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HABs & Neural Networks. SESSION 5. Fisheries , marine protected areas , population outbursts , biodiversity shifts. Artificial neural network approach to population dynamics of Harmful Algal Blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo- nitzschia . - PowerPoint PPT Presentation

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Diapositiva 1

Artificial neural network approach to population dynamics of Harmful Algal Blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia.HABs & Neural NetworksCarles Guallar, Margarita Fernndez-Tejedor, Maximino Delgado and Jorge Diognecarlesguallar@gmail.com

Barcelona, 29 November 2013

SESSION 5. Fisheries, marine protected areas, population outbursts, biodiversity shifts

1

Alfacs Bay (Ebro Delta)

Karlodinium spp.Pseudo-nitzschia spp.HABs & Neural NetworksThe study site was Alfacs bay situated in the Ebro Delta. This ecosystem has an important aquaculture industry. This is possible because of the high concentration of phytoplankton in this waters. However, recurrently harmful algae is the responsible of the blooms, with negative consequences for the shellfish industry.Therefore, developing a tool to predict these harmful phytoplankton outbursts will be very useful to prevent economic losses and to improve the management of the shellfish harvesting areasFor this study, two species were chosenKarlodinium is an ictiotoxic species first detected in 1994 an associated to shellfish mortalities. In this episodes, the cells exceed the 20 millions per literThe other species was Pseudonitzschia, it is associated with domoic acid2HABs & Neural Networks

Input layer Hidden layer Output layerVariable 1Variable 5Variable 4Variable 3Variable 2ForecastCharacteristics:- Feedforward neural network- Sigmoid function- Backpropagation with momentum term and flat spot elimination

0.550.600.650.700.75Longitude E40.6540.7040.75Latitude NHABs & Neural NetworksEnvironmental & PhytoplanktonMeteorologicalEbro River flow ratesHABs & Neural NetworksUnique data set

HABs & Neural Networks

Quantitative detection limit3.1PhytoplanktoncountsClassificationPrediction> 3.1< 3.1PresenceAbsenceCells L-1HABs & Neural Networks

Log10 (Karlodinium spp.)Lag (weeks)5 previous weeks

Log10 (Pseudo-nitzschia spp.)Lag (weeks)5 previous weeks- Deep water temperature (5th prev. week)

- Wind gust (3rd prev. week)

- Irradiance (8th prev. week)

- Atmosferic pressure (Log10, 5th prev. week)

- Ebro River flow rate (Log10, 5th prev. week)- Deep water temperature (14th prev. week)

- Wind velocity (10th prev. week)

- Water column salinity (6th prev. week)

- Atmosferic pressure (Log10, 13th prev. week)

- Ebro River flow rate (Log10, 1st prev. week)

HABs & Neural NetworksKarlodiniumPseudo-nitzschiaMisclassification error (%)One-step week Absence-Presence modelsError characteristicsAbsenceError characteristicsPresence

HABs & Neural NetworksKarlodiniumPseudo-nitzschiaCoefficient of determination (R2)One-step week Prediction models

HABs & Neural NetworksNeural Interpretation DiagramAbsence-Presence models

Karlodinium modelPseudo-nitzschia modelPresenceAbsencePresenceAbsenceHABs & Neural NetworksNeural Interpretation DiagramPrediction models

Pseudo-nitzschia model

Karlodinium modelLog10(Cells L-1)Log10(Cells L-1)

HABs & Neural NetworksConnection Weight ApproachPseudo-nitzschia modelsKarlodinium models

Absence-Presence modelsPrediction modelsHABs & Neural NetworksConnection Weight ApproachBiological vs Environmental variables

Absence-PresencePredictionKarlodiniumKarlodiniumPseudo-nitzschiaPseudo-nitzschia

HABs & Neural NetworksConclusions:

Neural network models were developed to predict Pseudo-nitzschia spp. and Karlodinium spp.

The population dynamics for Pseudo-nitzschia spp. and Karlodinium spp. were similar for the whole ecosystem.

The big size of the neural network models highlights the complexity of the phytoplankton dynamics in Alfacs Bay.

Environmental variables are important factors to drive phytoplankton dynamics in Alfacs Bay.

Thank you very much.

Aknowledgments:

Sistema de Observacin y Alerta de Proliferacin de Microalgas Nocivas en Zonas de Produccin Acucola Marina (PURGADEMAR; IPT-2011-1707-310000).

Programa de seguiment de la qualitat de les aiges, molluscs i fitoplancton txic a les zones de producci de marisc del litoral catal de la DGPiAM.HABs & Neural Networks