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. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.
Methods: In order to separate information on compositional similarity possibly present in mean EIVs, a new variable was introduced, calculated as a weighted average of randomized species EIVs. The performance of these mean randomized EIVs was compared with that of the mean real EIVs on the one hand and random values (randomized mean EIVs) on the other. To demonstrate the similarity issue, differences between samples were correlated with dissimilarity matrices based on various indices. Next, the three mean EIV variables were tested in canonical correspondence analysis (CCA), detrended correspondence analysis (DCA), analysis of variance (ANOVA) between vegetation clusters, and in regression on species richness. Subsequently, a modified permutation test of significance was proposed, taking the similarity issue into account. In addition, an inventory was made of studies published in the Journal of Vegetation Science and Applied Vegetation Science between 2000 and 2010 likely reporting biased results due to the similarity issue.Results: Using mean randomized EIVs, it is shown that compositional similarity is inherited into mean EIVs and most resembles the inter-sample distances in correspondence analysis, which itself is based on iterative weighted averaging. The use of mean EIVs produced biased results in all four analysis types examined: unrealistic (too high) explained variances in CCA, too many significant correlations with ordination axes in DCA, too many significant differences between cluster analysis groups and too high coefficients of determination in regressions on species richness. Modified permutation tests provided ecologically better interpretable results. From 95 studies using Ellenberg indicator values, 36 reported potentially biased results. Conclusions: No statistical inferences should be made in analyses relating meanEIVs with other variables derived from the species composition as this can produce highly biased results, leading to misinterpretation. Alternatively, a modified permutation test using mean randomized EIVs can sometimes be used.
We develop a novel class of measures to quantify sample completeness of a biological survey. The class of measures is parameterized by an order q ≥ 0 to control for sensitivity to species relative abundances. When q = 0, species abundances are disregarded and our measure reduces to the conventional measure of completeness, that is, the ratio of the observed species richness to the true richness (observed plus undetected). When q = 1, our measure reduces to the sample coverage (the proportion of the total number of individuals in the entire assemblage that belongs to detected species), a concept developed by Alan Turing in his cryptographic analysis. The sample completeness of a general order q ≥ 0 extends Turing's sample coverage and quantifies the proportion of the assemblage's individuals belonging to detected species, with each individual being proportionally weighted by the (q − 1)th power of its abundance. We propose the use of a continuous profile depicting our proposed measures with respect to q ≥ 0 to characterize the sample completeness of a survey. An analytic estimator of the diversity profile and its sampling uncertainty based on a bootstrap method are derived and tested by simulations. To compare diversity across multiple assemblages, we propose an integrated approach based on the framework of Hill numbers to assess (a) the sample completeness profile, (b) asymptotic diversity estimates to infer true diversities of entire assemblages, (c) non‐asymptotic standardization via rarefaction and extrapolation, and (d) an evenness profile. Our framework can be extended to incidence data. Empirical data sets from several research fields are used for illustration.
Aim: To propose a modification of the TWINSPAN algorithm that enables production of divisive classifications that better respect the structure of the data. Methods: The proposed modification combines the classical TWINSPAN algorithm with analysis of heterogeneity of the clusters prior to each division. Four different heterogeneity measures are involved: Whittaker's beta, total inertia, average Sørensen dissimilarity and average Jaccard dissimilarity. Their performance was evaluated using empirical vegetation datasets with different numbers of plots and different levels of heterogeneity. Results: While the classical TWINSPAN algorithm divides each cluster coming from the previous division step, the modified algorithm divides only the most heterogeneous cluster in each step. The four tested heterogeneity measures may produce identical or very similar results. However, average Jaccard and Sørensen dissimilarities may reach extreme values in clusters of small size and may produce classifications with a highly unbalanced cluster size. Conclusions: The proposed modification does not alter the logic of the TWINSPAN classification, but it may change the hierarchy of divisions in the final classification. Thus, unsubstantiated divisions of homogeneous clusters are prevented, and classifications with any number of terminal clusters can be created, which increases the flexibility of TWINSPAN.
