Assessing the response of an urban stream ecosystem to salinization under different flow regimes.

Urban streams are exposed to a variety of anthropogenic stressors. Freshwater salinization is a key stressor in these ecosystems that is predicted to be further exacerbated by climate change, which causes simultaneous changes in flow parameters, potentially resulting in non-additive effects on aquatic ecosystems. However, the effects of salinization and flow velocity on urban streams are still poorly understood as multiple-stressor experiments are often conducted at pristine rather than urban sites. Therefore, we conducted a mesocosm experiment at the Boye River, a recently restored stream located in a highly urbanized area in Western Germany, and applied recurrent pulses of salinity along a gradient (NaCl, 9 h daily of +0 to +2.5 mS/cm) in combination with normal and reduced current velocities (20 cm/s vs. 10 cm/s). Using a comprehensive assessment across multiple organism groups (macroinvertebrates, eukaryotic algae, fungi, parasites) and ecosystem functions (primary production, organic-matter decomposition), we show that flow velocity reduction has a pervasive impact, causing community shifts for almost all assessed organism groups (except fungi) and inhibiting organic-matter decomposition. Salinization affected only dynamic components of community assembly by enhancing invertebrate emigration via drift and reducing fungal reproduction. We caution that the comparatively small impact of salt in our study can be due to legacy effects from past salt pollution by coal mining activities >30 years ago. Nevertheless, our results suggest that urban stream management should prioritize the continuity of a minimum discharge to maintain ecosystem integrity. Our study exemplifies a holistic approach for the assessment of multiple-stressor impacts on streams, which is needed to inform the establishment of a salinity threshold above which mitigation actions must be taken.

The Asymmetric Response Concept explains ecological consequences of multiple stressor exposure and release.

Our capacity to predict trajectories of ecosystem degradation and recovery is limited, especially when impairments are caused by multiple stressors. Recovery may be fast or slow and either complete or partial, sometimes result in novel ecosystem states or even fail completely. Here, we introduce the Asymmetric Response Concept (ARC) that provides a basis for exploring and predicting the pace and magnitude of ecological responses to, and release from, multiple stressors. The ARC holds that three key mechanisms govern population, community and ecosystem trajectories. Stress tolerance is the main mechanism determining responses to increasing stressor intensity, whereas dispersal and biotic interactions predominantly govern responses to the release from stressors. The shifting importance of these mechanisms creates asymmetries between the ecological trajectories that follow increasing and decreasing stressor intensities. This recognition helps to understand multiple stressor impacts and to predict which measures will restore communities that are resistant to restoration.

Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.

Diatoms represent one of the morphologically and taxonomically most diverse groups of microscopic eukaryotes. Light microscopy-based taxonomic identification and enumeration of frustules, the silica shells of these microalgae, is broadly used in aquatic ecology and biomonitoring. One key step in emerging digital variants of such investigations is segmentation, a task that has been addressed before, but usually in manually captured megapixel-sized images of individual diatom cells with a mostly clean background. In this paper, we applied deep learning-based segmentation methods to gigapixel-sized, high-resolution scans of diatom slides with a realistically cluttered background. This setup requires large slide scans to be subdivided into small images (tiles) to apply a segmentation model to them. This subdivision (tiling), when done using a sliding window approach, often leads to cropping relevant objects at the boundaries of individual tiles. We hypothesized that in the case of diatom analysis, reducing the amount of such cropped objects in the training data can improve segmentation performance by allowing for a better discrimination of relevant, intact frustules or valves from small diatom fragments, which are considered irrelevant when counting diatoms. We tested this hypothesis by comparing a standard sliding window / fixed-stride tiling approach with two new approaches we term object-based tile positioning with and without object integrity constraint. With all three tiling approaches, we trained Mask-R-CNN and U-Net models with different amounts of training data and compared their performance. Object-based tiling with object integrity constraint led to an improvement in pixel-based precision by 12-17 percentage points without substantially impairing recall when compared with standard sliding window tiling. We thus propose that training segmentation models with object-based tiling schemes can improve diatom segmentation from large gigapixel-sized images but could potentially also be relevant for other image domains.

Epiphytic diatom community structure and richness is determined by macroalgal host and location in the South Shetland Islands (Antarctica).

The marine waters around the South Shetland Islands are paramount in the primary production of this Antarctic ecosystem. With the increasing effects of climate change and the annual retreat of the ice shelf, the importance of macroalgae and their diatom epiphytes in primary production also increases. The relationships and interactions between these organisms have scarcely been studied in Antarctica, and even less in the volcanic ecosystem of Deception Island, which can be seen as a natural proxy of climate change in Antarctica because of its vulcanism, and the open marine system of Livingston Island. In this study we investigated the composition of the diatom communities in the context of their macroalgal hosts and different environmental factors. We used a non-acidic method for diatom digestion, followed by slidescanning and diatom identification by manual annotation through a web-browser-based image annotation platform. Epiphytic diatom species richness was higher on Deception Island as a whole, whereas individual macroalgal specimens harboured richer diatom assemblages on Livingston Island. We hypothesize this a possible result of a higher diversity of ecological niches in the unique volcanic environment of Deception Island. Overall, our study revealed higher species richness and diversity than previous studies of macroalgae-inhabiting diatoms in Antarctica, which could however be the result of the different preparation methodologies used in the different studies, rather than an indication of a higher species richness on Deception Island and Livingston Island than other Antarctic localities.