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

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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mtext>LCBD</mml:mtext></mml:mrow> <mml:annotation>$$ \mathrm{LCBD} $$</mml:annotation></mml:semantics> </mml:math> ), which is typically regressed against environmental predictors. However, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mtext>LCBD</mml:mtext></mml:mrow> <mml:annotation>$$ \mathrm{LCBD} $$</mml:annotation></mml:semantics> </mml:math> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mtext>LCBD</mml:mtext></mml:mrow> <mml:annotation>$$ \mathrm{LCBD} $$</mml:annotation></mml:semantics> </mml:math> -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.