Modelling biodiversity related ecosystem processes as a Complex Adaptive System

Supervision Team

Background

Evidence is accumulating that biodiversity is a strong determinant of the ecosystem services delivered by an ecosystem and therefore ultimately the form of sustainable use of a system (Loreau et al 2001, Tilman et al 2001, Sala and Knowlton 2006, Butler et al 2007, Palumbi et al 2009). Thus, a steep decline in biodiversity has potentially larger consequences for ecosystems (of which humans are part) other than just the lost of species and direct implications for humans use of those species. Moreover there is significant potential for global change to lead to shifting diversity locally, regionally and globally (Vitousek et al 1997). Unfortunately, current ecosystem models (e.g. Ecopath with Ecosim (Christensen and Walters 2004) or Atlantis (Fulton et al 2004)) that are used to consider natural resource management and the impacts of human activities and climate change do not include an explicit handling of shifts in biodiversity. While complicated, these models will not be able to explore the full range of system dynamics (especially those likely to dominate under changing conditions) if they neglect the fundamental adaptability of system components.

The field of complex adaptive systems (CAS) (Holland 1992) offers a dynamic view in which fixed values for species composition of modelled groups (and associated parameters) are replaced with rules that are a function of long term (e.g. species) and short term (typically individual) history, embracing the adaptive capacity of nature. Ultimately, the flexibility of CAS based models of biodiversity needs to be married with existing process-based ecosystem models to allow for exploration of effective adaptive management.

References

Butler, S.J., Vickery, J.A., and Norris, K. (2007) Farmland biodiversity and the footprint of agriculture. Science, 315: 381–384

Christensen, V. and Walters, C. (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, 172: 109–139

Fulton, E.A., Fuller, M., Smith, A.D.M. and Punt, A.E. (2004) Ecological indicators of the ecosystem effects of fishing: final report. Australian Fisheries Management Authority Report, R99/1546.

Holland, J.H. (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge, Mass: MIT Press

Loreau, M., Naeem, S., Inchausti, P., et al. (2001) Biodiversity and ecosystem functioning: current knowledge and future challenges. Science, 294: 804–808

Palumbi, S.R., Sandifer, P.A., Allan, J.D., et al (2009) Managing for ocean biodiversity to sustain marine ecosystem services. Frontiers in Ecology and Environment, 7: 204–211

Sala, E. and Knowlton, K. (2006) Global marine biodiversity trends. Annual Reviews of Environmental Resources, 31: 93–122

Tilman, D., Fargione, F., Wolff, B., et al (2001) Forecasting agriculturally driven global environmental change. Science, 292: 281–284

Vitousek, P.M., Mooney, H.A., Lubchenco, J. and Melillo, J.M. (1997) Human Domination of Earths Ecosystems. Science, 277: 494-499

Project outline and objectives

This project will be aimed at developing quantitative models of how biodiversity and associated ecosystem functioning changes through time, particularly in marine ecosystems. This will begin with a systematic investigation of existing approaches to modelling biodiversity in a dynamic ecosystem setting and then move on to the development of sub-models for ecosystem models (e.g. Atlantis), representing biodiversity in a variety of non-stationary ways.

The specific objectives of the project are:

1. Identify the temporal and spatial scale of adaptation and change, and whether it is feasible to include these processes in existing ecosystem models based largely at a functional group level or whether a new generation of models is needed (this will then dictate what form of models are developed in later stages, whether they will be stand alone or nest within existing modelling frameworks).
2. Identify different existing approaches to modelling biological adaptation; this will involve evaluating the different approaches - such as trait-based modelling (e.g. Norberg 2004, Merico et al. 2009), “systems of indefinite diversity” (Bruggeman and Kooijman 2007), and pattern-oriented modelling (e.g. Grimm et al. 2005) - in terms of functionality, effectiveness, computer efficiency and model complexity.
3. Construction of sub-models for ecosystem models (or stand alone models depending on the outcome of objective 1), representing biodiversity in a variety of non-stationary ways.
4. Use the models to consider biodiversity under changing conditions (potentially also identifying key related processes and their importance for biodiversity in the test ecosystems).

Altogether this work will consolidate information on how to model ecosystem processes as a Complex Adaptive System.

References

Bruggeman, J. and Kooijman, S.A.L.M. (2007) A biodiversity-inspired approach to aquatic ecosystem modeling Limnololgy and Oceanography, 52, 1533–1544

Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., Thulke, H., Weiner, J., Wiegand, T., DeAngelis, D.L. (2005) Pattern-oriented modeling of agent-based complex systems: Lessons from ecology, Science, 310, 0000987-991

A. Merico, J. Bruggeman, K. Wirtz, 2009, A trait-based approach for downscaling complexity in plankton ecosystem models, Ecological Modelling, In Press

J. Norberg, 2004, Biodiversity and ecosystem functioning: A complex adaptive systems approach, Limnol. Oceanogr., 49(4, part 2), 1269–1277