Fish Monitoring in Kornati National Park: Baited, Remote, Underwater Video (BRUV) Versus Trammel Net Sampling

We evaluated (1) the suitability of two alternative methods for fish monitoring: trammel net sampling and BRUV (Baited Remote Underwater Video), and (2) the potential to cross-calibrate the methods based on a set of shared species with high catch probabilities. A statistical power analysis concluded that BRUV can be conducted with sufficient sample size to perceive small changes in fish populations with high power, and therefore can be used as a sentinel monitoring method. We found that fish species detected by both methods amounted to almost a third of the number of species in each method’s catch, and that 90% of these species are candidates for cross-calibration. 74% of the species at BRUV and 50% at trammel had occurrence probabilities above 10%, a reasonable threshold allowing stock assessment of these species. The sampled and predicted total species richness, extrapolated from the species accumulation curves, were almost identical across methods. We conclude that cross-calibrating the two methods and eventual replacement of the trammel method with non-destructive BRUV is feasible. The most effective areas of improvement are increased BRUV night-sampling effort and increased total sampling size to increase the statistical power of BRUV as a monitoring tool. This work has been supported under the Croatian Science Foundation under the project COREBIO (3107)

Semantic Segmentation for Posidonia Oceanica Coverage Estimation

One method of assessing the ecological status of seagrass is the analysis of videographic images for variables such as total aerial cover. Georeferenced images can be collected and matched by location over time, and any changes in coverage can be compared statistically to the expected null hypothesis. Since the manual analysis of large datasets approaching over a million images is not feasible, automated methods are necessary. Because of the wide variation in underwater conditions affecting light transmission and reflection, including biological conditions, deep learning methods are necessary to distinguish seagrass from non-seagrass portions of images. Using deep semantic segmentation, we evaluated several deep neural network architectures, and found that the best performer is the DeepLabv3Plus network, at close to 88% (intersection over union). We conclude that the deep learning method is more accurate and many times faster than human annotation. This method can now be used for scoring of large image datasets for seagrass discrimination and cover estimates. Our code is available on GitHub: https://enviewfulda.github.io/LookingForSeagrassSematicSegmentatio

The ADRIREEF database: natural/artificial reefs and wrecks in the Adriatic Sea

The present database collects data for artificial and natural reefs, and wrecks along the Adriatic Sea. Data has been collected from a large survey conducted through the ADRIREEF Interreg projects' partners, undergone a wide data harmonization in order to report answers to a consistent format, integrating survey data with literature data (scientific and grey), and data coming from different research projects. Parameters describing each natural, artificial reef and wreck are referable to four different groups: reef identification and geolocalisation information, summary of the characteristics of the area hosting the reef, aspects of the reefs that may also have an effect on the usage of the reef, present and/or possible future reef exploitation. In order to better visualize data, a webGIS has also been put in place and it is reachable at the address: https://adrireef.github.io/sandbox3/.The database counts 285 observations falling in Italian, Croatian and International waters. The database is available in a unique CSV file, where each element is described by 51 parameters, including coordinates in decimal degrees (WGS84 coordinate reference system) and bottom depth in meters, making data 3D. More information about data and single columns explanation is available in the attached README file