The evolution of the upwelling regime off west Portugal between 1941 and 2000 was investigated. Monthly averages of the longshore (meridional) wind component at four coastal stations of the Institute for Meteorology were computed and subject to linear regression analysis. Several comparisons were made among stations until a final regression model was reached. The resulting residuals were checked for the presence of red noise, and pairwise correlation coefficients were estimated for residuals of different stations. To complement this study, monthly sea-surface temperature averages were computed for six regions off west Portugal and subject to a similar procedure. In both analyses, it was concluded that the Portuguese upwelling regime has weakened since the 1940s. The waning of the northerly, upwelling-favourable winds was significant throughout the traditional upwelling season (April-September). Sea-surface temperature showed a steady year-round increase from 1941 onwards, in both offshore (+0.002°C/year) and coastal (+0.010°C/year) regions.

[1] A dynamic process convolution model (DPCM) is used to investigate the evolution and spatial distribution of monthly ocean temperature anomalies in the Portugal Current System. The analysis is performed with 20th century standard depth measurements from the National Oceanographic Data Center, ranging from the surface to 500 m depth. The proposed DPCM decomposes the temporal variability into short-term non-linear components and long-term linear trends, with both components varying smoothly across latitude, longitude and depth. An important feature of the DPCM is that it allows the assessment of trend significance without ad hoc corrections, since the residuals are spatially and temporally uncorrelated. In the analyzed period, an overall warming of coastal surface waters off the west Iberian Peninsula is found, together with fading crossshelf temperature gradients and increased coastal stratification. Since previous studies also found that upwelling-favorable winds have weakened from the 1940s onward, these results most likely reflect a long-term weakening of the coastal upwelling regime. Transient periods of temperature change are also described and associated with known variability in the North Atlantic, and a final discussion on the link between the observed trends and anthropogenic forcing on climate is presented.Citation: Lemos, R. T., and B. Sansó (2006), Spatio-temporal variability of ocean temperature in the Portugal Current System,

a b s t r a c tThe ability to predict the distribution of threatened marine predators is essential to inform spatially explicit seascape management. We tracked 99 individual black-browed albatrosses Thalassarche melanophris from two Falkland Islands' colonies in 2 years. We modeled the observed distribution of foraging activity taking environmental variables, fisheries activity (derived from vessel monitoring system data), accessibility to feeding grounds and intra-specific competition into account. The resulting models had sufficient generality to make reasonable predictions for different years and colonies, which allows temporal and spatial variation to be incorporated into the decision making process by managers for regions and seasons where available information is incomplete. We also illustrated that long-ranging birds from colonies separated by as little as 75 km can show important spatial segregation at sea, invalidating direct or uncorrected extrapolation from one colony to neighboring ones. Fisheries had limited influence on albatross distribution, despite the well known scavenging behavior of these birds. The models developed here have potentially wide application to the identification of sensitive geographical areas where special management practices (such as fisheries closures) could be implemented, and would predict how these areas are likely to move with annual and seasonal changes in environmental conditions.

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a b s t r a c tIn this work we present a novel surplus production model for fisheries stock assessment. Our goal is to enhance parameter estimation and fitting speed. The model employs a production function that differs from the canonical logistic (Schaefer) and Gompertz (Fox) functions, but is still connected to the Pella-Tomlinson formulation. We embed this function in a state-space model, using observed catchper-unit-effort indices and measures of fishing effort as input. From the literature we derive Bayesian prior densities for all model hyperparameters (carrying capacity, catchability, growth rate and error variance), as well as the state (annual stock biomass). We use the well-studied Namibian hake fishery as a case study, via which we compare the Schaefer, Fox and Pella-Tomlinson models with the new model. We also develop a package for the software R, which employs a Shiny application for data exploration, model specification, and output analyses. Posterior densities of hyperparameters and reference points agree across models. Identifiability issues emerge in the more cumbersome Pella-Tomlinson model. The new model yields small but consistent improvements in precision. It also renders implementation faster and easier, with no hidden truncation of negative biomasses. We conclude by discussing theoretical and practical extensions to this new model.

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