Drivers of metacommunity dynamics in river-floodplain fish: A path modeling approach

Metacommunity theory offers a compelling framework for understanding the processes that govern biodiversity patterns across space and time. Yet, a persistent challenge remains: integrating the wide array of ecological drivers into a unified model using observational data from complex, dynamic ecosystems. In this study, we present a novel, process-explicit path modelling approach that bridges recent theoretical advances in metacommunity ecology with empirical data. Focusing on fish communities in the floodplains of the Danube River, we leverage environmental DNA (eDNA) metabarcoding to characterize community composition across a spatio-temporal network of sites. We partition beta diversity into its species replacement and richness difference components and apply structural equation modelling to evaluate the relative influence of multiple ecological drivers—including spatial and temporal dispersal, demographic stochasticity, abiotic filtering and interspecific interactions. Our results re..., , # Drivers of metacommunity dynamics in river-floodplain fish: A path modeling approach

Dataset DOI: [10.5061/dryad.wstqjq301](10.5061/dryad.wstqjq301)

## Description of the data and file structure

### Project Overview

This project processes and transforms pairwise site data from lake ecosystems for ecological analysis, focusing on environmental filtering, demographic stochasticity, patch dynamics, interspecific interactions, and beta-diversity components.
The workflow covers data preparation, transformation, standardization, and conversion to symmetric matrices, producing an RData file ready for Structural Equation Modeling (SEM) and other downstream analyses.

### Workflow Summary

1. **Data preparation** — Pairwise site-time dataset and reference metadata.
2. **Data transformation** — Long-to-wide conversion, skewness check, variable transformation, and standardization.
3. **Matrix generation** — Conversion of variables into symmetric distance matrices.
4. **Statistical analysis**...,