Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) across Central and Eastern Europe emphasising forest ecosystems

Biodiversity Data Journal 13: e175486 doi: 10.3897/BDJ.13.e175486 Data Paper Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) across Central and Eastern Europe emphasising forest ecosystems Tomasz Pawłowicz ‡ Bialystok University of Technology, Bialystok, Poland Corresponding author: Tomasz Pawłowicz ([email protected]) Academic editor: Leonardo Fernandez Received: 20 Oct 2025 | Accepted: 09 Nov 2025 | Published: 25 Nov 2025 Citation: Pawłowicz T (2025) Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) across Central and Eastern Europe emphasising forest ecosystems. Biodiversity Data Journal 13: e175486. https://doi.org/10.3897/BDJ.13.e175486 Abstract Background A continental-scale, georeferenced checklist of slime moulds (Eumycetozoa) for Central and Eastern Europe, supplemented with standardised environmental covariates and with a particular emphasis on forest ecosystems, has not previously been available. The absence of a harmonised corpus has constrained statistically supported tests of habitatand substrate-related patterns and limited objective gap-mapping, particularly within forest ecosystems, where microclimatic buffering, dead-wood continuity and stand history are expected to be decisive; it has also hindered rigorous evaluation of slime moulds’ role as bioindicators of forest habitat types, substrate associations and gradients in anthropogenic pressure (naturalness). New information Literature discovery spanned multidisciplinary and domain-specific platforms; inclusion required a determinable taxon, a locality at least to country level and a year. Records ‡ © Pawłowicz T. 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. were de-duplicated conservatively; names were harmonised to a single authority (Eumycetozoa.com) with GBIF Species backbone as a fallback and higher taxonomy was filled consistently. The resource comprises presence-only occurrences, a taxonomically standardised checklist and a reference set; the curated bibliography comprises 528 bibliographic entries. Coverage spans Austria, Belarus, Czechia, Estonia, Germany, Hungary, Latvia, Liechtenstein, Lithuania, Moldova, Poland, Russia (European part), Slovakia, Slovenia, Switzerland and Ukraine. Event dates range from 1857 to 2025-08-01 and support ranges and mixed precision. Environmental content includes elevation, consolidated forest class, substrate category, habitat pressure, microhabitat, pH, air temperature, annual precipitation and stand age; controlled vocabularies comprise eight consolidated forest classes, ten substrate categories and seven habitatpressure classes. The dataset is released under CC-BY-4.0 (Creative Commons Attribution 4.0 International), employs reproducible DwC mapping and stable identifier versioning and is suited to ecological and biogeographic analyses, including forestfocused modelling and gap analyses. Keywords slime moulds, forest ecosystems, Central Europe, Eastern Europe, environmental covariates, substrate, habitat pressure, georeferenced occurrences Introduction Slime moulds (Eumycetozoa) are amoebozoan protists that produce fruiting bodies and occupy diverse terrestrial microhabitats, especially in temperate forests (Adl et al. 2012, Leontyev et al. 2019, Stephenson 2023). They exploit moisture‑buffered microhabitats, such as coarse woody debris, bryophyte mats and leaf litter, where they ingest bacteria and other microorganisms and contribute to nutrient turnover (Stephenson 2011, Fukasawa et al. 2015, Leontyev and Schnittler 2017). Phenotypic plasticity — including transitions between flagellated swarm cells and myxamoebae and the formation of dormant microcysts — facilitates persistence under hydric variability in forest environments (Liu et al. 2013, Ing and Stephenson 2022). The dataset and checklist adopt the GBIF‑aligned framework — phylum Eumycetozoa (Mycetozoa) (dataset fields aligned to GBIF; narrative usage: Eumycetozoa) — with classes Dictyosteliomycetes (Dictyostelia), Myxomycetes (Myxogastrea) and Protosteliomycetes (Protosporangiida) and name usage standardised accordingly (Lado 2019, Leontyev et al. 2019). To date, a continent-scale synthesis for Central and Eastern Europe has not been available, precluding robust tests of habitatand substrate-related patterns