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NATURE PODCAST
14 January 2022

Recreating the lost sounds of spring

How citizen science is helping us hear lost soundscapes.

Geoff Marsh

Geoff Marsh

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The researcher resurrecting our declining soundscapes.

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As our environments change, so too do the sounds they make — and this change in soundscape can effect us in a whole host of ways, from our wellbeing to the way we think about conservation. In this Podcast Extra we hear from one researcher, Simon Butler, who is combining citizen science data with technology to recreate soundscapes lost to the past. Butler hopes to better understand how soundscapes change in response to changes in the environment, and use this to look forward to the soundscapes of the future.Nature Communications: Bird population declines and species turnover are changing the acoustic properties of spring soundscapesNever miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed

doi: https://doi.org/10.1038/d41586-022-00023-8

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Comparison of spectral water index methodsFigure 3 shows the results of different spectral water index methods. Through overlay analysis with the original image and detailed visual analysis, it was found that AWEIsh had the best extraction performance and could accurately identify the boundary of the water body. NDWI, MNDWI, and EWI had different degrees of leakage extraction; NDWI and EWI had an evident leakage extraction in the northwest corner of the image, and the water leakage extraction of MNDWI was mainly concentrated in the middle of the image. There was a lot of water body misidentified in WI, especially in the southeast corner of the image.Figure 3Results of water extraction from different spectral water index methods. (a) The original image. The threshold values and extracted water bodies from (b) NDWI, (c) MNDWI, (d) EWI, and (f) AWEIsh methods determined by the OTSU algorithm. (e) The extraction result of WI.Full size imageBased on the visual interpretation of the water boundary, 100 test samples were selected and the confusion matrix32 was calculated to obtain the extraction accuracy of the water body from three aspects: commission error, omission error, and overall accuracy (Table 1). As seen from the table, the overall accuracy of AWEIsh was the highest, attaining a value of 99.14%, with extremely low commission and omission errors. WI had the lowest overall accuracy and the highest commission error, and could not distinguish water bodies and low reflectivity features effectively. The overall accuracies of NDWI, MNDWI, and EWI were similar. Comparing the results of the visual interpretation and quantitative analysis, the rapid extraction model of surface water based on the Google Earth Engine utilizing AWEIsh index was used for assessing the spatiotemporal changes of water bodies.Table 1 Accuracy comparison of different spectral water index methods.Full size tableTime series analysis of typical reservoir areaTo understand the inter-annual variation trend and intra-annual variation of the reservoir area in the dry zone of Sri Lanka, time series analysis was conducted with the Maduru Oya Reservoir as the case study area. The Maduru Oya Reservoir is the second largest reservoir in Sri Lanka, located in the east-central region, which is the main water source for irrigation and drinking, and has a high incidence of chronic kidney disease of unknown aetiology (CKDu). Figure 4 shows the inter-annual and intra-annual variations of Maduru Oya Reservoir area.Figure 4Observed area change in the Maduru Oya Reservoir. (a) Inter-annual variation of the Maduru Oya Reservoir area; (b) Intra-annual variation of the Maduru Oya Reservoir area in 2017.Full size imageFigure 4 shows that the inter-annual fluctuation of Maduru Oya Reservoir area is slight, while the intra-annual fluctuation is significant. From 1988 to 2018, the reservoir area showed an overall increasing trend with slight float; the smallest area was recorded in 1992 (27.43 km2) and the largest area in 2013 (42.97 km2) (Fig. 4a). The rainy season in the dry zone of Sri Lanka occurs from October to February, and the dry season occurs from March to September. In 2017, the maximum area of the Maduru Oya Reservoir was noted in February, and the minimum area was noted in September. The area in February was 2.24 times bigger than that of September, with a difference of 31.58 km2. The maximum area of reservoirs or lakes generally occurs at the end of the wet season (February), and the minimum area occurs at the end of the dry season (September)2, which is consistent with the occurrence of maximum and minimum area in the Maduru Oya Reservoir in 2017(Fig. 4b). The area of the reservoir increased significantly in May during the dry season. According to meteorological data33, there were persistent strong winds and torrential rains in Sri Lanka in May 2017, resulting in an abnormal increase in the reservoir area. Generally, the period in which the area increased was from October to February (rainy season), while March to September (dry season) was the period in which the area decreased regardless of the influence of abnormal weather factors. The intra-annual fluctuation of the reservoir was severe, and there was a risk of drought and flooding at the same time. This observation implied that the seasonal regulation of water resources must be focussed in the future.Analysis of spatiotemporal change of inland lakes and reservoirsTo systematically analyze the spatiotemporal variation characteristics of inland water in Sri Lanka in recent years, and considering the cloud cover of Landsat-5/8 images, 1995, 2005 and 2015 were selected as the study year with an interval of 10 years. The distribution information of surface water in three stages was obtained by running the rapid extraction model of surface water in the Google Earth Engine. According to statistics, the surface water areas of Sri Lanka in 1995, 2005, and 2015 were 1654.18 km2, 1964.86 km2, and 2136.81 km2, respectively. In the past 20 years, the water area of Sri Lanka has increased significantly. To further analyse the spatiotemporal changes of inland lakes and reservoirs, a 5-m buffer data of rivers in 2015 were produced in ArcGIS10.3 software; further, the area corresponding to the river channels were removed from the three images and only the lagoon areas were preserved. Lagoons are ubiquitous in the coastal areas of Sri Lanka, with flood discharge, aquaculture, coastal protection, and other functions34. The results consisting of the extracted lakes, reservoirs, and lagoons are shown in Fig. 5.Figure 5Water extraction results for Sri Lanka in 1995, 2005, and 2015. The administrative boundary data of Sri Lanka comes from the Humanitarian Data Exchange (HDX) open platform (https://data.humdata.org). The maps were generated by geospatial analysis of ArcGIS software (version ArcGIS 10.3; http://www.esri.com/software/arcgis/arcgis-for-desktop).Full size imageThe overall water area of lakes and reservoirs in Sri Lanka showed an increasing trend from 1995 to 2015, and the lagoon area increased over these 20 years (Fig. 5). Because the lagoon does not belong to inland freshwater sensu stricto, the corresponding statistical analysis was not included in the following step. According to statistics, the total area covered of lakes and reservoirs in Sri Lanka were 1020.41 km2, 1270.53 km2, and 1417.68 km2 in 1995, 2005, and 2015 respectively. In the past 20 years, the area of lakes and reservoirs in Sri Lanka has increased by a considerable margin, attaining a value of 397.27 km2. To further analyse the spatiotemporal variation of inland lakes and reservoirs, they were divided into four grades according to their area: I ( More

