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Global land-use data
For the historical time period 1700–2016, we used reconstructions of global cropland, pasture, and urban areas from the HYDE 3.2 dataset49 (available from https://doi.org/10.17026/dans-25g-gez3). Whilst HYDE 3.2 provides land-use data as far back as 10,000 BCE, we began our analysis in the year 1700, prior to which global land-use data are subject to increased uncertainty49,50. A total of 47 maps, including lower and upper uncertainty bounds, are available at 10-year intervals between 1700 and 2000, and at 1-year intervals between 2000 and 2016. These data were upscaled from their original spatial resolution of 0.083° to a 0.5° grid by summing up the cropland, pasture, or urban areas of all 0.083° grid cells contained in a given 0.5° cell.
For the period 2020–2100, we used 0.5°-resolution 10-year time-step projections of global cropland, pasture, and urban areas from the AIM model51 (available from https://doi.org/10.7910/DVN/4NVGWA), covering Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0 and 8.5, and Shared Socio-economic Pathways (SSPs) 1–5. The dataset contains all possible combinations of these emission and socio-economic trajectories with the exception of RCP 2.6/SSP 3, and RCP 8.5/SSPs 1–4. The data were harmonised with the HYDE 3.2 data by adding the differences between HYDE 3.2 and AIM cropland, pasture and urban area maps in the year 2010 to the AIM future land use projections. We refer to refs. 27,28,29,52 for details of the emission and socio-economic pathways, and to ref. 28 for a comparison between the AIM model and other integrated assessment models.
Global biome data
We used the BIOME4 vegetation model53 (available from https://pmip2.lsce.ipsl.fr/synth/biome4.shtml) to simulate the distribution of global potential natural biomes between the years 1700 and 2000, and between 2020 and 2100 for each of the four climate-change scenarios considered here (RCPs 2.6, 4.5, 6.0, 8.5), at a spatial resolution of 0.5°. Inputs required by BIOME4 include global mean atmospheric CO2 concentration, and gridded monthly means of temperature, precipitation, and percent sunshine. Past and RCP-specific future CO2 levels were obtained from refs. 54 and 55, respectively. The climatic data were generated as follows. For the period 1700–1900, we used annual simulations from the HadCM3 climate model56 (available from https://esgf-node.llnl.gov/search/cmip5/; Experiments ‘past1000’ and ‘historical’, Ensemble ‘r1i1p1’). For the period 1901–2016, we used 0.5° resolution annual observational data57 (available from https://doi.org/10.5285/10d3e3640f004c578403419aac167d82). For the period 2020–2100, and for each RCP (2.6, 4.5, 6.0, 8.5), we used annual simulations from the HadGEM2-ES climate model58, the MIROC5 climate model59 and the CSIRO-Mk3.6.0 climate model60 (available from https://esgf-node.llnl.gov/search/cmip5/; for each climate model and each RCP, we used averages from Ensembles ‘r1i1p1’, ‘r2i1p1’, ‘r3i1p1’, ‘r4i1p1’). We downscaled and bias-corrected both the pre-1901 HadCM3 simulations and the future HadGEM2-ES, MIROC5, and CSIRO-Mk3.6.0 simulations using the delta method61. This method is based on applying the difference between simulated and observed climate at times at which both are available (here we used the 1900–1930 period for the historical data, and the year 2006 for the future data) to the simulated climate at points in time at which only simulated data exist (i.e., pre-1901 and post-2016) in order to correct systematic biases in the climate model61,62. The delta method also serves to spatially downscale the simulated climate to the 0.5° resolution of the observational data.
For the computation of the global biome distribution at a point in time t, we used as inputs for BIOME4 the atmospheric CO2 concentration and gridded monthly climate values averaged across the time interval [t – 30 years, t]. Biome simulations were performed at 10-year intervals for both the historical and the future period. The complete time series of global biome simulations are available as Supplementary Movies 1–13.
Estimation of species’ habitat ranges
We estimated the geographic habitat ranges of an individual bird, mammal, and amphibian species through time following the general methodology in ref. 23. Our approach combines the following data:
I.
Spatial polygon data of species-specific extents of occurrence of all known birds63 (available from http://datazone.birdlife.org/species/requestdis), mammals, and amphibians64 (available from https://www.iucnredlist.org/).