and obscuring spatial gaps that warrant targeted sampling. Forests are a primary venue for slime-mould diversity, where moisture buffering, dead-wood continuity and canopy structure jointly shape community composition; these traits also underpin their role as bioindicators of habitat type, substrate associations and gradients in anthropogenic pressure (naturalness) (Stephenson 2011, Liu et al. 2015, Fukasawa et al. 2015, Leontyev and 2Pawłowicz T Schnittler 2017, de Basanta and Lado 2025). A dataset that unifies taxonomy, georeferencing practice and environmental descriptors across countries furnishes the comparability needed for cross‑regional analyses and for forecasting responses to climate and land‑use change. Standardised covariates — elevation, precipitation, pH, stand age, substrate category, consolidated forest class and habitat pressure — enable reproducible tests of hypothesised drivers, while maintaining strict traceability to the original sources. By documenting georeferencing policies (± 10 km assignability, WGS84, explicit uncertainty–precision coupling) and providing full bibliographic provenance, the resource enables reproducible, region‑wide analyses and evidence‑based identification of under‑sampled habitats and areas for targeted surveys. General description Purpose:To provide a regional, forest‑focused evidence base for slime moulds (Eumycetozoa) in Central and Eastern Europe that supports cross‑study comparability, presence‑only modelling and statistically robust tests of environmental drivers (elevation, precipitation, pH, stand age) across consolidated forest‑habitat, substrate and management‑pressure classes. Occurrences are Darwin Core–mapped and paired with standardised, forest‑relevant covariates; taxonomy is harmonised to a GBIF‑aligned usage, while preserving verbatim identifications to maintain historical signal and transparent reconciliation. Coverage spans the three classes Dictyosteliomycetes (Dictyostelia), Myxomycetes (Myxogastrea) and Protosteliomycetes (Protosporangiida). Sampling methods Description:Temporal coverage uses eventDate with mixed precision (single dates and bounded intervals), spanning 1857–2025; where only a publication year existed, it served as a proxy. Spatial coverage is a 16‑country Central‑ and Eastern‑European domain restricted to the European part of Russia (≤ 60° E); coordinates, when present, are in WGS84. Taxonomic scope is Eumycetozoa (Mycetozoa), covering Dictyosteliomycetes, Myxomycetes and Protosteliomycetes. Data mobilisation occurred in 2022–2025 with subsequent GBIF/Darwin Core harmonisation. Sampling description:Sources were identified via multidisciplinary and disciplinary bibliographic platforms and publisher portals; non‑Roman scripts were transliterated using ISO 9:1995 (E) for Cyrillic, preferring established English exonyms where available. Culture‑independent detections (eDNA/metabarcoding) were included; all such occurrences correspond to the locality of the original sampled material. For each eligible source, verbatim text was captured for taxonomy, locality (≥country), year, coordinates/elevation (if given) and habitat descriptors. Inclusion required a determinable taxon, a country‑level (or finer) locality and a year (sampling or publication as proxy). Provenance was encoded via basisOfRecord (HumanObservation or MaterialCitation). Potential duplicates were screened at publication/locality granularity, retaining distinct Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 3 sites or dates as separate occurrences. Text was normalised (UTF‑8) prior to Darwin Core mapping. Quality control:Taxonomy followed a two‑track approach: verbatimIdentification preserves printed usage; scientificName/scientificNameAuthorship provide the accepted form (nameAccordingTo fixed), with higher ranks filled consistently and taxonRank restricted to genus/species. Geospatial validation enforced valid latitude/longitude ranges and signs, WGS84 datum, positive non‑zero coordinateUncertaintyInMetres and discrete coordinatePrecision values; georeferenceVerificationStatus, georeferencedBy, with decisions noted in georeferenceRemarks. Temporal normalisation used ISO‑like eventDate formats (year, year‑month, full dates, intervals) cross‑checked against year/ month/day (ISO 8601), leaving unknown components blank. CSV hygiene ensured fixed column counts, correct