Classification Tree Models for Predicting Distributions of Michigan Stream Fish from Landscape Variables

Traditionally, fish habitat requirements have been described from local‐scale environmental variables. However, recent studies have shown that studying landscape‐scale processes improves our understanding of what drives species assemblages and distribution patterns across the landscape. Our goal was to learn more about constraints on the distribution of Michigan stream fish by examining landscape‐scale habitat variables. We used classification trees and landscape‐scale habitat variables to create and validate presence‐absence models and relative abundance models for Michigan stream fishes. We developed 93 presence‐absence models that on average were 72% correct in making predictions for an independent data set, and we developed 46 relative abundance models that were 76% correct in making predictions for independent data. The models were used to create statewide predictive distribution and abundance maps that have the potential to be used for a variety of conservation and scientific purposes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141481/1/tafs0976.pd

Developing User‐Friendly Habitat Suitability Tools from Regional Stream Fish Survey Data

We developed user‐friendly fish habitat suitability tools (plots) for fishery managers in Michigan; these tools are based on driving habitat variables and fish population estimates for several hundred stream sites throughout the state. We generated contour plots to show patterns in fish biomass for over 60 common species (and for 120 species grouped at the family level) in relation to axes of catchment area and low‐flow yield (90% exceedance flow divided by catchment area) and also in relation to axes of mean and weekly range of July temperatures. The plots showed distinct patterns in fish habitat suitability at each level of biological organization studied and were useful for quantitatively comparing river sites. We demonstrate how these plots can be used to support stream management, and we provide examples pertaining to resource assessment, trout stocking, angling regulations, chemical reclamation of marginal trout streams, indicator species, instream flow protection, and habitat restoration. These straightforward and effective tools are electronically available so that managers can easily access and incorporate them into decision protocols and presentations.Received April 9, 2010; accepted November 8, 2010Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141005/1/nafm0041.pd

The effect of streamflow on the short-term carrying capacity of a stream for juvenile smallmouth bas

Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/115680/1/39015029537258.pd

Distributions of Stream Fishes and their Relationship to Stream Size and Hydrology in Michigan’s Lower Peninsula

We examined the distribution and abundance patterns of 69 fish species that commonly occur in the rivers of Michigan’s lower peninsula to develop a simple, empirically based model for describing fish assemblages. We used cluster analysis to group fishes that shared similar abundance patterns at 226 stream sites. The 17 clusters we identified explained about 39% of the variation in species abundances among the stream sites, providing a reasonable, albeit simplified, picture of general associations of fishes in lower Michigan streams. Known ecological differences among species and further analyses suggested that a measure of cluster abundance should not be used to predict the abundances of its constituent species. We selected catchment area (CA) and low‐flow yield (LFY; 90% exceedence flow divided by catchment area) as axes for plotting fish distributions and rivers because these variables link catchment‐scale features of the landscape to multiple, site‐scale characteristics of stream habitat (e.g., temperature, velocity, and depth) important to fishes. As a measure of groundwater loading to streams, LFY, which integrates the geological, landform, and soil characteristics of catchments, reached its highest values in basins predominated by highly permeable soils and relatively steep topography. Plots of fish clusters and species abundances on LFY‐CA axes provided insight into the structure of fish assemblages in lower Michigan streams. When plotted on LFY−CA axes, the 17 fish clusters were distributed in a meaningful pattern that reflected stream size and temperature preferences of constituent species. The LFY‐CA axes provided an empirically derived framework for comparing Michigan streams and for assessing the physical and biological potential of different river reaches. This has allowed fishery managers to better explain, justify, and build public support for river management plans and actions. Although the relationships among LFY, CA, and fish abundances we describe are specific to lower Michigan streams, our approach could be used to develop similar models specific to other regions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141068/1/tafs0070.pd

Rating the Potential Suitability of Habitat in Michigan Stream Reaches for Arctic Grayling

Present-day environments and anticipated future conditions often pose a significant challenge to efforts to reintroduce extirpated species, highlighting the need for collaborative, thorough approaches to reintroductions. Such is the case in Michigan, where numerous partners are working to reintroduce Arctic Grayling Thymallus arcticus with hopes of reestablishing self-sustaining populations. With . 47,000 km of coldwater stream habitat in the state and limited numbers of eggs for reintroductions, a prioritization framework was needed to provide a standardized, fine-scale method for rating suitability of streams for reintroductions. Through facilitated discussions with stakeholders and experts, we developed an overall prioritization framework for rating Michigan streams with components evaluating a reach’s thermal, instream habitat, biological, and connectivity characteristics. Within the context of this broader framework, we developed the habitat rating component for assessing suitability of instream conditions for egg, fry, juvenile, and adult life stages of Arctic Grayling. Life-stage-specific habitat metrics and scoring criteria from this effort were used to rate habitat conditions for 45 reaches in tributaries of Michigan’s Manistee River, enabling identification of reaches likely having instream habitat most suitable for Arctic Grayling. Numbers of reaches meeting or exceeding 60%, 70%, and 80% of the maximum score for overall habitat suitability were 31, 8, and 1. Upon completion of the fish assemblage and connectivity components, the prioritization framework and habitat rating process described here will be used for comparing suitability among streams throughout the historical range of Arctic Grayling in Michigan and guiding reintroduction efforts. Though it will take considerable time before instream habitat suitability criteria can be evaluated for all life-stages of Arctic Grayling in Michigan, the collaborative stream prioritization framework developed for Arctic Grayling reintroduction can be readily adapted to reintroduction efforts for other species elsewhere

Use of GIS‐Derived Landscape‐Scale Habitat Features to Explain Spatial Patterns of Fish Density in Michigan Rivers

Both site‐ and landscape‐scale processes play important roles in the biological communities of rivers. Understanding the influences of these processes on fish abundance can help direct management and research efforts toward appropriate habitat variables and scales. We used multiple linear regression analysis of a regional fish and habitat database to determine the feasibility of using geographical information systems (GIS)–derived landscape‐scale habitat variables to explain the spatial variation in the density of five sport fish species (Chinook salmon Oncorhynchus tshawytscha, steelhead O. mykiss, brown trout Salmo trutta, brook trout Salvelinus fontinalis, and white sucker Catostomus commersonii) in the rivers of Michigan’s Lower Peninsula. We compared these models with those developed using site‐scale variables traditionally measured in the field. Landscape‐scale riverine habitat variables obtained through GIS analysis and modeling of catchment characteristics accounted for 18–69% of the variation in fish density. Landscape estimates of mean July water temperature were negatively correlated with the density of brook trout, brown trout, and Chinook salmon. Drainage area was negatively correlated with the density of steelhead and white suckers, and 90% exceedence flow yield (a measure of flow stability) was positively correlated with the density of Chinook salmon and steelhead. Site‐scale habitat variables explained less (12–57%) of the variation in fish density than landscape‐scale variables. In the site‐scale models, depth was negatively related to all species’ densities, and the percentage of soft substrates was positively correlated only with white suckers. Although there was still much unexplained variation in density, our models provide insight into key habitat variables that influence fish density patterns on a large scale.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141069/1/nafm1411.pd