General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding

Insurance effects of biodiversity can stabilize the functioning of multispecies ecosystems against environmental variability when differential species’ responses lead to asynchronous population dynamics. When responses are not perfectly positively correlated, declines in some populations are compensated by increases in others, smoothing variability in ecosystem productivity. This variance reduction effect of biodiversity is analogous to the riskspreading benefits of diverse investment portfolios in financial markets. We use data from the BIODEPTH network of grassland biodiversity experiments to perform a general test for stabilizing effects of plant diversity on the temporal variability of individual species, functional groups, and aggregate communities. We tested three potential mechanisms: reduction of temporal variability through population asynchrony; enhancement of long-term average performance through positive selection effects; and increases in the temporal mean due to overyielding. Our results support a stabilizing effect of diversity on the temporal variability of grassland aboveground annual net primary production through two mechanisms. Two-species communities with greater population asynchrony were more stable in their average production over time due to compensatory fluctuations. Overyielding also stabilized productivity by increasing levels of average biomass production relative to temporal variability. However, there was no evidence for a performance-enhancing effect on the temporal mean through positive selection effects. In combination with previous work, our results suggest that stabilizing effects of diversity on community productivity through population asynchrony and overyielding appear to be general in grassland ecosystems

The role of legumes as a component of biodiversity in a cross- European study of grassland biomass nitrogen

To investigate how plant diversity loss affects nitrogen accumulation in above-ground plant biomass and how consistent patterns are across sites of different climatic and soil conditions, we varied the number of plant species and functional groups (grasses, herbs and legumes) in experimental grassland communities across seven European experimental sites (Switzerland, Germany, Ireland, United Kingdom (Silwood Park), Portugal, Sweden and Greece). Nitrogen pools were significantly affected by both plant diversity and community composition. Two years after sowing, nitrogen pools in Germany and Switzerland strongly increased in the presence of legumes. Legume effects on nitrogen pools were less pronounced at the Swedish, Irish and Portuguese site. In Greece and UK there were no legume effects. Nitrogen concentration in total above-ground biomass was quite invariable at 1.66 +/- 0.03% across all sites and diversity treatments. Thus, the presence of legumes had a positive effect oil nitrogen pools by significantly increasing above-ground biomass, i.e. by increases in vegetation quantity rather than quality. At the German site with the strongest legume effect on nitrogen pools and biomass, nitrogen that was fixed symbiotically by legumes was transferred to the other plant functional groups (grasses and herbs) but varied depending on the particular legume species fixing N and the non-legume species taking it up. Nitrogenfixation by legumes therefore appeared to be one of the major functional traits of species that influenced nitrogen accumulation and biomass production, although effects varied among sites and legume species. This study demonstrates that the consequences of species loss on the nitrogen budget of plant communities may be more severe if legume species are lost. However, our data indicate that legume species differ in their N-2 fixation. Therefore, loss of an efficient N-2-fixer (Trifolium in our study) may have a greater influence on the ecosystem function than loss of a less efficient species (Lotus in our study). Furthermore, there is indication that P availability in the soil facilitates the legume effect on biomass production and biomass nitrogen accumulation

Biodiversity and ecosystem functioning: reconciling the results of experimental and observational studies

Biodiversity and ecosystem functioning research has been some of the most controversial of the last decade but rapid progress has been made by deriving hypotheses from the differing view points and challenging them with appropriate experimental and analytical tests (Loreau et al. 2001). Here we address some recent criticisms of the BIODEPTH project (Thompson et al. 2005) and show that: 1. While legume species play an important role in the BIODEPTH results, patterns are not generally consistent with the multispecies sampling effect for legumes proposed by Huston & McBride (2002) as suggested in Thompson et al. (2005). 2. The BIODEPTH results are also not consistent with transient biodiversity effects. Levels of species diversity were generally maintained over the 3 years of the project (i.e. little competitive exclusion) and diversity-productivity relationships in BIODEPTH generally strengthened during the experiments

Appendix B. Summary of the analysis of the temporal CVs as a function of species richness and the organizational level.

Summary of the analysis of the temporal CVs as a function of species richness and the organizational level

Supplement 1. The data sets used in the paper with detailed descriptions.

