Assessing Habitat Suitability Models for the Deep Sea: Is Our Ability to Predict the Distributions of Seafloor Fauna Improving?

Abstract

Methods that predict the distributions of species and habitats by developing statistical relationships between observed occurrences and environmental gradients have become common tools in environmental research, resource management, and conservation. The uptake of model predictions in practical applications remains limited, however, because validation against independent sample data is rarely practical, especially at larger spatial scales and in poorly sampled environments. Here, we use a quantitative dataset of benthic invertebrate faunal distributions from seabed photographic surveys of an important fisheries area in New Zealand as independent data against which to assess the usefulness of 47 habitat suitability models from eight published studies in the region. When assessed against the independent data, model performance was lower than in published cross-validation values, a trend of increasing performance over time seen in published metrics was not supported, and while 74% of the models were potentially useful for predicting presence or absence, correlations with prevalence and density were weak. We investigate the reasons underlying these results, using recently proposed standards to identify areas in which improvements can best be made. We conclude that commonly used cross-validation methods can yield inflated values of prediction success even when spatial structure in the input data is allowed for, and that the main impediments to prediction success are likely to include unquantified uncertainty in available predictor variables, lack of some ecologically important variables, lack of confirmed absence data for most taxa, and modeling at coarse taxonomic resolution.