55 research outputs found

    TCERG1L allelic variation is associated with cisplatin-induced hearing loss in childhood cancer, a PanCareLIFE study.

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    In children with cancer, the heterogeneity in ototoxicity occurrence after similar treatment suggests a role for genetic susceptibility. Using a genome-wide association study (GWAS) approach, we identified a genetic variant in TCERG1L (rs893507) to be associated with hearing loss in 390 non-cranial irradiated, cisplatin-treated children with cancer. These results were replicated in two independent, similarly treated cohorts (n = 192 and 188, respectively) (combined cohort: P = 5.3 × 10-10, OR 3.11, 95% CI 2.2-4.5). Modulating TCERG1L expression in cultured human cells revealed significantly altered cellular responses to cisplatin-induced cytokine secretion and toxicity. These results contribute to insights into the genetic and pathophysiological basis of cisplatin-induced ototoxicity

    TRY plant trait database - enhanced coverage and open access

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    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

    Traditional plant functional groups explain variation in economic but not size-related traits across the tundra biome

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    Aim Plant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits. Location Tundra biome. Time period Data collected between 1964 and 2016. Major taxa studied 295 tundra vascular plant species. Methods We compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species-level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species-level traits. Results Traditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species-level trait expression. Main conclusions Traditional functional groups only coarsely represent variation in well-measured traits within tundra plant communities, and better explain resource economic traits than size-related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling.Peer reviewe

    Global plant trait relationships extend to the climatic extremes of the tundra biome

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    The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world.Peer reviewe

    Ecological Indicator Values for Europe (EIVE) 1.0

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    Aims: To develop a consistent ecological indicator value system for Europe for five of the main plant niche dimensions: soil moisture (M), soil nitrogen (N), soil reaction (R), light (L) and temperature (T). Study area: Europe (and closely adjacent regions). Methods: We identified 31 indicator value systems for vascular plants in Europe that contained assessments on at least one of the five aforementioned niche dimensions. We rescaled the indicator values of each dimension to a continuous scale, in which 0 represents the minimum and 10 the maximum value present in Europe. Taxon names were harmonised to the Euro+Med Plantbase. For each of the five dimensions, we calculated European values for niche position and niche width by combining the values from the individual EIV systems. Using T values as an example, we externally validated our European indicator values against the median of bioclimatic conditions for global occurrence data of the taxa. Results: In total, we derived European indicator values of niche position and niche width for 14,835 taxa (14,714 for M, 13,748 for N, 14,254 for R, 14,054 for L, 14,496 for T). Relating the obtained values for temperature niche position to the bioclimatic data of species yielded a higher correlation than any of the original EIV systems (r = 0.859). The database: The newly developed Ecological Indicator Values for Europe (EIVE) 1.0, together with all source systems, is available in a flexible, harmonised open access database. Conclusions: EIVE is the most comprehensive ecological indicator value system for European vascular plants to date. The uniform interval scales for niche position and niche width provide new possibilities for ecological and macroecological analyses of vegetation patterns. The developed workflow and documentation will facilitate the future release of updated and expanded versions of EIVE, which may for example include the addition of further taxonomic groups, additional niche dimensions, external validation or regionalisation

    TRY plant trait database - enhanced coverage and open access

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    This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant 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.Peer reviewe

    Species trait shifts in vegetation and soil seed bank during fen degradation

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    Fens in Central Europe are characterised by waterlogged organic substrate and low productivity. Human-induced changes due to drainage and mowing lead to changes in plant species composition from natural fen communities to fen meadows and later to over-drained, degraded meadows. Moderate drainage leads to increased vegetation productivity, and severe drainage results in frequent soil disturbances and less plant growth. In the present article, we analyse changes in plant trait combinations in the vegetation and the soil seed bank as well as changes in the seed bank types along gradient of drainage intensity. We hypothesize that an increase in productivity enhances traits related to persistence and that frequent disturbance selects for regeneration traits. We use multivariate statistics to analyse data from three disturbance levels: undisturbed fen, slightly drained fen meadow and severely drained degraded meadow. We found that the abundance of plants regenerating from seeds and accumulating persistent seed banks was increasing with degradation level, while plants reproducing vegetatively were gradually eliminated along the same trajectory. Plants with strong resprouting abilities increased during degradation. We also found that shifts in trait combinations were similar in the aboveground vegetation and in soil seed banks. We found that the density of short-term persistent seeds in the soil is highest in fen meadows and the density of long-term persistent seeds is highest in degraded meadows. The increase in abundance of species with strong regeneration traits at the cost of species with persistence-related traits has negative consequences for the restoration prospects of severely degraded sites

    TRY plant trait database - enhanced coverage and open access

    Get PDF
    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

    TRY plant trait database – enhanced coverage and open access

    Get PDF
    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

    Ecological Indicator Values of Europe (EIVE) 1.0: a powerful open-access tool for vegetation scientists

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    Background: Ecological indicator values (EIVs) have a long tradition in vegetation ecological research in Europe. EIVs characterise the ecological optimum of species along major environmental gradients using ordinal scales. Calculating mean indicator values per plot is an effective way of bioindication. Following first systems in Russia and Central Europe, about two dozen EIV systems have been published for various parts of Europe. Aims: As there was no EIV system available at European scale that could be used for broad-scale analyses, e.g. in the context of the European Vegetation Archive (EVA), we develop such a system for the first time for the vascular plants of Europe. Location: Europe. Methods: We compiled all national and major regional EIV systems and harmonized their plant nomenclature with a newly developed contemporary European taxonomic backbone (EuroSL 1.0). Using regression, we rescaled the individual EIV systems for the main parameters to continent-wide quasi-metric scales, ranging from 1 to 99. The data from each individual system were then translated into a probability curve approximated with a normal distribution, weighed with the logarithm of the area represented and summed up across the systems. From the European density curve we extracted then a mean and a variance, which characterise the distribution of this species along this particular ecological gradient. Results and conclusions: Our consensus approach of integrating the expert knowledge of all existing EIV systems allowed deriving the first consistent description of the ecological behaviour for a significant part of the European vascular flora. The resulting Ecological Indicator Values of Europe (EIVE) 1.0 will be published open access to allow bioindication beyond country borders. Future releases of EIVE might contain more parameters, non-vascular plants and regionalisation or could be re-adjusted and extended to hitherto non-covered species through co-occurrence data from EVA
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