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Article

Butterfly Community Diversity in the Qinling Mountains

by
Jinze Ren
,
Shuying Li
,
Mengdi He
and
Yalin Zhang
*
Key Laboratory of Plant Protection Resources and Pest Management, Ministry of Education, Entomological Museum, College of Plant Protection, Northwest A&F University, Yangling, Xianyang 712100, China
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(1), 27; https://doi.org/10.3390/d14010027
Submission received: 18 December 2021 / Revised: 30 December 2021 / Accepted: 31 December 2021 / Published: 2 January 2022
(This article belongs to the Section Biodiversity Conservation)
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Abstract

:
The Qinling Mountains are one of the oldest mountain ranges in China and a global biodiversity research and conservation hotspot. However, there is a lack of systematic research and survey of butterfly diversity in this region. Based on the butterfly taxa, combined with the changes in natural climate, altitude gradient and season in the Qinling Mountains, the butterfly diversity and community structure changes in 12 counties in the middle Qinling Mountains were analyzed by transect surveys and platform data analyses. A total of 9626 butterflies were observed, belonging to 427 species across 175 genera and 5 families. The species richness on the southern slope of the Qinling Mountains was higher than on the northern slope. We also studied the variation in alpha and beta diversity of butterflies. The results show that butterfly species were abundant and the highest diversity was found at the middle altitudes (1000–2000 m). Moreover, there were obvious seasonal differences in both species and number of butterflies. The community similarity in spring, summer and autumn was low, with limited species co-existing. The butterflies in the Qinling Mountains reserve area were the most abundant, exhibiting no significant difference with those in the ecotone and the farm area. Finally, we did an assessment of butterflies as endangered and protected species. In conclusion, our long-term butterfly survey data show that human disturbance and climate and environmental changes jointly shape the butterfly diversity in the middle of the Qinling Mountains.

1. Introduction

Biodiversity is the material basis for human survival and development, as well as the premise of ecosystem sustainability [1]. However, the scale of population growth and human intervention, to a great extent, has led to a loss of biodiversity [2,3,4]. In recent years, the protection of biodiversity has attracted more attention from the international community [5,6]. The quantification of the spatial distribution of species diversity is an important prerequisite for biodiversity conservation [7]. Climate change indicators are closely linked to threats to biodiversity [8]. For example, in the northern hemisphere, species abundance increases with the increase in temperature and natural conditions and there is a distinct spatial pattern (i.e., a temporal classification alteration) [9].
Butterflies are excellent for studies at the population and ecosystem levels, because they are easily monitored, caught, tagged and identified in nature [10]. In the face of increasing global change and habitat degradation, butterflies have become the preferred indicator for monitoring and evaluating environmental changes in their habitats due to their sensitivity to environmental changes [11,12]. For example, in North America, a major climate-induced shift in butterflies is slowly under way, with warm-adapted species expanding northward and cold-adapted species retreating [13]. In temperate regions, microclimate is also a key factor affecting insect population dynamics. Different butterfly lineages have different microclimate and microhabitat responses [14]. Butterflies also play important roles in the ecosystem, such as pollinating plants [15,16,17,18,19]. Theoretically, maintaining the species diversity and abundance of pollinating butterflies should guarantee the pollination service function of agroforestry ecosystems to a certain extent [16]. Butterfly diversity makes up about 25 percent of Western Mediterranean fauna, the vast majority of which consists of non-cryptic species. Although the frequency of cryptic species was uniform, their distribution pattern determines most of the replacement of beta diversity [20].
As one of the oldest mountain ranges in China, the Qinling Mountains occupy an important position in the biogeography of China [21,22]. It is the dividing line between the subtropical zone and the warm temperate zone and also the dividing line between the Palearctic region and the Oriental region in the world zoogeographic divisions [23,24]. Taibai Mountain, the main peak of the Qinling Mountains, is 3767 m above sea level and is to the east of the Qinghai–Tibet Plateau [25,26]. The complex vegetation types and diverse climate make Qinling a “treasure house of biodiversity” [22,27]. As early as 1996, the Global Environmental Protection Fund (GEF) invested more than USD 10 million in the construction of the Qinling Nature Reserve group. The vegetation types in the middle Qinling Mountains are well known, but there has been little research on butterflies [28,29,30]. The species composition, richness and spatial distribution pattern of butterfly biodiversity in this region are not clear, which poses obstacles to resource conservation. At present, there are many studies on butterfly community diversity and its influencing factors, especially the effects of habitat differences and climate factors on butterfly diversity [31,32,33]. For example, changes in altitude can significantly change the richness of butterflies [34,35]. In the study of the mountains in northern Oaxaca, Mexico, the increase in altitude leads to changes in temperature and humidity, which reduces the abundance of butterfly species [36]. However, in Pyrcz’s research, the abundance of butterflies in the higher elevations of the Ecuadorian Andes was higher than that in the lower elevations [37]; seasonal variation plays a decisive role in the spatial distribution of butterfly communities. Butterfly species usually synchronize their life cycles with seasonality, different seasons bring different precipitation, which directly affects the change in butterfly species. In the study of Rio Doce State Park, butterfly diversity was more abundant in the dry season than in the wet one and their contribution to species turn over and nestedness variations overlap with seasonal variations [38].
Our research study mainly focuses on three issues: (1) the status of butterfly diversity in the middle Qinling Mountains; (2) the variation in butterfly community diversity with altitude gradients, habitat types and seasons in this area; (3) assessment of endangered and protected species of butterflies in this area. Over the past two years (from May 2020 to September 2021) we carried out a survey of the butterfly diversity in 12 counties in this region (Figure 1A). We analyzed the spatial distribution of butterflies in each family in combination with samples in the platform. Meanwhile, by studying the alpha and beta diversity of butterflies under environmental conditions, we compared the variation in butterfly community diversity with elevation gradient, habitat and season. For the first time, we systematically reveal the distribution pattern and biogeographical characteristics of butterflies on the northern and southern slopes of the middle Qinling Mountains, which is of great significance to better protect the ecological diversity.

