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Sample collectionThe 267 samples were collected between 2009 and 2018 from 41 locations expanding from the subarctic region to the tropics (Fig. 1, Auxillary Table S1)10 and processed using the same analytical pipeline (Fig. 2). The majority of the samples were collected using a depth-discrete Limnos tube-sampler (Limnos, Poland), with the exception of the samples from La Plata reservoir (Puerto Rico), which were collected using horizontal Van Dorn sampler (5 L capacity) and samples from Lake Loclat, which were collected using a deployed PVC-inlet connected to a peristaltic pump via tubing. Of all the lakes, 29 were sampled during the open water season and the majority of the lakes were sampled once. For 12 of the lakes only surface samples taken during the ice-covered period in winter were available, and one of the Swedish lakes (Lake Lomtjärnan) was sampled twice during the ice-covered period. Moreover, a total of 5 samples (one depth profile) from the time series of the Swiss lake (Loclat) were taken from under the ice. Time series samples were taken for Lake Loclat (seven time points, Auxillary Table S1)10 and for Lake Mekkojärvi (22 time points, see Saarenheimo et al.11 for details). For most lakes and ponds, samples were collected from multiple depths, including samples from the oxic surface layer (epilimnion), the layer with steepest change in oxygen concentration and temperature (metalimnion) and from the layer where oxygen levels were below the detection limit (hypolimnion). The exception to this were the 12 Swedish lakes sampled during ice-covered period, and five shallow ponds in Canada, for which only one sample from the oxic surface layer was taken (see Auxillary Table S1)10.Fig. 2Overview of the workflow from sample collection to mOTUs.Full size imageFrom two of the lakes, Lake Lomtjärnan in Sweden and Lake Alinen Mustajärvi in Finland, samples were collected also for single cell sorting. From both locations samples were preserved in glycerol-TE (gly-TE) and from Lomtjärnan samples were preserved also using phosphate buffered saline (PBS). For both preservants, the samples were flash frozen in liquid nitrogen after first incubating for 1 minute at ambient temperature.Simultaneous to collection of the DNA samples, also samples for environmental variables were taken. Variables included temperature, pH, conductivity, oxygen, total and dissolved nutrients (P and N species), gases (CO2 or dissolved inorganic carbon and methane (CH4)), total or dissolved organic carbon, iron, sulfate and chlorophyll a (Auxillary Table S1 and Auxillary Table S210 for the methods). As the samples were collected during multiple years and by different research groups, there was some variation for the procedures between the different sampling occasions, leading to variation in the final set of environmental data across the samples.DNA extraction and metagenome sequencingMost of the DNA samples were collected on 0.2 µm Sterivex filters (Millipore), except for the time-series samples collected from Loclat, which were collected by vacuum filtration onto 47 mm polycarbonate membrane filters with 0.2 μm pore size, and time series samples from Finnish Lake Mekkojärvi, for which the water for DNA extraction was collected from epilimnion (0–0.5 m), metalimnion (0.5–1 m) and hypolimnion (1–3 m) and pooled samples from each stratum were stored in 100 ml plastic containers and frozen at −20 °C and eventually freeze-dried (Alpha 1–4 LD plus, Christ). For all filter samples, water was filtered until the filter clogged. All filters were stored frozen (−20 to −80 °C) until the extraction of DNA. For all samples, DNA was extracted using PowerSoil DNA extraction kit (MoBio, Carlsbad, CA, USA) following the manufacturer’s instructions and the DNA concentrations were measured using Qubit dsDNA HS kit (Thermo Fisher Scientific Inc.).Sequencing libraries were prepared from 10 or 20 ng of DNA using the ThruPLEX DNA-seq Prep Kit according to the manufacturer’s preparation guide. Briefly, the DNA was fragmented using a Covaris E220 system, aiming at 400 bp fragments. The ends of the fragments were end-repaired and stem-loop adapters were ligated to the 5′ ends of the fragments. The 3′ end of the stem loop were subsequently extended to close the nick. Finally, the fragments were amplified and unique index sequences were introduced using 7 cycles of PCR followed by purification using AMPure XP beads (Beckman Coulter).The quality of the libraries was evaluated using the Agilent Fragment Analyzer system and the DNF-910-kit. The adapter-ligated fragments were quantified by qPCR using the Library quantification kit for Illumina (KAPA Biosystems/Roche) on a CFX384Touch instrument (BioRad) prior to cluster generation and sequencing.The sequencing libraries were pooled and subjected to cluster