<h2>File List</h2><blockquote> <table> <tbody><tr> <td><div>1.</div></td> <td><a href="BiodepthStability.zip">BiodepthStability.zip</a></td> </tr> <tr> <td><div>2.</div></td> <td><a href="BiodepthSpeciesBiomass.txt">BiodepthSpeciesBiomass.txt</a></td> </tr> <tr> <td><div>3.</div></td> <td><a href="BiodepthSpeciesCodes.txt">BiodepthSpeciesCodes.txt</a></td> </tr> <tr> <td><div>4.</div></td> <td><a href="BiodepthSpeciesNames.txt">BiodepthSpeciesNames.txt</a></td> </tr> <tr> <td><div>5.</div></td> <td><a href="BiodepthBiomass.txt">BiodepthBiomass.txt</a></td> </tr> <tr> <td><div>6.</div></td> <td><a href="TemporalCVs.txt">TemporalCVs.txt</a></td> </tr> <tr> <td><div>7.</div></td> <td><a href="TemporalCVsRandomized.txt">TemporalCVsRandomized.txt</a></td> </tr> <tr> <td><div>8.</div></td> <td><a href="SpatialCVs.txt">SpatialCVs.txt</a></td> </tr> <tr> <td><div>9.</div></td> <td><a href="CVandMeanDbar.txt">CVandMeanDbar.txt</a></td> </tr> <tr> <td><div>10.</div></td> <td><a href="TempCVCovar2Mix.txt">TempCVCovar2Mix.txt</a></td> </tr> <tr> <td><div>11.</div></td> <td><a href="RankBiomass.txt">RankBiomass.txt</a></td> </tr> <tr> <td><div>12.</div></td> <td><a href="Eveness.txt">Eveness.txt</a></td> </tr> </tbody></table> </blockquote><h2>Description</h2><blockquote> <p>1. BiodepthStability.zip contains 11 data files described below. Data files are in ASCII format (tab-delimited text files).<br> <br> 2. BiodepthSpeciesBiomass.txt contains 5802 datapoints with the following variables:<br> year: variable for year of experiment 1 to 3<br> site: factor for site with 8 levels: Germany, Portugal, Switzerland, Greece, Ireland, Sweden, Sheffield (UK), Silwood (UK)<br> block: factor for blocks within each site with 15 levels (A to O), two levels per site except Portugal with one level only<br> plot: factor to distinguish each plot at each site with 480 levels<br> sr: variable for sown species richness with levels 1, 2, 3, 4, 8, 11, 12, 14, 16, 18, 32<br> fr: variable for sown functional richness with levels 1, 2, 3<br> fgc: factor for functional group composition. g for grass, f for forb and l for legume , levels 1-7, 1=g, 2=f, 3=l, 4=gf, 5=gl, 6=fl, 7=gfl<br> mix.nest: variable treating same composition from different sites as different "ecotypes", levels 1-235 (fully nested within sites)<br> mix: variable for identical species compositions, levels 1-200 (partially crossed)<br> grass,forb,legume: contrasts for functional groups presence in a mixture, levels 0 or 1 (No,Yes)<br> funct: factor for the three functional groups grasses, forbs and legumes, three levels g, f, l and NA (for community level responses)<br> GRASS,FORB,LEG: individual functional group contrasts, levels 0 or 1 (No,Yes)<br> species: factor to distinguish each species, identical Linnean species at different sites are coded as different "ecotype" species, 186 levels, species coding see below<br> biomass: aboveground biomass in g/m² on species over the first three years</p> <p> 3. BiodepthSpeciesCodes.txt contains:<br> speciescode: factor to distinguish each species, identical Linnean species at different sites are coded as different "ecotype" species, 186 levels, species coding: First letter stands for functional group (g=grass, h=herb, l=legume) followed by site number (1-8), followed by the first three letters of genus then the first three letters of the specific name and then a number to distinguish any doubles in the data set (non present in this data set)<br> genus: Linnean genus<br> species: Linnean species name <br>   <br> 4. BiodepthSpeciesNames.txt contains:<br> abbrevation: the first three letters of genus then the first three letters of the specific name<br> genus: Linnean genus<br> species: Linnean species name </p> <p> 5. BiodepthBiomass.txt contains:<br> year: variable for year of experiment 1 to 3<br> site: factor for site with 8 levels: Germany, Portugal, Switzerland, Greece, Ireland, Sweden, Sheffield (UK), Silwood (UK)<br> block: factor for blocks within each site with 15 levels (A to O), two levels per site except Portugal with one level only<br> plot: factor to distinguish each plot at each site with 480 levels<br> sr: variable for sown