2. Materials and Methods

2.1. Study Region

The area we investigated is located at 31.8–34.1° N and 105–111° E in Shaanxi Province and is traversed by the Qinling Mountains from west to east. A total of 12 counties in the middle part of the Qinling Mountains were investigated (Figure 1B), including Zhouzhi County (ZZ), Huyi District (HY), Meixian County (MX), Taibai County (TB), Qishan County (QS), Chencang District (CC) and Weibin District (WB), Liuba County (LB), Chenggu County (CG), Foping County (FP), Ningshan County (NS) and Shiquan County (SQ). The altitude span was 480–3800 m. The Qinling Mountains are the boundary between the subtropical zone and the warm temperate zone. The northern and southern landscapes of the Qinling Mountains are different. On the northern slope, there are warm temperate mixed coniferous, broad-leaved and deciduous broad-leaved forests on mountain brown soil and in a mountain brown land zone. The southern slope is a mixed deciduous broad-leaved forest containing evergreen broad-leaved tree species of the northern subtropics [39]. Due to the large height difference, climate, soil and plants all follow an obvious vertical distribution law. The characteristics of warm temperate zone, cold temperate zone and cold zone are presented along with the elevation from bottom to top. Because of the special geographical location of the Qinling Mountains, the distribution of animals and plants on the northern and southern slopes have been affected for a long time and the species to the north and south of the Qinling Mountains mix.

2.2. Data Collection

From May 2020 to September 2021, we conducted transect surveys in 12 counties in the central Qinling Mountains. All butterfly data are based on standardized transect counts (“Pollard walk”) [40,41]. This method is widely used to investigate and monitor butterfly populations and communities and has considerable value when investigating differences in species abundance between locations [42,43]. We established 1–2 transect lines from each county in the middle Qinling Mountains, with a total of 30 transect lines, including 17 transect lines on the northern slope and 13 transect lines on the other slope. Each transect was repeatedly sampled three times a year, in spring (March, April and May), summer (June, July and August) and autumn (September, October and November). We conducted two years of continuous surveys, under the conditions of temperature suitable for butterfly activities and sufficient sunlight (>17 °C, 9:00–17:00). During the survey, a recorder took notes on butterfly species, habitat information, GPS coordinates and marked line-like tracks on a 1:10,000 map. Each transect was 2 km long and divided into 10 sections with a walking speed of 1.5 km/h. The species, number and activities of all butterflies at 2.5 m to the left of the translocation line and 5 m above the line were recorded and ecological photos were taken. The other worker watched for and caught the butterflies. The same individual specimens and the butterflies behind were not counted repeatedly [44]. The butterflies that could not be identified were captured and photographed, then wrapped in a triangular paper and brought back to the laboratory for identification [45,46,47].
Another part of butterfly data came from “National Animal Collection Resource Center” [48] and “China teaching specimen standardized integration and resource sharing platform” [49]. We screened out all butterfly transect information that had been recorded in these 12 counties through the above two platforms and combined their recorded altitude, season and habitat with the data we collected for unified alpha and beta analyses.
In the study of mountain vegetation in the middle part of Qinling Mountains, the difference in mountain vegetation coverage at low, middle and high elevations is obvious and the trend is different. Depending on the type of vegetation cover, the altitude of the observation area (365–3772 m) was divided into three gradients: low (<1000 m), medium (1000–2000 m) and high (>2000 m). The <1000 m gradient is mainly deciduous broadleaf forest belt containing evergreen trees and the 1000–1500 m gradient is mainly deciduous oak forest belt, while the >2000 m gradient is mainly coniferous forest belt [28]. Combining the transects and the ecological photos we took, we divided the habitat information of each butterfly specimen. The first part is the butterflies in the reserve. They were observed by us in some national parks and nature reserves. Most of transects in this area were located in the interior of the forest and had not suffered large-scale human interference. The second part is the butterflies of the ecotone zone. The ecotone is an area centered on the edge of the reserve and extends 2 km inward and outward. The transects of these areas were established at the edge of the forest, with a small amount of human interference. The last part is the butterflies on the farm. This part of the area is mainly composed of natural forests, secondary grasslands, vegetable fields and abandoned farmland. There is a lot of human use of land (kiwi and grape growing areas) and disturbance (farming and weeding). Additionally, because our collection was repeated in different seasons, we also analyzed the population dynamics of butterflies in spring, summer and autumn.

2.3. Analysis Methods

We used “ArcGIS” software to study the species abundance and the proportion of each family in each county. ArcGIS can represent the data more intuitively on the map [50]. The number of butterfly species in each county with family composition were visualized on a map using ArcMap 10.0 to show the spatial distribution of species richness.
As for comparing the alpha diversity of butterfly communities at different elevations, habitats and seasons, we used an asymptotic diversity estimate based on Hill numbers of order “q”, which is more statistically rigorous than other diversity measures [51,52]. The species richness (q = 0), Shannon diversity (q = 1) and Simpson diversity (q = 2) were calculated using the sparse extrapolation R package “iNEXT” based on the relationship between the number of individuals and species sampled [53].
Species richness (q = 0) is only concerned with the existence of species and counts species equally without considering their relative abundance. The larger the value is, the more species are in the community. Shannon diversity (q = 1, exponential of Shannon entropy) was estimated in proportion to species abundance, which can be interpreted as the effective number of common species in the community. Common species are species that appear more frequently in ecological surveys, but their numbers are not necessarily dominant. Simpson diversity (q = 2, inverse of Simpson concentration) was estimated by counting dominant species and interpreted as the effective number of dominant species in the community. The alpha diversity of the butterfly community (q = 0, 1, 2) was estimated by a 200 bootstrap re-sampling with a 95% confidence interval [52,54,55,56].
In order to assess the beta diversity of the butterfly community composition under different factors such as elevation habitat and season, an abundance non-metric multidimensional scaling analysis (NMDS) was performed using the R software “vegan” package based on species richness [57]. The NMDS is a data analysis method that simplifies research objects (samples or variables) in a multidimensional space to a low-dimensional space for positioning, analysis and classification, while preserving the original relationships between objects [58]. Its basic feature is that the similarity or dissimilarity of data between objects is regarded as a monotone function of the distance between points and, on the basis of maintaining the order relationship of the original data, the new data column of the same order is used to replace the original data for the metric multidimensional scaling analysis [59,60]. Finally, we built three separate models by similarity analysis (ANOSIM) in the R software “vegan” package to perform the corresponding similarity analysis on the previously analyzed dissimilarity matrix (NMDS) before quantifying the similarities and significant differences between different altitudes, habitats and seasons. The ANOSIM analysis is mainly used to analyze the similarity between high-dimensional data groups and provide a basis for the significance evaluation of data differences. A significance value less than 0.05 is generally considered to constitute a significant difference and, when the significance value is higher than 0.05, the difference between groups is not considered statistically significant [61]. The ANOSIM statistic “R” compares the mean of ranked dissimilarities between groups to the mean of ranked dissimilarities within groups. The higher the R value, the more dissimilar the groups are in terms of community composition [62].
To assess the degree of protection of the butterflies we collected, we looked through the Chinese Red List books and the list of butterfly protected species on the IUCN website [63]. The IUCN Red List is an important indicator of the health of the world’s biodiversity and is used to classify endangered species worldwide. It can provide information and catalyze actions for biodiversity conservation and policy changes, which are essential for protecting the natural resources we need for survival.

