Variations in ray flower numbers of Common Daisy (Bellis perennis L.) – the hidden cues of the Fibonacci order

Carsten Neumann

PLOS ONE, 2026, vol. 21, issue 8, 1-13

Abstract: In this study, I provide empirical evidence on the number of ray flowers in Bellis perennis L. to evaluate the widely accepted assumption that floral architecture and other natural phenomena follow numerical sequences such as the Fibonacci order. The aim is to contribute to a broader understanding of the mathematical principles underlying the organization of natural systems, particularly the interplay between deterministic patterns and inherent stochasticity. Between April and May 2024, a total of n = 563 Bellis perennis individuals were sampled across 34 distinct populations in the Federal State of Brandenburg, northeastern Germany. The number of ray flowers per individual follows a well-defined probabilistic distribution that is best described by the discrete negative binomial type II model and the continuous inverse gamma probability density function. While most individuals exhibit ray flower counts between 34 and 56, a pronounced frequency peak occurs between 40 and 48, with a mode of 42 and a median of 46, both stable across spatial and temporal sampling conditions. Notably, there are two distinct local deviations identified by both theoretical distributions that occur at ray flower counts of 34 and 55, corresponding precisely to two values from the Fibonacci sequence. These deviations are neither pure statistical outliers nor indicators of multimodality; rather, they represent mathematically constructible features within a stochastic framework. To characterize this phenomenon, I introduce the concept of “improbable recurrency”, referring to deterministic structures that emerge from, and are only detectable within, inherent stochastic patterns of natural systems. Improbable recurrency exemplifies the co-constitutive relationship between stochasticity and determinism, forming a shared quantitative logic through which the human mind interprets biological complexity.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0348529

DOI: 10.1371/journal.pone.0348529

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