Temperature and stoichiometric dependence of phytoplankton traits

Direct and indirect cumulative effects of temperature, nutrients, and light on phytoplankton growth

Temperature and resource availability are pivotal factors influencing phytoplankton community structures. Numerous prior studies demonstrated their significant influence on phytoplankton stoichiometry, cell size, and growth rates. The growth rate, serving as a reflection of an organism's success within its environment, is linked to stoichiometry and cell size. Consequently, alterations in abiotic conditions affecting cell size or stoichiometry also exert indirect effects on growth. However, such results have their limitations, as most studies used a limited number of factors and factor levels which gives us limited insights into how phytoplankton respond to environmental conditions, directly and indirectly. Here, we tested for the generality of patterns found in other studies, using a combined multiple-factor gradient design and two single species with different size characteristics. We used a structural equation model (SEM) that allowed us to investigate the direct cumulative effects of temperature and resource availability (i.e., light, N and P) on phytoplankton growth, as well as their indirect effects on growth through changes in cell size and cell stoichiometry. Our results mostly support the results reported in previous research thus some effects can be identified as dominant effects. We identified rising temperature as the dominant driver for cell size reduction and increase in growth, and nutrient availability (i.e., N and P) as dominant factor for changes in cellular stoichiometry. However, indirect effects of temperature and resources (i.e., light and nutrients) on species' growth rates through cell size and cell stoichiometry differed across the two species suggesting different strategies to acclimate to its environment.

Do all algae grow faster in environments replenished by reclaimed water? Examples of two effluents produced in Beijing

Reclaimed water with nitrogen, phosphorus, and other contaminants may trigger algal blooms during its ecological utilization in replenishing rivers or lakes. However, the effect of reclaimed water on algal growth rates is not well understood. In this study, the growth potentials of algae in terms of Cyanophyta, Chlorophyta, and Bacillariophyta, as well as mixed algae in both regular culture medium and reclaimed water produced from treatment plants in Beijing with similar N and P concentrations, were compared to evaluate whether reclaimed water could facilitate algal growth. In addition, reclaimed water was also sterilized to verify the impact of bacteria's presence on algal growth. The results indicated that most algae grew faster in reclaimed water, among which the growth rate of Microcystis aeruginosa even increased by 5.5 fold. The growth of mixed algae in reclaimed water was not enhanced due to the strong adaptive ability of the community structure. Residual bacteria in the reclaimed water were found to be important contributors to algal growth. This work provided theoretical support for the safe and efficient utilization of reclaimed water.

Predicting effects of multiple interacting global change drivers across trophic levels

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Environmental impacts on single-cell variation within a ubiquitous diatom: The role of growth rate

Morphological and physiological characteristics of phytoplankton cells are highly sensitive to changes in environmental conditions and, in turn, influence the dynamics of phytoplankton populations and communities. To cope with environmental change, trait variability and phenotypic plasticity may play an important role. Since global change comprises simultaneous changes in abiotic parameters, we assessed the impact of multiple drivers on functional traits of the diatom Thalassiosira (Conticribra) weissflogii by manipulating concurrently temperature, pCO₂, and dissolved nitrogen:phosphorus (N:P) ratio. We tested three scenarios: ambient (ambient temperature and atmospheric pCO₂; 16 N:P ratio), moderate future scenario (+1.5°C and 800 ppm CO₂; 25 N:P ratio), and more severe future scenario (+3°C and 1000 ppm CO₂; 25 N:P ratio). We applied flow cytometry to measure on single-cell levels to investigate trait variability and phenotypic plasticity within one strain of diatoms. Growth rates differed significantly between the treatments and were strongly correlated with cell size and cellular chlorophyll a content. We observed a negative correlation of growth rate with chlorophyll a variability among single strain populations and a negative correlation with the phenotypic plasticity of cell size, i.e. when growth rates were higher, the cell size cell-to-cell variability within cultures was lower. Additionally, the phenotypic plasticity in cell size was lower under the global change scenarios. Overall, our study shows that multiple traits are interlinked and driven by growth rate and that this interconnection may partly be shaped by environmental factors.

Novel cyanotoxin-producing Synechococcus in tropical lakes

Picocyanobacteria are small cyanobacteria, being about 0.8-1.5 µm in size. They are present in freshwater environments all over the world and are known to cause harmful algal blooms, although their effects are not well understood. Algal blooms are important to manage because they threaten freshwater resources, with potentially severe effects on ecological and human health. There is also increased urgency due to urbanization and climate change trends which are expected to exacerbate these bloom dynamics. These changes are expected to especially favour picocyanobacteria groups, emphasizing the need for better characterization of their effects in the environment. In this study, we report the discovery that Synechococcus sp. could produce cylindrospermopsin (CYN) and anatoxin-a (ATX). This ability had never been previously reported for this species. Their toxin genes were also partial compared to other major producers such as Raphidiopsis sp. and Anabaena sp., demonstrating potentially unique synthesis pathways that provides insight into the various mechanisms of genetic variation that drives toxin synthesis. The Synechococcus sp. strains were found to produce about 9.0 × 10^-5-6.8 × 10^-4 fg CYN cell^-1 and 4.7 × 10^-4-1.5 × 10^-2 fg ATX cell^-1. The potential for Synechococcus sp. to be toxic highlights a global concern due to its widespread distribution, and through environmental trends that increasingly favour its productivity within freshwater systems around the world.

Beyond nitrogen: phosphorus - estimating the minimum niche dimensionality for resource competition between phytoplankton

The niche dimensionality required for coexistence is often discussed in terms of the number of limiting resources. N and P limitation are benchmarks for studying phytoplankton interactions. However, it is generally agreed that limitation by small numbers of resources cannot explain the high phytoplankton diversity observed in nature. Here, we parameterised resource competition models using experimental data for six phytoplankton species grown in monoculture with nine potential limiting resources. We tested predicted species biomass from these models against observations in two-species experimental mixtures. Uptake rates were similar across species, following the classic Redfield ratio. Model accuracy levelled out at around three to five resources suggesting the minimum dimensionality of this system. The models included the resources Fe, Mg, Na and S. Models including only N and P always performed poorly. These results suggest that high-dimensional information about resource limitation despite stoichiometric constraints may be needed to accurately predict community assembly.

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.