Abstract
In the deep sea, biological data are often sparse; hence models capturing relationships between observed fauna and environmental variables (acquired via acoustic mapping techniques) are often used to produce full coverage species assemblage maps. Many statistical modelling techniques are being developed, but there remains a need to determine the most appropriate mapping techniques. Predictive habitat modelling approaches (redundancy analysis, maximum entropy and random forest) were applied to a heterogeneous section of seabed on Rockall Bank, NE Atlantic, for which landscape indices describing the spatial arrangement of habitat patches were calculated. The predictive maps were based on remotely operated vehicle (ROV) imagery transects high-resolution autonomous underwater vehicle (AUV) sidescan backscatter maps. Area under the curve (AUC) and accuracy indicated similar performances for the three models tested, but performance varied by species assemblage, with the transitional species assemblage showing the weakest predictive performances. Spatial predictions of habitat suitability differed between statistical approaches, but niche similarity metrics showed redundancy analysis and random forest predictions to be most similar. As one statistical technique could not be found to outperform the others when all assemblages were considered, ensemble mapping techniques, where the outputs of many models are combined, were applied. They showed higher accuracy than any single model. Different statistical approaches for predictive habitat modelling possess varied strengths and weaknesses and by examining the outputs of a range of modelling techniques and their differences, more robust predictions, with better described variation and areas of uncertainties, can be achieved. As improvements to prediction outputs can be achieved without additional costly data collection, ensemble mapping approaches have clear value for spatial management.
Highlights
As the anthropogenic footprint extends deeper into our oceans, reliable descriptions of the seafloor and the species present are required to devise appropriate management and conservation measures
Based on the results obtained, we examined whether ensemble maps, which take into account predictions and uncertainties from more than one model (Araújo and New, 2007; Marmion et al, 2009b), could further improve predictions
For each of the species assemblages considered, Area under the curve (AUC) values showed all models to perform better than could be expected by chance (Table 2)
Summary
As the anthropogenic footprint extends deeper into our oceans, reliable descriptions of the seafloor and the species present are required to devise appropriate management and conservation measures. Full coverage biological sampling is often not an option, and hierarchical approaches involving nested survey designs are often employed. They involve a combination of broader-scale geological map creation based on acoustic data, and detailed ground-truthing biological studies covering smaller spatial extents, often taking the form of imagery transects (Elvenes et al, 2014; Robert et al, 2015). The broader-scale geological maps can be used to define habitat patches allowing the relationships between the spatial arrangement of these patches within the surrounding landscape and their effect on species spatial patterns (Turner and Gardner, 1991) to be examined, modelled and used to make biological predictions across the larger extent covered by the acoustic surveys.
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