Data from: Variability in community productivity: mediating effects of vegetation attributes
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https://datadryad.org/dataset/doi:10.5061/dryad.5g1d01v
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资源简介:
1. Plant productivity varies though time in response to environmental
fluctuations. Reducing variability in productivity requires an improved
understanding of how plant community attributes interact with
environmental fluctuations to influence plant growth dynamics. We
evaluated links between two community attributes, species diversity and
abundance-weighted values of specific leaf area (SLA), and temporal
variability in grassland productivity at patch (local) and aggregate
(multi-patch) spatial scales. 2. Aggregate communities were created by
combining patches of spatially-distinct communities of perennial plant
species from grassland biodiversity experiments in Texas, USA.
Inter-annual variability in aboveground net primary productivity (ANPP) of
aggregate communities was analyzed as a function of two multiplicative
components, mean temporal variability in the ANPP of patches and temporal
synchrony in ANPP dynamics among patches. We used regression analyses to
determine whether temporal variability in aggregate ANPP and its
components were correlated with either species diversity or
community-weighted SLA over 5 years. 3. Temporal variability in ANPP of
aggregate communities was strongly correlated with temporal variability in
patch ANPP. Increasing mean SLA reduced ANPP variability of aggregate
communities by increasing mean productivity. Increased temporal changes in
patch-scale SLA further reduced temporal variability in aggregate ANPP by
reducing effects of precipitation fluctuations on productivity.
Conversely, increasing species diversity over the narrow range measured
increased temporal variability in aggregate ANPP. High diversity was
associated with reduced dominance of temporally-stable C4 grasses. 4. Our
results implicate means and patch-scale temporal dynamics in community SLA
as potential indicators of variability in grassland primary productivity
through time.
提供机构:
Dryad
创建时间:
2018-02-21



