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A general framework for modeling population abundance data.

Panagiotis Besbeas1, Byron J T Morgan2

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

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Summary
This summary is machine-generated.

Hidden Markov models offer a flexible framework for analyzing animal population dynamics from time-series survey data. These models can distinguish between different population models, but simulations show distinguishing latent processes is difficult without strong density dependence.

Keywords:
Beverton-HoltGompertzMoran-RickerViterbihidden Markov modelsstate-space models

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Area of Science:

  • Ecology
  • Population Dynamics
  • Statistical Modeling

Background:

  • Ecological time-series data from wild animal surveys are often analyzed using state-space population dynamics models.
  • Commonly used latent processes include Gompertz, Beverton-Holt, and Moran-Ricker models.
  • Existing methods can be limited in flexibility for handling complex data structures and model comparisons.

Purpose of the Study:

  • To demonstrate the utility of hidden Markov model (HMM) methodology for fitting diverse population dynamics models to animal survey data.
  • To highlight the flexibility of HMMs in accommodating various data features, such as multiple observations and unequal sampling intervals.
  • To compare the performance of different latent process models within the HMM framework.

Main Methods:

  • Application of hidden Markov model (HMM) methodology to analyze ecological time-series data.
  • Modeling population abundance on natural or log scales, incorporating multiple observations per sampling occasion.
  • Utilizing information criteria for model comparison and bootstrap methods for testing density dependence.
  • Fitting various latent process and observation models using the HMM framework.

Main Results:

  • HMMs provide a flexible and robust framework for fitting a wide range of population dynamics models.
  • Results were robust to the necessary discretization of the state variable in the HMMs.
  • No significant difference was found between Gompertz, Beverton-Holt, and Moran-Ricker latent models in maximized likelihood values for the studied datasets.
  • Simulations indicated that ecological time series often lack sufficient information to reliably distinguish between alternative latent processes when density dependence is weak.

Conclusions:

  • Hidden Markov models offer a powerful and adaptable approach for analyzing complex ecological time-series data.
  • The choice between specific latent process models (Gompertz, Beverton-Holt, Ricker) may have limited impact on model fit for certain datasets, especially without strong density dependence.
  • Further research may be needed to develop methods for better distinguishing between population dynamics models when data exhibit weak density dependence.