Updated on 2023/10/20

写真a

 
WATANABE Shuntaro
 
Organization
Research Field in Science, Science and Engineering Area Graduate School of Science and Engineering (Science) Department of Science Biorogy Program Assistant Professor
Title
Assistant Professor
Contact information
メールアドレス

Degree

  • Doctor (Environmental Science) ( 2014.11   The University of Shiga Prefecture )

Research Interests

  • Plant Ecology

Research Areas

  • Life Science / Ecology and environment

  • Environmental Science/Agriculture Science / Environmental dynamic analysis

  • Life Science / Forest science

Research History

  • Kagoshima University   Research Field in Science, Science and Engineering Area Graduate School of Science and Engineering (Science) Department of Science Biorogy Program   Assistant Professor

    2020.4

  • Kagoshima University   Research Field in Science, Science and Engineering Area Graduate School of Science and Engineering (Science) Earth and Environmental Sciences Course   Assistant Professor

    2020.3

Professional Memberships

  • THE SOCIETY FOR THE STUDY OF SPECIES BIOLOGY

  • THE ECOLOGICAL SOCIETY OF JAPAN

 

Papers

  • Yuri Maesako, Shuntaro Watanabe, Yudai Ogawa .  Factors influencing the distribution of <i>Pteris wallichiana</i> J. Agardh (Pteridaceae) in Kasugayama Primeval Forest , Nara Prefecture, Japan. .  Vegetation Science40 ( 1 ) 1 - 7   2023.8Factors influencing the distribution of <i>Pteris wallichiana</i> J. Agardh (Pteridaceae) in Kasugayama Primeval Forest , Nara Prefecture, Japan.Reviewed

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

  • Shuntaro Watanabe, Muneo Matsuda, Chikako Ohtsuka .  Natural History of Musashino Minamisawa (IV): Origin of Hybrids of the Genus Phoxinus in the Ochiai River and Changes in the Ecosystem by Domestic Invasive Fishes .  Bulletin of Jiyu Gakuen College of Liberal Arts8 ( 1 ) 17 - 29   2023.8Natural History of Musashino Minamisawa (IV): Origin of Hybrids of the Genus Phoxinus in the Ochiai River and Changes in the Ecosystem by Domestic Invasive FishesReviewed

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (bulletin of university, research institution)  

    DOI: 10.19019/jiyu.8.1_17

  • Masanori Onishi, Shuntaro Watanabe, Tadashi Nakashima, Takeshi Ise .  Practicality and Robustness of Tree Species Identification Using UAV RGB Image and Deep Learning in Temperate Forest in Japan .  Remote Sensing14 ( 7 ) 1710 - 1710   2022.4Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    Identifying tree species from the air has long been desired for forest management. Recently, combination of UAV RGB image and deep learning has shown high performance for tree identification in limited conditions. In this study, we evaluated the practicality and robustness of the tree identification system using UAVs and deep learning. We sampled training and test data from three sites in temperate forests in Japan. The objective tree species ranged across 56 species, including dead trees and gaps. When we evaluated the model performance on the dataset obtained from the same time and same tree crowns as the training dataset, it yielded a Kappa score of 0.97, and 0.72, respectively, for the performance on the dataset obtained from the same time but with different tree crowns. When we evaluated the dataset obtained from different times and sites from the training dataset, which is the same condition as the practical one, the Kappa scores decreased to 0.47. Though coniferous trees and representative species of stands showed a certain stable performance regarding identification, some misclassifications occurred between: (1) trees that belong to phylogenetically close species, (2) tree species with similar leaf shapes, and (3) tree species that prefer the same environment. Furthermore, tree types such as coniferous and broadleaved or evergreen and deciduous do not always guarantee common features between the different trees belonging to the tree type. Our findings promote the practicalization of identification systems using UAV RGB images and deep learning.

    DOI: 10.3390/rs14071710

    Web of Science

  • Shuntaro Watanabe;Yuri Maesako .  Co-occurrence pattern of congeneric tree species provides conflicting evidence for competition relatedness hypothesis .  PeerJ9   e12150 - e12150   2021.11Reviewed

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:PeerJ  

    In plants, negative reproductive interaction among closely related species (<italic>i.e.</italic>, reproductive interference) is known to hamper the coexistence of congeneric species while facilitation can increase species persistence. Since reproductive interference in plants may occur through interspecific pollination, the effective range of reproductive interference may reflects the spatial range of interspecific pollination. Therefore, we hypothesized that the coexistence of congeners on a small spatial scale would be less likely to occur by chance but that such coexistence would be likely to occur on a scale larger than interspecific pollination frequently occur. In the present study, we tested this hypothesis using spatially explicit woody plant survey data. Contrary to our prediction, congeneric tree species often coexisted at the finest spatial scale and significant exclusive distribution was not detected. Our results suggest that cooccurrence of congeneric tree species is not structured by reproductive interference, and they indicate the need for further research to explore the factors that mitigate the effects of reproductive interference.

