DOI: 10.5061/dryad.1vhhmgr6n
title: DNA metabarcoding reveals wolf dietary patterns in the northern Alps and Jura mountains

description: Understanding predator-prey interactions is crucial for wildlife
 management and human-wildlife coexistence, particularly in multi-use
 landscapes such as Western Europe. As wolves (Canis lupus) recolonize
 their former habitats, knowledge of their diet is essential for
 conservation planning and public acceptance. However, data from such
 regions is so far scarce, in particular for the Alpine region and the Jura
 Mountains.  As opportunistic hunters, wolves adapt their diet to
 the local prey structure, suggesting that abundant wild ungulates are the
 main prey source. We also expect diet composition to be influenced by
 region, season, and social status. DNA metabarcoding has emerged as a
 powerful tool in ecological research, offering high-resolution insights
 into dietary composition, yet its application in carnivores remains
 limited. Using a DNA metabarcoding approach, we analyzed 698 wolf scat
 samples collected in Switzerland from 2017 to 2024. We found red deer
 (Cervus elaphus) was the dominant prey in most areas, and together with
 roe deer (Capreolus capreolus) and chamois (Rupicapra rupicapra),
 accounted for 80.8% of the retrieved sequences. We found similarities in
 prey selectivity between the Alps and the Jura Mountains, but found
 significant differences across seasons and between pack and non-pack
 wolves. This study provides the first detailed dietary analysis of wolves
 in the Northern Alps and Jura Mountains, offering critical insights for
 wildlife management. Our findings highlight the importance of wild
 ungulates in wolf diet and underscore the value of non-invasive DNA
 approaches as a reliable conservation and biomonitoring tool.
description: <strong>Scat collection and sampling
 context</strong> Scat samples were collected
 opportunistically across Switzerland as part of the national wolf
 monitoring by trained authorities (Marucco et al. 2023), and had already
 been genetically assigned to specific wolf individuals by STR
 (microsatellite) genotyping (Dufresnes et al. 2019). Our analysis is based
 on 698 samples collected between 02.01.2017 and 07.04.2024 from different
 regions and seasons. These samples belong to 250 genetically identified
 wolves (males = 155, females = 93, unknown = 2, pack = 181, non-pack =
 69). As the scat samples were not collected systematically or randomly, we
 have taken into account the context in which they were found (i.e., at a
 livestock kill, a carcass of a wild prey, or independently of a carcass).
 We compared the proportions of livestock and natural prey species between
 the three contexts with Pearson’s Chi-squared test (Pearson 1900). We also
 tested whether the inclusion of samples found next to carcasses changed
 the overall result significantly. <strong>DNA
 extraction</strong> Scat samples were stored in
 alcohol and kept at 4 °C, until they could be extracted in a purpose-built
 low-DNA content laboratory at the University of Lausanne. For the DNA
 extraction, we used the QIAamp DNA Stool kit (Qiagen, Hilden, Germany),
 which is specifically optimized for scat samples. We followed the
 manufacturer protocol from step 6, i.e., the protocol was started directly
 with the addition of InhibitEX to 200mg of wet scat sample, skipping the
 addition of buffer ASL. Extracted samples were diluted 5-fold before PCR
 amplification. <strong>Generalist primer and
 blocking primer</strong> DNA extracts were
 amplified using a generalist primer pair highly specific for vertebrates
 (Vert01,(Taberlet et al. 2018)). The barcode targets the 12S mitochondrial
 rDNA region and amplifies within a range of 56 to 132bp. To limit the
 amplification of wolf sequences, we added a blocking primer for
 <em>Canis lupus</em>
 (5’-CTATGCTTAGCCCTAAACATAGATAATTTTACAACAAAATAATT-C3-3’) to the PCR
 reaction mix (Jarman et al. 2002). The first 6 bases of the 5’-end of the
 blocking primer overlap the last 6 bases of the 3’-end of the
 Vert01_forward primer, inhibiting the amplification of <em>C.
