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The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean

Journal paper
Moritz Sebastian Schmid, Cyril Aubry, Jordan Grigor, Louis Fortier
The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography,
Publication year: 2016



• 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 for the publication!

The effect of particle properties on the depth profile of buoyant plastics in the ocean

Journal paper
Merel Kooi, Julia Reisser, Boyan Slat, Francesco F. Ferrari, Moritz S. Schmid, Serena Cunsolo, Roberto Brambini, Kimberly Noble, Lys-Anne Sirks, Theo E. W. Linders, Rosanna I. Schoeneich-Argent & Albert A. Koelmans
Kooi, M. et al. The effect of particle properties on the depth profile of buoyant plastics in the ocean. Sci. Rep. 6, 33882; doi: 10.1038/srep33882 (2016).
Publication year: 2016

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: 

or at researchgate:




Bridging the gap from student to senior scientist: recommendations for engaging early career scientists in professional biological societies

Journal paper
Humphries, G.R.W., Flemming, S.A., Hammer, S., Hirata, K., Kappes, M.A., Kappes, P., Magnusdottir, E., Major, H., Mcduie, F., McOmber, K., Orben, R.A., Schmid, M.S., Wille, M.
Marine Ornithology 44(2):157–166
Publication year: 2016
Despite their long-standing and central role in the dissemination, promotion and defense of science, scientific societies currently face a unique combination of economic, social and technological changes. As a result, one of the most pressing challenges facing many societies is declining membership due to reduced recruitment and a failure to retain members, particularly early-career scientists (ECSs). To ensure that professional biological societies retain long-term viability and relevance, the recruitment and retention of ECSs needs to be a main priority. Here we propose a series of recommendations that we, a group of ECSs, believe will help professional societies better integrate and retain ECSs. We discuss each recommendation and detail its implementation using examples from our personal experiences in the global seabird research and management communities and from our collective experience as members of several professional societies. We believe these recommendations will not only help recruit and retain ECSs as society members, but will also directly benefit the organizations themselves.

A first overview of open access digital data for the Ross Sea: complexities, ethics, and management opportunities.

Journal paper
Huettmann, F., Schmid, M.S., Humphries, G.R.W.
Hydrobiologia 761:97–119,
Publication year: 2015



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 ( 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.



Seasonal observations and machine-learning-based spatial model predictions for the common raven (Corvus corax) in the urban, sub-arctic environment of Fairbanks, Alaska

Journal paper
A. P. Baltensperger, T. C. Mullet, M. S. Schmid, G. R. W. Humphries, L. Kövér, F. Huettmann (2013)
Polar Biol 36:1587–1599
Publication year: 2013



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