Similarity Analysis of Macroecology

TL;DR

This paper uses Buckingham's theorem to derive a dimensionless similarity framework for ecosystems, linking macroecological patterns to intrinsic and extrinsic variables, and providing a new method to analyze ecosystem complexity and dynamics.

Contribution

It introduces a novel similarity analysis approach for ecosystems, connecting macroecological patterns to fundamental variables without detailed ecosystem functions.

Findings

Derived a relationship between ecosystem complexity and macro variables.

Identified a control parameter for ecosystem dynamics.

Provided a method to detect rapid ecosystem changes.

Abstract

We perform a full similarity analysis of an idealized ecosystem using Buckingham's $\Pi$ theorem to obtain dimensionless similarity parameters given that some (non- unique) method exists that can differentiate different functional groups of individuals within an ecosystem. We then obtain the relationship between the similarity parameters under the assumptions of (i) that the ecosystem is in a dynamically balanced steady state and (ii) that these functional groups are connected to each other by the flow of resource. The expression that we obtain relates the level of complexity that the ecosystem can support to intrinsic macroscopic variables such as density, diversity and characteristic length scales for foraging or dispersal, and extrinsic macroscopic variables such as habitat size and the rate of supply of resource. This expression relates these macroscopic variables to each other, generating commonly observed macroecological patterns; these broad trends simply reflect the similarity property of ecosystems. We thus find that details of the ecosystem function are not required to obtain these broad macroecological patterns this may explain why they are ubiquitous. Departures from our relationship may indicate that the ecosystem is in a state of rapid change, i.e., abundance or diversity explosion or collapse. Our result provides normalised variables that can be used to isolate the trend in one ecosystem variable from another, providing a new method for isolating macroecological patterns in data. A dimensionless control parameter for ecosystem complexity emerges from our analysis and this will be a control parameter in dynamical models for ecosystems based on energy flow and conservation and will order the emergent behaviour of these models.