Journal Search Engine

to

Search Advanced Search Adode Reader(link)
Download PDF Export Citation Korean Bibliography
ISSN : 1226-9999(Print)
ISSN : 2287-7851(Online)
Korean J. Environ. Biol. Vol.43 No.3 pp.266-274
DOI : https://doi.org/10.11626/KJEB.2025.43.3.266

Influence of land use on butterfly diversity in riparian ecosystems: A comparison of agricultural and non-agricultural areas

Seo-Yun Jo, Sei-Woong Choi*
Department of Environmental Education, Mokpo National University, Muan 58554, Republic of Korea
*Corresponding author Sei-Woong Choi Tel. 061-450-2783 E-mail. choisw@mokpo.ac.kr

Contribution to Environmental Biology


▪ Despite their ecological significance, empirical evaluations of butterfly communities along land-use gradients in riparian environments are limited.


▪ This research demonstrates that non-agricultural landscapes, characterized by less human interference, support greater butterfly diversity compared to agricultural zones.


▪ The results underscore the importance of incorporating metrics such as species dominance and evenness into biodiversity assessments and conservation strategies.


25/07/2025 24/08/2025 29/08/2025

Abstract


This study investigated butterfly communities in riparian zones adjacent to dams, comparing agricultural (AG) and non-agricultural (NAG) areas. From April to October 2024, biweekly butterfly surveys were conducted using a standardized line transect method across three dam regions in Jeollanam-do, South Korea. NAG sites consistently showed higher species richness and total abundance than AG sites, although these differences were not statistically significant. Extrapolated diversity estimates indicated variations between the two areas, except for species richness. Seasonal patterns of species richness and abundance differed between land-use types. Rank-abundance distribution analysis revealed that NAG areas had more even species distributions, best described by the Log-normal model, while AG areas exhibited steep dominance patterns consistent with the Zipf-Mandelbrot model. The five most dominant species comprised 73.4% of individuals in AG sites, compared to just 51.4% in NAG sites. NMDS ordination illustrated distinct community clustering between NAG and AG sites, though PERMANOVA and MRPP did not identify statistically significant group differences. These findings suggest that reduced disturbance and greater habitat heterogeneity in NAG areas promote more resilient and balanced butterfly communities. The study emphasizes the ecological importance of natural riparian zones and highlights the potential of butterfly assemblages as indicators for biodiversity monitoring and conservation planning.


butterfly , riparian , land-use , dominance , evenness

초록

    1. INTRODUCTION

    The rapid decline in biodiversity has emerged as a critical environmental issue worldwide, with habitat alteration caused by human activities exerting profound impacts on the diversity and stability of biological communities (Chazdon 2003;McKinney 2008). Insect communities, including butterflies, have experienced considerable reductions in diversity and abundance, particularly in North American and Europe, where urban expansion and agricultural intensification have transformed natural landscapes (Forister et al. 2010;Fox et al. 2015).

    Among the key drivers, agricultural intensification operates at both local and landscape scales through pesticide application, monoculture practices and simplification of landscape structure which collectively reduce habitat heterogeneity and fragment ecological networks (Tscharntke et al. 2005;Hendrickx et al. 2007). These changes diminish critical resources for insect, leading to altered community composition and reduced ecological stability (Billeter et al. 2008).

    Butterflies are widely used as bioindicators due to their host plant specificity, sensitivity to environmental changes and short life cycles (Thomas 2005;Bonebrake et al. 2010). Prior studies have shown that butterfly diversity is positively associated with natural land cover and negatively affected by agricultural intensity, emphasizing the importance of land use patterns at both local and landscape scales (Öckinger and Smith 2006). In addition, butterflies use riparian habitats for their resources such as nectar and larval host plants (An and Choi 2021).

