Research Article |
Corresponding author: Csaba Domokos ( csaba.domokos@milvus.ro ) Academic editor: Cristian-Remus Papp
© 2024 Csaba Domokos, Sebastian Collet, Carsten Nowak, Ferenc Jánoska, Bogdan Cristescu.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Domokos C, Collet S, Nowak C, Jánoska F, Cristescu B (2024) Brown bear occurrence along a proposed highway route in Romania’s Carpathian Mountains. In: Papp C-R, Seiler A, Bhardwaj M, François D, Dostál I (Eds) Connecting people, connecting landscapes. Nature Conservation 57: 41-67. https://doi.org/10.3897/natureconservation.57.107283
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Linear transportation infrastructure threatens terrestrial mammals by altering their habitats, creating barriers to movement and increasing mortality risk. Large carnivores are especially susceptible to the negative effects of roads due to their wide-ranging movements. Major road developments are planned or ongoing throughout the range of the Romanian brown bear (Ursus arctos) population, which is numerically the largest in the European Union. The planned A8 (Tîrgu Mureș–Iași–Ungheni) highway crosses the Romanian Eastern Carpathians on their entire width, posing a risk to the Romanian and broader Carpathian transboundary bear population. In the summers of 2014, 2017 and 2020, we surveyed an 80 km-long section of the planned highway using 68 hair traps with lure mounted in pairs along the route. We aimed to assess bear occurrence, genetic connectivity across the proposed highway and to estimate the minimum number and sex ratio of bears present in the area. With an effort of 3,519 hair trapping days (17 days / trap / session), we identified 24 individuals from the 45 collected hair samples, with a higher prevalence of female bears (male:female sex ratio of 1:1.3). We documented functional connectivity across the planned highway through parent-offspring (4 cases), full-sib (2 cases) and half-sib (24 cases) genetic relationships amongst sampled individuals. Terrain ruggedness and longitude were the most important predictors of bear occurrence from our analysis of detections at hair trap locations. Bears consistently occurred in the vicinity of the planned highway when in rugged terrain of the western section of the study area and were often detected close to human settlements (< 1 km). Even at this stage, without the A8 highway constructed, connectivity is likely already limited by the existing extensive network of settlements and restricted to a few important linkage areas still free of developments. Additional threats to bears and other wildlife in the area include poaching and large numbers of free-ranging dogs. We provide recommendations to mitigate these threats.
Carpații Orientali, detection survey, habitat fragmentation, hair trapping, non-invasive survey, road ecology, Ursus arctos
The loss, degradation and fragmentation of habitats represent major threats to terrestrial mammal diversity around the globe (
The relationship between roads and brown bears (Ursus arctos) is complex, because road effects can be area- and/or sex-specific, may vary by time of day and season and can be influenced by traffic volume (
Some bears decrease their use of areas near roads or avoid these altogether, suggesting that roads can cause effective habitat loss at varying scales (
Traffic volumes are negatively correlated with road permeability for bears (
North American studies advocate for limiting road access (
Romania is an important stronghold for brown bears in Europe, hosting approximately 6,000 individuals (
The planned A8 (Tîrgu Mureș–Iași–Ungheni) highway, linking the city of Tîrgu Mureș in the west to the national border between Romania and the Republic of Moldova in the east, has been identified as a major threat to brown bear habitat connectivity (
The planned A8 highway is designed to traverse the Romanian Eastern Carpathians and their foothills on a west–east axis. This study covers an 80 km-long segment of the central section (Section 2) of the highway, between the villages of Ditrău in the west and Leghin in the east, representing 37.4% of the total length (Fig.
Route of the planned A8 highway and location of the study area in Romania’s Eastern Carpathians (A) and detailed map of the study area, with planned brown bear hair trap locations (n = 74) situated in pairs along an 80 km-long section of the planned A8 highway (B).
Even without the planned A8 highway, the study area is partially fragmented by human settlements which are contiguous in some areas. Unlike for the western section of this highway, no purpose-built wildlife crossing structures have been planned for this 80 km-long section by the Environmental Permit (
We designed a sampling scheme to quantify the occurrence, functional connectivity across the planned highway, minimum population size and sex ratio of brown bears in the vicinity of the proposed highway. Using a Geographic Information System (GIS), we divided a shapefile of the highway route into 1 km-long segments. We generated points in pairs at the end of each 1-km highway segment, with one point on either side of the highway and all points at a set 500 m from the route. Pairs of points of which at least one fell inside a GIS layer of settlements were discarded, resulting in a total of 74 points arranged in 37 pairs (Fig.
