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Article

Forest Quality and Available Hostplant Abundance Limit the Canopy Butterfly of Teinopalpus aureus

by
Lu Wang
1,2,
Hui Wang
2,3,
Yuhang Zha
1,2,
Heyi Wei
4,
Fusheng Chen
1,2 and
Juping Zeng
1,2,*
1
Key Laboratory of National Forestry and Grass and Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
2
The Station of Observation and Research of Jiulianshan, Longnan 341701, China
3
Jiulianshan National Nature Reserve of Jiangxi, Longnan 341701, China
4
Geodesign Research Centre, Jiangxi Normal University, Nanchang 330022, China
*
Author to whom correspondence should be addressed.
Insects 2022, 13(12), 1082; https://doi.org/10.3390/insects13121082
Submission received: 15 October 2022 / Revised: 7 November 2022 / Accepted: 12 November 2022 / Published: 24 November 2022
(This article belongs to the Collection Butterfly Diversity and Conservation)
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Abstract

:

Simple Summary

We carried out multi-year investigations on habitat selection of early stage individuals of the forest canopy species Teinopalpus aureus in Jiulianshan, South China, by comparing the low-mountain and middle-mountain regions as well as three types of forest: primeval broadleaf forest, secondary broadleaf forest, and coniferous forest. It was found that this butterfly exclusively occurred in the middle-mountain region and only preferred primeval broadleaf forests. This could mainly be driven by the specific larval hostplants (i.e., three Magnoliaceae species). Such resources were superior in the middle-mountain region, including plant abundance, diversity, tree height, DBH (Diameter at breast height), etc., and such a resource advantage was more concentrated in the primeval broadleaf forests. In particular, the abundance and diversity of hostplant trees with an exposed crown, which is demanded by this butterfly in its oviposition and in the subsequent larval development, were higher in the primeval broadleaf forests. Therefore, both the forest quality and the availability of the hostplants together limited the occurrence of this canopy butterfly.

Abstract

Hostplant limitation is a key focus of the spatial interaction between a phytophagous butterfly and a hostplant. The possible drivers related to the hostplants are species richness, abundance, or availability, but no consensus has been reached. In this study, we investigated the butterfly–hostplant interaction using the case of the forest canopy butterfly T. aureus in Asia, whose narrow distribution is assumed to be limited by its exclusive hostplant, Magnoliaceae, in tropic and subtropic regions. We recorded the Magnoliaceae species, as well as plant and butterfly individuals in transect, and we collected tree traits and topography variables. The results confirm that this butterfly is limited by the hostplants of their larval stage. The hostplants occurred exclusively in the middle-mountain region, with preference only for primeval forests. The hostplant resource was superior in the middle-mountain region, particularly concentrating in primeval forests. The hostplant’s abundance, together with altitude and habitat types, was critical to this butterfly’s occurrence, while those hostplant trees with an exposed crown, which are demanded by this butterfly in its oviposition, were the best drivers of positive butterfly–hostplant interactions. Therefore, the hostplant’s limitation was mainly determined by the availability of the hostplant. This case study supports the hypothesis that the limitation on this butterfly’s occurrence was driven by the hostplant’s availability, and it suggests that protecting high-quality forests is a valuable activity and essential in the conservation of canopy butterflies.

