The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams
Abstract Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labou...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14130 |
| _version_ | 1827824144522149888 |
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| author | Frédéric deSchaetzen Mikko Impiö Basil Wagner Patryk Nienaltowski Michael Arnold Martin Huber Matthias Meyer Jenni Raitoharju Luiz G. M. Silva Roman Stocker |
| author_facet | Frédéric deSchaetzen Mikko Impiö Basil Wagner Patryk Nienaltowski Michael Arnold Martin Huber Matthias Meyer Jenni Raitoharju Luiz G. M. Silva Roman Stocker |
| author_sort | Frédéric deSchaetzen |
| collection | DOAJ |
| description | Abstract Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour‐intensive sampling methods that result in data of low temporal and spatial resolution. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine‐learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images riverine organisms as they drift through the device. After being imaged, organisms are released into the environment unharmed. A machine‐learning classifier is used after field sampling to identify drifting organisms. Therefore, RODI provides a non‐invasive sampling method that can quantify organism drift at unprecedented temporal resolution. Multiple deployments have served to validate the performance of the technology in the field. In its current implementation, images are captured continuously for 1.5 h at 50 frames per second. We demonstrate that the quality of the resulting images enables a convolutional neural network classifier to identify organisms to the family level. The weighted F1 score, a metric for the performance of the classifier, was 94%, based on training and testing on a field‐collected dataset consisting of 4598 images of 285 organisms belonging to seven classes (one species, five families and one order). In conclusion, this work provides a proof of concept, demonstrating the viability of the deployment of RODI as an automated, in situ organism drift sampler. This novel approach offers the possibility to advance our fundamental understanding of the drift of riverine organisms and how this is affected by human impacts in natural streams while, at the same time, can serve as a cost‐effective tool for biodiversity monitoring. |
| first_indexed | 2024-03-12T02:21:47Z |
| format | Article |
| id | doaj.art-14e4d3b9b4b84de5b4fd348bdac599da |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-12T02:21:47Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-14e4d3b9b4b84de5b4fd348bdac599da2023-09-06T04:43:40ZengWileyMethods in Ecology and Evolution2041-210X2023-09-011492341235310.1111/2041-210X.14130The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streamsFrédéric deSchaetzen0Mikko Impiö1Basil Wagner2Patryk Nienaltowski3Michael Arnold4Martin Huber5Matthias Meyer6Jenni Raitoharju7Luiz G. M. Silva8Roman Stocker9Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering ETH Zurich Zurich SwitzerlandProgramme for Environmental Information Finnish Environment Institute Helsinki FinlandEcology Unit Kraftwerke Oberhasli AG Innertkirchen SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering ETH Zurich Zurich SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering ETH Zurich Zurich SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering ETH Zurich Zurich SwitzerlandEcology Unit Kraftwerke Oberhasli AG Innertkirchen SwitzerlandFaculty of Information Technology University of Jyväskylä Jyväskylä FinlandDepartment of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering ETH Zurich Zurich SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering ETH Zurich Zurich SwitzerlandAbstract Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour‐intensive sampling methods that result in data of low temporal and spatial resolution. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine‐learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images riverine organisms as they drift through the device. After being imaged, organisms are released into the environment unharmed. A machine‐learning classifier is used after field sampling to identify drifting organisms. Therefore, RODI provides a non‐invasive sampling method that can quantify organism drift at unprecedented temporal resolution. Multiple deployments have served to validate the performance of the technology in the field. In its current implementation, images are captured continuously for 1.5 h at 50 frames per second. We demonstrate that the quality of the resulting images enables a convolutional neural network classifier to identify organisms to the family level. The weighted F1 score, a metric for the performance of the classifier, was 94%, based on training and testing on a field‐collected dataset consisting of 4598 images of 285 organisms belonging to seven classes (one species, five families and one order). In conclusion, this work provides a proof of concept, demonstrating the viability of the deployment of RODI as an automated, in situ organism drift sampler. This novel approach offers the possibility to advance our fundamental understanding of the drift of riverine organisms and how this is affected by human impacts in natural streams while, at the same time, can serve as a cost‐effective tool for biodiversity monitoring.https://doi.org/10.1111/2041-210X.14130benthic invertebratescomputer visionfishmachine learningmonitoringneural network |
| spellingShingle | Frédéric deSchaetzen Mikko Impiö Basil Wagner Patryk Nienaltowski Michael Arnold Martin Huber Matthias Meyer Jenni Raitoharju Luiz G. M. Silva Roman Stocker The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams Methods in Ecology and Evolution benthic invertebrates computer vision fish machine learning monitoring neural network |
| title | The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams |
| title_full | The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams |
| title_fullStr | The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams |
| title_full_unstemmed | The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams |
| title_short | The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams |
| title_sort | riverine organism drift imager a new technology to study organism drift in rivers and streams |
| topic | benthic invertebrates computer vision fish machine learning monitoring neural network |
| url | https://doi.org/10.1111/2041-210X.14130 |
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