  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.
