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Are dominant plant species more susceptible to leaf‐mining insects? A case study at Saihanwula Nature Reserve, China

Abstract Dominant species significantly affect interspecific relationships, community structure, and ecosystem function. In the field, dominant species are often identified by their high importance values. Selective foraging on dominant species is a common phenomenon in ecology. Our hypothesis is that dominant plant groups with high importance values are more susceptible to leaf‐mining insects at the regional level. Here, we used the Saihanwula National Nature Reserve as a case study to examine the presence–absence patterns of leaf‐mining insects on different plants in a forest‐grassland ecotone in Northeast China. We identified the following patterns: (1) After phylogenetic correction, plants with high importance values are more likely to host leafminers at the species, genus, or family level. (2) Other factors including phylogenetic isolation, life form, water ecotype, and phytogeographical type of plants have different influences on the relationship between plant dominance and leafminer presence. In summary, the importance value is a valid predictor of the presence of consumers, even when we consider the effects of plant phylogeny and other plant attributes. Dominant plant groups are large and susceptible targets of leaf‐mining insects. The consistent leaf‐mining distribution pattern across different countries, vegetation types, and plant taxa can be explained by the “species‐area relationship” or the “plant apparency hypothesis.”

The above mentioned selective foraging on dominant species is a common phenomenon in ecological systems. Why do the dominant tree taxa in zonal vegetation host more parasites than subordinate taxa do; that is, why do "the outstanding usually bear the brunt of attack?" One explanation is that dominants are generally apparent plants, which might attract more consumers . According to plant apparency, ecological apparency, and optimal foraging hypotheses, apparent dominants are more likely to be found and preferred by parasites, natural enemies, pollinators, and humans (Feeny, 1976;Gonçalves et al., 2016;Phillips & Gentry, 1993;Schlinkert et al., 2015). Plant dominance can facilitate the evolutionary adaptation of consumers, and many consumers use plant defensive compounds to locate host plants (Smilanich, Fincher, & Dyer, 2016).
The larvae of leafminers feed on and live inside leaf tissues between the upper and lower epidermis and produce distinct leaf mines, which may persist for many days (Hering, 1951;Liu, Dai, & Xu, 2015). Therefore, leaf mines might provide important insights regarding the life history, taxonomy, interspecific relationships, and evolution of leaf-mining insects (Hirowatari, 2009;Liu et al., 2015).
In this study, we used Saihanwula National Nature Reserve as a case study to examine the presence-absence patterns of leafmining insects on different plants in a forest-grassland ecotone in Northeast China. To the best of our knowledge, there are fewer publications on the occurrence of leaf mining on different plants in East Asia than there are in Europe, America, and Australia. Different from our previous work on the relationship between plant apparency or phylogenetic isolation and plant utilization by leafminers and other consumers at the global scale , our hypothesis in this study is that dominant plant groups with high importance values are more susceptible to leaf-mining insects at the regional level.
Although there are many studies on leafminer species diversity based on plant characteristics, our study might be the first to use the importance value to study the leafminer species-to-area relationship. Moreover, in the previous work, we fit the dependence of consumer incidence on plant apparency or plant phylogeny separately , while in this study, we adopted phylogenetic generalized linear mixed model to consider plant apparency and plant phylogeny together in a model.
Saihanwula, as a National Natural Reserve, is under strict regulation and protection. Therefore, its vegetation has not changed as radically as the surrounding unprotected area. Moreover, host selection of leafminers might not only relate to the current general vegetation structure but may also show lags and accumulated responses to the plant composition of past decades (Godfray, 1984;Sugiura, 2010). It might be difficult to completely survey all vegetation again at the regional scale, as in the Saihanwula. In particular when considering only the presence-absence of leaf mine in a plant, the reuse of historical vegetation data might be reasonable at this stage.

