Remigiusz Pielech

Dr. Remigiusz Pielech

Department of Forest Biodiversity, Faculty of Forestry, Institute of Ecology and Silviculture, 31-425 Kraków, Poland
orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
15 May 2020 in Science
Verified
Journal Article
Closed access
PDF file
FZFlorian Zellweger
Florian Zellweger
PFPieter De Frenne
Pieter De Frenne
JLJonathan Lenoir
Jonathan Lenoir
PVPieter Vangansbeke
Pieter Vangansbeke
KVKris Verheyen
Kris Verheyen
DCDavid Coomes
David Coomes
Local factors restrain forest warming: Microclimates are key to understanding how organisms and ecosystems respond to macroclimate change, yet they are frequently neglected when studying biotic responses to global change. Zellweger et al. provide a long-term, continental-scale assessment of the effects of micro- and macroclimate on the community composition of European forests (see the Perspective by Lembrechts and Nijs). They show that changes in forest canopy cover are fundamentally important for driving community responses to climate change. Closed canopies buffer against the effects of macroclimatic change through their cooling effect, slowing shifts in community composition, whereas open canopies tend to accelerate community change through local heating effects. Science , this issue p. 772 ; see also p. 711

26 July 2020 in Applied Vegetation Science
Verified
Journal Article
Open access
PDF file
MCMilan Chytrý
Milan Chytrý
LTLubomír Tichý
Lubomír Tichý
SHStephan M. Hennekens
Stephan M. Hennekens
IKIlona Knollová
Ilona Knollová
JJJohn A. M. Janssen
John A. M. Janssen
JSJoop H. J. Schaminée
Joop H. J. Schaminée
Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats.Location: Europe.Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat.Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination.Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.

11 January 2023 in Journal of Vegetation Science
Verified
Journal Article
Open access
LTLubomír Tichý
Lubomír Tichý
IAIrena Axmanová
Irena Axmanová
JDJürgen Dengler
Jürgen Dengler
RGRiccardo Guarino
Riccardo Guarino
FJFlorian Jansen
Florian Jansen
MCMilan Chytrý
Milan Chytrý
Aims: Ellenberg‐type indicator values are expert‐based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator‐value system proposed by Heinz Ellenberg and co‐authors for Central Europe by incorporating other systems of Ellenberg‐type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized data set of Ellenberg‐type indicator values applicable at the European scale.Methods: We collected European data sets of indicator values for vascular plants and selected 13 data sets that used the nine‐, ten‐ or twelve‐degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these data sets. Based on species’ co‐occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value.Results: We provide a new data set of Ellenberg‐type indicator values for 8908 European vascular plant species (8168 for light, 7400 for temperature, 8030 for moisture, 7282 for reaction, 7193 for nutrients, and 7507 for salinity), of which 398 species have been newly assigned to at least one indicator value.Conclusions: The newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large‐scale studies of the European flora and vegetation or for gap‐filling in regional data sets. The European indicator values and the original and taxonomically harmonized regional data sets of Ellenberg‐type indicator values are available in the Supporting Information and the Zenodo repository.

22 October 2015 in Applied Vegetation Science
Verified
Journal Article
Closed access
PDF file
JDJan Douda
Jan Douda
KBKarel Boublík
Karel Boublík
MSMichal Slezák
Michal Slezák
IBIdoia Biurrun
Idoia Biurrun
JNJosef Nociar
Josef Nociar
NZNiklaus Zimmermann
Niklaus Zimmermann

19 October 2022 in Global Ecology and Biogeography
Verified
Journal Article
Closed access
GMGabriele Midolo
Gabriele Midolo
THTomáš Herben
Tomáš Herben
IAIrena Axmanová
Irena Axmanová
CMCorrado Marcenò
Corrado Marcenò
RPRicarda Pätsch
Ricarda Pätsch
MCMilan Chytrý
Milan Chytrý

22 June 2021 in Journal of Vegetation Science
Verified
Journal Article
Open access
PDF file
IBIdoia Biurrun
Idoia Biurrun
Remigiusz PielechRemigiusz Pielech
Remigiusz Pielech
IDIwona Dembicz
Iwona Dembicz
FGFrançois Gillet
François Gillet
ŁKŁukasz Kozub
Łukasz Kozub
JDJürgen Dengler
Jürgen Dengler
Aims: Understanding fine‐grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine‐grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups).Location: Palaearctic biogeographic realm.Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class.Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi‐natural) grasslands and natural grasslands are the richest vegetation type. The open‐access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats.Conclusions: The GrassPlot Diversity Benchmarks provide high‐quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation‐plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.