Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches

Stafford, R., Smith, V.A., Husmeier, D. , Grima, T. and Guinn, B.-A. (2013) Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches. Current Zoology, 59(3), pp. 403-417.

Abstract

Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically inferior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions between species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ‘stable’ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime optimisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change (or, in practice, any other disturbance) on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to measureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Husmeier, Professor Dirk
• Authors: Stafford, R., Smith, V.A., Husmeier, D., Grima, T., and Guinn, B.-A.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Current Zoology
• ISSN: 1674-5507