Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?
ABSTRACT: Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power (R2 > 0.8) and low error in predicting new observations. Interestingly, not all of the predictors present the same weight in the modelling of growth rate. A set of specific parameters, such as non-photochemical fluorescence quenching (NPQ) and the quantum yield of carbon production in the first model, and lipid, protein and carbohydrate contents for the second one, strongly covary with cell growth rate regardless of the taxonomic position of the phytoplankton species investigated. This reflects a set of specific physiological adjustments covarying with growth rate, conserved among taxonomically distant algal species that might be used as guidelines for the improvement of modern primary production models. The high predictive power of both sets of cellular traits for growth rate is of great importance for applied phycological studies. Our approach may find application as a quality control tool for the monitoring of phytoplankton populations in natural communities or in photobioreactors.
Project description:Nitrogen stress is an important control on the growth of phytoplankton and varying responses to this common condition among taxa may affect their relative success within phytoplankton communities. We analyzed photosynthetic responses to nitrogen (N) stress in two classes of phytoplankton that often dominate their respective size ranges, diatoms and prasinophytes, selecting species of distinct niches within each class. Changes in photosynthetic structures appeared similar within each class during N stress, but photophysiological and growth responses were more species- or niche-specific. In the coastal diatom Thalassiosira pseudonana and the oceanic diatom T. weissflogii, N starvation induced large declines in photosynthetic pigments and Photosystem II (PSII) quantity and activity as well as increases in the effective absorption cross-section of PSII photochemistry (?'PSII). These diatoms also increased photoprotection through energy-dependent non-photochemical quenching (NPQ) during N starvation. Resupply of N in diatoms caused rapid recovery of growth and relaxation of NPQ, while recovery of PSII photochemistry was slower. In contrast, the prasinophytes Micromonas sp., an Arctic Ocean species, and Ostreococcus tauri, a temperate coastal eutrophile, showed little change in photosynthetic pigments and structures and a decline or no change, respectively, in ?'PSII with N starvation. Growth and PSII function recovered quickly in Micromonas sp. after resupply of N while O. tauri failed to recover N-replete levels of electron transfer from PSII and growth, possibly due to their distinct photoprotective strategies. O. tauri induced energy-dependent NPQ for photoprotection that may suit its variable and nutrient-rich habitat. Micromonas sp. relies upon both energy-dependent NPQ and a sustained, energy-independent NPQ mechanism. A strategy in Micromonas sp. that permits photoprotection with little change in photosynthetic structures is consistent with its Arctic niche, where low temperatures and thus low biosynthetic rates create higher opportunity costs to rebuild photosynthetic structures.
Project description:Photosynthetic microalgae have a high potential for the production of biofuels and highly valued metabolites. However, their current industrial exploitation is limited by a productivity in photobioreactors that is low compared to potential productivity. The high cell density and pigment content of the surface layers of photosynthetic microalgae result in absorption of excess photons and energy dissipation through non-photochemical quenching (NPQ). NPQ prevents photoinhibition, but its activation reduces the efficiency of photosynthetic energy conversion. In Chlamydomonas reinhardtii, NPQ is catalyzed by protein subunits encoded by three lhcsr (light harvesting complex stress related) genes. Here, we show that heat dissipation and biomass productivity depends on LHCSR protein accumulation. Indeed, algal strains lacking two lhcsr genes can grow in a wide range of light growth conditions without suffering from photoinhibition and are more productive than wild-type. Thus, the down-regulation of NPQ appears to be a suitable strategy for improving light use efficiency for biomass and biofuel production in microalgae.