quoting/escaping, LF endings and UTF‑8 encoding; numeric fields use dots and plain decimals. Locality handling preserved verbatimLocality and harmonised names to English exonyms or consistent transliteration; stateProvince was retained as supplied. Country names and countryCode were validated as paired fields. Elevation, when present, was duplicated to minimumElevationInMetres and maximumElevationInMetres. Step description:A schematic overview of the occurrence‑data curation workflow from source intake to versioned release is shown in Fig. 6. A detailed, step‑by‑step operational description follows below (steps 1–23) and corresponds directly to the elements depicted in the schematic. Figure 1. Spatial distribution of Eumycetozoa records aggregated to 25‑km hexagons; colour classes show counts per hexagon (0, 1–5, 6–20, 21–50, 51–100, 100–200, ≥200). Coverage spans 16 countries in Central and Eastern Europe, with records limited to the European part of the Russian Federation (≤ 60° E).  4Pawłowicz T Figure 2. Family composition of Eumycetozoa records is shown as percentages; with counts summarised in the underlying dataset.  Figure 3. Trait coverage across consolidated forest classes, substrate categories and habitat‑pressure classes; bar lengths indicate the proportion of records mapped to each controlled label.  Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 5 Figure 4. Environmental coverage for pH, air temperature (°C) and annual precipitation (mm yr⁻¹) shown as histograms (bins as in dataset workflow); medians are indicated to aid interpretation.  Figure 5. (A) Cumulative Eumycetozoa records by year (1857–2025), reflecting the historical growth of the literature-derived database; (B) Annual additions aggregated into five-year bins (dark-blue bars; values expressed as records per year), with a connecting line joining bin mid-points.  6Pawłowicz T Figure 6. Schematic overview of the occurrence data curation workflow from source intake to versioned release.  Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 7 • Step 1. Establish the project namespace and templates; register controlled vocabularies (8 consolidated forest classes, 10 substrate categories, 7 habitat‑pressure classes) and unit/rounding rules for dynamicProperties keys. • Step 2. Discover and screen sources against inclusion criteria; register provisional metadata and eligibility flags. • Step 3. Acquire full texts; standardise encoding to UTF‑8; create Harvard‑style associatedReferences and normalise punctuation and spacing (full bibliography is listed comprehensively in Suppl. material 1). • Step 4. Record evidence type and de‑duplicate at publication/locality granularity: set basisOfRecord ( HumanObservation or MaterialCitation); screen duplicates with the key {authors + year + title + country + verbatimLocality + DOI}. • Step 5. Extract verbatim taxon strings into verbatimIdentification; attribute identifiedBy per source (or authors when unspecified); set the authority in nameAccordingTo. • Step 6. Reconcile names to accepted usage: populate scientificName and scientificNameAuthorship; fill higher ranks with taxonRank restricted to “genus”/“ species”. • Step 7. Parse and normalise dates: write eventDate (single date or start/end interval) and parsed components year, month, day (ISO 8601); where sampling date is absent, use publication year (year‑only). • Step 8. Harmonise countries and codes: populate country (chosen lexicon) and countryCode (ISO‑3166‑1 alpha‑2); retain provider stateProvince without additional normalisation. • Step 9. Build locality pair: keep exact verbatimLocality; generate harmonised locality (English exonym where established, otherwise consistent transliteration). • Step 10. Transcribe supplied coordinates into decimalLatitude and decimalLongitude; capture verbatimCoordinates, verbatimCoordinateSystem and verbatimSRS when provided; set geodeticDatum = WGS84. • Step 11. Geocode absent/ambiguous sites via authoritative gazetteers and apply the ± 10 km localisation rule; record georeferencedBy, georeferenceVerificationStatus (“verified”/“requires verification”). • Step 12. Couple uncertainty to precision: assign non‑zero coordinateUncertaintyInMetres and apply the six‑value mapping to coordinatePrecision {0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001}; round decimalLatitude/decimalLongitude accordingly. • Step 13. Handle elevation: parse verbatimElevation and duplicate single values into minimumElevationInMetres and maximumElevationInMetres; keep integers in metres. • Step 14. Map habitat: derive one consolidatedForestCategory (8 fixed labels) (Table 3) from source wording; store detailed verbatim stand text in forestTypeDetailed. 