Assessment of Habitat Representation across a Network of Marine Protected Areas with Implications for the Spatial Design of Monitoring

Abstract

Networks of marine protected areas (MPAs) are being adopted globally to protect ecosystems and supplement fisheries management. The state of California recently implemented a coast-wide network of MPAs, a statewide seafloor mapping program, and ecological characterizations of species and ecosystems targeted for protection by the network. The main goals of this study were to use these data to evaluate how well seafloor features, as proxies for habitats, are represented and replicated across an MPA network and how well ecological surveys representatively sampled fish habitats inside MPAs and adjacent reference sites. Seafloor data were classified into broad substrate categories (rock and sediment) and finer scale geomorphic classifications standard to marine classification schemes using surface analyses (slope, ruggedness, etc.) done on the digital elevation model derived from multibeam bathymetry data. These classifications were then used to evaluate the representation and replication of seafloor structure within the MPAs and across the ecological surveys. Both the broad substrate categories and the finer scale geomorphic features were proportionately represented for many of the classes with deviations of 1-6% and 0-7%, respectively. Within MPAs, however, representation of seafloor features differed markedly from original estimates, with differences ranging up to 28%. Seafloor structure in the biological monitoring design had mismatches between sampling in the MPAs and their corresponding reference sites and some seafloor structure classes were missed entirely. The geomorphic variables derived from multibeam bathymetry data for these analyses are known determinants of the distribution and abundance of marine species and for coastal marine biodiversity. Thus, analyses like those performed in this study can be a valuable initial method of evaluating and predicting the conservation value of MPAs across a regional network.

Application of species distribution models to explain and predict the distribution, abundance and assemblage structure of nearshore temperate reef fishes

“…The methods for creating these geomorphic classifications can be found in Young & Carr (2015). The methods for creating these geomorphic classifications can be found in Young & Carr (2015).…”

“…The second method used non-spatial geomorphic classifications extracted from the CSMP seafloor maps. The methods for creating these geomorphic classifications can be found in Young & Carr (2015). The six geomorphic classification categories were based on the three-dimensional complexity of the rocky reef: low complexity, low-to-moderate complexity, moderate complexity, moderate-to-high complexity, high complexity and high complexity on sloping terrain.…”

China’s little-known efforts to protect its marine ecosystems safeguard some habitats but omit others

“…Prior studies have also used sediment type to inform ecological representativeness of MPA networks ( 32 , 38 ). We attempted to include sediment type as well, but data for China were only available across small independent studies, and there were no data sources available—Chinese or international—that encompassed the entire EEZ.…”

An ecological framework for informing permitting decisions on scientific activities in protected areas

“…The habitat data collected and compiled in association with MPA establishment [ 47 – 49 , 51 , 52 ] served as a model for estimating habitat abundance (area) in California’s MPAs. For offshore locations, habitat areas were obtained using high-resolution digital elevation models, raster datasets that consist of depth values at regularly spaced intervals (e.g.…”