II.
Species-specific biome requirements63,64 (data also available from the above websites).

III.
Maps of global potential natural biome distributions corresponding to the relevant climatic conditions through time (i.e., reconstructions for the past, and RCP-specific projections for the future).

IV.
Maps of global cropland, pasture, and urban areas through time (i.e., reconstructions for the past, and RCP- and SSP-specific projections for the future).

The data I–IV were used to estimate the habitat range of individual species at a given point in time as illustrated in Fig. 4 and detailed in the following. In a first step, we used species-specific extents of occurrence (data I), which represent the outermost geographic limits of species’ observed, inferred or projected occurrences1. These spatial envelopes do not account for the distribution of natural or artificial land cover within that area, and therefore generally extend substantially beyond a species’ actual area of occupancy65,66. We first remapped extents of occurrence from their original spatial polygon format to a 0.083° resolution grid using the ‘rasterise’ function of the ‘raster’ package in R, which maps spatial polygons to those raster grid cells whose centres are contained within the polygons. For each species, we then determined the proportion of 0.083° cells contained in each 0.5° grid cell that represents the species’ extent of occurrence. This provides an estimate of the proportion of each 0.5° grid cell that is contained in the species’ extent of occurrence. Compared to the rasterising extent of occurrence directly to a 0.5° grid, this approach provides for more accurate estimates of species’ ranges and reduces the number of species that are not included in our analysis because their extents of occurrence do not overlap with any grid cell centre.
Fig. 4: Method of estimating potential natural and actual range for the example of the bat-eared fox (Otocyon megalotis) in the year 1900.

Here, for visualisation purposes, cropland, pasture, and urban areas were aggregated into one map; in reality, our method checks each of them separately against species’ artificial habitat preferences.

Full size image

In a second step, we refined the derived species-specific maps of the proportion of 0.5° grid cells contained in species’ extents of occurrence by combining them with species-specific biome requirements and maps of global biome distributions. Species-specific biome requirements (data II) include one or more habitat categories (cf. Supplementary Table 1), in which each species is known to occur. A species was estimated as being present in a grid cell contained in its previously derived extent of occurrence under the potential natural biome at a given point in time if the species’ list of habitat categories contained the local (i.e., grid cell-specific) potential natural biome at the relevant time (data III; see above). This required matching IUCN habitat categories (https://www.iucnredlist.org/resources/habitat-classification-scheme) with the biome categories of the Biome4 vegetation model, which was done as shown in Supplementary Table 1. In this way, we subset extents of occurrences by only retaining grid cells where the natural biome type is included in a species’ list of suitable habitat categories. The result of this step represents a species’ estimated potential natural habitat range (i.e., in the hypothetical absence of anthropogenic land use) at a given point in time.
In a third step, we estimated actual habitat ranges by including maps of global land use through time. Each species’ actual habitat range at a given time was derived by removing any unsuitable anthropogenic land from the previously estimated potential natural range. Historical and projected future land use maps (data IV; see above) provide the fraction of each grid cell that is occupied by cropland, pasture or urban areas. These data were combined with information on which of these three artificial land cover types, if any, species can occur in, which is also included in the list of species’ biome requirements (data II). This allowed us, for each grid cell contained in a species’ potential natural range at a given time, to estimate the proportion of the grid cell that contained suitable habitat. A species’ actual habitat range size was then obtained as the sum of the areas of the remaining suitable habitat from all relevant grid cells.
We applied the above method at each point in time for which global land use data is available (see above). In this way, we obtained potential natural ranges and actual ranges for 47 points in time between 1700 and 2016—using the baseline as well as lower and upper uncertainty bounds of the HYDE 3.2 land-use reconstructions—, and for nine points in time between 2020 and 2100—using the 16 combinations of future climatic and socio-economic pathways (see above), each of which, in turn, was considered based on climate data from three alternative models. Thus, we considered a total of 141 historical and 432 future scenarios.
Since the global distribution of natural biomes varies over time as the result of (naturally or anthropogenically) changing climatic conditions, the sizes of potential natural habitat ranges are time-dependent. This motivates to consider range changes in relation to the potential natural ranges estimated at a particular reference time, for which we chose the year t0 = 1850, representing a modern pre-industrial baseline. Denoting the potential natural range and the actual range of a species i at a time t by (A_i^{{mathrm{potential}}}(t)) and (A_i^{{mathrm{actual}}}(t)), respectively, the range change associated with species i at time t as the result of the distribution of biomes and land use at that time was calculated at as

