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Wolf, J. M.

Publications and source records attributed to Wolf, J. M..

2 recordsLinked to original sources

Bull Trout passage at beaver dams in two Montana streams

Beaver (Castor canadensis) translocation and mimicry is an increasingly popular tool for process-based restoration of degraded streams. Processes influenced by beaver restoration include stream-floodplain connectivity, multi-threaded channel formation, enhanced riparian condition and fire resiliency, and streamflow attenuation. Beaver activity can also be a nuisance in agricultural settings, increase invasive species abundance, and create in-stream barriers to fish. Previous studies indicate that spring-spawning salmonid species can pass beaver dams in higher proportions than fall-spawning species. Thus, restoration or mimicry of beavers in streams containing fall-spawning, threatened Bull Trout (Salvelinus confluentus) is of concern to many biologists. We evaluated Bull Trout passage at beaver dams in two Montana streams: Meadow Creek (East Fork Bitterroot River drainage) in summer 2020 and Morrison Creek (Middle Fork Flathead River drainage) from 1997 to 2011. In Meadow Creek, 16% of PIT-tagged Bull Trout which entered a large beaver dam complex were detected upstream of some dams, but no fish moved through the entire 1 km complex. In Morrison Creek, redds were more likely than random to be located below dams, but two or more redds were found upstream of at least one dam in 6 of 9 years dams were present. These results suggest that beaver dams can affect the movement of Bull Trout, but that passage depends on the characteristics of individual dams and reach geomorphology. Our methods cannot distinguish between inhibition of fish movement and selection of beaver-created habitats by fish. Therefore, we suggest future research on beaver restoration in streams with Bull trout.

ecology↗

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

While the promise of electronic medical record and biobank data is large, major questions remain about patient privacy, computational hurdles, and data access. One promising area of recent development is pre-computing non-individually identifiable summary statistics to be made publicly available for exploration and downstream analysis. In this manuscript we demonstrate how to utilize pre-computed linear association statistics between individual genetic variants and phenotypes to infer genetic relationships between products of phenotypes (e.g., ratios; logical combinations of binary phenotypes using and and or) with customized covariate choices. We propose a method to approximate covariate adjusted linear models for products and logical combinations of phenotypes using only pre-computed summary statistics. We evaluate our methods accuracy through several simulation studies and an application modeling various fatty acid ratios using data from the Framingham Heart Study. These studies show consistent ability to recapitulate analysis results performed on individual level data including maintenance of the Type I error rate, power, and effect size estimates. An implementation of this proposed method is available in the publicly available R package pcsstools.

genetics↗