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1. A benchmark for computational analysis of animal behavior, using animal-borne tags

Author

Hoffman, Benjamin, Cusimano, Maddie, Baglione, Vittorio, Canestrari, Daniela, Chevallier, Damien, DeSantis, Dominic L., Jeantet, Lorène, Ladds, Monique A., Maekawa, Takuya, Mata-Silva, Vicente, Moreno-González, Víctor, Trapote, Eva, Vainio, Outi, Vehkaoja, Antti, Yoda, Ken, Zacarian, Katherine, and Friedlaender, Ari

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which can elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, as well as a modeling task and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 hours of data collected from 149 individuals across nine taxa. In addition, using BEBE, we test a novel self-supervised learning approach to identifying animal behaviors based on bio-logger data, using a deep neural network pre-trained with self-supervision on data collected from human wrist-worn accelerometers. We show that this approach out-performs common alternatives, especially in a setting with a low amount of training data. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/BEBE, to enable community use of BEBE as a point of comparison in methods development., Comment: For associated code repositories, see https://github.com/earthspecies/BEBE/ and https://github.com/earthspecies/BEBE-datasets/ . For data repository, see https://zenodo.org/record/7947104

Published
2023

2. Learning interaction rules from multi-animal trajectories via augmented behavioral models

Author

Fujii, Keisuke, Takeishi, Naoya, Tsutsui, Kazushi, Fujioka, Emyo, Nishiumi, Nozomi, Tanaka, Ryoya, Fukushiro, Mika, Ide, Kaoru, Kohno, Hiroyoshi, Yoda, Ken, Takahashi, Susumu, Hiryu, Shizuko, and Kawahara, Yoshinobu

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Extracting the interaction rules of biological agents from movement sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the interactions from observed time-series data; however, this framework ignores the structures and assumptions of the generative process in animal behaviors, which may lead to interpretational problems and sometimes erroneous assessments of causality. In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models. We adopt an approach for augmenting incomplete multi-agent behavioral models described by time-varying dynamical systems with neural networks. For efficient and interpretable learning, our model leverages theory-based architectures separating navigation and motion processes, and the theory-guided regularization for reliable behavioral modeling. This can provide interpretable signs of Granger-causal effects over time, i.e., when specific others cause the approach or separation. In experiments using synthetic datasets, our method achieved better performance than various baselines. We then analyzed multi-animal datasets of mice, flies, birds, and bats, which verified our method and obtained novel biological insights., Comment: 24 pages, 5 figures, to appear in NeurIPS 2021

Published
2021

4. Improved Activity Forecasting for Generating Trajectories

Author

Ogawa, Daisuke, Tamaki, Toru, Hirakawa, Tsubasa, Raytchev, Bisser, Kaneda, Kazufumi, and Yoda, Ken

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting. We modify reward function with $L_p$ norm and propose convolution into value iteration steps, which is called convolutional value iteration. Experimental results with seabird trajectories (43 for training and 10 for test), our method is best in terms of MHD error and performs fastest. Generated trajectories for interpolating missing parts of trajectories look much similar to real seabird trajectories than those by the previous works., Comment: The 2019 International Workshop on Frontiers of Computer Vision (IW-FCV2019)

Published
2019

6. Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification

Author

Beal, Martin, Catry, Paulo, Phillips, Richard A., Oppel, Steffen, Arnould, John P.Y., Bogdanova, Maria I., Bolton, Mark, Carneiro, Ana P.B., Clatterbuck, Corey, Conners, Melinda, Daunt, Francis, Delord, Karine, Elliott, Kyle, Fromant, Aymeric, Granadeiro, José Pedro, Green, Jonathan A., Halsey, Lewis, Hamer, Keith C., Ito, Motohiro, Jeavons, Ruth, Kim, Jeong-Hoon, Kokubun, Nobuo, Koyama, Shiho, Lane, Jude V., Lee, Won Young, Matsumoto, Sakiko, Orben, Rachael A., Owen, Ellie, Paiva, Vitor H., Patterson, Allison, Pollock, Christopher J., Ramos, Jaime A., Sagar, Paul, Sato, Katsufumi, Shaffer, Scott A., Soanes, Louise, Takahashi, Akinori, Thompson, David R., Thorne, Lesley, Torres, Leigh, Watanuki, Yutaka, Waugh, Susan M., Weimerskirch, Henri, Whelan, Shannon, Yoda, Ken, Xavier, José C., and Dias, Maria P.