generation and paired-end sequencing with 150 bp read length S2/S4 flow-cells and the NovaSeq 6000 system (Illumina Inc.) using the v1 chemistry according to the manufacturer’s protocols. Negative controls were included to the sequencing as well as 1% of PhiX control library as a positive control.Base calling was done on the instrument by RTA (v3.3.3, 3.3.5, 3.4.4) and the resulting.bcl files were demultiplexed and converted to fastq format with tools provided by Illumina Inc., allowing for one mismatch in the index sequence. Additional statistics on sequence quality were compiled with an in-house script from the fastq-files, RTA and CASAVA output files. Sequencing was performed by the SNP&SEQ Technology Platform in Uppsala, Sweden.Single-cell sorting and DNA amplificationAll Gly-TE cryopreserved samples were thawed and diluted in 1 xPBS if needed while all plates with PBS were UV-treated with a dose of 2 J prior to sorting. Samples collected from both lakes were sorted, and then screened for organisms belonging to candidate phyla radiation. Samples collected from Lake Lomtjärnan were additionally subjected to sorting based on autofluorescence to identify and sequence cells belonging to lineage Chlorobia.For obtaining SAGs from representatives of the candidate phyla radiation (CPR), samples were first stained with 1 x SYBR Green I for approximate 30 minutes. Subsequent single cell sorting was performed with a MoFlo Astrios EQ (Beckman Coulter, USA) cell sorter using a 488 nm laser for excitation, 70 µm nozzle, sheath pressure of 60 psi and 0.1 µm sterile filtered 1x PBS as sheath fluid. Individual cells were deposited into empty 384-well plates (Biorad, CA USA) UVed at 2 Joules using a CyCloneTM robotic arm and the most stringent single cell sort settings (single mode, 0.5 drop envelope). Green fluorescence (488–530/40) was used as trigger and sort decisions were made based on combined gates of 488–530/40 Height log vs 488–530/40 Area log and 488–530/40 Height log vs SSC with increasing side scatter divided up in three different regions. Flow sorting data was interpreted and displayed using the associated software Summit v 6.3.1. Next, individual cells were subject to lysis, neutralization and whole genome amplification using MDA based on the protocol and workflow described by Rinke et al.12 but with several modifications. Reagent mastermixes were added using the MANTIS liquid dispenser (Formulatrix) and the LV or HV silicone chips. The lysozyme, D2 buffer, stop solution and MDA-mastermix were each dispensed with its own chip. Most MDA-reactions were run using the phi29 from ThermoFisher but a few were run with a more heat-stable phi29, EquiPhi also provided by ThermoFisher. The MDA reaction was carried out in a total volume of 5.2 µl. Thawed, sorted cells were first pre-treated with 400 nl/well of 12 U/µl of Ready-Lyse™ Lysozyme Solution (R1804M, Lucigen) at room temperature for 15 minutes before adding 400 nl Qiagen lysis buffer D2 followed by incubation at 95 °C for 10 seconds and 10 minutes on ice. Reactions were neutralized by adding 400 nl Qiagen Stop solution. Four µl of MDA mix containing 1x reaction buffer, 0.4 mM dNTP, 0.05 mM exonuclease-resistant Hexamers, 10 mM DTT, 1.7 U phi29 DNA polymerase (ThermoFisher Scientific) and 0.5 µM Syto13 was added to a final reaction volume of 5.2 µl. All reagents except SYTO13 were UV decontaminated at 2 Joules in a UV crosslinker. The whole genome amplification was run at 30 °C for 7 or 10 h followed by an inactivation step at 65 °C for 5 min. The reaction was monitored in real time by detection of SYTO13 fluorescence every 15 minutes using a FLUOstar® Omega plate reader (BMG Labtech, Germany) or a qPCR instrument. The EquiPhi protocol was run as previously described for ThermoFisher phi29 with the following exceptions; the EquiPhi polymerase was added in 1U/reaction, reaction buffer included with the polymerase was used and the reaction was carried out at 45 °C. The single amplified genome (SAG) DNA was stored at −20 °C until further PCR screening, library preparation and Illumina sequencing.The CPR SAGs were screened using the bacterial PCR primers targeting the 16 S rRNA gene, Bact_341 F and Bact_805 R13. The reactions were run in a LightCycler 480 PCR machine (ROCHE, MA USA) in 10 µl and a final concentration of 1 x LightCycler480 SYBR Green I Master mix, 0.25 µM of each primer and 2 µl of 60 to 80 times diluted SAGs. Following a 3 min denaturation at 95 °C, targets were amplified for 40 cycles of 95 °C for 10 s, 55 °C for 20 s, 72 °C for 30 s and a final 10 min extension at 72 °C followed by melting curve analysis. The products were purified using the NucleoSpin Gel and PCR clean-up purification kit (Macherey-Nagel, Germany), quantified using the Quant-iT TM PicoGreen® dsDNA assay kit (Invitrogen, MA USA) in a FLUOstar® Omega microplate reader (BMG