Brown trout redd superimposition in relation to spawning habitat availability

The relationship between redd superimposition and spawning habitat availability was investigated in the brown trout (Salmo trutta L.) population inhabiting the river Castril (Granada, Spain). Redd surveys were conducted in 24 river sections to estimate the rate of redd superimposition. Used and available microhabitat was evaluated to compute the suitable spawning habitat (SSH) for brown trout. After analysing the microhabitat characteristics positively selected by females, SSH was defined as an area that met all the following five requirements: water depth between 10 and 50 cm, mean water velocity between 30 and 60 cm s)1, bottom water velocity between 15 and 60 cm s)1, substrate size between 4 and 30 mm and no embeddedness. Simple regression analyses showed that redd superimposition was not correlated with redd numbers, SSH or redd density. A simulation-based analysis was performed to estimate the superimposition rate if redds were randomly placed inside the SSH. This analysis revealed that the observed superimposition rate was higher than expected in 23 of 24 instances, this difference being significant (P menor que 0.05) in eight instances and right at the limit of statistical significance (P = 0.05) in another eight instances. Redd superimposition was high in sections with high redd density. High superimposition however was not exclusive to sections with high redd density and was found in moderate- and low-redd-density sections. This suggests that factors other than habitat availability are also responsible for redd superimposition. We argue that female preference for spawning over previously excavated redds may be the most likely explanation for high superimposition at lower densities.

Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.