Habitat representativeness score (HRS): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species

The occurrence of most species is linked to the distribution of specific combinations of environmental variables that define their occupied niche. As a result, the relationship between environmental variables and species occurrence can be used to model species distribution. However, when collecting data to construct such models, it is preferable to ensure that the survey coverage is representative of all available habitat combinations within the area as a whole to ensure that the model does not under- or over-estimate the actual species distribution. By using multi-variate statistical techniques, a habitat representativeness score (HRS) can be calculated to provide an objective assessment of whether a specific survey coverage will collect (or has collected) data that are representative of all available habitat variable combinations in an area. To demonstrate this approach, HRSs calculated using principal component analysis were used to assess the minimum number of evenly-spaced parallel north-south surveys required to adequately survey two study areas with differing levels of environmental heterogeneity for all available combinations of four habitat variables. For the more environmentally homogeneous study area, the HRS suggests that for this survey design a minimum of five evenly-spaced parallel transects, covering around 5% of the study area, would be required to obtain representative survey coverage for these four variables. However, for the more heterogeneous study area, at least eight evenly-spaced parallel transects, covering around 9% of the study area, would be required. Therefore, for a given survey design, more survey effort is required to obtain a representative survey coverage when the survey area is more variable. In both cases, conducting fewer surveys than these minimum values would produce an unrepresentative data set and this could potentially lead to the production of species distribution models that do not accurately reflect the true species distribution.