# Accounting for the Influence of Community Turnover Along Environmental Gradients on Compositional Uniqueness

## TL;DR

This paper introduces a new method to better understand how environmental factors influence biodiversity by distinguishing between different types of community changes.

## Contribution

The paper introduces Generalised Dissimilarity Uniqueness Models (GDUM) to disentangle directional and non-directional drivers of beta diversity.

## Key findings

- GDUM improves the interpretability of LCBD–environment relationships by accounting for directional turnover.
- The framework distinguishes between environmental filtering and stochastic processes affecting biodiversity.
- GDUM is consistent with conventional models but offers better generalisability for biodiversity projections.

## Abstract

Compositional uniqueness has become increasingly relevant for understanding how local communities contribute to regional biodiversity. The most widely used metric is the Local Contribution to Beta Diversity (LCBD), which is typically regressed against environmental predictors. However, LCBD can vary either because of environmental processes that affect the overall variance in community composition, or because communities change directionally along environmental gradients. The latter implies that LCBD–environment relationships can strongly depend on how the environment is sampled. To address this issue, we introduce Generalised Dissimilarity Uniqueness Models (GDUM), a framework that embeds effects on community uniqueness within pairwise dissimilarity modelling. GDUMs are consistent with conventional uniqueness models, while explicitly accounting for directional changes in composition. This distinction disentangles directional and non‐directional drivers of beta diversity, such as environmental filtering versus stochastic processes. By improving interpretability and generalisability, GDUM is a useful tool for understanding beta diversity patterns and projecting biodiversity responses.

Compositional uniqueness is commonly quantified using the Local Contribution to Beta Diversity (LCBD), which is typically regressed against environmental predictors. However, LCBD–environment relationships can arise either from changes in overall compositional variance or from directional turnover along environmental gradients, making inference sensitive to how environmental gradients are sampled. We introduce Generalised Dissimilarity Uniqueness Models (GDUM), which embed uniqueness within pairwise dissimilarity modelling to disentangle directional and non‐directional drivers of beta diversity.