Namil Chung

Potential Impacts of Global Warming on the Diversity and Distribution of Stream Insects in South Korea

Globally, the East Asian monsoon region is one of the richest environments in terms of biodiversity. The region is undergoing rapid human development, yet its river ecosystems have not been well studied. Global warming represents a major challenge to the survival of species in this region and makes it necessary to assess and reduce the potential consequences of warming on species of conservation concern. We projected the effects of global warming on stream insect (Ephemeroptera, Odonata, Plecoptera, and Trichoptera [EOPT]) diversity and predicted the changes of geographical ranges for 121 species throughout South Korea. Plecoptera was the most sensitive (decrease of 71.4% in number of species from the 2000s through the 2080s) order, whereas Odonata benefited (increase of 66.7% in number of species from the 2000s through the 2080s) from the effects of global warming. The impact of global warming on stream insects was predicted to be minimal prior to the 2060s; however, by the 2080s, species extirpation of up to 20% in the highland areas and 2% in the lowland areas were predicted. The projected responses of stream insects under global warming indicated that species occupying specific habitats could undergo major reductions in habitat. Nevertheless, habitat of 33% of EOPT (including two-thirds of Odonata and one-third of Ephemeroptera, Plecoptera, and Trichoptera) was predicted to increase due to global warming. The community compositions predicted by generalized additive models varied over this century, and a large difference in community structure in the highland areas was predicted between the 2000s and the 2080s. However, stream insect communities, especially Odonata, Plecoptera, and Trichoptera, were predicted to become more homogenous under global warming.

Response of Fish Communities to Various Environmental Variables across Multiple Spatial Scales

A better understanding of the relative importance of different spatial scale determinants on fish communities will eventually increase the accuracy and precision of their bioassessments. Many studies have described the influence of environmental variables on fish communities on multiple spatial scales. However, there is very limited information available on this topic for the East Asian monsoon region, including Korea. In this study, we evaluated the relationship between fish communities and environmental variables at multiple spatial scales using self-organizing map (SOM), random forest, and theoretical path models. The SOM explored differences among fish communities, reflecting environmental gradients, such as a longitudinal gradient from upstream to downstream, and differences in land cover types and water quality. The random forest model for predicting fish community patterns that used all 14 environmental variables was more powerful than a model using any single variable or other combination of environmental variables, and the random forest model was effective at predicting the occurrence of species and evaluating the contribution of environmental variables to that prediction. The theoretical path model described the responses of different species to their environment at multiple spatial scales, showing the importance of altitude, forest, and water quality factors to fish assemblages.

Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas

Abstract The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of R and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.

Evaluation of global warming effects on the geographical distribution of weeds in paddy fields by characterizing germination time and morphological factors

Abstract Global warming, a consequence of climate change, alters rice-paddy ecosystems, especially through the changes of both growth rate of plants and the occurrences of pests, and affects both rice crop production and biodiversity. In this study, factors related to the germination temperatures of 80 weed species in paddy fields were analyzed to elucidate the effect of warming on morphological (leaf size), phenological (germination time), and population (distribution) responses. A self-organizing map (SOM) was used to classify the weed species on the basis of 5 factors related to germination temperature: the minimum, maximum, and optimum temperatures and the minimum and maximum optimal range. Climate data for the Korean Peninsula during 4 different decades (1990s, 2020s, 2050s, and 2080s) were obtained from a regional climate change model following the A1B emission scenario of the Intergovernmental Panel on Climate Change. Changes in the germination time and range of potential habitable areas for the weed species were estimated on the basis of the patterns of the SOM. The species associated with relatively lower germination temperatures tended to have smaller leaves, shorter stems, and earlier flowering and germination times than the species associated with higher germination temperature. The potential germination area increased progressively with rising temperature. The degree of potential increase in germination area was the greatest in the 2080s when the weeds could germinate in most of the southern Korean Peninsula. These results suggest that studying the patterns of germination temperature through SOM could provide necessary information for characterizing the germination of weeds on the basis of various characteristics (e.g., morphology, phenology, and distribution) and would be useful for maintaining agricultural productivity and agroecosystem biodiversity under global warming.

Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea.

Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.