• Parasites, Hosts and Diseases
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• v.22 no.1
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• pp.102-108
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• 1984

This paper is the result of adult female mosquito and larval collection in U.S. Army Installations in Korea from 1979 to 1983. New Jersey light traps were operated for adult collection from May to October. The primary concern of this surveillance is to determine when to recommend insecticide spraying for mosquito control in the Army areas. The 5th Preventive Medicine Unit have developed an "index" level of female mosquitoes in a light trap similar to other U.S. Army Agencies in other parts of the world. When 10 female mosquitoes are reached on two consecutive trap-night, or 5 known vector females are collected, fogging is recommended in the trap areas. 1. Mosquito collections were conducted in 12 U.S. Army areas by operating 39 New Jersey light traps. Mosquitoes collected from the areas were identified to be 17 species comprising 3 genera. Anopheles sinensis (40%), Culex tritaeniorhynchus (31%), Aedes vexans nipponii (19 %) and Culex pipiens pallens (10%) appeared to be the most common species in the areas. 2. The species, population density and monthly appearance of adult mosquitoes were found to be almost the same in the all provinces involved. And Japanese Encephalitis vector mosquitoes, Culex tritaeniorhynchus, showed their seasonal fluctuation from July to September with a peak in August each year. 3. Larval habitats confirmed in the Army areas were categorized into 16 types as shown in Table 3. The mosquito larvae collected in those habitats were identified to be 15 species representing 4 genera. Most breeding sites in the Army areas were those which are activated during the wet season. 4. More mosquitoes were collected from the Kyungki Province than from the other Provinces. The reason for more collection of mosquitoes from military installations in the Kyungki Province appears to be the geographic characteristics surrounded by rice fields, marshes and other stagnant water areas.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.333-343
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• 2015

This research aims at identifying the goshawk's possible and replaceable breeding ground by using the MaxEnt prediction model which has so far been insufficiently used in Korea, and providing evidence to expand possible protection areas for the goshawk's breeding for the future. The field research identified 10 goshawk's nests, and 23 appearance points confirmed during the 3rd round of environmental research were used for analysis. 4 geomorphic, 3 environmental, 7 distance, and 9 weather factors were used as model variables. The final environmental variables were selected through non-parametric verification between appearance and non-appearance coordinates identified by random sampling. The final predictive model (MaxEnt) was structured using 10 factors related to breeding ground and 7 factors related to appearance area selected by statistics verification. According to the results of the study, the factor that affected breeding point structure model the most was temperature seasonality, followed by distance from mixforest, density-class on the forest map and relief energy. The factor that affected appearance point structure model the most was temperature seasonality, followed by distance from rivers and ponds, distance from agricultural land and gradient. The nature of the goshawk's breeding environment and habit to breed inside forests were reflected in this modeling that targets breeding points. The northern central area which is about $189.5 km^2$(2.55 %) is expected to be suitable breeding ground. Large cities such as Cheongju and Chungju are located in the southern part of Chungcheongbuk-do whereas the northern part of Chungcheongbuk-do has evenly distributed forests and farmlands, which helps goshawks have a scope of influence and food source to breed. Appearance point modeling predicted an area of $3,071 km^2$(41.38 %) showing a wider ranging habitat than that of the breeding point modeling due to some limitations such as limited moving observation and non-consideration of seasonal changes. When targeting the breeding points, a specific predictive area can be deduced but it is difficult to check the points of nests and it is impossible to reflect the goshawk's behavioral area. On the other hand, when targeting appearance points, a wider ranging area can be covered but it is less accurate compared to predictive breeding point since simple movements and constant use status are not reflected. However, with these results, the goshawk's habitat can be predicted with reasonable accuracy. In particular, it is necessary to apply precise predictive breeding area data based on habitat modeling results when enforcing an environmental evaluation or establishing a development plan.