Abstract Sedimentary ancient DNA (SedaDNA) is an emerging tool to reconstruct past biodiversity with high taxonomic resolution. Its growing popularity has stimulated an increasing complexity of SedaDNA data production (e.g., DNA extraction, amplification, and sequencing; authentication of molecules; bioinformatics). Conversely, less attention has been devoted to how appropriate statistical analyses can help to extract ecological information from SedaDNA. Until now, ecological studies based on SedaDNA have taken limited advantage of the multiple statistical and numerical methods available for analysis. Here, we present a range of numerical approaches that can be particularly useful to multispecies ecological analysis on SedaDNA, with a special focus on biodiversity studies on macroorganisms. We discuss the advantages and complexity of such methods and describe how some of them can be optimized for ecological analyses of SedaDNA‐based metabarcoding data, with a special focus on SedaDNA studies. First, site occupancy‐detection models can help to better ascertain the variation through time of the occurrence of target species and to identify the factors determining their detection through time. Second, several approaches can be used to estimate variation of relative abundance. Even though methods for abundance estimation have major limitations, they can provide useful information on temporal variation of ecosystem functions. Third, approaches exist to obtain better measures of species diversity, while taking into account the uncertainties of species abundance and identification. Fourth, techniques of clustering, ordination, and constrained ordination allow identification of temporal trends and testing of candidate drivers of community variation. Finally, structural equation models can be used to assess complex causal relationships among biodiversity, human activities, and environment. SedaDNA studies can make use of a broad panel of analytical approaches, which can improve our understanding of long‐term biodiversity changes, maximizing the information we can obtain from past ecosystems