Labtech, Germany) and submitted for identification by Sanger sequencing at Eurofin Genomics. All SAGs were further screened using the newly designed primers targeting the phylum Parcubacteria 684F-OD1 (3′ GTAGKRRTRAAATSCGTT 5′) and 784 R (5′ TAMNVGGGTATCTAATCC -3′). These primers target with good specificity 67% of Parcubacteria in the SILVA database14. Parcu-PCR was run at 3 min at 95 °C, 40 cycles of 95 °C for 10 s, 55 °C for 20 s, 72 °C for 30 s and a final 10 min extension at 72 °C followed by melting curve analysis. The products were purified using the NucleoSpin Gel and PCR clean-up purification kit (Macherey-Nagel, Germany), quantified using the Quant-iT TM PicoGreen® dsDNA assay kit (Invitrogen, MA USA) in a FLUOstar® Omega microplate reader (BMG Labtech, Germany) and submitted for identification by Sanger sequencing at Eurofin Genomics.To recover Chlorobia single amplified genomes, sorting was done in 2016 on a MoFlo™ Astrios EQ sorter (Beckman Coulter, USA) using a 488 and 532 nm laser for excitation, a 70 μm nozzle, a sheath pressure of 60 psi, and 0.1 μm filtered 1x PBS as sheath fluid. An ND filter ND = 1 and the masks M1 and M2 were used. The trigger channel was set to the forward scatter (FSC) at a threshold of 0.025% and sort regions were defined on autofluorescence using laser 532 nm and band pass filters 710/45 and 664/22. Three populations were sorted based on differences in autofluorescence signals. The sort mode was set to single cell with a drop envelope of 0.5. The target populations were sorted at approximately 400 events per second into 96-well plates containing 1 µl 1x PBS per well with either 1 or 10 cells (positive control) deposited. A few wells remained empty (no cell sorted) were kept as negative controls. Sorted plates were stored frozen at −80 °C.The subsequent whole genome amplification was performed in 2018 using the REPLI-g Single Cell kit (QIAGEN) following the instructions provided by the manufacturer but with total reaction volume reduced to 12.5 µl. The denaturation reagent D2, stop solution, water, and reagent tubes and strips were UV-treated at 2.5 J. The lysis was changed slightly to 10 min at 65 °C, followed by 5 min on ice before adding the stop solution. To the master mix containing water, reaction buffer, and the DNA the polymerase we added SYTO 13 (Invitrogen) at a final concentration of 0.5 µM. The amplification was performed at 30 °C for 8 hours in a plate reader with fluorescence readings every 15 min. The reaction was stopped by incubating it for 5 min at 65 °C. The plate was stored for less than a week at −20 °C. Amplified DNA was mixed thoroughly by pipetting up and down 20 times before diluting it 50x and 100x in nuclease-free water. The DNA was screened for bacterial 16 S rRNA applying the primers Bact_341 F (5′- CCTACGGGNGGCWGCAG- 3′) and Bact_805 R (5′- GACTACHVGGGTATCTAATCC-3′)13 using the LightCycler® 480 SYBR Green I Master (Roche) kit. The PCR mix contained 1.5 µl diluted amplified DNA, 1x the LightCycler® 480 SYBR Green I Master mix, 0.25 µM of each primer, and nuclease-free water in a total reaction volume of 10 µl. The PCR cycling (5 min at 95 °C, followed by 40 cycles of 10 sec at 95 °C, 20 sec at 60 °C, 30 sec at 72 °C) was followed by meltcurve analysis on the LightCycler® 480 Instrument (Roche). DNA of confirmed Chlorobia was sent to sequencing as outlined below.Library preparation and Illumina sequencing of the single cellsFor the CPR-targeted analysis, Illumina libraries were prepared from sixty SAGs mainly selected from the screening procedure in a PCR-free workflow using the sparQ DNA Frag & Library Prep Kit (Quantabio) and IDT for Illumina TruSeq UD Indexes (Illumina). Libraries were prepared from 50–250 ng of MDA-products in 25% of the recommended reaction volumes according to manufacturer’s instructions. The MDA-products were fragmented for 7 minutes (5 minutes for 4 samples) without using the DNA Frag Enhancer Solution. Library insert sizes were determined using Bioanalyzer High Sensitivity DNA Kit (Agilent). Each library was quantified using the KAPA Library Quantification kit (Roche) in 5 µl reaction volumes in a 384-well plate run on LightCycler 480 (Roche) to allow equimolar pooling before sequencing on Illumina HiSeqX v2.5 PE 2 × 150 bp including negative and positive (PhiX) controls.For the Chlorobia-targeted sequencing, amplified DNA from 23 SAGs were quantified individually with Qubit dsDNA HS assay kit (ThermoFisher Scientific) and diluted to 0.2 ng/ul in nuclease free water. Sequencing libraries were prepared with Nextera XT DNA Library Preparation Kit and combinatorial combinations of molecular identifiers in the Nextera XT Index Kit (Illumina, CA USA) according to manufacturer’s instructions. Libraries with an average length of 1200 bp were quantified with Qubit dsDNA HS assay kit to allow pooling of equal amounts