    This study aimed to compare diversity and structure of butterfly community between non-agricultural (NAG hereafter) and agricultural (AG hereafter) areas within riparian zones of South Korea. Riparian ecosystems can be sensitive to anthropogenic pressures, as land use intensity often increases along river systems, with upstream areas typically retaining more natural vegetation and downstream areas more frequently converted to agricultural land (Allan 2004;Potts et al. 2010;Schürings et al. 2023). While this general gradient is well recognized, the present study examined butterfly communities between NAG and AG area established within riparian zones to examine how land use type along the riparian zones influences diversity and community structure. We hypothesized that the sites within NAG areas, due to their higher habitat heterogeneity and lower disturbance, would support higher species richness, whereas the sites within AG area would be dominated by a few disturbance-tolerant species. To test this, we conducted a field-based comparison across contrasting habitat types and analyzed the resulting butterfly community patterns.

    2. MATERIALS AND METHODS

    2.1. Study sites and survey methods

    Between April and October 2024, butterfly surveys were carried out every two weeks at six locations surrounding three major dams in southwestern Korea- Jangseong, Naju, and Dongbok-situated in Jeollanamdo. Each dam area included two survey sites: one AG and one NAG, totaling six sites (Table 1). AG sites were defined as transects with ongoing agricultural activity either within or directly adjacent to the area. The crops grown included rice and persimmon orchards in Jangseong, rice, garlic, and green onions in Naju, and rice in Dongbok. In contrast, NAG sites consisted of secondary forest or abandoned farmland, located approximately 500 meters from the nearest cropland in Jangseong, and about 450 meters away in both Naju and Dongbok. The distance between AG and NAG sites within each dam region ranged from 4 to 5 kilometers.

    At each survey site, a line transect with approximately 1.1 to 1.3 kilometers in length was selected. During the survey, we walked along a fixed route for 30 minutes, recording all butterflies detected within a 5-meter radius in all directions-left, right, and above, and surveys were conducted only under favorable weather conditions. To ensure consistency, both AG and NAG sites within the same region were surveyed on the same day and under identical weather conditions. A total of 78 butterfly surveys were conducted across the study area: 28 in Jangseong, 26 in Naju, and 24 in Dongbok. The majority of butterfly species were identified visually, referencing the available literature (Joo et al. 2021). For species that were challenging to identify-particularly small or morphologically similar taxa such as those in the Lycaenidae family-individuals were captured for closer examination and subsequently released after identification.

    2.2. Data analysis

    To compare the species richness (number of species) and abundance (number of individuals) of butterfly communities between AG and NAG in riparian zones, survey data from April to October 2024 were pooled by habitat type (NAG vs. AG). These three paired values (one pair per region) were then used as replicates for the Wilcoxon signed-rank test to examine differences in richness and abundance between habitat types. We also calculated three levels of Hill numbers using iNEXT: q=0 represents species richness where all species are weighted equally, q=1 corresponds to the Shannon diversity index which emphasizes common species, and q=2 corresponds to the Simpson diversity index which gives more weight to dominant species (Chao et al. 2014).

    To assess seasonal variation, butterfly data were aggregated on a monthly basis from April through October. For each month, species richness and abundance were calculated and subsequently compared between the two habitat types, AG and NAG using Wilcoxon signed-rank test.

    To characterize the rank-abundance distribution (RAD) of butterfly species in each habitat type, five RAD models-Broken-stick, Preemption (Geometric), Log-normal, Zipf and Zipf-Mandelbrot-were fitted to the observed abundance data using the radfit function from the vegan package (Oksanen et al. 2019) in R (ver.4.4.2; R Core Team 2024). Model performance was evaluated based on Akaike Information Criterion (AIC), with lower values indicating better fit.

    We compared the relative dominance of the five most abundant species across all survey sites. To identify dominant species, we first pooled data from all locations and selected the top five species based on total abundance. These species were then statistically compared between NAG and AG using Chi-squared tests. This method was chosen to maintain consistency in assessing dominance patterns across regions, acknowledging that locally dominant species may vary by site. It also aimed to highlight the primary contributors to each community and reveal differences in species dominance between land-use types.

