Long range dependence and the dynamics of exploited fish populations

TL;DR

This paper explores long range dependence in natural processes, focusing on fish population data, and highlights the importance of higher order analysis beyond traditional correlation methods.

Contribution

It introduces methods to detect higher order long range dependence in ecological data, specifically in exploited fish populations, extending existing analysis tools.

Findings

Fish populations exhibit higher order long range dependence.

Traditional second order correlation methods may miss complex dependence structures.

Deeper stochastic analysis reveals insights into ecological dynamics.

Abstract

Long range dependence or long memory is a feature of many processes in the natural world, which provides important insights on the underlying mechanisms that generate the observed data. The usual tools available to characterize the phenomenon are mostly based on second order correlations. However, the long memory effects may not be evident at the level of second order correlations and may require a deeper analysis of the nature of the stochastic processes. After a short review of the notions and tools used to characterize long range dependence, we analyse data related to the abundance of exploited fish populations which provides an example of higher order long range dependence.