Coastal retreat and/or advance adjacent to defences in England and Wales

Retreat and advance of shoreline position occurs naturally, and also as a result of defences which are constructed to prevent erosion and flooding. Retreat more commonly manifests itself down-drift of defences due to a sediment deficit causing the coast to become ‘set-back’. Advance normally develops due to sediment accumulation up-drift of a barrier inhibiting longshore drift, resulting in the coast becoming ‘set-forward’. Many examples of set-backs and set-forwards are recorded, but their location, number and cause is not known on a national scale. Using the Futurecoast aerial photographs, approximately 200 localities were identified as set-back or set-forward in England and Wales, with half situated in the Eastern and South East regions of England. Half of the total set-backs or set-forwards were on cliffed coasts, and half on low-lying coasts. Without local knowledge it is difficult to distinguish between set-backs and set-forwards. Set-backs often indicate higher retreat rates, thus threatening cliff-top infrastructure which requires defence upgrade and extensions, as well as raising maintenance costs. Monitoring set-backs is important for shoreline management, because as retreat continues, set-backs evolve and artificial headlands form and grow. This is reinforced by the shift from hard defence policies towards softer engineering approaches, managed realignment and limited intervention.     

Does phylogenetic inertia explain the evolution of ineffective antipredator behavior in a sunfish-salamander system?

The streamside salamander, Ambystoma barbouri, exhibits ineffective antipredator behavior (high emergence rate from refuge, and high activity while out of refuge) and thus suffers heavy predation in stream pools with sunfish. A. barbouri evolved relatively recently from an ancestor that closely resembled a sister species, A. texanum, which breeds in fishless, ephemeral ponds. Sunfish thus represent a relatively new selection pressure for A. barbouri. Phylogenetic inertia predicts that (1) A. texanum should be very poor at coping with fish and (2) because it has only recently been exposed to fish, A. barbouri should still be poor at avoiding fish, but due to its recent exposure to fish, A. barbouri should be better than A. texanum at coping with sunfish. Experimental results provided mixed support for these predictions. As predicted, A. texanum suffered heavy sunfish predation. Compared to A. texanum, A. barbouri showed a greater tendency to initiate alarm moves that enhanced escape success from fish. However, in both the presence and absence of fish, A. barbouri showed higher emergence rates from refuge and higher movement while out of refuge than A. texanum. These behaviors tend to increase exposure to sunfish, i.e., for these key behaviors, A. barbouri apparently evolved in the wrong direction as far as fish predation is concerned. Due to these offsetting effects (increased exposure to fish, increased escape success), A. barbouri is no better at surviving with sunfish than A. texanum. A possible explanation for the high activity of A. barbouri is its use of highly ephemeral habitats (relative to A. texanum) that favor the evolution of higher activity, feeding, and developmental rates for A. barbouri relative to A. texanum. Received: 15 May 2000 / Revised: 3 August 2000 / Accepted: 18 August 2000     

PM2.5 speciation trends network: evaluation of whole-system uncertainties using data from sites with collocated samplers

The objectives of this paper are to contrast the relative variability of replicate laboratory measurements of selected chemical components of fine particulate matter (PM) with total variability from collocated measurements and to compare the magnitudes of the uncertainties determined from collocated sampler data with those currently being provided to U.S. Environmental Protection Agency (EPA)'s Air Quality System (AQS) database by RTI International (RTI). Pointwise uncertainty values are needed for modeling and data analysis and should include all the random errors affecting each data point. Total uncertainty can be decomposed into two primary components: analytical measurement uncertainty and sampling uncertainty. Analytical measurement uncertainties are relatively easy to calculate from routine quality control (QC) data. Sampling uncertainties, on the other hand, are comparatively difficult to measure. In this paper, the authors describe data from collocated samplers to provide a snapshot of whole-system uncertainty for several important chemical species. The components of uncertainty were evaluated for key species from each of the analytical methods employed by the PM2.5 Speciation Trends Network (STN) program: gravimetry, ion chromatography (IC), X-ray fluorescence (XRF), and thermal-optical analysis for organic carbon and elemental carbon. The results show that the laboratory measurement uncertainties are typically very small compared with uncertainties calculated from the differences between samples collected from collocated samplers. These differences are attributable to the "field" components uncertainty, which may include contamination and/or losses during shipping, handling, and sampling, as well as other distortions of the concentration level due to flow and sample volume variations. Uncertainties calculated from the collocation results were found to be generally similar to the uncertainties currently being loaded into EPA's AQS system, with some exceptions described below.