bioRxivEcologyPreprint
Does where you live influence what you are made of? Spatial correlates of chemical traits across commonly occurring boreal plants
T. R. Heckford, S. J. Leroux, E. Vander Wal, M. Rizzuto, J. Balluffi-Fry, I. C. Richmond, Y. F. Wiersma
Abstract
ContextSpatially explicit drivers of foliar chemical traits link plants to ecosystem processes to reveal landscape functionality. Specifically, foliar elemental, stoichiometric, and phytochemical (ESP) compositions represent key indicator traits. ObjectivesHere, we investigate the spatial drivers of foliar ESP at the species level and across species at the trait level for five commonly occurring boreal forest understory plants. MethodsOn the island of Newfoundland, Canada, we collected foliar material from four chronosequenced forest grids. Using response variables of foliar elemental (C, N, P, percent and quantity), stoichiometric (C:N, C:P, N:P), and phytochemical (terpenoids) composition, we tested multiple competing hypotheses using spatial predictors of land cover (e.g., coniferous, deciduous, mixedwood), productivity (e.g., enhanced vegetation index), biotic (e.g., stand age/height, canopy closure) and abiotic (e.g., elevation, aspect, slope) factors. ResultsWe found evidence to support spatial relationships of foliar ESP for most species (mean R2 = 0.22, max = 0.65). Spatial variation in elemental quantity traits of C, N, P were related to land cover along with biotic and abiotic factors for 2 of 5 focal species. Notably, foliar C, C:P, and sesquiterpene traits between different species were related to abiotic factors. Similarly, foliar terpenoid traits between different species were related to a combination of abiotic and biotic factors (mean R2 = 0.26). ConclusionsSpatial-trait relationships mainly occur at the species level, with some commonalities occurring at the trait level. By linking foliar ESP traits to spatial predictors, we can map plant chemical composition patterns that influence landscape-scale ecosystem processes.
- organism: plant (other)
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: statistical model
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