Field studiesPhotosynthetic carbon fixation was investigated at eight different stations in the Pearl River estuary of the South China Sea (Fig. 1a and Supplementary Table 1), where the phytoplankton assemblages were dominated by diatoms45 during the time of our investigation (June 2015). Samples were collected from 10 to 20 m depths and transferred immediately into 50 mL quartz tubes and sealed to prevent gas exchange. The samples were inoculated with 100 μL of 5 μCi (0.185 MBq) NaH14CO3 solution for 2.15 h. All the incubations were carried out under incident solar radiation, attenuated with neutral density filters to simulate light intensities at the sampling depths, and the temperature was controlled with flow-through surface seawater.After incubation, the cells were filtered onto glass-fiber filters (25 mm, Whatman GF/F, USA) and stored at −20 ° C until measurement, during which the filters were exposed to HCl fumes overnight and dried (20 °C, 6 h) to remove unincorporated NaH14CO3 as CO2. The incorporated radioactivity was measured by liquid scintillation counting (LS 6500, Beckman Coulter, USA), and photosynthetic carbon fixation rates were estimated as previously reported46. Since the measurements were carried out under varying and low light levels similar to in situ levels at depths of 10 and 20 m, we normalized the photosynthetic rates to light intensity (μmol C (μg Chl a)−1 h−1 (μmol photons m−2 s−1)−1) to obtain the light use efficiency of photosynthesis (PLUE). This was done to allow for a meaningful comparison among different stations according to the linear relationship of photosynthetic carbon fixation under low solar irradiance levels46, which lies within the range of sunlight levels used in the present fieldwork ( More