Graphical Abstract

1. Introduction

Hostplant limitation (HPL) is a key focus of the spatial interaction between a phytophagous butterfly and a hostplant [1,2,3,4], and it can be affected by the (1) presence or absence of the hostplant [5], (2) the hostplant’s abundance (HPAb) and species richness (HPSR) [6], and (3) the hostplant’s availability (HPAv) [7,8,9]; however, a consensus is still lacking thus far in this regard. In this study, by using the oligophagous butterfly case of Teinopalpus aureus in Jiulianshan, South China, we attempted to illustrate the roles of hostplant abundance (HPAb), hostplant species diversity (HPSD), and hostplant availability (HPAv) in the formation of the hostplant limitation.
The accelerated decline in biodiversity with global warming [10,11] and the changes in the distribution and dynamics of the population of butterflies (as sensitive indicators) have attracted significant attention throughout the world. For oligophagous and monophagous butterflies, it is critical to know their distributions and the changes in their HPLs. They have a relatively narrow diet width, being dependent on their hostplants, and in terms of distribution, they are more likely to be limited by both abiotic (climate, topography, etc.) [3] and biotic factors (e.g., bottom-up control by hostplants) [12]. Thus, in nature, these specialists often occur in narrow, specific regions [5] corresponding to the constraint from the fundamental niche to the realized one [13]. Furthermore, future climate changes will certainly affect the ability of butterfly populations to persist, especially in cases where plants and butterflies react asynchronously [14,15], which could cause a spatial mismatch between butterfly and hostplant distributions [1,3]. This will undoubtedly lead to further deterioration of the situation of these specialists [16], leading to accelerated species extinction and a rapid decline in regional diversity [17]. As such, strategically establishing their key habitat as a protection area is a basic element in the conservation of these populations [18]. According to the functional concept of “resource-based habitats” [19,20,21], it is necessary to focus on the key consuming resources (e.g., hostplants) associated with habitat conservation and to conduct field investigations to determine the basic habitat demands and occurrence patterns under the effect of an HPL in order to provide efficient information (e.g., prioritizing resources or locations) for conservation-related decisions [22].
Mountains are key to habitat conservation in this changing world, given their coverage of a wide spectrum of environmental conditions, particularly vertical gradients [23]. In Asia’s tropical and subtropical regions, mountains are widely distributed and forests are usually lush, developing continuously from low to high elevations along vertical gradients with distinct layer structures. These mountain forests provide a variety of plant resources and microhabitats for phytophagous butterflies, conserving most of the richness of large swallowtail species [24]. Most species are endemic to this region, including the two endemic butterflies Byasa impediens [25] and Bhutanitis thaidina [26], and they always explore their favorite microhabitats and form specific preferences for long-term adaptation. For example, butterflies from the species Teinopalpus, Meandrusa, Pazala, Agehana, Graphium, etc. prefer to stay in the unique evergreen broadleaved canopies of mountain forests throughout their early stages, where the larvae usually feed on the treetop leaves of certain evergreen trees (Lauraceae, Magnoliaceae species, etc. [24,27,28]) because these trees provide favorable sunshine conditions for the incubation of their large-sized eggs. Therefore, it is assumed that these canopy butterflies can be constrained by their hostplants (e.g., HPL) and the forest canopy quality, and their actual distributions may be further narrowed by the combined effect of these factors. However, considering the uncertain active behaviors and range during the adult stages of these species [19,20,21], this study only focused on the relationship between the butterflies’ early stages (i.e., egg and larva) and the hostplants. We expected to analyze the HPL based on the clear resource-based habitat quality as well as the characteristics involved.
Teinopalpus aureus is one of the largest swallowtail butterflies that is endemic to tropical and subtropical regions in Asia [24,27,28]. It seems to be constrained to mountains with steep terrain and primeval forest cover [27,29,30,31,32]. It is rare to encounter this species in the field, but it is known as a flagship species [33] and has received close attention from entomologists, environmentalists, and the public [34,35,36]. However, it is still listed as a DD (data-deficient) species by the IUCN (International Union for Conservation of Nature), and it is still unclear what indeed constrains its occurrence. The two sister species of the genus Teinopalpus, T. aureus and T. imperialis Hope, occur only in some narrow mountain areas. They both prefer to live in treetops during their early stages of life (including the egg and larval stages) and seem to depend on evergreen broad-leaved forests [37,38], since the known hostplants are both from Magnoliaceae, a dominant family in these mountain forests. Obviously, due to being canopy butterflies, fieldwork on these species is challenging. For example, Igarashi [38] first investigated T. imperialis in Nepal and northern India in 1962, but it was not until 1986 that the author made a breakthrough in northern India and successfully identified the first larval hostplant of Magnolia campbellii Hook, revealing the biological characteristics of T. imperialis in the field [38]. The mysterious veil of T. aureus was not uncovered until 2004 by Zeng [39] in the Dayaoshan Mountains in Guangxi Province, South China, where the author successfully identified two hostplants, Parakmeria nitida (W.W. Smith) Law and Michelia chapensis Dandy, through the challenging method of “climbing up trees artificially and then checking leaves one by one in the canopy”. The butterfly’s biological characteristics were also revealed [30,40]. Subsequently, by using Zeng’s challenging method, some hostplants of Magnoliaceae in T. aureus were also identified through several other field investigations, including M. foveolata Merr. ex Dandy and M. maudiae Dunn in the Jiulianshan Mountains [31], M. foveolata in the Jinggangshan Mountains [40], as well as M. maudiae in the Pingshan Mountains [41]. Therefore, the two oligophagous butterflies, T. aureus and T. imperialis, are assumed to be consistent with the Magnoliaceae species in terms of the local distribution and occurrence, for example, involving the HPL effect.
To collect sufficient field data for the analysis of the HPL effect on T. aureus, we used the ready-made case of Jiulianshan, South China, where monitoring data have been collected during the butterfly flight period since 2004. Based on previous records, the general range of adult activity in Jiulianshan was determined, to be the same as for other geographical populations [30,40,41], this butterfly was also shown to go through two generations per year in this area [31]. Moreover, as for this oligophagous butterfly, previous local efforts have identified three larval hostplants: M. foveolata, M. maudiae [31], and Manglietia fordiana Oliv, all belonging to Magnoliaceae. So, in the case of Jiulianshan, the existing data or works suggest that further studies related to the spatial interaction between this rare oligophagous butterfly and the known hostplants or the possible HPL effect should be conducted. This time, field investigations were carried out along previous transect lines as well as along some new lines to record Magnoliaceae species, the individual locations (longitude, latitude, altitude, etc.) of different butterfly stages (egg, larvae, and adult), and the abovedescribed hostplants. We aimed to illustrate the butterfly’s habitat requirements and the HPL effect based on three resource levels: Magnoliaceae plants, three hostplant species, and the available hostplants. Through applying the resource-based habitat concept, we attempted to determine: (1) the factors impacting the habitat selection of T. aureus, (2) whether the HPL effect exists, and (3) how it impacts and constrains the butterfly’s local occurrence, for example, based on resource abundance (e.g., HPAb), diversity (e.g., HPSD), or availability (e.g., HPAv).

2. Materials and Methods

2.1. Study Area

This study was conducted in the Jiulianshan Mountains, which are the eastern core part (E 114°22′50″–114°31′32″, N 24°29′18″–24°38′55″) of the Nanling Mountain Range, located in the Jiulianshan National Nature Reserve on the boundary between Jiangxi and Guang-dong Province in South China. The elevation in this study area ranges from 280 to 1430 m in altitude and is mainly composed of medium and low mountains (Figure 1). Under the combined influence of continental and marine climates as well as the effect of mountainous terrain, this area is warm and humid. Mountain forests develop well and stay green throughout the year under good hydrothermal conditions.
As a National Nature Reserve, this area mainly focuses on protecting the typical subtropical evergreen broad-leaved forests and biodiversity [42]. However, due to various forms of human interference throughout history, several types of forest are currently present, including (1) primeval forests, for example, evergreen deciduous broad-leaved mixed forests and montane dwarf forests, which have been subjected to early protection actions (since 1957) and low interference and have retained their ecological integrity [42]; (2) secondary forests, which are recovering areas that have undergone selective cutting or small-area deforestation and still have incomplete ecological integrity, for example, secondary evergreen broad-leaved forests; (3) artificial forests, mainly pine (Pinus massoniana) or Chinese fir (Cunninghamia lanceolata) coniferous forests that were planted artificially in areas undergoing large-area deforestation; (4) bamboo forests, which are areas dominated by Moso bamboo (Phyllostachys eduli); and (5) other open forests, such as the forest areas around farmlands and villages [42]. In previous field observations, it was found that T. aureus almost always appears in close forests [30,31,37,40,41], so we did not use the transect data from bamboo and open forests in the following analysis. That is, we mainly focused on the differences among the primeval forests, secondary forests, and coniferous forests.

2.2. Investigations of T. aureus and Magnoliaceae Trees

From 2018 to 2021, we carried out transect counting randomly along mountain roads, forest trails, ridges, and streams in the study area (Figure 1), and the GPS data for all Magnoliaceae species, tree, and butterfly individuals within 20 m on both sides of transects were recorded, including the tree DBH and height. As for the butterfly, we recorded current as well as the previous data sourced from Lin et al. [31]. In our records, we marked the individual trees of known hostplants if they contained butterfly eggs or larvae. Field investigations were conducted throughout the year, while butterfly data were collected from April to June during the first generation, as well as from August to October during the second generation [30,31,37].
In the butterfly investigations, we used direct and indirect investigation methods to record eggs or larvae in the tree canopy of Magnoliaceae. In the direct method, we checked every leaf reached after climbing up trees or by lowering tree branches using a hook. In the indirect method, we checked all suspected leaf-feeding marks and feces of larvae on the ground under the canopy of known hostplant trees, according to the descriptions presented in Zeng [39] and Lin et al. [31].

