Hierarchical multi-population viability analysis

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.