Distribution and genetic status of brown bears in FYR Macedonia: implications for conservation

Abstract Conservation and management of large carnivores is often hampered by the lack of information of basic biological parameters. This is particularly true for brown bears (Ursus arctos) in the Former Yugoslav Republic (FYR) of Macedonia. The bear population in this country is important, as it links bear populations of the central part of the DinaricPindos population and the endangered population to the south in Greece. The aim of this study was to assess bear presence in FYR Macedonia and to provide the first evaluation of the genetic status of the species in this country. Bear presence was assessed through a questionnaire and sign surveys, while the genetic status of the species was evaluated through noninvasive genetic sampling from power poles and microsatellite analysis. The results of the study indicate the continuous and permanent presence of brown bears in FYR Macedonia from the border to Kosovo in the northwest, along the border to Albania and Greece in the south; bear presence around Mount Kožuf in the south of the country was seasonal. High levels of genetic diversity were recorded, and it appears that this bear population is currently not threatened by low genetic variability. Cross-border movements of bears between FYR Macedonia and Greece were documented, indicating the presence of an interconnected population and outlining the necessity for a coordinated international approach in the monitoring and conservation of the species in southeastern Europe

A reduced SNP panel to trace gene flow across southern European wolf populations and detect hybridization with other Canis taxa

[EN] Intra- and inter-specific gene flow are natural evolutionary processes. However, human-induced hybridization is a global conservation concern across taxa, and the development of discriminant genetic markers to differentiate among gene flow processes is essential. Wolves (Canis lupus) are affected by hybridization, particularly in southern Europe, where ongoing recolonization of historic ranges is augmenting gene flow among divergent populations. Our aim was to provide diagnostic canid markers focused on the long-divergent Iberian, Italian and Dinaric wolf populations, based on existing genomic resources. We used 158 canid samples to select a panel of highly informative single nucleotide polymorphisms (SNPs) to (i) distinguish wolves in the three regions from domestic dogs (C. l. familiaris) and golden jackals (C. aureus), and (ii) identify their first two hybrid generations. The resulting 192 SNPs correctly identified the five canid groups, all simulated first-generation (F1) hybrids (0.482≤Qi≤0.512 between their respective parental groups) and all first backcross (BC1) individuals (0.723≤Qi≤0.827 to parental groups). An assay design and test with invasive and non-invasive canid samples performed successfully for 178 SNPs. By separating natural population admixture from inter-specific hybridization, our reduced panel can help advance evolutionary research, monitoring, and timely conservation management.We thank S. Czarnomska, A. Galov, J. Harmoinen, E. Velli, D. Battilani, P. Aragno, P. Genovesi, and the Mam- mal Research Institute, Polish Academy of Sciences, for their assistance. We are also grateful to two anonymous reviewers for their constructive feedback that greatly improved our manuscript. Funding was provided to ISPRA by the Italian Ministry of Environment (MATTM; Direzione Tutela della Natura) and Regione Emilia Romagna (Assessorato Agricoltura) within a multi-year collaborative project to genotype and monitor the Italian wolf population. AVS was supported by a senior postdoctoral fellowship from Insubria University, Italy. RG was sup- ported by a research contract from the Portuguese Foundation for Science and Technolog

Vpliv različnih dejavnikov okolja in vzorčenja na uspešnost genotipizacije vzorcev iztrebkov, zbranih na terenu: primer pri rjavem medvedu