2.3. Calculation of the α Diversity Index and Determination of the Transect Length Unit

We recorded six Magnoliaceae species in the study area: Michelia skinneriana Dunn, M. chapensis, M. fordiana (including Manglietia yuyuanensis Law), Magnolia odora (Chun) Figlar & Noot., M. maudiae, and M. foveolata [43]. We used the formula H′ = −∑Pi LnPi (where Pi is the relative abundance of species) to calculate the Shannon–Wiener index as the α diversity index of the transect line. We first calculated cumulative values, 10 m in length, in three typical transect lines that extended from the foot of the mountain to the top. Then, we fitted three logistic response curves for these values against the transect length (Figure 2). The results indicated that, as the length increased, the growth of the index values first accelerated and then reached points of deceleration inflection (e.g., 188 m, 226 m, and 295 m, respectively) and entered a stable status. As such, in order to ensure the stability of the index, we used 300 m as the transect length unit in the following analysis in accordance with the method of Ye et al. [44]. Lastly, we divided all the transect lines into a certain number of 300 m sections and ensured each section contained only one type of habitat and was <100 m in relative elevation, as well as having an interval of >50 m from other sections. We did not use transect sections of less than 300 m in the analysis.

2.4. Environmental Data Collection

Considering that the butterfly T. aureus requires a mountain topography for its occurrence [45], we collected DEM data with a resolution of 30 m from www.usgs.gov (accessed on 30 March 2020) and used these to generate elevation, slope, and aspect raster data in ArcGIS 10.5 (Esri, Redlands, CA, USA). We further calculated the slope direction index of TRASP using the formula TRASP = {1 − cos[(π/180) × (Aspect−30)]}/2. This index has values from 0 to 1, indicating a change in environment from wet and cold to dry and hot conditions [46]. We also collected data on two variables, tree age and forest type, from the local Forest Resource Inventory (FRI) database.
Previous field investigations showed that T. aureus males always prefer to be active on certain mountaintops, where they are usually waiting for females or mating chances [37]. These mountaintops are almost always above 1000 m in altitude. These behaviors and situation were also observed in Jiulianshan [31]. Therefore, we sorted transect sections into two groups, low-mountain transects (LMT) and the middle-mountain transects (MMT) (Figure 1), according to the regional elevation of mountaintops. Furthermore, we sorted the MMT areas into the occupied areas (OA), where T. aureus (adults and early stages) was recorded each year, and the non-occupied areas (NOA), where the butterfly was never observed (Figure 1) [19].

2.5. Hostplant Availability Definition

It was found that female T. aureus butterflies almost always lay eggs on sites with tree canopies exposed, as shown in previous observations, maybe due to the more favorable sunshine conditions [41]. Therefore, for the three known hostplants in Jiulianshan, M. fordiana, M. foveolata, and M. maudiae, we roughly classified the tree individuals into available and non-available groups by comparing their heights with the average tree height and defined availability as hostplant trees taller than the average height. Subsequently, we calculated the abundance and diversity indices of the available plants, in the same way as for the other two plant resource levels: Magnoliaceae plants and hostplants.

2.6. Statistical Analysis

We used the non-metric multidimensional scaling analysis (NMDS) method to evaluate the contributions of the variables (see Table 1) to the distributions of LMT and MMT sections in descending dimensions [45]. We further used the Mann–Whitney U test method to compare the differences among variables between LMT and MMT as well as between occupied areas and non-occupied areas. We used Bailey’s method to determine factors affecting butterfly habitat selection (such as preference, avoidance, or random) in three types of forest, primeval forest, secondary forest, and coniferous forest, by comparing the actual (Pi) and expected (Pio) proportions utilized. The differences in environmental variables were also compared among these three habitat types using the Kruskal–Wallis method. Lastly, for the variables with significant differences between occupied areas and non-occupied areas, we used the stepwise discriminant analysis to identify key or driving variables or factors associated with butterfly habitat selection. In this study, we did not use adult records, and the level of significance was set as p < 0.05. All statistics were conducted in SPSS 25 (IBM, Armonk, NY, USA) and R 4.2.1 [47].

3. Results

3.1. Distribution of T. aureus Driven by the Resource Quality of Hostplants

Figure 3 shows an interpretive ranking (Stress = 0.076, R2 = 0.98) in the descending dimension space by NMDS based on the 13 environmental variables presented in Table 1. A comprehensive gradient change in hostplant quality is presented comprehensively using MDS1 and MDS2, with the biggest contributors being identified as the AHPSD (Available hostplants’ species diversity), HPSD (all hostplants’ species diversity), MSD (all Magnoliaceae species diversity), AHPAb (Available hostplants’ abundance), and HPAb (all hostplants’ abundance) (Figure 3A). The transect sections of the early butterfly stages (e.g., the occupied MMT in Figure 3B) were significantly more inclined to be located at the high-quality side of a gradient in terms of distribution compared to others.

3.2. Habitat Preference and Requirements of T. aureus in Terms of Occurrence

Using Bailey’s method, we compared the actual and expected proportions utilized among three habitat types: primeval forests, secondary forests, and coniferous forests. The results in Table 2 show that in the early stages, the butterflies prefer primeval forests and avoid secondary forests and coniferous forests for habitat selection (Table 2). The comparison of 12 environmental variables between occurrence and non-occurrence transects of T. aureus larvae shows significant differences for 10 variables but not for the 2 topography variables (aspect and slope) (Table 3). Overall, occupied transects have higher altitudes, older tree ages, and larger Magnoliaceae plants than non-occupied transects, and they have richer and more diverse Magnoliaceae resource conditions (including hostplant resources and available hostplant resources) (Table 3).

3.3. Distribution and Drivers in T. aureus

Using the significant variables presented in Table 2 and Table 3 to conduct a stepwise discriminant analysis, we constructed an optimal model with four selected environmental variables: AHPAb, Altitude, Habitat, and HPAb. The discriminant accuracy was 95% in this model. A positive relationship was observed between the former three variables and butterfly occurrence, while a negative one was found for HPAb, according to the discriminant coefficients and the Wilk’s λ results presented in Table 4. AHPAb was identified as the most critical variable or the key driver in the distribution of the early stages of T. aureus.
As for the effect of altitude on butterflies, it was shown that better resource conditions for Magnoliaceae plants exist in MMT, including plant abundance and diversity, as well as functional traits such as DBH, tree height, and tree age (Figure 4). The better resource conditions were also shown to exist in OA but did not include the condition of plant abundance, which was not found to be significantly different between OA and NOA (Figure 5).
Similarly, as for the influences of habitat, we found that the best resource conditions for Magnoliaceae only existed in primeval forests. These were therefore the preferred habitat over secondary forests and coniferous forests (Figure 6). To be noted, although no significant difference existed regarding the abundance index for all Magnoliaceae plants and all hostplants between primeval forests and secondary forests, with the same situation occurring for the unavailable hostplants (p = 0.93), a significant difference was found for the available portion of hostplants (Figure 6). In primeval forests, the abundance was strongly associated with the diversity in this part, while such a relationship did not occur in all Magnoliaceae plants or in all hostplants (Figure 7).