    Community composition analysis was performed using a species-by-site abundance matrix. A Non-metric Multidimensional Scaling (NMDS) analysis was conducted using the metaMDS function in the vegan package (Oksanen et al. 2019) in R (ver.4.4.2; R Core Team 2024). To test statistical significance in community dissimilarity between habitat types, permutational multivariate analysis of variance (PERMANOVA) and multiresponse permutation procedure (MRPP) were also conducted.

    3. RESULTS

    A total of 1,490 individuals from 34 butterfly species were recorded across all survey site. A total of 31 species (837 individuals; 56.2%) were recorded in NAG and 27 species (653 individuals; 43.8%) in AG. Although richness and abundance were higher in NAG, statistical analysis revealed no significant differences between these two habitats (Wilcoxon signed-rank tests: species richness, V=6, p=0.17; and abundance, V=3, p=1.0) (Fig. 1).

    Rarefaction and extrapolation analyses based on sample size (standardized to the maximum of 1,306 individuals) showed that the estimated species richness (q=0) was 32.4 species (95% confidence interval (CI): 29.31- 35.49) in NAG and AG 30.44 species (95% CI: 24.4- 36.48) in AG. The Shannon diversity (q=1) was 16.77 (15.32-18.22) in NAG and 9.72 (8.86-10.58) in AG and the Simpson diversity (q=2) was 11.17 (9.88-12.46) in NAG and 5.83 (5.19-6.46) in AG (Fig. 2). Both Shannon and Simpson diversity indices were significantly higher in NAG areas compared to AG areas.

    Monthly variations in species richness and abundance revealed contrasting seasonal patterns between NAG and AG (Fig. 3). In NAG, species richness peaked in May, followed by a steady decline through the summer months, tapering off toward October. In contrast, AG exhibited a bimodal richness pattern, with distinct peaks in April and August. Abundance trends also diverged between the two land-use types: NAG showed two pronounced peaks in May and September, while AG displayed a single peak in July.

    The rank-abundance plots showed a distinct difference in community structure between NAG and AG areas (Fig. 4). In NAG, the species abundance declined more gradually with rank. In contrast, AG exhibited a steep curve, reflecting strong dominance by a few species. The results of model fitting showed that the Lognormal model best explained the species distribution in NAG areas (AIC=159.03), whereas the Zipf-Mandelbrot models provided the best fit for AG areas (AIC= 136.47) (Table 2).

    To assess differences in the composition of dominant species, the top five most abundant species were compared between two areas: Pieris rapae (284 individuals, 19.1%), Zizeeria maha (254, 17%), Eurema mandarina (206, 13.8%), Colias erate (101, 6.8%) and Hestina persimilis (64, 4.3%). These five species accounted for 51.4 % in NAG and 73.4% in AG. Comparison of four dominant butterfly species in two areas showed statistically significant differences P. rapae (NAG: 59, AG: 225; χ2=50.96, p<0.0001), Z. maha (NAG: 173, AG: 81; χ2= 18.49, p<0.0001), C. erate (NAG: 27, AG: 74; χ2= 18.63, p<0.0001) and H. persimilis (NAG: 51, AG: 13; χ2=13.92, p=0.0002). E. mandarina (NAG: 120, AG: 86) showed a slight difference but was not statistically significant (χ2=0.33, p=0.57) (Table 3). When all five dominant species were grouped together and compared against the remaining species, a significant difference in abundance by areas was observed (χ2=73.57, p< 0.0001) (Table 3).

    Non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarity indicated a clear separation between NAG and AG communities (stress=2×10-10, Fig. 5). However, statistical testing using PERMANOVA (F=2.4, p=0.1, R2=0.38) and MRPP (δ=0.4528, A= 0.1205, p=0.1) showed no significant differences between the two groups.

