• An automatic zooplankton identification model has been developed for 114 taxonomic categories.
• The model successfully distinguishes species and stages.
• Various model validations show high model performance for identifying key zooplankton taxa.
• The model makes unprecedented insights into the fine scale vertical distribution of taxa possible.
We deployed the Lightframe On-sight Keyspecies Investigation (LOKI) system, a novel underwater imaging system providing cutting-edge imaging quality, in the Canadian Arctic during fall 2013. A Random Forests machine learning model was built to automatically identify zooplankton in LOKI images. The model successfully distinguished between 114 different categories of zooplankton and particles. The high resolution taxonomical tree included many species, stages, as well as sub-groups based on animal orientation or condition in images. Results from a machine learning regression model of prosome length (R2=0.97) were used as a key predictor in the automatic identification model. Model internal validation of the automatic identification model on test data demonstrated that the model performed with overall high accuracy (86%) and specificity (86%). This was confirmed by confusion matrices for external testing results, based on automatic identifications for 2 complete stations. For station 101, from which images had also been used for training, accuracy and specificity were 85%. For station 126, from which images had not been used to train the model, accuracy and specificity were 81%. Further comparisons between model results and microscope identifications of zooplankton in samples from the two test stations were in good agreement for most taxa. LOKI’s image quality makes it possible to build accurate automatic identification models of very high taxonomic detail, which will play a critical role in future studies of zooplankton dynamics and zooplankton coupling with other trophic levels.
Also see researchgate at goo.gl/ZctMep for the publication!
Most studies on buoyant microplastics in the marine environment rely on sea surface sampling. Consequently, microplastic amounts can be underestimated, as turbulence leads to vertical mixing. Models that correct for vertical mixing are based on limited data. In this study we report measurements of the depth profile of buoyant microplastics in the North Atlantic subtropical gyre, from 0 to 5 m depth. Microplastics were separated into size classes (0.5–1.5 and 1.5–5.0 mm) and types (‘fragments’ and ‘lines’), and associated with a sea state. Microplastic concentrations decreased exponentially with depth, with both sea state and particle properties affecting the steepness of the decrease. Concentrations approached zero within 5 m depth, indicating that most buoyant microplastics are present on or near the surface. Plastic rise velocities were also measured, and were found to differ significantly for different sizes and shapes. Our results suggest that (1) surface samplers such as manta trawls underestimate total buoyant microplastic amounts by a factor of 1.04–30.0 and (2) estimations of depth-integrated buoyant plastic concentrations should be done across different particle sizes and types. Our findings can assist with improving buoyant ocean plastic vertical mixing models, mass balance exercises, impact assessments and mitigation strategies.
Available here: http://www.nature.com/articles/srep33882
or at researchgate: https://goo.gl/E3CY9N
It is now understood that the Ross Sea stands as one of the last relatively pristine (ocean) areas. Many decades of international research have been carried out under the Antarctic Treaty System stipulating that data acquired under this scheme must be shared with the global community. In line with Carlson (Nature 469:293, 2011, Polar Research 10.3402/polar.v32i0.20789, 2013), we find little evidence of enforcement towards making digital geographic information systems (GIS) project data available online for the wider Ross Sea ecosystem. While it is possible to find easily >40 digital datasets for most areas and pixels worldwide, despite many decades of research in the Ross Sea, only app. 100 digital datasets can be found for the study area. It simply shows that data from many studies in the region are not available. High-quality population and trend data explicit in space and time are mostly missing in the public realm, e.g., from the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR.org). This presents an ethical dilemma because it still appears that sufficient data exist for a pro-active and pre-cautionary management of this region. No coherent and efficient management scheme truly exists and is applied for this precious part of the world now heavily affected by global stressors and mismanagement of data and resources.
The common raven (Corvus corax) is an abundant generalist of the northern hemisphere, known to congregate and roost near human-related food sources. Due to a growing human-footprint and associated anthropogenic food subsidies, raven populations have increased dramatically over the past several years throughout the USA. The sub-arctic region has also witnessed increased urbanization and industrialization, and ravens have taken advantage of these changes. During 2004 and 2006, we surveyed parking lots on a bi-weekly basis in the city of Fairbanks in interior Alaska, showing an influx of ravens in winter. Between 2010 and 2012, we documented the presence and absence of ravens at a permanent set of 30 suspected raven locations and 21 randomized locations within the city limits of Fairbanks. We used machine learning (RandomForests) and 12 spatial GIS datasets from the Fairbanks North Star Borough to accurately model-predict the relative occurrence of ravens during winter and summer in Fairbanks. Our research showed a positive correlation between raven occurrence and commercial and residential zones in both winter and summer, as well as an inverse geographic relationship between ravens and the waste transfer station in the study area in winter, and a direct correlation near restaurants in summer. These results emphasize the link that ravens have with commercial, anthropogenic food sources, and how Fairbanks and its subsidized, urban habitat may be shaping part of the wider sub-arctic biodiversity landscape.
Keywords Common raven Fairbanks Alaska Distribution model Machine learning Subsidized predator