Temperature and stoichiometric dependence of phytoplankton traits

Abstract

Understanding the links between intraspecific trait variability and environmental gradients is an important step toward unravelling the mechanisms that link species performance to environmental variation. Here, we performed a comparative, experimental study to investigate variability of cellular traits in three prokaryotic and three eukaryotic freshwater phytoplankton species along gradients of temperature and nitrogen:phosphorus ratio (N:P) in laboratory microcosms. Temperature and N:P strongly affect phytoplankton growth and are changing due to climate change and eutrophication. Metabolic theory and allometric scaling predict that smaller organisms should be favored at higher temperatures through improved metabolic uptake partly due to greater surface area to volume ratios. In addition, chlorophyll a (chl a) concentration should increase due to higher chlorophyll synthesis in response to light limitation at higher cell densities. We found that cell volume both increased and decreased with temperature, whereas intermediate N:P yielded higher growth rates and more extreme conditions yielded bigger cell volumes. Species growth responses to these gradients were distinct and not related to phylogenetic differences. Meaningfully coupled traits like the chl a fluorescence and cell volume shifted consistently and can improve our understanding of individual cell responses to abiotic drivers. This study showed that intraspecific trait variability of freshwater phytoplankton harbors potential for short term acclimation to environmental gradients. Finally, the high trait variability in some species has strong implications for their ecology and the accuracy of predictions where responses may differ when based on mean or fixed trait values.

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

“…2013; Hofmann et al . 2019). They contain species that are slower ( Oocystis , Desmodesmus , Chroococcus ) and faster growing ( Acutodesmus , Synechocystis, Microcystis ), with phycobilins (all cyanobacteria) or without (all green algae), more sensitive to warming ( Chrococcus, Desmodesmus ) or less ( Acutodemus, Synechocystis ) and high cell wall resistance ( Desmodesmus, Acutodesmus ) and low ( Oocystis ) (Dunker & Wilhelm 2018).…”

Resolving the consequences of gradual phenotypic plasticity for populations in variable environments

“…A second set of experiments highlights the broad range of environmental regimes where population growth cannot be adequately predicted without accounting for gradual plasticity. Our results concern a single, but significant, environmental driver (temperature) and a particular study system (unicellular phytoplankton; ecologically important and an emerging model for studying plasticity [Hofmann et al 2019, Rescan et al 2020). However, gradual plasticity likely affects many systems, drivers, and ecological processes.…”

Pollen analysis using multispectral imaging flow cytometry and deep learning

“…Multispectral imaging flow cytometry in combination with deep learning has recently been demonstrated as a tool for phytoplankton identification and quantification (Dunker et al ., 2018; Dunker, 2019), allowing algae traits to be described in detail and enabling new research regarding the roles of functional traits in shaping patterns of coexistence and diversity in this important functional group of species (Hofmann et al ., 2019). Here, we propose a similar application for pollen, aiming primarily for fast and accurate identification and counting of pollen grains.…”