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 Networks

Carles Guallar, Margarita Fernández-Tejedor, Maximino Delgado and Jorge Diogènecarlesguallar@gmail.com

Barcelona, 29 November 2013

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

Alfacs Bay (Ebro Delta)

Karlodinium spp. Pseudo-nitzschia spp.

HABs & Neural Networks

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input hidden 1 hidden 2 output

t- 1

t- 2

t- 3

t- 4

t- 5

t

Input layer Hidden layer Output layer

Variable 1

Variable 5

Variable 4

Variable 3

Variable 2

Forecast

Characteristics:- Feedforward neural network- Sigmoid function- Backpropagation with momentum term and flat spot elimination

0 . 5 5 0 . 5 6 0 . 5 7 0 . 5 8 0 . 5 9 0 . 6 0 . 6 1 0 . 6 2 0 . 6 3 0 . 6 4 0 . 6 5 0 . 6 6 0 . 6 7 0 . 6 8 0 . 6 9 0 . 7 0 . 7 1 0 . 7 2 0 . 7 3 0 . 7 4 0 . 7 54 0 . 5 5

4 0 . 5 6

4 0 . 5 7

4 0 . 5 8

4 0 . 5 9

4 0 . 6

4 0 . 6 1

4 0 . 6 2

4 0 . 6 3

4 0 . 6 4

4 0 . 6 5

I AC I A

C AE A

P A

0.55 0.60 0.65 0.70 0.75

Longitude E

40.65

40.70

40.75

Latit

ude

N

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Environmental & Phytoplankton

Meteorological Ebro River flow rates

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Unique data set

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Quantitative detection limit3.1

Phytoplanktoncounts Classification

Prediction> 3.1

< 3.1

Presence

Absence

Cells L-1

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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)

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Karlodinium Pseudo-nitzschia

Misclassification error (%)

One-step week Absence-Presence models

Error characteristicsAbsence

Error characteristicsPresence

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Karlodinium Pseudo-nitzschia

Coefficient of determination (R2)

One-step week Prediction models

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Neural Interpretation DiagramAbsence-Presence models

Karlodinium model

Pseudo-nitzschia model

PresenceAbsence

PresenceAbsence

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Neural Interpretation DiagramPrediction models

Pseudo-nitzschia model

Karlodinium model

Log10(Cells L-1)

Log10(Cells L-1)

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Connection Weight Approach

Pseu

do-n

itzsc

hia

mod

els

Kar

lodi

nium

mod

els

Absence-Presence models Prediction models

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Connection Weight ApproachBiological vs Environmental variables

Absence-Presence Prediction

KarlodiniumKarlodinium

Pseudo-nitzschiaPseudo-nitzschia

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Conclusions:

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

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

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

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

Thank you very much.

Aknowledgments:

- Sistema de Observación y Alerta de Proliferación de Microalgas Nocivas en Zonas de Producción Acuícola Marina (PURGADEMAR; IPT-2011-1707-310000).

- Programa de seguiment de la qualitat de les aigües, mol·luscs i fitoplancton tòxic a les zones de producció de marisc del litoral català de la DGPiAM.

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