadata \| Download File )
Data Table:	SiteList.csv ( View Metadata \| Download File )
Data Table:	SpList.csv ( View Metadata \| Download File )
Other Data:	data_descriptor.pdf ( View Metadata \| Download File )

Involved Parties

Data Set Owners:

Individual:

Masahiro Nakamura

Organization:

Wakayama Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University

Address:

Wakayama Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University,

Hokkaido 649-4563 Japan

Data Set Contacts:

Individual:

Masahiro Nakamura

Organization:

Wakayama Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University

Address:

Hirai,

Kozagawa, Wakayama 649-4563 Japan

Associated Parties:

Individual:

Takahumi Hino

Organization:

Network Center of the Forest and Grassland Survey of the Monitoring Sites 1000 Project, Japan Wildlife Research Center, c/o Tomakomai Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Tomakomai, Japan

Address:

The National Ainu Museum,

Shiraoi, Japan

Individual:	Yuri Kannno
Organization:	Wakayama Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Wakayama, Japan

Individual:	Shin Abe
Organization:	Forestry and Forest Products Research Institute, Tsukuba, Japan

Individual:	Tetsuto Abe
Organization:	Kyushu Research Center, Forestry and Forest Products Research Institute, Kumamoto, Japan

Individual:	Tsutomu Enoki
Organization:	Faculty of Agriculture, Kyushu University, Fukuoka, Japan

Individual:	Toshihide Hirao
Organization:	The University of Tokyo Chichibu Forest, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Chichibu, Japan

Individual:

Tsutom Hiura

Organization:

Tomakomai Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Tomakomai, Japan

Address:

Department of Ecosystem Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo,

Tokyo Japan

Individual:	Kazuhiko Hoshizaki
Organization:	Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan

Individual:

Hideyuki Ida

Organization:

Institute of Nature Education in Shiga Heights, Faculty of Education, Shinshu University, Yamanouchi, Japan

Address:

Faculty of Education, Shinshu University, Nagano, Japan,

Individual:

Ken Ishida

Organization:

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

Address:

Amami Ecosystem Study Group,

Individual:	Masayuki Maki
Organization:	Botanical Gardens, Tohoku University, Sendai, Japan

Individual:	Takashi Masaki
Organization:	Forestry and Forest Products Research Institute, Tsukuba, Japan

Individual:

Shoji Naoe

Organization:

Forestry and Forest Products Research Institute, Tsukuba, Japan

Address:

Tohoku Research Center, Forestry and Forest Products Research Institute,

Morioka, Japan

Individual:	Mahoko Noguchi
Organization:	Tohoku Research Center, Forestry and Forest Products Research Institute, Morioka, Japan

Individual:	Tatsuya Otani
Organization:	Shikoku Research Center, Forestry and Forest Products Research Institute, Kochi, Japan

Individual:	Takanori Sato
Organization:	Ecohydrology Research Institute, The University of Tokyo Forests, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Seto, Japan

Individual:	Michinori Sakimoto
Organization:	Field Science Education and Research Center, Kyoto University, Kyoto, Japan

Individual:

Hitoshi Sakio

Organization:

Field Center for Sustainable Agriculture and Forestry, Faculty of Agriculture, Niigata University, Sado, Japan

Address:

Sado Island Center for Ecological Sustainability, Niigata University,

Sado, Japan

Individual:	Masahiro Takagi
Organization:	Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan

Individual:	Atsushi Takashima
Organization:	Yona Field, Subtropical Field Science Center, Faculty of Agriculture, University of the Ryukyus, Kunigami, Japan

Individual:	Naoko Tokuchi
Organization:	Field Science Education and Research Center, Kyoto University, Kyoto, Japan

Individual:

Shunsuke Utsumi

Organization:

Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Horokanai, Japan

Address:

Field Science Center for Northern Biosphere, Hokkaido University,

Sapporo, Japan

Individual:

Amane Hidaka

Organization:

Address:

Graduate School of Agriculture, Kyoto University,

Kyoto Japan

Individual:	Masahiro Nakamura
Organization:	Wakayama Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Wakayama, Japan