| Data collection
Plant attribute data, including importance value, were obtained from the records of Saihanwula Nature Reserve (Li et al., 1998(Li et al., , 2005Zhang, 2007): (1) In each forest community type, a 20 × 20 m main plot was chosen. Trees were investigated individually within each 10 × 10 m subplot. Shrubs and tree seedlings were investigated in five subplots of 5 × 5 m at the four corners and the center of the main plot. Herbaceous species were investigated inside three 1 × 1 m subplots within each shrub subplot. (2) In each shrub community type, a 20 × 20 m main plot was chosen. Shrub or grass individuals were recorded within five 5 × 5 m subplots or three 1 × 1 m subplots, respectively, similar to the investigation conducted in the forest communities. (3) In each herbaceous community type, a 10 × 10 m main plot was chosen, twenty 1 × 1 m subplots were set up, and grass individuals were recorded.
The data were carefully reviewed and corrected for data consistency. The importance value (IV) of one tree species is the average of its relative density (RD), relative frequency (RF), and relative GBH (girth at breast height, i.e., 1.3 m from the ground; RG) (Equation 1), whereas the IV of one grass species is the average of its RD, RF, and relative coverage (RC) (Equation 2).
where RD = the density of a species/the total density of all species, RF = the frequency of a species/the sum of all frequencies, RG = the GBH of a tree species/the sum of all GBH values, and RC = the coverage of a grass species/the sum of all coverage values (Curtis & McIntosh, 1951;Gonmadje et al., 2011;Mori et al., 1983;Zhang, 2007).  (Li et al., 1998).
where TIV j is the total importance value of plant species j, RTIV j is the relative total importance value of plant species j, IV ij is the importance value of species j in the ith community type, AR i = the area of the ith community type/the total area, C is the number of community types, and S is the number of plant species (Li et al., 1998).  (Li et al., 1998). The group here could be categorized according to plant life form, water ecotype, phytogeographic distribution type, taxon (i.e., family or genus), and other plant attributes. (1) where GIV m is the total importance value of plant group m, RTIV m is the relative total importance value of plant group m, N m is the number of plant species in plant group m, TIV n is the total importance value of plant species n, and M is the total number of plant groups (Li et al., 1998).

| Host plant sampling
Leaf mines (i.e., the distinct feeding marks left by leafminers) can remain visible for a considerable period (Liu et al., 2015), including after larvae have emerged or after leaf fall. When we encountered damage on leaves from an inconclusive source, we carefully assessed whether the mesophyll tissues were eaten while both the upper and lower leaf epidermis were maintained (or at least the outer wall remaining undamaged) (Liu et al., 2015).
Sampling sites and the corresponding survey trails were systematically chosen according to vegetation maps, historical data, and expert knowledge. Our sampling sites and trails covered and represented all 10 vegetation subtypes (cold-temperate deciduous needle-leaved forest, cold-temperate evergreen needle-leaved forest, typical deciduous broad-leaved forest, montane Populus-Betula deciduous forest, temperate deciduous broad-leaved thicket, montane evergreen broad-leaved thicket, meadow steppe, typical steppe, forb meadow, and Carex meadow) and most of the typical formations in the natural reserve ( Figure 1). In July 2014 and October 2015, we (3-5 individuals per investigation group, with at least one experienced local guide) carefully examined all the trees, shrubs, and grasses that were visible along the studied trails and attempted to sample as many plant species with leaf mines as possible. Branches with mined leaves were collected and placed in plastic re-sealable bags in the field. The host plants were then identified and recorded. Host plants and mined leaves were scanned, and their digital images were stored in our laboratory for future studies. When living larvae were found, we attempted to rear the mining species.
During the studied period, if we could not find any leaf mines in one plant species, we assumed that leaf-mining damage was absent from the plant species.

| Data preparation
However, some leaf-mining species and their life histories in China (including Saihanwula) remain unknown for the following reasons: (1) Many leaf mines were empty; (2) many leafminers died in transport or in the laboratory; (3) many leafminers were parasitized by parasitoid wasps; (4) some leafminer groups could not be identified at the species or even genus level as there were no available taxonomists with expertise in these groups, especially in the unfamiliar Chinese species; (5) no long-term investigations of Chinese leafminers were officially performed on either the national or regional level beyond the preliminary work of our group. Moreover, there might be some types of gregarious leaf miners whose larvae share a single mine.
Therefore, in this study, we had to consider the presence-absence of leaf mines at the regional level rather than the individual number, incidence rate, or leaf area damage. However, when we collect enough detailed data in the future, the latter quantitative parameters may provide more valuable information than the former binary presence-absence data, especially at the community level.

| Plant phylogeny and statistical analyses
As closely related organisms are more likely to share similar biological traits, PGLMMs (phylogenetic generalized linear mixed models) can be adopted to correct for phylogenetic effects ( Table 1). That is, the incidence probability of leaf mines among plant groups increased positively with TIV in a logistic way ( Figure 2). Unexpectedly, the regression coefficients (B) of the nonphylogenetic logistic models (i.e., GLMs)

| Roles of plant phylogeny and plant dominance
were nearly equal to those obtained with binaryPGLMM, and the intercepts of the GLMs were nearly equal to those obtained with phyloglm (Table 1).  Figure 2c).

Rank of plant species group
Relative group importance value (RGIV) Notes. A total of 254 plant genera with available importance values were recorded in Saihanwula. These plant genera were ranked based on their importance values and then classified into 12 groups (21 genera per group). The two plant genera with the smallest nonzero importance values were omitted. Host ratio = number of host genera/total number of genera in each group (i.e., 21).