4. Discussion

Phytophagous butterflies, especially oligophagous and monophagous species, are constrained in terms of their occurrence and distribution by “hostplant limitations (HPLs)” [48]. However, whether HPL is related to hostplant abundance (HPAb) and hostplant species richness (HPSR) [6] or is driven by hostplant availability (HPAv) [8,9] remains uncertain. This case study, conducted in the Jiulianshan Mountains in South China, confirmed the biological interaction between the oligophagous butterfly T. aureus and its exclusive Magnoliaceae hostplants. The results support the idea that the HPL is mainly driven by hostplant availability (in abundance and diversity) rather than simply by hostplant abundance. Actually, we identified a negative interaction between butterfly and hostplant abundance and a strongly positive one between butterfly and hostplant availability.
We used the NMDS method to construct a descending dimension space, which expressed a comprehensive gradient change in the hostplants’ quality, and clearly showed that T. aureus butterflies are inclined to occur in the high-quality side of the area (see Figure 3). The quality of an area was determined by the hostplant abundance (e.g., HPAb, MPAb, and AHPAb) and diversity (e.g., HPSR, including MSD, HPSD, and AHPSD). The comparison of occurrence and non-occurrence transects also showed that most indices of occurrence transects were better than those of non-occurrence transects. These results illustrate the strict requirements of host plant resources for the canopy butterfly T. aureus.
However, according to the functional concept of “resource-based habitats” [19,20,21], hostplants in a region cannot be used completely by butterflies, because if the hostplants are considered to be key consumables, at least two parts can be included: (1) the available part, which is findable (to be encountered) and utilizable in the wild and has a real function (i.e., availability), and (2) the unavailable part, which does not show any function, either temporally or spatially. This distinction could be necessary for the understanding of HPL. Most butterflies usually demand high-quality habitats during their early life stages (e.g., egg, larva), including suitable microhabitat conditions [25,26,49,50] and utilities related to their hostplants. These requirements may be associated with some specific functions, such as an increase in the release of a key stimulus, namely, oviposition-induced volatiles, with increased light intensity [51], which directly determine the availability of hostplants for butterflies, especially the specialists. Similarly, in the case of T. aureus, the high demand for the oviposition position is reflected by the low-density and scattered pattern of egg distribution in the canopy [41], and our results show that the availability of Magnoliaceae hostplants can be determined by the requirement of sunshine-exposed branches, which implies that females could be also attracted by an increase in the release of some key stimulus from sunny leaves in oviposition [51,52]; further studies on this topic would be valuable. Through measuring and comparing tree heights, we found that the abundance of the available hostplants (i.e., AHPAb) has a positive interaction with butterfly occurrence, and it was also identified as the most critical driver (Table 4) of the HPL of T. aureus. On the contrary, a negative interaction was unexpectedly found between all hostplants’ abundance (HPAb) and butterfly occurrence, which may be due to the interference of unexposed trees; that is, the unavailable portion of hostplants does not match the butterfly’s early stages in distribution, perhaps due to a lack of stimulus for oviposition [51,52]. After selective felling, the abundance of tree seedlings in the secondary forests is even higher than that of the primeval forests [53] in the early recovery stage, and these seedlings are partially retained and subsequently take part in the process of restoration. However, most Magnoliaceae plants grow slowly [54]. Although the secondary forests have been in recovery for decades in the Jiulianshan Mountains [42], most of the hostplants still have their treetops unexposed and are not available as consumables. As such, the effect of HPL cannot be simply attributed to all hostplants (e.g., the HPAb), and the real contributor could be just the available portion (e.g., the HPAv).
T. aureus is a typical mountain forest species that often appears in the middle mountains above 1000 m at altitude. According to the Encounter-frequency hypothesis [55], this butterfly should be more inclined to select plants distributed in the middle mountains as host sources because of the higher encounter rate of these plants. Our results show that the hostplant resource in the middle-mountain areas is better in terms of diversity (e.g., MSD and HPSD) and abundance (e.g., MPAb and HPAb) than in the low mountain areas, and better availability (e.g., AHPSD and AHPAb) is assured in the middle mountains (Figure 4), which supports the above assumption. However, in addition to the effect of high elevation, another necessary condition is the habitat quality demanded by T. aureus.
In terms of habitat selection, the butterfly showed a “preference” for primeval forests, while showing “avoidance” of the low-quality habitats in secondary forests and coniferous forests in Bailey’s analysis (Table 2). Actually, only the primeval forests are able to simultaneously provide the highest species diversity or richness for the three known hostplants of the T. aureus canopy butterfly. The availability of hostplants (e.g., AHPAb) in the primeval forests was found to be significantly better than in secondary forests. However, it should be noted that when the availability was not considered, there was no significant difference between the primeval forest and secondary forest habitats in terms of the abundance of hostplants (e.g., HPAb and MPAb) (Figure 6). This means that the available portion truly represents the quality of hostplants (see the association between abundance and diversity in Figure 7) in a region, and the change in gradient reflected by this part was more than that of the habitat quality.
By comparing the occupied areas (OA) and non-occupied areas (NOA), we found some useful indicators reflecting the habitat quality demanded by T. aureus, including the tree age, height, and DBH, as well as the species diversity indices of the hostplant (e.g., MSD, HPSD, and AHPSD). However, the individual abundance of the hostplants (e.g., MPAb, HPAb, and AHPAb) could not be used as such indicators, because they did not differ between OA and NOA (Figure 5). A high hostplant abundance can only be contributed to a single hostplant species, which is not always consistent with a high-quality habitat. For instance, in our case study, we found that the M. maudiae hostplant was abundant in the OA and NOA, but the other two hostplant species, M. foveolata and M. fordiana, were rarely encountered in the NOA. Such asynchronism illustrates that the abundance index (e.g., HPAb, AHPAb) does not directly reflect the habitat quality, which means that the hostplant diversity is a better indicator of ‘resource-based habitat’ than the simple hostplant abundance [19]. Thus, it is necessary to simultaneously consider the habitat quality demanded by the butterflies when applying the concept of the “availability” of hostplants. Only in this way can we figure out the real contribution of the hostplant availability (e.g., AHPAb) to the HPL effect. We also suggest that it is important to pay more attention to the areas with high hostplant diversity in butterfly habitat conservation.

