Spatial properties of sessile benthic organisms and the design of repeat visual survey transects

Abstract

Monitoring the impacts of pressures, such as climate change, on marine benthic ecosystems is of high conservation priority. Novel imaging technologies, such as autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and towed systems, now give researchers the ability to monitor benthic ecosystems over large spatial and temporal scales. The design of monitoring programmes that use such technologies is currently hindered by a lack of information about the typical abundance and spatial distributions of target indicators and the level of sampling required to detect changes. A further complicating factor is that these sampling platforms are often not able to be exactly relocated when conducting repeat surveys. How the spatial properties of benthic organisms influence the estimates of cover, given alternative designs that vary in the geolocation precision of transects and the sampling intensity of images, is explored. A geostatistical modelling approach is used to quantify the spatial distribution of 20 key deep‐water invertebrate species at a long‐term monitoring site. The parameter estimates from these models are then used to simulate repeat transects with geolocation error and different levels of sampling. Results suggest that species with short effective ranges (i.e. those with strong spatial dependence over relatively short distances) and large spatial variance, which suggests strong spatial dependence effects, will require greater sampling effort to achieve a given standard of precision. Spatial offsets of 2 m, typical of an AUV, are unlikely to have dramatic impacts on the precision of estimates when sufficient images are sampled, but offsets of 10 m that are typical of towed systems may require a prohibitively high sampling effort for some species. These findings have important implications for benthic monitoring programmes, and highlight the importance of considering the interactions between sampling design, the technical limitations of survey equipment, and the spatial properties of indicator species.

Spatially balanced designs for transect‐based surveys

“…Instead, we recommend model-based estimators that can account for the inherent spatial patterns and auto-correlation that are likely to be manifest in the data (e.g. Foster, Hosack, Hill, Barrett, & Lucieer, 2014;Perkins, Hosack, Foster, Hill, & Barrett, 2019, for transect-based examples). Note that model-based analyses also remove the computationally demanding task, inherited from BAS, of calculating the observed spatially balanced inclusion probabilities (Robertson et al, 2013;Robertson, McDonald, Price, & Brown, 2017).…”

Monitoring the resilience of a no-take marine reserve to a range extending species using benthic imagery

“…Sampling platforms such as Autonomous Underwater Vehicles (AUVs), towed and drop camera systems and Remotely Operated Vehicles (ROVs) have allowed collection of large amounts of data over greater areas and deeper depths than have been traditionally surveyed using diver-based approaches [ 23 , 24 ]. Imagery or video footage from these platforms can typically be geolocated, allowing for spatial components to be incorporated into subsequent analyses; including the co-location of observations with mapping products such as multibeam sonar (e.g., [ 25 ]) and the analysis of spatial patterns in distributions of target species (e.g., [ 26 ]). NTR monitoring programs utilising these platforms include Australia’s Integrated Marine Observing System (IMOS) AUV program and California’s Marine Protected Area ROV monitoring program.…”