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Hierarchical multi-population viability analysis.

Douglas R Leasure1, Seth J Wenger1, Nathan D Chelgren2

  • 1University of Georgia, 203 D.W. Brooks Drive, Athens, Georgia, 30602, USA.

Ecology
|November 30, 2018
PubMed
Summary
This summary is machine-generated.

A new hierarchical model improves extinction risk assessments for threatened species with limited data. This approach enhances conservation by integrating information across isolated populations, crucial for effective wildlife management.

Keywords:
Lahontan cutthroat troutRicker modelconservationextinction riskhierarchical Bayesian time seriesimperfect detectionisolated populationsobservation modelpopulation viability analysisremoval sampling

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Area of Science:

  • Ecology
  • Conservation Biology
  • Quantitative Biology

Background:

  • Conventional population viability analysis (PVA) requires extensive data, limiting its application to species with sparse monitoring data across isolated populations.
  • Existing PVA models often fail to account for incomplete detection and sampling biases, potentially leading to inaccurate extinction risk estimates.
  • Effective conservation of threatened species necessitates robust methods for assessing population dynamics and extinction risks, especially with limited or infrequent monitoring data.

Purpose of the Study:

  • To develop and present a hierarchical multi-population viability analysis (PVA) model designed to enhance inference from sparse data by enabling information sharing among populations.
  • To explicitly incorporate observation and sampling sub-models to address challenges of incomplete detection and sampling biases in population monitoring.
  • To apply and validate this novel hierarchical PVA model using historical data from threatened Lahontan cutthroat trout populations in the Great Basin, USA.

Main Methods:

  • Developed a hierarchical multi-population PVA model integrating theoretical ecology concepts with statistical frameworks.
  • Incorporated explicit observation and sampling sub-models to handle imperfect detection and biases in electrofishing survey data.
  • Utilized GIS and remote sensing for environmental variables (temperature, flow, vegetation) and field data for nonnative trout densities, applied to 155 isolated Lahontan cutthroat trout populations (1985-2015).

Main Results:

  • Population growth rates of Lahontan cutthroat trout were positively correlated with colder stream temperatures.
  • Nonnative trout presence significantly reduced the carrying capacity for native trout populations.
  • Extinction risks were influenced by environmental stochasticity, population size, water temperature, and densities of nonnative trout.

Conclusions:

  • Hierarchical multi-population PVA effectively bridges site-level data with population-level processes, maximizing the utility of sparse datasets for conservation.
  • The developed model provides robust estimates of population dynamics, extinction risks, and associated uncertainties, crucial for informed management decisions.
  • A graphical user interface facilitates scenario simulation and assessment of management actions, aiding in the conservation of threatened species like Lahontan cutthroat trout.