5. Conclusions

This case study in Jiulianshan clarifies that the forest canopy butterfly T. aureus prefers primeval forests, while avoiding other types of forest mainly due to two hierarchies of needs: (1) the basic hierarchy of needs, i.e., forest quality requirements, including elevation (e.g., middle-mountains), forest components (e.g., old-growth forests), forest structure (e.g., tree height and DBH of Magnoliaceae), and forest function (e.g., evergreen canopy), and (2) the advanced hierarchy of needs, especially the requirements for the key consumables of hostplants, including the hostplant species richness (e.g., MSD, HPSD, and AHPSD) and hostplant abundance (e.g., MPAb, HPAb, and AHPAb). Obviously, it is only after the basic needs are satisfied that the habitat can support the occurrence of this rare butterfly, and at this time, the HPL effect is then critically driven by the hostplant availability, rather than simply by the hostplant abundance alone. In a word, the severe requirements in terms of habitat and hostplant availability limit the occurrence of this canopy butterfly, which explains why, although the secondary forests in the area have been recovered for decades, the local population is still confined to a narrow, specific region, i.e., the last remaining primeval forests in Jiulianshan. Our results suggest that maintaining the quality of forest-based habitats with Magnoliaceae hostplants is critical for T. aureus conservation. This case study also has some useful implications for the conservation of other forest canopy butterflies.

Author Contributions

Conceptualization, J.Z.; methodology, L.W. and H.W. (Heyi Wei); software, L.W.; validation, F.C. and J.Z.; formal analysis, L.W.; investigation, L.W., H.W. (Hui Wang) and Y.Z.; resources, L.W. and J.Z.; data curation, L.W., H.W. (Hui Wang) and Y.Z.; writing—original draft preparation, L.W.; writing—review and editing, H.W. (Heyi Wei), F.C. and J.Z.; visualization, L.W. and J.Z.; supervision, F.C. and J.Z.; project administration, J.Z.; funding acquisition, F.C. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (31160430 and 31760640) and partly supported by “Science and Technology Innovation Project of Jiangxi Forestry Bureau (202233)” and “Open Fund of PYHKF Key Laboratory (PYHKF-2020-03)” project.

Data Availability Statement

Data can be provided on request from the corresponding author.

