How do simple evolutionary strategies and investment optimizations affect ecological patterns? The case of generalized Taylor's Law
Stefano Garlaschi, Samuele Stivanello

TL;DR
This paper investigates how evolutionary strategies and investment optimizations influence Taylor's Law in ecological populations, revealing that the law's exponent can vary widely and may be affected by rare events and sampling artifacts.
Contribution
It demonstrates that evolutionary strategies do not constrain the range of Taylor's Law exponents, highlighting the role of rare events and sampling in empirical observations.
Findings
TL exponent can assume any real value due to model parameter regions.
The shape of these regions is unaffected by strategies or optimization objectives.
Rare events significantly influence the TL exponent, suggesting sampling artifacts in empirical data.
Abstract
Taylor's Law (TL) relates the variance to the mean of a random variable via power law. In ecology it applies to populationsand it is a common empirical pattern shared among different ecosystems. Measurements give power law exponent to be between 1 and 2, and more often to cluster around 2, whereas theoretical models predict TL exponent can assume any real value. In this paper, adopting the framework of multiplicative growth models in a Markovian environment, we investigate the possibility of evolutionary strategies to be responsible for TL exponent to be in a finite range. We implement three different strategies the individuals can follow and for each strategy set two different optimization investment objectives. In all the studied cases we find TL exponent can assume any real value due to the existence of regions of the model parameters in which the exponent can diverge. Furthermore,…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Ecosystem dynamics and resilience · Evolution and Genetic Dynamics
