Spatiotemporal Range

In order to capture as much of the potential variation as possible, the samples analysed included Pj elements of 0. excavata from most of its spatiotemporal range (see Table 14.1). Assignment of these elements to 0. excavata was based on published opinions and the active input of conodont workers with experience in this taxon. The full data set is provided in Appendix 5. Original sample sizes ranged between 9 and 44 elements. Inequality in sample size can mask differences in variance between samples, so samples were reduced through random subsampling to produce sample sizes of between 10 and 20 (except for the poorly preserved American topotype material). 

Higher tier mixture extrapolation approaches such as this are used in practice, by calculating site-specific msPAF values, across a broad range of assessment questions. A practical example is provided by De Zwart (2005), who studied the impacts of pesticide use in The Netherlands. In this study, the specific mode of action of the pesticide was taken into account, as shown in Box 5.2. This analysis resulted in spatiotemporal indicators of relative toxic pressure across The Netherlands (Figure 5.3). 

Also the presence of certain cations was shown to change the nonlinear behavior of formic acid oxidation qualitatively. Examples include Cu2+ ions, whose addition induced complex oscillation patterns as well increased frequency oscillations [136], and Bi3+ ions, that were found to significantly enhance the current density as well as the existence range of oscillations and spatiotemporal self-organization (see Section 4.2) [137],... 

A large parameter range in which cluster patterns dominated the spatiotemporal dynamics was also observed for the oscillatory oxidation of H2 in the presence of electrosorbing cations and anions [175], Characteristically, the cluster-type patterns were found at lower concentrations of Cu2+ ions compared with the pulse-type motions shown in Fig. 56. Examples of two-phase and three-phase clusters are depicted in Fig. 63. In these figures the homogeneously oscillating mode has been subtracted. 

An analysis of the recent observation data [30,31] shows that baroclinic Rossby waves that are generated off the eastern coasts in the northern parts of the Pacific and Atlantic oceans in a period of about a year represent their dominant non-stationary dynamical response to the annual cycle of the atmospheric forcing in the latitudinal range from 10-15° to 45-50°N. In so doing, their mean phase velocities (0.02-0.03 ms 1 at 40-45°N) are higher than the theoretical values (about 0.01 ms-1). A similar situation is observed in the Black Sea as well [27]. In [32], several reasons of this phenomenon were listed such as the interaction with more large-scale non-stationary processes, topographic and nonlinear effects, and insufficient duration and spatiotemporal resolution of the observation data. 

In microscale models the explicit chain nature has generally been integrated out completely. Polymers are often described by variants of models, which were primarily developed for small molecular weight materials. Examples include the Avrami model of crystallization,- and the director model for liquid crystal polymer texture. Polymeric characteristics appear via the values of certain constants, i.e. different Frank elastic constant for liquid crystal polymers rather than via explicit chain simulations. While models such as the liquid crystal director model are based on continuum theory, they typically capture spatiotemporal interactions, which demand modelling on a very fine scale to capture the essential effects. It is not always clearly defined over which range of scales this approach can be applied. 

All the complex behavior described so far in this Chapter arises from the diffusive coupling of the local dynamics which in the homogeneous case have simple fixed points as asymptotic states. If the local dynamics becomes more complex, the range of possible dynamic behavior in the presence of diffusion becomes practically unlimited. It is clear that coupling chaotic subsystems could produce an extremely rich dynamics. But even the case of periodic local dynamics does so. Diffusively coupled chemical or biological oscillators may become synchronized (Pikovsky et ah, 2003), or rather additional instabilities may arise from the spatial coupling. This may produce target waves, spiral patterns, front instabilities and several different types of spatiotemporal chaos or phase turbulence (Kuramoto, 1984). 

By their very nature, tasks which enable one to measure spatiotemporal accuracy are complex or higher-level sensory-motor tasks. These place demands on a large number of lower-level PRs such as visual acuity, dynamic visual perception, range of movement, strength, simple reaction times, acceler-ation/deacceleration, static steadiness, dynamic steadiness, prediction, memory, open-loop movements, concentration span, attention switching, that is, central executive function or supervisory attentional system (multitask abilities), utilization of preview, and learning. 

---