$${Delta}A_ileft( t right) = 100{mathrm{% }} cdot left( {frac{{A_i^{{mathrm{actual}}}(t)}}{{A_i^{{mathrm{potential}}}(t_0)}} – 1} right).$$
(1)

Species whose potential natural habitat range size in the reference year t0 = 1850 (i.e., the range size estimated in the absence of anthropogenic land use and based on the global distribution of biomes in 1850) is zero, (A_i^{{mathrm{potential}}}left( {t_0} right) = 0), were not included in the analysis as, in this case, changes in range size are not defined. Based on the set (left{ {{Delta}A_ileft( t right)} right}_{i = 1,2, ldots }) of the individual range changes of all species through time, we calculated range change percentiles at each point in time (Fig. 1a), and determined the proportion of species that have experienced the loss of a given percentage of their baseline range (Fig. 1b). Similarly as in Eq. (1), we also computed the range change attributed only to climate-change-induced biome changes, (100% cdot left( {A_i^{{mathrm{potential}}}(t)/A_i^{{mathrm{potential}}}(t_0) – 1} right)) (Supplementary Fig. 1).
Analyses were conducted using Matlab R2019a67 and R v3.6.368.
Method discussion
Whilst the available climate data for a given point in time only allows us to assign one primary natural biome type to each 0.5° grid cell, microclimates within cells may, in reality, result in the presence of different biomes in parts of a cell that are not represented in our data. By design of the approach used here, grid cells containing a non-primary biome that is suitable for a species, whilst the estimated primary biome is not, do not contribute to our estimation of the species’ habitat range. Conversely, grid cells containing a non-primary biome that is not suitable for a species, whilst the primary biome is suitable, would be included in their entirety in the species’ estimated range. This may lead us to underestimate the range sizes of species typically occurring in non-primary biomes in areas in which the estimated primary biomes are not suitable for the species, and to overestimate the range sizes of species typically occurring in the estimated primary biome in areas where other biomes also occur that are not suitable. Higher-resolution biome data could, in principle, reduce inaccuracies; however, generating such data in a reliable manner is not trivial. We are not aware of indications that this aspect of the approach would either systematically increase or decrease our overall estimates for range size changes across species in Fig. 1a.
Our estimation of species’ habitat range sizes does not take into account habitat connectivity within or across grid cells. In principle, this can result in disconnected patches being included in a species’ estimated range, despite in reality being too small to represent potentially suitable habitat. However, neither species-specific data on the minimum size that spatially connected areas must not fall below before becoming non-viable nor reliable very-high-resolution land use and biome data, both of which would be needed to fully accommodate this issue, are currently available.
Although species’ extents of occurrence are based not only on known, but also inferred and projected occurrences, the data remain very likely biased as the result of range contractions that occurred before the beginning of the systematic collection and mapping of species’ distributions, and that cannot be fully reconstructed. Whilst this may lead us to underestimate the absolute range sizes of species, it does not necessarily imply that we either systematically underestimate or overestimate the percentage change of species’ ranges through time.
We chose the 0.5° resolution for our analysis as both the 1901–2016 observational climate data (and therefore also the pre-1901 and future climate data, which were downscaled using the observational data) and the projections of future land use are only available at this resolution. Attempts to further downscale these data would likely involve significant additional uncertainties. We are not aware of indications that an increase in the resolution of the analysis (if indeed the necessary datasets were available) would result in a systematic increase or decrease of either the absolute range sizes or the percentage change of range sizes relative to the baseline sizes, estimated here, at any point in time.
Species-specific extents of occurrence and habitat preferences have been argued to be subject to uncertainty69; however, uncertainty estimates (quantitative or otherwise) are not provided with the data. In our main analysis, we therefore used the available data at face value. However, to verify that our results are not overly impacted by specific species, we performed the following bootstrapping analysis. Based on the set of species-specific range changes of all 16,919 species, estimated for the year 2016, we randomly sampled 16,919 values from this set with replacement a total of 104 times. For each of these 104 sets of range change estimates, we calculated 10%–90% percentiles analogous to Fig. 1a. For each percentile, we then calculated the mean and standard deviation of the computed 104 values. The result, shown in Supplementary Fig. 5, demonstrates that the uncertainties of our estimates with respect to specific species are very small, indicating that our results are robust with respect to potential uncertainties in the species data.