 lupus</em> target DNA. Given that the most abundant vertebrate DNA
 on a wolf scat is wolf DNA, adding a blocking primer provides a solution
 to increase the proportion of prey sequences obtained, and reduce the
 ratio of redundant wolf sequences in the final sequencing output. We
 tested the performance of the blocking primer through quantitative-PCR
 (qPCR), by comparing the amplification success of the scat samples with
 increasing blocking primer final concentrations (2.5μM, 5μM, 10μM et and
 0μM). After examining the qPCR results, a blocking
 primer final concentration of 2μM was deemed sufficient for the first
 sequencing run. However, many of the samples from this first sequencing
 run only produced wolf sequences, so these were resequenced with a higher
 final concentration of blocking primer (4<strong>μ</strong>M)
 in the subsequent second and third sequencing runs, which mainly contained
 other additional samples. <strong>DNA
 metabarcoding and sequencing</strong> Forward and
 reverse Vert01 primer pairs were combined with an identifying tag,
 consisting of eight variable nucleotides with at least five bases
 difference between tags, to identify the sample source of each sequence.
 Spacers were also added to the 5’-end, i.e. a few random nucleotides of
 varying length (1 to 3), to increase complexity for enhanced cluster
 detection during the sequencing run. PCR reaction mix
 was as follows: AmpliTaq Gold 360 Master Mix 1x, tagged forward and
 reverse primers at 0.2μM, BSA at 0.16 mg/ml and <em>Canis
 lupus</em> Blocking primer at 2-4μM. The final volume per well was
 20μL, including 2μL of DNA template. The PCR thermal profile started with
 denaturation at 95°C for 10 minutes, followed by 40 cycles of
 amplification. Each cycle was composed of 30 seconds at 95°C, 30 seconds
 at 49°C and one minute at 72°C, before a final elongation at 72°C for
 seven minutes. Each scat was amplified in triplicate. For each PCR plate,
 we added positive controls (an equimolar mix of five different species:
 <em>Pelophylax ibericus,</em> <em>Rana
 italica,</em> <em>Timema</em> sp., <em>Cyanoliseus
 patagonus and</em> Myotis capaccini); extraction controls, to assess
 the contamination that might have occurred during the DNA extraction part;
 negative controls, i.e. water, to assess potential contamination during
 the PCR process; and blank controls, i.e. empty wells, which were used to
 assess tag-jumps and sequencing errors during the bioinformatics
 part. Amplification success was verified on a subset of
 samples through electrophoresis on a 2% agarose gel. PCR products were
 then pooled together by plate into a single tube, but amplified positive
 controls were removed beforehand. After PCR amplification and pooling,
 amplicons were purified using the MiniElute PCR purification kit (Qiagen,
 Hilden, Germany). Purified pools were quantified using a Qubit® 2.0
 Fluorometer (Life Technology Corporation, USA) and sent to the Fragment
 analyzer (Advanced Analytical Technologies, USA) to quantify the length
 and abundance of the amplicons. Library preparation for
 sequencing was done following the Tagsteady protocol (Carøe and Bohmann
 2020). After adaptors were added, libraries were validated with a fragment
 analyzer (Advanced Analytical Technologies, USA), to verify the adaptors
 had correctly attached to the amplicons. Final libraries were quantified
 through qPCR, normalised and pooled before 150 paired-end sequencing on an
 Illumina MiniSeq sequencing platform (Illumina, San Diego, CA, USA).
 Because of the sequential arrival of sequences, sequencing was done in
 three separate runs, all using a Mid output Kit (Illumina, San Diego, CA,
 USA).
