Abstract: Abstract Mixing models are used throughout earth and environmental science to quantify the relative contributions of sources to mixtures, based on chemical or isotopic tracers. Often, however, some end-members are missing or their tracer distributions overlap, precluding the use of conventional mixing models. Here I show how these constraints can be overcome by exploiting the information contained in tracer time-series fluctuations. This approach, ensemble end-member mixing analysis (EEMMA), can potentially quantify many sources using a single tracer, even if their mean concentrations are indistinguishable. EEMMA can also quantify source contributions when some sources are unknown, and even infer the tracer time series of a missing source. Benchmark tests with synthetic data verify the reliability of this approach, thus expanding the range of mixing models that can be quantified using tracer time series. An R script is provided for the necessary calculations, including error propagation.