The development of formally data-constrained models of ecological phenomena is increasingly popular. While debate about good and bad practice in data-constrained modelling rages on, few doubt that the direct incorporation of empirical evidence into the formulation of ecological models is a good idea. However, the methods involved have traditionally been technically demanding, sometimes soaking up months to years of research time, and so there is keen interest in developing computational methods that make it less of a technically demanding challenge to develop models and combine them with data appropriately within ecological research.


I will describe the success we have had to date in employing the simple and fast inference library Filzbach within various research projects. Filzbach is a code library that enables Bayesian parameter inference via Markov Chain Monte Carlo, using the Metropolis Hastings algorithm, that in comparisons is one of the fastest and easiest to use inference libraries available. I will underline why we have found it particularly useful in recent published research, particularly in the development of the first fully data constrained global terrestrial carbon model, but will also highlight limitations, both with the algorithm and with the user interface that point to new and improved formal data constraining approaches. I will then go on to show our next generation of tools designed to lower the technical overhead in conducting data constrained modelling even further. I will show results from a recent study to investigate the mechanisms underpinning phytoplankton blooms in the north atlantic made within the tool as an example. In this case, the only code I wrote is the formal functional specification of the classical nutrient-phytplankton-zooplankton model – all the rest, from climate and environmental data fetch, through parameter inference, to probabilistic forecasts were all made within our new tool. We hope innovations such as these will hasten the pace at which we produce demonstrably useful predictive models in ecology.