Abstract
Scalable assessments of biodiversity are required to successfully and adaptively manage coastal ecosystems. Assessments must account for habitat variations at multiple spatial scales, including the small scales (<100 m) at which biotic and abiotic habitat components structure the distribution of fauna, including fishes. Associated challenges include achieving consistent habitat descriptions and upscaling from in situ-monitored stations to larger scales. We developed a methodology for (a) determining habitat types consistent across scales within large management units, (b) characterizing heterogeneities within each habitat, and (c) predicting habitat from new survey data. It relies on clustering techniques and supervised classification rules and was applied to a set of 3,145 underwater video observations of fish and benthic habitats collected in all reef and lagoon habitats around New Caledonia. A baseline habitat typology was established with five habitat types clearly characterized by abiotic and biotic attributes. In a complex mosaic of habitats, habitat type is an indispensable covariate for explaining spatial variations in fish communities. Habitat types were further described by 26 rules capturing the range of habitat features encountered. Rules provided intuitive habitat descriptions and predicted habitat type for new monitoring observations, both straightforwardly and with known confidence. Images are convenient for interacting with managers and stakeholders. Our scheme is (a) consistent at the scale of New Caledonia reefs and lagoons (1.4 million km(2)) and (b) ubiquitous by providing data in all habitats, for example, showcasing a substantial fish abundance in rarely monitored soft-bottom habitats. Both features must be part of an ecosystem-based monitoring strategy relevant for management. This is the first study applying data mining techniques to in situ measurements to characterize coastal habitats over regional-scale management areas. This approach can be applied to other types of observations and other ecosystems to characterize and predict local ecological assets for assessments at larger scales.