3. Results

3.1. Species Richness and Its Spatial Pattern

We counted a total of 9626 butterflies, of which 6583 were collected from our line transects in the past two years and the rest were pulled from the platform (Table S1). These butterflies comprise 427 species in 175 genera (Table S2). Nymphalidae had the highest diversity index and species richness, with 74 genera accounting for 42.28% of the total genera. The number of individuals and the species richness of Papilionidae were the lowest, with 11 genera accounting for 6.28% (Table 1).
The species richness of butterflies in the 12 counties ranged from 36 to 273 species (Figure 2). Zhouzhi County (ZZ) had the highest species abundance, while Qishan County (QS) had the lowest species abundance. As shown in Figure 1, counties with high species richness (ZZ, TB and NX) are concentrated in the central part of the Qinling Mountains, where there are more protected areas. Chencang District (CC), Weibin District (WB) and Chenggu County (CG), all of which had a low–middle abundance, are located at the edge of the foothills in the middle Qinling Mountains. Of course, this difference in richness is not absolute. The Huyi District (HY) was also characteristically high in species richness. On the whole, species abundance in counties located on the southern slope of the Qinling Mountains was higher than on the northern slope.

3.2. Butterfly Alpha Diversity Patterns

In the middle part of the Qinling Mountains, the cumulative species curve did not reach an asymptote, but there was obvious separation among different elevation types. These results show that species diversity decreased significantly in order “q” (0–2), with fewer common or dominant species at low abundance. There were significant differences in butterfly community diversity at different elevations. The mid-altitude area (1000–2000 m) had the highest number of species, followed by the low-altitude area (<1000 m), with the high-altitude area showing the lowest number of species (Figure 3A). The species abundance first increased and then decreased from low elevation to high elevation. An estimated average of 424 species was found at mid-altitude, while only 74 were found at high altitude (Table 2). The seasonal accumulative curves in this region are obviously different (Figure 3D). In addition, estimated species richness was the highest in summer (466 ± 28.43) and the lowest in autumn (140 ± 21.69) (Table 2). Although the species richness in spring was higher than in autumn, the Shannon diversity (37.98 ± 1.64) and Simpson diversity (11.04 ± 0.52) were lower than in autumn (Shannon diversity = 38.71 ± 1.75 and Simpson diversity = 22.36 ± 1.03), indicating that the effective number of common species and dominant species was higher in autumn, when the abundance was low (Table 2). In terms of habitat, the accumulative curves of the three habitat types are also clearly separated (Figure 3G). Butterflies in the reserve achieved great advantages in estimated species richness (475 ± 33.74), Shannon diversity (144.45 ± 3.50) and Simpson diversity (58.06 ± 2.06) (Table 2). Although the number of individual butterflies (2841) on the farm was higher than the number recorded in the ecotone (2634). However, estimated species (324 ± 19.57) in the ecotone, Shannon diversity (85.92 ± 2.54) and Simpsons diversity (36.14 ± 1.60) are all higher than the values obtained on the farm (estimated species = 280 ± 20.30, Shannon diversity = 62.70 ± 1.98 and Simpson diversity = 25.62 ± 1.16) (Table 2). It shows that, as the intensity of human interference increases, there are fewer and fewer butterfly species.

3.3. Butterfly Beta Diversity Pattern

According to the NMDS altitude analysis chart, the observed species in the high-altitude area were obviously dispersed, with significant spatial differences and greater similarity differences between the low-altitude area and high-altitude area. Some species of butterflies distributed in the middle and lower elevations were abundant and overlapped with those at high and low elevations (Figure 3B). At the same time, the NMDS seasonal map showed that there was clear separation in each season and the overlap degree of species distribution within a season was low, with only a small portion of common crossing areas at the edges of the three seasons (Figure 3E). The NMDS habitat map shows that most of the overlapping areas were in the reserve, ecotone and farm and the similarity among butterfly groups in different habitats was high (Figure 3H). Finally, based on the similarity ANOSIM analysis, there were significant differences between different altitude types (R = 0.20, significance = 0.01) and different seasons (R = 0.32, significance < 0.01) and the similarity coefficient was low (Figure 3C,F). The composition of the butterfly community has its own characteristics. Some species with strong adaptability and a wide distribution can overlap, but the shared species are limited. Regarding the different type of habitat (R = 0.01, significance > 0.05), the overlap area of butterflies was higher in different habitats, but the community similarity was not significant (Figure 3I).