Ratio of host
TA B L E 3 Relationship between the total importance value of plant genus groups and the ratio of leaf-mining insect hosts among plant genera

| D ISCUSS I ON
In this study, we measured plant dominance using the importance value, which is the sum of the relative density, relative frequency, and relative basal area of the plant group (Curtis & McIntosh, 1951 species can partially explain differences in leafminer richness in Britain (Claridge & Wilson, 1982). A majority of the variation in species richness among agromyzid miners on Britain umbellifers was attributed to the distribution area, local abundance, number of habitats occupied, and body size of different host plants (Fowler, Lawton, Lawton, Fowler, & Lawton, 1982;Lawton & Price, 1979).
Other factors may account for some variation in the speciesarea regression between plant dominance and leafminer incidence (Claridge & Wilson, 1982;Lawton & Price, 1979). In general, biotic factors play much important roles than abiotic ones in leaf-mining distribution patterns (Sinclair & Hughes, 2008a). Plant phylogeny, which is highly related to plant chemistry, may have large influences on the species-area relationship of leafminers (Claridge & Wilson, 1982;Godfray, 1984). Among the plant species of different life forms, tree groups did not exhibit the highest total importance values but were much more likely to suffer leaf-mining damage than any other life form in Saihanwula (Table 5). Among plant species of different water ecotypes in Saihanwula, plants in extremely dry or wet environments had very little likelihood of hosting leafminers (Table 6). In the same way, no leafminers were discovered at two driest places in Australia (Sinclair & Hughes, 2008a); aquatic habitats may be unfavorable for the agromyzid leafminers (Lawton & Price, 1979). The presence-absence of leaf mining might be obviously related to leaf physical traits such as leaf size, leaf length, leaf thickness, or leaf form (Dai, Zhu, Xu, Liu, & Wang, 2011;Fowler et al., 1982;Godfray, 1984;Lawton & Price, 1979;Sinclair & Hughes, 2008a). Adult leafminers should lay eggs on leaves that are large enough for the larvae to complete their life histories (Dai et al., 2011). Therefore, many leafminers prefer larger leaves to smaller ones (Faeth, 1991;Hileman & Lieto, 1981).
In contrast, plant phylogenetic isolation, life history, interspecific competition, and natural enemies had no important impacts on the number of agromyzid flies on the British Umbelliferae (Lawton & Price, 1979).
Although the influence of importance value on the presenceabsence of leaf mines was not independent of plant phylogenetic relationships, the role of plant dominance on the probability of being mined was clear (Table 1, Figure 2). One possible explanation for the similar regression coefficients or intercepts between the PGLMMs and nonphylogenetic logistic models is that the close relatives of the dominant plants were more dominant than the other plants and were thus more susceptible to plant parasites.
Vegetation parameters such as density, frequency, coverage, diversity, and importance value have been used to measure the apparency or dominance of plant species (Gonçalves et al., 2016;Guo & Rundel, 1997). Higher dominance is associated with more host-consumer encounters (random placement hypothesis) and more ecological niches for consumers (habitat diversity hypothesis) (Miller, 2012;Strona & Fattorini, 2014 Gonçalves-Alvim, Fernandes, & Goncalves-Alvim, 2001;Lawton & Price, 1979;Mendonça, 2007;Price, 1977;Veldtman & McGeoch, 2003;Ward & Spalding, 1993 In summary, dominant plant groups are large and susceptible targets for leaf-mining insects even when we consider the effects of plant phylogeny and other plant attributes. Such a consistent leafmining distribution pattern across different countries, vegetation types and plant taxa can be explained by the "species-area relationship" (i.e., the leafminer species incidence to plant importance value relationship) or the "species-apparency relationship."

ACK N OWLED G M ENTS
The authors would like to thank Chengqing Liao, Wanhua Liu, Guosheng Li, and Fang Zhou from the Leafminer Group for help with the field investigation and sample collection. We also acknowledge Mr. Changlin Xiang from Saihanwula National Nature Reserve for plant identification. We thank American Journal Experts (AJE) for English language editing of the manuscript (http://www.aje. com/r/NRW49). Thanks are also due to the six anonymous reviewers and the editors for their helpful comments on earlier versions of this article. This study was funded by the National Natural Science

DATA ACCE SS I B I LIT Y
Data for this study are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.sc3fr20

CO N FLI C T O F I NTE R E S T
None declared.

AUTH O R CO NTR I B UTI O N S
X.D. conceived and designed the study, performed the fieldwork, managed the project, analyzed the data, and wrote the manuscript.
C.L. extracted the data from publications, rechecked the field data, reanalyzed all the data, and modified the vegetation map. J.X.
helped identify leaf mines and guide the writing of Chinese version manuscript. Q.G. helped to analyze the data and improve the manuscript in English. W.Z. performed the fieldwork and analyzed the data. Z.Z. wrote the first version of the manuscript in Chinese.
Bater provided the background data of Saihanwula and aided the fieldwork.