Environmental effects on Octopus vulgaris landings in northwest Spanish waters.

Gersom CostasIsabel BrunoGraham J. Pierce

2014I CES Annual Science Conference. A Coruña (Spain).Theme Session P:Operational solutions for cephalopod fisheries and culture. P06

Aim

Identifying the most important environmental variables influencing Octopus abundance in Northwestern Iberian Peninsula

• Short-lived species ( ~12 ~24 months) • Non-overlapping generations, 1or 2 cohorts in fishery• Very rapid growth • Semelparous species. High fecundity rates. • Shore zone. Variety of substrates

one single spawning peak in spring in the Galician waters~24 monthsParalarvae in the plankton for _40 days, after bottom 4 months

Life History

Material and Methods

Data of artisanal octopus fishery logbooks from the Spanish Administration.

Artisanal trap fleet in northwestern iberian peninsula from 2003 to 2012

Environmental data.

Abbreviation Name Periodicity Source

sst Sea Surface temperature

Montly http://rda.ucar.edu/datasets/

cloudneess cloudness Montly http://rda.ucar.edu/datasets/

wind_f wind strengh Montly http://rda.ucar.edu/datasets/

press sea level pressure Montly http://rda.ucar.edu/datasets/

NAO North atlatic Oscillation Annual http://www.cpc.ncep.noaa.gov/

mean_upwell Upwelling index Quarterly www.indicedeafloramiento.ieo.es

0

500

1000

1500

2000

2500

Penas

Altas

BaixasLand

ings

(t)

Reporting obligation of fishing operation in logbooks of fishing vessels >10 m in length.

Comparative : sales at fish markets- logbooks

Material and Methods

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

-12 -8 -5 -2 1 4

41

42

43

44

E0 E1 E2 E3 E4 E5

10 9 8 7 6 5 4

11

12

13

14

15

16

17

0

50

100

150

200

1 2 3 4

quarter

CP

UE

(kg/

day)

area

Altas

Baixas

Penas

0

1000

2000

3000

1 2 3 4

Quarter

Effo

rt (m

ean_

Nda

ys)

Rias Altas

Rias Baixas

Cabo Peñas

Material and Methods

Material and Methods

0

5

10

15

20

1 2 3 4

Quarter

win

d (

0.1

m/s

)

0

2

4

6

8

1 2 3 4

Quarter

Clo

ud

ne

ss

1000

1020

1040

1 2 3 4

Quarter

pres

siom

(mB

)

10

15

20

1 2 3 4

Quarter

Tem

p (º

C)

NAO

UPWELLING

Seasonal and Trend decomposition using Loess

The response: LPUEThe initial predictor variables with the input terms:

1. sea surface temperature (SST)2. wind strength (wind_f)3. sea level pressure (press) 4. cloudiness (cloud)5. Annual NAO index previous year (lag -1)6. Upwelling index Quarter 1 previous year (lag -1)7. Upwelling index Quarter 4 of 2 previous year (lag -2)8. Month9. ICES Statistical Rectangle

GAM for investigating the influence the influence of environmental variables over octopus abundance

Material and Methods

-1 0 1

-30

theoretical quantiles

devi

ance

res

idua

ls

2.0 3.0 4.0

-30

Resids vs. linear pred.

linear predictor

resi

dual

sHistogram of residuals

Residuals

Fre

quen

cy

-3 -2 -1 0 1

030

2.0 3.0 4.0

14

Response vs. Fitted Values

Fitted Values

Res

pons

e

-1.0 -0.5 0.0

-1.0

-0.5

0.0

0.5

nao

s(n

ao

,2.9

5)

-1200 -800 -400 0 200

-1.0

-0.5

0.0

0.5

mean_upw

s(m

ea

n_

up

w,2

.19

)

ResultsCabo Peñas

The final optimum model is:

log(lpue) ~ s(upwell_Q1(lag-1), 4) + s(nao(lag-1), 4) + month +sr

R-sq.(adj) = 0.356 Deviance explained = 44.6%GCV score = 0.53333 Scale est. = 0.45573 n = 159

-1.0 -0.5 0.0

-0.6

-0.2

0.2

nao

s(n

ao

,3)

-1000 -500 0

-0.6

-0.2

0.2

mean_upw

s(m

ea

n_

up

w,2

.52

)

-1500 -1000 -500 0

-0.6

-0.2

0.2

mean_upw2

s(m

ea

n_

up

w2

,2.9

6)

ResultsRias Altas

The final optimum model is:

log(lpue) ~ s(upwell_Q1(lag-1), 4) + s(upwell_Q4(lag-2), 4) + s(nao(lag-1), 4) + month +sr

R-sq.(adj) = 0.564 Deviance explained = 59.3%GCV score = 0.11235 Scale est. = 0.10472 n = 463

1 2 3 4 5 6 7

-0.4

0.0

0.4

cloud

s(cl

ou

d,2

.61

)

1010 1015 1020 1025

-0.4

0.0

0.4

pres

s(p

res,

2.6

7)

15 16 17 18

-0.4

0.0

0.4

sst

s(ss

t,2

.87

)

-1.0 0.0 1.0

-1.5

1.0

theoretical quantiles

devia

nce r

esid

uals

3.0 4.0 5.0

-1.5

1.0

Resids vs. linear pred.

linear predictor

resid

uals

Histogram of residuals

Residuals

Fre

quency

-1.5 0.0 1.0

0150

3.0 4.0 5.0

2.5

5.0

Response vs. Fitted Values

Fitted Values

Response

-1.0 -0.5 0.0

-0.4

0.0

0.4

nao

s(n

ao

,2.0

2)

ResultsRias Baixas

-800 -600 -400 -200 0

-0.4

0.0

0.4

mean_upw

s(m

ea

n_

up

w,2

.95

)

-2000 -1000 0 500

-0.4

0.0

0.4

mean_upw2

s(m

ea

n_

up

w2

,2.9

6)

R-sq.(adj) = 0.595 Deviance explained = 61.9%GCV score = 0.117 Scale est. = 0.11004 n = 573

The final optimum model is:

log(lpue) ~ s(upwell_Q1(lag-1), 4) + s(upwell_Q4(lag-2), 4) + s(nao(lag-1), 4) + s(sst, 4) + s(cloud, 4)+ s(press, 4)+ month +sr

Discussion

• Negative NAO index in previous year creating no favourable conditions for

octopus abundance

• Effects of upwelling pulses after paralarval stage can be associated to

variability of recruitment and posterior abundance in 3 areas.

• Oceanographic variables have significant effects over Octopus abundance

just in southern area (Rias Baixas).

• Importance of environmental factors in 3 geographic areas in relation on

octopus abundance.