of the libraries based on mass. The libraries were sequenced on an Illumina MiSeq v3 PE 2 × 300 bp including negative and positive (PhiX) controls.Data processing of the metagenome and single cell sequencesThe metagenome sequencing resulted in a total of ~107 paired-end reads of length 2 × 150 bp, amounting to a total of total 3 Tbp. The raw data was trimmed using Trimmomatic (version 0.36; parameters: ILLUMINACLIP:TruSeq 3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36)15 (Auxillary Table S3)10. The trimmed data was assembled using Megahit (version 1.1.13)16 with default settings. Two types of assemblies were done, single sample assemblies for all the samples individually and a total of 53, mainly lake-wise, co-assemblies (see Auxillary Table S4)10, some samples of the Canadian ponds have also been coassembled with previously sequenced libraries of the same sample (see Auxillary Table S5)10. The relevant quality controlled reads were mapped to all the assemblies using BBmap17 with default settings and the mapping results were used to bin the contigs using Metabat (version 2.12.1, parameters –maxP 93 –minS 50 -m 1500 -s 10000)18. Genes of obtained bins were predicted and annotated using Prokka (version 1.13.3)19 using standard parameters except for the bin containing all the unbinned contigs where the –metagenome flag was used. Single-cell libraries were processed similarly to the metagenomes, but without the binning step, and using the single-cell variant of the SPAdes20 assembler instead of Megahit.Prokaryotic completeness and redundancy of all bins from Metabat and for all assembled single cells were computed using CheckM (version 1.0.13)21 (Auxillary Tables S6 and S7 for MAGs and SAGs, respectively)10. Average Nucleotide Identity (ANI) for all bin-pairs was computed with fastANI (version 1.3)22. The bins were clustered into metagenomic Operational Taxonomic Units (mOTUs) starting with 40% complete genomes with less than 5% contamination. Genome pairs with ANI above 95% were clustered into connected components. Additionally, less complete genomes were recruited to the mOTU if its ANI similarity was above 95%. Bins were taxonomically annotated in a two-step process. GTDB-Tk (version 102 with database release 89)23 was used first with default settings. Using this classification an lca database for SourMASH (version 1.0)24 was made. This database as well as one based on the GTDB release 89 was then used with SourMASH’s lca classifier for a second round of classification of bins that were not annotated with GTDB-tk (Auxillary Table S8)10.The taxonomic diversity of the bacterial (Fig. 3) and archaeal (Fig. 4) mOTUs, respectively, were visualized in a tree format. The trees were computed using GTDB-tk with one representative MAG per mOTU of the stratfreshDB, and one random representative genome per family of the GTDB. Trees were visualized using anvi’o25.Fig. 3Bacterial diversity of the stratfreshDB27. The insert illustrates the quality of the MAGs and SAGs included in the tree. Interactive version of the tree with more information available at https://anvi-server.org/moritzbuck/bacterial_diversity_of_the_stratfreshdb.Full size imageFig. 4Archaeal diversity of the stratfreshDB27. The insert illustrates the quality of the MAGs included in the tree. Interactive version of the tree with more information available at https://anvi-server.org/moritzbuck/archaeal_diversity_of_the_stratfreshdb.Full size image More

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SampleA total of 620 carnivore tooth pits were included in the present study. These samples included tooth marks produced by;

Brown Bears (Ursus arctos, Ursidae, 69 pits)

Spotted Hyenas (Crocuta crocuta, Hyaenidae, 86 pits)

Wolves (Canis lupus, Canidae, 80 pits)

African Wild Dogs (Lycaon pictus, Canidae, 89 pits)

Foxes (Vulpes vulpes, Canidae, 53 pits)

Jaguars (Panthera onca, Felidae, 77 pits)

Leopards (Panthera pardus, Felidae, 84 pits)

Lions (Panthera leo, Felidae, 82 pits)

Samples originated from a number of different sources, including animals kept in parks as well as wild animals. Samples obtained from wild animals included those produced by foxes as well as wolves. The only sample containing both wild and captive animals was the wolf sample. Preliminary data from these tooth pits revealed animals in captivity to have highly equivalent tooth pit morphologies to wild animals ((vert d vert ) = 0.125, p = 9.0e−14, BFB = 1.4e+11), while tooth scores revealed otherwise ((vert d vert ) = 0.152, p = 0.99, BFB = 3.7e+01 against (H_{a})). Under this premise, and so as to avoid the influence of confounding variables that go beyond the scope of the present study, tooth scores were excluded from the present samples and are under current investigation (data in preperation). Nevertheless, other research have shown tooth pits to be more