Marine phytoplankton functional types exhibit diverse responses to thermal change

Marine phytoplankton generate half of global primary production, making them essential to ecosystem functioning and biogeochemical cycling. Though phytoplankton are phylogenetically diverse, studies rarely designate unique thermal traits to different taxa, resulting in coarse representations of phytoplankton thermal responses. Here we assessed phytoplankton functional responses to temperature using empirically derived thermal growth rates from four principal contributors to marine productivity: diatoms, dinoflagellates, cyanobacteria, and coccolithophores. Using modeled sea surface temperatures for 1950–1970 and 2080–2100, we explored potential alterations to each group’s growth rates and geographical distribution under a future climate change scenario. Contrary to the commonly applied Eppley formulation, our data suggest phytoplankton functional types may be characterized by different temperature coefficients (Q10), growth maxima thermal dependencies, and thermal ranges which would drive dissimilar responses to each degree of temperature change. These differences, when applied in response to global simulations of future temperature, result in taxon-specific projections of growth and geographic distribution, with low-latitude coccolithophores facing considerable decreases and cyanobacteria substantial increases in growth rates. These results suggest that the singular effect of changing temperature may alter phytoplankton global community structure, owing to the significant variability in thermal response between phytoplankton functional types.


March 2021
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We conducted a meta-analysis of thermal response data to characterize key phytoplankton functional types (PFTs) and their relative abilities to cope with ocean warming. This included assessing each PFTs thermal dependency (Q10) and static capacity for thermal change. The PFTs we evaluated included four principal contributors to marine productivity: diatoms (strains (n)=135), dinoflagellates (n=46), coccolithophores (n=30), and cyanobacteria (n=32). We then employed sea surface temperature projections from an ensemble of Earth System Models to assess how PFT growth and geographical range may be altered in a future ocean.
Beginning with a growth rate compilation by Thomas et al. (2012) . This resulted in a compilation of thermal growth rates from four functional groups, which comprised 243 strains and 3,246 discrete growth rate measurements from a broad range of temperatures and locations. In total, our dataset contained the following growth rate measurements (N): coccolithophores (N=202), cyanobacteria (N=502), diatoms (N=1794), and dinoflagellates (N=748).
Analyses for this study were conducted using data from the literature. We used 99th quantile regressions, according to the method outlined in Bissinger et al. (2008), to determine whether the sample size from each phytoplankton functional type was sufficient; both in terms of the number and thermal range of thermal growth rate measurements.
This study does not include new experimental findings, as data was compiled from the literature. When growth data was not made available in spreadsheet form, GraphClick software (version 3.0.3) was employed to digitize rate measurements from published figures. An ensemble mean of modeled sea surface temperatures from two 20-year time frames (1950-1970; 2080-2100) were utilized from the Coupled Model Intercomparison Project phase 5 (CMIP5).
Thermal growth rates were compiled from literature published from 1935 to 2020. They include phytoplankton collected globally, but are restricted to marine or estuarine sources.
We focused our efforts on marine phytoplankton, excluding freshwater species which have evolved under physical dynamics that strongly differ from those of marine environments. Thermal growth rates followed the selection criteria outlined in Thomas et al. (2012), with a few modifications. We broadened our criteria to include growth rates measured at greater than 80 µmol photons m2s-1 when day length equaled 24 hours, allowing for the inclusion of more cyanobacteria. We eliminated studies which exposed strains to fluctuating nutrient concentrations, as there was concern about the comparability of the resulting reaction norms. Additionally, the cyanobacteria group was constrained to eliminate diazotrophic species, which are characterized by fundamentally different physiological processes, which could impact group characterizations. Though diazotrophs are significant ecological contributors, the data available was deemed insufficient to characterize them independently (see Sampling strategy). Additionally, dinoflagellate growth rates were verified to be autotrophically obtained (strains grown on medium only).
R code and growth data have been made available so analyses can be reproduced.
Phytoplankton functional groups were assigned according to information from the original studies. Data was not otherwise grouped.
Data for this study was collected from the literature prior to analysis and growth data can be traced back to its original sources.