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Geology topics

Chris S Allen

Publications and source records attributed to Chris S Allen.

3 recordsLinked to original sources

Combining expert knowledge of a threatened trout distribution with sparse occupancy data for climate-related projection

Objective To evaluate the vulnerability of Bull Trout Salvelinus confluentus to potential climate changes across its range in Oregon, we compiled disparate expert knowledge of the distribution of spawning and rearing and combined these probabilistic statements as data along with documented records of breeding and rearing in a joint occupancy model. Methods The joint expert knowledge–occupancy model, which was based on discrete patches of cold water (≤13°C) suitable for spawning and rearing, permitted the association of true occupancy with climate and other explanatory variables while accounting for variation in detection probability. We then applied estimated relationships of patch occupancy with explanatory variables to projected coldwater patch configurations in the years 2040 and 2080. Result Projections of the kilometers of occupied coldwater patch in future decades suggest precipitous declines if current relationships of occupancy with environmental variables are maintained. Impacts of climate changes in future decades manifest directly through the outright loss of coldwater patches and increases in winter high flows but also indirectly by increased isolation. Conclusion Combining probabilistic statements of species distributions from knowledgeable experts with sparse occupancy data may be a robust and timely alternative when large numbers of repeated occupancy surveys are infeasible.

Oregon

Engaging stakeholders to develop a decision support model of conservation risk and management capacity to prioritize investments in Bull Trout recovery

Rarely are sufficient resources available to support the full suite of management actions to promote recovery of a species across their entire distribution. Decision support models are a tool that can inform natural resource management decisions with consideration of the perspectives from a variety of stakeholders who work across large geographic and jurisdictional extents. We offer an example of a decision support model that was developed by several Federal and State natural resource agencies to rank Bull Trout Salvelinus confluentus core areas for prioritizing conservation investment within Oregon, USA. We engaged State level decision makers to identify parameters believed to be influential in determining funding allocations for Bull Trout core areas. Parameters were linked in a model framework that was further refined with input from local Bull Trout experts with knowledge specific to the various core areas. The model produces a relative priority value that is a combination of the conservation risk to the species and the management capacity to address threats. A series of sensitivity analyses suggests that Bull Trout persistence and threat score are most influential in determining the relative priority of a core area, and life-history and genetic diversity are least influential. One of the more powerful products from this work is an interactive web-based application ( https://das.ecosphere.fws.gov/public/obts/ ) that anyone can use to explore how their beliefs in parameter values will affect the relative priority of Bull Trout core areas across Oregon. Our modeling effort is an example of engaging stakeholders with different roles in species recovery and across a large geographic area to create a clearer path forward in allocating limited resources for species recovery. This approach can be employed to address a number of natural resource management situations across species and habitats.

Oregon