Enhancing fish Underwater Visual Census to move forward assessment of fish assemblages: An application in three Mediterranean Marine Protected Areas

Abstract

Monitoring fish assemblages is needed to assess whether Marine Protected Areas (MPAs) are meeting their conservation and fisheries management goals, as it allows one to track the progress of recovery of exploited species and associated communities. Underwater Visual Census techniques (UVC) are used to monitor fish assemblages in MPAs. UVCs should be adapted to fish abundance, body-size and behaviour, which can strongly affect fish detectability. In Mediterranean subtidal habitats, however, UVC strip transects of one surface area (25x5 m2) are commonly used to survey the whole fish assemblage, from large shy fish to small crypto-benthic fish. Most high trophic level predators (HTLPs) are large shy fish which rarely swim close to divers and, consequently, their abundance may be under-estimated with commonly used transects. Here, we propose an improvement to traditional transect surveys to better account for differences in behaviour among and within species. First, we compared the effectiveness of combining two transect surface areas (large: 35x20 m2; medium: 25x5 m2) in quantifying large, shy fish within and outside Mediterranean MPAs. We identified species-specific body-size thresholds defining a smaller and a larger size class better sampled by medium and large transects respectively. Combining large and medium transects provided more accurate biomass and species richness estimates for large, shy species than using medium transects alone. We thus combined the new approach with two other transect surface areas commonly used to survey crypto-benthic (10x1 m2) and necto-benthic (25x5 m2) species in order to assess how effectively MPAs protection the whole fish assemblage. We verified that MPAs offer significant protection for HTLPs, their response in terms of biomass and density increase in MPAs was always higher in magnitude than other functional groups. Inside MPAs, the contribution of HTLP reached >25% of total fish biomass, against < 2% outside MPAs. Surveys with multiple transect surface areas allow for a more realistic assessment of the structure of the whole fish assemblage and better assessment of potential recovery of HTLPs within reserves of HTLP.

Small-Scale Coastal Fishing Shapes the Structure of Shallow Rocky Reef Fish in the Aegean Sea

“…All species were recorded except from those belonging to the families of Blenniidae, Gobiidae, and Tripterygiidae. Despite their important role in the ecology of shallow rocky reefs (Tiralongo et al, 2016), obtaining reliable diversity, density and biomass estimates for these highly diverse, small, crypto-benthic species would need a different type of sampling approach (e.g., Patzner, 1999;Tiralongo et al, 2016;Prato et al, 2017), which could not be followed due to fieldworkrelated logistical constraints.…”

Survey of Fish Behavior Analysis by Computer Vision

“…They compared the two methods to find that HD video is a more cost-effective monitoring technique and indicated that HD video allows for effective fish identification by computer vision due to its larger screen size over UVC technology, as well as longer image analysis and more systematic operation. Prato, et al, applied UVC to monitor the fish combination of multiple sections in Marine reserve and can realize realistic assessment of the whole Marine fish composite structure [50].…”

Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas

“…grouper), and (3) transect C: 10 m x 1 m for crypto-benthic fishes (e.g. Gobidae) (Prato et al, 2017 for further methodological details) (see Suppl. mat.…”

“…At each transect, each species encountered was identified and its abundance recorded. Fish size (total length) was recorded within 5 cm size classes for large sized fishes (species maximum size > 50 cm), 2 cm size classes for other necto-benthic fish species and 1 cm for small crypto-benthic fishes (following Prato et al, 2017). Based on density and size data, fish biomass was estimated using length-weight relationships (Froese & Pauly, 2017).…”