We entered the coordinates of each of the 74 points described above in a hand-held GPS unit and accessed the points by driving and hiking to the sites. We deployed a hair trap station at each point during three survey sessions (2014, 2017, 2020). Surveys occurred in summer (June–July in 2014, July–August in 2017 and 2020). Hair traps were active for 17 days during each survey, after which the stations were retrieved from the field. Hair-trap stations were deployed within a 50 m buffer of each predetermined point, selecting areas with trees whenever possible. Stations consisted of a single strand of 4-prong barbed wire, mounted at a height of 50 cm that delimited a small area (6–16 m2). The barbed wire was secured with U nails to at least three trees, if present or to 1.5 m-long, sharpened poles that we carried to the site and hammered into the ground.
At the centre of the area enclosed by the barbed wire, we constructed a small mound from locally available woody debris and rocks, onto which we poured 0.5 l of scent lure. The scented mound was unreachable for bears unless they crossed the barbed wire. We prepared the lure prior to each survey session, using 40 kg of Atlantic mackerel (Scomber scombrus) that we left rotting for 12 months in sealed plastic barrels. We then added 30 l of fresh, salted cattle blood and left the mixture to rot for an additional 3 months, before bottling it.
We checked the barbed wire at each station after 17 days for hair samples. Obtained hairs were visually examined to classify them as brown bear vs. other species. Examiners were experienced wildlife biologists accompanied by gamekeepers who had handled hairs of bears and other mammals, as well as physically handled bears for > 10 years. Hairs from other, clearly identifiable species were discarded after recording the non-target species. Hair samples from bears or of unclear origin were collected and labelled. Hairs located on the same group of four barbs were always collected as a single sample. Hairs located on neighbouring or almost neighbouring groups of barbs (i.e. 10–20 cm apart) were also collected as part of the same sample, unless they were obviously different in colour, length or texture. Hairs located further than 20 cm apart were always collected as separate samples, even if they seemed similar. We used medical tweezers to transfer samples from the barbed wire to envelopes, cleansing them after each use through burning with a lighter to avoid cross-sample contamination. Samples were stored individually in filter paper envelopes placed inside individual ziplock plastic bags that contained a bag of silica gel.
All pre-PCR molecular steps were conducted in a laboratory dedicated to the processing of environmental samples following standard routines for avoidance of contamination (
For confirmed bear samples we amplified 13 unlinked autosomal microsatellite markers: Msut2 (
The software ML-relate (
Error rates for microsatellite genotyping were assessed via three basic statistics: Allelic dropout (AD) was calculated for heterozygote consensus genotypes as the proportion of one of the two consensus alleles missing across replicates (including wrong alleles); false allele rate (FA) was calculated for homozygote consensus genotypes as the proportion of additional alleles present across replicates; amplification success was calculated as the proportion of failed loci across all replicates. Error rates were calculated within samples across replicates and summarised over all samples.
We considered a suite of covariates that could a priori be hypothesised to influence bear occurrence (Table
Covariates for modelling brown bear occurrence along a planned highway in Romania’s Eastern Carpathians.
Covariate | Code | Units | Data range | Linearity | Covariate justification (potential influence to be tested in the models) | References |
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Habitat | ||||||
Abiotic | ||||||
Terrain Ruggedness Index | TRI | Unitless (index) | 0.43–13.86 | Non-linear | Rugged terrain offers habitat security by limiting human access and providing better cover |
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Biotic | ||||||
Habitat | Habitat | Categorical | Pasture, mixed forest, conifer forest, deciduous forest | Non-linear | Pastures, deciduous and mixed forests provide feeding opportunities for bears. All three forest types provide cover for the species. |
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Human Influence | ||||||
Longitude | Longitude | Degree | 25.55–26.22 | Linear | Poaching was suspected to occur on a west to east gradient, with easternmost areas having higher poaching pressure | none (area specific) |
Distance to nearest settlement | DistSett | Metre | 0–4,484.54 | Non-linear | The proximity of settlements can filter the bear population for individuals more tolerant towards people and/or actively avoiding larger/more aggressive conspecifics |
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We used ordinal logistic regression to investigate brown bear occurrence as a function of covariates hypothesised to influence bear habitat use. In ordinal logistic regression, the dependent variable is structured to have multiple discrete values in an assigned order. Although our data involved repeated surveys at the same set of stations, an occupancy modelling approach was not appropriate because the assumption of population closure for the survey duration was not fulfilled. Occurrence in our analytical framework took three values corresponding to situations where a bear was detected: no detection in the three survey sessions (1), detection in one of the three sessions (2) and detection in two or three of the three sessions (3). This method follows the approach of
We generated a set of 15 candidate models that were either univariate or included combinations of covariates. A correlation matrix including all covariates showed that the variables were not highly correlated (r <|0.6|) and could, therefore, be included in the same model structure. The models were included in three categories corresponding to three hypotheses: Habitat (n = 1), Human (n = 7) and combined Habitat and Human influences (n = 7). We ranked models using delta AICc and calculated evidence ratios for supported models (delta AICc < 2 and delta AICc < delta AICc of the null model).
