Morphology, phylogeny, and taxonomy of two species of colonial volvocine green algae from Lake Victoria, Tanzania.

The biodiversity and taxonomy of colonial volvocine green algae are important in ancient lakes in tropical regions. However, few taxonomic studies of these algae have been conducted in African ancient lakes. Here, we describe two species of colonial volvocine green algae in cultures originating from water samples from Lake Victoria, an ancient lake in Africa. One was identified as an undescribed morphological species of Eudorina; E. compacta sp. nov. This new species can be distinguished from other Eudorina species by its compactly arranged vegetative cells that form a hollow ellipsoidal colony. The other was identified as Colemanosphaera charkowiensis. The genus Colemanosphaera is new to Africa

Automated classification of schools of the silver cyprinid Rastrineobola argentea in Lake Victoria acoustic survey data using random forests

The dagaa classification reported here was supported specifically by several Scottish Funding Council Global Challenge Research Fund (GCRF) grants from the University of St Andrews and the University of Strathclyde, by a GCRF Networking Grant to ASB and RJK from the UK Academy of Medical Sciences (GCRFNG\100371), and a Royal Society International Collaboration Award to ASB and Rhoda Tumwebaze, LVFO (ICA\R1\180123).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 120kHz 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) for 49081 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 manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.Publisher PDFPeer reviewe

Automated classification of schools of the silver cyprinid <i>Rastrineobola argentea</i> in Lake Victoria acoustic survey data using random forests

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 120kHz 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) for 49081 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 manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.</p