Hornik J.,University of South Bohemia |
Janecek S.,Centaurea |
Janecek S.,Academy of Sciences of the Czech Republic |
Klimesova J.,Academy of Sciences of the Czech Republic |
And 6 more authors.
Plant Ecology | Year: 2012
Species-area curves are often employed to identify factors affecting biodiversity patterns. The aim of this study was to determine how model choice affects biological interpretation of SAC parameters at a small scale in wet, temperate meadows (Železné hory Mts, Czech Republic). We estimated 88 species-area curves in nested plots on areas ranging from 0. 01 to 4 m2 at 22 localities using four different models (Arrhenius, Gleason, and their log transformations). Relationships were tested between the parameters of the fitted curves (slope and intercept) and a number of environmental and vegetation characteristics (environmental-water table, pH, nutrient availability, organic matter content; community-productivity, evenness; and individual plant-shoot cyclicity, persistence of connection among ramets, multiplication rate, dispersal ability). Species diversity was calculated for 0. 01, 1, and 4 m2. The corrected Akaike information criterion was used to identify the best model. The models differed in their sensitivity to environmental, community, and individual plant characteristics. The spatial scale that was the most suitable for revealing the factors underlying species diversity was the smallest considered (0. 01 m2). The most important factors were spatial pattern in community structure (evenness, lateral spread), plant mobility (lateral spread and persistence), and soil properties. Although Gleason model showed better fit to data (both non-log and log transformation) and its intercept was more sensitive to tested biological characteristics, the Arrhenius model was more sensitive when correlating biological characteristics and slope. Choice of model according to best fit criteria restricts possibilities of biological interpretation and deserves further study. © 2012 Springer Science+Business Media B.V.
Chytry M.,Masaryk University |
Drazil T.,Administration of the Slovensky Raj |
Hajek M.,Masaryk University |
Kalnikova V.,Masaryk University |
And 32 more authors.
Preslia | Year: 2015
We provide an inventory of the sites and vegetation types in the Czech Republic and Slovakia that contain the highest numbers of vascular plant species in small areas of up to 625 m2. The highest numbers of species were recorded in semi-natural grasslands, in which we report four new world records for fine-scale species richness: 17 species of vascular plants in 0.0044 m2 in a mountain meadow in the Krkonoše Mts, 52 and 63 species in 0.25 and 0.5 m2, respectively, in the Kopanecké lúky meadows in the Slovak Paradise (Slovenský raj), and 109 species in 16 m2 in the Porážky meadows in the White Carpathians (Bílé Karpaty). The previous world record of 43 species in 0.1 m2 was equalled in the Čertoryje meadows in the White Carpathians, however, the previous record referred to shoot presence while the new record considers only the species rooted in the plot. We interpreted and corrected the data from the Czech Republic that Wilson et al. (2012) used to compile a list of world records and provide an updated list. The updated list contains five world records from the Czech Republic and two from Slovakia. The most species-rich grasslands and forests in the Czech Republic and Slovakia are concentrated in regions with base-rich soils in the Western Carpathians, especially in the flysch zone in SE Moravia and the Czech-Slovak borderland, and in limestone and volcanic areas in central Slovakia. The richest types of non-forest vegetation include semi-dry base-rich meadows (Bromion erecti and Cirsio-Brachypodion pinnati), base-rich pastures and mesic meadows (Cynosurion cristati and Arrhenatherion elatioris), Nardus stricta grasslands (Violion caninae and Nardo strictae-Agrostion tenuis) and some wet meadows and natural subalpine grasslands. A special type of species-rich herbaceous to open woodland vegetation develops as successional stages on gravel accumulations in Carpathian rivers after severe flooding. The maximum counts of vascular plant species in non-forest vegetation in the Czech Republic and Slovakia are 7 species/0.0009 m2, 11/0.0011 m2, 12/0.004 m2, 17/0.0044 m2, 23/0.01 m2, 37/0.04 m2, 43/0.1 m2, 52/0.25 m2, 63/0.5 m2, 82/1 m2, 88/4 m2, 109/16 m2, 116/25 m2, 131/49 m2 and 133/100 m2. While the maximum counts for plots smaller than 0.5 m2 are from various regions and probably mainly depend on appropriate management, the maximum counts for plots larger than 0.5 m2 are for two areas only, the south-eastern part of the White Carpathians and Kopanecké lúky meadows, suggesting the importance of regionally specific landscape processes for high species richness at such scales. Czech and Slovak forest vegetation is much poorer than grasslands, reaching maxima of 100, 109 and 118 species in plots of 100, 400 and 500 m2, which are considerably smaller than global maxima for temperate forests. Most of the species-rich sites occur on base-rich soils, in habitats with intermediate values of environmental factors, are subject to low-intensity management or natural disturbance, occur in landscapes with large areas of natural and semi-natural vegetation and probably have a long historical continuity.