We report the results as odds ratios, which we obtained through using the exponential of the parameter estimate(s) of the predictor(s) in the top model(s). For a one-unit increase in each predictor, odds ratios > 1 indicate an increase and odds ratios < 1 a decrease in the odds of bear occurrence.
We used QGIS v.3.16.15 for GIS procedures and R Studio v.2021.09.0 Build 351 for all statistical analyses.
Overall, 68 hair-trap stations were active for 17 consecutive days across all sessions. Sampling effort was 1,156 trapping days in 2014 and 2017, respectively 1,207 trapping days in 2020. Three additional hair-trap stations active only in 2020 were excluded from modelling bear occurrence, but included in all other analyses. Three other locations that had been planned for sampling were excluded due to the presence of shepherd camps or livestock water troughs in all survey years.
During the three survey sessions, we collected a total of 89 hair samples. Mitochondrial control region sequencing was successful for 86 of the 89 analysed samples (96.6%). Half of the samples (n = 45, 50.6%) could be assigned to brown bears: 12 in 2014, 27 in 2017 and six in 2020. The samples originated from 12 hair traps in 2014 (17.7% of all mounted traps), 11 in 2017 (16.2% of all mounted traps) and five in 2020 (7% of all mounted traps). Bear hair was almost exclusively collected from hair trap locations west of Lake Bicaz (43 of the 45 samples). The two exceptions were samples collected in 2017 from the same hair trap that was the westernmost location sampled east of Lake Bicaz. During fieldwork east of the Lake, we only observed bear sign (tracks of a single animal) once in 2017. In contrast, we often encountered bear sign (tracks, scats, excavated anthills, peeled tree bark) west of Lake Bicaz.
Twenty of the 68 traps active across all three sessions registered bear hair (29.4%). Additionally, one trap active only in 2020 also captured bear hair. Fourteen traps were successful during a single session (including one active only in 2020), whereas seven traps yielded bear hair samples in two sessions each (Fig.
Brown bear hair trapping success along an 80 km-long section of the planned A8 highway during three survey sessions in the summers of 2014, 2017 and 2020 (A–C).
We identified a total of three haplotypes, namely BG1 and Ro2 (
We documented other wildlife and domestic species depositing hair at the hair trapping stations. Domestic dogs as the most frequently detected species overall (more than bears) were detected in all three survey years (2014, 2017, 2020: 15, 28, 22 locations, respectively) and so were wild boar (Sus scrofa; 2, 1, 4 locations) and red deer (Cervus elaphus; 1, 1, 3 locations). Roe deer (Capreolus capreolus) were detected in two survey years (2014, 2017: 1, 2 locations), just as cattle (2014, 2017: 2, 8 locations) and horses (2014, 2020: 1, 1 locations). Red fox (Vulpes vulpes; 2020: 2 locations) and sheep (2014: 1 location) were each detected in one survey year. Additionally, unidentified Canis sp. (either dogs or wolves [Canis lupus], as mitochondrial haplotypes w4, w11 and w19 following
Only one model that had an intermediate number of parameters received support (model 7 with two parameters; Table
Ranking of brown bear occurrence models across a planned highway route in Romania’s Eastern Carpathians. The supported model is illustrated in bold font.
