| 1. |
- Dengler, Juergen, et al.
(författare)
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GrassPlot - a database of multi-scale plant diversity in Palaearctic grasslands
- 2018
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Ingår i: Phytocoenologia. - : Schweizerbart. - 0340-269X. ; 48:3, s. 331-347
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Tidskriftsartikel (refereegranskat)abstract
- GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001;... 1,000 m(2)) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata. However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetationplot databases, such as the European Vegetation Archive (EVA) and the global database " sPlot". Its main aim is to facilitate studies on the scale-and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board.
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| 2. |
- Biurrun, Idoia, et al.
(författare)
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Benchmarking plant diversity of Palaearctic grasslands and other open habitats
- 2021
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Ingår i: Journal of Vegetation Science. - Oxford : John Wiley & Sons. - 1100-9233 .- 1654-1103. ; 32:4
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Tidskriftsartikel (refereegranskat)abstract
- Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association for Vegetation Science.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. © 2021 The Authors.
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| 3. |
- Jiroušek, Martin, et al.
(författare)
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Classification of European bog vegetation of the Oxycocco‐Sphagnetea class
- 2022
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Ingår i: Applied Vegetation Science. - : Wiley. - 1402-2001 .- 1654-109X. ; 25:1, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Aims: Classification of European bog vegetation (Oxycocco- Sphagnetea class); iden -tification of diagnostic species for the class and vegetation subgroups (orders and alliances); development of an expert system for automatic classification of vegetation plots; and production of distribution maps of the Oxycocco- Sphagnetea class and its alliances.Location: Europe.Methods: A data set of vegetation- plot records was compiled to include various bog types over most of the European continent. An unsupervised classification (beta- flexible linkage method, Sørensen distance measure) and detrended correspondenceanalysis (DCA) ordination were applied. Formal definitions of syntaxa based on spe -cies presence and covers, and respecting the results of the unsupervised classifica-tion, were developed and included in a classification expert system.Results: The Oxycocco- Sphagnetea class, its two orders (Sphagno- Ericetalia tetralicisand Sphagnetalia medii) and seven compositionally distinct alliances were formally de -fined. In addition to the syntaxa included in EuroVegChecklist, three new alliances were distinguished: Rubo chamaemori- Dicranion elongati (subarctic polygon and palsa mires); Erico mackaianae- Sphagnion papillosi (blanket bogs of the northwestern IberianPeninsula); and Sphagno baltici- Trichophorion cespitosi (boreal bog lawns). The latter alliance is newly described in this article.Conclusions: This first pan- European formalized classification of European bog veg -etation partially followed the system presented in EuroVegChecklist, but suggested three additional alliances. One covers palsa and polygon mires, one covers Iberian bogs with endemics and one fills the syntaxonomical gap for lawn microhabitats in boreal bogs. A classification expert system has been developed, which allows assign -ment of vegetation plots to the types described.
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| 4. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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