Seasonal plankton dynamics along a cross-shelf gradient off northern Spain

The development of population models able to reproduce the dynamics of zooplankton is a central issue when trying to understand how a changing environment would affect zooplankton in the future. Using 10 years of monthly data on phytoplankton and zooplankton abundance in the Bay of Biscay from the IEO's RADIALES time-series programme, we built non-parametric Generalized Additive Models (GAMs) able to reproduce the dynamics of plankton on the basis of environmental factors (nutrients, temperature, upwelling and photoperiod). We found that the interaction between these two plankton components is approximately linear, whereas the effects of environmental factors are non-linear. With the inclusion of the environmental variability, the main seasonal and inter-annual dynamic patterns observed within the studied plankton assemblage indicate the prevalence of bottom-up regulatory control. The statistically deduced models were used to simulate the dynamics of the phytoplankton and zooplankton. A good agreement between observations and simulations was obtained, especially for zooplankton. We are presently developing spatio-temporal GAM models for the North Sea based on the Continuous Plankton Recorder database.

Non-linearities, regime shifts and recovery: The recent influence of climate on Black Sea chlorophyll

The Black Sea ecosystem experienced severe eutrophication-related degradation during the 1970s and 1980s. However, in recent years the Black Sea has shown some signs of recovery which are often attributed to a reduction in nutrient loading. Here, SeaWiFS chlorophyll a (chl a), a proxy for phytoplankton biomass, is used to investigate spatio-temporal patterns in Black Sea phytoplankton dynamics and to explore the potential role of climate in the Black Sea's recovery. Maps of chl a anomalies, calculated relative to the 8 year mean, emphasize spatial and temporal variability of phytoplankton biomass in the Black Sea, particularly between the riverine-influenced Northwest Shelf and the open Black Sea. Evolution of phytoplankton biomass has shown significant spatial variability of persistence of optimal bloom conditions between three major regions of the Black Sea. With the exception of 2001, chl a has generally decreased during our 8 year time-series. However, the winter of 2000–2001 was anomalously warm with low wind stress, resulting in reduced vertical mixing of the water column and retention of nutrients in the photic zone. These conditions were associated with anomalously high levels of chl a throughout much of the open Black Sea during the following spring and summer. The unusual climatic conditions occurring in 2001 may have triggered a shift in the Black Sea's chl a regime. The long-term significance of this recent shift is still uncertain but illustrates a non-linear response to climate forcing that makes future ecosystem changes in the pelagic Black Sea ecosystem difficult to predict.

Are Fast Responses More Random? Testing the Effect of Response Time on Scale in an Online Choice Experiment

Scepticism over stated preference surveys conducted online revolves around the concerns over “professional respondents” who might rush through the questionnaire without sufficiently considering the information provided. To gain insight on the validity of this phenomenon and test the effect of response time on choice randomness, this study makes use of a recently conducted choice experiment survey on ecological and amenity effects of an offshore windfarm in the UK. The positive relationship between self-rated and inferred attribute attendance and response time is taken as evidence for a link between response time and cognitive effort. Subsequently, the generalised multinomial logit model is employed to test the effect of response time on scale, which indicates the weight of the deterministic relative to the error component in the random utility model. Results show that longer response time increases scale, i.e. decreases choice randomness. This positive scale effect of response time is further found to be non-linear and wear off at some point beyond which extreme response time decreases scale. While response time does not systematically affect welfare estimates, higher response time increases the precision of such estimates. These effects persist when self-reported choice certainty is controlled for. Implications of the results for online stated preference surveys and further research are discussed.