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Data for: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders

Cite this dataset

Nowak, Sabina et al. (2024). Data for: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders [Dataset]. Dryad. https://doi.org/10.5061/dryad.qfttdz0qn

Abstract

We assessed changes in the population size, density, and diet composition of wolves inhabiting the Romincka Forest (RF), an area of 480 km2 situated along the state border between Poland, Russian Federation (Kaliningrad), and Lithuania. We compared the results of our research in 2020-2021 with data from other projects conducted since 1999. We found that both packs living in RF had transboundary territories. The number of packs was stable over 21 years, the average pack size almost doubled (from 4-4.5 to 7.5-8 wolves per pack), the total wolf number increased 1.8 times, reaching 15-16 wolves, the density increased 1.5 times up to 3.1-3.3 wolves/100 km2 in winter 2020/2021. Our analyses of 165 scats revealed that beavers Castor fiber made up 45.6% of food biomass in the wolf diet in 2020, which was 3.4 times more than in 1999-2004 (n=84 scats, 13.4%). Wild ungulates constituted 44.8% of the wolf food biomass in 2020, 1.6 times less than before (71.1%). In our study, among wild ungulates, wolves primarily consumed roe deer Capreolus capreolus (22.6% of food biomass), then wild boars Sus scrofa (13.7%) and red deer Cervus elaphus (5.0%), while moose Alces alces was eaten rarely (0.4%). We also recorded domestic dogs (4.9% of food biomass) and cattle (3.1%). The food niche breadth was wider (B=2.31) than in the earlier period (B=1.84), and the Pianka index showed moderate similarity in food composition between both periods (α=0.816). In November 2022, due to the migration crisis, a 199 km impermeable fence along the state border with Kaliningrad was erected, which blocked access to 48% of the RF area that was regularly used by the resident wolf packs. This may cause wolf numbers to decrease and isolation from the central part of the Baltic wolf population to which they belong, according to our DNA analyses.

README: Dataset for paper: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders

https://doi.org/10.5061/dryad.qfttdz0qn

The dataset provides data to assess the wolf numbers and diet in the Romincka Forest in northern Poland.

Description of the data and file structure

Data are grouped into three files:

Nowak_Repository_genotyping.txt. Results of genetic fingerprinting based on 13 DNA microsatellite markers for non-invasive samples found during the fieldwork in the Romincka Forest, along with reference samples from Baltic, Central European, and Carpathian wolf subpopulations. This is a TAB-separated file that contains the following columns:

(1) ID - identification number of the sample;

(2) sex - sex of the individual based on the analysis of DBX intron 6 and DBY intron 7;

Followed by columnes with numerical data for allele sizes of 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140, FH2161, vWF, PEZ17 and CPH5.

Nowak_Repository_wolf_diet.txt. Results of the analysis of wolf scats found in the Romincka Forest in 2020. This is a TAB-separated file with the following columns:

(1) Region - name of the forest tract;

(2) Year - a year when the sample was collected;

(3) mass_(g) - dry mass (in grams) of the scat content after washing;

And in the following columns with names of food items (hare, small_rodents, beaver, moose, wild_boar, red_deer, roe_deer, Cervide_indet, cattle, dog, plant_matter, artificial) is a dry mass of the scat content which refers to identified food items.

Name of food items refers to: hare (Lepus europaeus), small_rodents (small rodents, <50g, not identified to species), beaver (Castor fiber), moose (Alces alces), wild boar (Sus scrofa), red deer (Cervus elaphus), roe deer (Capreolus capreolus), Cervide indet. (cervids not identified to species level), cattle (domestic cattle Bos taurus), dog (Canis familiaris), plant matter (plant remains), artificial (artificial matter, e.g. plastic bags, etc.).

Nowak_Repository_tracking.txt. List of tracks, videos from camera trapping, direct observations, scats, and urinations obtained during the study. This is a TAB-separated text file, divided into the following columns:

(1) forest - with the name of the forest tract;

(2) state_forest_division - with the name of the division of the Polish State Forest Service;

(3) species - with the name of the studied species (grey wolf);

(4) N - number of individuals (number of individuals is provided for tracking, direct observations, and camera trapping; in case of scats number of individuals could not be provided, thus there is NA);

(5) age_category - two general age classes were provided: ad for adults or juv for juveniles;

(6) season - it indicates a study season which begins on April 1st and finishes 31st March following year;

(7) month;

(8) type - type of data: direct observations, tracks, scats, video.

