Development of habitat suitability criteria for trout in small streams

Abstract

We observed 2863 trout in the wild to determine habitat utilization in small streams of the Kings River basin in California's Sierra Nevada mountains. The habitat utilization data were used to develop habitat suitability functions that provide input variables to the instream flow incremental methodology (IFIM) habitat assessment model of the U.S. Fish and Wildlife Service.Observations of habitat utilization of rainbow trout (Salmo gairdneri), brown trout (Salmo trutta), and brook trout (Salwelinwfontinalis) were obtained during the summer months of 1983 and 1984. The observations were made in small streams with discharges ranging from 0.7 m3 s-' to 0.03 m3 s-'. The streams are at elevations of 1250 to 2530 m. Equal effort was applied to observing undisturbed trout in all habitat types. Snorkeling proved to be the most effective method of observation.Individual trout of all species and life stages were most often observed in the lower half of the water column, utilizing low-velocity currents of less than 3.0cm s-'.From the depth and velocity utilization data, several forms of habitat suitability functions were developed and evaluated:1. Univariate depth and velocity functions derived from frequency histogram analysis.2. Univariate depth and univariate velocity exponential polynomial models. Bivariate depth and velocity exponential polynomial models.Univariate exponential polynomial models provided the best fit to the data for each species, based on visual comparisons of response surfaces and contour plots, and comparisons of computed sums of squared errors. Bivariate models fitted to the data resulted in greater sums of squared errors than multiplicative aggragation of univariate models, and frequently predicted utilization at zero depth.The habitat suitability functions derived from the univariate exponential polynomial models provided the best input to the IFIM habitat assessment models. KEY woKI>s lnstrcam flow Trout Suitability critcrin

Application of Probabilistic Neural Networks to microhabitat suitability modelling for adult brown trout (Salmo trutta L.) in Iberian rivers

“…Given the large amount of information about its habitat selection at the microscale, it was necessary to prioritize by selecting some benchmarking studies that were mainly located in the Mediterranean context and had applied multivariate techniques (Ayllón et al, 2010;Lambert and Hanson, 1989;Vismara et al, 2001). However the multivariate approach is not as widespread as the univariate.…”

“…Substrate index 6, 7 and 8). Lambert and Hanson (1989) also developed bivariate polynomial functions from data collected in small mountains streams of the King River Basin in the Sierra Nevada of California, with noticeable…”

“…Several data-driven multivariate techniques have been applied in freshwater fish habitat suitability modelling, specifically at the microhabitat scale. Ranging from simple bivariate polynomial functions (Lambert and Hanson, 1989;Vismara et al, 2001) to more complex fuzzy rule base models (Jorde et al, 2001). Logistic regression has been used by some researchers to develop habitat suitability models for brown trout (Ayllón et al, 2010;Hayes and Jowett, 1994), but Generalized Additive Models (GAMs) have been also used to predict the habitat suitability and possible interactions among variables (Jowett and Davey, 2007).…”

Simulating Spawning and Juvenile Rainbow Trout (Oncorhynchus mykiss) Habitat in Colorado River Based on High-Flow Effects

“…Fish preference curves are, in turn, founded on literature, laboratory studies and frequency of the field observation (which has certain values within a variable range of values for fish habitat). The preference curves applied in this study are mainly taken from a specific fish study on the Colorado River and only four indices which significantly affected fish habitat were chosen [36][37][38]. These fish preference curves are specific for spawning and juvenile rainbow trout.…”

Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

“…The habitat suitability models at microhabitat scale range from simple habitat suitability index (HSI) and bivariate polynomial functions (Lambert & Hanson, 1989) to more complex fuzzy rule base models (Jorde et al, 2001;Mouton et al, 2008), such as multilayer perceptron ensembles (Muñoz-Mas et al, 2014a) or probabilistic neural networks (Muñoz-Mas et al, 2014b). The mesohabitat scale areas usually classified as riffles, runs or pools (Angermeier & Schlosser, 1989) that exhibit important differences among them related to water velocity, channel morphology and type of substrate.…”