Stream Assessment and Monitoring
A Function-Based Framework for Stream Assessment & Restoration Projects
Stream restoration efforts have increased significantly in the US over the past few decades and are now recognized as a billion-dollar industry. These restoration efforts stem from centuries of abuse as humans continue to alter the riverine landscape for a variety of purposes, including farming, logging, mining and development on the floodplain, and the subsequent need for channelization and flood control. These activities have significantly diminished the natural functions of our stream corridors. Today stream corridor restoration efforts seek to improve or restore these lost functions. A variety of federal, state and local programs, along with efforts from non-profit organizations, provide funding for these programs. The goals are varied and range from simple streambank stabilization projects to watershed scale restoration. For these projects to be successful it is important to know why the project is being completed and what techniques are best suited to restore the lost functions. Knowing why a project is needed requires some form of functional assessment followed by clear project goals. To successfully restore stream functions, it is necessary to understand how these different functions work together and which restoration techniques influence a given function. It is also imperative to understand that stream functions are interrelated and build on each other in a specific order, a functional hierarchy. If this hierarchy is understood, it is easier to establish project goals. And with clearer goals, it is easier to evaluate project success.
Sampling strategies for estimating brook trout effective population size
The influence of sampling strategy on estimates of effective population size (Ne) from single-sample genetic methods has not been rigorously examined, though these methods are increasingly used. For headwater salmonids, spatially close kin association among age-0 individuals suggests that sampling strategy (number of individuals and location from which they are collected) will influence estimates of Ne through family representation effects. We collected age-0 brook trout by completely sampling three headwater habitat patches, and used microsatellite data and empirically parameterized simulations to test the effects of different combinations of sample size (S = 25, 50, 75, 100, 150, or 200) and number of equally-spaced sample starting locations (SL = 1, 2, 3, 4, or random) on estimates of mean family size and effective number of breeders (Nb). Both S and SL had a strong influence on estimates of mean family size and ^ Nb; however the strength of the effects varied among habitat patches that varied in family spatial distributions. The sampling strategy that resulted in an optimal balance between precise estimates of Nb and sampling effort regardless of family structure occurred with S = 75 and SL = 3. This strategy limited bias by ensuring samples contained individuals from a high proportion of available families while providing a large enough sample size for precise estimates. Because this sampling effort performed well for populations that vary in family structure, it should provide a generally applicable approach for genetic monitoring of iteroparous headwater stream fishes that have overlapping generations.
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