 <strong>Bioinformatics</strong>
 The bioinformatic processing of the raw sequence output and first
 filtering was done using the <em>OBITools *package (Boyer 2016). In
 brief, forward and reverse sequences were aligned with a quality score of
 at least 40. Joined sequences were assigned to samples based on the unique
 tag combinations attached to the primer pairs. Assigned sequences were
 then de-replicated, retaining only unique sequences, to greatly reduce the
 size of the files and the computing time. Afterwards, sequences with less
 than 10 reads per library were discarded as well as those not fitting the
 range of the Vert01 barcode lengths. We then calculated pairwise
 dissimilarities between reads using the *obiclean</em> function.
 Lesser abundant sequences with single nucleotide dissimilarity were
 clustered into the most abundant ones. Then, we used the
 <em>Sumaclust</em> algorithm (Mercier 2013) to further refine
 the resulting clusters based on a sequence similarity of 97 %. It relies
 on the same clustering algorithm as UCLUST (Prasad, Madhusudanan, and
 Jaganathan 2015) and it is used to identify erroneous sequences produced
 during amplification and sequencing, which are derived from its main
 (centroid) sequence. The retained sequences were
 taxonomically assigned to taxa with a database for Vert01 (Supplementary
 Information), which was generated using the EMBL database (European
 Molecular Biology Laboratory). To ensure the accuracy of our automatic
 taxonomic assignment, these operational taxonomic units (OTUs) were then
 double-checked using BLASTn on the NCBI database.  Data
 cleaning process continued in R (version 4.0.2). We used the *metabaR
 *package (Zinger et al. 2021) to refine the sequencing output. The package
 uses the sample type from each plate, i.e., sample, extraction, negative,
 blank or positive control, to flag sequences based on their potential to
 be contaminants. For example, sequences which are more abundant in
 extraction and negative controls than in samples are labelled as
 contaminant sequences and removed. The package also compares replicates
 between them, and discards the ones that are too different from the
 others, using all the differences between replicates from each sample as
 reference. We further removed replicates with less than 100 reads in
 total, as we considered them as low coverage. This process was done
 separately for each library and allowed us to produce clean datasets,
 which were directly used for the statistical analysis.
 Remaining PCR replicates for each individual scat were then
 merged. The reads within each PCR replicate were pooled to calculate the
 presence/absence (PA) of each OTU, the proportional count of OTUs, i.e.,
 Frequency of occurrence (FOO) and proportion of sequences, i.e., Relative
 read abundance (RRA). Overall, after bioinformatic data cleaning, we
 obtained vertebrate sequences for all the samples, i.e., 698.
 Specifically, 665 samples produced at least one non-wolf sequence, while
 33 produced only wolf sequences, despite the addition of the blocking
 primer. <strong>Prey occurrence in dietary data
 & statistical analysis</strong> We linked
 the results from the metabarcoding to the seasonal period (spring – early
 summer = April - July, late summer - autumn = August - November, winter =
 December - March), geographical region and social status predictors. The
 attribution of scats to one of the two social statuses was carried out by
 overlapping the spatial location of the scat with polygons of pack
 territories present in a given year. Scats collected outside polygons were
 assigned to non-pack wolves. Non-pack wolves therefore include dispersers,
 resident single wolves or pairs. Pairs were regarded as packs if they
 reproduced in the same year. We ran our statistical
 analysis regarding the influence of the predictors on diet composition on
 PA, FOO and RRA of the five most frequent OTUs, later called prey or
 consumed species. However, we based our results on RRA, as it incorporates
 detected species in a more quantitative way compared to PA or FOO (P. D.
 Lamb et al. 2019). Converting read proportion to PA or FOO can have
 drawbacks, i.e., overestimating the importance of small and rare species
 because they are counted equally alongside items consumed in greater
 proportions. RRA may therefore give a more accurate image of the actual
 consumed proportions (Deagle et al. 2018), although its quantitative
 accuracy is still an ongoing debate. Each of these metrics was averaged
 across all samples to provide a mean value per prey item for the general
 visualization figures (Figure 3, Figure 4, Supporting
 Information). We first created a distance matrix with
 <em>Bray-Curtis</em> dissimilarity with the <em>metaMDS
 *function of the *vegan *package (Oksanen et al. 2025) and then visualized
 the dissimilarities with non-metric multidimensional scaling NMDS across
 the predictors (Buzan et al. 2024; Ricotta and Podani 2017; Wang et al.