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Natural diets of tropical birds vary within speciesWe collected 62 regurgitated samples (using the tartar emetic method 22) from multiple tropical bird species representing four bird orders (Columbiformes–Pigeons, Coraciiformes–Kingfishers, Psittaciformes–Parrots, and Passeriformes–Passerines). First, we characterized diet components visually and then through metabarcoding of 52 of these samples using universal primers targeting invertebrates (Cytochrome c oxidase subunit I: COI gene) and plants (Internal transcribed spacer 2: ITS2 gene) (Table S1 and Fig. 2). Through visual identification, we identified plant material in 26 samples. The most common visually identified invertebrate orders were Araneae (spiders—27 samples), and Coleoptera (beetles—27 samples) (Table S2). Metabarcoding sequences were analysed using the OBITools software25. Overall, we found 47 plant operational taxonomic units (OTUs—97% sequence similarity threshold) and 180 invertebrate OTUs (Table S3). Plant items were dominated by the orders Rosales (27.7% OTUs), Fabales (8.5% OTUs), and Sapindales (8.5% OTUs). Except for four OTUs, all plants were identified to the genus level. Of the invertebrate OTUs, 54 belonged to feather mites (known feather symbionts), endoparasites, and rotifers (likely due to accidental consumption along with drinking water), and these OTUs were removed from further analyses, leaving 126 potential dietary invertebrate OTUs. Invertebrate samples were dominated by the classes Insecta (67.5% OTUs) and Arachnida (28.6% OTUs). At the order-level, dietary items were mainly represented by Araneae (spiders—28.6% OTUs), Hemiptera (true bugs—15.9% OTUs), Diptera (flies—14.3% OTUs), and Lepidoptera (moths and butterflies—10.3% OTUs). However, 77% of the invertebrate OTUs could not be identified to genus level, highlighting the limited research on genotyping invertebrate communities in Papua New Guinea.Figure 2Natural diets of wild birds vary between individuals of the same species and the results of the two identification methods of dietary components (visual identification and metabarcoding). Relative abundances based on the presence/absence of data of different dietary components are indicated in colours. Only invertebrates are separated into taxonomic orders as visual identification is unable to identify plant orders. Individuals depicted with asterisks had both crop microbiome and diet samples (dataset 1), while black font represents individuals with both cloacal microbiomes and diet samples (dataset 2). Individuals are clustered according to the species (each species is given a six-letter code name) and their literature-based dietary guilds. The order of the species is indicated with illustrations (Columbiformes–Pigeons, Coraciiformes–Kingfishers, Passeriformes–Passerines and Psittaciformes–Parrots), while ‡ represents diet samples with a complete consensus between the two identification methods.Full size imageDiet item identification differed markedly between visual and metabarcoding methods (Fig. 2, Tables S2 and S3). The diet components of individuals also varied notably within species (Figs. 2 and S1). Only diets of 12 out of 52 individuals were fully congruent between the two methods (Fig. 2). Of these 12 samples, eight had only plant material. Identification of invertebrate orders also differed between the two methods (Fig. 2, Table 1). Both methods identified the arthropod orders Hemiptera, Diptera, Orthoptera (crickets and locusts), and Araneae in the same samples (Fig. 2 and Table 1), while metabarcoding detected lower proportions of Coleoptera than the visual identification (Table 1).Table 1 Comparison between diet items identified in the regurgitated samples from the two approaches (visual identification and metabarcoding).Full size tableComparison of microbiomes and consumed diet itemsFor subsequent comparisons of diets and microbiomes, we utilised individual datasets from both visual identification (diet components identified at the order level) and metabarcoding (both OTU and order level), and a combination (order level) of both approaches (for details see “Methods” section on identifying prey items). Due to differences between the diet identification methods, a combination of the results was used to circumscribe the full diversity of consumed diets and to account for inherent biases associated with the two methods (i.e., the inability to identify plant material and smaller body parts of invertebrates visually, and extraction and sequencing biases associated with metabarcoding). We separated the microbiome dataset into three datasets due to sequencing limitations: dataset 1 included 12 birds with successfully sequenced crop microbiomes and diets identified using both methods, dataset 2 included 27 birds with successfully sequenced cloacal microbiomes and diets, and