informative than tooth scores when considering morphology20,23.When working with tooth mark morphologies, preference is usually given to marks found on long bone diaphyses. This is preferred considering how diaphyses are denser than epiphyses, and are thus more likely to survive during carnivore feeding. Nevertheless, when working with captive or semi-captive animals, controlling the bones that carnivores are fed is not always possible. This is due to the rules and regulations established by the institution where these animals are kept64. While this was not an issue for the majority of the animals used within the present study, in the case of P. pardus, animals were only fed ribs in articulation with other axial elements. In light of this, a careful evaluation on the effects this may have on the analogy of our samples was performed (Supplementary Appendix 2). These reflections concluded that in order to maintain a plausible analogy with tooth marks produced by other animals on diaphyses, tooth marks could only be used if found on the shaft of bovine ribs closest to the tuburcle, coinciding with the posterior and posterior-lateral portions of the rib, and farthest away from the costochondral junction65. This area of the rib corresponds to label RI3 described by Lam et al.65. Moreover, with a reported average cortical thickness of 2.3mm (± 0.13 mm) and Bone Mineral Density of (4490 kg/m^{3} [213.5, 334.6])66, bovine ribs are frequently employed in most bone simulation experiments used in agricultural as well as general surgical sciences. Finally, considering the grease, muscle and fat content of typical domestic bovine individuals67, alongside the general size of P. pardus teeth, it was concluded that the use of rib elements for this sample was the closest possible analogy to the tooth marks collected from other animals.Carnivores were fed a number of different sized animals, also dependent in most cases on the regulations established by the institution where these animals are kept64. Nevertheless, recent research has found statistical similarities between tooth marks found on different animals25, with the greatest differences occurring between large and small sized animals. Needless to say, considering the typical size of prey some of these carnivores typically consume, this factor was not considered of notable importance for the present study25 (Supplementary Appendix 1).For the purpose of comparisons, animals were split into 5 groups according to ecosystem as well as taxonomic family. From an ecological perspective, two datasets were defined; (1) the Pleistocene European Taxa dataset containing U. arctos, V. vulpes, C. crocuta, P. pardus, P. leo and C. lupus; and (2) the African Taxa dataset containing C. crocuta, P. pardus, L. pictus and P. leo. When considering taxonomic groupings, animals were separated into 3 groups, including; (1) the Canidae dataset, including V. vulpes, L. pictus and C. lupus; (2) the Felidae dataset, including P. pardus, P. onca and P. leo; and (3) a general Taxonomic Family dataset, including all Canidae in the same group, all Felidae in the same group, followed by Hyaenidae and Ursidae. Some complementary details on each of these carnivores have been included in Supplementary Appendix 1.All experiments involving carnivores were performed in accordance with the relevant ethical guidelines as set forth by park keepers and general park regulations. No animals were sacrificed specifically for the purpose of these experiments. Likewise, carnivores were not manipulated or handled at any point during the collection of samples. Collection of chewed bones were performed directly by park staff and assisted by one of the authors (JY). The present study followed the guidelines set forth by ARRIVE (https://arriveguidelines.org/) wherever necessary. No licenses or permits were required in order to perform these experiments. Finally, in the case of animals in parks, bone samples were provided by the park according to normal feeding protocols. More details can be consulted in the Extended Samples section of the supplementary files.3D modelling and landmark digitisationDigital reconstructions of tooth marks were performed using Structured Light Surface Scanning (SLSS)68. The equipment used in the present study was the DAVID SLS-2 Structured Light Surface Scanner located in the C.A.I. Archaeometry and Archaeological Analysis lab of the Complutense University of Madrid (Spain). This equipment consists of a DAVID USB CMOS Monochrome 2-Megapixel camera and ACER K11 LED projector. Both the camera and the projector were connected to a portable ASUS X550VX personal laptop (8 GB RAM, Intel® CoreTM i5 6300HQ CPU (2.3 GHz), NVIDIA GTX 950 GPU) via USB and HDMI respectively. The DAVID’s Laser Scanner Professional Edition software is stored in a USB Flash Drive. Equipment were calibrated using a 15 mm