3.4. Evaluation of Endangered and Protected Butterflies in the Middle Qinling Mountains

According to the Red List of Species in China (Volume iii): Invertebrates [64], 170 are Chinese endemic species and 44 species are protected, including 16 vulnerable (VU) and 28 near-threatened (NT) species; among them, Troides aeacus and Bhutanitis thaidina have been listed as “National two levels of protection animals” in China. In addition, we also found seven protected species according to the IUCN Red List of Threatened Species, including one endangered (EN), one near-threatened (NT) and five least-concern (LC) species (Table S3).

4. Discussion

The counties in the middle part of the Qinling Mountains are divided into two types. One includes the warm temperate coniferous, broadleaf mixed and deciduous broadleaf forests and the northern slope mountain brown soil and mountain brown land zone. Their butterflies belong to the Palearctic Realm (including WB, CC, ZZ, TB, northern HY and QS). The other group is the deciduous broad-leaved mixed forest with evergreen broad-leaved tree species in the northern subtropics and the southern slope of yellow–brown soil and yellow–brown land zone. Their butterflies belong to the Oriental Realm (including FP, NS, LB, TB and southern CG). Overall, the southern slope of the northern mountains shows abundance of butterfly species by blocking the cold of the north Qinling. The north and south form two different climate regions, where the northern region is cold and dry, while the southern one is hot and humid; butterflies in the middle part of the Qinling Mountains seem to prefer the southern slope with higher temperature and humidity [39,65,66]. In addition, the species richness of TB, ZZ and NS counties is relatively high, which is closely related to human disturbance factors and degree of protection. In the transect survey, we found that these three counties have more protected areas, less human disturbance and more virgin forests and undeveloped ecosystems. In terms of species and numbers, the closer the county is to the Qinling Mountains, the richer and higher the butterfly diversity.
Combined with our collected data and platform data, the biodiversity of butterflies in the ecosystem of the middle Qinling Mountains is affected by altitude gradient, seasonal change and habitat type. The dilution curve of Alpha species richness indicates that more species could be observed in plots with different seasons, elevation gradients and habitat types. From the perspective of spatial and temporal distribution patterns, although butterfly communities in different altitudes and seasons share some species, species composition differs greatly and community similarity is low, which provides a scientific basis for species diversity monitoring and conservation in this region.
Butterfly groups are sensitive to environmental and climate changes and are good ecological probes [11]. Elevation gradient changes are an important factor affecting biodiversity because there are many environmental variables, notably temperature and humidity [31,32,33], and different combinations of vegetation types that contribute to environmental heterogeneity at different altitudes [34]. In the mountain ecosystem, altitude is also an important factor shaping butterfly community composition. Species richness along the altitude gradient presents three modes: increasing, decreasing or a mid-altitude peak [35]. In studies of butterfly communities in the Himalayan Yanshan Mountains and the Alps, butterfly populations first increase and then decline sharply with altitude. In this study, the highest butterfly diversity was observed in the mid-altitude area of the central Qinling Mountains, which supports the peak pattern of mid-altitude diversity. At the highest altitudes (>3000 m), on the top of Taibai Mountain, the peak of the middle part of the Qinling Mountains in Taibai county, only a small number of Nymphalidae and Papilionidae butterflies were found, which were all collected during the summer. Butterfly communities were the most abundant in coniferous and broadleaved mixed forests.
In addition, butterfly groups may respond to seasonality to some extent, which may be related to rising temperature and precipitation. The seasonal effects of precipitation on species diversity reflect the general operation of non-neutral mechanisms in natural communities. The optimum development period of butterfly larvae is correlated with the availability of plant leaves and new plant tissues and the availability of resources regulates the activity patterns of butterflies and affects butterfly diversity [67,68]. The precipitation in the middle part of the Qinling Mountains is mainly concentrated from August to October. The butterfly community richness and abundance were highest in summer, because it was in the period of the highest temperature and drought transition, which was consistent with Lourenço’s study [38]. Interestingly, the number of species and species abundance in spring were significantly higher than in autumn, but the Shannon diversity and Simpson diversity indices were lower than in summer, indicating that the effective number of common species and dominant species was higher in autumn than in spring and that species abundance could not accurately reflect the level of biodiversity. Because estimates of abundance assign equal weight to all species, they do not contain information about relative abundance of species.
Moreover, the NMDS map analysis indicated that butterfly communities in farmland, protected areas and marginal interlacing areas were highly similar, that most butterfly communities overlapped and that only a few butterflies were unique in their respective habitat types. The protected area surveyed was the most abundant habitat type and it is clear that the protected area had the most abundant and diverse butterfly species. Farmland with greater human disturbance has fewer species and simpler community structures [69,70]. However, there were no significant community differences in different habitats, indicating that butterflies in the Qinling Mountains live in a wide range and many butterflies appear in different environments.
According to the Red List of Species in China (Volume iii): Invertebrates, we evaluated the endangered species level of the 427 butterfly species collected, including 170 endemic species in China, 44 protected species and 2 wild animals under state key protection of Class II in China. We also found seven protected species in the IUCN Red List of Threatened Species. This could help develop better conservation measures for these endangered butterfly species. Understanding the combined impact of climate change and habitat loss on biodiversity is urgent in the field of ecology and conservation. Our research study on the middle Qinling area on butterfly diversity provides a database to help biologists provide more suitable protection schemes, reduce human activities and maintain the stability of the habitats. This is a basis to protect the butterfly species diversity, minimize the interference of secondary forest and mixed forest understory economic crops and promote the natural restoration or increase the heterogeneity of the environment. For example, more protected areas, more diversity monitoring and seasonal observation in areas where protected species are common would help protect endangered species. At the same time, in farmland planting areas, advocating the implementation of green ecological planting and the rational use of pesticides, as well as reducing environmental pollution, would effectively protect species diversity.

5. Conclusions

This is the first systematic ecological research study of the butterfly biodiversity of 12 counties in the central Qinling Mountains. The distribution of five butterfly families in each county is more sufficiently understood. In addition, due to different climate, terrain and vegetation types, the northern slope of the middle Qinling Mountains had better butterfly diversity than the southern slope. Additionally, season and altitude are key factors that affect the number and species of butterfly species. These studies are crucial to the conservation of insect diversity in the Qinling Mountains.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d14010027/s1, Table S1: Collection and platform list of butterfly species, Table S2: Distribution pattern of butterflies in the middle Qinling Mountains., Table S3: List of endangered and protected butterfly species in the middle Qinling Mountains.