dataset 3 included 17 birds for which we obtained successfully sequenced crop and cloacal microbiomes (Table S1). Prior to subsequent analyses, each microbiome dataset was rarefied to even sequencing depths using the sample with the lowest number of sequences26 (Fig. S2).Crop microbiome similarity did not align with the consumed diet similarity (dataset 1)Out of the collected crop samples (N = 62), samples from only 19 individuals were successfully sequenced for their microbiomes. Of these individuals, we acquired diet samples for 12 individuals. Bacterial 16S rRNA MiSeq sequences were analysed using the DADA2 pipeline27 within QIIME228. There were 351,867 bacterial sequences (mean ± SD: 29,322 ± 33,009) in the crop microbiomes prior to rarefaction (Table S4). After rarefaction, bacterial sequences were identified to 615 amplicon sequence variants (ASVs—100% sequence similarity). Crop microbiomes were dominated by Proteobacteria (53.6%), Actinobacteria (18.9%), and Firmicutes (17.9%). Alpha diversities of individual microbiomes were calculated using the diversity function in the microbiome package29 and they did not differ significantly between host orders [Chao1 richness: Kruskal Wallis (KW) χ2 = 4.559, df = 3, p = 0.2271; Shannon’s diversity index: χ2 = 2.853, df = 3, p = 0.4149], or literature-based dietary guilds (Chao1 richness: KW χ2 = 4.317, df = 2, p = 0.1155; Shannon’s diversity index: KW χ2 = 2.852, df = 2, p = 0.2403) (Fig. S3).The compositional differences of crop microbiomes were investigated with the adonis2 function in the vegan package30 using permutational multivariate analyses of variance tests (PERMANOVA). These analyses revealed that the bird host order did not influence the crop microbiome composition (PERMANOVA10,000 permutations: Bray–Curtis: F = 1.251, R2 = 0.0993, p = 0.1911; Jaccard: F = 1.154, R2 = 0.0962, p = 0.2191) (Fig. S1). The effect of feeding guild was masked by host order as they are strongly correlated in this dataset. Furthermore, the lack of an effect of host taxa on crop microbiomes may be a result of the small sample sizes.We further investigated whether alpha diversity of the crop microbiomes was influenced by the diet item diversity of individuals. The Chao1 richness estimates of the microbiomes and the richness of the consumed diet items (number of different diet items based on the combined results) of individuals were not significantly correlated (Table S5), suggesting that the diet richness does not impact crop microbiome richness. However, Shannon’s diversity index of crop microbiomes and diet diversity were marginally significantly negatively associated (Table S5). This suggests that despite the lack of an association between diet and microbiome richness, crop microbiome evenness could be influenced by diet diversity.We then explored the association between the crop microbiome composition and the consumed diets, investigating correlations between Bray–Curtis and Jaccard dissimilarities of microbiomes, and Jaccard dissimilarity of diets using Mantel tests in the vegan package30. The compositional similarity of the diets based on any of the methods (visual, metabarcoding—both OTU and order-level separately, and combined) did not correlate significantly with crop microbiome compositions (Table 2 and Fig. S4). We observed similar non-significant associations between diets and microbiomes when investigating host orders separately (Table S6). This suggests that overall crop microbiomes of individuals are not completely modelled by the composition of the consumed diets.Table 2 Results of Mantel tests between the crop (dataset 1) and the cloacal (dataset 2) microbiome similarities (measured with both Bray–Curtis and Jaccard distances) and the consumed diet similarities (measured with Jaccard distances).Full size tableHost-taxon specific cloacal microbes are associated with different diet items (dataset 2)We obtained 27 individuals from 15 bird species with successfully sequenced cloacal microbiomes and diet samples (based on both metabarcoding and visual identification). Prior to rarefying, we acquired 818,272 bacterial sequences from the cloacal swab samples (mean ± SD: 30,306 ± 20,903) (Table S7). After rarefaction, bacterial sequences were assigned to 1,324 ASVs that belonged to Actinobacteria (35.9%), Proteobacteria (32.6%), Firmicutes (21.2%) and Tenericutes (5.0%). Cloacal microbiome alpha diversity did not differ significantly between different bird orders (Chao1 richness: KW χ2 = 2.624, df = 3, p = 0.4532; Shannon’s diversity: χ2 = 6.595, df = 3, p = 0.0861) or literature-based dietary guilds (Chao1 richness: KW χ2 = 1.128, df = 3, p = 0.7703; Shannon’s diversity: KW χ2 = 1.673, df = 3, p = 0.6429) (Fig. S5).However, cloacal microbiome beta diversity was significantly influenced by host bird order (PERMANOVA10,000 permutations: Bray–Curtis: F = 2.159, R2 = 0.2055, p  More