markerboard, using additional macro lenses attached to both the projector and the camera in order to obtain optimal resolution at this scale. Once calibrated the DAVID SLS-2 produces a point cloud density of up to 1.2 million points which can be exported for further processing via external software.The landmark configuration used for this study consists of a total of 30 landmarks (LMs)21; 5 fixed Type II landmarks18 and a (5 times 5) patch of semilandmarks69 (Fig. S2). Of the 5 fixed landmarks, LM1 and LM2 mark the maximal length (l) of each pit. For the correct orientation of the pit, LM1 can be considered to be the point along the maximum length furthest away from the perpendicular axis marking the maximum width (w). LM2 would therefore be the point closest to said perpendicular axis (see variables (d_{1}) and (d_{2}) in Fig. S2 for clarification). LM3 and LM4 mark the extremities of the perpendicular axis (w) with LM3 being the left-most extremity and LM4 being the right-most extremity. LM5 is the deepest point of the pit. The semilandmark patch is then positioned over the entirety of the pit, so as to capture the internal morphology of the mark.Landmark collection was performed using the free Landmark Editor software (v.3.0.0.6.) by a single experienced analyst. Inter-analyst experiments prior to landmark collection revealed the landmark model to have a robustly defined human-induced margin of error of 0.14 ± 0.09 mm (Median ± Square Root of the Biweight Midvariance). Detailed explanations as well as an instructional video on how to place both landmarks and semilandmarks can be consulted in the Supplementary Appendix and main text of Courtenay et al.21.Geometric morphometricsOnce collected, landmarks were formatted as morphologika files and imported into the R free software environment (v.3.5.3, https://www.r-project.org/). Initial processing of these files consisted in the orthogonal tangent projection into a new normalized feature space. This process, frequently referred to as Generalized Procrustes Analysis (GPA), is a valuable tool that allows for the direct comparison of landmark configurations18,19,70. GPA utilises different superimposition procedures (translation, rotation and scaling) to quantify minute displacements of individual landmarks in space71. This in turn facilitates the comparison of landmark configurations, as well as hypothesis testing, using multivariate statistical analyses. Nevertheless, considering observations made by Courtenay et al.20,21,25 revealed tooth mark size to be an important conditioning factor in their morphology, prior analyses in allometry were also performed72. From this perspective, allometric analyses first considered the calculation of centroid sizes across all individuals; the square root of the sum of squared distances of all landmarks of an object from their centroid18. These calculations were then followed by multiple regressions to assess the significance of shape-size relationships. For regression, the logarithm of centroid sizes were used. In cases where shape-size relationships proved significant, final superimposition procedures were performed excluding the scaling step of GPA (form).In addition to these analyses, preliminary tests were performed to check for the strength of phylogenetic signals73. This was used as a means of testing whether groups of carnivores produced similar tooth pits to other members of the same taxonomic family. For details on the phylogenies used during these tests, consult Fig. S1 and Supplementary Appendix 1.For the visualisation of morphological trends and variations, Thin Plate Splines (TPS) and central morphological tendencies were calculated19,71. From each of these mean landmark configurations, for ease of pattern visualisation across so many landmarks, final calculations were performed using Delaunay 2.5D Triangulation algorithms74 creating visual meshes of these configurations in Python (v.3.7.4, https://www.python.org/).Once normalised, landmark coordinates were processed using dimensionality reduction via Principal Components Analyses (PCA). In order to identify the optimal number of Principal Component Scores (PC Scores) that best represented morphological variance, permutation tests were performed calculating the observed variance explained by each PC with the permuted variance over 50 randomized iterations75. Multivariate Analysis of Variance (MANOVA) tests were then performed on these select PCs to assess the significance of multivariate morphological variance among samples.Geometric Morphometric applications were programmed in the R programming language (Sup. Appendix 8).Robust statisticsWhile GPA is known to normalize data76, this does not always hold true. Under this premise, caution must be taken when performing statistical analyses on these datasets. Taking this into consideration, prior to all