Published
2023
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13. Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks

Author

Gunner, Richard M., Holton, Mark D., Scantlebury, Mike D., van Schalkwyk, O. Louis, English, Holly M., Williams, Hannah J., Hopkins, Phil, Quintana, Flavio, Gómez-Laich, Agustina, Börger, Luca, Redcliffe, James, Yoda, Ken, Yamamoto, Takashi, Ferreira, Sam, Govender, Danny, Viljoen, Pauli, Bruns, Angela, Bell, Stephen H., Marks, Nikki J., Bennett, Nigel C., Tonini, Mariano H., Duarte, Carlos M., van Rooyen, Martin C., Bertelsen, Mads F., Tambling, Craig J., and Wilson, Rory P.

Published
2021
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14. How often should dead-reckoned animal movement paths be corrected for drift?

Author

Gunner, Richard M., Holton, Mark D., Scantlebury, David M., Hopkins, Phil, Shepard, Emily L. C., Fell, Adam J., Garde, Baptiste, Quintana, Flavio, Gómez-Laich, Agustina, Yoda, Ken, Yamamoto, Takashi, English, Holly, Ferreira, Sam, Govender, Danny, Viljoen, Pauli, Bruns, Angela, van Schalkwyk, O. Louis, Cole, Nik C., Tatayah, Vikash, Börger, Luca, Redcliffe, James, Bell, Stephen H., Marks, Nikki J., Bennett, Nigel C., Tonini, Mariano H., Williams, Hannah J., Duarte, Carlos M., van Rooyen, Martin C., Bertelsen, Mads F., Tambling, Craig J., and Wilson, Rory P.

Published
2021
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18. Understanding Animal Behavior Using Their Trajectories : A Case Study of Gender Specific Trajectory Trends

Author

Ardakani, Ilya, Hashimoto, Koichi, Yoda, Ken, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Streitz, Norbert, editor, and Konomi, Shin’ichi, editor

Published
2018
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19. Finding Discriminative Animal Behaviors from Sequential Bio-Logging Trajectory Data

Author

Sakuma, Takuto, Nishi, Kazuya, Yamazaki, Shuhei J., Kimura, Koutarou D., Matsumoto, Sakiko, Yoda, Ken, Takeuchi, Ichiro, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Streitz, Norbert, editor, and Konomi, Shin’ichi, editor

Published
2018
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22. Global assessment of marine plastic exposure risk for oceanic birds

