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Exact inference for integrated population modelling.

P Besbeas1,2, B J T Morgan2

  • 1Department of Statistics, Athens University of Business and Economics, 10434 Athens, Greece.

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Summary
This summary is machine-generated.

This study introduces an exact method for integrated population modelling using hidden Markov models (HMMs), avoiding Kalman filter approximations. This approach enhances data analysis for ecological populations like owls and lapwings.

Keywords:
Little owlsNorthern lapwingshidden Markov modelsmigrationparameter redundancystate-space models

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Area of Science:

  • Statistical Ecology
  • Population Dynamics
  • Ecological Modelling

Background:

  • Integrated population modelling (IPM) combines time-series and survey data.
  • Classical IPM often uses Kalman filters for time-series likelihood approximation.
  • Hidden Markov models (HMMs) are suitable for discrete state-space systems.

Purpose of the Study:

  • To present an exact, flexible IPM method using HMMs, bypassing Kalman filter approximations.
  • To apply the HMM-based IPM to real-world ecological data.
  • To compare HMM approaches and identify modelling efficiencies.

Main Methods:

  • Developed an exact IPM framework utilizing Hidden Markov Models (HMMs).
  • Applied the method to Little owl and Northern lapwing population data.
  • Explored first- and second-order HMMs and state-space dimensionality.
  • Grouped states for high-dimensional HMMs to manage computational complexity.

Main Results:

  • The HMM-based IPM proved exact and flexible, avoiding approximations.
  • Analysis of Little owls revealed parameter redundancy affecting immigration estimation.
  • Northern lapwing modelling showed first-order HMMs were more efficient.
  • State-space grouping was necessary for lapwing HMMs.

Conclusions:

  • The proposed HMM-based IPM offers an exact and versatile alternative to traditional methods.
  • The approach provides new insights into population dynamics parameters, such as immigration.
  • Methodological choices in HMMs (order, state-space dimension) impact efficiency and require careful consideration.