The paper investigates how different field conditions and sample characteristics influence genotyping success in field-collected brown bear scat samples. Genotyping performance of 413 samples collected in a pilot study in southern Slovenia was evaluated, andstatistical modelling was used to control confounding between pre- dictor variables and to quantify their specific effects ongenotyping success. The best predictors of genotyping success were subjectively estimated scat age, sampling month, and contents of ascat. Even when the other confounded variables were controlled for, genotyping success dropped rapidly with the age estimate, from 89% (82-94%) for 0-day scats to 33% (19-52%) for scats estimated to be 5 days old. Sampling month was also an important predictor, and samples collected during the bear hyperphagia period in late summer/autumn performed considerably better (90%,78-96%) than the samples collected in spring / early summer (66%, 57-74%). This effect was stronger for fresh than for older samples. Effects of different food types were also considerable, but less important for practical use. Since noninvasive genetic sampling already became the key method for surveying wild populations of many species, efficiency of studies is becoming increasingly important. Understanding the effect of the month of sampling allows the field season to be timed for maximum genotyping success, while subjective scat age provides a useful metric that indicates a sample’s viability for genotyping, allowing for prioritization of samples and culling of non-viable samples before resources are wasted for their analysis. This provides higher useful data yields per invested resources and may ultimately lead to better study results.V članku je predstavljen učinek različnih terenskih pogojev in lastnosti vzorca na uspešnost genotipizacije iztrebkov rjavega medveda, nabranih na terenu. Ocenil sem uspešnost genotipizacije 413 vzorcev, zbranih v pilotni študiji v južni Sloveniji ter uporabil statistično modeliranje za popravek motenja med spremenljivkami in kvantifikacijo njihovih učinkov na uspeh genotipizacije. Uspeh genotipizacije so najbolje pojasnili subjektivno ocenjena starost vzorca, mesec vzorčenja in vsebina iztrebka. Tudi ko sem kontroliral moteče spremenljivke, je uspešnost z višjo oceno starosti hitro padala, od 89 % (82 – 94 %) pri iztrebkih starih 0 dni na 33 % (19 – 52 %) za iztrebke ocenjene kot stare 5 dni. Pomembna pojasnjevalna spremenljivka je tudi mesec vzorčenja, saj so imeli iztrebki, zbrani v obdobju hiperfagije medvedov pozno poleti in jeseni znatno višjo uspešnost (90 %, 78 – 96 %) kot iztrebki zbrani pomladi in zgodaj poleti (66 %, 78 –96 %). Ta učinek je bil izrazitejši za sveže kot za starejše vzorce. Učinki različne prehrane so bili prav tako precejšnji, kar pa je za praktično uporabo manjšega pomena. Neinvazivno genetsko vzorčenje je že postalo ključna metoda za preučevanje prostoživečih populacij številnih živalskih vrst, zato postaja učinkovitost takšnih študij vse bolj pomembna. Razumevanje učinka meseca vzorčenja nam omogoča načrtovanje terenskegadela tako, da bo uspešnost genotipizacije kar najvišja. Po drugi strani nam subjektivna ocena starosti iztrebka podaja dobro merilo uporabnosti vzorca in nam omogoča prioritizacijo vzorcev ter odstranitev slabih vzorcev, preden porabimo sredstva za njihovo analizo. To omogoča višji izplen uporabnih podatkov glede na porabljena sredstva in delo ter lahko prispeva k boljšim rezultatom študije

Effects of different environmental and sampling variables on the genotyping success in field-collected scat samples: a brown bear case study

The paper investigates how different field conditions and sample characteristics influence genotyping success in field-collected brown bear scat samples. Genotyping performance of 413 samples collected in a pilot study in southern Slovenia was evaluated, andstatistical modelling was used to control confounding between pre- dictor variables and to quantify their specific effects ongenotyping success. The best predictors of genotyping success were subjectively estimated scat age, sampling month, and contents of ascat. Even when the other confounded variables were controlled for, genotyping success dropped rapidly with the age estimate, from 89% (82-94%) for 0-day scats to 33% (19-52%) for scats estimated to be 5 days old. Sampling month was also an important predictor, and samples collected during the bear hyperphagia period in late summer/autumn performed considerably better (90%,78-96%) than the samples collected in spring / early summer (66%, 57-74%). This effect was stronger for fresh than for older samples. Effects of different food types were also considerable, but less important for practical use. Since noninvasive genetic sampling already became the key method for surveying wild populations of many species, efficiency of studies is becoming increasingly important. Understanding the effect of the month of sampling allows the field season to be timed for maximum genotyping success, while subjective scat age provides a useful metric that indicates a sample’s viability for genotyping, allowing for prioritization of samples and culling of non-viable samples before resources are wasted for their analysis. This provides higher useful data yields per invested resources and may ultimately lead to better study results

Vignette for "resamplediversity" package.

User friendly, hands-on, example based tutorial to the data and functions provided in resamplediversity package. Includes a walk-through through the analyses presented in the manuscript

R package "resamplediversity" containing genotypes of brown bears from Dinaric Mountains and all functions required to use the reference population approach.

This is an R package containing all data and functions for running analyses presented in the manuscript. You will need R (www.r-project.org). You will need to install adegenet package (command: install.packages("adegenet")), and then this downloaded file (command: install.packages(file.choose(),repos=NULL)). Select resamplediversity_1.0.zip when the "File" dialog pops up. When the package is installed, invoke it from within R with library(resamplediversity). There is a user-friendly vignette: vignette("resamplediversity")