Author

Clark, Bethany L., primary, Carneiro, Ana P. B., additional, Pearmain, Elizabeth J., additional, Rouyer, Marie-Morgane, additional, Clay, Thomas A., additional, Cowger, Win, additional, Phillips, Richard A., additional, Manica, Andrea, additional, Hazin, Carolina, additional, Eriksen, Marcus, additional, González-Solís, Jacob, additional, Adams, Josh, additional, Albores-Barajas, Yuri V., additional, Alfaro-Shigueto, Joanna, additional, Alho, Maria Saldanha, additional, Araujo, Deusa Teixeira, additional, Arcos, José Manuel, additional, Arnould, John P. Y., additional, Barbosa, Nadito J. P., additional, Barbraud, Christophe, additional, Beard, Annalea M., additional, Beck, Jessie, additional, Bell, Elizabeth A., additional, Bennet, Della G., additional, Berlincourt, Maud, additional, Biscoito, Manuel, additional, Bjørnstad, Oskar K., additional, Bolton, Mark, additional, Booth Jones, Katherine A., additional, Borg, John J., additional, Bourgeois, Karen, additional, Bretagnolle, Vincent, additional, Bried, Joël, additional, Briskie, James V., additional, Brooke, M. de L., additional, Brownlie, Katherine C., additional, Bugoni, Leandro, additional, Calabrese, Licia, additional, Campioni, Letizia, additional, Carey, Mark J., additional, Carle, Ryan D., additional, Carlile, Nicholas, additional, Carreiro, Ana R., additional, Catry, Paulo, additional, Catry, Teresa, additional, Cecere, Jacopo G., additional, Ceia, Filipe R., additional, Cherel, Yves, additional, Choi, Chang-Yong, additional, Cianchetti-Benedetti, Marco, additional, Clarke, Rohan H., additional, Cleeland, Jaimie B., additional, Colodro, Valentina, additional, Congdon, Bradley C., additional, Danielsen, Jóhannis, additional, De Pascalis, Federico, additional, Deakin, Zoe, additional, Dehnhard, Nina, additional, Dell’Omo, Giacomo, additional, Delord, Karine, additional, Descamps, Sébastien, additional, Dilley, Ben J., additional, Dinis, Herculano A., additional, Dubos, Jerome, additional, Dunphy, Brendon J., additional, Emmerson, Louise M., additional, Fagundes, Ana Isabel, additional, Fayet, Annette L., additional, Felis, Jonathan J., additional, Fischer, Johannes H., additional, Freeman, Amanda N. D., additional, Fromant, Aymeric, additional, Gaibani, Giorgia, additional, García, David, additional, Gjerdrum, Carina, additional, Gomes, Ivandra Soeli Gonçalves Correia, additional, Forero, Manuela G., additional, Granadeiro, José P., additional, Grecian, W. James, additional, Grémillet, David, additional, Guilford, Tim, additional, Hallgrimsson, Gunnar Thor, additional, Halpin, Luke R., additional, Hansen, Erpur Snær, additional, Hedd, April, additional, Helberg, Morten, additional, Helgason, Halfdan H., additional, Henry, Leeann M., additional, Hereward, Hannah F. R., additional, Hernandez-Montero, Marcos, additional, Hindell, Mark A., additional, Hodum, Peter J., additional, Imperio, Simona, additional, Jaeger, Audrey, additional, Jessopp, Mark, additional, Jodice, Patrick G. R., additional, Jones, Carl G., additional, Jones, Christopher W., additional, Jónsson, Jón Einar, additional, Kane, Adam, additional, Kapelj, Sven, additional, Kim, Yuna, additional, Kirk, Holly, additional, Kolbeinsson, Yann, additional, Kraemer, Philipp L., additional, Krüger, Lucas, additional, Lago, Paulo, additional, Landers, Todd J., additional, Lavers, Jennifer L., additional, Le Corre, Matthieu, additional, Leal, Andreia, additional, Louzao, Maite, additional, Madeiros, Jeremy, additional, Magalhães, Maria, additional, Mallory, Mark L., additional, Masello, Juan F., additional, Massa, Bruno, additional, Matsumoto, Sakiko, additional, McDuie, Fiona, additional, McFarlane Tranquilla, Laura, additional, Medrano, Fernando, additional, Metzger, Benjamin J., additional, Militão, Teresa, additional, Montevecchi, William A., additional, Montone, Rosalinda C., additional, Navarro-Herrero, Leia, additional, Neves, Verónica C., additional, Nicholls, David G., additional, Nicoll, Malcolm A. C., additional, Norris, Ken, additional, Oppel, Steffen, additional, Oro, Daniel, additional, Owen, Ellie, additional, Padget, Oliver, additional, Paiva, Vítor H., additional, Pala, David, additional, Pereira, Jorge M., additional, Péron, Clara, additional, Petry, Maria V., additional, de Pina, Admilton, additional, Pina, Ariete T. Moreira, additional, Pinet, Patrick, additional, Pistorius, Pierre A., additional, Pollet, Ingrid L., additional, Porter, Benjamin J., additional, Poupart, Timothée A., additional, Powell, Christopher D. L., additional, Proaño, Carolina B., additional, Pujol-Casado, Júlia, additional, Quillfeldt, Petra, additional, Quinn, John L., additional, Raine, Andre F., additional, Raine, Helen, additional, Ramírez, Iván, additional, Ramos, Jaime A., additional, Ramos, Raül, additional, Ravache, Andreas, additional, Rayner, Matt J., additional, Reid, Timothy A., additional, Robertson, Gregory J., additional, Rocamora, Gerard J., additional, Rollinson, Dominic P., additional, Ronconi, Robert A., additional, Rotger, Andreu, additional, Rubolini, Diego, additional, Ruhomaun, Kevin, additional, Ruiz, Asunción, additional, Russell, James C., additional, Ryan, Peter G., additional, Saldanha, Sarah, additional, Sanz-Aguilar, Ana, additional, Sardà-Serra, Mariona, additional, Satgé, Yvan G., additional, Sato, Katsufumi, additional, Schäfer, Wiebke C., additional, Schoombie, Stefan, additional, Shaffer, Scott A., additional, Shah, Nirmal, additional, Shoji, Akiko, additional, Shutler, Dave, additional, Sigurðsson, Ingvar A., additional, Silva, Mónica C., additional, Small, Alison E., additional, Soldatini, Cecilia, additional, Strøm, Hallvard, additional, Surman, Christopher A., additional, Takahashi, Akinori, additional, Tatayah, Vikash R. V., additional, Taylor, Graeme A., additional, Thomas, Robert J., additional, Thompson, David R., additional, Thompson, Paul M., additional, Thórarinsson, Thorkell L., additional, Vicente-Sastre, Diego, additional, Vidal, Eric, additional, Wakefield, Ewan D., additional, Waugh, Susan M., additional, Weimerskirch, Henri, additional, Wittmer, Heiko U., additional, Yamamoto, Takashi, additional, Yoda, Ken, additional, Zavalaga, Carlos B., additional, Zino, Francis J., additional, and Dias, Maria P., additional

