Disruption of ecological networks in lakes by climate change and nutrient fluctuations.

Climate change interacts with local processes to threaten biodiversity by disrupting the complex network of ecological interactions. While changes in network interactions drastically affect ecosystems, how ecological networks respond to climate change, in particular warming and nutrient supply fluctuations, is largely unknown. Here, using an equation-free modelling approach on monthly plankton community data in ten Swiss lakes, we show that the number and strength of plankton community interactions fluctuate and respond nonlinearly to water temperature and phosphorus. While lakes show system-specific responses, warming generally reduces network interactions, particularly under high phosphate levels. This network reorganization shifts trophic control of food webs, leading to consumers being controlled by resources. Small grazers and cyanobacteria emerge as sensitive indicators of changes in plankton networks. By exposing the outcomes of a complex interplay between environmental drivers, our results provide tools for studying and advancing our understanding of how climate change impacts entire ecological communities.

A legacy of invasive sun corals: Distinct mobile invertebrate assemblages at near-reef coral-dominated rubble.

Fast-growing and reproducing sun corals have successfully invaded rocky reefs around the Atlantic Ocean, markedly reducing the diversity of fouling invertebrates and macroalgae, and profoundly changing the composition of reef-associated mobile invertebrates. Here, we address sun-coral rubble depositions and report, for the first time, the effects of sun corals on near-reef soft-bottom invertebrate assemblages. Abundance, richness and diversity were higher at rubble habitats compared to bare sandy grounds, which could be a positive effect of substrate complexity. All those parameters were also higher at rubble patches dominated by sun-coral fragments compared to rubble patches dominated by pebbles or shell fragments, also suggesting possible additive effects of coral-borne chemical attraction (sun-coral specific, as inputs of other coral species were virtually absent). Different epifaunal groups were exclusive of rubble habitats and a subset of those exclusive of sun-coral rubble, explaining the incremental richness across habitats. The relative abundance of the two dominant groups - polychaetes (p) and amphipods (a) - contributed the most to the observed contrasts on community structure, as their proportion (p:a) changed from 10:1 in bare sand to nearly co-dominance in coral rubble. While previous research suggested that spreading sun corals reduce prey supply for fish foraging on reef walls, our results suggest they may increase prey abundance and diversity at the adjacent non-consolidated habitat, possibly reshaping trophic pathways connecting the benthic and the pelagic environment.

Deep Learning Classification of Lake Zooplankton.

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, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

Underwater dual-magnification imaging for automated lake plankton monitoring.

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 expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.