Model_code | Model_set | Model_structure | K | ResDev | AIC | AICc | dAICc | ER |
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7 | Human Influence | TRI + Longitude | 4 | 94.0 | 102.0 | 102.7 | 0.0 | 1.0 |
8 | Human Influence | DistSett + TRI + Longitude | 5 | 93.9 | 103.9 | 104.8 | 2.2 | 2.9 |
3 | Human Influence | TRI | 3 | 100.4 | 106.4 | 106.8 | 4.1 | 8.0 |
6 | Human Influence | DistSett + Longitude | 4 | 98.8 | 106.8 | 107.4 | 4.7 | 10.6 |
5 | Human Influence | DistSett + TRI | 4 | 99.3 | 107.3 | 108.0 | 5.3 | 14.1 |
14 | Human Influence & Habitat | Habitat + TRI + Longitude | 7 | 92.5 | 106.5 | 108.4 | 5.7 | 17.3 |
4 | Human Influence | Longitude | 3 | 102.0 | 108.0 | 108.4 | 5.7 | 17.4 |
15 | Human Influence & Habitat | Habitat + DistSett + TRI + Longitude | 8 | 92.2 | 108.2 | 110.6 | 8.0 | 54.0 |
10 | Human Influence & Habitat | Habitat + TRI | 6 | 98.4 | 110.4 | 111.7 | 9.1 | 92.9 |
0 | Null | Null | 2 | 108.3 | 112.3 | 112.5 | 9.8 | 134.2 |
13 | Human Influence & Habitat | Habitat + DistSett + Longitude | 7 | 97.2 | 111.2 | 113.0 | 10.4 | 178.3 |
12 | Human Influence & Habitat | Habitat + DistSett + TRI | 7 | 97.6 | 111.6 | 113.5 | 10.8 | 219.1 |
11 | Human Influence & Habitat | Habitat + Longitude | 6 | 100.5 | 112.5 | 113.9 | 11.2 | 274.2 |
2 | Human Influence | DistSett | 3 | 108.3 | 114.3 | 114.7 | 12.0 | 401.0 |
1 | Habitat | Habitat | 5 | 107.1 | 117.1 | 118.1 | 15.4 | 2198.3 |
9 | Human Influence & Habitat | Habitat + DistSett | 6 | 107.1 | 119.1 | 120.4 | 17.8 | 7194.3 |
Genotyping of brown bear hair samples was successful for 34 (75.6%) samples. Calculation of error rates showed a mean allelic dropout rate of 0.17 (SD = 0.28), a mean false allele rate of 0.03 (SD = 0.14) and a mean amplification success of 0.78 (SD = 0.27). Out of the 34 samples, nine were excluded from further analysis, as they originated from four individuals that had already been identified on the same hair traps, during the same survey year (2017). We identified a total of 24 individual bears across the three survey sessions. Sex was successfully determined for 21 (87.5%) of the 24 individuals: nine were males and 12 females, resulting in a sex ratio (male:female) of 1:1.3. The largest number of individuals was identified in the year 2017 (nfemale = 7, nmale = 3, nunknown = 2), followed by year 2014 (nfemale = 4, nmale = 5) and 2020 (nfemale = 2, nmale = 1, nunknown = 1).
Only one female bear was recaptured in our study, both within and across survey years, amongst different hair trap locations. The animal was detected in 2014 and 2017 on neighbouring hair traps located on the same side (south) of the planned highway route.
We found four cases of parent-offspring (PO) relationships and two full-sibs (FS) in the dataset (Table
Successfully genotyped bears (n = 24), relatedness and movements in relation to the planned highway route implied by detected relatedness. Hair traps A were located to the north, hair traps B to the south of the planned highway route, with numbers increasing from west to east.