Published
2023
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23. Corrigendum to “Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification” [Biol. Conserv. 281 (2023) 109994]

Author

Beal, Martin, primary, Catry, Paulo, additional, Phillips, Richard A., additional, Oppel, Steffen, additional, Arnould, John P.Y., additional, Bogdanova, Maria I., additional, Bolton, Mark, additional, Carneiro, Ana P.B., additional, Clatterbuck, Corey, additional, Conners, Melinda, additional, Daunt, Francis, additional, Delord, Karine, additional, Elliott, Kyle, additional, Fromant, Aymeric, additional, Granadeiro, José Pedro, additional, Green, Jonathan A., additional, Halsey, Lewis G., additional, Hamer, Keith C., additional, Ito, Motohiro, additional, Jeavons, Ruth, additional, Kim, Jeong-Hoon, additional, Kokubun, Nobuo, additional, Koyama, Shiho, additional, Lane, Jude V., additional, Lee, Won Young, additional, Matsumoto, Sakiko, additional, Orben, Rachael A., additional, Owen, Ellie, additional, Paiva, Vitor H., additional, Patterson, Allison, additional, Pollock, Christopher J., additional, Ramos, Jaime A., additional, Sagar, Paul, additional, Sato, Katsufumi, additional, Shaffer, Scott A., additional, Soanes, Louise, additional, Takahashi, Akinori, additional, Thompson, David R., additional, Thorne, Lesley, additional, Torres, Leigh, additional, Watanuki, Yutaka, additional, Waugh, Susan M., additional, Weimerskirch, Henri, additional, Whelan, Shannon, additional, Yoda, Ken, additional, Xavier, José C., additional, and Dias, Maria P., additional

Published
2023
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27. Refining seabird marine protected areas by predicting habitat inside foraging range - a case study from the global tropics

