Explaining spatial variability in stream habitats using both natural and management‐influenced landscape predictors

Abstract

ABSTRACT1. The distribution and composition of in-stream habitats are reflections of landscape scale geomorphic and climatic controls. Correspondingly, Pacific salmon (Oncorhynchus spp.) are largely adapted to and constrained by the quality and complexity of those in-stream habitat conditions. The degree to which lands have been fragmented and managed can disrupt these patterns and affect overall habitat availability and quality.2. Eleven in-stream habitat features were modelled as a function of landscape composition. In total, 121 stream reaches within coastal catchments of Oregon were modelled. For each habitat feature, three linear regression models were applied in sequence; final models were composed of the immutable and management-influenced landscape predictors that best described the variability in stream habitat.3. Immutable landscape predictors considered proxies for stream power described the majority of the variability seen in stream habitat features. Management-influenced landscape predictors, describing the additional human impacts beyond that which was inherently entwined with the immutable predictors, explained a sizeable proportion of variability. The largest response was seen in wood volume and pool frequency.4. By using a sequential linear regression analysis, management-influenced factors could be segregated from natural gradients to identify those stream habitat features that may be more sensitive to land-use pressures. These results contribute to the progressing notion that the conservation of freshwater resources is best accomplished by investigating and managing stream systems from a landscape perspective.

Deep‐Seated Landslides Drive Variability in Valley Width and Increase Connectivity of Salmon Habitat in the Oregon Coast Range

“…), to describe relationships between instream habitat and landscape conditions (Anlauf et al. ), and to understand patterns of juvenile Coho Salmon habitat use (Steel et al. ).…”

“…Geomorphic features and measurements are georeferenced, allowing for distance downstream and habitat unit lengths to be calculated in a geographic information system (GIS) (Moore et al 1997(Moore et al , 2007. A robust literature using AIP field survey data has demonstrated the relevance and utility of this methodology to explain patterns of Coho Salmon occupancy (Steel et al 2012;Flitcroft et al 2014), to describe relationships between instream habitat and landscape conditions (Anlauf et al 2011), and to understand patterns of juvenile Coho Salmon habitat use (Steel et al 2016).…”

Generalized additive models to predict adult and young brown trout (Salmo truttaLinnaeus, 1758)densities in Mediterranean rivers

“…On a larger scale, macro-scale HSM analyse the abiotic factors controlling the spatial patterns of species distributions in river networks, catchments or river basins. This approach has been successfully applied using several techniques, including Generalized Linear Models (Anlauf et al, 2011), Multivariate Adaptive Regression Splines (Leathwick et al, 2005) and Artificial Neural Networks (Olaya-Mar ın et al, 2012). These studies combined variables derived from several sources and applied different techniques to reveal potentially suitable areas for the target species (Fukuda et al, 2013).…”

Discontinuities concentrate mobile predators: quantifying organism–environment interactions at a seascape scale

“…Creating effective complexity indices is challenging . Existing indices have proven useful but often only measure a few variables, use qualitative variables, do not explain why a particular set of variables were chosen, or simply do not predict distribution better than individual habitat features (Clark et al 2004, Gratwicke and Speight 2005, Anlauf et al 2011, França et al 2012, Kovalenko et al 2012. Our three indices that combined individual discontinuities showed how habitat heterogeneity was linked to striped bass distribution.…”