2. Effect of mowing, fertilization and dominant removal on ecosystem characteristics and species trait composition. Jan Leps, Jiri Dolezal, and David Zeleny Department of Botany, University of South Bohemia, Ceske Budejovice, Czech Republic VISTA Project

3. Mowing, Fertilization, Dominant removal in factorial experiment (eight possible combinations), each in three replications 2m x 2m plots - central 1m 2 (or smaller) sampled Mowing and fertilization: feasible combinations of land (un)use types Molinia removal: effect of dominant under different regimes Experiment started in 1994 (baseline data) Data from 2004 are used here

4. Where: Ohrazeni meadow, South Bohemia, southern part of the Czech republic Molinion (Molinia caerulea) dominated meadow Originally mown once a year

7. Traditional meadow: mown, unfertilized, without removal; up to 40 species per m 2

8. Unmown, unfertilized, no removal: Molinia dominant, litter abundant abandoned meadow

9. Unmown, unfertilized, Molinia removed

10. Fertilized, mown, no removal (productive meadow)

11. Fertilized, unmown, no removal: abandoned eutrofized meadow

12. RDA - “ecosystem properties”

13. Number of species - Fertilisation has the fairly strongest effect (negative) - mowing and removal positive effect

15. Molinia suppresses species richness only in unmown plots Unfertilised plots

16. H’ (and similarly evenness) is affected mainly by mowing (positively) Evenness: no effect is significant, but relatively largest effect of mowing

18. Mowing - effect on litter; large amount of litter suppresses vegetation development in spring Molinia - the largest producer of litter, particularly in unmown plots

19. Response of species traits to treatments - three matrices

20. Use of weighted averages - each site is characterized by average value of traits of constituent species, weighted by species abundance (e.g. proportion in biomass) For the categorial variables, we get proportion of each category. To be analyzed by Redundancy analysis (RDA - for all variables together), or by GLM (ANOVA) for individual variables separately

21. RDA - “species traits” (treatment specific)

22. Proportion of four (functional?) groups in various treatments.

23. In mown plots, other grass species replaced the removed Molinia successfully, in unmown, the empty space was occupied by forbs and graminoids In unmown plots, there is no functional replacement for removed dominant (Molinia)

24. Constant and plastic traits: e.g. life form is not affected by either mowing or fertilization, it is constant. Plant height (and similarly leaf nutrient content, specific leaf area - SLA, etc.) change with condition, sometimes considerably. Change in aggregated characteristics can be caused either by change in species composition (for height: daisy is replaced by sunflower) or by trait variability (when fertilized, daisy grows taller). [Without further study, it is impossible to distinguish pure phenotypic plasticity from genetically caused variability.] Suggested method: Repeated measurement (=split plot) ANOVA, treatments being the between plot effects, and the two trait values being the repeated measure: nonspecific (average over treatments) and specific for each treatment. The interaction between specificity and main effect signifies variability of the trait.

25. Plant height Ferilized plots support species that are genuinely taller. Comparison of nonspecific (constant for all the treatments) and specific values suggests that in mown plots, the same species are taller in fertilized conditions. In mown plots, the effect is more pronounced and the direction of species composition change and trait variability is the same.

26. Reversed question: Could species traits predict species response to treatments? (Useful e.g. in conservation studies.) First calculate the species response to a factor (by partial constrained RDA), and then try to predict the response with species trait(s) used as predictors. Plants not able to grow tall disappear after fertilization

27. Cirsium palustre

28. The approach based on aggregated averages is dependent on dominants - it is useful for studying species traits together with ecosystem functioning. The trait plasticity can be distinguised from change in species composition. However, the subordinate species are not reflected in the analyses. The approach based on individual species needs fixed species traits (because they are predictors). It is more useful in nature conservation and biodiversity projects (e.g. to estimate, which species might be endangered due to land use changes).

29. Thank you for your attention!