Phytoplankton competition and resilience under fluctuating temperature.

Environmental variability is an inherent feature of natural systems which complicates predictions of species interactions. Primarily, the complexity in predicting the response of organisms to environmental fluctuations is in part because species' responses to abiotic factors are non-linear, even in stable conditions. Temperature exerts a major control over phytoplankton growth and physiology, yet the influence of thermal fluctuations on growth and competition dynamics is largely unknown. To investigate the limits of coexistence in variable environments, stable mixed cultures with constant species abundance ratios of the marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana, were exposed to different temperature fluctuation regimes (n = 17) under high and low nitrogen (N) conditions. Here we demonstrate that phytoplankton exhibit substantial resilience to temperature variability. The time required to observe a shift in the species abundance ratio decreased with increasing fluctuations, but coexistence of the two model species under high N conditions was disrupted only when amplitudes of temperature fluctuation were high (±8.2°C). N limitation caused the thermal amplitude for disruption of species coexistence to become lower (±5.9°C). Furthermore, once stable conditions were reinstated, the two species differed in their ability to recover from temperature fluctuations. Our findings suggest that despite the expectation of unequal effect of fluctuations on different competitors, cycles in environmental conditions may reduce the rate of species replacement when amplitudes remain below a certain threshold. Beyond these thresholds, competitive exclusion could, however, be accelerated, suggesting that aquatic heatwaves and N availability status are likely to lead to abrupt and unpredictable restructuring of phytoplankton community composition.

High predictability of direct competition between marine diatoms under different temperatures and nutrient states.

The distribution of marine phytoplankton will shift alongside changes in marine environments, leading to altered species frequencies and community composition. An understanding of the response of mixed populations to abiotic changes is required to adequately predict how environmental change may affect the future composition of phytoplankton communities. This study investigated the growth and competitive ability of two marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana, along a temperature gradient (9-35°C) spanning the thermal niches of both species under both high-nitrogen nutrient-replete and low-nitrogen nutrient-limited conditions. Across this temperature gradient, the competitive outcome under both nutrient conditions at any assay temperature, and the critical temperature at which competitive advantage shifted from one species to the other, was well predicted by the temperature dependencies of the growth rates of the two species measured in monocultures. The temperature at which the competitive advantage switched from P. tricornutum to T. pseudonana increased from 18.8°C under replete conditions to 25.3°C under nutrient-limited conditions. Thus, P. tricornutum was a better competitor over a wider temperature range in a low N environment. Being able to determine the competitive outcomes from physiological responses of single species to environmental changes has the potential to significantly improve the predictive power of phytoplankton spatial distribution and community composition models.

Predictions of response to temperature are contingent on model choice and data quality.

The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates, are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and suboptimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured) but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best-fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.