North Atlantic subtropical mode waters and ocean memory in HadCM3

A study of the formation and propagation of volume anomalies in North Atlantic Mode Waters is presented, based on 100 yr of monthly mean fields taken from the control run of the Third Hadley Centre Coupled Ocean-Atmosphere GCM (HadCM3). Analysis of the temporal and. spatial variability in the thickness between pairs of isothermal surfaces bounding the central temperature of the three main North Atlantic subtropical mode waters shows that large-scale variability in formation occurs over time scales ranging from 5 to 20 yr. The largest formation anomalies are associated with a southward shift in the mixed layer isothermal distribution, possibly due to changes in the gyre dynamics and/or changes in the overlying wind field and air-sea heat fluxes. The persistence of these anomalies is shown to result from their subduction beneath the winter mixed layer base where they recirculate around the subtropical gyre in the background geostrophic flow. Anomalies in the warmest mode (18 degrees C) formed on the western side of the basin persist for up to 5 yr. They are removed by mixing transformation to warmer classes and are returned to the seasonal mixed layer near the Gulf Stream where the stored heat may be released to the atmosphere. Anomalies in the cooler modes (16 degrees and 14 degrees C) formed on the eastern side of the basin persist for up to 10 yr. There is no clear evidence of significant transformation of these cooler mode anomalies to adjacent classes. It has been proposed that the eastern anomalies are removed through a tropical-subtropical water mass exchange mechanism beneath the trade wind belt (south of 20 degrees N). The analysis shows that anomalous mode water formation plays a key role in the long-term storage of heat in the model, and that the release of heat associated with these anomalies suggests a predictable climate feedback mechanism.

Strategies and biases in location memory in Williams syndrome

Individuals with Williams syndrome (WS) demonstrate impaired visuo-spatial abilities in comparison to their level of verbal ability. In particular, visuo-spatial construction is an area of relative weakness. It has been hypothesised that poor or atypical location coding abilities contribute strongly to the impaired abilities observed on construction and drawing tasks [Farran, E. K., & Jarrold, C. (2005). Evidence for unusual spatial location coding in Williams syndrome: An explanation for the local bias in visuo-spatial construction tasks? Brain and Cognition, 59, 159-172; Hoffman, J. E., Landau, B., & Pagani, B. (2003). Spatial breakdown in spatial construction: Evidence from eye fixations in children with Williams syndrome. Cognitive Psychology, 46, 260-301]. The current experiment investigated location memory in WS. Specifically, the precision of remembered locations was measured as well as the biases and strategies that were involved in remembering those locations. A developmental trajectory approach was employed; WS performance was assessed relative to the performance of typically developing (TD) children ranging from 4- to 8-year-old. Results showed differential strategy use in the WS and TD groups. WS performance was most similar to the level of a TD 4-year-old and was additionally impaired by the addition of physical category boundaries. Despite their low level of ability, the WS group produced a pattern of biases in performance which pointed towards evidence of a subdivision effect, as observed in TD older children and adults. In contrast, the TD children showed a different pattern of biases, which appears to be explained by a normalisation strategy. In summary, individuals with WS do not process locations in a typical manner. This may have a negative impact on their visuo-spatial construction and drawing abilities. (c) 2007 Elsevier Ltd. All rights reserved.

Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.