News Article | February 3, 2016
Although there are >2,800 plant species in Great Britain27, only 1,341 of them are common enough to have been encountered in the Countryside Survey. Of these, the 454 commonest species accounted for 99% of national plant cover in 2007. More than half of these 454 species are unrewarding to pollinators (mainly bryophytes, pteridophytes, gymnosperms and wind-pollinated angiosperms28), leaving 220 species that are likely to contribute substantially to floral resources at a national scale. We focus here on these 220 species, along with an additional 50 species that we believe to be locally important floral sources (for example, Buddleja davidii, Impatiens glandulifera, Knautia arvensis). Together, these 270 plant species provide a focal set of potential importance in national nectar provision (Supplementary Table 11). Of the 270 species, 175 were surveyed in the field from February 2011 to October 2012, mainly in the south of England. When possible (112 species), nectar was collected from plants in at least two populations in two locations. For three species (Caltha palustris, Lamium purpureum and Sinapis arvensis), half the nectar samples, and for Viola arvensis all the samples, were collected from pot-grown plants, because insufficient flowering field populations were found. For the remaining species, nectar was collected from plants in one field population. When possible, the different populations were sampled on different dates, thus providing some measure of variation due to differences in location and weather. Note that nectar was collected in only 1–2 sites per species, and so intraspecific variation in production per flower was not assessed (but see Supplementary Information). Nectar was collected from ten single flowers in each population between 09:00 and 16:00 h (median 20 and range 5–30 flowers collected per species in total; see Extended Data Fig. 6 and Supplementary Information for site correlation); these had been bagged (using 1.4 × 1.7 mm fabric mesh) for 24 h to prevent depletion by nectar-feeding insects. When possible (76 species), glass microcapillaries (1 and 5 μl Minicaps, Hirshmann, Eberstadt, Germany) were used directly to collect the nectar, otherwise single flowers were rinsed twice with 1–5 μl of distilled water added to the nectaries with a pipette for 1 min, and the diluted nectar solution was collected. The sugar concentration of nectar (%; g sucrose per 100 g solution) was measured by using a hand-held refractometer modified for small volumes (Eclipse, Bellingham and Stanley, Tunbridge Wells, UK). The amount of sugar produced per flower basis over 24 h (s; μg of sugars per flower per 24 h) was calculated using the formula29 s = 10dvC where v is the volume collected (μl), and d is the density of a sucrose solution at a concentration C (g sucrose per 100 g solution) as read on the refractometer. The density of the sucrose solution was calculated by the formula29 d = 0.0037921C + 0.0000178C2 + 0.9988603. The number of open flowers per unit area of vegetative cover (flower density) was estimated for 179 species by placing five quadrats (0.5 m × 0.5 m) haphazardly on each flowering population (median 10 quadrats, range 1–20 quadrats; see Extended Data Fig. 6 and Supplementary Information for site correlation). In each quadrat, we counted the number of open floral units of the focal species (a ‘floral unit’ is one or multiple flowers that can be visited by insects without flying30; for example a composite flower head of daisy, Bellis perennis). We also counted the number of open flowers present in one typical open floral unit in each quadrat. Vegetative cover for each plant species was estimated using a point-quadrat approach with the cross-strings of the quadrat: cover was expressed as proportional to the number of the 36 cross-points covered by the foliage of the species of interest in each quadrat. For trees, instead of using quadrats, we counted the number of floral units in a 3D cube (0.5 × 0.5 × 0.5 m) that was placed in the outer areas of foliage. This was extrapolated to the whole column situated above the unit of vegetative cover by measuring the height of tree foliage with an inclinometer (PM-5/360 PC Suunto) and by estimating the distribution of the flowers within the tree foliage (subjectively assessed scores: from 1 for a strongly biased flower distribution on the outer edges of the foliage to 5 for a homogeneous full flower distribution). Given that flower density is not constant throughout the flowering season, we estimated variations in flower density according to a triangular function from the estimated peak of flowering through the flowering season which was documented from recorded phenologies28, 31, 32 (see Supplementary Information and Extended Data Fig. 6 for phenology parameter relationships). An alternative nectar rectangular phenology productivity database was also generated by keeping nectar productivity of each species constant throughout the flowering season; this was used to perform sensitivity analyses. The mean nectar sugar content from a single flower (produced over a 24 h period) was