Estimates of temporal delays in biome shifts in response to climatic changes70 are currently not available with the global coverage that would allow us to further refine our approach of assuming that biomes at a given point in time are determined by the climatic conditions in the preceding 30 years. This also applies to data on the dispersal speeds of plant functional types, and their effect on potential delays in colonisations of previously climatically unsuitable areas33; current studies on this topic are too spatially scarce to inform our approach. In our main analysis, we therefore followed the assumption commonly made in global vegetation models of no seed dispersal limitations71. However, to explore the impact of this assumption, we also repeated our analysis based on the extreme scenario of biomes not shifting at all between the present (year 2016) and 2100. The estimated range size changes (Supplementary Fig. 6) are quantitatively similar to the results of our main analysis (Fig. 3), consistent with our assessment of the overall stronger impact of land use compared to climate-driven biome changes. Qualitatively, i.e., in terms of how different RCP/SSP scenarios rank relative to each other, results are equivalent to those of our main analysis.
As noted in the Introduction, our estimates of future habitat ranges represent upper estimates of species’ actual geographic distributions. In particular, our main analysis does not account for species’ ability to migrate to areas that will become suitable habitat at a future point in time but are not at present. However, our framework allows us to examine the effect of excluding such areas from the estimated habitat range. We repeated our analysis of future changes in habitat range sizes, but considered a grid cell as part of a species’ range only if the local biomes estimated for both the relevant point in the future and for the present (year 2016) were included in the species’ list of biome requirements. In other words, grid cells outside of species’ current potential natural habitat ranges were not counted towards their future range sizes, assuming that species are not able to migrate at all. This represents an extreme scenario that will underestimate most species’ mobility (e.g., over half of the species considered here can fly) and their ability to track biome shifts. Since the habitat range derived for a species in this manner is a subset of the one estimated in our main analysis, projected range losses based on this approach are, by design, higher (Supplementary Fig. 7). Qualitatively, results are equivalent to those in Fig. 3 in terms of how different RCP/SSP scenarios rank relative to each other.
As the empirical data on species’ habitat preferences only provide categorical biome requirements, not continuous climatic envelopes, the method used here does not account for range changes due to changes in climatic conditions that are too small to manifest as biome changes. However, estimating precise climatic envelopes of species can be subject to considerable uncertainty and be highly sensitive to the way in which they are estimated (see below). By construction of the method used here, species’ ranges over time vary within the extents of occurrence provided with the empirical data, and do not exceed those. Justification for this assumption is provided by the fact that potential natural ranges (and, much more, actual ranges) are generally well-contained within extents of occurrence, with the former accounting for an average of 64% of the area of the latter in the reference year 1850, thus providing ample space for range shifts and expansions within the boundaries. Additional evidence that the restriction of habitat ranges to the extents of occurrence does not prevent significant range expansions can be seen in the sizeable number of species that have already experienced such range expansions (Fig. 1a and Supplementary Fig. 1) or are predicted to do so in future scenarios of strong global warming (Supplementary Fig. 1 and Supplementary Fig. 3a).
Climate niche models estimate statistical relationships between climatic conditions and species’ spatial distributions, and apply these to climate projections in order to estimate future distribution patterns72. By design, they have great potential for mapping species’ distributions under a high degree of complexity in terms of possible predictor variables and their interactions, which has made the approach very useful in scenarios where the number of species, the geographic region and/or the temporal scale considered is relatively small so that statistical challenges are well-manageable73,74,75. In an analysis involving a large number of species, points in time, and different climatic and land-use scenarios considered here, the challenges commonly faced by climate nice models, specifically in terms of ensuring the robustness of the underlying statistical model and the estimated parameters, and avoiding unwanted artefacts in the extrapolation behaviour76,77,78,79,80,81, would be very difficult to manage. By operating directly and transparently on the empirical data of species’ extents of occurrence and biome requirements, and not being reliant on any particular statistical model or parameterisation, the approach used here provides the robustness needed at this scale of data23,82.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Stratigraphic context of the Portfjeld Formation
The Portfjeld Formation is the lowermost formation of the Franklinian Basin in southern Peary Land (Figs. 1 and 2a, b), resting unconformably on Mesoproterozoic sandstones of the Independence Fjord Group and localized erosionally truncated outliers of Neoproterozoic tillites and associated carbonates of inferred Marinoan affinity (for regional stratigraphic reviews, see17,18). The carbonate-dominated Portfjeld Formation is overlain, at a karstified unconformity (Fig. 2c), by transgressive fluvial to marine shelf siliciclastics of the Buen Formation. The sandstone-dominated lower member of the Buen Formation yields trace fossils of early Cambrian age19, while the mudstone-dominated upper member contains rich faunas of Cambrian Series 2 (Stage 3–4) age20.
Fig. 1: Field photographs of locality of the Portfjeld biota.