Author Contributions

Conceptualization, J.R. and Y.Z.; methodology, field survey and investigation, J.R., S.L. and M.H.; software, J.R.; validation, Y.Z., S.L. and M.H.; formal analysis, J.R.; resources and data curation, Y.Z.; writing—original draft preparation, J.R. and S.L.; writing—review and editing, Y.Z.; visualization, supervision, project administration and funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research study was funded by The Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China (2019HJ2096001006), the National Natural Science Foundation of China (32170469,31750002) and the National Animal Collection Resource Center, China.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are grateful to John Richard Schrock (Emporia State University, Emporia, KS, USA) for revising the manuscript. We also would like to express our appreciation to personnel who assisted during our investigation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ma, K. New opportunities for mainstreaming biodiversity conservation. Biodivers. Sci. 2015, 23, 557–558. [Google Scholar] [CrossRef] [Green Version]
  2. Paul, V.M.; Ho, V.C. Farmers, biodiversity and plant protection: Developing a learning environment for sustainable tree cropping systems. Int. J. Agric. Sustain. 2004, 2, 67–76. [Google Scholar]
  3. Crist, E.; Mora, C.; Engelman, R. The interaction of human population, food production, and biodiversity protection. Science 2017, 356, 260–264. [Google Scholar] [CrossRef] [PubMed]
  4. Butchart, S.H.M.; Walpole, M.; Collen, B.; Van Strien, A.; Scharlemann, J.P.W.; Almond, R.E.A.; Baillie, J.E.M.; Bomhard, B.; Brown, C.; Bruno, J.; et al. Global Biodiversity: Indicators of Recent Declines. Science 2010, 328, 1164–1168. [Google Scholar] [CrossRef] [PubMed]
  5. You, M.S. Conservation and utilization of the insect diversity in China. Chin. Biodivers. 1997, 5, 135–141. [Google Scholar]
  6. Sayer, C.A.; Carr, J.A.; Darwall, W.R.T. A critical sites network for freshwater biodiversity in the Lake Victoria Basin. Fish. Manag. Ecol. 2018, 26, 435–443. [Google Scholar] [CrossRef]
  7. Kaschner, K.; Tittensor, D.P.; Ready, J.; Gerrodette, T.; Worm, B. Current and Future Patterns of Global Marine Mammal Biodiversity. PLoS ONE 2011, 6, e19653. [Google Scholar] [CrossRef] [PubMed]
  8. Linquist, S.; Varner, G.; Newman, J. Precis of defending biodiversity. Biol. Philos. 2020, 35, 1–4. [Google Scholar] [CrossRef]
  9. Pilotto, F.; Kühn, I.; Adrian, R.; Alber, R.; Alignier, A.; Andrews, C.; Bäck, J.; Barbaro, L.; Beaumont, D.; Beenaerts, N.; et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 2020, 11, 3486. [Google Scholar] [CrossRef]
  10. Nowicki, P.; Settele, J.; Henry, P.-Y.; Woyciechowski, M. Butterfly Monitoring Methods: The ideal and the Real World. Isr. J. Ecol. Evol. 2008, 54, 69–88. [Google Scholar] [CrossRef]
  11. Forister, M.L.; McCall, A.C.; Sanders, N.J.; Fordyce, J.A.; Thorne, J.H.; O’Brien, J.; Waetjen, D.P.; Shapiro, A.M. Com-pounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl. Acad. Sci. USA 2010, 107, 2088–2092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. González-Megías, A.; Menéndez, R.; Roy, D.; Brereton, T.O.M.; Thomas, C.D. Changes in the composition of British butterfly assemblages over two decades. Glob. Change Biol. 2008, 14, 1464–1474. [Google Scholar] [CrossRef]
  13. Breed, G.A.; Stichter, S.; Crone, E.E. Climate-driven changes in northeastern U.S. butterfly communities. Nat. Clim. Change 2012, 3, 142–145. [Google Scholar] [CrossRef]
  14. Checa, M.F.; Rodríguez, J.; Willmott, K.R.; Liger, B. Microclimate variability significantly affects the composition, abundance and phenology of butterfly communities in a highly threatened neotropical dry forest. Fla. Entomol. 2014, 97, 1–13. [Google Scholar] [CrossRef]
  15. Munyuli, M.B.T. Odore Butterfly Diversity from Farmlands of Central Uganda. Psyche A J. Èntomol. 2012, 2012, 1–23. [Google Scholar] [CrossRef] [Green Version]
  16. Xie, Z.; Wang, Y.; Cao, J.; Wang, J.; An, J. Ecological resilience of pollination in the face of pollinator decline: Content, mechanism and perspective. Biodivers. Sci. 2021, 29, 980–994. [Google Scholar] [CrossRef]
  17. Zhang, Z.; Cui, X.; Zhou, J.; Huang, C.; Deng, J.; Tang, X.; Luo, S. Pollination of the Orchid Habenaria rhodocheila by the Swallowtail Butterfly Papilio helenus in Subtropical Evergreen Broad-Leaved Forests in Southern China. Flora-Morphol. Distrib. Funct. Ecol. Plants 2021, 274, 151736. [Google Scholar] [CrossRef]
  18. Tzortzakaki, O.; Kati, V.; Panitsa, M.; Tzanatos, E.; Giokas, S. Butterfly diversity along the urbanization gradient in a densely-built Mediterranean city: Land cover is more decisive than resources in structuring communities. Landsc. Urban Plan. 2019, 183, 79–87. [Google Scholar] [CrossRef]
  19. Ramirez, N. Pollination specialization and time of pollination on a tropical Venezuelan plain: Variations in time and space. Bot. J. Linn. Soc. 2004, 145, 1–16. [Google Scholar] [CrossRef]
  20. Vodă, R.; Dapporto, L.; Dinca, V.E.; Vila, R. Cryptic matters: Overlooked species generate most butterfly beta-diversity. Ecography 2014, 38, 405–409. [Google Scholar] [CrossRef]
