Aim Quantifying and predicting change in large ecosystems is an important research objective for applied ecologists as human disturbance effects become increasingly evident at regional and global scales. However, studies used to make inferences about large-scale change are frequently of uneven quality and few in number, having been undertaken to study local, rather than global, change. Our aim is to improve the quality of inferences that can be made in meta-analyses of large-scale disturbance by integrating studies of varying quality in a unified modelling framework that is informative for both local and regional management.
Innovation Here we improve conventionally structured meta-analysis methods by including imputation of unknown study variances and the use of Bayesian factor potentials. The approach is a coherent framework for integrating data of varying quality across multiple studies while facilitating belief statements about the uncertainty in parameter estimates and the probable outcome of future events. The approach is applied to a regional meta-analysis of the effects of loss of coral cover on species richness and the abundance of coral-dependent fishes in the western Indian Ocean (WIO) before and after a mass bleaching event in 1998.
Main conclusions Our Bayesian approach to meta-analysis provided greater precision of parameter estimates than conventional weighted linear regression meta-analytical techniques, allowing us to integrate all available data from 66 available study locations in the WIO across multiple scales. The approach thereby: (1) estimated uncertainty in site-level estimates of change, (2) provided a regional estimate for future change at any given site in the WIO, and (3) provided a probabilistic belief framework for future management of reef resources at both local and regional scales.