Phytoplankton performance in the lab predicts occurrence in the field across a global temperature gradient

Biologists aim to predict where species will survive and thrive as the planet warms. To do so, we often rely on data-hungry species distribution models (SDMs) that use associations between species occurrences and environmental predictors to capture the realised niche. An alternative basis for predictions is to experimentally quantify the effect of environmental drivers on performance, which captures the fundamental niche. We presently do not know which of these approaches represents a better path towards accurate forecasts. SDMs may depend too strongly on present-day environmental covariation, which will change in the future. In contrast, a major shortcoming of experiments is that they ignore most environmental drivers to focus on one or two. Quantifying how well fundamental and realised niches agree today would help establish how useful both SDMs and experiments are likely to be. We therefore compared both niches in 39 relatively common marine phytoplankton species. The temperature-dependence of population growth rate was characterised with a thermal performance curve model applied to lab experimental data, and the temperature-dependence of species occurrence probability estimated with SDMs applied to a global compilation of marine presence records. We found a fairly strong, near 1:1 relationship between measures of thermal niche centre: the median growth temperature in the lab and the median occurrence temperature in the field (R2 = 0.49). We also found a modest positive relationship between measures of thermal niche width, the growth niche width and the occurrence niche width (R2 = 0.24). This agreement should increase our confidence in environmental preferences inferred with SDMs. It also suggests that simple experiments can reliably constrain species ranges and help forecast range shifts. This has important implications for forecasting community composition and ecosystem processes, as we ought to be able to predict range shifts in biogeochemically-important taxa such as diatoms and nitrogen-fixing cyanobacteria.