Community ecological modelling as an alternative to physiographic classifications for marine conservation planning

Accurate mapping of marine species and habitats is an important yet challenging component of establishing networks of representative marine protected areas. Due to limited biological data, marine classifications based on abiotic data are often used as surrogates to represent biological patterns. We tested the surrogacy of an existing physiographic marine classification using non-metric multidimensional scaling and permutational analysis of variance to determine whether species composition was significantly different among physiographic units. We also present an alternative ecological classification that incorporates biological and environmental data in a community modeling approach. We use data on 174 species of demersal fish and benthic invertebrates to identify mesoscale biological assemblages in a 100,000 km 2 study area in the northeast Pacific Ocean. We identified assemblages using cluster analysis then used a random forest model with 12 environmental variables to delineate mesoscale ecological units. Our community modelling approach resulted in five geographically coherent ecological units that were best explained by changes in depth, temperature and salinity. Our model showed high predictive performance (AUC = 0.93) and the resulting ecological units represent more distinct species assemblages than those delineated by physiographic variables alone. A strength of our analysis is the ability to map model uncertainty to identify transition zones at unit boundaries. The output of this study provides a biotic driven classification that can be used to better achieve representativity in the MPA planning process.