Roving Divers Surveying Fish in Fixed Areas Capture Similar Patterns in Biogeography but Different Estimates of Density When Compared With Belt Transects

A. Rassweiler, A. K. Dubel, G. Hernan, D. J. Kushner, J. E. Caselle, J. L. Sprague, L. Kui, T. Lamy, S. E. Lester, R. J. Miller
2020 Frontiers in Marine Science  
Fish abundance and diversity are core measurements taken by many nearshore marine monitoring projects. The most common approaches for counting fish include belt transects and timed counts by roving divers, each with its own limitations. Here we evaluate a fish counting method developed by the Channel Islands National Park's Kelp Forest Monitoring Program (KFMP), in which roving observers make fish counts that are standardized both by the time taken (30 min) and the area sampled (2,000 m 2 ).
more » ... ed (2,000 m 2 ). This method is potentially very useful because it combines an advantage of simple timed counts -the ability to rapidly sample a large area -with the potential to calculate area-specific density of fish, not just their relative abundance. However, the method has not been comprehensively evaluated and it is uncertain whether fish can effectively be counted in such a large target area within the allotted time. Fortunately, many sites surveyed with this method are also sampled with a more standard fish counting approach of belt transects, both by the KFMP and by the Partnership for Interdisciplinary Studies of Coastal Oceans. Here we compare estimates of fish density obtained through the area-standardized roving diver method and belt transect methods. In paired samples we find substantial and species-specific differences in densities estimated by each method. Considering all fish taxa together we find that roving divers are likely undersampling the target area. Despite considerable species-level variation, the different methods produce similar estimates of average diversity and find similar regional and temporal patterns in fish abundance, demonstrating that they can successfully be used in parallel even if the datasets cannot be easily combined. These analyses can guide the interpretation of roving diver data for basic research and management decisions.
doi:10.3389/fmars.2020.00272 fatcat:rydo5dniw5g7vatowyzhdwpng4