Data Set Characteristics

Geographic Region:

Geographic Description:

Japan

Bounding Coordinates:

West:	128.23 degrees
East:	142.28 degrees
North:	26.74 degrees
South:	44.37 degrees

Time Period:

Begin:

2014-01-01

End:

2015-12-31

Taxonomic Range:

Classification:

Rank Name:

Family

Rank Value:

Schisandraceae

Classification:

Rank Name:

Genus

Rank Value:

Illicium

Classification:

Rank Name:	Species
Rank Value:	Illicium anisatum L.
Common Name:	anisatum

Classification:

Rank Name:

Family

Rank Value:

Lauraceae

Classification:

Rank Name:

Genus

Rank Value:

Machilus

Classification:

Rank Name:	Species
Rank Value:	Machilus thunbergii Sieb. et Zucc.
Common Name:	thunbergii

Classification:

Rank Name:

Family

Rank Value:

Magnoliaceae

Classification:

Rank Name:

Genus

Rank Value:

Magnolia

Classification:

Rank Name:	Species
Rank Value:	Magnolia obvata Thumb.
Common Name:	obvata

Classification:

Rank Name:

Family

Rank Value:

Cercidiphyllaceae

Classification:

Rank Name:

Genus

Rank Value:

Cercidiphyllum

Classification:

Rank Name:	Species
Rank Value:	Cercidiphyllum japonicum Sieb. et Zucc.
Common Name:	japonicum

Classification:

Rank Name:

Family

Rank Value:

Hamamelidaceae

Classification:

Rank Name:

Genus

Rank Value:

Distylium

Classification:

Rank Name:	Species
Rank Value:	Distylium racemosum Sieb. et Zucc.
Common Name:	racemosum

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Betula

Classification:

Rank Name:	Species
Rank Value:	Betula ermanii Cham.
Common Name:	ermanii

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Betula

Classification:

Rank Name:	Species
Rank Value:	Betula grossa Sieb. et Zucc.
Common Name:	grossa

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Carpinus

Classification:

Rank Name:	Species
Rank Value:	Carpinus cordata Bl.
Common Name:	cordata

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Carpinus

Classification:

Rank Name:	Species
Rank Value:	Carpinus laxiflora (Sieb. et Zucc.) Bl.
Common Name:	laxiflora

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Carpinus

Classification:

Rank Name:	Species
Rank Value:	Carpinus tschonoskii Maxim.
Common Name:	tschonoskii

Classification:

Rank Name:

Family

Rank Value:

Betulaceae

Classification:

Rank Name:

Genus

Rank Value:

Ostrya

Classification:

Rank Name:	Species
Rank Value:	Ostrya japonica Sargent
Common Name:	japonica

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Castanea

Classification:

Rank Name:	Species
Rank Value:	Castanea crenata Sieb. et Zucc.
Common Name:	crenata

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Castanopsis

Classification:

Rank Name:	Species
Rank Value:	Castanopsis cuspidata (Thunb. ex Murray) Schottky
Common Name:	cuspidata

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Castanopsis

Classification:

Rank Name:	Species
Rank Value:	Castanopsis sieboldii (Makino) Hatusima ex Yamazaki et Mashiba subsp. lutchuensis (Koidz.) H. Ohba
Common Name:	sieboldii

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Castanopsis

Classification:

Rank Name:	Species
Rank Value:	Castanopsis spp.
Common Name:	spp.

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Fagus

Classification:

Rank Name:	Species
Rank Value:	Fagus crenata Bl.
Common Name:	crenata

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Fagus

Classification:

Rank Name:	Species
Rank Value:	Fagus japonica Maxim.
Common Name:	japonica

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Quercus

Classification:

Rank Name:	Species
Rank Value:	Quercus acuta Thunb. ex Murray
Common Name:	acuta

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Quercus

Classification:

Rank Name:	Species
Rank Value:	Quercus crispula Bl.
Common Name:	crispula

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Quercus

Classification:

Rank Name:	Species
Rank Value:	Quercus salicina Bl.
Common Name:	salicina

Classification:

Rank Name:

Family

Rank Value:

Fagaceae

Classification:

Rank Name:

Genus

Rank Value:

Quercus

Classification:

Rank Name:	Species
Rank Value:	Quercus serrata Thunb. ex Murray
Common Name:	serrata

Classification:

Rank Name:

Family

Rank Value:

Juglandaceae

Classification:

Rank Name:

Genus

Rank Value:

Pterocarya

Classification:

Rank Name:	Species
Rank Value:	Pterocarya rhoifolia Sieb. et Zucc.
Common Name:	rhoifolia

Classification:

Rank Name:

Family

Rank Value:

Salicaceae

Classification:

Rank Name:

Genus

Rank Value:

Idesia

Classification:

Rank Name:	Species
Rank Value:	Idesia polycarpa Maxim.
Common Name:	polycarpa

Classification:

Rank Name:

Family

Rank Value:

Rosaceae

Classification:

Rank Name:

Genus

Rank Value:

Prunus

Classification:

Rank Name:	Species
Rank Value:	Prunus verecunda (Koidz.) Koehne
Common Name:	verecunda

Classification:

Rank Name:

Family

Rank Value:

Malvaceae

Classification:

Rank Name:

Genus

Rank Value:

Tilia

Classification:

Rank Name:	Species
Rank Value:	Tilia japonica (Miq.) Simonkai
Common Name:	japonica

Classification:

Rank Name:

Family

Rank Value:

Sapindaceae

Classification:

Rank Name:

Genus

Rank Value:

Acer

Classification:

Rank Name:	Species
Rank Value:	Acer mono Maxim.
Common Name:	mono

Classification:

Rank Name:

Family

Rank Value:

Sapindaceae

Classification:

Rank Name:

Genus

Rank Value:

Acer

Classification:

Rank Name:	Species
Rank Value:	Acer rufinerve Sieb. et Zucc.
Common Name:	rufinerve

Classification:

Rank Name:

Family

Rank Value:

Sapindaceae

Classification:

Rank Name:

Genus

Rank Value:

Acer

Classification:

Rank Name:	Species
Rank Value:	Acer sieboldianum Miq.
Common Name:	sieboldianum

Classification:

Rank Name:

Family

Rank Value:

Sapindaceae

Classification:

Rank Name:

Genus

Rank Value:

Acer

Classification:

Rank Name:	Species
Rank Value:	Acer ukurunduense Trautv. et Meyer
Common Name:	ukurunduense

Classification:

Rank Name:

Family

Rank Value:

Sapindaceae

Classification:

Rank Name:

Genus

Rank Value:

Aesculus

Classification:

Rank Name:	Species
Rank Value:	Aesculus turbinata Bl.
Common Name:	turbinata

Classification:

Rank Name:

Family

Rank Value:

Cornaceae

Classification:

Rank Name:

Genus

Rank Value:

Swida

Classification:

Rank Name:	Species
Rank Value:	Swida controversa (Hemsl) Soják

Classification:

Rank Name:

Family

Rank Value:

Clethraceae

Classification:

Rank Name:

Genus

Rank Value:

Clethra

Classification:

Rank Name:	Species
Rank Value:	Clethra barbinervis Sieb. et Zucc.
Common Name:	barbinervis

Classification:

Rank Name:

Family

Rank Value:

Pentaphylacaceae

Classification:

Rank Name:

Genus

Rank Value:

Cleyera

Classification:

Rank Name:	Species
Rank Value:	Cleyera japonica Thunb.
Common Name:	japonica

Classification:

Rank Name:

Family

Rank Value:

Theaceae

Classification:

Rank Name:

Genus

Rank Value:

Schima

Classification:

Rank Name:	Species
Rank Value:	Schima wallichii (DC.) Korthals
Common Name:	wallichii

Classification:

Rank Name:

Family

Rank Value:

Theaceae

Classification:

Rank Name:

Genus

Rank Value:

Stewartia

Classification:

Rank Name:	Species
Rank Value:	Stewartia monadelpha Sieb. et Zucc.
Common Name:	monadelpha

Classification:

Rank Name:

Family

Rank Value:

Oleaceae

Classification:

Rank Name:

Genus

Rank Value:

Fraxinus

Classification:

Rank Name:	Species
Rank Value:	Fraxinus platypoda Oliv.
Common Name:	platypoda

Classification:

Rank Name:

Family

Rank Value:

Araliaceae

Classification:

Rank Name:

Genus

Rank Value:

Kalopanax

Classification:

Rank Name:	Species
Rank Value:	Kalopanax pictus (Thunb.) Nakai
Common Name:	pictus

Classification:

Rank Name:

Family

Rank Value:

Aquifoliaceae

Classification:

Rank Name:

Genus

Rank Value:

Ilex

Classification:

Rank Name:	Species
Rank Value:	Ilex macropoda Miq.
Common Name:	macropoda

Classification:

Rank Name:

Family

Rank Value:

Aquifoliaceae

Classification:

Rank Name:

Genus

Rank Value:

Ilex

Classification:

Rank Name:	Species
Rank Value:	Ilex pedunculosa Miq.
Common Name:	pedunculosa

Sampling, Processing and Quality Control Methods

Step by Step Procedures

Step 1:

Description:

Study sites and data acquisition

Herbivory data were obtained at 19 natural forest sites as part of the Monitoring Sites 1000 Project, with one to five permanent plots located within each site. These sites covered the major climate zones and biogeographic regions in Japan (Ministry of the Environment 2001, http://www.env.go.jp/press/press.php?serial=2908, last accessed on October 6, 2020) (Fig. 1, Table 1; see also Ishihara et al., 2011) as well as the four major forest types. Plots were classified to four forest types based on the dominant tree species; evergreen conifer forest (EC), broadleaf and conifer mixed forest (BC), deciduous broadleaf forest (DB), and evergreen broadleaf forest (EB) (Ishihara et al., 2011). The majority of the surveyed forest were old-growth or older secondary forests, but some were secondary forests aged <100 years. We classified forests stands to three age categories: old growth (OG), old secondary (OS), and secondary (S) (Ishihara et al., 2011; see Supporting Information 1 for details). The mean annual temperature and precipitation and mean maximum snow depth during 1981–2010 were extracted from the Mesh Climate Data 2010 database distributed by the Japan Meteorological Agency (2012). The database provides climate variables estimated at 1-km spatial resolution. Mean annual temperature was corrected to account for altitudinal difference between a given plot and the 1-km cell mean using a lapse rate of 0.55°C per 100 m. Database estimates for snow depth may contain inaccuracies (i.e., over- or underestimation) given that snow depth is highly spatially heterogeneous. Therefore, we provide snow depths reported in other publications and personal observations in Supporting Information 1 and Table S1. Additional ecological data collected at the 19 study sites were available from publications, including seasonal patterns and inter-annual dynamics of litterfall (Suzuki et al., 2012), forest stand structure, composition, and dynamics (Ishihara et al., 2010), as well as ground-dwelling beetle community and understory variables (Niwa et al., 2016). At the Shiiba site (SI), only tree census data (Ishihara et al., 2010) were available. Additional ecological information including understory vegetation, disturbance history, and soil parameters collected from study sites are provided in Supporting Information 1 and partially summarized in Table S1.

Step 2:

Description:

