Incorporating spatial context into the analysis of salmonid habitat relations

Habitat use and availability studies have long been used to understand fish–habitat relations and develop predictive models in fisheries management. Lapointe (Chapter 17, this volume) addresses many of the strengths of this approach, specifically for quantifying linkages between geomorphology and fish habitat. Despite the welldocumented limitations of correlative models that relate the population density of organisms to habitat characteristics (Railsback et al., 2003; Lancaster and Downes, 2010), this approach still has much to offer aquatic ecology in terms of setting the broader context within which ecological processes (i.e., growth, reproduction, and behaviour) interact and influence the distribution and abundance of organisms (Thomson et al., 1996). This applies particularly to gravel-bed rivers, which (i) are dynamic spatially and temporally, (ii) range in size from several to tens of metres in width, and (iii) are characterized by flows that make experimentation and manipulation difficult at scales that are relevant to the diverse life histories of lotic fishes (Fausch et al., 2002). Thus, it is expected that statistical models that assess habitat associations and seek to predict the distribution and abundance of fish in relation to physical factors (e.g., channel form, flow, and water quality) will continue playing a role – albeit in conjunction with process-based investigations of movement, growth, and behaviour – in studying and conserving lotic fishes. It is along these lines, and specifically regarding salmonid fishes, that this chapter responds to the review and synthesis provided by Lapointe (Chapter 17, this volume). Habitat preferences are highly “complex and contextual” (see Section 17.2). In spite of centuries of work and the availability of sophisticated computer modelling tools, there is much room for improvement in describing and quantifying fish–habitat relations (Walton and Cotton, 1676; Bjornn and Reiser, 1991; Smith, 1994; Guisan et al., 2002; Railsback and Harvey, 2002; Rosenfeld, 2003). The challenge lies not just in having the appropriate statistical tools, but in obtaining more complete information on where the fish are located and what is available to them in both space and time. Lapointe acknowledges that there are many potential explanations for this problem and suggests that “Part of the poor predictive power of conventional, patch-scale spawning habitat preferences just mentioned may be due to a scale mismatch concerning broader characteristics that control local reproductive success. ” The specific issue of a scale mismatch as one such explanation for the poor fit of fish–habitat models is a propos and has been described for stream fishes at scales ranging frommetres to hundreds of kilometres (Dunham and Vinyard, 1997; Peterson and Dunham, 2010). However, this raises the general question: Why is the predictive power of fish– habitat models so poor? This general question is particularly germane to the aim of the chapter by Lapointe, which is to provide some background for geomorphologists whomay be interested in relating physical patterns in fluvial geomorphology to biological responses to address the needs of natural resource managers. In the following discussion, we address the question raised by Lapointe of why it is so difficult to predict salmonid–habitat relations. We acknowledge that this cannot be an exhaustive treatment of the subject and therefore identify what we believe are several key issues that demonstrate the importance of explicitly incorporating spatial context into the analysis of fish–habitat data. Specifically, our goals are to (i) describe uncertainty in fish–habitat relations as is reflected in correlative models, (ii) elucidate the potential problems with using density (i.e., abundance normalized by area or length) as a response variable in statistical models, (iii) illustrate how uncertainty in correlative models can