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Integrating population dynamics models and distance sampling data: a spatial hierarchical state-space approach.

Khurram Nadeem1, Jeffrey E Moore2, Ying Zhang1

  • 1Department of Mathematics & Statistics, Acadia University, Wolfville, Nova Scotia, B4P 2R6, Canada.

Ecology
|November 19, 2016
PubMed
Summary

This study introduces a spatial state-space model to analyze wildlife population dynamics using distance sampling data, effectively estimating density dependence and spatial variation even with limited data. The framework improves ecological inference for population management.

Keywords:
Akaike information criterionGaussian processRicker modeldensity dependencedistance samplingfin whale (Balaenoptera physalus)maximum likelihood estimationmodel identifiabilitynonlinear autoregressive modelspatial modellingstate-space models

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Area of Science:

  • Ecology
  • Population Dynamics
  • Statistical Modeling

Background:

  • Wildlife population models (e.g., Gompertz, Ricker) quantify density dependence but are challenged by observation error and missing data in abundance time series.
  • Current methods often pool spatial data, potentially losing resolution.
  • State-space models offer a solution by jointly estimating data and population dynamics, but spatial extensions are complex.

Purpose of the Study:

  • To develop and validate a spatial state-space modeling framework for estimating stochastic population dynamics (SPD) directly from spatially referenced distance sampling data.
  • To incorporate spatial variance (covariance) in population growth within the hierarchical model.
  • To demonstrate the feasibility of likelihood-based inference, including diagnostics and model selection, using a data cloning algorithm.

Main Methods:

  • Developed a unified hierarchical state-space modeling framework for spatial SPD models.
  • Utilized line-transect distance sampling data, allowing direct estimation from spatially referenced, short time series.
  • Employed a data cloning algorithm for likelihood-based inference, model selection, and estimability diagnostics.

Main Results:

  • The hierarchical state-space framework efficiently estimates underlying dynamical parameters and spatial abundance distributions.
  • Analysis of fin whale (Balaenoptera physalus) data (1991-2014) revealed strong density regulation despite a short time series (7 surveys).
  • Reliable estimates of fin whale densities and spatial variability in intrinsic growth potential were obtained.

Conclusions:

  • The developed integrative framework enables direct estimation of spatial population dynamics from distance sampling data.
  • It effectively infers key ecological characteristics like density regulation and spatial variation in growth potential.
  • This approach enhances ecological inference for wildlife populations, even with limited or sparse data.