Patterns of species abundanceIn their analysis of the fossil assemblage of VM4, Luzón et al.1 indicate that herbivorous taxa comprise the bulk of the fauna. Their data, compiled in Table 1, show that herbivore remains represent 94.2% (1492/1578) of NISP and 78.8% (41/52) of MNI values for large mammals. These figures are close to those of VM3, 93.5% (6570/7027) and 84.4% (287/340), respectively (Table 1). A χ2 test shows that these differences are not statistically significant (p  > 0.3 in both cases). Among herbivores, Luzón et al.1 indicate that E. altidens is the species most abundantly preserved, both in frequency of remains and number of individuals, followed by cervids, bison, caprines, and megaherbivores (i.e., elephant, rhino, and hippo). This is also the situation in VM3 according to data compiled in Table 1: for example, the NISP value of E. altidens represents 31.8% (124/390) of the remains of large mammal identified in VM4 and 49.6% (2937/5924) in VM3. Although this difference is statistically significant (χ2 = 46.408, p  0.75). The difference based on NISP values seems high, but it falls within the range expected from variations in abundance data from different years for the ungulate prey more common in Serengeti, where the frequencies of Thomson’s gazelle, wildebeest, and zebra fluctuated in the late sixties between 18.9–56.3%, 21.3–42.8%, and 11.1–15.7%, respectively15,16. Finally, P. brevirostris is the species most represented among carnivores in both assemblages according to NISP values (Table 1), 26.8% (15/56) in VM4 and 30.0% (122/407) in VM3 (χ2 = 0.241, p  > 0.6), followed by canids, ursids and felids.The distribution of NISP and MNI values among taxa in VM4 and VM3 was further analysed using an approach based on contingency tables. The table for NISP values shows a significant χ2 value (Table 2, left part). This results from some differences in taxa abundance between the assemblages compared, which are reflected in the adjusted residuals: remains of megaherbivores and carnivores (excluding hyaenas) are represented in VM4 by higher frequencies than those expected from a random, homogeneous distribution, while they are underrepresented in VM3. This applies to the estimates obtained for VM4 using the data of Luzón et al.1 and our own data (Tables S1, S2). The NISP values estimated for P. brevirostris by Luzón et al.1 suggest a higher frequency of this carnivore in VM4 than in VM3, as indicated by the adjusted residual. However, the abundance of hyaena remains in our dataset for VM4 does not depart significantly from the expectations, as happens in VM3. Given that the database of Luzón et al.1 includes less than half of the remains of large mammals included in our database (Table 2), this suggests that the high frequency of P. brevirostris reported in VM4 results from poor sampling. The remains of other carnivores are more abundantly represented in VM4 than in VM3. However, it must be noted that a study of 24 dens of the three living hyaenas showed that the abundance of carnivore remains is highly variable, even among dens of the same species17. The distribution of MNI values among taxa in VM4 and VM3 (Table 2, right part) does not differ from the expectations of a random distribution according to the low χ2 value of the contingency table. Only the adjusted residual for megaherbivores, which are slightly over-represented in VM4 according to the data of Luzón et al.1, is statistically significant, while their abundance in VM3 is slightly lower than expected. Moreover, the probabilities of obtaining in the randomization tests the cumulative χ2 values observed for the NISP and MNI values of each species (p  0.97, respectively; Fig. S4) are equivalent to those obtained with their groupings in Table 2.Table 2 Contingency tables for the abundance of large mammals in the assemblages of the two excavation quarries of Venta Micena compared in this study, VM4 (a: data published by Luzón et al.1 for the fossils unearthed during the years 2005 and 2019–2020; b: unpublished data analysed by M.P. Espigares for the fossils of 2005 and 2013–2015) and VM3 (updated from Ref.9).Full size tableIn summary, the comparison of the faunal assemblages from both excavation quarries (Tables 1, 2) only shows some minor differences in taxa abundance for horse, megaherbivores, and carnivores other than the hyaena, as well as the presence in VM3 of some remains of two small ungulates (a roe deer-sized cervid and a chamois-sized bovid) and two small carnivores (Table 1), which are not reported by Luzón et al.1. Given their comparatively low number of specimens studied at VM4, it is reasonable to expect that the latter taxa, which are poorly represented in VM3, will also appear in VM4 during future excavations.Age mortality profilesLuzón et al.1 indicate that