Individual | Sex | Detected on hair trap (survey year) | Haplotype | Clade/lineage | Related with (relatedness) | Movement implied by relatedness in relation to highway route |
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RO_UA001 | ♂ | A06 (2014) | BG1 | west | RO_UA004 (HS); RO_UA011 (HS); RO_UA022 (HS) | along; across; along |
RO_UA002 | ♀ | A13 (2014) | Ro2 | east | RO_UA014 (PO); RO_UA015 (PO); RO_UA018 (HS); RO_UA021 (HS) | along; along; along; across |
RO_UA003 | ♂ | A18 (2014) | Ro2 | east | RO_UA007 (FS); RO_UA009 (HS); RO_UA022 (HS) | across; across; along |
RO_UA004 | ♀ | A25 (2014) | BG1 | west | RO_UA001 (HS); RO_UA011 (HS) | along; across |
RO_UA005 | ♀ | B03 (2014) | BG1 | west | RO_UA006 (HS); RO_UA013 (HS); RO_UA025 (HS) | along; across; along |
RO_UA006 | ♀ | B14 (2014); B13 (2017) | BG1 | west | RO_UA005 (HS); RO_UA019 (HS) | along; along |
RO_UA007 | ♂ | B18 (2014) | Ro2 | east | RO_UA003 (FS); RO_UA020 (FS) | across; across |
RO_UA008 | ♂ | B20 (2014) | BG1 | west | RO_UA016 (HS); RO_UA019 (HS) | across; along |
RO_UA009 | ♂ | B24 (2014) | BG1 | west | RO_UA003 (HS); RO_UA019 (HS) | across; along |
RO_UA011 | ♂ | B03 (2017) | Ro2 | east | RO_UA001 (HS); RO_UA004 (HS); | across; across |
RO_UA012 | ♀ | A13 (2017) | Ro2 | east | RO_UA013 (PO); RO_UA022 (HS) | none (same hair trap); along |
RO_UA013 | ? | A13 (2017) | Ro2 | east | RO_UA005 (HS); RO_UA012 (PO); RO_UA015 (HS) | across; none (same hair trap); along |
RO_UA014 | ♀ | A14 (2017) | Ro2 | east | RO_UA002 (PO); RO_UA016 (HS); RO_UA018 (HS) | along; along; along |
RO_UA015 | ♀ | A14 (2017) | Ro2 | east | RO_UA002 (PO); RO_UA013 (HS); RO_UA018 (HS) | along; along; along |
RO_UA016 | ♀ | A25 (2017) | H7 | west | RO_UA008 (HS); RO_UA014 (HS); RO_UA017 (PO) | across; along; none (same hair trap) |
RO_UA017 | ♀ | A25 (2017) | H7 | west | RO_UA016 (PO); RO_UA018 (HS); RO_UA019 (HS) | none (same hair trap); along; across |
RO_UA018 | ♂ | A16 (2017) | Ro2 | east | RO_UA002 (HS); RO_UA014 (HS); RO_UA015 (HS); RO_UA017 (HS) | along; along; along; along |
RO_UA019 | ♂ | B17 (2017) | BG1 | west | RO_UA006 (HS); RO_UA008 (HS); RO_UA009 (HS); RO_UA017 (HS) | along; along; along; across |
RO_UA020 | ♀ | A19 (2017) | Ro2 | east | RO_UA007 (FS); RO_UA021 (HS); RO_UA024 (HS) | across; across; across |
RO_UA021 | ? | B19 (2017) | Ro2 | east | RO_UA002 (HS); RO_UA020 (HS) | across; across |
RO_UA022 | ♀ | A04 (2020) | Ro2 | east | RO_UA001 (HS); RO_UA003 (HS); RO_UA012 (HS) | along; along; along |
RO_UA023 | ? | A06 (2020) | BG1 | west | – | – |
RO_UA024 | ♂ | B06 (2020) | BG1 | west | RO_UA020 (HS) | across |
RO_UA025 | ♀ | B17 (2020) | Ro2 | east | RO_UA005 (HS) | along |
Using non-invasive repeat survey methodology, we assessed the distribution and documented the minimum local population size of brown bears along a planned highway route in Romania, as part of an effort to collect information before highway construction. We detected bears at 21 sampling stations along the planned highway route, but with a more restricted distribution than expected and a concentration of presence in the western part of the study area. Our study did not succeed in producing direct evidence of bears crossing the planned highway route (e.g. same individual detected by hair traps on both sides of the future highway). Nevertheless, we provide genetic evidence that the population uses both sides of the planned development, including the detection of related animals on both sides of the highway route.
