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

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Current Zoology 2013年第3期59卷 403-417页

作者： Richard STAFFORD V. Anne SMITH Dirk HUSMEIER thomas grima Barbara-ann GUINN School of Applied Sciences Boumemouth University BH12 5BB UK School of Biology University of St Andrews St Andrews KY 16 9ST UK School of Mathematics and Statistics University of Glasgow G 12 8QW UK Department of Life Sciences University of Bedfordshire LU1 3JU UK

Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, 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 be- tween 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 op- timisation 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 meas- ureable 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.

关键词： Regime shift Phase shift Altemative stable state Intertidal Food web Resilience

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