    DOI: 10.7717/peerj.12150

    Other Link: https://peerj.com/articles/12150.xml

  • Shuntaro Watanabe, Kazuaki Sumi, Takeshi Ise .  Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests .  BMC Ecology20 ( 1 )   2020.12Reviewed

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    Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    <title>Abstract</title><sec>
    <title>Background</title>
    Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identification of target objects from visual data using computer techniques is one of the most promising techniques to reduce the costs and labor for vegetation mapping. Although deep learning and convolutional neural networks (CNNs) have become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation is still difficult. In this study, we investigated the effectiveness of adopting the chopped picture method, a recently described protocol for CNNs, and evaluated the efficiency of CNN for plant community detection from Google Earth images.


    </sec><sec>
    <title>Results</title>
    We selected bamboo forests as the target and obtained Google Earth images from three regions in Japan. By applying CNN, the best trained model correctly detected over 90% of the targets. Our results showed that the identification accuracy of CNN is higher than that of conventional machine learning methods.


    </sec><sec>
    <title>Conclusions</title>
    Our results demonstrated that CNN and the chopped picture method are potentially powerful tools for high-accuracy automated detection and mapping of vegetation.


    </sec>

    DOI: 10.1186/s12898-020-00331-5

    Other Link: http://link.springer.com/article/10.1186/s12898-020-00331-5/fulltext.html

  • Shuntaro Watanabe, Kazuaki Sumi, Takeshi Ise .  Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests .  BMC Ecology   2020.11Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forestsReviewed

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    Language:English   Publishing type:Research paper (scientific journal)  

  • 渡部俊太郎, 大西信徳, 皆川まり, 伊勢武史 .  深層学習による画像認識技術の生態学への応用 -植物種と植生の識別を中心に- .  保全生態学研究   2020.3深層学習による画像認識技術の生態学への応用 -植物種と植生の識別を中心に- Reviewed

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

  • Shuntaro Watanabe, Masanori Onishi, Mari Minagawa, Takeshi Ise .  深層学習による画像認識技術の生態学への応用-植物種と植生の識別を中心に- .  Japanese Journal of Conservation Ecology   2020.3深層学習による画像認識技術の生態学への応用-植物種と植生の識別を中心に-Reviewed

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)  

  • 前迫ゆり、渡部俊太郎、中野智之 .  田辺湾畠島の植生 .  地域自然史と保全41 ( 2 ) 131 - 141   2020田辺湾畠島の植生Reviewed

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

  • 前迫ゆり、渡部俊太郎、中野智之 .  田辺湾畠島の植生 .  地域自然史と保全   2019.12田辺湾畠島の植生 Reviewed

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

  • Takeshi Ise, Shigeki Ikeda, Shuntaro Watanabe, Kazuhito Ichii .  Regional-scale data assimilation of a terrestrial ecosystem model: leaf phenology parameters are dependent on local climatic conditions. .  Frontiers in environmental science   2018.9Regional-scale data assimilation of a terrestrial ecosystem model: leaf phenology parameters are dependent on local climatic conditions. Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.3389/fenvs.2018.00095

  • Shuntaro Watanabe, Koh‑Ichi Takakura, Yuko Kaneko, Naohiko Noma, Takayoshi Nishida .  Skewed male reproductive success and pollen transfer in a small fragmented population of the heterodichogamous tree Machilus thunbergii. .  Journal of Plant Research   2018.2Skewed male reproductive success and pollen transfer in a small fragmented population of the heterodichogamous tree Machilus thunbergii. Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Ise T, Ikeda S, Watanabe S, Ichii K .  Regional-scale data assimilation of a terrestrial ecosystem model: leaf phenology parameters are dependent on local climatic conditions. .  Frontiers in Environmental Science6   1 - 10   2018Regional-scale data assimilation of a terrestrial ecosystem model: leaf phenology parameters are dependent on local climatic conditions.Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Watanabe S, Sumi K, Ise T .  Using deep learning for bamboo forest detection from Google Earth images. .  bioRxiv   2018Using deep learning for bamboo forest detection from Google Earth images.

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  • Watanabe S, Takakura K, Kaneko Y, Noma N, Nishida T .  Skewed male reproductive success and pollen transfer in a small fragmented population of the heterodichogamous tree Machilus thunbergii. .  Journal of Plant Research131   623 - 631   2018Skewed male reproductive success and pollen transfer in a small fragmented population of the heterodichogamous tree Machilus thunbergii.Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Shuntaro Watanabe, Yuko Kaneko, Yuri Maesako, Naohiko Noma .  Detecting the Early Genetic Effects of Habitat Degradation in Small Size Remnant Populations of Machilus thunbergii Sieb. et Zucc. (Lauraceae) .  International Journal of Forestry Research   2017.2Detecting the Early Genetic Effects of Habitat Degradation in Small Size Remnant Populations of Machilus thunbergii Sieb. et Zucc. (Lauraceae)Reviewed