We found a positive association between bear occurrence and terrain ruggedness. When confronted with human disturbance, such as in human-dominated landscapes of Europe, bears may select rugged terrain (
Most hair traps that detected bears were close (< 1 km) to human settlements (mean ± SD distance of all hair traps to human settlements was 1.35 ± 1.33 km). Mountainous villages in Romania commonly comprise solitary houses or small groups of homesteads, which are often not recorded as part of settlements in Corine Land Cover (i.e. discontinuous urban fabric). Thus, some hair traps that registered bears were even closer to buildings than revealed by the land-cover layer. Our results are in accordance with previous studies reporting that in Romania bears regularly use human-dominated landscapes and in general habitats in the proximity of human settlements (
Longitude of the hair trap location was a good predictor of bear presence, with westernmost hair traps more successful. One possible explanation for this pattern is habitat fragmentation of the region in the west–east direction by Lake Bicaz due to its large size, as well as numerous contiguous settlements around it. However, poaching with firearms is also an issue of concern around the Lake and in the region east of it (Anonymous, Harghita County Police Inspectorate, Miercurea Ciuc, Romania, personal communication 2015, 2016). As these are some of the best bear habitats in Romania (
The habitat types in which the hair traps were mounted did not influence the success rate of collecting bear hair samples. In Romania, during summer when our surveys were conducted, female bears typically select mixed forests, whereas males select all three forest types: deciduous, mixed and conifer (
Although we identified 24 distinct bear individuals in the three surveys, we expected to detect a larger number of individuals. A possible reason is the close proximity of sampling stations to human settlements, which can act as a filter for the bear population, selecting for subordinate individuals or demographics of age or reproductive classes that are more tolerant towards human presence and/or actively avoid larger/more aggressive conspecifics.
Widespread, cryptic poaching could have contributed to relatively low bear detection rates in our study. Due to low densities and slow reproductive rates, large carnivores are especially vulnerable to poaching and previous studies have documented substantial effects of illegal killings on large carnivore demography (
Even if we were unable to determine the sex of three of the 24 individual bears we identified, the data are indicative of a large population segment of females (1:1.3 [male:female]). This is comparable to the 1:1.6 sex ratio estimated for the Romanian Southern Carpathians (
We showed that domestic dogs are present throughout the region, at least during the summer. The frequent detection of dogs at the hair traps is likely due to the presence of large numbers of guardian dogs accompanying livestock, stray dogs or dogs associated with human settlements. A similar finding was recorded in a study of wolf diet which revealed the importance of dogs in the diet of wolves in the south-eastern Carpathian Mountains (
We also genetically confirmed the presence of unidentified Canis sp. (either dogs or wolves) in several locations. There is a possibility that at least some of these samples originated from wolves, in particular, the ones identified as haplotype w4, which occasionally occurs in dogs, but is commonly found in Romanian wolves (
Connectivity on the north-south axis is relatively limited in the study area due to existing human settlements. Completion of the A8 highway could potentially further impede bear movements in important bear habitats centrally located in the Romanian Eastern Carpathians and their foothills. This area provides a vital link to other national-level populations located further North, including Ukraine, Slovakia and Poland (
We express our gratitude to all who assisted us during fieldwork, namely Ágoston Pál, Károly Illyés, Károly Pál (professional gamekeepers), Ulysse Faure, Attila Marton and Sándor Olivér Murányi. We thank Berardino Cocchiararo and the lab team at the Senckenberg Centre for Wildlife Genetics for genetic analysis and Gregor Rolshausen (Senckenberg) for assistance in error rate calculation. Gábor Bóné assisted in the preparation of datasets for modelling, while Tibor Sós helped in the finalisation of maps. We thank Cristian-Remus Papp and two anonymous reviewers for constructive feedback that greatly enhanced the quality of the manuscript. The study occurred in the framework of Milvus Group’s Brown bear conservation and research programme in a model area in Romania.
The authors have declared that no competing interests exist.
No ethical statement was reported.
This long-term initiative has received financial support from Aktionsgemeinschaft Artenschutz (Germany), Bears in Mind (the Netherlands), Bernd Thies Foundation (Switzerland), Columbus Zoo and Aquarium (United States), EuroNatur (Germany), Fresno Chaffe Zoo (United States), Stichting Wildlife (the Netherlands) and Stiftung für Bären (Germany).
B. Cristescu and C. Domokos conceived and designed the study. C. Domokos collected the data. S. Collet and C. Nowak analyzed the genetic data. B. Cristescu and C. Domokos analyzed the occurrence data. C. Domokos led the writing of the manuscript. All authors contributed critically to drafts and approved the final draft.
Csaba Domokos https://orcid.org/0000-0002-9973-7779
Sebastian Collet https://orcid.org/0000-0002-7309-5448
Carsten Nowak https://orcid.org/0000-0002-3139-1951
Ferenc Jánoska https://orcid.org/0000-0002-6200-0829
Bogdan Cristescu https://orcid.org/0000-0003-2964-5040
All of the data that support the findings of this study are available in the main text.