1. TNA FishAut SARTI-UPC, Spain General Assembly 2017
Fanelli Emanuela ENEA-Marine Environment Research Center La Spezia (Italy)
Aguzzi Jacopo ICM-CSIC Barcelona (Spain)
Marini Simone ISMAR-CNR la Spezia (Italy)
Azzurro Ernesto ISPRA Livorno (Italy)
Del Rio-Fernandez Joaquin SARTI-UPC Vilanova i la Geltrú (Spain)
1st FixO3 TNA Call
SARTI-UPC, Spain
General Assembly 2017
Vilanova i la Geltrú, Spain; 27 – 29 June 2017

2. TNA FishAut OUTLINE Main Objectives Status/Progress
WP 09 – TNA
OUTLINE
Main Objectives
Status/Progress
Cruises/Number of Access Days
Main Results
Other aspects

3. TNA FishAut 1. Main Objectives Biological objectives
WP 09 – TNA
1. Main Objectives
FISHAUT: Analysis of FISH community structure and trophic relationships by AUTomated video imaging at a coastal cabled observatory
Biological objectives
Explore multispecies temporal variability, as a product of activity rhythms and environmental forcing
Technological implementations
Develop automated video imaging procedure to classify and count fishes in different habitat context

4. TNA FishAut 2. Status/Progress
WP 09 – TNA
2. Status/Progress
The project ended 2016 (last meeting in Barcelona in June )
All objectives successfully achieved
2 manuscripts in preparation (one for the ecological monitoring and one for the technological implementation, expected submission late summer-autumn 2017)

5. 3. Cruises/Number of Access Days (one year)
TNA Project Title
WP 09 – TNA
3. Cruises/Number of Access Days (one year)
Use of image data
Integration of data with information from previous years;
Use of oceanographic data (T, S, Turbidity, Chlorophyll)
Use of atmospheric data (Irradiance, Wind Speed-Direction, T=
Satellite data (Chl and sst)

6. TNA FishAut 4. Main Results Paper 1
WP 09 – TNA
4. Main Results
Paper 1
Environmental drivers of intra- and interannual changes in coastal fish assemblages by multiparametric observatory monitoring
Fanelli E.,  Sbragaglia V. , Azzurro E., Marini S., Costa C., Del Rio J., Toma D., Aguzzi J. 
Analyses of image data from 2012 to 2014
Coupling with oceanographic and atmospheric variables
Univariate and multivariate analysis, Linear models (GLM, DistLM)
Seasonal patterns in fish assemblage structure
patterns are maintained through years
environmental drivers of community changes, both at seasonal and inter-annual scale

7. TNA FishAut 4. Main Results
WP 09 – TNA
4. Main Results
Overall significant differences between day vs. night fish assemblage with different species dominance
Source df MS
Pseudo-F
Year 2
1875.8
4.33**
Month 10
1946.1
4.49***
YearxMonth 20
648.86
1.49ns
Residuals
336
433.35
Total
368
Significant differences occurred at interannual (among years) and intrannual level (across months)

8. TNA FishAut 4. Main Results
WP 09 – TNA
4. Main Results
Different environmental variables drove the observed pattern in each year, i.e. in 2012 a good DISTLM model (R2=0.47) with 4 variables: irradiance, chlorophyll, Chla (delayed 1-month) and sst
CCA plot for 2012 data

9. TNA FishAut 4. Main Results Paper 2
WP 09 – TNA
4. Main Results
Paper 2
Tracking Fish Abundance by Underwater Image Recognition: a Real World Case
Marini S., Azzurro E., Fanelli E., Toma D., del Rio-Fernandez J., Sbragaglia V., Aguzzi A.
Comparisons between the results obtained during 2012 and 2013 by automation with outputs of the visual fish counting.
Image analysis and recognition methodology combining an image segmentation process and an image-feature extraction process together (supervised machine learning approach) coupled with a K-fold Cross-Validation framework.
Development of automated fish recognition method, robust with respect to the most critical acquisition conditions that can be found into an unconstrained environment, well coping with visual inspection performance changes upon environmental variability

10. TNA FishAut 4. Main Results
WP 09 – TNA
4. Main Results
Pearson correlation between the observed and recognized time-series where each test image was tagged with the water turbidity and bio-fouling level.
The more the bio-fouling score increases the more the correlation between the two time series decreases. Differently, the level of turbidity do not affects relevantly the automate recognition performance.
Fragment of the time-series obtained through the test dataset observation (red line) and the fragment of time-series automatically extracted by the image classifier (blue line) in the same period.
The automate image classifier captures the temporal dynamics of the time-series obtained through the visual inspection.

11. TNA FishAut 5. Other aspects
WP 09 – TNA
5. Other aspects
Linking biological data with environmental information (i.e., oceanographic and atmospheric) is crucial for ecosystem-based monitoring approach
The automated recognition approach is enough general to be installed on cabled observatories (e.g. OBSEA, AcquaAlta, SmartBay, ONC, JAMSTEC….) for real-time abundance time-series extraction
Multiparametric and automated long-term ecosystem monitoring is of relevance for the MSFD (descriptor 1=biodiversity; 2=alien species; 3=commercial fish and shellfish species, 4=food webs, and 10=marine litter).