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

2.50
Hdl Handle:
http://hdl.handle.net/10547/578874
Title:
Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches
Authors:
Stafford, Richard; Smith, V.Anne; Husmeier, Dirk; Grima, Thomas; Guinn, Barbara-Ann
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.
Affiliation:
Bournemouth University; University of St Andrews; University of Glasgow; University of Bedfordshire
Citation:
Stafford, R., Smith, V.A., Husmeier, D., Grima, T. & Guinn, B.A. (2013) 'Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches'. Current Zoology (59) pp403-417.
Publisher:
Current Zoology
Issue Date:
2013
URI:
http://hdl.handle.net/10547/578874
Additional Links:
http://www.currentzoology.org/temp/%7B6B8F7E61-2553-4246-80C5-DAF0373ABFDC%7D.pdf
Type:
Article
Language:
en
ISSN:
1674-5507
Appears in Collections:
Biomedicine and Nutrition Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorStafford, Richarden
dc.contributor.authorSmith, V.Anneen
dc.contributor.authorHusmeier, Dirken
dc.contributor.authorGrima, Thomasen
dc.contributor.authorGuinn, Barbara-Annen
dc.date.accessioned2015-09-29T08:43:17Zen
dc.date.available2015-09-29T08:43:17Zen
dc.date.issued2013en
dc.identifier.citationStafford, R., Smith, V.A., Husmeier, D., Grima, T. & Guinn, B.A. (2013) 'Predicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approaches'. Current Zoology (59) pp403-417.en
dc.identifier.issn1674-5507en
dc.identifier.urihttp://hdl.handle.net/10547/578874en
dc.description.abstractEcological 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.en
dc.language.isoenen
dc.publisherCurrent Zoologyen
dc.relation.urlhttp://www.currentzoology.org/temp/%7B6B8F7E61-2553-4246-80C5-DAF0373ABFDC%7D.pdfen
dc.subjectregime shiften
dc.subjectphase shiften
dc.subjectalternative stable stateen
dc.subjectintertidalen
dc.subjectfood weben
dc.subjectresilienceen
dc.titlePredicting ecological regime shift under climate change: new modelling techniques and potential of molecular-based approachesen
dc.typeArticleen
dc.contributor.departmentBournemouth Universityen
dc.contributor.departmentUniversity of St Andrewsen
dc.contributor.departmentUniversity of Glasgowen
dc.contributor.departmentUniversity of Bedfordshireen
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