    4. DISCUSSION

    This study compared butterfly communities within riparian zones, focusing on differences between nonagricultural (NAG) and agricultural (AG) areas. Although NAG showed higher species richness compared to AG, these differences were not statistically significant when tested using the Wilcoxon signed-rank test. However, Shannon (q=1) and Simpson (q=2) diversity indices in iNEXT-based extrapolation analyses were significantly higher in NAG compared to AG. This indicates that butterfly individuals in NAG were distributed more evenly among species, contribution to higher Shannon and Simpson diversity values. In contrast, AG sites were dominated by a few disturbancetolerant species, which reduced evenness and thereby lowered diversity indices despite similar levels of species richness (Magurran 1988;Hillebrand et al. 2008;MacDonald et al. 2017).

    Monthly occurrence patterns also differed between NAG and AG. NAG showed clear seasonal fluctuations with richness and abundance peaking in May and September, whereas AG maintained relatively stable but lower values with a single peak in July. These trends suggest that diverse and continuous floral and host resources in NAG support multiple generations and stable populations, while agricultural disturbance creates temporally simplified habitats (Potts et al. 2010;Fox et al. 2014). Similar findings have shown reduced survival and diversity under resource-limited agricultural landscapes (Lebeau et al. 2016;Wix et al. 2019). Although this study was limited to a single year, the observed patterns support the hypothesis that NAG provides more stable seasonal habitats than AG.

    To further investigate differences in community structure, rank-abundance distribution (RAD) models were applied. In AG areas, the Zipf-Mandelbrot model showed the best fit, indicating high dominance by a few species and lower evenness. In contrast, the Lognormal model best described the NAG community, reflecting a more balanced and equitable distribution of species. These results were consistent with the RAD curves, in which AG displayed a steep slope, whereas NAG showed a more gradual decline. Such patterns quantitatively demonstrate how anthropogenic disturbances lead to ecological monopolization and reduced community evenness (Harpole and Tilman 2007;Hillerbrand et al. 2007, 2008). This study found no significant difference in species richness between NAG and AG. However, species dominance patterns differed, with a higher proportion of dominant species observed in AG. It is increasingly recognized that species richness alone is insufficient to explain ecosystem structure and function. Particularly in human-modified ecosystems, dominance by a few species can reduce ecological resilience and increase vulnerability.

    Analysis of dominant species composition further highlighted these trends. In AG, disturbance-tolerant species such as Pieris rapae and Colias erate were highly dominant, and the top five species accounted for 73.4% of all individuals. P. rapae primarily exploits cruciferous crops (e.g., cabbage, radish, rapeseed), whereas C. erate depends on leguminous crops, which are widely cultivated in agricultural landscapes. These host-plant associations explain their frequent occurrence in farmlands and adjacent habitats, and support their classification as representative indicator taxa of agricultural ecosystems (Kim et al. 2021). Similarly, a nationwide study of agroecosystems also identified P. rapae and C. erate as the major dominant species, highlighting that agricultural landscapes tend to simplify community structures by favoring a few disturbance-tolerant species (Lee and Choi 2023). In contrast, NAG showed a more even distribution of diverse species including Zizeeria maha and Hestina persimilis, with the top five species comprising only 51.4%. Goulson et al. (2005) reported dominance by a few species in resource-poor, disturbed habitats, and Fleishman et al. (1999) noted lower evenness and higher dominance of widespread species in agricultural landscapes. Comparison of dominant species, and a pooled comparison of all five also showed a significant difference between two areas, indicating that NAG maintains a more balanced and diverse community, whereas AG is prone to ecological monopolization by a few species. The variation in dominance patterns directly relates to ecological stability and suggests that analyzing species dominance provides valuable insights into disturbance effects on insect communities. An and Choi (2021) observed that butterfly communities in riparian zones with greater proportions of grasslands and forested areas exhibited higher diversity and a greater abundance of specialist species. This suggests that both local habitat complexity and surrounding landscape features contribute to maintaining ecologically balanced insect communities. The relatively higher proportion of “other species” in NAG implies that factors such as host plant diversity, heterogeneous vegetation and limited human disturbance collectively support higher ecological resilience in NAG.

