Comparing copepod time-series in the north of Spain: Spatial autocorrelation of community composition

Four time-series of copepod species biomass in the north of Spain were contrasted to demonstrate spatial autocorrelation of local communities and their responses to short-term local and regional variability in oceanographic conditions. The series represented coastal and oceanic environments along a marked gradient of influence of seasonal upwelling from Galicia to the Mar Cantábrico (S Bay of Biscay), and each one included at least 10 years of continuous data collected at monthly frequency. Community composition (i.e. species number and diversity) was very consistent through the region, but local variations in the presence of new species and the relative proportions of common species allowed for the characterisation of the response to the environment at each site. Small-sized species were more frequent near the coast. A few species, however, captured the main patterns of variability in all series. Calanus helgolandicus and Acartia (mainly Acartia clausi) were generally the main contributors to total biomass, while other species as Paracalanus parvus and Clausocalanus spp. were important only at some locations. Most copepod indices were positively correlated with upwelling, either considering the whole community (biomass, species richness and diversity) or individual species, but only in the coastal series analysed since 1991. Copepods in the nearby ocean, however, showed negative correlations with upwelling in the period 1960–1986. The effects of upwelling may have been modulated by local factors, as showed by the increases in biomass, number of species and diversity in associations with increases in sea surface temperature in Galicia, while in the Mar Cantábrico only the warming-tolerant species increased and those typical of upwelling decreased. Density stratification of the water column was associated with decreases in total copepod biomass in Galicia, while it favoured the increase in species richness in the Mar Cantábrico. Nearly all significant responses of copepods to environmental variability were delayed by up to 5 months, showing the importance of considering time-lags in the analysis of temporal responses of zooplankton.

Keeping a sense of proportion but losing all perspective: a critique of Gorard's notion of the 'Politician's Error'

In 1999 Stephen Gorard published an article in this journal in which he provided a trenchant critique of what he termed the `politician's error' in analysing differences in educational attainment. The main consequence of this error, he argued, has been the production of misleading findings in relation to trends in educational performance over time that have, in turn, led to misguided and potentially damaging policy interventions. By using gender differences in educational attainment as a case study, this article begins by showing how Gorard's notion of the politician's error has been largely embraced and adopted uncritically by those within the field. However, the article goes on to demonstrate how Gorard's own preferred way of analysing such differences – by calculating and comparing proportionate changes in performance between groups – is also inherently problematic and can lead to the production of equally misleading findings. The article will argue that there is a need to develop a more reliable and valid way of measuring trends in educational performance over time and will show that one of the simplest ways of doing this is to make use of existing, and widely accepted, measures of effect size.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.