@article{oai:kanagawa-u.repo.nii.ac.jp:00014056,
 author = {岩崎, 貴也 and 奥田, 真未 and 安藤, 温子 and 松尾, 歩 and 陶山, 佳久 and 中濱, 直之 and 泉, 進},
 journal = {Science Journal of Kanagawa University},
 month = {Jun},
 note = {Vegetation damage caused by the rapid increase of Japanese sika deer (Cervus nippon ) has become a major problem in many areas of Japan. Deer have preferences for plant species, which change according to the region, season, and surrounding vegetation. Therefore, to clarify the impact of deer on local vegetation, it is important to understand the overall trend in other areas and then further investigate the behavior and feeding habits of deer in those areas. The Shonan Hiratsuka Campus of Kanagawa University is a place with a high plant diversity where the pre-construction satoyama environment still remains. However, deer have begun to invade this area. In this study, we used DNA metabarcoding technology on deer feces to identify the foraging plants of deer in this area with two cpDNA regions (rbc L and trn L). In total, OTUs identified as Fagaceae, Asteraceae, Poaceae, and Oxalidaceae were detected with many sequence reads. These groups contain many species that are considered to be deerpreferred plants, which is consistent with the results of this study. In addition, the species that we could identify such as Impatiens textorii, Persicaria thunbergi, Pueraria lobata, and Carex japonica , were also known to be deer-preferred plants. While spring sample included a variety of families such as Poaceae, Rosaceae, and Urticaceae, half of the reads from the early summer sample were identified as Asteraceae, and half of the reads from the autumn sample were Fagaceae, showing seasonal differences. This result suggests that deer may use new buds and new leaves in spring, flowers in early summer, and nuts and litter in autumn as a guide when selecting plants to feed on., Departmental Bulletin Paper, 原著, 2020年度 神奈川大学総合理学研究所共同研究助成論文},
 pages = {59--67},
 title = {DNAメタバーコーディング解析で推定されたニホンジカの採食植物 ： 神奈川大学湘南ひらつかキャンパスの例},
 volume = {32},
 year = {2021}
}