All ecophysiological processes depend strongly on the environment. This poses a major challenge to our attempts to model and forecast dynamics because (1) we need to incorporate the environmental dependence in process-based models, (2) the environment is multi-dimensional, and (3) the experiments needed to parameterise this multi-dimensional dependence are too large and complex. How do we solve this problem? I propose that time series datasets and flexible modelling approaches (including machine learning) can provide imperfect but usable parameterisations. I demonstrate using a 20-year plankton time series from Lake Constance that we can use interpretable machine learning methods to obtain realistic estimates of how growth varies across multiple dimensions: temperature, light, nutrients, predators. The 4-dimensional ‘growth response surfaces’ are consistent with lab experiments, and can be used as input in process-based models. Furthermore, they can be used to derive important ecological inferences. Comparing how all species perform across environmental parameter space, I find no evidence for any of the commonly hypothesized trade-offs believed to govern ecological dynamics, including gleaner vs. opportunist, growth rate vs. competitive ability, or nutrient competitive ability vs. light competitive ability. Instead, I find evidence for a more complex and unexplored co-existence mechanism. Trade-offs appear to be multidimensional, suggesting that ignoring any of these environmental dimensions will lead to incorrect inferences and forecasts of dynamics and biodiversity.

Primary Presenter: Mridul Thomas, University of Geneva (mridul.thomas@unige.ch)

Authors:

Ursula Gaedke, University of Potsdam  (ursula.gaedke@uni-potsdam.de)