Project description:Biodiversity can strongly influence trophic interactions. The nutritional quality of prey communities and how it is related to the prey diversity is suspected to be a major driver of biodiversity effects. As consumer growth can be co-limited by the supply of several biochemical components, biochemically diverse prey communities should promote consumer growth. Yet, there is no clear consensus on how prey specific diversity is linked to community biochemical diversity since previous studies have considered only single nutritional quality traits. Here, we demonstrate that phytoplankton biochemical traits (fatty acids and sterols) can to a large extent explain Daphnia magna growth and its apparent dependence on phytoplankton species diversity. We find strong correlative evidence between phytoplankton species diversity, biochemical diversity, and growth. The relationship between species diversity and growth was partially explained by the fact that in many communities Daphnia was co-limited by long chained polyunsaturated fatty acids and sterols, which was driven by different prey taxa. We suggest that biochemical diversity is a good proxy for the presence of high food quality taxa, and a careful consideration of the distribution of the different biochemical traits among species is necessary before concluding about causal links between species diversity and consumer performance.
Project description:Infection and lysis of phytoplankton by viruses affects population dynamics and nutrient cycles within oceanic microbial communities. However, estimating the quantitative rates of viral-induced lysis remains challenging in situ. The modified dilution method is the most commonly utilized empirical approach to estimate virus-induced killing rates of phytoplankton. The lysis rate estimates of the modified dilution method are based on models which assume virus-host interactions can be represented by a single virus and a single host population with homogeneous life-history traits. Here, using modeling approaches, we examine the robustness of the modified dilution method in multi-strain, complex communities. We assume that strains differ in their life history traits, including growth rates (of hosts) and lysis rates (by viruses). We show that trait differences affect resulting experimental dynamics such that lysis rates measured using the modified dilution method may be driven by the fastest replicating strains; which are not necessarily the most abundant in situ. We discuss the implications of using the modified dilution method and alternative dilution-based approaches for estimating viral-induced lysis rates in marine microbial communities.
Project description:Phytoplankton play an important role as primary producers and thus can affect higher trophic levels. Phytoplankton growth and diversity may, besides other factors, be controlled by seasonal temperature changes and increasing water temperatures. In this study, we investigated the combined effects of temperature and diversity on phytoplankton growth. In a controlled laboratory experiment, monocultures of 15 freshwater phytoplankton taxa (green algae, cyanobacteria, and diatoms) as well as 25 mixed communities of different species richness (2-12 species) and taxa composition were exposed to constant temperatures of 12, 18, and 24 °C. Additionally, they were exposed to short-term daily temperature peaks of +4 °C. Increased species richness had a positive effect on phytoplankton growth rates and phosphorous content at all temperature levels, with maximum values occurring at 18 °C. Overyielding was observed at almost all temperature levels and could mostly be explained by complementary traits. Higher temperatures resulted in higher fractions of cyanobacteria in communities. This negative effect of temperature on phytoplankton diversity following a shift in community composition was most obvious in communities adapted to cooler temperatures, pointing to the assumption that relative temperature changes may be more important than absolute ones.
Project description:Chemical micropollutants occur worldwide in the environment at low concentrations and in complex mixtures, and how they affect the ecology of natural systems is still uncertain. Dynamics of natural communities are driven by the interaction between individual organisms and their growth environment, which is mediated by the organisms' expressed phenotypic traits. We tested whether exposure to a mixture of 12 pharmaceuticals and personal care products (PPCP) influences phenotypic trait diversity in lake phytoplankton communities and their ability to regulate biomass production to fit environmental changes (response capacity). We exposed natural phytoplankton assemblages to three mixture levels in permeable microcosms maintained at three depths in a eutrophic lake for one week, during which the environmental conditions were fluctuating. We studied individual-level traits, phenotypic diversity and community biomass. PPCP reduced individual-level trait variance and overall community phenotypic diversity, but maintained higher standing phytoplankton biomass compared to untreated controls. Estimated effect sizes of PPCP on traits and community properties were very large (partial Eta-squared > 0.15). The PPCP mixture antagonistically interacted with the natural environmental gradient in habitats offered by different depths and, at concentrations comparable to those in waste-water effluents, prevented communities from converging to the same phenotypic structure and total biomass of unexposed controls. We show that micropollutants can alter individual-level trait diversity of lake phytoplankton communities and therefore their capacity to respond to natural environmental gradients, potentially affecting aquatic ecosystem processes.
