リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Evaluation of Deep Learning-Based Monitoring of Frog Reproductive Phenology」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Evaluation of Deep Learning-Based Monitoring of Frog Reproductive Phenology

Kimura, Kaede Sota, Teiji 京都大学 DOI:10.1643/h2023018

2023.11

概要

To evaluate the utility of a deep-learning approach for monitoring amphibian reproduction, we examined the classification accuracy of a trained model and tested correlations between calling intensity and frog abundance. Field recording and count surveys were conducted at two sites in Kyoto City, Japan. A convolutional neural network (CNN) model was trained to classify the calls of five anuran species. The model achieved 91–100% precision and 75–98% recall per species, with relatively lower performance on less abundant species. Computational experiments investigating the effects of the number and seasonality of the training samples showed that models trained on larger datasets from broader recording seasons performed better. Calling activity was high when males were abundant (Pearson's r = 0.45–0.66), although correlations between the calling activity and the number of pairs in amplexus were generally weaker. Our results suggest that deep learning is an effective tool for reconstructing the reproductive phenology of male anurans from field recordings. However, caution is required when applying to rare species and when inferring female reproductive activity.

この論文で使われている画像

参考文献

AudioMoth: evaluation of a smart open acoustic

device for monitoring biodiversity and the

Bermant, P. C. 2021. BioCPPNet: automatic

bioacoustic source separation with deep neural

networks. Scientific Reports 11:23502.

Besson, M., J. Alison, K. Bjerge, T. E. Gorochowski,

T. T. Høye, T. Jucker, H. M. R. Mann, and C. F.

environment. Methods in Ecology and Evolution

9:1199–1211.

Howard, J., and S. Gugger. 2020. Fastai: a layered

API

for

deep

learning.

Information.

An

International Interdisciplinary Journal 11:108.

Clements. 2022. Towards the fully automated

Kahl, S., C. M. Wood, M. Eibl, and H. Klinck. 2021.

monitoring of ecological communities. Ecology

BirdNET: A deep learning solution for avian

Letters 25:2753–2775.

diversity

monitoring.

Ecological

Informatics

61:101236.

large alluvial plain in Japan. Wetlands 42:106.

Keitt, T. H., and E. S. Abelson. 2021. Ecology in the

age of automation. Science 373:858–859.

M.

Brigham,

Recommendations

and

E.

for

Bayne.

acoustic

McFee, B., A. Metsai, M. McVicar, S. Balke, C.

Thomé, C. Raffel, F. Zalkow, A. Malek, Dana, K.

Knight, E. C., K. C. Hannah, G. J. Foley, C. D. Scott,

R.

spatial scales on paddy field-breeding frogs in a

2017.

recognizer

Lee, O. Nieto, D. Ellis, J. Mason, E. Battenberg,

Thassilo.

2022.

librosa:

0.9.1.

https://doi.org/10.5281/zenodo.6097378

performance assessment with application to five

Norouzzadeh, M. S., A. Nguyen, M. Kosmala, A.

common automated signal recognition programs.

Swanson, M. S. Palmer, C. Packer, and J. Clune.

Avian Conservation and Ecology 12:14.

2018. Automatically identifying, counting, and

LeBien, J., M. Zhong, M. Campos-Cerqueira, J. P.

describing wild animals in camera-trap images with

Velev, R. Dodhia, J. L. Ferres, and T. M. Aide.

deep learning. Proceedings of the National

2020. A pipeline for identification of bird and frog

Academy of Sciences of the United States of

species in tropical soundscape recordings using a

America 115:E5716–E5725.

convolutional

neural

network.

Ecological

Informatics 59:101113.

Japanese Toad, Bufo japonicus japonicus. VIII.

LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep

learning. Nature 521:436–444.

Rowley.

2022.

Climatic factors influencing the breeding activity.

Japanese Journal of Ecology 35:527–535.

Liu, G., R. T. Kingsford, C. T. Callaghan, and J. J.

L.

Okuno, R. 1985. Studies on the natural history of the

Anthropogenic

habitat

modification alters calling phenology of frogs.

