Predicting habitat suitability of filter-feeder communities in a shallow marine environment, New Zealand.

The distribution of benthic ecosystems, dominated by filter-feeding communities, is highly influenced by the seabed geomorphology. However, the spatial variation in settlement of these species is also affected by near-bottom currents and any changes in light, nutrient concentration and food quality often associated with increases of suspended sediment concentrations within the water column. Detailed predictions of the geographic distribution of filter-feeder species and a deeper understanding of the physical processes influencing their distribution patterns is key for effective management and conservation. To date, predictive distribution modelling has been derived essentially from geomorphological parameters, mainly using spatially limited observations. In this study, seabed mapping, oceanographic modelling, hydrographic records and biological observations are integrated to provide high-resolution prediction of filter-feeder habitat distribution within Queen Charlotte Sound/Totaranui and Tory Channel/Kura Te Au, South Island of New Zealand. The aim is to evaluate potential suitable habitat areas for filter-feeders to inform where habitat restoration management should focus efforts to recover communities such as the horse mussel (Atrina zelandica) or the green-lipped mussel (Perna canaliculus), both of which have high economic impact in New Zealand. To accomplish this, Maximum Entropy (MaxEnt) predictive modelling was used to produce Habitat Suitability (HS) maps, using geomorphological parameters and seafloor classification information. Final HS maps also incorporated oceanographic and sediment dynamic information, showing that filter-feeder habitat distribution is highly influenced by the hydrodynamics and sedimentary processes apart from the seafloor geomorphology. Filter-feeder communities inhabit quiescent areas, limited by depth, slope and sediment type; and coincide with regions presenting low near-bottom currents and low turbidity levels. Additionally, the obtained results reveal the effects of the coastal settlements and major marine traffic routes, limiting the suitable habitats to areas with less human impact. This study demonstrates that a multidisciplinary approach is crucial to better predict the spatial distribution of benthic communities, which is key to improve benthic habitat restoration and recovery assessments.

Development of an ecologic marine classification in the new zealand region.

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.