This dataset provides zooplankton density and biomass information for each station visited during the three past Lake Superior CSMI surveys summarized by major taxonomic groups. Also included is the design weights for the surveys, which allow you to do the various statistical tests that we did in the paper. This dataset is associated with the following publication: Pawlowski, M., and M. Sierszen. A lake-wide approach for large lake zooplankton monitoring: Results from the 2006–2016 Lake Superior Cooperative Science and Monitoring Initiative surveys. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(4): 1015-1027, (2020).

This database contains climatic, hydrologic, water quality and biological information for the Lake Beysehir catchment, Turkey. The dataset includes meteorological data (precipitation, air temperature, wind speed, solar radiation, relative humidity), discharges for the main inflows and lake outflow, lake water level, water chemistry data for inflows and lake. In addition, lake biological data (phytoplankton, zooplankton, fish and macrophyte) is avaiable. Data was compiled during the METU-DPT-TEAB project, EU-FP7 REFRESH project and EU-FP7 MARS project . More information on this dataset can be found in the Freshwater Metadatabase - MARS_09 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=MARS_09).

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 μm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem can expand our understanding of both applied and fundamental plankton ecology. Our work leads us to conclude that the utilization of the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.

The PLANKTON*NET data provider at the Alfred Wegener Institute for Polar and Marine Research is an open access repository for plankton-related information. It covers all types of phytoplankton and zooplankton from marine and freshwater areas. PLANKTON*NET's greatest strength is its comprehensiveness as for the different taxa image information as well as taxonomic descriptions can be archived. PLANKTON*NET also contains a glossary with accompanying images to illustrate the term definitions. PLANKTON*NET therefore presents a vital tool for the preservation of historic data sets as well as the archival of current research results. Because interoperability with international biodiversity data providers (e.g. GBIF) is one of our aims, the architecture behind the new planktonnet@awi repository is observation centric and allows for mulitple assignment of assets (images, references, animations, etc) to any given observation. In addition, images can be grouped in sets and/or assigned tags to satisfy user-specific needs . Sets (and respective images) of relevance to the scientific community and/or general public have been assigned a persistant digital object identifier (DOI) for the purpose of long-term preservation (e.g. set "Plankton*Net celebrates 50 years of Roman Treaties", handle: 10013/de.awi.planktonnet.set.495) More information on this dataset can be found in the Freshwater Metadatabase - BFE_70 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_70).