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Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biodiversity of seminatural meadows under various management regimes:

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Presentation on theme: "Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biodiversity of seminatural meadows under various management regimes:"— Presentation transcript:

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2 Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biodiversity of seminatural meadows under various management regimes: a 16 years experimental study

3 OHRAZENI – a seminatural meadow Regular mowing ceased in late eighties

4 Molinia caerulea Nardus stricta

5 Species diversity and “interesting plants” (e.g. red list species) concentrated in “traditional”, i.e. mown, unfertilized Dactylorhiza majalis Senecio rivularis

6 Carex pulicaris C. hartmanii 14 Carex species

7 Scorzonera humilis

8 Myosotis nemorosa

9 Factorial experiment, 3 replications Mowing (once a year, in June) Ferilization (65 [50] g of commercial NPK/m 2 - 12% N (nitrate and ammonium), 19% P (as P 2 O 5 ) and 19% K (as K 2 O)) Dominant (i.e. Molinia caerulea) removal (in spring 1995, but re-weeding necessary time from time) Yielding 24 plots, 2m × 2m each Central 1m x 1m sampled, followed by detailed analysis of 50cm × 50cm grid of 10cm × 10 cm – including seedling counts Experiment started 1994, baseline data available

10 Ohrazení (http://mapy.atlas.cz)

11 Detailed recording of vegetation in all the 16 years Sprouts of woody plants removed

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13 Response of plant community to the treatments in terms of Species richness and composition Species traits –Methodological notes: how to analyse the species traits?

14 Mown - unfertilized (=traditional)

15 Mown - unfertilized & Molinia removed

16 Mown - fertilized (=intensive)

17 Mown - fertilized & Molinia removed

18 Unmown - unfertilized (=abandoned)

19 Unmown - unfertilized & Molinia removed

20 Unmown - fertilized (=abandoned eutrofized meadow)

21 Unmown - fertilized & Molinia removed

22 Problem for interpretation, in late years, Molinia removal would have little effect in fertilized mown (as it have very low cover also in the control plots) Error bars =95% confidence intervals

23 Species richness

24 In mown plots, increase of the number of species during first six years, regardless of removal of Molinia, in unmown plots, removal has positive effect on species richness NSP of vascular plants per m 2

25 In unmown plot, continuous decrease. Initial positive effect of removal ceased after 10 yrs. In mown plots, initial increase (5 years) followed by decrease, no effect of removal.

26 Take home message Increase in soil nutrients can lead to competitive exclusion - nevertheless, in community of established perennial plants, the exclusion can take rather long (six years in our case).

27 Species richness dynamics depends on spatial scale Repeated measures ANOVA 3 main plot factors (Mowing, Fertilization, Removal) 2 Rep Mes faktors – year and plot size (from 10x10cm 2 to 50x50cm 2 ) Number of species log transformed – i.e. relative change of the richness Very long ANOVA table

28 Only selected terms will be interpretted Many interactions significant, effects are not additive

29 During the first eight years, number of species increases on small plots, but is constant on larger plots

30 Positive effect of mowing is most pronounced on the small spatial scale Data from 2008, averaged over fertilization and removal treatments 10x10 50x50

31 In small plots, the effect of removal is more pronounced in non-fertilized plots, in large plots, in fertilized plots

32 Seedling number - Average over 1996-2007

33 Species composition

34 DCA - Molinia is passive species - log transformed cover. Starting points in 1994 show the random variability - the divergence of trajectories show the differentiation according to treatments between 1994 and 2007. Centroids of Year x Treatment

35 Principal response curves triangles - mown circles unmown full symbol - fertil. open symbol - unfert. solid line - control broken l. - removal

36 Principal response curves Multivariate counterpart of Repeated measurement ANOVA - the first axis, which is plotted against the time, captures the main differentiation among categories of YEAR * TREATMENT interaction The common temporal trend is subtracted from the data - YEAR as covariable(s) The horizontal axis corresponds to the control (in our case, unmown, unfertilized, no removal)

37 Principal response curves triangles - mown circles unmown full symbol - fertil. open symbol - unfert. solid line - control broken l. - removal

38 The species lost from the community under various management types Are not a random subsample, but species with specific ecological characteristics (contrary to Hubbell’s theory) IMPORTANCE OF SPECIES TRAITS (mostly based on 2004 biomass data)

39 Two approaches to analyse species trait response Species based: can we predict the species response on the basis of its traits? –Traits are predictors of species response (trait value is a fixed characteristics of individual species) Community (plot) based: how do the community (weighted) average [or variability] respond to environmental characteristics? –Traits (averages, variability) are response. Trait plasticity can be evaluated

40 Species based approach 1. Calculate the environmental response for each individual species (we have used constrained ordination framework / RDA) 2. Predict the species response on the basis of species traits (various regressions, regression trees)

41 Species response to fertilization (RDA score, positive values mean that the species gains from fertilization)

42 Species response to mowing (RDA score, positive values mean that the species is supported by mowing)

43 Regression tree prediction of response to mowing Unassisted seed disperals Tall plants rosettes

44 Potential height is good predictor of response to fertilization and mowing With increasing nutrients, the plants are released from competition for nutrients, but simultaneously, the importance of competition for light increases - the taller plants are in advantage. The taller plants are harmed more by mowing Higher asymmetry of competition for light (in comparison with competition for nutrients) explains decline in species richness.

45 Methodological note 1 Regression tree – highly non-parametric regression approach –Well designed to account for the non-additivity The tall plants are always harmed by mowing In a group of “not so tall” plants, also other factors play a role: e.g. those with a rosette respond more positively

46 Methodological note 2 Species based approach – species are considered independent observations – problem of phylogenetic relatedness –Do we need phylogenetic correction? –To which extend could be the similarity of species responses explained by the similarity of their traits, and to which extend by the phylogenetic relatedness?

47 The role of the dominant (Molinia caerulea) consequences of its removal

48 Litter - Molinia produces large amount of slowly decaying litter. Its removal causes decrease of litter amount (with exception of mown & fertilized conditions) As a consequence, seedling recruitment is supported by Molinia removal. Molinia competes also after its death.

49 In the presence of Molinia, the peak of biomass is shifted from June to August (and is slightly higher) - only weak statistical support Unfertilized plots only shown The presence of a single species can considerably shift the seasonal biomass dynamics

50 In mown plots, the removed Molinia is replaced by other grasses, however, there is no other grass which would be able to replace Molinia in unmown plots. Take home message: The dominant species (when removed) is not always replaced with the species from the same functional group.

51 Molinia is rather extreme in some traits, e.g. Very late phenology Very slow rate of litter decomposition Very deep and strong roots Extremely constant biomass over the years It is very likely that the presence/absence of Molinia has crucial role in various ecosystem processes (e.g. nutrient cycling)

52 Iva Spackova, Alena Vitova, Petr Macek, Francesco de Bello, Jiri Dolezal, Vojtech Lanta, Jonathan Titus, Eva Chaloupecka, Katerina Palkova, David Zeleny Thanks for the help Thanks for money Czech Science Foundation (GACR) Framework V – VISTA project

53 Thank you for your attention


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