Author

Miller, Mark, Hemson, Graham, Toit, Julie Du, Mcdougall, Andrew, Miller, Peter, Mizutani, Akira, Trevail, Alice, Small, Alison, Ravache, Andreas, Beard, Annalea, Bunce, Ashley, Poli, Caroline, Surman, Chris, Gonzalez-zamora, Diego, Clingham, Elizabeth, Vidal, Eric, Mcduie, Fiona, Machovsky-capuska, Gabriel, Cumming, Graeme, Humphries, Grant, Weimerskirch, Henri, Shamoun-baranes, Judy, Henry, Leeann, Wood, Hannah, Young, Hillary, Kohno, Hiroyoshi, Gonzalez-sols, Jacob, Cecere, Jacopo, Veen, Jan, Neumann, Jessica, Shephard, Jill, Green, Jonathan, Castillo-guerrero, José, Sommerfeld, Julia, Dossa, Justine, Bourgeois, Karen, Yoda, Ken, Mcleay, Lachlan, Calabrese, Licia, Mendez, Loriane, Soanes, Louise, Nicoll, Malcolm, Derhé, Mia, Gilmour, Morgan, Diop, Ngone, James, Nicholas, Carr, Pete, Austin, Rhiannon, Freeman, Robin, Clarke, Rohan, Mott, Rowan, Maxwell, Sarah, Saldanha, Sarah, Shaffer, Scott, Oppel, S., Votier, Stephen, Yamamoto, Takashi, Militão, Teresa, Beger, Maria, and Congdon, Bradley

Subjects
Transferability, Great Barrier Reef, Foraging niche, Foraging Radius, Ecological Niche Model, Marine Protected Area
Abstract

Conservation of breeding seabirds typically requires detailed data on where they feed at sea. Ecological niche models (ENMs) can fill data gaps, but rarely perform well when transferred to new regions. Alternatively, the foraging radius approach simply encircles the sea surrounding a breeding seabird colony (a foraging circle), but overestimates foraging habitat. Here, we investigate whether ENMs can transfer (predict) foraging niches of breeding tropical seabirds between global colonies, and whether ENMs can refine foraging circles. We collate a large global dataset of tropical seabird tracks (12000 trips, 16 species, 60 colonies) to build a comprehensive summary of tropical seabird foraging ranges and to train ENMs. We interrogate ENM transferability and assess the confidence with which unsuitable habitat predicted by ENMs can be excluded from within foraging circles. We apply this refinement framework to the Great Barrier Reef (GBR), Australia to identify a network of candidate marine protected areas (MPAs) for seabirds. We found little ability to generalise and transfer breeding tropical seabird foraging niches across all colonies for any species (mean AUC: 0.56, range 0.4-0.82). Low global transferability was partially explained by colony clusters that predicted well internally but other colony clusters poorly. After refinement with ENMs, foraging circles still contained 89% of known foraging areas from tracking data, providing confidence that important foraging habitat was not erroneously excluded by greater refinement from high transferability ENMs nor minor refinement from low transferability ENMs. Foraging radii estimated the total foraging area of the GBR breeding seabird community as 2,941,000 km2, which was refined by excluding between 197,000 km2 and 1,826,000 km2 of unsuitable foraging habitat. ENMs trained on local GBR tracking achieved superior refinement over globally trained models, demonstrating the value of local tracking. Our framework demonstrates an effective method to delineate candidate MPAs for breeding seabirds in data-poor regions.