    NMDS visualization further supported this pattern, showing more cohesive clustering among NAG sites and a dispersed pattern in AG. This supports that habitat naturalness contributes to community stability, while anthropogenic disturbance increases variability-consistent (Öckinger and Smith 2006). Although PERMANOVA and MRPP results were not statistically significant, likely due to the small number of sites and short duration of the study, the observed trend toward community separation suggests a potential ecological difference (Anderson 2001).

    5. CONCLUSION

    Overall, the study demonstrated that human disturbances in agricultural landscapes may influence butterfly community composition and diversity. NAG supported more even species distributions and lower dominance, indicating a more stable ecological structure. These results emphasize the critical role of habitat naturalness in sustaining balanced and resilient biological communities. Moreover, community-based assessments using indicator species like butterflies can serve as practical tools in developing regional biodiversity conservation strategies.

    However, the study faced limitations due to a small number of survey sites which weakened statistical power. Additionally, the effects of factors such as pesticide use, host plant distribution and vegetation diversity were not quantitatively assessed, limiting inference about causal drivers of community structures. Future research should integration of quantitative disturbance metrics and species-specific ecological analyses. In particular, modeling the relationship between land-use intensity and butterfly community responses could provide a robust foundation for biodiversity conservation planning.

    This highlights the need for conservation and management strategies to mitigate the negative impacts of agricultural disturbances on butterfly biodiversity in riparian areas. In agricultural landscapes, it is essential to ensure the continuous availability of nectar sources and host plants, reduce excessive use of pesticides and herbicides, and limit crop monocultures to maintain heterogeneous vegetation structures. Additionally, establishing buffer zones adjacent to farmland that resemble surrounding natural habitats could enhance habitat connectivity across both agricultural and non-agricultural areas. Integrating these conservation and management strategies into agricultural policies and land-use intensity planning in riparian ecosystem is expected to contribute to the preservation of biodiversity and the resilience of this ecosystem.

    ACKNOWLEDGEMENTS

    This study was supported by a research grant from the National Research Foundation of Korea (RS-2023- 00272745) and the Rural Development Administration, funded by the Korean government (MAFRA) (RS- 2024-00397542).

    CRediT authorship contribution statement

    SY Jo: Investigation, Methodology, Data analysis, Writing-Original draft. SW Choi: Conceptualization, Methodology, Data analysis, Writing-Original draft, Supervision, Funding acquisition.

    Declaration of Competing Interest

    The authors declare no conflicts of interest

    Figure

    KJEB-43-3-266_F1.jpg

    Comparison of (A) species richness and (B) abundance between non-agricultural areas (NAG, blue) and agricultural areas (AG, red). Results are based on the Wilcoxon signed-rank test: species richness, V=6, p=0.174; abundance, V=3, p=1.0. Data were collected from three dams in southern South Korea. n.s. Not significant.

    KJEB-43-3-266_F2.jpg

    Rarefaction and extrapolation curves for butterfly diversity in non-agricultural (NAG) and agricultural (AG) areas based on sample size. Diversity estimates are shown for (A) species richness (q=0), (B) Shannon diversity (q=1), and (C) Simpson diversity (q=2). Solid lines represent rarefied values, while dashed lines represent extrapolated values. Shaded areas indicate 95% confidence intervals.

    KJEB-43-3-266_F3.jpg

    Monthly variation in (A) species richness and (B) abundance of butterflies in agricultural (AG, red) and non-agricultural (NAG, blue) areas from April to October 2024. Values are presented as mean±standard deviation (SD).

