LAUSR

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging, but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimising sampling strategies therefore is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. eDNA can detect diverse taxa simultaneously, but to date has rarely been applied across large spatial scales relevant for conservation planning. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to under-sampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna. Simulations were carried out representing different sampling strategies (random vs systematic), frames (ferry routes vs unconstrained) and number of sampling points. SSDMs were remade from different sampling simulations and compared to the ‘perfect knowledge’ SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimise potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective eDNA sampling strategies using commercial vessel routes globally. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis, and can recover meaningful data on spatiotemporal biodiversity patterns.