multiplied up to the nectar content of a single floral unit (number of flowers in a floral unit), then to the amount of nectar per unit area (number of flowers per m2), to the amount of nectar per unit area for each month (variation in flower density over the flowering season) and finally to the amount of nectar per unit area per year. Despite relatively low sample sizes per species compared to species-specific studies, our estimates of sugar production were well correlated with published values both per flower per day and per area per year (Extended Data Fig. 6 and Supplementary Information). This empirical method provided the nectar productivity values for 161 plant species among the 175 initially surveyed (nectar productivity could not be scaled up for some species due to mismatches with phenological data, see Supplementary Information). To model the nectar productivity of the plant species that could not be surveyed in the field, we used a predictive modelling approach. We first analysed variation in the nectar values from the surveyed species. A linear model was fitted to annual nectar sugar productivity (log (x + 1) transformed) as a function of plant traits. Plants traits were mainly collected from the BiolFlor database33, and included: ‘flower shape’, ‘breeding system’, ‘life span’, the degree of ‘dicliny’, the maximum ‘height’, the ‘flowering period’ and ‘family’ (see Supplementary Information for definitions). The estimates from the most parsimonious statistical model based on AIC criterion (Supplementary Table 6, N = 153; adjusted r2 = 0.55) were used to predict the annual nectar sugar productivity for the initial list of surveyed and unsurveyed species on the basis of their traits. To check the validity of the predicted values, we adopted a repeated ‘leave-one-out’ approach to model successively all the excluded values from the empirically derived data sets. Then, we applied a standardized major axis regression on the log (x + 1) transformed empirically derived and modelled nectar values of the surveyed species (Extended Data Fig. 6). We predicted the nectar values for 252 species; and giving priority to empirical and default values, we included 94 of them in our database. An alternative nectar productivity database was also generated by considering only the species with empirical nectar values; this was used to perform sensitivity testing. For four crop species harvested before flowering—onion (Allium cepa), cabbage (Brassica oleracea cultivated), turnip (Brassica rapa) and radish (Raphanus sativus)—we assigned a value of zero for nectar productivity. A zero value was also assigned to Helianthemum nummularium, despite the missing flower density data, given that we collected no nectar in flowers. In the Countryside Survey vegetation data set, some taxa are only identified at the genus level; we interpreted these taxa to represent the commonest species in the genus (for example, Centaurea sp. was interpreted as Centaurea nigra). For 10 species out of the initial list of 270 it was not possible to quantify nectar production, leading to a total of 260 species with quantified annual and monthly nectar productivity values (161 values from empirical research, 94 modelled values, and 5 default values, Supplementary Table 11). All the above steps of scaling-up process are summarized in Supplementary Table 7. Spatio-temporal variations in nectar provision at the national scale were calculated by combining our nectar productivity data set with vegetation and land cover data already recorded during the Countryside Survey19. The Countryside Survey is a national survey of plant communities conducted in 1978, 1990, 1998 and 2007 in Great Britain (England, Wales and Scotland). The survey was conducted by selecting 1-km sample squares at random from 32 land classes19 representing physiographically similar sampling domains throughout Great Britain, ensuring an unbiased representation of the British non-urban landscape. Within each square, a random, stratified sample of five areal (nonlinear) square plots (200 m2) was established and the presence and the percentage cover of all vascular plant species were recorded. These plots were classified to 17 habitat classes, but we only used data from 11 habitats: acid grassland, arable land, bog, bracken, broadleaf woodland, calcareous grassland, conifer, fen, improved grassland, neutral grassland and shrub heath (Supplementary Table 8 for habitat description). The habitats not used were inland rock, littoral rock/supralittoral rock, littoral sediment/supralittoral sediment, montane and urban habitats; these were excluded due to low sample sizes. Even though urban habitats probably contribute to the national nectar provision, we were unable to include this habitat in this study because the Countryside Survey was not designed to survey urban areas. In 1.14% of Countryside Survey plots, two or more habitats were attributed to the same plot; these were excluded for this study. Additional plots were used to sample linear features in each 1 km square, covering hedgerows, streamsides and road verges (1 × 10 m and oriented along the linear