Landscape construction in non-gradient dynamics: A case from evolution

TL;DR

This paper constructs a generalized adaptive landscape for a two-loci population model with non-gradient dynamics, demonstrating that populations can increase adaptiveness without following the steepest gradient, thus advancing Wright's evolutionary theory.

Contribution

It introduces a generalized adaptive landscape applicable to non-gradient dynamics, expanding the understanding of evolutionary trajectories in complex models.

Findings

Generalized landscape exists for non-gradient dynamics.

Populations can increase adaptiveness without steepest ascent.

This approach broadens Wright's landscape theory for complex systems.

Abstract

Adaptive landscape has been a fundamental concept in many branches of modern biology since Wright's first proposition in 1932. Meanwhile, the general existence of landscape remains controversial. The causes include the mixed uses of different landscape definitions with their own different aims and advantages. Sometimes the difficulty and the impossibility of the landscape construction for complex models are also equated. To clarify these confusions, based on a recent formulation of Wright's theory, the current authors construct generalized adaptive landscape in a two-loci population model with non-gradient dynamics, where the conventional gradient landscape does not exist. On the generalized landscape, a population moves along an evolutionary trajectory which always increases or conserves adaptiveness but does not necessarily follow the steepest gradient direction. Comparisons of different aspects of various landscapes lead to a conclusion that the generalized landscape is a possible direction to continue the exploration of Wright's theory for complex dynamics.