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How range residency and long-range perception change encounter rates.

Ricardo Martinez-Garcia1, Christen H Fleming2, Ralf Seppelt3

  • 1ICTP South American Institute for Fundamental Research & Instituto de Física Teórica - UNESP, Rua Dr. Bento Teobaldo Ferraz 271, Bloco 2 - Barra Funda 01140-070 São Paulo, SP Brazil; Dept. of Ecology & Evolutionary Biology, Princeton University, Princeton NJ 08544, USA.

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Area of Science:

  • Ecology
  • Theoretical Ecology
  • Movement Ecology

Background:

  • Encounter rates are fundamental to understanding ecological interactions and population dynamics.
  • Traditional models often assume uniform space use and local perception, based on Brownian motion.
  • Empirical evidence increasingly shows non-uniform space use and nonlocal perception in animals.

Purpose of the Study:

  • To investigate how realistic animal movement and perception strategies affect pairwise encounter rates.
  • To derive new analytical expressions for encounter rates under Ornstein-Uhlenbeck motion.
  • To compare predictions from Ornstein-Uhlenbeck motion with Reflected Brownian Motion.

Main Methods:

  • Derived analytical expressions for encounter rates using Ornstein-Uhlenbeck (OU) motion.
  • Compared OU-based encounter predictions with those from Reflected Brownian Motion (RBM).
  • Analyzed the influence of perception scale and home-range size on encounter rates in both models.

Main Results:

  • Ornstein-Uhlenbeck motion, incorporating non-uniform space use and distinct home ranges, provides a more realistic framework.
  • Encounter rate predictions differ significantly when realistic movement and perception are considered compared to RBM.
  • The interplay between perception scale and home-range size critically influences encounter rates.

Conclusions:

  • Neglecting empirically supported animal behaviors leads to systematic biases in encounter-rate predictions.
  • Ornstein-Uhlenbeck motion offers a more accurate theoretical basis for understanding ecological interactions.
  • Accurate modeling of movement and perception is essential for advancing ecological theory.