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1155/2017/9410626

  • Shuntaro Watanabe, Yuko Kaneko, Yuri Maesako, Naohiko Noma .  Detecting the Early Genetic Effects of Habitat Degradation in Small Size Remnant Populations of Machilus thunbergii Sieb. et Zucc. (Lauraceae) .  International Journal of Forestry Research2017   2017Detecting the Early Genetic Effects of Habitat Degradation in Small Size Remnant Populations of Machilus thunbergii Sieb. et Zucc. (Lauraceae)Reviewed

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Hindawi Limited  

    Habitat degradation caused by human activities has reduced the sizes of many plant populations worldwide, generally with negative genetic impacts. However, detecting such impacts in tree species is not easy because trees have long life spans. Machilus thunbergii Sieb. et Zucc. (Lauraceae) is a dominant tree species of broad-leaved evergreen forests distributed primarily along the Japanese coast. Inland habitats for this species have become degraded by human activities. To investigate the effects of habitat degradation on genetic structure, we compared the genetic diversities of mature and juvenile trees of five M. thunbergii populations around Lake Biwa in Japan. Allelic diversity was influenced by past lineage admixture events, but the effects of forest size were not clear. On the other hand, the inbreeding coefficient of the juvenile stage was higher in small populations, whereas large populations maintained panmictic breeding. Also, the extent of genetic differentiation was greater in juveniles than in mature trees. We detected the early genetic effects of habitat degradation in small, isolated M. thunbergii populations, indicating that habitat degradation increases inbreeding and genetic differentiation between populations.

    DOI: 10.1155/2017/9410626

    Scopus

  • Shuntaro Watanabe, Naohiko Noma, Takayoshi Nishida .  Flowering phenology and mating success in the heterodichogamous tree Machilus thunbergii Sieb. et Zucc (Lauraceae). .  Plant Species Biology   2016.3Flowering phenology and mating success in the heterodichogamous tree Machilus thunbergii Sieb. et Zucc (Lauraceae).Reviewed

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1111/1442-1984.12078

  • Shuntaro Watanabe, Yuko Kaneko, Yuri Maesako, Naohiko Noma .  Range expansion and lineage admixture of the Japanese evergreen tree Machilus thunbergii in central Japan .  Journal of Plant Research   2014.11Range expansion and lineage admixture of the Japanese evergreen tree Machilus thunbergii in central Japan Reviewed

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1007/s10265-014-0650-2

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Books

  • 植物の超階層生物学 : ゲノミクス×フェノミクス×生態学でひもとく多様性

    渡部俊太郎, 大西信徳, 伊勢武史( Role: Contributor ,  画像情報に基づく 植物・植生の判別とその発展 ー深層学習による技術発展を中心にー)

    文一総合出版  2023.8  ( ISBN:9784829962107

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    Total pages:339p, 図版ivp   Language:Japanese

    CiNii Books

  • Monitoring tropical insects in the 21st century International journal

    Greg Lamarre, Tom Fayle, Simon Segard, Benita Laird-Hopkins, Akihiro Nakamura, Daniel Souto-Vilarósa, Shuntaro Watanabe, Yves Basset( Role: Joint author)

    Elsevier  2020.3 

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    Language:English Book type:Scholarly book

    DOI: https://doi.org/10.1016/bs.aecr.2020.01.004

  • Monitoring tropical insects in the 21st century

    Lamarre GP, Fayle TM, Segar ST, Laird-Hopkins B, Nakamura A, Souto-Vilarós D, Watanabe S, Basset Y( Role: Contributor)

    2020.3 

MISC

  • Applications of image recognition technology for biodiversity monitoring Invited

    Shuntaro Watanabe, Takeshi Ise

    Image Laboratory   32 ( 1 )   45 - 50   2021.1

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

    J-GLOBAL

Awards

  • Plant Species Biology Best Paper Award

    2017   The Society for the Study of Species Biology  

    Watanabe Shuntaro

Research Projects

  • 同時開花植物の花色のバラツキを左右する機構の解明

    Grant number:21K17915  2021.4 - 2024.3

    日本学術振興会  科学研究費助成事業 若手研究  若手研究

    渡部 俊太郎

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    Authorship:Principal investigator 

    Grant amount:\4680000 ( Direct Cost: \3600000 、 Indirect Cost:\1080000 )

  • Classifying vegetation using deep learning: clarification of characteristics of vegetation

    Grant number:18H03357  2018.4

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

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    Grant type:Competitive

 

Teaching Experience

  • Environmental Forestry

    2022
    Institution:The University of Shiga Prefecture

  • 地域自然環境実習

    2021
    Institution:鹿児島大学

  • 野外実験演習

    2021
    Institution:九州大学

  • 生物学概論

    2021
    Institution:鹿児島大学

  • 植物生態学

    2021
    Institution:鹿児島大学

  • 多様性生物学実験

    2021
    Institution:鹿児島大学

  • 森林環境学I

    2019
    Institution:滋賀県立大学

  • 基礎スキル演習1

  • フィールドプラクティス2

  • フィールドスタジオ演習2

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