a Portfjeld Formation—basal Buen sandstones at Midsommersøer, notice the conspicuous band of dark cherty dolomites. b Detail of Portfjeld Formation, west of Midsommersøer, with the same darky cherty dolomites overlain by thrombolitic mounds, showing the lithostratigraphic horizon yielding the Portfjeld biota. c View looking east along Wandel Dal with Midsommersøer, taken from the fossil locality (off shot left). Scale bar valid for a.

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Fig. 2: Geography, geology, and stratigraphy of the study area, North Greenland.

a Geological map showing the sample locality at Midsommersøer, North Greenland. b Stratigraphic schemes in northern Ellesmere Island and North Greenland. c Stratigraphic section through the Portfjeld Formation at the western end of Midsommersøer compared with δ13Ccarb (o/oo PDB) values indicating the Shuram–Wonoka anomaly (about 570–560 Ma), “F” indicates the sample locality.

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The Portfjeld Formation comprises two discrete stratigraphic packages separated by a regionally developed karstic unconformity. The lower succession, about 170-m thick, is dominated by dolostones with rare limestones and represents two transgressive–regressive cycles of a carbonate ramp. Typical facies include hummocky cross-stratified intraclast-rich grainstones and cherty dark dolostones of the mid- and outer ramp, and ooid–pisoid grainstones and varied microbial facies of the inner ramp, including columnar and meter-scale domal stromatolites and thrombolitic bioherms. The capping hiatal surface shows penetrative and multi-generational karstic features extending some 40 m beneath the surface (Fig. 2c), including extensive interstratal solution, brecciation, and successive cave/vug/fracture fills and cementation, testifying to a protracted period of subaerial exposure. The transgressive succession of the upper Portfjeld Formation (c. 70–90 m thick) comprises fluvial sandstones and mudstones succeeded by high-energy shallow marine carbonate and siliciclastic facies, truncated upwards by dolines and karstic collapse structures at the Portfjeld–Buen formation boundary.
Chemostratigraphy shows that the δ13C of the Portfjeld Formation carbonate samples range from +4‰ to −8‰. Positive δ13C values persist over the lower c. 40 m of the formation, before a marked negative shift of 12‰ down to values of −8‰. A more gradual increase characterizes the δ13C values up-section through the karstified strata to the karstic unconformity at 167 m, following which there is a clear stabilization in δ13C values to values around 0 to −1‰ for the remainder of the succession (Fig. 2c).
The δ13C database for Neoproterozoic carbonate sections has proliferated within the last 30 years to the point where a δ13C compilation curve can act as a chemostratigraphic correlation tool for newly studied sections. Utilizing chemostratigraphy as a chronology tool involves the correlation of globally coherent geochemical perturbations and trends in vertical carbonate successions, within a broadly understood timeframe. This is particularly useful when attempting to refine age estimates for successions that lack abundant biostratigraphical and/or radiometric data. Utilizing the most up-to-date δ13C chemostratigraphic framework21, the asymmetric negative δ13C excursion and more gradual recovery displayed by the mid-section of the Portfjeld Formation can be aligned with the most extreme C-isotope variation recorded in Earth’s history: the Shuram–Wonoka anomaly. The form and magnitude (∼12‰) of this δ13C excursion, as well as a nadir value of −8‰, are unique to the Shuram–Wonoka anomaly and deter its alignment with other Neoproterozoic excursions, as well as the Basal Cambrian Isotope Excursion, BACE21. The Shuram–Wonoka anomaly is recognized intercontinentally in Late Ediacaran strata22 and provides a broad chronostratigraphic marker to constrain the biostratigraphy presented in this study. Williams and Schmidt22 noted that the Wonoka excursion spanned an interval of up to 10 Myr. from about 570 to 560 Ma and was recognized in shallow marine shelf environments on three palaeocontinents with low palaeolatitudes ( More

