Quantifying wave exposure in shallow temperate reef systems: applicability of fetch models for predicting algal biodiversity
Hill, N and Pepper, A and Puotinen, M and Hughes, M and Edgar, GJ and Barrett, NS and Stuart-Smith, RD and Leaper, R, Quantifying wave exposure in shallow temperate reef systems: applicability of fetch models for predicting algal biodiversity, Marine Ecology - Progress Series, 417, (November) pp. 83-95. ISSN 0171-8630 (2010) [Refereed Article]
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.