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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.1
- 수록면
- 386 - 402 (17page)
- DOI
- https://doi.org/10.15681/kswe.2025.41.5.386
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초록· 키워드
This study examined the relationship between community indices and area for benthic macroinvertebrates across different mesohabitats (riffle, run, pool, riparian) in the Jojong Stream, Gyeonggi Province, Korea, utilizing four non-probabilistic and fifteen probabilistic distribution models. Rarefaction-based estimates of expected species richness revealed significant discrepancies from observed values in smaller survey areas, indicating potential distortions in ecological interpretation. Most community indices-including species richness, abundance, diversity, dominance, evenness, and the benthic macroinvertebrate (BMI) saprobic index-varied with sampling area. Specifically, species richness and abundance consistently increased with area, while diversity and dominance tended to align even at smaller scales. In riffle habitats, BMI appeared relatively unaffected by area. Although the four-parameter generalized logistic power function showed a high overall fit, its complexity and risk of overfitting limit its practical application. In contrast, three-parameter models such as lognormal, Weibull, inverse Weibull, gamma, and generalized exponential distributions provided comparable accuracy with greater efficiency. The estimated habitat carrying capacity for species diversity was highest in main channel flows and riffles. Additionally, differential entropy indicated high heterogeneity in main channels and riparian zones, attributed to complex environmental factors such as substrate variability and vegetation. To accurately assess species-area relationships and habitat capacity at the site level, increased sampling effort is necessary. The lognormal model exhibited the highest accuracy, suggesting that larger stream systems support greater biodiversity, particularly where spatial heterogeneity enhances ecological richness.
#Benthic macroinvertebrates
#Community index
#Habitat carrying capacity
#Habitat heterogeneity
#Mesohabitat
#Probability distribution model
#Sample size
#Survey area
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