Regional estimates of a range-extending ecosystem engineer using stereo-imagery from ROV transects collected with an efficient, spatially balanced design

The redistribution of marine ecosystem engineers in response to changing climate is restructuring endemic benthic communities globally. Therefore, developing and implementing efficient monitoring programs across the complete depth range of these marine ecosystem engineers is often an urgent management priority. Traditionally, many monitoring programs have been based on a systematically selected set of survey locations that, while able to track trends at those sites through time, lack inference for the overall region being monitored. This study trialled a probabilistic sampling design to address this need, taking advantage of an important prerequisite for such designs, extensive multibeam echosounder (MBES) mapping, to inform a spatially balanced sample selection. Here, we allocated 170 remotely operated vehicles (ROVs) transects based on a spatially balanced probabilistic sampling design across three locations with extensive mapping. Generalized additive models were used to estimate the density and associated barren cover of the range-expanding ecosystem engineer, the long spined urchin (Centrostephanus rodgersii). Estimates were generated at a reef-wide scale across three locations on the east coast of Tasmania, Australia, representing the leading edge of the species recent range extension. Model-based estimates of urchin density and barren cover incorporated seabed structure attributes, such as depth and ruggedness, with differences in these modelled relationships being identified between locations. Estimates ranged from 0.000065 individuals m^-2 and 0.018% barren cover in the Tasman Peninsula to 0.167 individuals m^-2 and 2.10% barren cover at Governor Island Marine Reserve, reflecting a north to south distributional gradient. This study highlights the value of combining probabilistic sampling designs, ROV transects, stereo video, and MBES mapping to generate reliable and robust estimates of important ecosystem species needed to protect reef-based fishery and conservation values via adaptive and informed management.