Modern samples Modern specimens used in this study are housed in the Australian Museum, Sydney (prefix AM M); Museum and Art Gallery of the Northern Territory, Alice Springs and Darwin (CAM, U); Museum Victoria, Melbourne (MV C, DTC); Flinders University Research Collection, Adelaide (FUR); Queensland Museum, Brisbane (QM A, J, JM), South Australian Museum, Adelaide (SAMA M); Western Australian Museum, Perth (WAM M); American Museum of Natural History, New York (AMNH); and Papua New Guinea National Museum and Art Gallery, Port Moresby (PNG MR). Sixteen extant species were sampled to capture a broad dietary spectrum, with an additional five species of *Dendrolagus *from New Guinea analyzed as a single unit, because no single species had adequate available samples (Table S1, Data S1). Fossil samples Paleontological specimens are housed in the South Australian Museum, Adelaide (SAMA P, FU). The sample originated from excavations of the Main Fossil Chamber deposit of Victoria Fossil Cave, Naracoorte, South Australia, which were led by Rod Wells (Flinders University) through the 1970s–1990s. The sequence consists of at least eight superposed infill sedimentary units (*19*, *42*). A flowstone capping the sequence provides a minimum age of ~213 ka (*22*). Using existing stratigraphic designations (*42*), unit 8 and upper unit 7 (depth bin 7E, see below) are dated to ~220 ka and ~226 ka, respectively (*21*). Here we assess the diets of the 14 best-represented VFC macropodid species (Table S1), excluding potoroines, because DMTA for modern, largely fungivorous potoroines has yet to be investigated. Insufficient samples could be attained for *Lagorchestes leporides *and *Procoptodon goliah*. Recently, *Protemnodon brehus *and *Prot. roechus *have been relegated to *nomina dubia*, and effectively replaced by *Prot. mamkurra *and* Prot. viator *(*12*). Unfortunately, the cheek dentition of these two species cannot be distinguished, so samples are here referred to as *Prot. mamkurra *due to its greater abundance in the VFC deposit based on specimens that can be identified to species level (I. Kerr, pers. comm. 21/3/2024). However, there remains a possibility that *Prot. viator *is present in the sample. Data acquisition Specimens were cleaned and cast using standard procedures (*43*), which have been shown to have high fidelity in replicating microwear surfaces (*44*). Casts were scanned using a Sensofar Plμ NEOX confocal microscope "Bruce" at Flinders University, (100X ELWD objective, neural aperture 0.80, blue light 460nm, spatial sampling 0.17µm, step height 2 singularities within cross-validated subsets were flagged as suspect and not included for comparison (Table S4; Data S2). To balance remaining models, those which fell in the top ten of the most measures were selected, or where multiple models performed equally at this, models were visually compared to see which best differentiated species. Where these were identical, the model with fewest parameters was chosen (Data S2; Figures S4–6). ANOVA models were then run to provide statistical support for differences evident in the final models constructed by LME modeling, as well as additional tests considering differences between *species* only. For the LME-based models, non-significant factors were dropped from comparison reiteratively until only significant factors remained, with significance defined as the F statistic *p<*0.05. As ANOVA cannot handle random effects, the effect of *specimen* was dropped from ANOVA comparisons. Because of this, a single scan was used for each specimen, with the most commonly sampled teeth and facets used to determine which scan was used. While removing any subsampling effects, this reduced the sample size to *n*=937 (see Table S1 'N. specimens' for the sample size for each species, and Data S1 for the full dataset). Post-hoc comparisons were undertaken using Tukey's HSD test of pairwise comparisons (Data S1). To visualize the dataset across the multiple variables being used, and help attain a simplified consensus, a Principal Components Analysis (PCA) was undertaken using the mean for each of the 15 final variables for each taxon. Subsampled data for modeling was removed prior to PCA visualization as per ANOVA dataset above (Data S1). A distinct set of analyses considered dietary change through time, restricted to specimens from Pit C in the Main Fossil Chamber of VFC, where the most reliable depth information is available (*42*). Analysis considered stratigraphic units 4–8, with the deepest units 4 and 5 combined due to low sample sizes (*42*). In contrast, the fossil-rich, 0.9-m-thick unit 7 was subdivided by depth into units 7A–7E, respectively (Table S5). Analysis was limited to the four most numerous VFC kangaroos: the macropodines *Macropus giganteus *and *Notamacropus rufogriseus*, and the sthenurines *Procoptodon browneorum *and *Pro. gilli *(Table S5). These four species still did not provide sufficient samples for LME modeling, so analysis was restricted to ANOVA comparisons. Datasets were compiled for each species and tested against stratigraphic unit in ANOVA comparisons for each microwear variable to determine if dietary change across time within a species was detectable. For *Ma. giganteus *and *N. rufogriseus, *this analysis was extended to include modern samples to further investigate dietary change over time. To assess how changes in available foods may impact on fitness at any particular time, relative abundances for these four species within each unit were calculated for comparison by dividing the minimum numbers of individuals for each species by the total minimum numbers of individuals for all macropodid species (excluding potoroines) in each unit. Data were acquired from the South Australian Museum Palaeontology register, with taxonomic identities of all specimens checked. Dietary analysis through time was then extended to consider all taxa lumped into subfamilies Macropodinae and Sthenurinae to see if broad trends were evident across or between these, using the same methodology. Microwear trends over time were then considered across the entire dataset to see if any taxonomically independent changes in diet indicative of changes in vegetation could be detected. A final set of analyses was undertaken to compare microwear collected here to an earlier study of macropodid microwear (*6*), which itself contained some data collected previously (*15*). Analysis was undertaken using *Asfc *and* epLsar *values published in (*6*), and limited to taxa that were at a generic level included in (*6, 15*) and here. Data were transformed using *bestNormalize* and analyses were made through ANOVA comparisons (Data S1). These considered firstly if there were broad differences between the studies, following (*45*, *54*) and interspecific comparisons. All data were collected using SensoMAP 7.1.2.7288 (Digital Surf), and analyzed in R. version 4.3.2 (*52*), using the *bestNormalize, car, factoextra, ggplot2, ggcorrplot, lme4, penxlsx, performance, and* stringr packages. Scripts used can be found at the Dryad archive for this article. In all results and discussion 'significant' results indicate *p*<0.05.

Identifying what drove the late Pleistocene megafaunal extinctions on the continents remains one of the most contested topics in historical science. This is especially so in Australia, which lost 90% of its large species by 40,000 years ago, more than half of them kangaroos. Determining causation has been obstructed by a poor understanding of their ecology. Using Dental Microwear Texture Analysis, we show that most members of Australia's richest Pleistocene kangaroo assemblage had diets that were much more generalized than their craniodental anatomy implies. Mixed feeding across most kangaroos pinpoints dietary flexibility as a key behavioral adaptation to climate-driven fluctuations in vegetation structure, dispelling the likelihood that late Pleistocene climatic variation was the sole or primary driver of their disappearance.

Funding provided by: Australian Research CouncilROR ID: https://ror.org/05mmh0f86Award Number: DP110100726 Funding provided by: Australian Research CouncilROR ID: https://ror.org/05mmh0f86Award Number: FT130101728 Funding provided by: Australian Research CouncilROR ID: https://ror.org/05mmh0f86Award Number: LE130100115 Funding provided by: Australian Research CouncilROR ID: https://ror.org/05mmh0f86Award Number: DP190103636