Aim We identify the main forest vegetation types in Taiwan, provide their formal definitions and describe their species composition, habitat affinities and distribution. Location Taiwan. Methods A data set of 9822 vegetation plots with environmental characteristics recorded in the field or derived from digital maps in GIS was compiled from historical literature and an extensive field survey. Using expert knowledge, 6574 of these plots were used to build a classification into broad vegetation types. The units of the resulting classification were formally defined using a Cocktail determination key, which can be used for the automatic assignment of new vegetation plots to these vegetation types. Results Twelve vegetation types of zonal forests and nine types of azonal forests were distinguished. Zonal types in the subtropical region, from high mountains to foothills, are Juniperus subalpine coniferous woodland, Abies–Tsuga upper‐montane coniferous forest, Chamaecyparis montane mixed cloud forest, Fagus montane deciduous broad‐leaved cloud forest, Quercus montane evergreen broad‐leaved cloud forest, Machilus–Castanopsis sub‐montane evergreen broad‐leaved forest, Phoebe–Machilus sub‐montane evergreen broad‐leaved forest and Ficus–Machilus semi‐evergreen foothill forest. Zonal types in the tropical region, from high mountains to foothills, are Pasania–Elaeocarpus montane evergreen broad‐leaved cloud forest, Drypetes–Helicia sub‐montane evergreen broad‐leaved forest, Dysoxylum–Machilus foothill evergreen broad‐leaved forest and Aglaia–Ficus foothill evergreen broad‐leaved forest. Azonal types are Illicium–Cyclobalanopsis tropical winter monsoon forest, Pyrenaria–Machilus subtropical winter monsoon forest, Diospyros–Champereia tropical rock‐outcrop forest, Zelkova–Quercus subtropical rock‐outcrop forest, Pinus successional woodland, Alnus successional woodland, Trema–Mallotus successional woodland, Scaevola–Hibiscus seashore woodland and Kandelia mangrove. Conclusions The diversity of forest vegetation in Taiwan is strongly structured by the temperature and moisture gradient. Along the temperature gradient, five altitudinal zones can be recognized. Azonal forest types develop at sites affected by the winter monsoon, on steep slopes, rocky soils, in seashore saline habitats and in places disturbed by fire, landslides and human activities. Zonal vegetation contains a higher ratio of endemic and Pacific species and occurs in wetter habitats, whereas azonal vegetation contains co‐existing species from different regions and usually occurs in drier habitats.
Aim Several hypotheses postulate that species invasion is affected by an interplay between the phylogenetic position of the invading species and the phylogenetic structure of the invaded community type. Some of them suggest that phylogenetic relatedness of invaders to native species promotes naturalization, because phylogenetically related alien species tend to have similar environmental adaptations as native species. Others predict that phylogenetic relatedness hampers naturalization because of stronger competition of aliens with native species and shared enemies. Here we ask how phylogenetic diversity of native species affects invasion across community types. Location Czech Republic. Methods All major plant community types at a national scale (n = 88) were characterized by their species pools, i.e. lists of species that can potentially occur there. Of the total number of 2306 species, 1785 were native, 246 were archaeophytes and 275 were neophytes. For each species pool, we related the number of alien species to the phylogenetic diversity of the native species pool, calculated as mean phylogenetic distance (MPD) and mean nearest taxon distance (MNTD), including null models. Results The number of alien species was related both to the phylogenetic structure of community types and to the phylogenetic position of alien species. Frequently disturbed herbaceous community types with strong phylogenetic clustering were more invaded than others, possibly due to disturbance acting as an environmental filter. Here, alien species increased the degree of phylogenetic clustering as they tended to be from the same lineages as native species. Such trends were not detected for phylogenetically more diverse community types such as forests. Main conclusions Our findings support the hypothesis that relatedness of invaders to native species promotes invasion because of their shared adaptations to the same environments. Alien species more strongly invade community types that are phylogenetically clustered, and because they tend to be related to native species, invaded community types become even more clustered.
Abstract. Variation partitioning of species composition into components explained by environmental and spatial variables is often used to identify a signature of niche-and dispersal-based processes in community assembly. Such interpretation, however, strongly depends on the quality of the environmental data available. In recent studies conducted in forest dynamics plots, the environment was represented only by readily available topographical variables. Using data from a subtropical broad-leaved dynamics plot in Taiwan, we focus on the question of how would the conclusion about importance of niche-and dispersal-based processes change if soil variables are also included in the analysis. To gain further insight, we introduced multiscale decomposition of a pure spatial component [c] in variation partitioning. Our results indicate that, if only topography is included, dispersalbased processes prevail, while including soil variables reverses this conclusion in favor of niche-based processes. Multiscale decomposition of [c] shows that if only topography was included, broad-scaled spatial variation prevails in [c], indicating that other as yet unmeasured environmental variables can be important. However, after also including soil variables this pattern disappears, increasing importance of meso-and fine-scaled spatial patterns indicative of dispersal processes.
A new dataset of ecological indicator values for species, subspecies and some varieties, hybrids and infrageneric species groups has been compiled for the vascular flora of the Czech Republic. Indicator values for light, temperature, moisture, (soil) reaction, nutrient availability and salinity were assigned to 2275 species and 801 other taxa, using the nine-degree (or 12-degree for moisture and 10-degree for salinity) ordinal scales proposed by Heinz Ellenberg for the flora of Germany. The values are compatible with Ellenberg indicator values, which were used as a baseline, but extensively revised based on our own field observations, literature, comparison with indicator value systems of other countries and an analysis of taxon co-occurrences in vegetation plots from the Czech National Phytosociological Database. Taxa in the Czech flora missing in the original Ellenberg tables were added. Compared with the original Ellenberg's dataset of indicator values, smaller proportions of taxa were classified as extremely basiphilous, extremely oligotrophic or strictly avoiding saline habitats. The revised values were tested by regressing unweighted site mean indicator values against measured environmental variables. In most cases, prediction of environmental conditions was slightly more accurate with the new Czech indicator values than with the original Ellenberg indicator values. The full dataset of indicator values is available in an electronic appendix to this paper.
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