Data on cryptogamic biota in relation to heavy metal concentrations in soil

The data presented here are related to the research article entitled “Cryptogamic communities as a useful bioindication tool for estimating the degree of soil pollution with heavy metals” (Rola and Osyczka, 2018) [1]. These data concern the relationships between epigeic cryptogamic biota and heavy metal concentrations in soil of areas associated with Zn–Pb industry. The presence of particular species and coverage of lichens and bryophytes as well as soil chemical parameters in relation to three different soil pollution classes and five habitat types are provided. Included data could be used to compare cryptogamic community structure and pollutant concentration levels with other Zn–Pb polluted areas.


a b s t r a c t
The data presented here are related to the research article entitled "Cryptogamic communities as a useful bioindication tool for estimating the degree of soil pollution with heavy metals" (Rola and Osyczka, 2018) [1]. These data concern the relationships between epigeic cryptogamic biota and heavy metal concentrations in soil of areas associated with Zn-Pb industry. The presence of particular species and coverage of lichens and bryophytes as well as soil chemical parameters in relation to three different soil pollution classes and five habitat types are provided. Included data could be used to compare cryptogamic community structure and pollutant concentration levels with other Zn-Pb polluted areas.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Subject area
Environmental pollution More specific subject area Soil pollution, Cryptogamic biota Type of data Table, figure How data was acquired The presence and coverage of lichen and bryophyte species were determined in study plots.
The following soil parameters were analysed: pH (electrometrically determined, Hach Lange HQ40d pH meter), organic carbon content (dry Data can be used as a base-line data for metal concentration levels in soils within areas associated with Zn-Pb industry.

Data
Data on the specific structure of cryptogamic communities in relation to soil chemical parameters in sites directly associated with the processing of Zn-Pb ores in southern Poland are presented (Fig. 1). Different types of anthropogenic and semi-natural habitats, i.e. post-smelting, post-flotation, postmining dumps, grassland or industrial wastes in smelter environs and psammophilous grassland, were considered. Analysis of cryptogamic biota within study plots with respect to the chemical parameters of the corresponding soil resulted in identification of three different pollution classes related to the concentration of heavy metals: low, high, and extreme (for details see Ref. [1]). The ranges of analysed chemical parameters for each class are presented in Table 1 and for particular habitat types in Table 2. As regard cryptogamic biota, altogether, 45 species, including 27 lichens and 18 bryophytes, were recorded ( Table 3). The presence of particular species in plots assigned to certain soil pollution class are shown in Fig. 2; whereas the presence in study plots representing particular habitat types in Figs. [3][4][5][6][7]. Details related to the determination of soil pollution classes and their chemical and biotic characteristics can be found in Ref. [1].      2. Experimental design, materials, and methods

Field studies and sampling
The fieldwork was conducted in the Silesia-Cracow Upland area, one of the most polluted regions in Poland, associated for centuries with the processing of Zn-Pb ores (Fig. 1). The sampling were conducted in the summer seasons of 2015 and 2016. Altogether, 210 plots, 1 m Â 1 m, representing homogenous patches of vegetation, were examined with respect to the presence and coverage of  Species presence matrix in the studied plots; the plots are arranged according to soil pollution classes. Dominants, species recorded in no less than half of the plots, and simultaneously with mean cover higher than 2% within at least one of the pollution classes, are separated on the left side. For abbreviations of species see Table 3.
determination based on a detailed examination of their morphology and, in the case of lichens, chemical features. Lichen secondary substances, required for the identification of certain species, were determined by means of TLC, following [7]. The nomenclature follows [8] and [9] for lichens and bryophytes, respectively. Additionally, percentage of total coverage of lichens and bryophytes was estimated for each plot. From 72 plots three soil subsamples, to a depth of 5 cm, were collected and bulked in one composite sample.  Table 3; for abbreviations of study sites see Fig. 1.  Table 3; for abbreviations of study sites see Fig. 1.

Chemical analysis of soil samples
The soil samples were dried and passed through a 2-mm sieve. Acidity (pH) was electrometrically determined in 1-M KCl suspensions with a Hach Lange HQ40d pH meter. Organic carbon content was measured using the dry combustion technique with a LECO SC-144DR Analyzer (LECO Corp., MI, USA) and total N content using the Kjeldahl method using Kjeltec 2300 Analyzer Unit (FOSS Tecator, Sweden). Soil samples (5 g DW) were digested with 70% HClO 4 (Merck, Suprapur) using a digester (FOSS Tecator 2020, Sweden). Subsequently, flame atomic absorption spectrometry using Varian Fast Sequential Atomic Absorption Spectrometer 280 (Varian, Australia) for Zn, Cd, Pb and Varian Zeeman Atomic Absorption Spectrometer 280 with Graphite Tube Atomizer 120 (Varian, Australia) for As was applied. Exchangeable forms of elements were determined by extracting 5 g DW with a 0.05-M EDTA solution and measured by means of flame atomic absorption spectrometry. Certified reference materials (CRM048-50G Sigma-Aldrich, BCR-483 Sigma-Aldrich, ISE-912 WEPAL -Wageningen University) were used for quality assurance. Appropriate solutions without samples were used as reagent blanks. The analyses were repeated three times and the mean values considered as one observation.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at https://doi.org/ 10.1016/j.dib.2018.05.137.  Table 3; for abbreviations of study sites see Fig. 1.