species richness with levels 1, 2, 3, 4, 8, 11, 12, 14, 16, 18, 32<br> fr: variable for sown functional richness with levels 1, 2, 3<br> fgc: factor for functional group composition. g for grass, f for forb and l for legume , levels 1-7, 1=g, 2=f, 3=l, 4=gf, 5=gl, 6=fl, 7=gfl<br> mix.nest: variable treating same composition from different sites as different "ecotypes", levels 1-235 (fully nested within sites)<br> mix: variable for identical species compositions, levels 1-200 (partially crossed)<br> grass,forb,legume: contrasts for functional groups presence in a mixture, levels 0 or 1 (No,Yes)<br> funct: factor for the three functional groups grasses, forbs and legumes, three levels g, f, l and NA (for community level responses)<br> GRASS,FORB,LEG: individual functional group contrasts, levels 0 or 1 (No,Yes)<br> species: factor to distinguish each species, identical Linnean species at different sites are coded as different "ecotype" species, 187 levels (with NA)<br> level: factor to distinguish stability measures at the species (Species), functional groups (Group) or mixture (Community) level<br> biomass: aboveground biomass in g/m² on species, functional group and community level over the first three years</p> <p> 6. TemporalCVs.txt contains:<br> site: factor for site with 8 levels: Germany, Portugal, Switzerland, Greece, Ireland, Sweden, Sheffield (UK), Silwood (UK)<br> sr: variable for sown species richness with levels 1, 2, 3, 4, 8, 11, 12, 14, 16, 18, 32<br> mix.nest: variable treating same composition from different sites as different "ecotypes"<br> level: factor to distinguish stability measures at the species (Species), functional groups (Group) or mixture (Community) level<br> tempCV: temporal coefficient of variation (ratio of the standard deviation, <i>σ</i>, to the mean, <i>μ</i>, expressed as percentage) of total aboveground biomass on species, functional group and community level over the first three years<br> tempSD: standard deviation of total aboveground biomass on species, functional group and community level over the first three years <br> tempMean: mean of total aboveground biomass on species, functional group and community level over the first three years  </p> <p> 7. TemporalCVsRandomized.txt contains:<br> site: factor for site with 8 levels: Germany, Portugal, Switzerland, Greece, Ireland, Sweden, Sheffield (UK), Silwood (UK)<br> sr: variable for sown species richness with levels 1, 2, 3, 4, 8, 11, 12, 14, 16, 18, 32<br> mix.nest: variable treating same composition from different sites as different "ecotypes"<br> level: factor to distinguish stability measures at the species (Species), functional groups (Group) or mixture (Community) level<br> tempCV: temporal coefficient of variation (ratio of the standard deviation, <i>σ</i>, to the mean, <i>μ</i>, expressed as percentage) of total aboveground biomass on species and community level over the first three years calculated after randomly assigning the two replicate plots of each mixture to either the population CV or the community CV omitting the functional group level.</p> <p> 8. SpatialCVs.txt contains:<br> year: variable for year of experiment 1 to 3<br> site: factor for site with 8 levels: Germany, Portugal, Switzerland, Greece, Ireland, Sweden, Sheffield (UK), Silwood (UK)<br> sr: variable for sown species richness with levels 1, 2, 3, 4, 8, 11, 12, 14, 16, 18, 32<br> mix.nest: variable treating same composition from different sites as different "ecotypes"<br> level: factor to distinguish stability measures at the species (Species), functional groups (Group) or mixture (Community) level<br> </p><p> 9. CVandMeanDbar.txt contains:</p></blockquote><p>...</p

Appendix F. Temporal mean and SD for individual sites.

Temporal mean and SD for individual sites

Appendix C. Temporal variability (CV) calculated after randomly assigning the two replicate plots of each mixture to either the population CV or the community CV omitting the functional group level.

Temporal variability (CV) calculated after randomly assigning the two replicate plots of each mixture to either the population CV or the community CV omitting the functional group level