8Pawłowicz T Country Code Country Code Austria AT Liechtenstein LI Belarus BY Lithuania LT Czechia CZ Moldova MD Estonia EE Poland PL Germany DE Russia (European part) RU Hungary HU Slovakia SK Latvia LV Slovenia SI Switzerland CH Ukraine UA Field / key Storage Units / format Normalisation & rounding Analytical role minimumElevationInMetres DwC topography m (integer) Single values duplicated to min/ max when only one elevation reported Elevational context for habitat comparisons maximumElevationInMetres DwC topography m (integer) As above Elevational range per record airTemperature_C dynamicProperties °C (numeric) Rounded to 1 decimal at ingestion Microclimatic covariate in forest settings annualPrecipitation_mmYr dynamicProperties mm yr⁻¹ (integer) Integer totals Macro‑climatic covariate; stratifies moist vs. dry forests pH dynamicProperties dimensionless Decimal commas from sources normalised to numeric dots; rounded to two decimals Substrate chemistry; particularly relevant for conifer‑derived detritus standAge_yr dynamicProperties years (integer) Integer; no fractions Indicator of forest structural maturity microhabitat dynamicProperties verbatim free text Source phrasing retained (substrate, host taxon, decay state, height) Fine‑scale context for substrate‑dependent taxa Table 1. Countries represented and ISO 3166 1 alpha 2 codes. Table 2. Environmental and forest centric attributes (storage, units and usage). Rounding policy at ingestion: temperature 0.1°C; pH 0.01; precipitation and stand age as integers. Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 9 string preserved separately for transparency. Environmental and forest context variables are standardised for cross‑study comparability and downstream modelling, with rounding rules enforced at ingestion (temperature to one decimal, pH to two decimals and precipitation/stand age as integers). Trait representation indicates broad coverage across consolidated forest classes (Table 3), substrates (Table 4) and management‑pressure categories (Fig. 3, Table 5). Observed ranges (dataset): minimumElevationInMetres −28– 2750 m; maximumElevationInMetres 0–3000 m; airTemperature_C −3.3–21°C; annualPrecipitation_mmYr 132–2000 mm yr⁻¹; pH 3.35–10.00; standAge_yr discrete values 35–210 yr. pH values derive predominantly from moist‑chamber incubations; source decimal commas were harmonised to numeric dots with two‑decimal rounding for analysis. Continuous covariates span wide environmental space (Fig. 4): pH 3.35–10.00 (median ≈ 5.8; n = 173), air temperature −3.3–21°C (median ≈ 6.6°C; n = 1361) and annual precipitation 132–2000 mm yr⁻¹ (median ≈ 588 mm yr⁻¹; n = 1813). Temporal coverage Data range:1857-1-01 - 2025-8-01. Notes:Temporal coverage is represented by eventDate, year, month and day, spanning historical to present records with mixed precision, including ISO‑like single dates and bounded intervals. In the supplied material, eventDate ranges from 1857 to 2025-08-01; where only a publication year was available, that year served as a proxy with month/day left blank. The data mobilisation window was February 2022–August 2025; cleaning and harmonisation to GBIF/Darwin Core occurred from June to October 2025. Record accumulation accelerates markedly in recent decades (Fig. 5). Usage licence Usage licence:Other IP rights notes:All occurrence records are released under the Creative Commons Attribution 4.0 International licence, encoded in the licence field as the short form “CC‑BY‑4.0”. Full bibliographic provenance is supplied per record in associatedReferences to support proper attribution when reusing the dataset. Data resources Data package title:Georeferenced Checklist and Occurrence Dataset of Slime Moulds (Eumycetozoa) Across Central and Eastern Europe Emphasising Forest Ecosystems Resource link: https://doi.org/10.15468/jqukxm 16 Pawłowicz T Alternative identifiers: https://ipt.pensoft.net/resource?r=eumycetozoa_central_ eastern_europe Number of data sets:1 Data set name:Georeferenced Checklist and Occurrence Dataset of Slime Moulds (Eumycetozoa) Across Central and Eastern Europe Emphasising Forest Ecosystems Character set:UTF‑8 (Unicode); LF line endings; diacritics preserved; numeric fields use the dot as the decimal separator. Download URL: https://ipt.pensoft.net/archive.do?r=eumycetozoa_central_ eastern_europe&v=1.2 Data format:Comma‑separated values (CSV) implementing the Darwin Core Occurrence core; environmental covariates serialised as minified JSON in dynamicProperties. Description: This dataset assembles georeferenced, presence‑only occurrence records and a taxonomically standardised regional checklist of slime moulds (Eumycetozoa) from 16 countries across Central and Eastern Europe Pawłowicz 2025. Coverage emphasises forest ecosystems and complements each record with harmonised descriptors of substrate, stand type and management pressure. Environmental attributes (including elevation, precipitation, pH, stand age and consolidated forest class) enable comparative ecological and biogeographic analyses, forest‑focused modelling and gap mapping. Full source citations are provided for all records. Column label Column description occurrenceID Persistent unique identifier with a 36-character base UUID/GUID (UUID v5) generated deterministically from normalised bibliographic and locality tokens and stable under georeference updates. basisOfRecord Nominal evidence type with two case‑sensitive values —HumanObservation (literature‑derived field records without a cited voucher) and MaterialCitation (records citing examined material/vouchers). occurrenceStatus Presence state, uniformly “PRESENT”, confirming that all rows represent confirmed presences. licence Short‑form reuse licence code applied to every record: CC‑BY‑4.0. associatedReferences Harvard‑style reference string for the source (authors, year, title, outlet, pagination/identifier), optionally including DOI/URL. eventDate Sampling/observation date encoded as ISO‑like single dates. year Four‑digit calendar year parsed from eventDate. month Integer month (1–12) parsed from eventDate. Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 17 day Integer day of month (1–31) parsed from eventDate. country Full English sovereign‑state name from the dataset’s 16‑country Central and Eastern Europe domain (Russian Federation records are European part only). countryCode ISO‑3166‑1 alpha‑2 uppercase country code corresponding to country. stateProvince Provider‑supplied subnational administrative unit (heterogeneous levels and endonyms retained; not harmonised). locality Harmonised Latin‑script site description using established English exonyms where available or consistent transliteration, constructed from verbatimLocality. verbatimLocality Exact locality string as printed in the source, preserving native language/script, diacritics and punctuation. decimalLatitude Geographic latitude in signed decimal degrees (dot separator), supplied only when localisable to ± 10 km and rounded to match coordinatePrecision. decimalLongitude Geographic longitude in signed decimal degrees (dot separator), supplied only when localisable to ± 10 km and rounded to match coordinatePrecision. geodeticDatum Geodetic reference datum for coordinates, uniformly “WGS84” in the delivered dataset. coordinateUncertaintyInMetres Non‑zero integer estimate of positional uncertainty (metres) accompanying any coordinates, documenting georeferencing precision. coordinatePrecision Plain‑decimal degree precision of the coordinates, taking one of six values (0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001) that reflect the reported resolution. georeferencedBy Personal name(s) of the individual(s) responsible for georeferencing or, where coordinates were copied verbatim, the publication authors. georeferenceVerificationStatus Binary georeferencing quality flag with values “verified” or “requires verification”. minimumElevationInMetres Minimum elevation in metres; single elevation statements are duplicated to this and maximumElevationInMetres, with the original string kept in verbatimElevation. maximumElevationInMetres Maximum elevation in metres paired with minimumElevationInMetres, derived from the same source statement where only one value is given. scientificName Accepted taxon name used for indexing, generally including authorship and year as curated against Eumycetozoa.com (GBIF backbone as fallback). scientificNameAuthorship Full nomenclatural authorship string associated with scientificName. verbatimIdentification Original taxonomic identification as printed in the source, retaining spelling, authorship format, qualifiers and infraspecific ranks. nameAccordingTo Authority to which the name usage is aligned, standardised to “Eumycetozoa.com” throughout the dataset. identifiedBy Name(s) of the identifier(s) of the taxon; where not stated in the source, the publication’s author(s) are recorded. 