Acknowledgments

The authors thank Li Xiu-shan and Zhang Cheng-sheng for reviewing the early manuscript. We are also grateful for the help and suggestions for the fieldwork from Zhu Xiang-fu and Hu Hua-lin of the Jiulianshan National Nature Reserve of Jiangxi, as well as Jia Feng-hai of Jiangxi University of Traditional Chinese Medicine. Important support was provided by the Huangniushi Nature Reserve of Guangdong during the field investigation.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.C.; Fromentin, J.M.; Hoegh-Guldberg, O.; Bairlein, F. Ecological Responses to Recent Climate Change. Nature 2002, 416, 389–395. [Google Scholar] [CrossRef] [PubMed]
  2. Schweiger, O.; Settele, J.; Kudrna, O.; Klotz, S.; Kühn, I. Climate Change Can Cause Spatial Mismatch Of Trophlcally Interacting Species. Ecology 2008, 89, 3472–3479. [Google Scholar] [CrossRef]
  3. Schweiger, O.; Heikkinen, R.; Harpke, A.; Hickler, T.; Klotz, S.; Kudrna, O.; Kühn, I.; Pöyry, J.; Settele, J. Increasing Range Mismatching of Interacting Species under Global Change Is Related to Their Ecological Characteristics. Glob. Ecol. Biogeogr. 2012, 21, 88–99. [Google Scholar] [CrossRef]
  4. Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-Resolution Global Maps of 21st Century Forest Cover Change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [Green Version]
  5. Hanspach, J.; Schweiger, O.; Kühn, I.; Plattner, M.; Pearman, P.B.; Zimmermann, N.E.; Settele, J. Host Plant Availability Potentially Limits Butterfly Distributions under Cold Environmental Conditions. Ecography 2014, 37, 301–308. [Google Scholar] [CrossRef]
  6. Quinn, R.M.; Caston, K.J.; Roy, D.B. Coincidence in the Distributions of Butterflies and Their Foodplants. Ecography 1998, 21, 279–288. [Google Scholar] [CrossRef]
  7. Dennis, R.L.H.; Shreeve, T.G.; Arnold, H.R.; Roy, D.B. Does Diet Breadth Control Herbivorous Insect Distribution Size? Life History and Resource Outlets for Specialist Butterflies. J. Insect. Conserv. 2005, 9, 187–200. [Google Scholar] [CrossRef]
  8. Berzitis, E.; Minigan, J.; Hallett, R.; Newman, J. Climate and Host Plant Availability Impact the Future Distribution of the Bean Leaf Beetle (Cerotoma Trifurcata). Glob. Change Biol. 2014, 20, 2778–2792. [Google Scholar] [CrossRef] [PubMed]
  9. Curtis, R.J.; Brereton, T.M.; Dennis, R.L.H.; Carbone, C.; Isaac, N.J.B. Butterfly Abundance Is Determined by Food Availability and Is Mediated by Species Traits. J. Appl. Ecol. 2015, 52, 1676–1684. [Google Scholar] [CrossRef] [Green Version]
  10. Grillo, O.; Venora, G. Biodiversity Loss in a Changing Planet; InTech: Rijeka, Croatia, 2011; ISBN 978-953-307-707-9. [Google Scholar]
  11. Cardinale, B.J.; Duffy, J.E.; Gonzalez, A.; Hooper, D.U.; Perrings, C.; Venail, P.; Narwani, A.; Mace, G.M.; Tilman, D.; Wardle, D.A.; et al. Biodiversity Loss and Its Impact on Humanity. Nature 2012, 486, 59–67. [Google Scholar] [CrossRef]
  12. Karban, R.; Grof-Tisza, P.; Maron, J.L.; Holyoak, M. The Importance of Host Plant Limitation for Caterpillars of an Arctiid Moth (Platyprepia Virginalis) Varies Spatially. Ecology 2012, 93, 2216–2226. [Google Scholar] [CrossRef] [Green Version]
  13. Hutchinson, G.E. Concluding Remarks. Cold Spring Harb. Symp. Quant. Biol. 1957, 22, 415–427. [Google Scholar] [CrossRef]
  14. Matter, S.F.; Doyle, A.; Illerbrun, K.; Wheeler, J.; Roland, J. An Assessment of Direct and Indirect Effects of Climate Change for Populations of the Rocky Mountain Apollo Butterfly (Parnassius Smintheus Doubleday). Insect Sci. 2011, 18, 385–392. [Google Scholar] [CrossRef]
  15. Stenoien, C.; Nail, K.R.; Zalucki, J.M.; Parry, H.; Oberhauser, K.S.; Zalucki, M.P. Monarchs in Decline: A Collateral Landscape-Level Effect of Modern Agriculture. Insect Sci. 2018, 25, 528–541. [Google Scholar] [CrossRef] [PubMed]
  16. Warren, M.; Hill, J.K.; Thomas, J.; Asher, J.A.; Fox, R.; Huntley, B.; Roy, D.B.; Telfer, M.; Jeffcoate, S.H.P.; Harding, P.; et al. Rapid Responses of British Butterflies to Opposing Forces of Climate and Habitat Change. Nature 2001, 414, 65–69. [Google Scholar] [CrossRef] [Green Version]
  17. Brereton, T.; Roy, D.; Greatorex-Davies, N. Thirty Years and Counting: The Contribution to Conservation and Ecology of Butterfly-Monitoring in the UK. Br. Wildl. 2006, 17, 162–170. [Google Scholar]
  18. Chowdhury, S.; Jennions, M.D.; Zalucki, M.P.; Maron, M.; Watson, J.E.M.; Fuller, R.A. Protected Areas and the Future of Insect Conservation. Trends Ecol. Evol. 2022, S0169-5347(22)00224-5. [Google Scholar] [CrossRef]
  19. Dennis, R.; Shreeve, T.; Van Dyck, H. Towards a Functional Resource-Based Concept for Habitat: A Butterfly Biology Viewpoint. Oikos 2003, 102, 417–426. [Google Scholar] [CrossRef]
  20. Dennis, R. Just How Important Are Structural Element as Habitat Components? Indications from a Declining Lycaenid with Priority Conservation Status. J. Insect Conserv. 2004, 8, 37–45. [Google Scholar] [CrossRef]
  21. Dennis, R.L.H. A Resource-Based Habitat View for Conservation: Butterflies in the British Landscape; John Wiley & Sons: Hoboken, NJ, USA, 2010; ISBN 978-1-4443-1526-4. [Google Scholar]
  22. Game, E.; Kareiva, P.; Possingham, H. Six Common Mistakes in Conservation Priority Setting. Conserv. Biol. J. Soc. Conserv. Biol. 2013, 27, 480–485. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Spehn, E.M.; Rudmann-Maurer, K.; Korner, C.; Maselli, D. Mountain Biodiversity and Global Change; Global Mountain Biodiversity Assessment (GMBA) of DIVERSITAS: Basel, Switzerland, 2012. [Google Scholar]
  24. Zhou, Y. The Monograph of Chinese Butterflies; Henan Science and Technology Press: Zhengzhou, China, 1994. [Google Scholar]
  25. Li, X.; Zhang, Y.-L.; Settele, J.; Franzén, M.; Schweiger, O. Long-Distance Dispersal and Habitat Use of the Butterfly Byasa Impediens in a Fragmented Subtropical Forest. Insect Conserv. Divers. 2012, 6, 170–178. [Google Scholar] [CrossRef]
  26. Gao, K.; Li, X.; Guo, Z.; Zhang, Y. The Bionomics, Habitat Requirements and Population Threats of the Butterfly Bhutanitis Thaidina in Taibai Mountain. J. Insect Conserv. 2014, 18, 29–38. [Google Scholar] [CrossRef]
  27. Igarashi, S. Life history of Teinopalpus aureus in Vietnam in comparison with that of T. imperialis. Butterflies 2001, 30, 4–24. [Google Scholar]
  28. Wu, C.S.; Xu, Y.F. Butterflies of China; Straits Publishing House: Fuzhou, China, 2017. [Google Scholar]
  29. Zeng, J.P.; Zhou, S.Y.; Luo, B.T.; Qin, K.; Wu, J.S. Life History of Teinopalpus aureus guangxiensis Chou et Zhou (Lepidoptera: Papilionidae). Guangxi Sciences 2007, 14, 323–326. [Google Scholar]
  30. Zeng, J.P.; Zhou, S.Y.; Luo, B.T.; Qin, K.; Liang, Y.L. Morphology and bionomics of the endangered butterfly golden kaiserihind, Teinopalpus aureus, in Dayaoshan of Guangxi. Chin. Bull. Entomol. 2008, 45, 457–464. [Google Scholar]
  31. Lin, B.Z.; Zhu, X.F.; Zeng, J.P.; Yuan, J.X. Research on Biological Characteristics of Teinoplpus aureus in Jiulianshan. For. Res. 2017, 30, 399–408.49. [Google Scholar] [CrossRef]
