| 1. |
- Kattge, Jens, et al.
(author)
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TRY plant trait database - enhanced coverage and open access
- 2020
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In: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Journal article (peer-reviewed)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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| 2. |
- Kehoe, Laura, et al.
(author)
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Make EU trade with Brazil sustainable
- 2019
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
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Journal article (other academic/artistic)
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| 3. |
- Zamora, Juan Carlos, et al.
(author)
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Considerations and consequences of allowing DNA sequence data as types of fungal taxa
- 2018
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In: IMA Fungus. - : INT MYCOLOGICAL ASSOC. - 2210-6340 .- 2210-6359. ; 9:1, s. 167-185
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Journal article (peer-reviewed)abstract
- Nomenclatural type definitions are one of the most important concepts in biological nomenclature. Being physical objects that can be re-studied by other researchers, types permanently link taxonomy (an artificial agreement to classify biological diversity) with nomenclature (an artificial agreement to name biological diversity). Two proposals to amend the International Code of Nomenclature for algae, fungi, and plants (ICN), allowing DNA sequences alone (of any region and extent) to serve as types of taxon names for voucherless fungi (mainly putative taxa from environmental DNA sequences), have been submitted to be voted on at the 11th International Mycological Congress (Puerto Rico, July 2018). We consider various genetic processes affecting the distribution of alleles among taxa and find that alleles may not consistently and uniquely represent the species within which they are contained. Should the proposals be accepted, the meaning of nomenclatural types would change in a fundamental way from physical objects as sources of data to the data themselves. Such changes are conducive to irreproducible science, the potential typification on artefactual data, and massive creation of names with low information content, ultimately causing nomenclatural instability and unnecessary work for future researchers that would stall future explorations of fungal diversity. We conclude that the acceptance of DNA sequences alone as types of names of taxa, under the terms used in the current proposals, is unnecessary and would not solve the problem of naming putative taxa known only from DNA sequences in a scientifically defensible way. As an alternative, we highlight the use of formulas for naming putative taxa (candidate taxa) that do not require any modification of the ICN.
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| 4. |
- Potapov, Anton M., et al.
(author)
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Global fine-resolution data on springtail abundance and community structure
- 2024
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In: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Journal article (peer-reviewed)abstract
- Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.
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| 5. |
- Prager, Case M., et al.
(author)
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Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment : An example from the WaRM Network
- 2022
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In: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 12:10
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Journal article (peer-reviewed)abstract
- A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities.
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| 6. |
- Pilotto, Francesca, et al.
(author)
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Meta-analysis of multidecadal biodiversity trends in Europe
- 2020
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 11:1
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Journal article (peer-reviewed)abstract
- Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. The global biodiversity decline might conceal complex local and group-specific trends. Here the authors report a quantitative synthesis of longterm biodiversity trends across Europe, showing how, despite overall increase in biodiversity metric and stability in abundance, trends differ between regions, ecosystem types, and taxa.
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| 7. |
- Redestig, Henning, et al.
(author)
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Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data
- 2009
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In: Analytical Chemistry. - Washington : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 81:19, s. 7974-7980
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Journal article (peer-reviewed)abstract
- Most mass spectrometry based metabolomics studies are semiquantitative and depend on efficient normalization techniques to suppress systematic error. A common approach is to include isotope-labeled internal standards (ISs) and then express the estimated metabolite abundances relative to the IS. Because of problems such as insufficient chromatographic resolution, however, the analytes may directly influence estimates of the IS, a phenomenon known as cross-contribution (CC). Normalization using ISs that suffer from CC effects will cause significant loss of information if the interfering analytes are associated with the studied factors. We present a novel normalization algorithm, which compensates for systematic CC effects that can be traced back to a linear association with the experimental design. The proposed method was found to be superior at purifying the signal of interest compared to current normalization methods when applied to two biological data sets and a multicomponent dilution mixture. Our method is applicable to data from randomized and designed experiments that use ISs to monitor the systematic error.
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