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The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations.

Christie A Bahlai1, Elise F Zipkin2

  • 1Department of Biological Sciences and Environmental Science and Design Research Initiative, Kent State University, Kent, Ohio, United States of America.

Plos Computational Biology
|January 16, 2020
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Summary
This summary is machine-generated.

A new Dynamic Shift Detector algorithm identifies critical parameter changes in population dynamics. This tool aids ecological understanding and conservation by pinpointing abrupt shifts in species

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Area of Science:

  • Ecology
  • Population Dynamics
  • Computational Biology

Background:

  • Environmental factors and internal population regulation rules can cause abrupt ecological transitions.
  • Identifying the timing and magnitude of these parameter shifts in natural populations is challenging.
  • Understanding these shifts is crucial for effective species conservation and management.

Purpose of the Study:

  • To introduce and evaluate the Dynamic Shift Detector (DSD) algorithm for identifying parameter changes in population dynamics.
  • To assess the DSD's accuracy in detecting shifts in simulated and real-world ecological data.
  • To demonstrate the algorithm's utility in informing ecological management and conservation strategies.

Main Methods:

  • The Dynamic Shift Detector algorithm iteratively fits subsets of population time series data.
  • Model selection is used to rank breakpoint combinations and assign relative weights to detected shifts.
  • Performance was evaluated using simulations and two case studies: an invasion process and a conservation scenario.

Main Results:

  • The DSD accurately identified simulated parameter shifts with 70-100% accuracy under low noise conditions.
  • The algorithm assigned high weights (>0.8) to true breaks and low weights (<0.2) to erroneous breaks.
  • Case studies revealed shifts linked to resource availability during invasion and changing management practices affecting hostplants.

Conclusions:

  • The Dynamic Shift Detector algorithm effectively identifies parameter shifts in population dynamics.
  • This tool can provide critical insights into ecological transitions and inform conservation and management decisions.
  • The DSD is valuable for understanding species' dynamics in the context of global environmental change.