feature). Each linear plot was also attributed to its nearest adjacent habitat. To investigate the most recent nectar patterns, we used the most comprehensive vegetation data set from the Countryside Survey 2007 that encompasses all nonlinear plots (2,576 plots in 2007). To focus on linear features, we included vegetation data from linear features plots (1,951 plots in 2007). To test for historical changes from 1978 to 2007, we used vegetation data from nonlinear plots shared between the 1978, 1990, 1998 and 2007 Countryside Surveys (529 shared plots in England and Wales and 768 in Great Britain; Supplementary Table 9). We focused on the shared plots across years because the Countryside Survey sampling design was modified over time (for example, from fixed to proportional plot number per land class from 1978 to 1990). The annual nectar productivity within each plot (kg per ha per year) is the sum of the nectar productivity of each species (kg per ha cover per year) weighted by their vegetative cover in the plot (%), assuming that the vegetative cover is representative of floral abundance (see Extended Data Fig. 7 and Supplementary Information for details). Nectar productivity values of plots were used to statistically estimate the annual nectar productivity for each habitat (kg per ha per year). The annual nectar provision of each habitat (kg per year) was computed from their annual habitat nectar productivity (kg per ha per year) multiplied by their respective national land covers for each survey (areas of habitats in ha from Countryside Surveys19, 34, 35; Supplementary Table 5). These were summed to estimate the annual national nectar provision in 1978, 1990, 1998 and 2007. For the 1930s period, areas of habitats (only available for England and Wales) were derived from the digitalized Dudley Stamp land utilization survey maps20; see Supplementary Information and Supplementary Table 5). Because nectar productivity can’t be assessed for this period, we quantified nectar provision in 1930, 1978, 1990, 1998 and 2007 assuming unchanged nectar productivity within habitats but using observed shifts in land cover among habitats across time. The national nectar provision of hedgerows was calculated from their mean nectar productivity (kg per ha per year) multiplied by their estimated area in England (length of hedgerows from Countryside Survey 2007 for England35, assuming a 1 m width). The contribution of habitat or species to the national nectar provision in 2007 is the fraction of nectar provided by these entities (in %). The amount of nectar offered by each habitat in 2007 is calculated from habitat nectar productivity (estimated value of habitat productivity) multiplied by its national area. The amount of nectar offered by each species in 2007 is calculated from the sum of its average nectar productivity stratified by habitat and multiplied by habitat national area. The contribution of habitat or species to the historical changes in national nectar provision is expressed by the absolute change (in kg of sugars), which is the difference in the amount of nectar produced by the entity during the time period considered. Relative change (in %) which is the absolute change multiplied by 100 and divided by the amount of nectar produced at the initial date, refers to the magnitude of change for each entity. Nectar diversity was estimated through two Shannon indexes (using ‘vegan’ package in R36) that encompass both the richness and the evenness of nectar producing sources (see Supplementary Information). The species nectar diversity index, based on the proportion of nectar produced by each species, was calculated as follows: where p is the proportional nectar contribution of plant species i and S is the total number of plant species in each plot. The functional nectar diversity index, based on the proportion of nectar produced by each floral morphology group, reflects the diversity of nectar sources in terms of resource accessibility for flower-visiting insects. Flower types were derived from Müller flower classification system recorded from the BiolFlor database33, which was condensed into five classes: pollen rewarding flowers, open, partly hidden, hidden, and bee flowers (see Supplementary Information). The functional nectar diversity index was computed as follows: where p is the proportional nectar contribution of flower type i and S is the total number of flower types in each plot. The annual nectar productivity (kg of sugars per ha per year), species nectar diversity (Shannon index of nectar contribution of plant species) and functional nectar diversity (Shannon index of nectar contribution of floral morphology groups) in 2007 were mapped at the British national scale using the Great Britain Land Cover Maps of 200737. Various options are available for managing habitats to provide floral resources for pollinators, some of which are eligible for grant aid under European Union funded agri-environment schemes. Agri-environment options within the English ‘Environmental Stewardship’ scheme included sowing nectar flower mixtures (EF4/HF4), sowing wild bird seed mixtures (EF2/HF2), creation