Sample collection
We sampled tissue from wild greater gliders from four locations that broadly represent the northern and southern distribution of the Petauroides latitudinal and longitudinal geographic range as part of a separate study to investigate relationships between animal physiology and climate (Fig. 4). The sites included Mount Zero-Taravale Australian Wildlife Sanctuary (19° 07′ 18″ S, 146° 04′ 42″ E, n = 18) and Blackbraes National Park (19° 34′ 39″ S, 144° 05′ 05″ E, n = 15) in North Queensland, and Bendoc State Forest (37° 10′ 35″ S, 148° 56′ 52″ E, n = 9) and Wombat State Forest (37° 29′ 50″ S, 144° 09′ 23″ E, n = 6) in Victoria. We then conducted additional field sampling in Redcliffe Vale, Queensland (21° 06′ 57″ S, 148° 56′ 58″ E, n = 18) (Fig. 4) as this was the suggested location of the proposed P. armillatus19,20. In addition, 12 museum specimen tissue samples were obtained from the Queensland Museum from greater gliders collected from northern, central and southern Queensland to investigate genetic structure in that area more broadly (Fig. 4). Additional information about the climate, geography and vegetation at each sample location can be found in Supplementary Material, (Table S1 online).
Figure 4

Location of the five study areas in eastern Australia in blue triangles (Blackbraes National Park (NP), Mount Zero-Taravale Sanctuary, Redcliffe Vale, Bendock State Forest (SF) and Wombat SF), and location of museum samples in orange squares (MS). The grey shading represents the current distribution of the greater glider from the Australian Species of National Environmental Significance Database49. This map was generated in R using the Australian coastline data from Geoscience Australia50 and multiple R packages (ggplot239, ggsn51, sp52,53, rgdal54, raster55).