Global Change Biology 28:6194–6208.

Pinheiro, J., D. Bates, and R Core Team. 2022.

nlme: linear and nonlinear mixed Effects models. R

package

version

3.1-162.

https://cran.r-

project.org/web/packages/nlme/index.html

Mac Aodha, O., R. Gibb, K. E. Barlow, E.

R Core Team. 2022. R: a language and environment

Browning, M. Firman, R. Freeman, B. Harder, L.

for statistical computing. R Foundation for

Kinsey, G. R. Mead, S. E. Newson, I. Pandourski,

Statistical

S. Parsons, J. Russ, A. Szodoray-Paradi … K. E.

Computing,

Vienna,

Austria.

https://www.R-project.org/

Jones. 2018. Bat detective-Deep learning tools for

Rowley, J. J. L., C. T. Callaghan, T. Cutajar, C.

bat acoustic signal detection. PLoS Computational

Portway, K. Potter, S. Mahony, D. F. Trembath,

Biology 14:e1005995.

P. Flemons, and A. Woods. 2019. FrogID: Citizen

Marques, T. A., L. Thomas, S. W. Martin, D. K.

scientists provide validated biodiversity data on

Mellinger, J. A. Ward, D. J. Moretti, D. Harris,

frogs of Australia. Herpetological Conservation and

and P. L. Tyack. 2013. Estimating animal

Biology 14:155–170.

population

acoustics.

Schneider, S., G. W. Taylor, S. C. Kremer, P.

Biological Reviews of the Cambridge Philosophical

density

using

passive

Burgess, J. McGroarty, K. Mitsui, A. Zhuang, J.

Society 88:287–309.

R. deWaard, and J. M. Fryxell. 2022. Bulk

Matsui, M., and N. Maeda. 2018. Encyclopedia of

arthropod abundance, biomass and diversity

Japanese frogs. Bun-ichi Sogo Shuppan, Tokyo,

estimation using deep learning for computer vision.

Japan.

Methods in Ecology and Evolution 13:346–357.

Matsushima, N., M. Hasegawa, and J. Nishihiro.

Shimada, T., A. Imamura, and N. Ohnishi. 2013. A

2022. Effects of landscape heterogeneity at multiple

study of larval phenologies of five anuran species in

Japanese paddy fields. Japanese Journal of

Herpetological Conservation and Biology 4:389–

Herpetology 2013:77–85.

402.

Shimoyama, R. 1993. Female reproductive traits in a

Womack, M. C., E. Steigerwald, D. C. Blackburn,

population of the pond frog, Rana nigromaculata,

D. C. Cannatella, A. Catenazzi, J. Che, M. S. Koo,

with prolonged breeding season. Japanese Journal

J. A. McGuire, S. R. Ron, C. L. Spencer, V. T.

of Herpetology 15:37–41.

Vredenburg, and R. D. Tarvin. 2022. State of the

Shirose, L. J., C. A. Bishop, D. M. Green, C. J.

amphibia 2020: a review of five years of amphibian

MacDonald, B. R. J., and N. J. Helferty. 1997.

research and existing resources. Ichthyology &

Validation tests of an amphibian call count survey

Herpetology 110:638–661.

technique in Ontario, Canada. Herpetologica

53:312–320.

Xie, J., R. Zeng, C. L. Xu, J. L. Zhang, and P. Roe.

2017. Multi-label classification of frog species via

Stowell, D. 2022. Computational bioacoustics with

deep learning. p. 187–193. In: 2017 IEEE 13th

deep learning: a review and roadmap. PeerJ

International Conference on e-Science (e-Science).

10:e13152.

Zuur, A. F., E. N. Ieno, N. Walker, A. A. Saveliev,

Weir, L., I. J. Fiske, and J. A. Royle. 2009. Trends

and G. M. Smith. 2009. Mixed effects models and

in anuran occupancy from northeastern states of the

extensions in ecology with R. Springer, New York.

North American Amphibian Monitoring Program.

...

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る