Between-taxon matching of common and rare species richness patterns

Aim: Our primary aim is to understand how assemblages of rare (restricted range) and common (widespread) species are correlated with each other among different taxa. We tested the proposition that marine species richness patterns of rare and common species differ, both within a taxon in their contribution to the richness pattern of the full assemblage and among taxa in the strength of their correlations with each other. Location The UK intertidal zone. Methods: We used high-resolution marine datasets for UK intertidal macroalgae, molluscs and crustaceans each with more than 400 species. We estimated the relative contribution of rare and common species, treating rarity and commonness as a continuous spectrum, to spatial patterns in richness using spatial crosscorrelations. Correlation strength and significance was estimated both within and between taxa. Results: Common species drove richness patterns within taxa, but rare species contributed more when species were placed on an equal footing via scaling by binomial variance. Between taxa, relatively small sub-assemblages (fewer than 60 species) of common species produced the maximum correlation with each other, regardless of taxon pairing. Cross-correlations between rare species were generally weak, with maximum correlation occurring between small sub-assemblages in only one case. Cross-correlations between common and rare species of different taxa were consistently weak or absent. Main conclusions: Common species in the three marine assemblages were congruent in their richness patterns, but rare species were generally not. The contrast between the stronger correlations among common species and the weak or absent correlations among rare species indicates a decoupling of the processes driving common and rare species richness patterns. The internal structure of richness patterns of these marine taxa is similar to that observed for terrestrial taxa.

Is Longer-Distance Migration Slowing? An Analysis of the Annual Record for England and Wales since the 1970s

This paper is prompted by the widespread acceptance that the rates of inter-county and inter-state migration have been falling in the USA and sets itself the task of examining whether this decline in migration intensities is also the case in the UK. It uses annual inter-area migration matrices available for England and Wales since the 1970s by broad age group. The main methodological challenge, arising from changes in the geography of health areas for which the inter-area flows are given, is addressed by adopting the lowest common denominator of 80 areas. Care is also taken to allow for the effect of economic cycles in producing short-term fluctuations on migration rates and to isolate the effect of a sharp rise in rates for 16-24 year olds in the 1990s, which is presumed to be related to the expansion of higher education. The findings suggest that, unlike for the USA, there has not been a substantial decline in the intensity of internal migration between the first two decades of the study period and the second two. If there has been any major decline in the intensity of address changing in England and Wales, it can only be for the within-area moves that this time series does not cover. This latter possibility is examined in a companion paper using a very different data set (Champion and Shuttleworth, 2016).

Is it time for standards and a common data exchange format for digital image analysis?

Digital image analysis is at a crossroads. While the technology has made great strides over the past few decades, there is an urgent need for image analysis to inform the next wave of large scale tissue biomarker discovery studies in cancer. Drawing parallels from the growth of next generation sequencing, this presentation will consider the case for a common language or standard format for storing and communicating digital image analysis data. In this context, image analysis data comprises more than simply an image with markups and attached key-value pair metrics. The desire to objectively benchmark competing platforms or a push for data to be deposited to public repositories much like genomics data may drive the need for a standard that also encompasses granular, cell-by-cell data.