The Pacific sand lance (Ammodytes personatus) is a key forage species for many commercially important fish (e.g. salmon and groundfish), marine birds, and whales found in nearshore coastal waters of British Columbia, Canada. Sand lance lack a swim bladder and have a requirement for low‐silt, medium‐coarse sandy sea‐bed habitat for burying. Little information is available describing the distribution of burying habitat, partly because there are no commercial fisheries for A. personatus in British Columbia. This information is required by habitat and wildlife managers to identify and protect uncommon patches of burying habitats from detrimental activities, including dredging, infilling, and oil spills. In this study, habitat distribution results from five suitability modelling algorithms were evaluated: maximum entropy, generalized linear model, generalized additive model, random forest, and an ensemble model of the latter three. The maximum entropy model had the highest performance score (area under the receiver operator characteristic curve was 0.78) and was selected as the model that most accurately identified the presence of suitable A. personatus burying habitat. Model results indicate that suitable burying habitat is primarily influenced by derived sea‐bed substrate, distance to estuary, distance to sand‐gravel beaches, and bottom sea temperature. Overall, the spatial modelling identified only 105 km2 of highly suitable sand lance burying habitat, or 2.6% of the study area (0–150 m), primarily in Haro Strait, along the east coast of Vancouver Island, and in northern regions of the strait near Cortes, Savary, and Harwood islands. Identification of this uncommon and patchy burying habitat will contribute to the ongoing conservation of an important coastal prey species.

RPAS (Remotely piloted aircraft systems, i.e., drones) present an efficient method for mapping schooling coastal forage fish species that have limited distribution and abundance data. However, RPAS imagery acquisition in marine environments is highly dependent on suitable environmental conditions. Additionally, the size, color and depth of forage fish schools will impact their detectability in RPAS imagery. In this study, we identified optimal and suboptimal coastal environmental conditions through a controlled experiment using a model fish school containing four forage fish-like fishing lures. The school was placed at 0.5 m, 1.0 m, 1.5 m, and 2.0 m depths in a wide range of coastal conditions and then we captured RPAS video imagery. The results from a cluster analysis, principal components, and correlation analysis of RPAS data found that the optimal conditions consisted of moderate sun altitudes (20–40°), glassy seas, low winds (<5 km/h), clear skies (<10% cloud cover), and low turbidity. The environmental conditions identified in this study will provide researchers using RPAS with the best criteria for detecting coastal forage fish schools.