Methods

Tracking. We tracked wolves by foot or by car, using the regular and dense network of dirt roads, routes, and other linear structures, and the plowed strip of soil along the borderline, across the whole Polish portion of RF, that wolves used for traveling and scent-marking. In snow-free seasons, we found tracks on mud or sand and followed them as far as were visible, usually at distances of 100-300 m, while in winter, snow cover allowed us to follow wolf tracks up to 10 km. Species identification was based on the shape and size of tracks and evidence of animal behavior during scent-marking. Additionally, track identification was verified with genetic analysis of scat and urine samples collected during tracking. In winter, we estimated the number of wolves in the tracked group on snow by counting the number of individual trails when wolves split, which usually happened on road junctions and was associated with intense scent-marking. We measured the length of the footprint of the front paw with claws and the distance between the heels of subsequent footprints in the track to distinguish between adults and pups. To record adult wolves and pups, we used camera traps Browning Spec Ops Advantage and Browning Spec Ops Edge, USA, which were set up to register 30s-long videos with a 1s delay between recordings. Altogether, we set up camera traps in 28 locations, mostly on roads and road junctions, which were defined based on evidence of wolf presence (tracks and scent marks) found during tracking and wolf response to our howl stimulation. Cameras worked for 112 camera days.

Genotyping. To distinguish between wolf family groups in RF, we applied genetic fingerprinting. Because our main goal was to genotype and distinguish between parental pairs and their relatives, which scent-mark the pack territory mostly on roads and junctions, we were looking for non-invasive samples (fresh scats and urine from snow) during wolf tracking on forest roads and trails within the whole forest tract. We put 3-4 cm long fragments of fresh wolf scats into plastic tubes (30 ml) fixed with 96% ethanol and stored at +4°C, while urine samples collected from snow, we mixed with two volumes of 96% ethanol and sodium acetate (100 mM final concentration) and kept at −20°C. All tubes with samples were described with date, GPS coordinates, and, whenever possible, the presumed status (breeder, subadult, male, or female) based on the behaviour of the tracked wolf. DNA isolation was performed in a separate cleanroom to avoid contamination. DNA from scats was isolated either with QIAamp DNA Stool Mini Kit (Qiagen) or Exgene™ Stool DNA Mini kit (GeneAll Biotechnology), while precipitated urine samples were isolated with Exgene™ Tissue SV kit (GeneAll Biotechnology). Genetic fingerprinting was based on 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140, FH2161, vWF, PEZ17 and CPH5. Additionally, DBX intron 6 and DBY intron 7 were used as sex markers. Details of laboratory analyses are described in the paper of Szewczyk et al. (2019. Sci. Rep.). Furthermore, we assigned the local wolves to the source population, comparing microsatellite genotypes from the study area with reference genotypes from the Central and Eastern European wolf populations (Baltic, Carpathian, and Central European Lowland). From each population, we selected 30 genotypes (from the dataset from Szewczyk et al. 2019, Sci. Rep.) and processed them together with genotypes from RF using STRUCTURE 2.3.4 software. Analyses were performed for a number of clusters (K) ranging from 1 to 10, with 10 iterations per K. The Evanno method applied in STRUCTURE HARVESTER was used to infer the most likely number of clusters.

Wolf diet. The diet composition was assessed by analyzing the content of wolf scats. Scats were collected opportunistically year-round on the dense grid of forest roads and other linear structures spanning the whole Polish RF and during long-distance tracking on snow. Each scat was placed in a paper envelope and described with date and GPS coordinates. Then scats were dried for a minimum of five days at 70°C in a laboratory drier to kill parasites. Socked scats were washed through a 0.5 mm-mesh sieve and then dried. The species eaten by wolves were identified based on hair, bones, teeth, and hooves found in scats using hair or osteological keys and reference material collected during earlier studies on the wolf foraging ecology. In cases where the identification of food items in the scat based on the hair and bones was inconclusive (n=19 samples), we determined the species based on the mtDNA isolated from the hair from each sample. We extracted DNA using Exgene™ Genomic DNA micro kit (GeneAll Biotechnology) with modification of manufacturer protocol with the elongated time of first incubation (until complete dissolution, from 2.5 hours to overnight incubation), with the result of 50 ul of DNA extract. The PCR solution, reaction conditions, and further analyses were conducted as described in Szewczyk et al. (2019, Sci. Rep.) with primers L15995 (Taberlet and Bouvet 1994) and H16498 (Fumagalli et al. 1996) proposed by Pun et al. (2009) for species identification from mixed samples.

Funding

National Science Center, Award: 2020/39/B/NZ9/01829