18 Pawłowicz T kingdom Higher‑rank field uniformly set to “Protozoa”. phylum Higher‑rank field uniformly set to “Mycetozoa”. class Taxonomic class with three permissible values: “Dictyosteliomycetes”, “Myxomycetes” or “Protosteliomycetes”. order Taxonomic order assigned as one of eleven levels recognised in the dataset. family Taxonomic family assigned as one of 24 families. genus Generic epithet only (single token, with no authorship or rank qualifiers). specificEpithet Species‑level epithet in lower case, recorded without the generic name or authorship. taxonRank Rank at which the identification is asserted, restricted to “genus” or “species” in this dataset. dynamicProperties Minified JSON container for environmental and forest covariates (airTemperature_C, annualPrecipitation_mmYr, pH, standAge_yr, microhabitat, forestTypeDetailed, consolidatedForestCategory, substrateCategory, habitatPressure). Additional information Conflicts of interest: none declared. Acknowledgements The research was supported by the Białystok University of Technology (WZ/WB-INL/ 2/2025). References • Adl S, Simpson AB, Lane C, Lukeš J, Bass D, Bowser S, Brown M, Burki F, Dunthorn M, Hampl V, Heiss A, Hoppenrath M, Lara E, le Gall L, Lynn D, McManus H, Mitchell ED, Mozley‐Stanridge S, Parfrey L, Pawlowski J, Rueckert S, Shadwick L, Schoch C, Smirnov A, Spiegel F (2012) The revised classification of eukaryotes. Journal of Eukaryotic Microbiology 59 (5): 429‑514. https://doi.org/10.1111/j.1550-7408.2012.00644.x • de Basanta DW, Lado C (2025) Phagocytes of the forest: Are myxomycetes defensive mutualists for host plants? European Journal of Protistology 99 https://doi.org/10.1016/ j.ejop.2025.126158 • Fukasawa Y, Takahashi K, Arikawa T, Hattori T, Maekawa N (2015) Fungal wood decomposer activities influence community structures of myxomycetes and bryophytes on coarse woody debris. Fungal Ecology 14: 44‑52. https://doi.org/10.1016/j.funeco. 2014.11.003 Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) ... 19 • Ing B, Stephenson S (2022) The history of the study of myxomycetes. Myxomycetes47‑96. https://doi.org/10.1016/b978-0-12-824281-0.00012-9 • Lado C (2019) An online nomenclatural information system of Eumycetozoa in the Catalogue of Life. The Catalogue of Life Partnership https://doi.org/10.15468/nlgobs • Leontyev D, Schnittler M (2017) The phylogeny of Myxomycetes. Myxomycetes83‑106. https://doi.org/10.1016/b978-0-12-805089-7.00003-2 • Leontyev DV, Schnittler M, Stephenson SL, Novozhilov YK, Shchepin ON (2019) Towards a phylogenetic classification of the Myxomycetes. Phytotaxa 399 (3). https:// doi.org/10.11646/phytotaxa.399.3.5 • Liu Q, Yan S, Dai J, Chen S (2013) Species diversity of corticolous myxomycetes in Tianmu Mountain National Nature Reserve, China. Canadian Journal of Microbiology 59 (12): 803‑813. https://doi.org/10.1139/cjm-2013-0360 • Liu Q, Yan S, Chen S (2015) Species diversity of myxomycetes associated with different terrestrial ecosystems, substrata (microhabitats) and environmental factors. Mycological Progress 14 (5). https://doi.org/10.1007/s11557-015-1048-9 • Pawłowicz T (2025) Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) across Central and Eastern Europe emphasising forest ecosystems. Occurrence dataset. URL: https://doi.org/10.15468/jqukxm • Stephenson S (2011) From morphological to molecular: studies of myxomycetes since the publication of the Martin and Alexopoulos (1969) monograph. Fungal Diversity 50 (1): 21‑34. https://doi.org/10.1007/s13225-011-0113-1 • Stephenson S (2023) Past and ongoing field-based studies of Myxomycetes. Microorganisms 11 (9). https://doi.org/10.3390/microorganisms11092283 Supplementary material Suppl. material 1: Dataset Bibligoraphy Authors: Tomasz Pawłowicz Data type: Bibliography Brief description: Dataset bibliography comprising of 528 bibliographic entries. Download file (68.20 kb) 20 Pawłowicz T