TY - JOUR
T1 - Automated classification of schools of the Silver Cyprinid Rastrineobola argentea in Lake Victoria acoustic survey data using random forests
AU - Proud, Roland
AU - Mangeni-Sande, Richard
AU - Kayanda, Robert
AU - Cox, Martin
AU - Nyamweya, Chrisphine
AU - Ongore, Collins
AU - Natugonza, Vianny
AU - Everson, Inigo
AU - Elison, Mboni
AU - Hobbs, Laura
AU - Boniphace Kashindye, Benedicto
AU - Mlaponi, Enock
AU - Taabu-Munyaho, Anthony
AU - Mwainge, Venny
AU - Kagoya, Esther
AU - Pegado, Antonio
AU - Nduwayesu, Evarist
AU - Brierley, Andrew S.
N1 - C International Council for the Exploration of the Sea 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is
properly cited.
PY - 2020/5/9
Y1 - 2020/5/9
N2 - Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple 'rule' that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have however been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 Million Tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) and associated environmental parameters (e.g. temperature, turbidity) for 49,081 manually-classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the expert manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time-series.
AB - Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple 'rule' that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have however been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 Million Tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) and associated environmental parameters (e.g. temperature, turbidity) for 49,081 manually-classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the expert manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time-series.
U2 - 10.1093/icesjms/fsaa052
DO - 10.1093/icesjms/fsaa052
M3 - Article
SN - 1054-3139
VL - 77
SP - 1379
EP - 1390
JO - ICES Journal of Marine Science
JF - ICES Journal of Marine Science
IS - 4
ER -