  32. Xing, S.; Au, T.F.; Dufour, P.C.; Cheng, W.; Landry Yuan, F.; Jia, F.; Vu, L.V.; Wang, M.; Bonebrake, T.C. Conservation of Data Deficient Species under Multiple Threats: Lessons from an Iconic Tropical Butterfly (Teinopalpus Aureus). Biol. Conserv. 2019, 234, 154–164. [Google Scholar] [CrossRef]
  33. Wang, W.L.; Suman, D.; Zhang, H.H.; Xu, Z.-B.; Fang-Zhou, M.; Hu, S.J. Butterfly Conservation in China: From Science to Action. Insects 2020, 11, 661. [Google Scholar] [CrossRef] [PubMed]
  34. Collins, N.M.; Morris, M.G. Threatened Swallowtail Butterflies of the World; IUCN: Gland, Switzerland, 1985; ISBN 978-2-88032-603-6. [Google Scholar]
  35. Gimenez The IUCN Red List of Threatened Species. Available online: https://www.iucnredlist.org/en (accessed on 3 September 2022).
  36. National Forestry and Grassland Administration (NFGA); Ministry of Agriculture and Rural Affairs (MARA). National Key Protected Wildlife List; Beijing, China. 2021. Available online: http://www.forestry.gov.cn/main/5461/20210205/122418860831352.html. (accessed on 13 June 2022).
  37. Zeng, J.P.; Zhou, S.Y.; Ding, J.; Luo, B.T.; Qin, K. Behavior characteristics and habitat adaptabilities of the endangered butterfly Teinopalpus aureus in Mount Dayao. Acta Ecol. Sin. 2012, 32, 6527–6534. [Google Scholar] [CrossRef]
  38. Igarashi, S. On the Life History of Teinopalpus Imperialis HOPE in Northern India and Its Phylogenetic Position in the Papilionidae. Lepid. Sci. 1987, 38, 115–151. [Google Scholar] [CrossRef]
  39. Zeng, J.P. Study on biological of Teinopalpus aureus Guangxi Chou et zhou. Master’s Thesis, Guangxi Normal University, Guilin, China, 2005. [Google Scholar]
  40. He, G.Q.; Jia, F.H. Research on population quantity and host plants of Teinoplpus aureus Mell in Jinggang Mountain. J. Nanchang Inst. Technol. 2012, 31, 68–70. [Google Scholar]
  41. Zeng, J.P.; Lin, B.Z.; Zhu, X.F.; Liu, L.Y. A Host Plant, Michelia maudiae, despread-istributed in South China for the Endangered Butterfly of Teinopalpus aureus. Acta Agric. Univ. Jiangxiensis 2014, 36, 550–555. [Google Scholar] [CrossRef]
  42. Liu, X.Z.; Xiao, Z.Y.; Ma, J.H. Scientific Survey and Study on the Forest Ecosystem in Jiangxi Jiulianshan Nature Reserve; China Forestry Publishing House: Beijing, China, 2009; pp. 287–311. [Google Scholar]
  43. Zeng, J.P.; Jin, Z.F.; Chen, F.S. Studies on Forest Ecology in Jiulianshan: Special Topics on Animals and Insects; Jiangxi Science and Technology Press: Nanchang, China, 2022. [Google Scholar]
  44. Ye, W.H.; Ma, K.P.; Ma, K.M.; Sang, W.G.; Gao, X.M. Studies on plant community diversity in Donglingshan mountain, Beijing, China IV. The influence of scale on α diversity. Acta Ecol. Sin. 1998, 18, 10–14. [Google Scholar]
  45. Roleček, J.; Vild, O.; Sladký, J.; Řepka, R. Habitat Requirements of Endangered Species in a Former Coppice of High Conservation Value. Folia Geobot. 2017, 52, 59–69. [Google Scholar] [CrossRef]
  46. Roberts, D.W.; Cooper, S.V. Concepts and Techniques of Vegetation Mapping. General technical report INT—U.S.; Department of Agriculture, Forest Service, Intermountain Research Station: Ogden, UT, USA, 1989. [Google Scholar]
  47. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022; Available online: https://www.R-project.org/. (accessed on 5 September 2022).
  48. Ramírez-Fischer, F.J.; Benyamini, D.; Vargas, H.A. An Endangered Hemiparasitic Shrub Is the Only Host Plant of the Little-Known Neotropical Hairstreak Strymon Flavaria (Lepidoptera: Lycaenidae) in the Arid Andes. J. Insect Conserv. 2016, 20, 923–928. [Google Scholar] [CrossRef]
  49. Scherer, G.; Fartmann, T. Occurrence of an Endangered Grassland Butterfly Is Mainly Driven by Habitat Heterogeneity, Food Availability and Microclimate. Insect Sci. 2021, 29, 1211–1225. [Google Scholar] [CrossRef]
  50. Modin, H.; Öckinger, E. Mobility, Habitat Selection and Population Connectivity of the Butterfly Lycaena Helle in Central Sweden. J. Insect Conserv. 2020, 24, 821–831. [Google Scholar] [CrossRef]
  51. Paré, P.W.; Tumlinson, J.H. Plant Volatiles as a Defense against Insect Herbivores. Plant Physiol. 1999, 121, 325–332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Fadamiro, H.; Chen, L.; Akotsen-Mensah, C.; Setzer, W.N. Antennal Electrophysiological Responses of the Giant Swallowtail Butterfly, Papilio Cresphontes, to the Essential Oils of Zanthoxylum Clava-Herculis and Related Plants. Chemoecology 2010, 20, 25–33. [Google Scholar] [CrossRef]
  53. Damptey, F.G.; Adofo, E.; Duah-Gyamfi, A.; Adusu, D.; Opuni-Frimpong, E. Logging Effects on Seedling Regeneration and Diversity in a Tropical Moist Semi-Deciduous Forest in Ghana. Geol. Ecol. Landsc. 2021, 1–12. [Google Scholar] [CrossRef]
  54. Liu, Y.H. Magnolias of China; Beijing Science & Technology Press: Beijing, China, 2004; p. 6. [Google Scholar]
  55. Southwood, T.R.E. The Number of Species of Insect Associated with Various Trees. J. Anim. Ecol. 1961, 30, 1–8. [Google Scholar] [CrossRef]
Insects 13 01082 g001 550
Figure 1. The geographical location of the study area, Jiulianshan National Nature Reserve (black line), and transect in low-mountain (black dotted line, with the top elevation < 1000 m, LMT) and middle-mountain (white dotted line, with the top elevation >1000 m, MMT) areas. The two ovals roughly indicate the butterfly occupied area (OA) and non-occupied area (NOA) for T. aureus.
Figure 1. The geographical location of the study area, Jiulianshan National Nature Reserve (black line), and transect in low-mountain (black dotted line, with the top elevation < 1000 m, LMT) and middle-mountain (white dotted line, with the top elevation >1000 m, MMT) areas. The two ovals roughly indicate the butterfly occupied area (OA) and non-occupied area (NOA) for T. aureus.
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Insects 13 01082 g002 550
Figure 2. The logistic response curves fitted with the Shannon–Wiener index of Magnoliaceae plants against the transect length in the Jiulianshan Mountains, showing the points (e.g., 188 m, 226 m, and 295 m) of deceleration inflection in three transect lines extending from the mountain foot to the top.
Figure 2. The logistic response curves fitted with the Shannon–Wiener index of Magnoliaceae plants against the transect length in the Jiulianshan Mountains, showing the points (e.g., 188 m, 226 m, and 295 m) of deceleration inflection in three transect lines extending from the mountain foot to the top.
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Insects 13 01082 g003 550
Figure 3. NMDS analysis of the transect sections, showing the contributions of environmental variables in Table 1 (A) and the distribution of the early butterfly stages of T. aureus occupied (Occupied MMT) or non-occupied sections of medium-mountain transects (MMT) as well as low-mountain transects (LMT) (B), see Table 1 for abbreviations.
Figure 3. NMDS analysis of the transect sections, showing the contributions of environmental variables in Table 1 (A) and the distribution of the early butterfly stages of T. aureus occupied (Occupied MMT) or non-occupied sections of medium-mountain transects (MMT) as well as low-mountain transects (LMT) (B), see Table 1 for abbreviations.
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Insects 13 01082 g004 550
