A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Riverine Organism Drift Imager : A new technology to study organism drift in rivers and streams (2023)


de Schaetzen, F., Impiö, M., Wagner, B., Nienaltowski, P., Arnold, M., Huber, M., Meyer, M., Raitoharju, J., Silva, L. G. M., & Stocker, R. (2023). The Riverine Organism Drift Imager : A new technology to study organism drift in rivers and streams. Methods in Ecology and Evolution, 14(9), 2341-2353. https://doi.org/10.1111/2041-210x.14130


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: de Schaetzen, Frédéric; Impiö, Mikko; Wagner, Basil; Nienaltowski, Patryk; Arnold, Michael; Huber, Martin; Meyer, Matthias; Raitoharju, Jenni; Silva, Luiz G. M.; Stocker, Roman

Lehti tai sarja: Methods in Ecology and Evolution

ISSN: 2041-210X

eISSN: 2041-210X

Julkaisuvuosi: 2023

Ilmestymispäivä: 19.05.2023

Volyymi: 14

Lehden numero: 9

Artikkelin sivunumerot: 2341-2353

Kustantaja: Wiley-Blackwell

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1111/2041-210x.14130

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/87107


Tiivistelmä

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.


YSO-asiasanat: joet; monitorointi; hermoverkot (biologia); konenäkö; kalat; koneoppiminen

Vapaat asiasanat: benthic invertebrates; computer vision; fish; machine learning; monitoring; neural network; rivers; streams


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Raportointivuosi: 2023

Alustava JUFO-taso: 3


Viimeisin päivitys 2024-03-04 klo 18:25