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Population viability analyses

Elements of a good PVA

  • include stakeholders in model design, validation and interpretation
  • builds on (long-term) high quality data
  • perform a careful model selection and compare alternative models where possible
  • include relevant parameters based on knowledge of the system and the literature and in consideration of gaps when implementing the model:
When implementing the model:
  • apply careful parameter estimation and parameterization
  • perform calibration, verification and validation
  • perform sensitivity analyses (preferably, global sensitivity analysis) and address uncertainty in a systematic and transparent way
  • compare the outcomes of alternative models where possible
  • differentiate among parameters affecting i) the model, ii) the real world and iii) those that are management relevant
  • rank management scenarios to support decision-making when communicating a PVA:
When communicating a PVA:
  • justify the above sections (design and implementation) - e.g. model selection
  • communicate the entire modeling cycle and justify decisions and assumptions along the way
  • report all inputs and outputs systematically to allow repeatability
  • use carefully selected time horizon and viability measures and reports using consistent units to allow comparability
  • demonstrate that the PVA serves its purpose by, e.g., leading to on-the-ground actions
  • enhance collective learning and potential generalization
  • peer review both the model (design, code, application) and the report

 

 
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