Project description:Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits-which determine responses to the abiotic and biotic environment-may help us rigorously link them. However, our capacity to measure traits in natural communities has thus far been limited. Here we compared the predictive power of trait richness (trait space coverage), evenness (regularity in trait distribution) and divergence (prevalence of extreme phenotypes) derived from individual-based measurements with two species-level metrics (taxonomic richness and evenness) when modelling the productivity of natural phytoplankton communities. Using phytoplankton data obtained from 28 lakes sampled at different spatial and temporal scales, we found that the diversity in individual-level morphophysiological traits strongly improved our ability to predict community resource-use and biomass yield. Trait evenness-the regularity in distribution of individual cells/colonies within the trait space-was the strongest predictor, exhibiting a robust negative relationship across scales. Our study suggests that quantifying individual microbial phenotypes in trait space may help us understand how to link physiology to ecosystem-scale processes. Elucidating the mechanisms scaling individual-level trait variation to microbial community dynamics could there improve our ability to forecast changes in ecosystem properties across environmental gradients.
Project description:Rising atmospheric CO 2 and ocean acidification are fundamentally altering conditions for life of all marine organisms, including phytoplankton. Differences in CO 2 related physiology between major phytoplankton taxa lead to differences in their ability to take up and utilize CO 2. These differences may cause predictable shifts in the composition of marine phytoplankton communities in response to rising atmospheric CO 2. We report an experiment in which seven species of marine phytoplankton, belonging to four major taxonomic groups (cyanobacteria, chlorophytes, diatoms, and coccolithophores), were grown at both ambient (500 ?atm) and future (1,000 ?atm) CO 2 levels. These phytoplankton were grown as individual species, as cultures of pairs of species and as a community assemblage of all seven species in two culture regimes (high-nitrogen batch cultures and lower-nitrogen semicontinuous cultures, although not under nitrogen limitation). All phytoplankton species tested in this study increased their growth rates under elevated CO 2 independent of the culture regime. We also find that, despite species-specific variation in growth response to high CO 2, the identity of major taxonomic groups provides a good prediction of changes in population growth and competitive ability under high CO 2. The CO 2-induced growth response is a good predictor of CO 2-induced changes in competition (R2 > .93) and community composition (R2 > .73). This study suggests that it may be possible to infer how marine phytoplankton communities respond to rising CO 2 levels from the knowledge of the physiology of major taxonomic groups, but that these predictions may require further characterization of these traits across a diversity of growth conditions. These findings must be validated in the context of limitation by other nutrients. Also, in natural communities of phytoplankton, numerous other factors that may all respond to changes in CO2, including nitrogen fixation, grazing, and variation in the limiting resource will likely complicate this prediction.
Project description:Understanding how changes in temperature affect interspecific competition is critical for predicting changes in ecological communities with global warming. Here, we develop a theoretical model that links interspecific differences in the temperature dependence of resource acquisition and growth to the outcome of pairwise competition in phytoplankton. We parameterised our model with these metabolic traits derived from six species of freshwater phytoplankton and tested its ability to predict the outcome of competition in all pairwise combinations of the species in a factorial experiment, manipulating temperature and nutrient availability. The model correctly predicted the outcome of competition in 72% of the pairwise experiments, with competitive advantage determined by difference in thermal sensitivity of growth rates of the two species. These results demonstrate that metabolic traits play a key role in determining how changes in temperature influence interspecific competition and lay the foundation for mechanistically predicting the effects of warming in complex, multi-species communities.
Project description:Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V c,max ), maximum electron transport rate (J max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c,max, J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c,max, but not for J max, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for V c,max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c,max. The PLSR model was able to accurately predict both lower V c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.