or enhancement of floristically enhanced buffer strips (HE10), re-introduction or continuation of haymaking (haymaking supplement HK18) and creation, restoration and maintenance of species-rich semi-natural grassland (HK6/7/8). These five options were selected as the most likely to provide floral resources for pollinators. Field study sites were located on farmland and nature reserves in which the following replicates of the pollinator habitats were present: nectar flower mixtures (n = 32), wild bird seed mixtures (n = 4), enhanced field margins/road verges (n = 7), hay meadows (n = 5) and species-rich grasslands (n = 7). These were existing habitats representing ongoing management by the land owners or land managers concerned. Transects 100 m long × 6 m wide were established in each habitat. The number of floral units of each flowering species was recorded on 1 to 3 occasions, in 20 × 1 m2 quadrats per transect. Annual nectar productivity (kg of sugars per ha per year) was calculated for each species at each site from the average estimated nectar productivity at the peak of the flowering season derived from the several counts of floral units across the flowering period (analogous to Supplementary Information). The values for the species present in each habitat were then summed to estimate productivity for each habitat. National areas of options providing floral resources in the English agri-environment scheme ‘Environmental Stewardship’ were extracted for 2007 for England (data for Great Britain were unavailable) from data supplied by Natural England38, 39. Mean nectar productivity per unit area was multiplied by the national area of each option to give nectar provision by that option (kg of sugars per year). The total contribution of nectar provision provided by Environmental Stewardship in England is a minimum value, as it has been compared to national provision estimated from vegetative cover rather than direct flower counts and we did not take into account the more limited floral resources potentially provided by other options. No statistical methods were used to predetermine sample size. Statistical analyses were carried out with Linear Mixed-Effect Models (lme function from ‘nlme’ package) in R 3.0.1 (ref. 36). To investigate the most recent nectar variations (2007), we analysed the log (x + 1) annual nectar productivity, species nectar diversity and functional nectar diversity according to the type of habitat (“HABITAT”; 11 habitats) of the nonlinear plots. The differences in log (x + 1) nectar productivity, species nectar diversity and functional nectar diversity between nonlinear and linear features were analysed according to the type of habitat (“HABITAT”; 11 habitats), the type of vegetation surveyed (“TYPE”; nonlinear vs linear features) and the interaction between these two terms. Countryside Survey square (“SQUARE”) was included as a random term in these models in order to account for the spatial auto-correlation of plots nested into 1 km squares. In order to investigate historical changes over recent decades (1978–2007), we analysed the log (x + 1) annual nectar productivity, species nectar diversity and functional nectar diversity computed from the shared nonlinear plots in 1978, 1990, 1998 and 2007 according to the type of habitat (“HABITAT”), the year (“YEAR”) considered as a categorical factor, and the interaction between these two terms. We included plots nested within square (“SQUARE/PLOTS”) as random terms to account for the spatial and temporal auto-correlation of the data in this latter model. This latter statistical test was repeated considering all shared plots in Great Britain or only those in England and Wales to provide estimates of habitat nectar productivity across time for distinct areas, allowing comparisons with earlier (1930s) habitat information only available for that latter area. Significant differences among modalities were analysed with multiple comparisons (single-step method adjusted P-values from glht function in ‘multcomp’ package in R36). Letter-based representation of all multiple comparisons was achieved from multcompLetters function in ‘multcompView’ package in R36. Model residuals were plotted to visually check that normality and homoscedasticity assumptions were satisfied. We re-ran the same analyses with the Countryside Survey vegetation data combined with (1) the alternative nectar rectangular phenology productivity database (created by keeping constant nectar productivity of each species during the flowering season); and (2) using only the empirical nectar productivity database, as sensitivity tests (Extended Data Fig. 4 and Supplementary Information). Plots were performed with ggplot2 package in R36. All box plots show the median, 25th and 75th percentiles (lower and upper hinges), trimmed ranges that extend from the hinges to the lowest and highest values within 1.5× inter-quartile range of the hinge (lower and upper whiskers) plus outliers (filled circles). Notches that extend 1.58× inter-quartile range/square root of the number of observations were represented to give a roughly 95 interval for comparing medians.