  21. Yue, M. The plant altitudinal spectrum of Qinling Mountains is complete and complex. For. Hum. 2015, 2, 76–81. [Google Scholar]
  22. Liu, X.; Zhang, Y.; Zhao, X.; He, X.; Cai, Q.; Zhu, Y.; He, B.; Jiu, Q. Introduction to the wildlife camera-trapping database of the middle Qinling Mountains. Biodivers. Sci. 2020, 28, 1075–1080. [Google Scholar] [CrossRef]
  23. Zhang, R.Z. Relict distribution of land vertebrates and Quaternary glaciation in China. Acta Zool. Sin. 2004, 50, 841–851. [Google Scholar]
  24. Kang, M.Y.; Zhu, Y. Discussion and analysis on the geo-ecological boundary in Qinling Range. Acta Ecol. Sin. 2007, 7, 118–128. [Google Scholar]
  25. Cao, F.; Wang, H.; Xia, G. Leafhopper (Hemiptera, Cicadellidae) community composition and diversity in different forest zones of Taibai Mountains. J. Northwest For. Univ. 2016, 6, 197–203. [Google Scholar]
  26. He, H.; Wei, C.; Liu, Y. The species diversity of ants in different habitats in Mt. Taibai. J. Northwest A F Univ. 2003, 31, 141–144. [Google Scholar]
  27. Yan, W.; Ji, S.; Shuai, L.; Zhao, L.; Zhu, D.; Zeng, Z.; Reserve, F.N.N. The spatial distribution patterns of mammal species diversity in Yangxian county on the southern slope of the Qinling Mountains. Biodivers. Sci. 2019, 27, 177–185. [Google Scholar] [CrossRef]
  28. Zhao, T.; Bai, H.; Deng, C.; Meng, Q.; Guo, S.; Qi, G. Topographic differentiation effect on vegetation cover in the Qinling Mountains from 2000 to 2016. Acta Ecol. Sin. 2019, 12, 4499–4509. [Google Scholar]
  29. Wu, H.; Xiao, N.N.; Lin, T.T. Relationships between functional diversity and species diversity of pine-oak mixed forest in Qinling Mountains and their environmental explanations. Ecol. Environ. Sci. 2020, 6, 1090–1100. [Google Scholar]
  30. Zhang, M.X.; Wang, D.X.; Peng, S.L.; Huang, Y.K.; Zhang, G.G. Community stability analysis for the oak-pine mixed forest in Qinling Mountains. Acta Ecol. Sin. 2015, 35, 2564–2573. [Google Scholar]
  31. Hodkinson, I.D. Terrestrial insects along elevation gradients: Species and community responses to altitude. Biol. Rev. 2005, 80, 489–513. [Google Scholar] [CrossRef] [Green Version]
  32. Chazot, N.; Willmott, K.R.; Endara, P.G.S.; Toporov, A.; Hill, R.I.; Jiggins, C.D.; Elias, M. Mutualistic mimicry and filtering by altitude shape the structure of Andean butterfly communities. Am. Nat. 2014, 183, 26–39. [Google Scholar] [CrossRef]
  33. Gallou, A.; Baillet, Y.; Ficetola, G.F.; Després, L. Elevational gradient and human effects on butterfly species richness in the French Alps. Ecol. Evol. 2017, 7, 3672–3681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Homeier, J.; Breckle, S.-W.; Günter, S.; Rollenbeck, R.T.; Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica 2010, 42, 140–148. [Google Scholar] [CrossRef]
  35. Rahbek, C. The role of spatial scale and the perception of large-scale species-richness patterns. Ecol. Lett. 2004, 8, 224–239. [Google Scholar] [CrossRef]
  36. Molina-Martínez, A.; León-Cortés, J.L.; Regan, H.M. Climatic and geometric constraints as driving factors of butterfly species richness along a Neotropical elevational gradient. J. Insect Conserv. 2013, 17, 1169–1180. [Google Scholar] [CrossRef]
  37. Pyrcz, T.W.; Janusz, W.; Raf, A.G. Diversity and distribution patterns of Pronophilina butterflies (Lepidoptera: Nymphalidae: Satyrinae) along an altitudinal transect in north-western Ecuador. Neotrop. Entomol. 2009, 38, 716–726. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Lourenço, G.M.; Luna, P.; Guevara, R.; Dáttilo, W.; Freitas, A.V.L.; Ribeiro, S.P. Temporal shifts in butterfly diversity: Responses to natural and anthropic forest transitions. J. Insect Conserv. 2020, 24, 353–363. [Google Scholar] [CrossRef]
  39. Zhong, Y.Z.; Li, K.H. A primary study on the climatic boundary effect of the join zone between Qinling Mountain and Huanghuai Plain. Geogr. Res. 1996, 4, 66–73. [Google Scholar]
  40. Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 1977, 12, 115–134. [Google Scholar] [CrossRef]
  41. Morris, M.G.; Pollard, E.; Yates, T.J. Monitoring Butterflies for Ecology and Conservation. J. Appl. Ecol. 1995, 32, 673. [Google Scholar] [CrossRef]
  42. Gall, L.F. Measuring the size of Lepidopteran populations. J. Res. Lepid 1985, 24, 97–116. [Google Scholar]
  43. Basset, Y.; Eastwood, R.O.D.; Sam, L.; Lohman, D.J.; Novotny, V.; Treuer, T.I.M.; Miller, S.E.; Weiblen, G.D.; Pierce, N.E.; Bunyavejchewin, S.; et al. Cross-continental comparisons of butterfly assemblages in tropical rainforests: Implications for biological monitoring. Insect Conserv. Divers. 2013, 6, 223–233. [Google Scholar] [CrossRef]
  44. Ma, F.Z.; Xu, H.G.; Chen, M.M.; Tong, W.J.; Wang, C.B.; Cai, L. Progress in construction of China Butterfly Diversity Observation Network (China BON-Butterflies). J. Ecol. Rural. Environ. 2018, 34, 27–36. [Google Scholar]
  45. Chou, I. Monographia Rhopalocerorum Sinensium; Henan Science and Technology Publishing House: Zhengzhou, China, 1994; pp. 12–453. [Google Scholar]
  46. Chou, I. Classification and Identification of Chinese Butterflies; Henan Science and Technology Publishing House: Zhengzhou, China, 1998; pp. 27–487. [Google Scholar]
  47. Wu, C.S.; Xu, Y.F. Butterflies of China; The Straits Publishing House: Fuzhou, China, 2017; pp. 5–2036. [Google Scholar]
