1. Quality control before data enter the database 2. Extracting biological knowledge from the database 1. Environmental control 2. Data collection 3. Deduction of information 4. Deduction of knowledge Two topics:
1. Quality control: Quality targets for experiments: Setup SOPs (standard operation procedures). SOPs for – reproducible – growth environments. SOPs for – reproducible - plant analysis. Collect information (history, phenotype) for every individual in a plant information database. Reliable, reproducible, transparent
The biologist’s perspective: plant size variability is an issue Plant variability across labs: Massonnet et al. (2010) Plant Phys.
What variability can one expect? n = 600 Mean = 1.41 n = 800 A meta-analysis of the effect of elevated CO2: 350 experiments with 800 mean values for 350 species Poorter & Navas (2003) New Phytol. 600 estimates of variability in plant size (standard deviation ln-transformed weight) Poorter & Garnier (1996) J. Exp. Bot.
Could the variation in growth response to elevated CO2 be explained simply by plant-to- plant variability? SD lnW # of plants P 20 0.214 P 50 0.318 P 80 0.5112 1. Assume a true W 700 /W 350 of 1.41 2. Draw at random 4, 8 or 12 plants from a population with 3 variabilities: B W 700 /W 350 ? 90.000 simulations
Yes, all observed variation in growth response could just happen to be caused by sampling too few individuals from too variable experimental populations: Poorter & Navas (2003) New Phytol.
Conclusion 1: - Quality control in your procedures is an issue - Biological variation is an equally important issue, and growth chambers are NOT solving this problem
2. Extracting biological knowledge from the database :
At the phenotypic level, there is – for plants – a lack of information structured in a database: TAIR, PLEXdb, Genevestigator, Drastic, CSB,DB, Germinate Leda Glopnet TRY (TurboVeg)? (Floral DB) Chloroplast 2010, Germinate
How do plants respond to their environment? Investigator A: Arabidopsis Trait x low light20units high light40units Investigator B: Brassica Trait x low light60units high light60units
The 2 experiments may actually tell the same thing:
The classical dose-response curve: Nutrient supply Yield Mitscherlich (1909)
The example of SLA vs Light: Light intensity Irradiance PFD PPFD PAR PFR µmol m -2 s -1 mol m -2 day -1 W m -2 lux cal cm -2 s -1 langley min -1 lumen foot -2 MJ ft-c SLA SLW SLM LSM LSW LMA g m -2 mg cm -2 µg cm -2 µg mm -2 m 2 kg -1 dm 2 g -1 mm 2 mg -1 cm 2 mg -1 MaMa SLA: leaf area / leaf dry mass
How can we achieve a clear picture from fragmented data?: SLA (m 2 kg -1 ) Daily Photon Irradiance (mol m -2 day -1 )
A literature analysis of >1100 data points (mean values) from >150 experiments on >300 species: DPI (mol m -2 day -1 ) SLA (m 2 kg -1 )
Four different experiments show that interspecific variation in SLA is large:
After scaling SLA relative to the (interpolated) value at a reference light intensity of 8 mol m -2 day -1 :
>1000 data points from >150 experiments on >300 species:
Median and the interquartile range for 7 light classes: P 10 P 90 P 50
10 th and 90 th percentiles give norm values, by which you can compare new experiments: The red line is an example of an outlying experiment Terminalia ivorensis
1. Light quantity (DPI) 2. Light quality (R/FR) 3. UV-B 4. CO 2 5. O 3 6. Nutrient availability (N, P, G) 7. Drought stress 8. Waterlogging 9. Submergence 10.Temperature 11. Salinity 12. Soil compaction Stress box Can we follow the same approach for other environmental factors?
SLA responses to light, gases, and nutrients: 10005030 700150600
SLA responses to water, temperature, salinity and soil compaction: 30090 70 300200 70
An overall non-linear equation to describe the response: r 2 = 0.72; PI = 2.92
Plasticity index: highest divided by lowest fitted value across a predefined range RangePI Irradiance 1 – 50mol m -2 day -1 2.92 CO 2 200 – 1200µmol mol -1 1.41 Salinity 0 – 100% seawater 1.23 Waterlogging - – + 1.12 Compaction 1.0 – 1.6g cm -3 1.05 R : FR 0.2 – 1.2 mol mol -1 1.00 UV-B 1 – 20kJ m -2 day -1 1.00 O3O3 5 – 100nmol mol -1 1.00 Nutrients 0.05 – 1rel. units 1.13 Water 0.05 – 1rel. units 1.25 Submergence - – + 1.95 Temperature 5 – 35 oCoC 2.24
- Species family / name - woody / herbaceous - deciduous / evergreen - shrub / tree - annual / perennial - N 2 fixing - C 3 / C 4 / CAM Species traits - Glasshouse, Growth chamber, OTC, Garden - Light (DPI) - Temperature (24h- average) - Substrate (hydroponics / soil, pot volume) Growth environment - Shade / Sun - Dry / Wet - Cold / Warm - Non-saline / Saline Environmental niche Are there differences between subgroups?
An example: tropical species are more plastic than boreal species
Growth chamber Glasshouse OTC, shade house Functional groups Most experiments with herbs were in growth chambers, most with trees were outside in shade houses:
SLA is just one trait, can we do the same for other traits?: Env. FactorSLATrait 2Trait 3Trait 4....Trait n 1 2 3 4 5 6 ... 12
PI = 1.22 n = 400 Yes, for example the % allocation of biomass to leaves as dependent on light intensity:
- Yield - RGR, ULR, LAR - SLA - LMF, SMF, RMF, (HI) Growth box (> 4) - PHOT actual - PHOT capacity (/m2, /g, /N) - gs,Transpiration, - c i /c a - J / V max - RESP leaf, stem, root, fruit (/g) Gas exchange (> 3) - [C], [N], [P] leaf, stem, root, fruit - Starch, Fructan - Nitrate - Sol. Sugars - Lignin - (Hemi-)Cellulose - Protein, Org. N - Org. acids - Minerals, Ash - Sol. Phenolics - Tannin - Construction costs - Delta 13C Chem. comp. (> 5) - leaf size - plant height - leaf thickness - leaf density (or FW / DW) - vol / % epidermis, mesophyll air spaces, sclerenchyma - cell size Morphology / anatomy (> 3) - Rubisco capacity - PEP carboxylase - SBPase - AGPase - NR - etc Enzyme box (> 4) - link to mRNA Do the same for these plant traits:
Conclusions: ► Is able to summarise data across many experiments ► Yields quantitative response curves ► As well as normal limits This meta-phenomics approach : ► Is applicable to (almost all) environmental factors ► Is applicable to all plant traits ► Will be very useful for modeling (global change, limiting factors)
Frank Gilmer, FZJ Uli Schurr, FZJ Thanks to: for more info see: - J. Exp. Bot. (2010) 61: 2043-2055 - www.metaphenomics.org Ismael Aranda Owen Atkin Corine de Groot Yulong Feng Jurg Franzaring Keith Funnell Yaskara Hayashida Vaughan Hurry Ken Krauss Dina Rhonzina Francesco Ripullone Catherine Roumet Peter Ryser Dylan Schwilk Susanne Tittmann Jan Henk Venema Rafael Villar Dina Rhonzina Francesco Ripullone Catherine Roumet Peter Ryser Dylan Schwilk Susanne Tittmann Jan Henk Venema Rafael Villar Gerard Bönisch, MPI-Jena Benjamin Bruns, FZJ
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