Structure and diversity of intertidal benthic diatom assemblages in contrasting shores: a case study from the Tagus estuary1

The structure of intertidal benthic diatoms assemblages in the Tagus estuary was investigated during a 2-year survey, carried out in six stations with different sediment texture. Nonparametric multivariate analyses were used to characterize spatial and temporal patterns of the assemblages and to link them to the measured environmental variables. In addition, diversity and other features related to community physiognomy, such as size-class or life-form distributions, were used to describe the diatom assemblages. A total of 183 diatom taxa were identified during cell counts and their biovolume was determined. Differences between stations (analysis of similarity (ANOSIM), R=0.932) were more evident than temporal patterns (R=0.308) and mud content alone was the environmental variable most correlated to the biotic data (BEST, rho=0.863). Mudflat stations were typically colonized by low diversity diatom assemblages (H' similar to 1.9), mainly composed of medium-sized motile epipelic species (250-1,000 mu m(3)), that showed species-specific seasonal blooms (e.g., Navicula gregaria Donkin). Sandy stations had more complex and diverse diatom assemblages (H' similar to 3.2). They were mostly composed by a large set of minute epipsammic species (<250 mu m(3)) that, generally, did not show temporal patterns. The structure of intertidal diatom assemblages was largely defined by the interplay between epipelon and epipsammon, and its diversity was explained within the framework of the Intermediate Disturbance Hypothesis. However, the spatial distribution of epipelic and epipsammic life-forms showed that the definition of both functional groups should not be over-simplified.

Effects of environmental conditions on the seasonal distribution of phytoplankton biomass in the North Sea

We study the spatial and seasonal variability of phytoplankton biomass (as phytoplankton color) in relation to the environmental conditions in the North Sea using data from the Continuous Plankton Recorder survey. By using only environmental fields and location as predictor variables we developed a nonparametric model (generalized additive model) to empirically explore how key environmental factors modulate the spatio-temporal patterns of the seasonal cycle of algal biomass as well as how these relate to the ,1988 North Sea regime shift. Solar radiation, as manifest through changes of sea surface temperature (SST), was a key factor not only in the seasonal cycle but also as a driver of the shift. The pronounced increase in SST and in wind speed after the 1980s resulted in an extension of the season favorable for phytoplankton growth. Nutrients appeared to be unimportant as explanatory variables for the observed spatio-temporal pattern, implying that they were not generally limiting factors. Under the new climatic regime the carrying capacity of the whole system has been increased and the southern North Sea, where the environmental changes have been more pronounced, reached a new maximum.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.