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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Northwest CASC > FY 2011 Projects > Rangewide Climate Vulnerability Assessment for Threatened Bull Trout > Approved Products ( Show all descendants )

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_ScienceBase Catalog
__National and Regional Climate Adaptation Science Centers
___Northwest CASC
____FY 2011 Projects
_____Rangewide Climate Vulnerability Assessment for Threatened Bull Trout
______Approved Products
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Abstract (from http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2014-0098#.VN_BFGjF_gl): Linked atmospheric and wildfire changes will complicate future management of native coldwater fishes in fire-prone landscapes, and new approaches to management that incorporate uncertainty are needed to address this challenge. We used a Bayesian network (BN) approach to evaluate population vulnerability of bull trout ( Salvelinus confluentus) in the Wenatchee River basin, Washington, USA, under current and future climate and fire scenarios. The BN was based on modeled estimates of wildfire, water temperature, and physical habitat prior to, and following, simulated fires throughout the basin. We found that bull trout population...
Bull Trout is the most cold-adapted fish in freshwaters of the Pacific Northwest. The species is listed as threatened under the U.S. Endangered Species Act, but climate warming may place the species at further risk. Climate warming may lead to warming of streams in summer and increasing probability of floods in winter, leading to widespread loss of habitat projected for Bull Trout. This project sought to further elaborate how these climate-related threats influence Bull Trout across five western states (OR, WA, ID, MT, NV). These states form the southern margin of the species’ range. We used predictions of temperatures in streams across approximately two-thirds of this extent to map coldwater streams or “patches”...
Parent Project: Range-Wide Climate Vulnerability Assessment for Threatened Bull Trout Publication Abstract: Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species’ range. We illustrated the method by forecasting suitable...