Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano- and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite ocean-colour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano- and pico-phytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure — designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a time-series of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme.

Temporal variability in total, micro- and nano-phytoplankton primary production at a coastal site in the Western English Channel

Primary productivity and subsequent carbon cycling in the coastal zone have a significant impact on the global carbon budget. It is currently unclear how anthropogenic activity could alter these budgets but long term coastal time series of hydrological, biogeochemical and biological measurements represent a key means to better understand past drivers, and hence to predicting future seasonal and inter-annual variability in carbon fixation in coastal ecosystems. An 8-year time series of primary production from 2003 to 2010, estimated using a recently developed absorption-based algorithm, was used to determine the nature and extent of change in primary production at a coastal station (L4) in the Western English Channel (WEC). Analysis of the seasonal and inter-annual variability in production demonstrated that on average, nano- and pico-phytoplankton account for 48% of the total carbon fixation and micro-phytoplankton for 52%. A recent decline in the primary production of nano- and pico-phytoplankton from 2005 to 2010 was observed, corresponding with a decrease in winter nutrient concentrations and a decrease in the biomass of Phaeocystis sp. Micro-phytoplankton primary production (PPM) remained relatively constant over the time series and was enhanced in summer during periods of high precipitation. Increases in sea surface temperature, and decreases in wind speeds and salinity were associated with later spring maxima in PPM. Together these trends indicate that predicted increases in temperature and decrease in wind speeds in future would drive later spring production whilst predicted increases in precipitation would also continue these blooms throughout the summer at this site.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

Demographic and temporal trends in out of hospital sudden cardiac death in belfast

Objective: To determine the epidemiology of out of hospital sudden cardiac death (OHSCD) in Belfast from 1 August 2003 to 31 July 2004.

Design: Prospective examination of out of hospital cardiac arrests by using the Utstein style and necropsy reports. World Health Organization criteria were applied to determine the number of sudden cardiac deaths.

Results: Of 300 OHSCDs, 197 (66%) in men, mean age (SD) 68 (14) years, 234 (78%) occurred at home. The emergency medical services (EMS) attended 279 (93%). Rhythm on EMS arrival was ventricular fibrillation (VF) in 75 (27%). The call to response interval (CRI) was mean (SD) 8 (3) minutes. Among patients attended by the EMS, 9.7% were resuscitated and 7.2% survived to leave hospital alive. The CRI for survivors was mean (SD) 5 (2) minutes and for non-survivors, 8 (3) minutes (p < 0.001). Ninety one (30%) OHSCDs were witnessed; of these 91 patients 48 (53%) had VF on EMS arrival. The survival rate for witnessed VF arrests was 20 of 48 (41.7%): all 20 survivors had VF as the presenting rhythm and CRI ? 7 minutes. The European age standardised incidence for OHSCD was 122/100 000 (95% confidence interval 111 to 133) for men and 41/100 000 (95% confidence interval 36 to 46) for women.

Conclusion: Despite a 37% reduction in heart attack mortality in Ireland over the past 20 years, the incidence of OHSCD in Belfast has not fallen. In this study, 78% of OHSCDs occurred at home.

Identification and bisection of temporal durations and tone frequencies: Common models for temporal and non-temporal stimuli.

Two experiments examined identification and bisection of tones varying in temporal duration (Experiment 1) or frequency (Experiment 2). Absolute identification of both durations and frequencies was influenced by prior stimuli and by stimulus distribution. Stimulus distribution influenced bisection for both stimulus types consistently, with more positively skewed distributions producing lower bisection points. The effect of distribution was greater when the ratio of the largest to smallest stimulus magnitude was greater. A simple mathematical model, temporal range frequency theory, was applied. It is concluded that (a) similar principles describe identification of temporal durations and other stimulus dimensions and (b) temporal bisection point shifts can be understood in terms of psychophysical principles independently developed in nontemporal domains, such as A. Parducci's (1965) range frequency theory.

Analysing spatio-temporal change by use of national historical Geographical Information Systems: Population change during and after the Great Irish Famine

Several countries have made large investments in building historical Geographical Information Systems (GIS) databases containing census and other quantitative statistics over long periods of time. Making good use of these databases requires approaches that explore spatial and temporal change.