Effort and potential efficiencies for aquatic non-native species early detection

Our objective was to determine the effort required for high-probability early detection of non-native zooplankton, benthic invertebrates, and fish using Duluth–Superior Harbor — a Great Lakes port under intense non-native species introduction pressure — as a case study. Initially, we allocated samples using a spatially balanced random design. We then resampled the harbor, but allocated samples to a few targeted areas. We detected 21 non-native invertebrate and 10 non-native fish species; however, many rare zooplankton and benthic invertebrates were likely missed. The two designs did not have significantly different species accumulation curves, but the targeted area design samples had higher species richness and detected non-native species with a significantly higher probability. It was possible to reduce the effort required to detect established non-native species. In contrast, the effort required to detect an ultra-rare, newly arrived species remained large. Based on statistical estimation theory, the effort required to detect 95% or more of species present could exceed enumerating 750 zooplankton samples (~500 000 individuals, ~90 species), 150 benthic invertebrate samples (~100 000 individuals, ~250 species), and 100 fish samples (~75 000 individuals, ~40 species).

Optimum probabilistic processing in colour perception. I. Colour discrimination

A comprehensive account of wavelength discrimination and colour satu­ration discrimination is given in terms of optimum probabilistic signal detection. The theory is a logical deduction from statistical estimation theory of the visual estimate of the spectral parameters of the stimulus. In place of geometrical concepts associated with colour-space geometry, stimulus discriminability is determined by optimum decision rules given by likelihood ratio tests on statistics that are postulated for the trichro­matic responses. The classical line element theory and its formulations are deduced to be discriminability measures between signals. The different mathematical forms of classical theory are shown to correspond to differ­ent statistical constraints.