Published
2023

36. Luck and tactics in foraging success: the case of the imperial shag

Author

Wilson, Rory P., Holton, Mark D., Neate, Andrew, Del’Caño, Monserrat, Quintana, Flavio, Yoda, Ken, and Gómez-Laich, Agustina

Subjects
Ecology, Aquatic Science, Ecology, Evolution, Behavior and Systematics
Abstract

It has been proposed that predators searching for prey acquire food according to a probabilistic framework, where success is based on ‘luck’ and the odds of success vary with prey abundance. If true, this has major ramifications for variation in the rates of energy acquisition within animal populations, which is particularly pertinent in offspring provisioning and breeding success, because smaller animals (the young) cannot starve for as long as the adults. However, despite much general speculation about rates of food acquisition, no study has measured whether food encounter is probabilistic in wild animals. We used animal-mounted cameras to document all prey captures by wild imperial shags Leucocarbo atriceps as they hunted underwater and show that, although they mostly do not have inter-prey acquisition time distributions that accord with a ‘luck-based’ framework assuming a constant probability of finding prey over time, there is no difference in the predicted amount of food captured between models that use the empirical data or theoretical Poisson-based fits of the data. We also noted considerable inter-individual differences in foraging success that far exceeded any differences between empirical and theoretical inter-prey acquisition time distributions. The data were used in a probabilistic foraging model that made explicit the mechanistic link between random prey encounters and food-dependent breeding success, indicating that ‘less lucky’ individuals could not provision their broods at rates commensurate with normal growth while the ‘lucky’ birds could do so easily. Given the nature of food encounter in these birds, coupled with substantial inter-individual variation in foraging success, we suggest that more successful individuals are particularly choosey about when, how and where to forage, which results in them operating with higher odds of success.

Published
2022

40. The role of wingbeat frequency and amplitude in flight power

Author

Krishnan, Krishnamoorthy, primary, Garde, Baptiste, additional, Bennison, Ashley, additional, Cole, Nik C., additional, Cole, Emma-L., additional, Darby, Jamie, additional, Elliott, Kyle H., additional, Fell, Adam, additional, Gómez-Laich, Agustina, additional, de Grissac, Sophie, additional, Jessopp, Mark, additional, Lempidakis, Emmanouil, additional, Mizutani, Yuichi, additional, Prudor, Aurélien, additional, Quetting, Michael, additional, Quintana, Flavio, additional, Robotka, Hermina, additional, Roulin, Alexandre, additional, Ryan, Peter G., additional, Schalcher, Kim, additional, Schoombie, Stefan, additional, Tatayah, Vikash, additional, Tremblay, Fred, additional, Weimerskirch, Henri, additional, Whelan, Shannon, additional, Wikelski, Martin, additional, Yoda, Ken, additional, Hedenström, Anders, additional, and Shepard, Emily L. C., additional

Published
2022
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44. The role of wingbeat frequency and amplitude in flight power: supplementary information

Author

Krishnan, Krishnamoorthy, Garde, Baptiste, Bennison, Ashley, Cole, Nik C., Cole, Emma-L., Darby, Jamie, Elliott, Kyle H., Fell, Adam, Gómez-Laich, Agustina, de Grissac, Sophie, Jessopp, Mark, Lempidakis, Emmanouil, Mizutani, Yuichi, Prudor, Aurélien, Quetting, Michael, Quintana, Flavio, Robotka, Hermina, Roulin, Alexandre, Ryan, Peter G., Schalcher, Kim, Schoombie, Stefan, Tatayah, Vikash, Tremblay, Fred, Weimerskirch, Henri, Whelan, Shannon, Wikelski, Martin, Yoda, Ken, Hedenström, Anders, and Shepard, Emily L. C.

Abstract

This file contains information on ethical approvals for the tagged birds, mixed model results of interaction between wingbeat amplitude, frequency and airspeed for tropic birds and the effect of sampling frequency on the estimation of acceleration signal amplitude for the manuscript "The role of wingbeat frequency and amplitude in flight power" published in the Journal of Royal Society Interface.

Published
2022
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47. Fréchet Kernel for Trajectory Data Analysis

Author

Takeuchi, Koh, primary, Imaizumi, Masaaki, additional, Kanda, Shunsuke, additional, Tabei, Yasuo, additional, Fujii, Keisuke, additional, Yoda, Ken, additional, Ishihata, Masakazu, additional, and Maekawa, Takuya, additional

Published
2021
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