Choosing between strategies for designing surveys: autonomous underwater vehicles.

Abstract

1. Autonomous underwater vehicles (AUV), which collect images of marine habitats, are now an established sampling tool. The use of AUVs is becoming more widespread as they offer a non-destructive method to survey substantial spatial areas. The design of AUV surveys has historically been based on expert knowledge andAUVspecific considerations, such as reducing geolocation error. The expert knowledge encompasses intuition, previous surveying experiences and holistic knowledge of the study region. 2. We investigate the statistical aspects to AUV survey design for estimation of percentage cover of key benthic biota. We investigate the presence of spatial autocorrelation in AUV data using model-based geostatistics and examine the effect of autocorrelation on survey design by examining different design strategies – methods for placing AUV transects. The design strategies are assessed by inspecting the expected bias and the expected standard deviation ofmodel predictions, where the model depends on the choice of design. 3. The AUV data exhibited a wide range of autocorrelation, from non-existent to substantial. The design strategies varied in their statistical performance and nearly all strategies had shortcomings. Design strategies that were consistently poor performers had (i) transects placed in parallel in a single spatial dimension and (ii) made no attempt to spread out the transects in space. The superior design types had more transect-to-transect separation (but not toomuch) and effectively spanned important covariates. 4. The results give guidelines to researchers designing AUV surveys for biological mapping and for monitoring. In particular, we demonstrate that any spatial design should seek spatial balance, such as would be introduced by a systematic or stratified component within a randomized design. Knowledge of the system under study should be incorporated and, if possible, should be done so in a formalmanner that is objective and repeatable.