Using a Stream Network Census of Fish and Habitat to Assess Models of Juvenile Salmonid Distribution

Abstract

We censused juvenile salmonids and stream habitat over two consecutive summers to test the ability of habitat models to explain the distribution of juvenile Coho Salmon Oncorhynchus kisutch, young‐of‐the‐year (age‐0) steelhead O. mykiss, and steelhead parr (age ≥1) within a network consisting of several different‐sized streams. Our network‐scale habitat models explained 27, 11, and 19% of the variation in density of juvenile Coho Salmon, age‐0 steelhead, and steelhead parr, respectively, but strong levels of spatial autocorrelation were typically present in the residuals. Explanatory power of base habitat models increased and spatial autocorrelation decreased with the sequential inclusion of variables accounting for the effects of stream size, year, stream, reach location, and a tertiary interaction term. Stream‐scale models were highly variable. Fish–habitat associations were rarely linear and ranged from negative to positive; the variable accounting for location of the habitat within a stream was often more important than the habitat variables. The limited success of our network‐scale models was apparently related to variation in the strength and shape of fish–habitat associations across and within streams and years. These results indicate that there are several potential limitations to extrapolating models to broader areas based only on spatially limited surveys.

Dams versus habitat: predicting the effects of dams on habitat supply and juvenile rainbow trout along the Santa Cruz River, Patagonia

“…Consistent with this finding, and our results, many studies have documented high juvenile density areas occurring in the proximity of spawning areas (Murphy et al, 1989;Foldvik et al, 2010;Flitcroft et al, 2014). Differences and similarities among these and our study, and in particular the overall importance of river location for explaining abundance patterns, highlight the potential limitations to extrapolating fish-habitat relationship models to broader areas or to streams other than those in which the data were collected (McMillan et al, 2013).…”

“…Roni et al (2012) used multiple regression models to examine the relationship between habitat variables and growth, survival, and emigration in juvenile Coho salmon also in two small western Washington rivers, finding that rkm correlated both positively and negatively with juvenile densities. In another study, McMillan et al (2013) used GAMs to examine the correlation between juvenile salmonid density and five stream habitat variables in the Calawah River, Washington. They found a negative association between densities of age-0 steelhead and wetted width (contrary to our results); however, the variable accounting for location of the habitat within a stream was more important than the habitat variables, a pattern similar to what we found in the Santa Cruz River.…”

“…First, non-parametric GAMs were fitted to explore the response (relative abundance) of juvenile rainbow trout to the predictor variables, applying the smoothing spline function (Hastie & Tibshirani, 1990;Hedger et al, 2005;McMillan et al, 2013). The objective of this step was twofold: identifying those variables among the 15 predictors to which the abundance of trout was more strongly related, and identifying the shape of the response to specific predictors.…”

Anadromy and residency in steelhead and rainbow trout (Oncorhynchus mykiss): a review of the processes and patterns

“…Oncorhynchus mykiss rear and spawn in a broad range of ephemeral and perennial streams (Boughton et al 2009;McMillan et al 2013). Variation in annual flow regime, particularly summer low flows, may represent a population bottleneck for nonmigratory fishes (Courter et al 2009).…”

Spatial variability in adult brook trout (Salvelinus fontinalis) survival within two intensively surveyed headwater stream networks

“…Many previous studies have examined associations of stream fishes with fluvial habitat characteristics. Lotic habitat heterogeneity has been linked to occurrence (Labbe and Fausch 2000;Rich et al 2003), abundance (Deschênes and Rodríguez 2007;Reeves et al 2011;McMillan et al 2013), and spatial population structure (Skalski et al 2008;Kanno et al 2011a) of stream fishes. However, very little is known about environmental drivers of spatial variability in population vital rates that might exist within complex stream networks.…”