    KJEB-43-3-266_F4.jpg

    Rank-abundance plot of butterfly communities in non - agricultural (NAG, blue) and agricultural (AG, red) areas. Species are ranked in descending order of abundance.

    KJEB-43-3-266_F5.jpg

    NMDS ordination of butterfly communities based on Bray- Curtis distance. Points represent different survey sites, with colors indicating non -agricultural (NAG, blue) and agricultural (AG, red) areas. JS, NJ, and HS refer to the regions of Jangseong (JS), Naju (NJ), and Dongbok (Hwasun, HS), respectively. Connecting lines indicate community similarity within the same area.

    Table

    Geographic information for each survey site from three dams in southern South Korea, including transect length

    NAG refers to non-agricultural area, and AG refers to agricultural area.

    Akaike Information Criterion (AIC) values for five rankabundance distribution (RAD) models applied to butterfly communities in non -agricultural (NAG) and agricultural (AG) areas

    Lower AIC values indicate a better model fit. The best-fitting model for each area is highlighted in bold.

    Chi-squared test results comparing the abundance of the top five dominant butterfly species between non-agricultural (NAG) and agricultural (AG) areas

    Reference

    1. Allan JD. 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 35:257-284.
    2. An JS and SW Choi. 2021. Butterflies as an indicator group of riparian ecosystem assessment. J. Asia -Pac. Entomol. 24: 195-200.
    3. Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26:32-46.
    4. Billeter R, J liira, D Bailey, R Bugter, P Arens, I Augenstein, … and PJ Edwards. 2008. Indicators for biodiversity in agricultural landscapes: a pan - European study. J. Appl. Ecol. 45:141- 150.
    5. Bonebrake TC, LC Ponisio, Cl Boggs and PREhrlich. 2010. More than just indicators: A review of tropical butterfly ecology and conservation. Biol. Conserv. 143:1381-1841.
    6. Chao A, NJ Gotelli, TC Hsieh, EL Sander, KH Ma, RK Colwell and AM Ellison. 2014. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84:45-67.
    7. Chazdon RL. 2003. Tropical forest recovery: Legacies of human impact and natural disturbances. Perspect. Plant Ecol. Evol. Syst. 6:51-71.
    8. Fleishman E, GT Austin, PF Brussard and DD Murphy. 1999. A comparison of butterfly communities in native and agricultural riparian habitats in the Great Basin, USA. Biol. Conserv. 89:209-218.
    9. Forister ML, AC McCall, NJ Sanders, JA Fordyce, JH Thorne, J O’Brien, DP Waetjen and AM Shapiro. 2010. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl. Acad. Sci. U. S. A. 107:2088- 2092.
    10. Fox R, TH Oliver, C Harrower, MS Parsons, CD Thomas and DB Roy. 2014. Long-term changes to the frequency of occurrence of British moths are consistent with opposing and synergistic effects of climate and land-use changes. J. Appl. Ecol. 51:949-957.
    11. Fox R, TM Brereton, J Asher, Ta August, MS Botham, NAD Bourn, … and DB Roy. 2015. The State of the UK’s Butterflies 2015. Butterfly Conservation and Centre for Ecology & Hydrology. Dorset, United Kingdom.
    12. Goulson D, ME Hanley, B Darvill, JS Ellis and ME Knight. 2005. Causes of rarity in bumblebees. Biol. Conserv. 122:1-8.
    13. Harpole WS and D Tilman. 2007. Grassland species loss resulting from reduced niche dimension. Nature 446:791-793.
    14. Hendrickx F, JP Maelfait, W van Wingerden, O Schweiger, M Speelmans, S Aviron, … and ROB Bugter. 2007. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44:340-351.
    15. Hillebrand H, DM Bennett and MW Cadotte. 2008. Consequences of dominance: A review of evenness effects on local and regional ecosystem process. Ecology 89:1510-1520.