two megaherbivores, elephant Mammuthus meridionalis and rhino Stephanorhinus aff. hundsheimensis, show frequencies of non-adults that are close to, or even higher than, those of adults, as happens in VM3 (Table 1). However, the low MNI counts for these species in VM4 do not allow to state this: for example, elephants are represented by a juvenile and an adult, which gives a frequency of 50% of non-adults; with a sample size of only two individuals, the 95% confidence interval calculated with a binomial approach for this percentage is 1.3–98.7% (Table 1). In S. hundsheimensis, the frequency of non-adults, 80% (4/5), has also a very wide confidence interval (28.4–99.5%). In three species of medium-to-large sized ungulates, E. altidens, the ancestor of water buffalo Hemibos aff. gracilis and P. verticornis, Luzón et al.1 report similar frequencies of adults and non-adults, while they indicate that Bison sp. shows a lower frequency of juveniles (Table 1). This is true for horse and deer (58.3% and 42.9% of non-adults, respectively), but Hemibos is only recorded by one adult individual, which means that the percentage of non-adults for this species is not reliable. Luzón et al.1 calculate the percentage of 33% non-adult bison over a sample of only three individuals, of which one is a juvenile: the confidence interval for non-adults (0.8–90.6%) comprises the frequencies for horse and megacerine deer (Table 1), which rules out their suggestion of a lower frequency of juveniles for this bovid. In contrast to VM4, the abundances of non-adult horse, bison and megacerine deer are similar in VM3 (Table 1), where they are represented by higher MNI counts (which makes their percentages reliable). A similar reasoning can be applied to the claim of Luzón et al.1 that adults outnumber calves and juveniles among smaller herbivores such as the Ovibovini Soergelia minor, the Caprini Hemitraus albus and the cervid Metacervocerus rhenanus: in these species, MNI counts are very low to calculate reliably the percentage of juveniles (see their confidence intervals in Table 1). In fact, Luzón et al.1 acknowledge this limitation when they write that “the total number of individuals in each species is too low to draw reliable conclusions on the resulting patterns” and “a prime-dominant, L- or U-shaped mortality profile cannot be clearly discerned”. The situation in VM3 is quite different (Table 1): MNI counts for the two ungulates better represented in the assemblage, E. altidens and P. verticornis, allowed to reconstruct U-shaped attritional mortality profiles (Fig. 2b), which evidenced that the hypercarnivores focused on young and old individuals in the case of large prey6,7.Patterns of skeletal abundanceThe limitations and inaccuracies cited above result from the small sample analysed by Luzón et al.1 in VM4 (1578 remains of large mammals of which only 420 could be determined taxonomically and anatomically, compared to 8150 and 6331 remains in VM3, respectively: Table 1). These limitations apply also to their inferences on the skeletal profiles of ungulates. For example, they indicate that species of herbivore size class 2 (50–125 kg: M. rhenanus, H. albus, and S. minor) show biased skeletal profiles, with a predominance of teeth and elements of the forelimb over those of the hindlimb. In VM3, these ungulates also show higher frequencies of teeth than of bones, which has been interpreted as evidence of the transport by P. brevirostris of small-to-medium sized ungulates as whole carcasses to their denning site, where the giant hyaenas fractured the bones for accessing their medullary cavities and this resulted in their underrepresentation compared to teeth7,8,9,10. In the case of the major limb bones of these species in VM4, the elements of the forelimb (12.9%, 13 bones out of 101 determined remains) are twice as abundant as those of the hindlimb (6.9%, 7 bones), but these percentages do not differ statistically (χ2 = 2.028, p = 0.1544), which indicates the effects of poor sampling. In the species of herbivore size class 3 (125–500 kg), Luzón et al.1 indicate that they are well represented by all anatomical elements (e.g., craniodental elements account for ~ 30% of the remains, while both axial and appendicular elements show frequencies  > 20%). This pattern is like the one reported in VM3 for medium-to-large sized ungulates7,8,9,10. However, Luzón et al.1 indicate a bias in the disproportionate amount of posterior limb remains compared to anterior limb specimens, which in their opinion contrasts with the more balanced representation of these elements observed in VM3. Specifically, the number of forelimb bones (13.8%, 54 out of 392 bones) is about half the abundance of hindlimb bones (25.3%, 99 bones). This difference is statistically significant (χ2 = 16.460, p  More