For biological analyses, we re-analyzed a dataset of 2022 supergene trees from an avian dataset [7 , 11]. We also use three sets of simulated datasets we used before: a 37-taxon mammalian dataset , a 45-taxon avian dataset , and datasets used for evaluating ASTRAL-II . The first two datasets are based on biological data and have a single species tree topology, whereas the last dataset is simulated using SimPhy  and has a different species tree per replicate and has heterogeneous parameters. Avian and mammalian datasets enable us to evaluate performance for relatively small numbers of species, varying ILS and the number of genes. The amount of ILS is changed by multiplying or dividing branch lengths by 2 or 5; shorter branches (0.2X and 0.5X) produce more ILS and longer branches reduce ILS (Additional file 1: Table S1). We create two collections for these datasets, one where we fix the number of genes (200 for mammalian and 1000 for avian) and vary the amount of ILS, and a second collection, where we fix the amount of ILS (to very high or 0.2X for mammalian and default 1X for avian) and vary the number of genes (200 to 3200 for mammalian and 200 to 2000 for avian). The simPhy dataset  has two collections, and is simulated to capture the range of reasonable biological datasets. In the simPhy-ILS collection, we fix the number of species to 201 and show three levels of ILS, ranging from moderate (10 million generations) to very high (500K generations), and for each case, we vary the number of genes (50, 200, 1000). For each case, we have 100 replicates, half with a speciation rate of 10−6 and the other half with 10−7. In the simPhy-size, we fix ILS to moderate and speciation rate to 10−6, and change the number of species from 10 to 500, with 50 replicates per dataset.