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Wild greater gliders were located through spotlight searches using high-powered, handheld torches (Ledlenser P7, Zweibrüder Optoelectronics GmbH and Co., Solingen, Germany) to detect greater glider eye shine. Individuals were then captured using a gas-powered, tranquilizer dart-gun (Montech Black Wolf; Tranquil Arms Company, VIC, Australia) and darts specifically designed for mammals between 400 and 2000 g (0.5 ml; Minidarts, Tranquil Arms Company) containing 30–60 mg Zoletil 100 (Zolazepan and Tiletamine 50:50; Virbac), depending on the estimated body mass of the animal. While still under sedation, captured individuals were weighed and sexed, and reproductive status was assessed. External measurements were taken with vernier callipers using external jaws to measure head length (tip of snout to occipital bone protuberance), head width (widest part of one zygomatic arch to the analogous location on the other side of the head), ear width (widest part of the ear when flattened), ear length (from tragus to the outermost edge of the ear, excluding fur), and knee to heal-hind limb (top of knee to base of the heel with limb flexed to ninety degrees). Body length (occipital bone protuberance to the base of the tail, following the spine, with head in-line with the plane of the body) and tail length (cloaca to the tip of the tail, excluding fur) were measured with a flexible tape measure. Each individual was marked with a PIT tag (AVID Microchip Company, CA, USA) implanted subcutaneously and a tissue sample was clipped from the margin of the ear for DNA analysis. All work involving live animals complied with animal ethics and relevant guidelines and regulations. The animal capture and tissue collection was approved by James Cook University (Animal Ethics Permits A2137, A2545).
Morphology data investigation
Principal components analysis of the eight measured morphological traits for greater gliders from the five sites (Taravale, Blackbraes, Redcliffe Vale, Bendoc, and Wombat) was performed in R, using the “prcomp” function. The plot was generated using the ggplot2 package37. We then used a canonical variate analysis (CVA) in R package MASS38 to analyse regional group structure (Northern, Central, and Southern) in the multivariate data. To explore whether there were significant differences in measured traits between sexes and account for unequal sample sizes, we used linear models with backward elimination variable selection method and Tukey’s post-hoc multiple comparison test. Linear models included each morphological trait as the response variable and region (Northern, Central, Southern), sex (male, female) and the interaction between region and sex. Insignificant variables were eliminated until only significant variables remained. Based on these results, sex was pooled and Tukey’s post-hoc multiple comparison test was used to compare measured morphological traits between regions. We also explored differences between sites with Tukey’s post-hoc multiple comparison test.
DNA extraction and sequencing
DNA was extracted by Diversity Arrays Technologies (DArT Pty Ltd, Canberra, Australia) using a NucleoMag 96 Tissue Kit (MachereyNagel, Duren, Germany) coupled with NucleoMag SEP (Ref. 744900) to allow automated separation of high-quality DNA on a Freedom Evo robotic liquid handler (TECAN, Miinnedorf, Switzerland). Tissue was first incubated overnight with proteinase K, adjusted in concentration depending on the tissue. Sequencing for SNP genotyping was done using DArTseq (DArT Pty Ltd, Canberra, Australia), which uses a combination of complexity reduction using restriction enzymes, implicit fragment size selection and next generation sequencing39, as described in detail by Kilian et al.40 and Georges et al.26. Essentially, DArTseq is an implementation of sequencing complexity-reduced representations41 and more recent applications on next generation sequencing platforms42,43. To achieve the most appropriate complexity reduction (the fraction of the genome represented, controlling average read depth and number of polymorphic loci), four combinations of restriction enzymes (Pstl enzyme combined with either Hpall, Sphl, Nspl or Msel) were evaluated and restriction enzyme combination of Pstl (recognition sequence 5′-CTGCAIG-3′) and Sphl (5′-GCATGIC-3′) was selected.
Amplification using polymerase chain reaction (PCR)26,44 and the conditions applied are as described in Georges et al.26. After PCR, equimolar amounts of amplification products from each sample were pooled and applied to cBot (Illumina) bridge PCR for sequencing on the Illumina Hiseq 2500. The sequencing (single end) was run for 77 cycles to yield sequence tags of 20–69 bp after removing adaptors.
SNP genotyping
