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This paper discusses dynamic factor analysis, a technique for estimating common trends in multivariate time series. Unlike more common time series techniques such as spectral analysis and ARIMA models, dynamic factor analysis can analyse short, non-stationary time series containing missing values. Typically, the parameters in dynamic factor analysis are estimated by direct optimisation, which means that only small data sets can be analysed if computing time is not to become prohibitively long and the chances of obtaining sub-optimal estimates are to be avoided. This paper shows how the parameters of dynamic factor analysis can be estimated using the EM algorithm, allowing larger data sets to be analysed. The technique is illustrated on a marine environmental data set.

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Greenstreet, S. P. R., Rogers, S. I., Rice, J. C., Piet, G. J., Guirey, E. J., Fraser, H. M., and Fryer, R. J. 2011. Development of the EcoQO for the North Sea fish community. – ICES Journal of Marine Science, 68: 1–11. Development of the Ecological Quality Objective (EcoQO) for the North Sea demersal fish community is described. Size-based metrics were identified as the most effective indicators of the state of the community, but such metrics are also sensitive to environmental influence. Redefining the large fish indicator (LFI) produced a metric more sensitive to fishing-induced change and therefore more useful to managers. Fish stocks were thought to be exploited at a sustainable rate in the early 1980s, so in a process echoing the precautionary approach to fish stock management, this was considered the reference period for the LFI, suggesting a value of 0.3 as the appropriate EcoQO. The LFI declined from around 0.3 in 1983 to 0.05 in 2001, followed by a recovery to 0.22 in 2008. However, analyses of the longer-term groundfish survey data suggest that, even were fishing pressure to be reduced to early 20th century levels, the LFI would be unlikely to rise much above a value of 0.3. The response of the LFI to variation in fishing pressure suggested a more complex relationship than anticipated, underscoring the need for operational theoretical size-resolved multispecies fish community models to support management towards broader ecosystem objectives.

The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Tw) for Scotland that predicts variability in both river temperature and climate sensitivity. Tw was modelled as a linear function of maximum daily air temperature (Ta), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Tw was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Tw under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.

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Marine sediments from coastal areas and voes in the Shetland and Orkney Islands were analysed for parent and branched 2- to 6-ring polycyclic aromatic hydrocarbons (PAHs) and geochemical biomarkers. Where possible 14 sediment samples were collected at random from each of 17 Shetland and 9 Orkney sites. The wide range of total PAH concentrations in sediments (i.e., < LOD up to 22619 ng g(-1) dry weight) was indicative of a variety of anthropogenic activities and different sediment types associated with the specific locations. Low PAH concentrations were determined in sandy sediments from areas of limited boat activity. The highest PAH concentrations were found in muddy sediment close to a boat repair yard. PAH concentration ratios were consistent with the main source of these compounds, in most areas, being pyrolysis. Geochemical biomarker (triterpane and sterane) profiles from the sediment were indicative, for some areas, of limited petrogenic input. Punds Voe was the only voe to show evidence of North Sea oil. PAH profiles were similar across sites within each island group, with any differences attributable to known local sources of PAHs. However, there was a clear difference in the PAH profiles of Shetland and Orkney sediments, with Orkney sediments having a higher proportion of the lighter alkylated PAHs.