Underwater Video as a Tool to Quantify Fish Density in Complex Coastal Habitats

Abstract

Habitat loss is a serious issue threatening biodiversity across the planet, including coastal habitats that support important fish populations. Many coastal areas have been extensively modified by the construction of infrastructure such as ports, seawalls, docks, and armored shorelines. In addition, habitat restoration and enhancement projects often include constructed breakwaters or reefs. Such infrastructure may have incidental or intended habitat values for fish, yet their physical complexity makes quantitatively sampling these habitats with traditional gears challenging. We used a fleet of unbaited underwater video cameras to quantify fish communities across a variety of constructed and natural habitats in Perdido and Pensacola Bays in the central northern Gulf of Mexico. Between 2019 and 2021, we collected almost 350 replicate 10 min point census videos from rock jetty, seawall, commercial, public, and private docks, artificial reef, restored oyster reef, seagrass, and shallow sandy habitats. We extracted standard metrics of Frequency of Occurrence and MaxN, as well as more recently developed MeanCount for each taxon observed. Using a simple method to measure the visibility range at each sampling site, we calculated the area of the field of view to convert MeanCount to density estimates. Our data revealed abundant fish assemblages on constructed habitats, dominated by important fisheries species, including grey snapper Lutjanus griseus and sheepshead Archosargus probatocephalus. Our analyses suggest that density estimates may be obtained for larger fisheries species under suitable conditions. Although video is limited in more turbid estuarine areas, where conditions allow, it offers a tool to quantify fish communities in structurally complex habitats inaccessible to other quantitative gears.

Optimising sampling of fish assemblages on intertidal reefs using remote underwater video

“…The metrics of MaxN T and species richness had an even lower ratio of 0.5:1, as the entire video can be watched at 2× speed for RUV data in these low abundance habitats. A recent study on RUVs suggested using Frequency of Occurrence, a presence/absence metric derived from species richness, in situations where a quick and robust assessment of fish assemblage composition is required ( Baker et al, 2022 ). Researchers could consider this method over subsampling when fast data processing is required.…”

“…Other field and simulation studies have shown that it can be less precise than MaxN, and potentially over-inflate zero counts ( Stobart et al, 2015 ; Campbell et al, 2015 ). Their relative value can change based on useage, as for RUVs in complex habitats there is high correlation between the two for structure-oriented species, but less so for mobile species ( Baker et al, 2022 ).…”

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

“…The experiment presented by (28) cannot be used to detect the exact location and species of fish that the proposed model has performed. The model built by (10) could not detect the species, has reduced performance at a lower resolution, and cannot pinpoint the exact location of the fish. The presented model has addressed all of these parameters.…”

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

“…The experiment presented by [73] cannot be used to detect the exact location and species of fish that the presented model has performed. The model built by [74] could not detect the species, has reduced performance at a lower resolution, and cannot pinpoint the exact location of the fish and all of these parameters have been addressed by the presented model. [75,81] had performance degradation at lower resolution images where the method proposed in the experiment had obtained higher accuracy, which is 96.5% for lower resolution as well.…”