Klimesova J.,Academy of Sciences of the Czech Republic |
Janecek S.,Academy of Sciences of the Czech Republic |
Hornik J.,Centaurea |
Hornik J.,University of South Bohemia |
Dolezal J.,Academy of Sciences of the Czech Republic
Preslia | Year: 2011
The role of clonal traits in a plant's response to changes in management of semi-natural grasslands is poorly known and the few studies examining their importance have yielded contradictory results. For a better understanding of the role of plant functional traits in determining competitive ability and clonal growth in response to early changes in management, we mowed and applied fertilizer to 22 wet meadows in the Železné hory Mts, Czech Republic.We used two methods of assessing abundance (plant cover and species frequency) to determine whether changes in frequency induced by changes in management are better predicted by clonal traits while changes in cover are mainly determined by competitive traits such as plant height. We evaluated (i) the response of individual species to changes in management and (ii) the response of the whole community, with and without taking abundance of individual plants into account, in order to separate the effect of local extinction and immigration from changes inabundance. The plant functional traits tested were generally found to be important soon after the changes in the management of the semi-natural grasslands occurred: competitively superior resident species (possessing tall erosulate, monocyclic shoots) that are able to spread far and multiply clonally (having a high clonal index) were favoured by applying fertilizer and/or suppressed by mowing. Some other traits supposed to be important in the response to changes in management did not change (persistence of connection between ramets). Results for the two methods of assessing abundance differed; however, neither was better at detecting the response of particular types of traits (i.e. relevant to clonal growth and competitive ability). The initial response of the whole community, with and without taking abundance of individual plants into account, was consistent indicating that species that went extinct possessed the same traits as those that decreased in abundance. The clonal index proved to bea useful characteristic of meadow plants. Our results further imply that (i) the method used to assess abundance significantly affects the output of analyses of the response of functional traits, and (ii) a comparison of analyses based on weighting abundance and unweighted means resulted in a deeper insight into the changes in the spectra of functional traits that occurred after changes in meadow management.
Camerlink I.,Wageningen University |
Ellinger L.,Centaurea |
Bakker E.J.,Wageningen University |
Lantinga E.A.,Wageningen University
Homeopathy | Year: 2010
Background: The use of antibiotics in the livestock sector is increasing to such an extent that it threatens negative consequences for human health, animal health and the environment. Homeopathy might be an alternative to antibiotics. It has therefore been tested in a randomised placebo-controlled trial to prevent Escherichia coli diarrhoea in neonatal piglets. Method: On a commercial pig farm 52 sows of different parities, in their last month of gestation, were treated twice a week with either the homeopathic agent Coli 30K or placebo. The 525 piglets born from these sows were scored for occurrence and duration of diarrhoea. Results: Piglets of the homeopathic treated group had significantly less E. coli diarrhoea than piglets in the placebo group (P < .0001). Especially piglets from first parity sows gave a good response to treatment with Coli 30K. The diarrhoea seemed to be less severe in the homeopathically treated litters, there was less transmission and duration appeared shorter. © 2009.
Janecek S.,Academy of Sciences of the Czech Republic |
de Bello F.,Academy of Sciences of the Czech Republic |
de Bello F.,University of South Bohemia |
Hornik J.,Centaurea |
And 12 more authors.
Journal of Vegetation Science | Year: 2013
Questions: To what extent do changes in management (abandonment and fertilization) affect plant functional and taxonomic diversity in wet meadow communities? To what extent do the changes in functional and taxonomic diversity depend on site productivity? Location: Železné hory Mts., Czech Republic. Methods: Experimental plots were established on 21 wet meadows differing in productivity and species composition. In each meadow, in 2007, four 1 × 1 m plots were established, representing a full factorial design with abandonment and fertilization as the factors. In each plot, the number of species present was recorded in 100 subplots (0.1 × 0.1 m) in the years 2007, 2009 and 2011. Different indicators of functional diversity (functional richness, functional evenness, and Rao′s quadratic entropy) were calculated using five functional traits (SLA, LDMC, seed mass, plant height and clonality). Both abundance-weighted and non-weighted diversity indices were calculated. Randomization tests (conducted with PERMANOVA) were used to assess the effect of site productivity and management on both α- and β-diversity components. Results: Meadows along the productivity gradient differed in functional and taxonomic diversity. Both abandonment and fertilization decreased taxonomic diversity. Whereas fertilization decreased functional richness and Rao′s quadratic entropy, abandonment decreased functional evenness. The changes in both taxonomic and functional diversity caused by abandonment and fertilization occurred faster in more productive meadows. Conclusions: The increased dominance of tall species with abandonment and fertilization, followed by the loss of species and the decrease in various indicators of functional diversity, suggest that increased competition for light resulted in increased trait convergence among co-existing species. In addition, many processes occurring after abandonment and fertilization depend on meadow productivity. Results suggest that abundance- and non-abundance-weighted diversity indices give complementary insights on community structure. These results imply that changes are needed in current meadow management and conservation. © 2012 International Association for Vegetation Science.