 2022). We then analyzed dispersion between levels of each predictor using
 *vegdist *and *betadisper *function with *Bray-Curtis</em>
 dissimilarity. We statistically tested for differences in dispersion with
 ANOVA, whereby significance does not presuppose ideal conditions for the
 further statistical comparison of the groups. \<br> We assessed
 differences in the diet composition with a permutational multivariate
 analysis of variance (PERMANOVA) (Anderson 2001) with *adonis2 *function
 of *vegan *package, 999 permutations and weights on number of samples per
 region. Finally, we tested for the influence of specific prey  species on
 the environmental variables (region, seasonal period, social status) with
 the similarity percentage method (SIMPER). *Simper *of vegan package
 compares levels of a categorical variable with a distance matrix as
 response matrix, in our case the RRA of each detected species per sample.
 We wrote the results of the diet composition in the language of evidence
 as a more nuanced approach(Muff et al. 2022). All data
 manipulation, visualization, and statistical analysis were performed using
 R software (R Core Team 2021). Key packages included *tidyverse *for data
 manipulation and visualization (Wickham et al. 2019), *sf *(Pebesma 2018)
 and *raster *(Hijmans et al. 2025) for spatial data handling, *tmap *for
 mapping (Tennekes 2018), and *vegan *was used for ecological and
 statistical analysis. For calculation of the consumed
 biomass we multiplied the RRA of consumed prey species with the minimum
 biomass required based on the field metabolic rate (Glowacinski and Profus
 1997) and an average body weight of 32kg, typical of an Italian wolf
 (Gazzola, Avanzinelli, et al. 2007). We used RRA, as it offers a more
 quantitative approach, suitable to the relative consumption of
 biomass.
description: # DNA metabarcoding reveals wolf dietary patterns in the northern Alps and
 Jura mountains Dataset DOI:
 [10.5061/dryad.1vhhmgr6n](10.5061/dryad.1vhhmgr6n) ## Description of the
 data and file structure This Wolf_diet_2025.txt file was generated on
 2025-07-09 by Florin Kunz and Eduard Mas-Carrió  GENERAL INFORMATION 1\.
 Title of Dataset: Data from: DNA Metabarcoding reveals wolf dietary
 patterns in the northern Alps and Jura mountains 2\. Author Information
 Florin Kunz1,2, Eduard Mas‐Carrió3, Philippine Surer1,2, Fridolin
 Zimmermann1,2, Philippe Christe1, Luca Fumagalli3,4 & Nina Gerber2 1
 Department of Ecology and Evolution, University of Lausanne, Lausanne,
 Switzerland\ 2 Foundation KORA - Carnivore Ecology and Wildlife
 Management, Ittigen BE, Berne, Switzerland\ 3 Laboratory for Conservation
 Biology, Department of Ecology and Evolution, University of Lausanne,
 Lausanne, Switzerland\ 4 Swiss Human Institute of Forensic Taphonomy,
 University Centre of Legal Medicine Lausanne-Geneva, Lausanne University
 Hospital and University of Lausanne, 1000 Lausanne 25, Switzerland 3\.
 Date of data collection: Between 02.01.2017 and 07.04.2024 4\. Geographic
 location of data collection: All samples collected from Switzerland.
 Subdivided in four geographical areas. From west to east: Southern Jura
 (n=97), Valais Alps (n=213), Southern Alps (n=68) and Eastern Swiss Alps
 (n=258). 5\. Funding sources that supported the collection of the data:
 FBM funding Vaud, Switzerland; SNF funding, Switzerland; Federal Office
 for the environment FOEN, Switzerland and Cantons of Switzerland. 6\.
