Management and conservation of ecosystems relies on biodiversity data; however, broad-scale biological data are often limited. Predictive modelling using environmental variables has recently proven a valuable tool in addressing this gap. Wave exposure is a particularly important environmental variable that structures shallow reef systems, but it is rarely quantified across the large areas often used for predictive studies. Therefore, we investigated approaches that quantify exposure and can be readily applied across a large area. We generated 6 quantitative indices that emphasise different aspects of exposure using a numerical wave model and cartographic fetch models. The utility of these indices for predictive modelling in shallow temperate reef systems was assessed by how well they explained community and genera-level algal patterns in Tasmania, Australia, which is a region that experiences a wide range of wave exposure conditions. Exposure indices were significant predictors of algal patterns, explaining up to 18% of community level patterns and up to 37% of the variance associated with the occurrence and cover of algal genera. Fetch-based indices in particular appear to be a viable option for quantifying exposure on shallow reefs. These indices can be generated within a Geographic Information System (GIS) program for specific sites of interest, along coastlines or on a grid, and are potentially accessible to ecologists. Quantification of exposure across broad regions using fetch indices will allow ecologists to makes advances in predictive modelling studies, but also facilitate studies that test the generality of hypotheses and mechanisms driving patterns previously observed using qualitative measures.KEY WORDS: Wave exposure index · Cartographic fetch modelling · Macro-algae · Biodiversity · Predictive models Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 417: [83][84][85][86][87][88][89][90][91][92][93][94][95] 2010 tion of shallow reef communities requires knowledge of biodiversity patterns across large regions. Because collecting biological data is labour intensive, difficult and expensive for the large areas relevant to management, these data are often limited and patchy. Where biological data are limited or lacking, predicting patterns of biodiversity using relationships between biodiversity and physical variables can facilitate conservation decision-making (Lehmann et al. 2002, Beger & Possingham 2008, Foster & Dunstan 2010.A range of physical data is now available across large regions to aid in the development of predictive models for the marine environment. These include data on sea surface temperature or ocean colour from satellites (e.g. MODIS and SeaWiFS) or Argo floats, ocean chemistry measurements (e.g. CSIRO Atlas of Regional Seas, CARS; Condie & Dunn 2006), bathymetry (e.g. Whiteway 2009) and sediment grain-size data (e.g. Marine Sediment database, MARS; Geoscience Australia). While these physical data have proven useful for ecosys...