Figure 4. Comparison of DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae between low-mountain transects (LMT) and middle-mountain transects (MMT) in Jiulianshan. See Figure 1 for transects and Table 1 for abbreviations; ns for p > 0.05, * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.
Figure 4. Comparison of DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae between low-mountain transects (LMT) and middle-mountain transects (MMT) in Jiulianshan. See Figure 1 for transects and Table 1 for abbreviations; ns for p > 0.05, * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.
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Insects 13 01082 g005 550
Figure 5. Comparison of DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae between the occupied and non-occupied areas of MMT in Jiulianshan. See Figure 1 for transects and Table 1 for abbreviations; ns for p > 0.05, * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.
Figure 5. Comparison of DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae between the occupied and non-occupied areas of MMT in Jiulianshan. See Figure 1 for transects and Table 1 for abbreviations; ns for p > 0.05, * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.
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Insects 13 01082 g006 550
Figure 6. Comparison of the DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae among the primeval forest, secondary forest, and coniferous forest habitats in Jiulianshan. The different lowercase letters indicate significant differences between groups and vice versa. See Figure 1 for transects and Table 1 for abbreviations.
Figure 6. Comparison of the DBH, tree height, tree age, plant abundance, and diversity in Magnoliaceae among the primeval forest, secondary forest, and coniferous forest habitats in Jiulianshan. The different lowercase letters indicate significant differences between groups and vice versa. See Figure 1 for transects and Table 1 for abbreviations.
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Insects 13 01082 g007 550
Figure 7. Abundance and diversity (Shannon–Wiener index) regression relationships for all Magnoliaceae plants, all hostplants, unavailable hostplants, as well as the available hostplants in primeval forests, showing the coefficients and p values. See Table 1 for abbreviations.
Figure 7. Abundance and diversity (Shannon–Wiener index) regression relationships for all Magnoliaceae plants, all hostplants, unavailable hostplants, as well as the available hostplants in primeval forests, showing the coefficients and p values. See Table 1 for abbreviations.
Insects 13 01082 g007
Table
Table 1. Environmental variables, abbreviations, and descriptions.
Table 1. Environmental variables, abbreviations, and descriptions.
Environmental VariablesAbbreviationsDescriptions
AltitudeAltitudethe altitude of each transect section’s center point
AspectAspectthe aspect of each transect section’s center point
SlopeSlopethe slope of each transect section’s center point
Average tree ageAge athe average tree age in each transect section
Diameter of breast heightDBH bthe mean DBH of all Magnoliaceae trees in each transect section
Tree heightHeight bthe mean tree height of all Magnoliaceae trees in each transect
section
All Magnoliaceae species
diversity		MSD bthe Shannon–Wiener index of all Magnoliaceae plants of each transect section
All Magnoliaceae plants
abundance		MPAb bthe individual number of all Magnoliaceae plants of each transect section
All hostplants species diversityHPSD bthe Shannon–Wiener index of all hostplants of each transect
section
All hostplants abundanceHPAb bthe individual number of all hostplants of each transect section
Available hostplants species
diversity		AHPSD bthe Shannon–Wiener index of the available hostplants with the height above the average tree height of each transect section
Available hostplants abundanceAHPAb bthe individual number of the available hostplants with the height above the average tree height of each transect section
Habitat typeHabitatthe habitat of each transect section, including primeval,
secondary, or coniferous forest
a Sourced from the local database of Forest Resource Inventory (FRI), the average forest tree age is determined by the dominant tree species; b Without considering the seedling individuals with DBH < 3 cm.
Table
Table 2. Habitat selection of T. aureus by the method of Bailey’s.
Table 2. Habitat selection of T. aureus by the method of Bailey’s.
Habitat TypesActual
Proportion
Utilized (Pi)		Expected Proportion Utilized
(Pio)		Bailey’s 95%
Confidence
Interval for Pi		Selection
Primeval forests 0.84620.21010.4372 ≤ Pi ≤ 0.9847Preference
Secondary forests 0.15380.68070.0021 ≤ Pi ≤ 0.4954Avoidance
Coniferous forests 00.10920.0000 ≤ Pi ≤ 0.0000Avoidance
Table
Table 3. Comparisons of environmental variables between occupied and non-occupied transect sections in T. aureus.
Table 3. Comparisons of environmental variables between occupied and non-occupied transect sections in T. aureus.
Environmental VariablesNon-Occupied
(n = 106)		Occupied
(n = 13)		p Value
Altitude662.68 ± 168.1 b983.00 ± 154.64 a<0.001 ***
Aspect0.52 ± 0.31 a0.34 ± 0.25 a>0.05 ns
Slope16.83 ± 8.78 b17.54 ± 6.09 a>0.05 ns
Age46.46 ± 11.77 b60.15 ± 10.54 a<0.001 ***
DBH8.43 ± 10.50 a17.51 ± 7.72 a<0.01 **
Height5.81 ± 6.91 b9.80 ± 3.11 a<0.01 **
MSD0.11 ± 0.27 b0.41 ± 0.37 a<0.001 ***
MPAb6.08 ± 11.70 b24.23 ± 27.94 a<0.001 ***
HPSD0.07 ± 0.22 b0.36 ± 0.32 a<0.001 ***
HPAb3.35 ± 7.33 b23.85 ± 28.13 a<0.001 ***
AHPSD0.04 ± 0.15 b0.29 ± 0.30 a<0.001 ***
AHPAb1.46 ± 3.48 b15.15 ± 17.33 a<0.001 ***
The different lowercase letters indicated significant differences between occupied and non-occupied sections and vice versa. ns for p > 0.05, ** for p < 0.01 and *** for p < 0.001. See Table 1 for abbreviations.
Table
Table 4. Discriminant coefficient and Wilk’s λ results for the four selected environmental variables included in the stepwise discriminant analysis.
Table 4. Discriminant coefficient and Wilk’s λ results for the four selected environmental variables included in the stepwise discriminant analysis.
Environmental VariablesStandardized Discriminant CoefficientWilk’s λp
AHPAb1.4640.613<0.001
Altitude0.4600.592<0.001
Habitat0.3340.572<0.001
HPAb−0.9240.571<0.001
See Table 1 for abbreviations.
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Wang, L.; Wang, H.; Zha, Y.; Wei, H.; Chen, F.; Zeng, J. Forest Quality and Available Hostplant Abundance Limit the Canopy Butterfly of Teinopalpus aureus. Insects 2022, 13, 1082. https://doi.org/10.3390/insects13121082

AMA Style

Wang L, Wang H, Zha Y, Wei H, Chen F, Zeng J. Forest Quality and Available Hostplant Abundance Limit the Canopy Butterfly of Teinopalpus aureus. Insects. 2022; 13(12):1082. https://doi.org/10.3390/insects13121082

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Wang, Lu, Hui Wang, Yuhang Zha, Heyi Wei, Fusheng Chen, and Juping Zeng. 2022. "Forest Quality and Available Hostplant Abundance Limit the Canopy Butterfly of Teinopalpus aureus" Insects 13, no. 12: 1082. https://doi.org/10.3390/insects13121082

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