 Recommended citation for this dataset: Kunz et al. (2025), Data from: DNA
 Metabarcoding reveals wolf dietary patterns in the northern Alps and Jura
 mountains, Dryad, Dataset #### DATA & FILE OVERVIEW 1\. Description of
 dataset Understanding predator-prey interactions is crucial for wildlife
 management and human-wildlife coexistence, particularly in multi-use
 landscapes such as Western Europe. As wolves (Canis lupus) recolonize
 their former habitats, knowledge of their diet is essential for
 conservation planning and public acceptance. However, data of such regions
 is so far scarce, in particular for the Alpine region and the Jura
 Mountains.  As opportunistic hunters, wolves adapt their diet to the local
 prey structure, suggesting that abundant wild ungulates are the main prey
 source. We also expect diet composition to be influenced by region, season
 and social status. DNA metabarcoding has emerged as a powerful tool in
 ecological research, offering high-resolution insights into dietary
 composition, yet its application in carnivores remains limited. Using a
 DNA metabarcoding approach, we analyzed 698 wolf scat samples collected in
 Switzerland from 2017 to 2024. ### Files and variables 2\. File List: Raw
 data P1 File name: DietKora-P1_S4_L001_R1_001.fastq.gz Description:
 Forward reads for P1 sequences File name:
 DietKora-P1_S4_L001_R2_001.fastq.gz Description: Reverse reads for P1
 sequences Raw data P2 File name: DietKora-P2_S1_L001_R1_001.fastq.gz
 Description: Forward reads for P2 sequences File name:
 DietKora-P2_S1_L001_R2_001.fastq.gz Description: Reverse reads for P2
 sequences Raw data P3 File name: DietKora-P3_S2_L001_R1_001.fastq.gz
 Description: Forward reads for P3 sequences File name:
 DietKora-P3_S2_L001_R2_001.fastq.gz Description: Reverse reads for P3
 sequences Raw data P4 File name: DietKora-P4_S3_L001_R1_001.fastq.gz
 Description: Forward reads for P4 sequences File name:
 DietKora-P4_S3_L001_R2_001.fastq.gz Description: Reverse reads for P4
 sequences Raw data P5 File name: DietKora-P5_S4_L001_R1_001.fastq.gz
 Description: Forward reads for P5 sequences File name:
 DietKora-P5_S4_L001_R2_001.fastq.gz Description: Reverse reads for P5
 sequences Raw data P6 File name: DietKora-P6_S5_L001_R1_001.fastq.gz
 Description: Forward reads for P6 sequences File name:
 DietKora-P6_S5_L001_R2_001.fastq.gz Description: Reverse reads for P6
 sequences Raw data TestDietKoraBlocking File name:
 TestDietKoraBlocking_S2_L001_R1_001.fastq.gz Description: Forward reads
 for TestDietKoraBlocking sequences File name:
 TestDietKoraBlocking_S2_L001_R2_001.fastq.gz Description: Reverse reads
 for TestDietKoraBlocking sequences Raw data Pool1 File name:
 Diet-Kora-2-Pool1_S1_L001_R1_001.fastq.gz Description: Forward reads for
 Pool1 sequences File name: Diet-Kora-2-Pool1_S1_L001_R2_001.fastq.gz
 Description: Reverse reads for Pool1 sequences Raw data Pool1 File name:
 Diet-Kora-2-Pool1_S1_L001_R1_001.fastq.gz Description: Forward reads for
 Pool1 sequences File name: Diet-Kora-2-Pool1_S1_L001_R2_001.fastq.gz
 Description: Reverse reads for Pool1 sequences Raw data Pool2 File name:
 Diet-Kora-2-Pool2_S2_L001_R1_001.fastq.gz Description: Forward reads for
 Pool2 sequences File name: Diet-Kora-2-Pool2_S2_L001_R2_001.fastq.gz
 Description: Reverse reads for Pool2 sequences Raw data Pool3 File name:
 Diet-Kora-2-Pool3_S3_L001_R1_001.fastq.gz Description: Forward reads for
 Pool3 sequences File name: Diet-Kora-2-Pool3_S3_L001_R2_001.fastq.gz
 Description: Reverse reads for Pool3 sequences Raw data Pool4 File name:
 Diet-Kora-2-Pool4_S4_L001_R1_001.fastq.gz Description: Forward reads for
 Pool4 sequences File name: Diet-Kora-2-Pool4_S4_L001_R2_001.fastq.gz
 Description: Reverse reads for Pool4 sequences File name: ngs_wolf_P1.txt
