Development and validation of numerical habitat models for juveniles of Atlantic salmon (Salmo salar)

Abstract

We evaluated the ability of numerical habitat models (NHM) to predict the distribution of juveniles of Atlantic salmon (Salmo salar) in a river. NHMs comprise a hydrodynamic model (to predict water depth and current speed for any given flow) and a biological model (to predict habitat quality for fish using water depth, current speed, and substrate composition). We implemented NHMs with a biological model based on (i) preference curves defined by the ratio of the use to the availability of physical conditions and (ii) a multivariate logistic regression that distinguished between the physical conditions used and avoided by fish. Preference curves provided a habitat suitability index (HSI) ranging from 0 to 1, and the logistic regression produced a habitat probabilistic index (HPI) representing the probability of observing a parr under given physical conditions. Pearson's correlation coefficients between HSI and local densities of parr ranged from 0.39 to 0.63 depending on flow. Corresponding values for HPI ranged from 0.81 to 0.98. We concluded that HPI may be a more powerful biological model than HSI for predicting local variations in fish density, forecasting fish distribution patterns, and performing summer habitat modelling for Atlantic salmon juveniles.

Assessing Nonlinearity in Fish Habitat Preference of Japanese Medaka (Oryzias latipes) Using Genetic Algorithm ^|^ndash; Optimized Habitat Prediction Models

“…To evaluate habitat preferences of target species, researchers have proposed various types of the models, for instance, the habitat suitability index (HSI 3,20 ), patterns of preference level 18 , a fuzzy rule-based model 13 , and many others 2,[4][5][6][7]11,12 . In nature, the habitat selections of target species are strongly affected by nonlinear and complex interactions between environmental factors, which is supported by previous researches using nonlinear equations in habitat prediction 9,10,21 . From this perspective, the method used to evaluate the interactions is one of the most important aspects contributing to the reliability of habitat evaluation techniques.…”

“…From this perspective, the method used to evaluate the interactions is one of the most important aspects contributing to the reliability of habitat evaluation techniques. For instance, to test the reliability of regression equations, Vadas & Orth 21 compared linear, polynomial, and product equations, and Guay et al 9,10 employed a logistic regression model to predict the spatial distribution of target fish. These studies employed a univariate approach, in which unit models that consider a single factor were developed and then these models were integrated to evaluate habitat preference based on several environmental factors.…”

Emergence of New Explanatory Variables for 2D Habitat Modelling in Large Rivers: The St. Lawrence Experience

“…Indirectly, it modifies environmental characteristics of the habitat, gas dissolution, density and surface tension (Wetzel, 2001 In classic habitat models, the habitat suitability index (HSI) based on preference curves has been widely used, but with limited accuracy (Scott and Shrivell, 1987;Bourgeois et al, 1996). Recently, Guay et al (2000) fast-flowing waters, while cyprinids prefer quieter, shallower waters. In addition, the analysis showed a highly significant negative effect of increasing light intensity on walleye, also consistent with literature on its biology (AIi et al, 1977 The probability of occurrence was simulated for the three species and for five water discharge scenarios between 5000 and 12,000 m3s-1.…”

Catchment‐scale mapping of surface grain size in gravel bed rivers using airborne digital imagery

“…Furthermore, Fausch et al [2002] propose that fluvial habitat modeling of aquatic species must be extended to the scale of the entire river. Given that grain size distribution is a fundamental parameter of aquatic habitat [Guay et al, 2000], catchment-scale grain estimations will be necessary. Since both field measurements and photosieving are not easily applicable at the river scale, there is a need for alternative methods.…”