  48. National Animal Collection Resource Center. Available online: http://museum.ioz.ac.cn/ (accessed on 9 June 2021).
  49. China Teaching Specimen Standardized Integration and Resource Sharing Platform. Available online: http://mnh.scu.edu.cn/ (accessed on 15 July 2021).
  50. Roberts, J.J.; Best, B.D.; Dunn, D.C.; Treml, E.A.; Halpin, P.N. Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environ. Model. Softw. 2010, 25, 1197–1207. [Google Scholar] [CrossRef]
  51. Hill, M.O. Diversity and Evenness: A Unifying Notation and Its Consequences. Ecol. 1973, 54, 427–432. [Google Scholar] [CrossRef] [Green Version]
  52. Chao, A.; Chiu, C.-H.; Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annu. Rev. Ecol. Evol. Syst. 2014, 45, 297–324. [Google Scholar] [CrossRef] [Green Version]
  53. Hsieh, T.C.; Ma, K.H.; Chao, A.; McInerny, G. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 2016, 7, 1451–1456. [Google Scholar] [CrossRef]
  54. Chao, A.; Gotelli, N.J.; Hsieh, T.C.; Sander, E.L.; Ma, K.H.; Colwell, R.K.; Ellison, A.M. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 2014, 84, 45–67. [Google Scholar] [CrossRef] [Green Version]
  55. Chao, A.; Kubota, Y.; Zelený, D.; Chiu, C.; Li, C.; Kusumoto, B.; Yasuhara, M.; Thorn, S.; Wei, C.; Costello, M.J.; et al. Quantifying sample completeness and comparing diversities among assemblages. Ecol. Res. 2020, 35, 292–314. [Google Scholar] [CrossRef]
  56. Chai, Z.; Wang, D. A comparison of species composition and community assemblage of secondary forests between the birch and pine-oak belts in the mid-altitude zone of the Qinling Mountains, China. PeerJ 2016, 4, e1900. [Google Scholar] [CrossRef] [Green Version]
  57. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R. Vegan: Community Ecology Package, R Package Version 2.5–7; 2019. Available online: http://vegan.r-forge.r-project.org/ (accessed on 5 May 2021).
  58. Noval Rivas, M.; Burton, O.T.; Wise, P.; Zhang, Y.Q.; Hobson, S.A.; Garcia Lloret, M.; Chehoud, C.; Kuczynski, J.; De-Santis, T.; Warrington, J.; et al. A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis. J. Allergy Clin. Immunol. 2013, 131, 201–212. [Google Scholar] [CrossRef]
  59. Prentice, I.C.; van der Maarel, E. Theory and Models in Vegetation Science; Springer: Heidelberg, Germany, 1987; Volume 69, pp. 57–68. [Google Scholar]
  60. Minchin, P.R. An evaluation of the relative robustness of techniques for ecological ordination. Plant Ecol. 1987, 69, 89–107. [Google Scholar] [CrossRef]
  61. Clarke, K.R. Non-parametric multivariate analysis of changes in community structure. Aust. J. Ecol. 1993, 18, 117–143. [Google Scholar] [CrossRef]
  62. Warton, D.I.; Wright, S.T.; Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 2012, 3, 89–101. [Google Scholar] [CrossRef]
  63. IUCN. The IUCN Red List of Threatened Species. Version 2019-3. 2019. Available online: http://www.iucnredlist.org/ (accessed on 9 October 2019).
  64. Wang, S.; Xie, Y. China Species Red List. Vol. III, Invertebrates; Higher Education Press: Beijing, China, 2005; pp. 51–850. [Google Scholar]
  65. Deng, C.H.; Bai, H.Y.; Ma, X.Y.; Zhao, T. Variation characteristics and its north-south differences of the vegetation phenology by remote sensing monitoring in the Qinling Mountains during 2000–2017. Acta Ecol. 2021, 41, 1068–1080. [Google Scholar]
  66. Yan, J.P. Comparison of Environmental Response between North and South of Qinling Mountains; Chinese Science Press: Beijing, China, 2006; p. 112. [Google Scholar]
  67. Hamer, K.C.; Hill, J.K.; Benedick, S.; Mustaffa, N.; Chey, V.K.; Maryati, M. Diversity and ecology of carrion- and fruit-feeding butterflies in Bornean rain forest. J. Trop. Ecol. 2006, 22, 25–33. [Google Scholar] [CrossRef] [Green Version]
  68. Murakami, M.; Ichie, T.; Hirao, T. Beta-diversity of lepidopteran larval communities in a Japanese temperate forest: Effects of phenology and tree species. Ecol. Res. 2008, 23, 179–187. [Google Scholar] [CrossRef]
  69. Fang, L.; Zhang, Y.; Xing, X. Butterfly community structure and diversity in Qinling National Botanical Garden, China. Biodivers. Sci. 2020, 28, 965–972. [Google Scholar] [CrossRef]
  70. Yi, L.; Dong, Y.; Miao, B.; Peng, Y. Diversity of butterfly communities in Gaoligong region of Yunnan. Biodivers. Sci. 2021, 29, 950–959. [Google Scholar] [CrossRef]
Diversity 14 00027 g001 550
Figure 1. The geographical position and topological character (A) and habitat type map of the ecological study region of the middle Qinling Mountains (B). The counties on the right are Zhouzhi County (ZZ), Huyi District (HY), Meixian County (MX), Taibai County (TB), Qishan County (QS), Chencang District (CC) and Weibin District (WB), Liuba County (LB), Chenggu County (CG), Foping County (FP), Ningshan County (NS) and Shiquan County (SQ).
Figure 1. The geographical position and topological character (A) and habitat type map of the ecological study region of the middle Qinling Mountains (B). The counties on the right are Zhouzhi County (ZZ), Huyi District (HY), Meixian County (MX), Taibai County (TB), Qishan County (QS), Chencang District (CC) and Weibin District (WB), Liuba County (LB), Chenggu County (CG), Foping County (FP), Ningshan County (NS) and Shiquan County (SQ).