hypothesis testing, normality tests were also performed. These included Shapiro tests and the inspection of Quantile–Quantile graphs. In cases where normality was detected, univariate hypothesis tests were performed using traditional parametric Analysis of Variance (ANOVA). For multivariate tests, such as MANOVA, calculations were derived using the Hotelling-Lawley test-statistic. When normality was rejected, robust alternatives to each of these tests were chosen. In the case of univariate testing, the Kruskal–Wallis non-parametric rank test was prefered, while for MANOVA calculations, Wilk’s Lambda was used.Finally, in light of some of the recommendations presented by The American Statistical Association (ASA), as debated in Volume 73, Issue Sup1 of The American Statistician77,78, the present study considers p-values of ( >2sigma ) from the mean to indicate only suggestive support for the alternative hypothesis ((H_{a})). (p ; > ; 0.005), or where possible, (3sigma ) was therefore used as a threshold to conclude that (H_{a}) is “significant”. In addition, Bayes Factor Bound (BFB) values (Eq. 1) have also been included alongside all corresponding p-Values79. Unless stated otherwise, BFBs are reported as the odds in favor of the alternative hypothesis (BFB:1). More details on BFB, Bayes Factors and the (p ; > ; 3sigma ) threshold have been included in Supplementary Appendix 3. General BFB calibrations in accordance with Benjamin and Berger’s Recommendation 0.379, as well as False Positive Risk values according to Colquhoun’s proposals80, have also been included in Table S20 of Supplementary Appendix 3.$$begin{aligned} BFB = frac{1}{-e ; p ; log (p)} end{aligned}$$
(1)
All statistical applications were programmed in the R programming language (Sup. Appendix 8).Computational learningComputational Learning employed in this study consisted of two main types of algorithm; Unsupervised and Supervised algorithms. The concept of “learning” in AI refers primarily to the creation of algorithms that are able to extract patterns from raw data (i.e. “learn”), based on their “experience” through the construction of mathematical functions38,81. The basis of all AI learning activities include the combination of multiple components, including; linear algebra, calculus, probability theory and statistics. From this, algorithms can create complex mathematical functions using many simpler concepts as building blocks38. Here we use the term “Computational Learning” to refer to a very large group of sub-disciplines and sub-sub-disciplines within AI. Deep Learning and Machine Learning are terms frequently used (and often debated), however, many more branches and types of learning exist. Under this premise, and so as to avoid complication, the present study has chosen to summarise these algorithms using the term “Computational”.Similar to the concepts of Deep and Machine Learning, many different types of supervision exist. The terms supervised and unsupervised refer to the way raw data is fed into the algorithm. In most literature, data will be referred to via the algebraic symbol x, whether this be a vector, scalar or matrix. The objective of algorithms are to find patterns among a group of x. In an unsupervised context, x is directly fed into the algorithm without further explanation. Algorithms are then forced to search for patterns that best explain the data. In the case of supervised contexts, x is associated with a label or target usually denominated as y. Here the algorithm will try and find the best means of mapping x to y. From a statistical perspective, this can be explained as (pleft( y vert x right) ). In sum, unsupervised algorithms are typically used for clustering tasks, dimensionality reduction or anomaly detection, while supervised learning is typically associated with classification tasks or regression.The workflow used in the present study begins with dimensionality reduction, as explained earlier with the use of PCA. While preliminary experiments were performed using non-linear dimensionality reduction algorithms, such as t-distributed Stochastic Neighbor Embedding (t-SNE)82 and Uniform Manifold Approximation and Projection (UMAP)83, PCA was found to be the most consistent across all datasets, a point which should be developed in detailed further research. Once dimensionality reduction had been performed, and prior to any advanced computational modelling, datasets were cleaned using unsupervised Isolation Forests (IFs)84. Once anomalies had been removed, data augmentation was performed using two different unsupervised approaches; Generative Adversarial Networks (GANs)38,39,40,41 and Markov Chain Monte Carlo (MCMC) sampling44. Data augmentation was performed for two primary reasons; (1) the simulation of larger datasets to ensure supervised algorithms have enough information to train from, and (2) to balance