Sampling design, field methodology, and preliminary analyses

A. Sampling design One permanent plot at each of the 19 sites was selected for litter collection. Plots ranged in size from 0.64 to 1.2 ha and were usually approximately 1 ha in size (see Supporting Information 1, SiteList.csv). Plots were placed or their shapes were adjusted to avoid forest roads and urban edges. Plots were typically divided into 10 x 10-m grid cells. Typically, 25 conical litter traps, each with a circular collection area of 0.5 m2, were installed within each plot (see Suzuki et al., 2012 for details of the litter traps). Traps were placed approximately 1 m above the ground at grid cell corners and were spaced 20 m apart. Litterfall that collected within the traps was collected monthly. Traps were either removed or placed on the ground during winter at sites that received snow. B. Field methodology Litterfall from these traps was then used for a visual assessment of leaf damage by leaf- chewing insects. Herbivory has been assessed visually in other publications (e.g., review by Kozlov et al., 2015). Although few studies have used litter traps for the visual assessment of herbivory (but see Hiura & Nakamura, 2013), litter traps are useful for collecting litterfall, especially for the assessment of multiple tree species over long periods. One technician visually assessed leaf damage blindly with respect to site. The protocol was standardized across all sites, enabling comparisons among forest types. At broadleaf forest sites, the 5 traps with the largest amount of litter were selected from all 25 traps. At BC sites, 5 traps located near broadleaf trees were selected. During the 4- month period when litterfall was highest (Table 2), we visually categorized the extent of herbivory on fallen leaves to six classes: no damage; 1–10% of leaf area lost; 11–25% loss; 26–50% loss; 51–75% loss; and >76% loss (Nakamura, Asanuma, & Hiura, 2010; Nakamura, Nakaji, Muller, & Hiura, 2014). This herbivory assessment was applied to all broadleaf tree species that were considered dominant at each site (up to 50% dominance, Table 3). In addition, oak (Quercus crispula, 8 sites in 2014, 8 sites in 2015) and beech (Fagus crenata, 8 sites in 2014, 9 sites in 2015) leaves were also assessed regardless of their dominance at sites. C. Preliminary analyses We preliminarily analyzed the latitudinal gradient in insect herbivory as observed in deciduous species, evergreen species, Q. crispula, and F. crenata in 2014 and 2015 using linear mixed models (LMM) in the R library lme4 package. In the models, the median herbivory rate of each leaf (i.e., 0%, 5.5%, 18%, 38%, 63%, and 88%, respectively) was treated as a response variable; latitude, year sampled, and their interaction were treated as fixed effect variables. The year sampled was treated as a factor. Plot ID was treated as a random effect variable. The likelihood-ratio chi-square test was used to determine whether the herbivory rate was significantly related to latitude. The preliminary analyses indicated that insect herbivory on deciduous species significantly increased toward higher latitudes (χ2 = 35.03, P < 0.001; Fig. 2a, Table 4), whereas that on evergreen species significantly decreased (χ2 = 4.82, P < 0.05; Fig. 2b, Table 4). These results were congruent with the latitudinal pattern in defense strategy in deciduous and evergreen species reported by Saihanna et al. (2018), who showed that deciduous species used physical defenses at lower latitudes, whereas evergreen species exhibited the opposite latitudinal defense patterns. In particular, insect herbivory on Q. crispula significantly increased with increasing latitude (χ2 = 8.99, P < 0.05; Fig. 3a, Table 4), and herbivory on F. crenata tended to increase with latitude, although the trend was not significant (Fig. 3b, Table 4). These results were congruent with the latitudinal pattern in beech herbivory reported by Hiura & Nakamura (2013). Our findings suggested that the latitudinal gradient of insect herbivory differed depending on leaf type (i.e., evergreen or deciduous). Leaf type is an important predictor of resource use; evergreen species typically show a more conservative resource-use strategy and slower growth rates (Givinish, 2002), whereas deciduous species show an exploitative resource-use strategy and higher growth rates (Reich, Ellsworth, & Walters, 1998). Our preliminary results suggest that the resource-use strategy may be an important determinant of the directionality of latitudinal gradients in insect herbivory.

Step 3:

Description:

Data verification procedures

We note that our data collection at two sites, Kamigamo (2014) and Wakayama (2015), was limited to a single year. The selected litter traps at some sites did not capture fallen leaves of some target tree species. Specifically, we did not capture leaves from Acer ukurunduense at Otanomosutaira in 2015, Castanea crenata at Ogawa in 2015, Illicium anisatum at Shiiba in 2014, Quercus acuta at Aya in 2014, and Ilex macropoda at Kamigamo in 2015. Thus, herbivory was not assessed for those species in those years.

Data Set Usage Rights

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

Access Control:
Auth System:	JaLTER
Order:	allowFirst
Allow:	[read]	public

Metadata download:

Ecological Metadata Language (EML) File