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Figure 2. Spatial distribution of species richness in 12 ecoregions. The pie charts represent the proportions of 5 butterfly families. The counties are Zhouzhi County (ZZ), Huyi District (HY), Meixian County (MX), Taibai County (TB), Qishan County (QS), Chencang District (CC) and Weibin District (WB), Liuba County (LB), Chenggu County (CG), Foping County (FP), Ningshan County (NS) and Shiquan County (SQ).
Figure 2. Spatial distribution of species richness in 12 ecoregions. The pie charts represent the proportions of 5 butterfly families. The counties are Zhouzhi County (ZZ), Huyi District (HY), Meixian County (MX), Taibai County (TB), Qishan County (QS), Chencang District (CC) and Weibin District (WB), Liuba County (LB), Chenggu County (CG), Foping County (FP), Ningshan County (NS) and Shiquan County (SQ).
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Figure 3. Butterfly diversity in the middle Qinling Mountains study sites. (A,D,G) Sample size-based rarefaction and extrapolation (dotted line segments) sampling curves for species richness (q = 0) for butterfly data from different altitudes, season and habitat types. (B,E,H) Nonmetric multidimensional scaling (NMDS) ordination of sites within different altitudes, seasons and habitat types based on taxonomic beta diversity. (C,F,I) Analysis of similarities (ANOSIM) in different altitude, season and habitat types. The border represents the interquartile range (IQR), the horizontal line represents the median value and the upper and lower tentacles represent 1.5 times the IQR range beyond the upper and lower quartile. “Between” represents differences between groups and the other three boxplots represent intra-group differences.
Figure 3. Butterfly diversity in the middle Qinling Mountains study sites. (A,D,G) Sample size-based rarefaction and extrapolation (dotted line segments) sampling curves for species richness (q = 0) for butterfly data from different altitudes, season and habitat types. (B,E,H) Nonmetric multidimensional scaling (NMDS) ordination of sites within different altitudes, seasons and habitat types based on taxonomic beta diversity. (C,F,I) Analysis of similarities (ANOSIM) in different altitude, season and habitat types. The border represents the interquartile range (IQR), the horizontal line represents the median value and the upper and lower tentacles represent 1.5 times the IQR range beyond the upper and lower quartile. “Between” represents differences between groups and the other three boxplots represent intra-group differences.
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Table
Table 1. Diversity indices of butterfly communities in the middle Qinling Mountains (mean ± SE).
Table 1. Diversity indices of butterfly communities in the middle Qinling Mountains (mean ± SE).
FamilyNo. of
Genera		No. of
Individuals		Species
Richness		Shannon
Diversity		Simpson
Diversity
Pieridae10266058 ± 13.8110.80 ± 0.30 5.71 ± 0.16
Papilionidae 1183336 ± 3.65 10.90 ± 0.43 7.49 ± 0.27
Lycaenidae491412126 ± 18.22 20.90 ± 0.89 9.93 ± 0.41
Nymphalidae744263238 ± 21.5179.44 ± 1.70 38.09 ± 1.31
Hesperiidae 3145895 ± 20.95 39.54 ± 2.38 27.43 ± 1.61
Table
Table 2. Butterfly diversity communities at different altitudes and in different seasons and habitats in the middle Qinling Mountains. Species richness and Shannon and Simpson diversity indices were estimated based on Hill numbers (q = 0, q = 1 and q = 2, respectively; mean ± SE).
Table 2. Butterfly diversity communities at different altitudes and in different seasons and habitats in the middle Qinling Mountains. Species richness and Shannon and Simpson diversity indices were estimated based on Hill numbers (q = 0, q = 1 and q = 2, respectively; mean ± SE).
Factors No. of IndividualsSpecies RichnessShannon DiversitySimpson Diversity
Altitude<1000 m2565261 ± 19.3645.27 ±1.5014.39 ± 0.71
1000–2000 m5516424 ± 25.57110.50 ± 2.3650.53 ± 1.29
>2000 m74101 ± 34.5166.98 ± 11.3644.27 ± 8.34
SeasonSummer6591466 ± 28.43133.18 ± 2.3663.43 ± 1.52
Spring2087251 ± 17.1437.98 ± 1.6411.04 ± 0.52
Autumn928140 ± 21.6938.71 ± 1.7522.36 ± 1.03
HabitatReserve4151475 ± 33.74144.45 ± 3.5058.06 ± 2.06
Ecotone2634324 ± 19.5785.92 ± 2.5436.14 ± 1.60
Farm2841280 ± 20.3062.70 ± 1.9825.62 ± 1.16
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Ren, J.; Li, S.; He, M.; Zhang, Y. Butterfly Community Diversity in the Qinling Mountains. Diversity 2022, 14, 27. https://doi.org/10.3390/d14010027

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Ren J, Li S, He M, Zhang Y. Butterfly Community Diversity in the Qinling Mountains. Diversity. 2022; 14(1):27. https://doi.org/10.3390/d14010027

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Ren, Jinze, Shuying Li, Mengdi He, and Yalin Zhang. 2022. "Butterfly Community Diversity in the Qinling Mountains" Diversity 14, no. 1: 27. https://doi.org/10.3390/d14010027

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Ren, J., Li, S., He, M., & Zhang, Y. (2022). Butterfly Community Diversity in the Qinling Mountains. Diversity, 14(1), 27. https://doi.org/10.3390/d14010027

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