Systems modelling for synergistic ecological-climate dynamics

For those interested in my current science research directions, I’ve just been funded for another 4 years by the Australian Research Council as a Professorial “Future Fellow“, to continue my work on stochastic systems modelling and scenario optimisation. Here are some details on the project:

Title: Systems modelling for synergistic ecological-climate dynamics

Summary of Proposal: The project aims to improve forecasts of the response of biodiversity to future climate change, and develop better on-ground conservation management. A systems modelling framework will be developed and tested against real-world data, to integrate a wide variety of biological and geophysical inputs and so produce more realistic predictions.

Many computer-based tools have been developed to simulate single-species demography and population dynamics and the effect of habitat loss, disease spread, response to harvest, and shifts in geographical ranges due to climate change. These applications can be individually sophisticated, yet they perform necessarily limited roles in isolation. I will implement a set of ‘meta-modelling’ applications to inter-link separate ecological simulators, by allowing sharing of data structures, parameters and outputs. Using a dynamical systems approach, I will develop and test a framework for multidisciplinary forecasting and sensitivity analysis, providing improved predictions of biodiversity response to the many and complex stressors of global change.

Here is the University of Adelaide media release and below are some further details on the aims and methods:

My strategic aims are to:

(a) determine the extent to which climate change might amplify or mitigate existing major anthropogenic threats to biodiversity (e.g. habitat degradation, overexploitation, and invasive species) and

(b) link this real-world data to novel statistical and computational systems models for predictive purposes.

The goal is to use available long-term data to model and forecast population responses (distributional range, fragmentation, viability, community interactions) to multiple stressors, in particular climate change.

The objectives of the proposed research are three-fold:

1. Develop regional ecological response models for threatened (and other important) species, using long-term data to evaluate the synergistic impact of multiple threats on population responses. These models will integrate existing ecosystem attributes and data related to demography, distribution, and autecology.

2. Partition population responses between anthropogenic, environmental and climatic stressors, identify historical population trends relative to contemporary climate change, and analyse emergent results from complex simulators.

3. Use integrated ecological response models to forecast population responses to future climatic conditions based on ecosystem attributes and regional down-scaled Global Climate Model outputs.

Given the scope and rate of climate change and other human impacts, it is now imperative that we find logistically feasible and cost-effective ways to prevent a cascading loss of biodiversity, from the local/regional to national/international scale, and thus maintain relatively healthy ecosystems in perpetuity. The project aims to improve forecasts of the response of biodiversity to future climate change, and so improve on-ground conservation management. A systems modelling framework will be developed, and tested against real-world data, to integrate a wide variety of biological and geophysical inputs and so produce more realistic predictions. Model development will be based on decades of on-ground monitoring and remote sensing data. Linking ecological response models to climate model projections affords an opportunity to project species and ecosystem responses for the next 50+ years, while providing a state-of-the-art forecasting tool for conservation and management agencies.

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Meta-modelling is an approach in which computational links are constructed between separately developed discipline-specific models (described in Nyhus et al. 2007). The concept is that individual simulators (existing or new models) can function as arbitrarily powerful stand-alone programs, with a meta-model framework being used to manage the integration of two or more system components, to create a dynamic ‘bottom-up’ simulation. By passing data structures and variables describing the state of the system between programs, this novel approach can be used to develop sophisticated applications without the need to focus on time-consuming (and often specialist) development of all individual processes. It also holds the promise of generating emergent, non-linear behaviour – much like the output produced by Earth Systems models, which include atmosphere, cryosphere and layered ocean models, dynamic vegetation simulators, etc. (IPCC 2007).

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I also hope to build in energy systems case studies into this work, as it’s now obviously a serious research interest of mine. There’s lots more in the formal proposal of course, but I’d be happy to answer any questions here on BNC on the details of the approach.