 Description: file to associate prey sequences to each individual wolf scat
 sample using the forward and reverse unique tag combinations for samples
 in library P1. File name: ngs_wolf_P2.txt Description: file to associate
 prey sequences to each individual wolf scat sample using the forward and
 reverse unique tag combinations for samples in library P2. File name:
 ngs_wolf_P3.txt Description: file to associate prey sequences to each
 individual wolf scat sample using the forward and reverse unique tag
 combinations for samples in library P3. File name: ngs_wolf_P4.txt
 Description: file to associate prey sequences to each individual wolf scat
 sample using the forward and reverse unique tag combinations for samples
 in library P4. File name: ngs_wolf_P5.txt Description: file to associate
 prey sequences to each individual wolf scat sample using the forward and
 reverse unique tag combinations for samples in library P5. File name:
 ngs_wolf_P6.txt Description: file to associate prey sequences to each
 individual wolf scat sample using the forward and reverse unique tag
 combinations for samples in library P6. File name: ngs_wolf_bloking.txt
 Description: file to associate prey sequences to each individual wolf scat
 sample using the forward and reverse unique tag combinations for samples
 in library TestDietKoraBlocking. File name: ngs_kora2_Pool1.txt
 Description: file to associate prey sequences to each individual wolf scat
 sample using the forward and reverse unique tag combinations for samples
 in library Pool1. File name: ngs_kora2_Pool2.txt Description: file to
 associate prey sequences to each individual wolf scat sample using the
 forward and reverse unique tag combinations for samples in library Pool2.
 File name: ngs_kora2_Pool3.txt Description: file to associate prey
 sequences to each individual wolf scat sample using the forward and
 reverse unique tag combinations for samples in library Pool3. File name:
 ngs_kora2_Pool4.txt Description: file to associate prey sequences to each
 individual wolf scat sample using the forward and reverse unique tag
 combinations for samples in library Pool4. File name:
 wolves_WCH_to_Kora.csv Description: file that translates the ID names of
 the wolf scat samples given by the LBC (i.e. WCH names) into the Kora ID
 names of the same wolf scat. Two different labelling systems were used
 along the project, with each sample having both a WCH and a Kora name. For
 clarity, we provide this csv file to homogenize the names to kora ids and
 simplify the analyses. File name:
 RRA_per_sample_only_preyspecies_and_env_covariates.csv Description: table
 with one row per scat sample, environmental covariates and one column per
 detected prey species (after filtering out contaminations and detections
 of Canis lupus). In each column the relative read abundances (RRAs) of the
 respective detections, recalculated without wolf RRA are listed. It is the
 input table to create the graphs shown in the main document of the
 manuscript and for the statistical analysis (NMDS, Permanova, Simper).
 File name: CameraTrapping_prey_availability_events.csv Description: table
 with all camera trapping events of four ungulates across the different
 camera trapping regions. Raw data for the creation of relative detection
 frequencies as input for the prey electivity of wolves. File name:
 Permanova_NMDS_RRA_all.Rmd Description: R-Markdown script used for
 statistical analysis of detected species and environmental covariates,
 such as region, seasons and social status. It includes data
 transformations from dwgr_diet_env-covariates_onlyPrey_suppMat.csv,
 creation of NMDS plots, SIMPER analysis and PERMANOVA. DATA-SPECIFIC
 INFORMATION FOR: RRA_per_sample_only_preyspecies_and_env_covariates 1\.
