Mapping, quantifying and comparing seascape heterogeneity of Southwest Indian Ridge seamounts

Context Seamounts are abundant geomorphological features creating seabed spatial heterogeneity, a main driver of deep-sea biodiversity. Despite its ecological importance, substantial knowledge gaps exist on the character of seamount spatial heterogeneity. Objectives This study aimed to map, quantify and compare seamount seascapes to test whether individual habitats and seamounts differ in geomorphological structuring, and to identify spatial pattern metrics useful to discriminate between habitats and seamounts. Methods We mapped and classified geomorphological habitat using bathymetric data collected at five Southwest Indian Ridge seamounts. Spatial pattern metrics from landscape ecology are applied to quantify and compare seascape heterogeneity in composition and configuration represented in resulting habitat maps. Results Whilst part of the same regional geological feature, seamounts differed in seascape composition and configuration. Five geomorphological habitat types occurred across sites, which within seamounts differed in patch area, shape and clustering, with ridge habitat most dissimilar. Across seamounts, the spatial distribution of patches differed in number, shape, habitat aggregation and intermixing, and outcomes were used to score seamounts on a gradient from low to high spatial heterogeneity. Conclusions Although seamounts have been conceptualised as similar habitats, this study revealed quantitative differences in seascape spatial heterogeneity. As variations in relative proportion and spatial relationships of habitats within seamounts may influence ecological functioning, the proposed quantitative approach can generate insights into within-seamount characteristics and seamount types relevant for habitat mappers and marine managers focusing on representational ecosystem-based management of seamounts. Further research into associations of sessile and mobile seamount biodiversity with seascape composition and configuration at relevant spatial scales will help improve ecological interpretation of metrics, as will incorporating oceanographic parameters.