Sequences generated from each lane were processed using proprietary DArT Pty Ltd analytical pipelines as described by Georges et al.26. In particular, one third of samples were processed twice from DNA, using independent adaptors, to allelic calls as technical replicates, and scoring consistency (repeatability) was used as the main selection criterion for high quality/low error rate markers. The DArT analysis pipelines have been tested against hundreds of controlled crosses to verify Mendelian behaviour of the resultant SNPs as part of their commercial operations. The resultant data set contained the SNP genotypes and various associated metadata of which CloneiD (unique identity of the sequence tag for a locus), repAvg (proportion of technical replicate assay pairs for which the marker score is identical), CallRate (proportion of individuals scored at a particular locus) and SnpPosition (position in the sequence tag at which the defined SNP variant base occurs) are of particular relevance to our analyses.
Additional SNP filtering
The SNP data and associated metadata were read into a genlight object ({adegenet}45) to facilitate processing with package dartR (version 1.8)46. We first removed all but one SNP from each sequence tag (12,782 SNPs removed) and retained only those loci supported by a read depth between 5 × and 100 × (8895 loci removed). Three individuals were removed from the dataset owing to an exceptionally poor call rate of less than 0.5 (MS9, MS12, MS8) and resultant monomorphic loci removed from the dataset. Loci with a repeatability less than 0.99 were removed (2380 loci) and finally, loci with a call rate of less than 0.95 were removed (9870 loci). We regard the data remaining after this additional filtering (11,317 SNP markers for 75 individuals) as highly reliable.
Visualization
Genetic similarity among individuals, populations and colour morphs was visualized using ordination (principal coordinates analysis or PCoA47) as implemented in the gl.pcoa and gl.pcoa.plot functions of dartR. A scree plot of eigenvalues guided the number of informative axes to examine48, taken in the context of the average percentage variation explained by the original variables (using the gl.pcoa.scree function in dartR).
Diagnosable units
Diagnosable units are populations or aggregations of populations that can be diagnosed by one or more fixed allelic differences. Individuals that were intermediate between two aggregations in the PCoA (T1 and T5) were removed as suspected hybrids or introgressed individuals and examined separately. A fixed difference analysis as implemented in dartR was undertaken, and pairs of populations with no fixed allelic differences were progressively amalgamated to yield a putative set of diagnosable operational taxonomic units (OTUs). Putative OTUs that were distinctive because of false positives (owing to sampling error) were identified using gl.fixed.diff() with test = TRUE in dartR (see Georges et al.26, R scripts are provided in the online data repository), and pairs of populations for which the count of fixed differences did not exceed the estimated false positive rate were also amalgamated. Counts of private alleles were obtained from gl.report.pa in dartR.
Genetic diversity
Expected heterozygosity was used as a measure of relative genetic diversity. Heterozygosity was obtained for each population from allele frequencies using the gl. report.heterozygosity function of dartR and pairwise comparisons of heterozygosity between populations were tested for significance using the gl.test.heterozygosity function in dartR (significance evaluated by re-randomizaton with 10,000 replicates).
Hybridisation
The genotypes of suspected hybrids/introgressed individuals (T1 and T5) were examined using New Hybrids28 without specifying parental source populations. Briefly, New Hybrids uses simulation to characterize likelihood bins for each of the parental populations, F1 hybrids generated by crossing the parentals, F2 hybrids, and backcrosses between the F1 hybrids and the parental populations. These bins are used to estimate the likelihood of an individual belonging to each bin, and these likelihoods are rendered and scaled to produce posterior probabilities of bin membership. Parameters were set as ThetaPrior = Jeffreys, PiPrior = Jeffreys, burnin = 10,000, sweeps = 10,000 and the default genotype frequency classes (P0, P1, F1, F2, F1 × P0, F1 × P1). New Hybrids gives a posterior probability of individual membership in each of the genotype frequency classes, allowing effective assignment of first generation hybrids (F1), second generation hybrids (F2) and backcrosses of F1 hybrids to the parental populations.
Relatedness
Two individuals with genotypes intermediate between Taravale/Blackbraes and Redcliffe Vale (T1 and T5) were found in the same cluster of trees in Taravale. Given their status and proximity, we wished to examine the possibility that they were in a parent–offspring relationship. We did this by assuming they were parent–offspring and examining the frequency of pedigree inconsistencies (e.g. one individual homozygous reference, the other homozygous alternate). Theoretically, the count of pedigree inconsistencies should be zero for true parent–offspring pairs, but errors in calling the SNPs generates false positives. To overcome this, we generated counts of pedigree inconsistencies for all pairs of individuals to generate a null expectation and identified putative pairs of parent–offspring as outliers using software to be included in the next release of dartR (gl.report.parent.offspring). More

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