Intercomparison of Two Fluorescent Dyes to Visualize Parasitic Fungi (Chytridiomycota) on Phytoplankton

Fungal microparasites (here chytrids) are widely distributed and yet, they are often overlooked in aquatic environments. To facilitate the detection of microparasites, we revisited the applicability of two fungal cell wall markers, Calcofluor White (CFW) and wheat germ agglutinin (WGA), for the direct visualization of chytrid infections on phytoplankton in laboratory-maintained isolates and field-sampled communities. Using a comprehensive set of chytrid-phytoplankton model pathosystems, we verified the staining pattern on diverse morphological structures of chytrids via fluorescence microscopy. Empty sporangia were stained most effectively, followed by encysted zoospores and im-/mature sporangia, while the staining success was more variable for rhizoids, stalks, and resting spores. In a few instances, the staining was unsuccessful (mostly with WGA), presumably due to insufficient cell fixation, gelatinous cell coatings, and multilayered cell walls. CFW and WGA staining could be done in Utermohl chambers or on polycarbonate filters, but CFW staining on filters seemed less advisable due to high background fluorescence. To visualize chytrids, 1 mu g dye mL(-1) was sufficient (but 5 mu g mL(-1) are recommended). Using a dual CFW-WGA staining protocol, we detected multiple, mostly undescribed chytrids in two natural systems (freshwater and coastal), while falsely positive or negative stained cells were well detectable. As a proof-of-concept, we moreover conducted imaging flow cytometry, as a potential high-throughput technology for quantifying chytrid infections. Our guidelines and recommendations are expected to facilitate the detection of chytrid epidemics and to unveil their ecological and economical imprint in natural and engineered aquatic systems.</p

Development of embodied capital: Diet composition, foraging skills, and botanical knowledge of forager children in the Congo Basin

The embodied capital theory states that the extended juvenile period has enabled human foragers to acquire the complex foraging skills and knowledge needed to obtain food. Yet we lack detailed data on how forager children develop these skills and knowledge. Here, we examine the seasonal diet composition, foraging behavior, and botanical knowledge of Mbendjele BaYaka forager children in the Republic of the Congo. Our data, acquired through long-term observations involving full-day focal follows, show a high level of seasonal fluctuation in diet and foraging activities of BaYaka children, in response to the seasonal availability of their food sources. BaYaka children foraged more than half of the time independent from adults, predominantly collecting and eating fruits, tubers, and seeds. For these most-consumed food types, we found an early onset of specialization of foraging skills in children, similar to the gendered division in foraging in adults. Specifically, children were more likely to eat fruit and seed species when there were more boys and men in the group, and girls were more likely than boys to collect tuber species. In a botanical knowledge test, children were more accurate at identifying plant food species with increasing age, and they used fruits and trunks for species identification, more so than using leaves and barks. These results show how the foraging activities of BaYaka children may facilitate the acquisition of foraging skills and botanical knowledge and provide insights into the development of embodied capital. Additionally, BaYaka children consumed agricultural foods more than forest foods, probably reflecting BaYaka’s transition into a horticultural lifestyle. This change in diet composition may have significant consequences for the cognitive development of BaYaka children

Resilience trinity: safeguarding ecosystem functioning and services across three different time horizons and decision contexts

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi-faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time-horizons: i) reactive, when there is an imminent threat to ES resilience and a high pressure to act, ii) adjustive, when the threat is known in general but there is still time to adapt management, and iii) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology, and engineering are often implicitly focussing on provident, adjustive, or reactive resilience, respectively, but these different notions and of resilience and their corresponding social, ecological, and economic trade-offs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer-term management actions are not missed while urgent threats to ES are given priority

Cell Wall Structure of Coccoid Green Algae as an Important Trade-Off Between Biotic Interference Mechanisms and Multidimensional Cell Growth

Coccoid green algae can be divided in two groups based on their cell wall structure. One group has a highly chemical resistant cell wall (HR-cell wall) containing algaenan. The other group is more susceptible to chemicals (LR-cell wall – Low resistant cell wall). Algaenan is considered as important molecule to explain cell wall resistance. Interestingly, cell wall types (LR- and HR-cell wall) are not in accordance with the taxonomic classes Chlorophyceae and Trebouxiophyceae, which makes it even more interesting to consider the ecological function. It was already shown that algaenan helps to protect against virus, bacterial and fungal attack, but in this study we show for the first time that green algae with different cell wall properties show different sensitivity against interference competition with the cyanobacterium Microcystis aeruginosa. Based on previous work with co-cultures of M. aeruginosa and two green algae (Acutodesmus obliquus and Oocystis marssonii) differing in their cell wall structure, it was shown that M. aeruginosa could impair only the growth of the green algae if they belong to the LR-cell wall type. In this study it was shown that the sensitivity to biotic interference mechanism shows a more general pattern within coccoid green algae species depending on cell wall structure

Data_Sheet_1.DOCX

<p>Coccoid green algae can be divided in two groups based on their cell wall structure. One group has a highly chemical resistant cell wall (HR-cell wall) containing algaenan. The other group is more susceptible to chemicals (LR-cell wall – Low resistant cell wall). Algaenan is considered as important molecule to explain cell wall resistance. Interestingly, cell wall types (LR- and HR-cell wall) are not in accordance with the taxonomic classes Chlorophyceae and Trebouxiophyceae, which makes it even more interesting to consider the ecological function. It was already shown that algaenan helps to protect against virus, bacterial and fungal attack, but in this study we show for the first time that green algae with different cell wall properties show different sensitivity against interference competition with the cyanobacterium Microcystis aeruginosa. Based on previous work with co-cultures of M. aeruginosa and two green algae (Acutodesmus obliquus and Oocystis marssonii) differing in their cell wall structure, it was shown that M. aeruginosa could impair only the growth of the green algae if they belong to the LR-cell wall type. In this study it was shown that the sensitivity to biotic interference mechanism shows a more general pattern within coccoid green algae species depending on cell wall structure.</p

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton

Abstract Background Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy with high throughput and latest development in machine learning techniques can overcome this hurdle. Results In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage during the batch cultivation. The results show the high potential of this approach, where species identity and their respective life cycle stage could be predicted with a high accuracy of 97%. Conclusions These findings could pave the way for reliable and fast phytoplankton species determination of indicator species as a crucial step in water quality assessment

Pollen analysis using multispectral imaging flow cytometry and deep learning

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen

Data from: Temperature effects on prey and basal resources exceed that of predators in an experimental community

Climate warming alters the structure of ecological communities by modifying species interactions at different trophic levels. Yet, the consequences of warming-led modifications in biotic interactions at higher trophic levels on lower trophic groups are lesser known. Here, we test the effects of multiple predator species on prey population size and traits, and subsequent effects on basal resources along an experimental temperature gradient (12-15C, 17-20C, and 22-25C). We experimentally assembled food web modules with two congeneric predatory mites (Hypoaspis miles and Hypoaspis aculeifer), and two Collembola prey species (Folsomia candida and Proisotoma minuta) on a litter and yeast mixture as the basal resources. We hypothesized that warming would modify interactions within and between predator species, and that these alterations would cascade to basal resources via changes in the density and traits (body size and lipid: protein ratio) of the prey species. The presence of congeners constrained the growth of the predatory species independent of warming despite warming increased predator density in their respective monocultures. We found that warming effects on both prey and basal resources were greater than the effects of predator communities. Our results further showed opposite effects of warming on predator (increase) and prey densities (decrease), indicating a warming-induced trophic mismatch, which are likely to alter food web structures. We highlight that warmer environments can restructure food webs by its direct effects on lower trophic groups rather than via modifying top-down effects