1. Environmental effects on Octopus vulgaris landings in northwest Spanish waters. Gersom Costas Isabel Bruno Graham J. Pierce 2014I CES Annual Science Conference. A Coruña (Spain). Theme Session P:Operational solutions for cephalopod fisheries and culture. P06

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

3. 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 months Paralarvae in the plankton for _40 days, after bottom 4 months Life History

4. 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. AbbreviationNamePeriodicitySource sstSea Surface temperature Montlyhttp://rda.ucar.edu/datasets/ cloudneesscloudnessMontlyhttp://rda.ucar.edu/datasets/ wind_fwind strenghMontlyhttp://rda.ucar.edu/datasets/ presssea level pressureMontlyhttp://rda.ucar.edu/datasets/ NAONorth atlatic OscillationAnnualhttp://www.cpc.ncep.noaa.gov/ mean_upwellUpwelling indexQuarterly www.indicedeafloramiento.ieo.es

5. Reporting obligation of fishing operation in logbooks of fishing vessels >10 m in length. Comparative : sales at fish markets- logbooks Material and Methods

6. Rias Altas Rias Baixas Cabo Peñas Material and Methods

7. NAO UPWELLING Seasonal and Trend decomposition using Loess

8.  The response: LPUE  The 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.Month 9.ICES Statistical Rectangle GAM for investigating the influence the influence of environmental variables over octopus abundance Material and Methods

9. Results Cabo 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

10. Results Rias 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

11. Results Rias Baixas 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

12. 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.