A framework for analyzing ecological trait-based models in multi-dimensional niche spaces

TL;DR

This paper introduces a theoretical framework for analyzing biodiversity patterns in multi-dimensional ecological niche spaces using string-based trait representations, enabling predictions of population distributions and identifying ecological drivers.

Contribution

It presents a novel mathematical approach that models ecological niches with string sequences and predicts long-term population distributions, advancing trait-based ecological modeling.

Findings

Linear theory accurately predicts long-term population distributions.

A new transform helps identify ecological drivers from biodiversity data.

The framework enables analysis of pattern-forming instabilities in niche models.

Abstract

We develop an theoretical approach for predicting biodiversity in multi-dimensional niche spaces, arising due to ecological drivers such as competitive exclusion. The novelty of our approach relies on the fact that ecological niches are described by sequences of strings, which allows us to describe multiple traits. We define the mathematical framework for analyzing pattern forming instabilities in these models, showing surprisingly that the analytic linear theory predicts the asymptotically long time population distributions of niches in the model. We propose a test for identifying ecological drivers in biodiversity distributions, based on representing ecosystem data by means of a certain transform introduced in the theory.