title: The plant ionome revisited by the nutrient balance concept.
abstract: Tissue analysis is commonly used in ecology and agronomy to portray plant nutrient signatures. Nutrient concentration data, or ionomes, belongs to the compositional data class, i.e., multivariate data that are proportions of some whole, hence carrying important numerical properties. Statistics computed across raw or ordinary log-transformed nutrient data are intrinsically biased, hence possibly leading to wrong inferences. Our objective was to present a sound and robust approach based on a novel nutrient balance concept to classify plant ionomes. We analyzed leaf N, P, K, Ca, and Mg of two wild and six domesticated fruit species from Canada, Brazil, and New Zealand sampled during reproductive stages. Nutrient concentrations were (1) analyzed without transformation, (2) ordinary log-transformed as commonly but incorrectly applied in practice, (3) additive log-ratio (alr) transformed as surrogate to stoichiometric rules, and (4) converted to isometric log-ratios (ilr) arranged as sound nutrient balance variables. Raw concentration and ordinary log transformation both led to biased multivariate analysis due to redundancy between interacting nutrients. The alr- and ilr-transformed data provided unbiased discriminant analyses of plant ionomes, where wild and domesticated species formed distinct groups and the ionomes of species and cultivars were differentiated without numerical bias. The ilr nutrient balance concept is preferable to alr, because the ilr technique projects the most important interactions between nutrients into a convenient Euclidean space. This novel numerical approach allows rectifying historical biases and supervising phenotypic plasticity in plant nutrition studies.
authors.affiliation: Équipe de Recherche en Sols Agricoles et Miniers, Department of Soils and Agrifood Engineering, Université Laval Québec, QC, Canada.
authors.forename: Serge-Étienne
authors.initials: SÉ
authors.lastname: Parent
authors.forename: Léon Etienne
authors.initials: LE
authors.lastname: Parent
authors.forename: Juan José
authors.initials: JJ
authors.lastname: Egozcue
authors.forename: Danilo-Eduardo
authors.initials: DE
authors.lastname: Rozane
authors.forename: Amanda
authors.initials: A
authors.lastname: Hernandes
authors.forename: Line
authors.initials: L
authors.lastname: Lapointe
authors.forename: Valérie
authors.initials: V
authors.lastname: Hébert-Gentile
authors.forename: Kristine
authors.initials: K
authors.lastname: Naess
authors.forename: Sébastien
authors.initials: S
authors.lastname: Marchand
authors.forename: Jean
authors.initials: J
authors.lastname: Lafond
authors.forename: Dirceu
authors.initials: D
authors.lastname: Mattos
authors.forename: Philip
authors.initials: P
authors.lastname: Barlow
authors.forename: William
authors.initials: W
authors.lastname: Natale
keywords: compositional data analysis; ionome classification; isometric log-ratio; numerical biases; nutrient interactions; plant nutrition