Biology subjects

Burfeid-Castellanos, A. M.

Publications and source records attributed to Burfeid-Castellanos, A. M..

3 recordsLinked to original sources

bioRxiv2024

Characterization of diatom communities in restored sewage channels in the Boye catchment, Germany

Restoration programs were initiated in different stream systems within the Boye catchment in the early 1990s to stop and reverse the negative impacts of anthropogenic disturbances. However, our knowledge of the effects of restoration works in this river network, specifically on benthic diatom communities, is still limited. Diatoms were used successfully to assess the impact of restoration works in a range of river networks around the world but received less attention in the Boye catchment. This study aimed to characterize benthic diatom communities in this restored river catchment, using digital microscopy methods. We collected samples in the spring and summer of 2020 at sites restored in different years (1995, 2005, 2010, 2012, and 2013) within the catchment. Our results showed no effect of season, restoration date (year), and location of the site along the streams on the most dominant diatom species. However, some rare taxa indicated significant variations between the seasons. Overall water quality in the streams ranged from moderate to very good, indicating the positive impacts of restoration works conducted in these former sewage channels.

ecology↗

bioRxiv2024

Assessment of microphytobenthos communities in the Kinzigcatchment using photosynthesis-related traits, digital light microscopy and 18S-V9 amplicon sequencing

Microalgae form an essential group of benthic organisms that respond swiftly to environmental changes. They are widely used as bioindicators of anthropogenic stressors in freshwater ecosystems. We aimed to assess the responses of microalgae communities to multiple environmental stressors in the Kinzig River catchment, home to a long-term ecological monitoring site, in Germany. We used a photosynthetic biomass proxy alongside community composition of diatoms assessed by digital light microscopy, and of microalgae by 18S-V9 amplicon sequencing, to characterise microalgae at 19 sampling sites scattered across the catchment. Our results revealed significant effects of physical and chemical factors on microalgae biomass and community compositions. We found that conductivity, water temperature and pH were the most important factors affecting microalgae community composition, as observed in both microscopy and amplicon analysis. In addition to these three variables, the effect of total phosphate on all microalgae, together with water discharge on the diatom (Bacillariophyta) communities, as assessed by amplicon analysis, may reveal taxon-specific variations in the ecological responses of different microalgal groups. Our results highlighted the complex relationship between various environmental variables and microalgae biomass and community composition. Further investigations, involving the collection of time series data, are required to fully understand the underlying biotic and abiotic parameters that influence these microalgae communities.

ecology↗

bioRxiv2022

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.

ecology↗