I been very vocal on my interest on FADs and the fact that they are both a treat and an opportunity. Their numbers (and impact) on the fishery are staggering. Yet on the other side, one could see it as the potential of having 65000 echo sunders providing is with an unprecedented level of understanding of the stock status in the Pacific, because the information is being collected as we speak… yet we are not there yet.
Tuna captain’s best friend but maybe there is chance for scientist too.
In the meantime, to use FAD data to better understand the ecology and behaviour of the “ecosystem” that ensembles underneath… and that VERY complex task is exactly what Blanca Orúe Montaner did for her PhD thesis working with AZTI and the Euskal Herriko Unibertsitatea/University of the Basque Country. She recently defended her work and is available here: Ecology and behavior of tuna and non-tuna species at drifting fish aggregating devices (DFADs) in the Indian Ocean using fishers’ echo-sounder buoys
I highly recommend you read the original , I will just quote part of the intro and the conclusions here, but I will also go into some specifics chapters over the next months as they are a wealth of info.
She was very clear on her task ahead:
The aim of this thesis is to describe the aggregation process, investigate the spatio-temporal distribution and environment preferences of tuna and non-tuna species aggregated with DFADs, using acoustic data provided by fishers´ echo-sounder buoys. This will contribute to the assessment and management of tropical tunas in the Western Indian Ocean as the will require knowledge about the ecology and behavior of tuna and non-tuna species associated with DFADs.
For achieving the aim of this PhD, acoustic data provided by more than 7500 echo-sounder buoys used by fishers in the Indian Ocean was collected, processed and analyzed for 2012-2015.
Conclusions and thesis
The conclusion of the first chapter with the aim “to establish standardized protocols to process fishers echo-sounders´ acoustic raw biomass estimates in order to use them for scientific purposes” is:
1. The implementation of the proposed standardized protocol eliminates errors and false positives found in data provided by fishers’ echo-sounder buoys and allows the collection of clean acoustic data useful for scientific purposes.
The conclusion of the second chapter with the aim “to progress towards improved remote biomass estimates by the echo-sounders equipping DFADs, following previous analysis proposed in the field, based on existing knowledge of the vertical distribution of non-tuna and tuna species at DFADs and mixed species target strengths (TS) and weights” is:
2. Although the application of behavior/size-based model improves acoustic biomass estimates provided originally by manufacturers, the improvement of the biomass estimates is not as large as expected; which suggest that a single model to convert acoustic signal into biomass is not enough to explain the large variability in these data and further improvements are needed.
The conclusions of the third chapter with the aim “to investigate the aggregation process of virgin (i.e. newly deployed) DFADs in the Western Indian Ocean using the acoustic records provided by fishers’ echo-sounder buoys, determining the first detection day of tuna and non-tuna species at DFAD and identifying the potential differences in the spatio-temporal dynamics of the aggregations” are:
3. The average period for the arrival of fishes to the DFADs (i.e. first day that the echo-sounder detected biomass) is 13.5±8.4 days for tuna and 21.7±15.1 days for non-tuna species; which indicates that tuna arrive at DFADs before non-tuna species.
4. The analysis shows a significant relationship between DFAD depth and detection time of tuna, suggesting a faster tuna colonization for deeper objects. For non-tuna species this relationship appears to be not significant. The influence of underwater structure on the aggregation process may be related to the vertical distribution of the species under DFADs.
5. The aggregation dynamics differ between area and monsoon periods in both tuna and non-tuna species. These differences could be explained by changes in the biophysical environment associated with seasonality, although there may be other social factors affecting the aggregation process of tuna and non-tuna species at DFADs, such as the density and abundance of the local tuna population or DFADs.
6. The results of this research could be used as a tool to assist on the sustainability of tuna fisheries, helping to design conservation measures for tuna and non-tuna species management, such as reducing the undesired catch.
The conclusions of the fourth chapter with the aim “to investigate the habitat preferences and distribution of tuna and non-tuna species associated with DFADs and environmental conditions in the Indian Ocean implementing Bayesian Hierarchical spatial models” are:
7. Results highlight species-specific spatial and temporal distributions as well as different relevant environmental factors for tuna and non-tuna species presence associated with DFADs, which suggest that both species group may have different habitat preferences.
8. The highest probabilities for tuna presence are found in warmer waters, with larger sea surface height and low eddy kinetic energy. In the case of non-tuna species presence on DFADs, the highest probabilities are found in colder and productive waters. For both groups, days at sea is relevant, with a higher probability of tuna and non-tuna presence on DFADS when the object stays longer at sea.
9. The relevance of buoy random effect and the spatial effect could indicate that there must be other ecological processes behind this associative behavior of tuna and non-tuna species at DFADs that are not being taken into account in this study (e.g. social behavior of tuna at DFADs or DFAD densities in the area).
10. The results of his study could contribute to design spatio-temporal conservation management measures (e.g. dynamic area closures) for both target and non-target species, using near real-time habitat predictions based on acoustic data provided by fishers’ echo-sounders and remote sensing systems.
The conclusions of the fifth chapter with the aim “to compare the spatio-temporal distribution of tuna in the Western Indian Ocean resulted from both fisheries-dependent (i.e. nominal catch data) and independent (i.e. acoustic data from fishers´ echo-sounder buoys) data using spatially-explicit Bayesian Hierarchical spatial models” are:
11. The predicted probability of occurrence for tuna developed using fisheries-dependent and fisheries-independent data show, in general, similar areas of tuna presence under the DFADs, however, some fine-scale differences are observed. The good level of overlap in the predictions indicate by the similarity statistics, considering the low correlation values, are most likely due to the fact that the large-scale habitat preference predictions considered in this study have very low probabilities outside the main fishing area, which may result in misleading similarity statistics.
12. The maps obtained with acoustic data allow identifying areas of high probability of tuna presence at DFADs with greater precision, whereas the maps derived from catch data do not indicate any variation of tuna distribution on a finer scale.
13. Results of monthly specific tuna distribution patterns under DFADs suggest that tuna species may have different habitat preferences depending on the season.
14. The results of this research highlight the great potential fishers´ echo-sounder buoys have for addressing key scientific questions on tuna ecology and behavior. Furthermore, these data could significantly contribute to the development of possible management measures, for example time–area closures, in which, by having greater precision in the spatio-temporal distribution of tuna associated with DFADs, management measures could be more efficient.
Finally, considering all these conclusions, the working hypothesis has been confirmed:
Results on the aggregation process, spatio-temporal distribution and environmental preferences of species associated with DFADs in the Indian Ocean, using acoustic data provided by fishers’ echo-sounder buoys, contribute to increase the knowledge of ecology and behavior of tuna and non-tuna species associated with DFADs, which could advice the scientific basis for DFAD fishery management, supporting tuna RFMOs in decision-making for a more sustainable management of the species and the fishery.