Could sampling make hares eat lynxes?

TL;DR

This paper highlights how slow sampling rates can distort population cycle data, leading to misinterpretations such as the hare-lynx paradox, by applying aliasing concepts from signal processing to ecological models.

Contribution

It introduces the concept of aliasing in population dynamics, demonstrating its effects through numerical simulations of the Lotka-Volterra model.

Findings

Slow sampling causes cycle inversion in prey-predator data

Inadequate sampling inflates cycle periods

Aliasing can lead to misinterpretation of ecological interactions

Abstract

Cycles in population dynamics are widely found in nature. These cycles are understood as emerging from the interaction between two or more coupled species. Here, we argue that data regarding population dynamics are prone to misinterpretation when sampling is conducted at a slow rate compared to the population cycle period. This effect, known as aliasing, is well described in other areas, such as signal processing and computer graphics. However, to the best of our knowledge, aliasing has never been addressed in the population dynamics context or in coupled oscillatory systems. To illustrate aliasing, the Lotka-Volterra model oscillatory regime is numerically sampled, creating prey-predator cycles. Inadequate sampling periods produce inversions in the cause-effect relationship and an increase in cycle period, as reported in the well-known hare-lynx paradox. More generally, slow acquisition rates may distort data, producing deceptive patterns and eventually leading to data misinterpretation.