 Number of variables: 29 2\. Number of cases/rows: 654 3\. Variable List:
 sample: ID of the scat sample region: regions used in analysis, where scat
 sample was collected seasons: three categories for seasons in which scat
 sample was collected, as used in the analysis socialStatus: “pack” or “no
 pack”, depending if the sample was collected in or outside of an occupied
 wolf pack territory. sampleOriginNew2: category of sampling context
 Species names: One column for each detected consumed species (after
 filtering out contaminations and Canis lupus, containing the relative read
 abundance, recalculated after removing detections of wolf. 4\. Missing
 data codes:          None 5\. Abbreviations used:          NA; not
 applicable DATA-SPECIFIC INFORMATION FOR:
 CameraTrapping_prey_availability_events 1\. Number of variables: 5 2\.
 Number of cases/rows: 15,022 3\. Variable List: session_name: name of the
 camera trap monitoring session in which the camera took the events.
 site_name: name of the site where the camera trap was placed
 animal_species: English name of the ungulate species pictured datetime:
 local date and time of the first picture of the camera trapping event
 region: the four camera trapping regions which we differentiate in the
 prey electivity analysis 4\. Missing data codes:          None 5\.
 Abbreviations used:          NA; not applicable DATA-SPECIFIC INFORMATION
 FOR: All Fastq.gz files Sequencing data is structured by libraries and
 were produced through 3 separate sequencing runs. Libraries from first
 sequencing run are P1-P6, sequencing 2 is TestDietKoraBlocking and
 sequencing 3 is Pool1-Pool4. NGS filter files have matching names to each
 library. DATA-SPECIFIC INFORMATION FOR: ngs_wolf_P1-P6.txt +
 ngs_wolf_blocking.txt + ngs_kora2_Pool1-4.txt (i.e. all files starting
 with “ngs”). 1\. Number of variables: 6 2\. Number of rows: 288 (except
 ngs_wolf_blocking.txt, which has 96). 3\. Variable list: Column1:
 Experiment name Column2: Replicate name Column3: Forward tag : Reverse tag
 Column4: Forward primer Column5: Reverse primer Column6: "F" -
 Needed to run Obitools + Position in the plate DATA-SPECIFIC INFORMATION
 FOR: wolves_WCH_to_Kora.csv Column1: kora (kora id name for the wolf scat
 sample). Column2: lbc (WCH id name for the wolf scat sample). Column3:
 id_kora (wolf individual name using kora nomenclature after genotyping,
 different from the wolf scat sample ID name from columns 1 and 2).
 Column4: id_lbc (wolf individual name using LBC nomenclature after
 genotyping, different from the wolf scat sample ID name from columns 1 and
 2). INSTRUCTIONS TO PROCESS RAW SEQUENCING OUTPUT 4 sequencing files, two
 files for each sampling year (Each year was extracted amplified and
 sequenced independently). 1\. Unzip raw files 2\. Join paired ends to have
 aligned dataset 3\. Use ngs_files for each Library respectively to assign
 sequences to each individual wolf scat sample. 4\. Use
 wolves_WCH_to_Kora.csv file to convert WCH wolf sample names to Kora
 samples names. Proceed analysis with Kora names. 5\. Use the file
 dwgr_diet_env-covariates_onlyPrey_suppMat.csv as input for data
 exploration with environmental covariates and as input for statistical
 analysis with Permanova_NMDS_RRA_all.Rmd. 6\. Calculate prey electivity by
 using CameraTrapping_prey_availability_events.csv and
 dwgr_diet_env-covariates_onlyPrey_suppMat.csv, both converted to relative
 prey presence and relative prey consumption, as input. ## Access
 information Other publicly accessible locations of the data: * NA Data was
 derived from the following sources: * NA