A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

A zero-stiffness elastic shell structure

A remarkable shell structure is described that, due to a particular combination of geometry and initial stress, has zero stiffness for any finite deformation along a twisting path; the shell is in a neutrally stable state of equilibrium. Initially the shell is straight in a longitudinal direction, but has a constant, nonzero curvature in the transverse direction. If residual stresses are induced in the shell by, for example, plastic deformation, to leave a particular resultant bending moment, then an analytical inextensional model of the shell shows it to have no change in energy along a path of twisted configurations. Real shells become closer to the inextensional idealization as their thickness is decreased; experimental thin-shell models have confirmed the neutrally stable configurations predicted by the inextensional theory. A simple model is described that shows that the resultant bending moment that leads to zero stiffness gives the shell a hidden symmetry, which explains this remarkable property.

Climatic effects on flood frequency: an example from southern Arizona

EXTRACT (SEE PDF FOR FULL ABSTRACT): After 1960, the Santa Cruz River at Tucson, Arizona, an ephemeral stream normally dominated by summer floods, experienced an apparent increased frequency of flooding coincident with an increased percentage of annual floods occurring in fall and winter. This shift reflects large-scale and low-frequency changes in the eastern Pacific Ocean, in part associated with El Niño-Southern Oscillation (ENSO) phenomena. ... Questions are raised about the validity of standard methods of flood-frequency analysis to estimate regulatory and designed floods.

Real-time climate monitoring using an AVHRR-based vegetation index

A normalized difference vegetation index (NDVI) has been produced and archived on a 1° latitude by 1° longitude grid between 55°S and 75°N. The many sources of data errors in the NDVI include cloud contamination, scan angle biases, changes in solar zenith angle, and sensor degradation. Week-to-week variability, primarily caused by cloud contamination and scan angle biases, can be minimized by temporally filtering the data. Orbital drift and sensor degradation introduces interannual variability into the dataset. These trends make the usefulness of a long-term climatology uncertain and limit the usefulness of the NDVI. Elimination of these problems should produce an index that can be used for climate monitoring.