datasets so each sample has the same size. Both MCMCs and GANs were trialed and tested using robust statistics to evaluate quality of augmented data41. Once the best model had been determined, each of the datasets were augmented so they had a total sample size of (n = 100). In the case of the Taxonomic Family dataset, augmentation was performed until all samples had the same size as the largest sample.Once augmented, samples were used for the training of supervised classification models. Two classification models were tried and tested; Support Vector Machines (SVM)85 and Neural Support Vector Machines (NSVM)86,87. NSVMs are an extension of SVM using Neural Networks (NNs)38 as feature extractors, in substituting the kernel functions typically used in SVMs. Hyperparameter optimization for both SVMs and NSVMs were performed using Bayesian Optimization Algorithms (BOAs)88.Supervised computational applications were performed in both the R and Python programming languages (Sup. Appendix 8). For full details on both unsupervised and supervised computational algorithms, consult the Extended Methods section of the Supplementary Materials.Evaluation of supervised learning algorithms took into account a wide array of different popular evaluation metrics in machine and deep learning. These included; Accuracy, Sensitivity, Specificity, Precision, Recall, Area Under the receiver operator characteristic Curve (AUC), the F-Measure (also known as the F1 Score), Cohen’s Kappa ((kappa )) statistic, and model Loss. Each of these metrics, with the exception of loss, are calculated using confusion matrices, measuring the ratio of correctly classified individuals (True Positive & True Negative) as well as miss-classified individuals (False Positive & False Negative). For more details see Supplementary Appendix 6.Accuracy is simply reported as either a decimal (left[ 0, 1right] ) or a percentage. Accuracy is a metric often misinterpreted, as explained in Supplementary Appendix 6, and should always be considered in combination with other values, such as Sensitivity or Specificity. Both Sensitivity and Specificity are values reported as decimals (left[ 0, 1right] ), and are used to evaluate the proportion of correct classifications and miss-classifications. AUC values are derived from receiver operator characteristic curves, a method used to balance and graphically represent the rate of correctly and incorrectly classified individuals. The closest the curve gets to reaching the top left corner of the graph, the better the classifier, while diagonal lines in the graph represent a random classifier (poor model). In order to quantify the curvature of the graph, the area under the curve can be calculated (AUC), with (AUC=1) being a perfect classifier and (AUC=0.5) being a random classifier. The (kappa ) statistic is a measure of observer reliability, usually employed to test the agreement between two systems. When applied to confusion matrix evaluations, (kappa ) can be used to assess the probability that a model will produce an output (hat{y}) that coincides with the real output y. (kappa ) values typically range between (left[ 0, 1right] ), with (kappa =1) meaning perfect agreement, (kappa =0) being random agreement, and (kappa =0.8) typically used as a threshold to define a near-perfect or perfect algorithm.While in the authors’ opinion, AUC, Sensitivity and Specificity values are the most reliable evaluation metrics for studies of this type (Supp. Appendix 6), for ease of comparison with other papers or authors who choose to use other metrics, we have also included Precision, Recall and F-Measure values. Precision and Recall values play a similar role to sensitivity and specificity, with recall being equivalent to sensitivity, and precision being the calculation of the number of correct positive predictions made. Precision and Recall, however, differ from their counterparts in being more robust to imbalance in datasets. F-Measures are a combined evaluation of these two measures. For more details consult Supplementary Appendix 6.Loss metrics were reported using the Mean Squared Error (Eq. 2);$$begin{aligned} MSE = frac{1}{n} sum _{i = 1}^{n} left( y_{i} – hat{y}_{i} right) ^{2} end{aligned}$$
(2)
Loss values are interpreted considering values closest to 0 as an indicator of greater confidence when using the model to make new predictions.Final evaluation metrics were reported when using algorithms to classify only the original samples, without augmented data. Augmented data was, therefore, solely used for training and validation. Finally, so as to assess the impact data augmentation has on supervised learning algorithms, algorithms were also trained on the raw data. This was performed using 70% of the raw data for training, while the remaining 30% was used as a test set. More

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