Back

The interaction of dispersal, species sorting, and priority effects, explains a unimodal response of β-diversity to immigration rates

Ortiz, E.; Ramos-Jiliberto, R.; Arim, M.

2026-01-02 ecology
10.64898/2025.12.31.697192 bioRxiv
Show abstract

A dominant perspective in metacommunity theory is that dispersal consistently homogenizes species composition across local communities. However, recent studies have reported a direct relationship between dispersal rate and {beta}-diversity, which has been attributed to the influence of priority effects and species sorting. The underlying mechanisms by which dispersal can either increase or decrease {beta}-diversity remain unclear, hindering our understanding of metacommunity assembly and the processes driving biodiversity patterns across time and space. We theoretically evaluated how purely random priority effects, priority effects conditioned by species dispersal abilities, and species sorting shape the {beta}-diversity-dispersal relationship in metacommunities open to exogenous dispersal (i.e., immigration) from a regional species pool. Our results show that the three evaluated processes foster community differentiation at intermediate exogenous dispersal rates when dispersal between communities (i.e. endogenous dispersal) is taking place. Additionally, the interaction between endogenous and exogenous dispersal homogenizes communities at low and high exogenous dispersal rates, determining a unimodal response of {beta}-diversity to increasing exogenous dispersal rates. Our findings contribute to a deeper mechanistic understanding of metacommunity assembly and offer a coherent explanation for previously observed empirical discrepancies, thereby challenging the traditional view of dispersal as a purely homogenizing process.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.