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Area of Science:

  • Ecology
  • Population Dynamics
  • Environmental Science

Background:

  • Time series analysis is crucial for understanding population regulation by density and environmental factors.
  • Existing methods struggle to accurately reconstruct environmental effects on population dynamics using commonly collected, low-resolution data.
  • Fast environmental interactions can be missed by infrequent population surveys.

Purpose of the Study:

  • To develop and validate a method for modeling population dynamics using mismatched time series data (high-resolution environmental, low-resolution abundance).
  • To assess the ability of the proposed method to detect environmental effects on population dynamics.
  • To investigate the impact of data resolution on detecting population-environment interactions.

Main Methods:

  • Developed a statistical approach to couple high-resolution environmental data with low-resolution population abundance data.
  • Utilized simulation studies to test the method's reliability in detecting linear and nonlinear environmental effects.
  • Applied the method to analyze the population dynamics of Florida's Snail kite using historical data.

Main Results:

  • The proposed method reliably detected linear environmental effects in simulations.
  • Detecting nonlinear environmental effects necessitates high-resolution covariate data, even with slow population turnover.
  • The method identified key environmental covariates for Snail kite population dynamics and suggested a potential phenological shift.

Conclusions:

  • The developed statistical framework enables reliable detection of population-environment interactions from coarsely surveyed time series data.
  • Low predictability of animal population growth by weather variables may stem from how covariate data is utilized.
  • Accurate utilization of high-resolution environmental data is key to understanding population dynamics and predicting responses to environmental change.