7. Heckford, T.R., Leroux, S.J., Vander Wal, E., Rizzuto, M., Balluffi-Fry, J., Richmond, I.C., Wiersma, Y.F. (2021). Spatially explicit correlates of plant functional traits inform landscape patterns of resource quality. Landscape Ecology.


  1. Context: Spatially explicit correlates of foliar elemental, stoichiometric, and phytochemical (ESP) traits represent links to landscape patterns of resource quality.
  2. Objectives: We investigate spatial correlates for multiple foliar ESP traits at the species level and across species at the trait level for five boreal forest understory plants.
  3. Methods: On the island of Newfoundland, Canada, we collected plot-level foliar material from four chronosequenced forest grids. We integrate plot-level response variables of foliar elemental (C, N, P, percent and quantity), stoichiometric (C:N, C:P, N:P), and phytochemical (terpenoids) traits, with spatial predictors available for the whole landscape to test multiple competing hypotheses. These hypotheses include the effects 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.
  4. Results: Spatial correlates of foliar ESP traits were generally species specific. However, at the trait level, some species shared spatial predictors, notably for foliar percent carbon, C:P, N:P, sesquiterpene traits. Here we highlight that foliar C, C:P, and sesquiterpene traits between different species were explained by abiotic spatial correlates alone. Similarly, foliar terpenoid traits between different species were related to a combination of abiotic and biotic factors (mean R2 =  0.26).
  5. Conclusions: Spatial-trait relationships mainly occur at the species level, with some commonalities at the trait level. By linking plot-level foliar ESP traits to spatial predictors, we can map plant chemical composition patterns that influence landscape-scale ecosystem processes and thus inform sustainable landscape management.

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