Ecological Archives --A3
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Appendix C. Details on the imputation analyses used in this manuscript.
Table C1. List of the traits for which some values necessitated imputation before those variables could be use to predict leaf standing herbivore damage observed in monocultures at the field site of the Jena Experiment (Germany).
| Variables | Nb | Species missing values | Method | Reference variables or species |
| RGR | 15 | Cirsium oleraceum; Crepis biennis; Galium mollugo; Geranium pratense; Knautia arvensis; Onobrychis viciifolia; Pastinaca sativa; Pimpinella major; Plantago media; Primula veris; Tragopogon pratensis; Trifolium fragiferum; Trifolium hybridum; Veronica chamaedrys | Multiple imputation | Seed mass; Beginning of seed shedding; Leaf lifespan; Shoot nitrogen concentration; Trampling tolerance |
| Leaf phosphorus concentration | 6 | Alopecurus pratensis; Cirsium oleraceum; Pastinaca sativa; Plantago media; Primula veris; Trifolium fragiferum | Multiple imputation | Leaf dry matter content; Leaf hemicellulose content; Leaf water-soluble content |
| Stem growth form | 1 | Taraxacum officinale | Comparison | Leontodon hispidus; L. autumnalis |
| Seed shedding height | 3 | Phleum pratense; Poa trivialis; Taraxacum officinale | Multiple regression | 7.51997 + 0.3019 × Maximum height + 0.45872 × Minimum height + 0.05004 × Typical height + 0.52805 × Vegetative height R2 = 0.8525 |
| Beginning of seed shedding | 6 | Centaurea jacea; Galium mollugo; Geranium pratense; Medicago x varia; Taraxacum officinale; Tragopogon pratensis | Comparison | Beginning of flowering + 1 |
| Period of seed shedding | 6 | Centaurea jacea; Galium mollugo; Geranium pratense; Medicago x varia; Taraxacum officinale; Tragopogon pratensis | Comparison | Period of flowering + 1,5 |
Notes: Nb: Number of missing values for a given trait; Method: "Comparison" means that the imputed variable has simply been compared with a similar complete variable or a similar species to deduce the missing values. "Multiple regression" means that the imputed variable has been regressed against several correlated variables to get an equation predicting the missing values. "Multiple imputation" means that the imputation has been done following Su et al. (2011); The fourth column lists the variables (or species) that have been used to impute the missing values of a given trait.
Literature Cited
Su, Y.-S., A. Gelman, J. Hill, and M. Yajima. 2011. Multiple Imputation with diagnostics (mi) in R: opening windows into the black box. Journal of statistical software 45:1–31.