    16. Hillebrand H, DS Gruner, ET Borer, MES Bracken, EE Cleland, JJ Elser, WS harpole, JT Ngai, EW Seabloo, JB Shurin and JE Smith. 2007. Consumer versus resource control of producer diversity depends on ecosystem type and producer community structure. Proc. Natl. Acad. Sci. U. S. A. 104:10904- 10909.
    17. Joo HJ, SS Kim, HC Kim, JD Son, YJ Lee and JS Joo. 2021. Butterflies of the Korean Peninsula: Classification and Ecology of 279 Species with English Descriptions. GeoBook. Paju, Korea.
    18. Kim MH, SG Choi, SW Choi, CH Jung, YJ Oh, MK Kim and JH Bang. 2021. Selection of 30 indicator species for agricultural ecosystems under climate change using the Analytic Hierarchy Process (AHP) and expert surveys. J. Climate Change Res. 12:421-429.
    19. Lebeau J, RA Wesselingh and H Van Dyck. 2016. Floral resource limitation severely reduces butterfly survival, condition and flight activity in simplified agricultural landscapes. Oecologia 180:421-427.
    20. Lee JY and SW Choi. 2023. Relationship between butterfly community and geographic location and ecological traits inhabiting agroecosystems. Korean J. Environ. Biol. 41:712-719.
    21. MacDonald ZG, SE Nielsen and JH Acorn. 2017. Negative relationships between species richness and evenness render common diversity indices inadequate for assessing long-term trends in butterfly diversity. Biodivers. Conserv. 26:617-629.
    22. Magurran AE. 1988. Ecological Diversity and Its Measurement. Springer. Dordrecht, Netherlands. p. 179.
    23. McKinney ML. 2008. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11:161-176.
    24. Öckinger E and HG Smith. 2006. Landscape composition and habitat area affects butterfly species richness in semi-natural grasslands. Oecologia 149:526-534.
    25. Oksanen J, FG Blanchet, M Friendly, R Kindt, P Legendre, D McGlinn, PR Minchin, RB O’Hara, GL Simpson, P Solymos, MHH Stevens, E Szoecs and H Wagner. 2019. vegan: Com munity Ecology Package. R package version 2.5 -6. https://CRAN.R-project.org/package=vegan
    26. Potts Sg, JC Biesmeijer, C Kremen, P Neumann, O Schweiger and WE Kunin. 2010. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25:345-353.
    27. R Core Team. 2024. R: A Language and Environment for Statistical Computing (Version 4.4.2). R Foundation for Statistical Computing. http://www.R-project.org/
    28. Schürings C. 2023. Impacts of agricultural land use types on the biodiversity and health of river ecosystems: A large-scale analysis. Ph.D. dissertation. Universität Duisburg -Essen. Duisburg/Essen, Germany.
    29. Thomas JA. 2005. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philos. Trans. R. Soc. B-Biol. Sci. 360:339-357.
    30. Tscharntke T, AM Klein, A Kruess, I Steffan-Dewenter and C Thies. 2005. Landscape perspectives on agricultural intensification and biodiversity - ecosystem service and management. Ecol. Lett. 8:857-874.
    31. Wix N, M Reich and F Scharrschmidt. 2019. Butterfly richness and abundance in flower strips and field margins: The role of local habitat quality and landscape context. Heliyon 5:e01636.

    Vol. 40 No. 4 (2022.12)

    Journal Abbreviation 'Korean J. Environ. Biol.'
    Frequency quarterly
    Doi Prefix 10.11626/KJEB.
    Year of Launching 1983
    Publisher Korean Society of Environmental Biology
    Indexed/Tracked/Covered By

    Contact info

    Any inquiries concerning Journal (all manuscripts, reviews, and notes) should be addressed to the managing editor of the Korean Society of Environmental Biology. Yongeun Kim,
    Korea University, Seoul 02841, Korea.
    E-mail: kyezzz@korea.ac.kr /
    Tel: +82-2-3290-3496 / +82-10-9516-1611