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The weighting of environmental variables in habitat modelling is important, especially for species with a poorly understood distribution. Traditional weighting schemes, such as arithmetic or geometric mean, often cause “gradient” habitat distribution patterns. We develop a new methodology that determines optimal variable weighting via a structured sensitivity analysis approach. This method considers the full spectrum of weighting combinations and uses multiple model selection criteria to select the best fit. We use a Northwest Pacific neon flying squid (Ommastrephes bartramii) fishery dataset (1998–2012) to compare our best performance habitat suitability index (BEST-HIS) with the traditional fixed methods, as well as to the more recent machine learning approach: boosted regression tree. Approaches were evaluated based on differences in habitat metrics, such as continuity, magnitude, and ratio of estimated unfavourable/favourable habitat. The BEST-HSI model generally outperformed the other three methods, though habitat metrics notably differed depending on weighting schemes used. The BEST-HSI approach is an efficient exploratory tool to investigate empirical relationships between organism presence and the environment, particularly for species with little known life history or migration information.

Optimal weighting in species habitat modeling: a case study from Ommastrephes bartramii in the Northwest Pacific Ocean