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AMOUNT OR PATTERN? GRASSLAND RESPONSES TO THE HETEROGENEITY AND AVAILABILITY OF TWO KEY RESOURCES
Author(s) -
Maestre Fernando T.,
Reynolds James F.
Publication year - 2007
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/06-0421
Subject(s) - holcus lanatus , biology , biomass (ecology) , lolium perenne , nutrient , ecology , grassland , spatial heterogeneity , ecosystem , microcosm , agronomy , edaphic , terrestrial ecosystem , productivity , biomass partitioning , perennial plant , soil water , macroeconomics , economics
Patterns of resource availability and heterogeneity shape the composition, productivity, and dynamics of plant assemblages in a wide variety of terrestrial ecosystems. Despite this, the responses of plant assemblages to simultaneous changes in the availability and heterogeneity of more than a single resource are virtually unknown. To fill this gap, microcosms consisting of assemblages formed by Lolium perenne , Plantago lanceolata , Anthoxantum odoratum , Holcus lanatus , and Trifolium repens were grown in a factorial experiment with the following treatments: nutrient availability (NA), water availability (WA), spatial nutrient heterogeneity (NH), and temporal water heterogeneity (WH). Assemblages exhibited precise root foraging patterns in response to nutrient heterogeneity, which were modified by NA and WA. A series of two‐ and three‐way interactions involving the four factors evaluated determined biomass production, the belowground : aboveground biomass ratio, the patterns of root biomass allocation with depth, and the relative contribution to aboveground biomass of Lolium and Anthoxanthum . In all cases, these interactions explained significant amounts of the variation found in the data. Our study demonstrates that considering the interactions between resource availability and heterogeneity allows for a refinement of predictions that can detectably reduce the error associated with extrapolating from single factor analyses.