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Abstract:
Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape
in any particular state relatively constant over large temporal and
spatial scales. The size distribution and return frequency of disturbance
events, and subsequent recovery processes, determine to
a large extent the spatial scale over which this old-growth steady
state develops. Here,we characterize thismosaic for a CentralAmazon
forest by integrating field plot data, remote sensing disturbance
probability distribution functions, and individual-based simulation
modeling. Results demonstrate that a steady state of patches of
varying successional age occurs over a relatively large spatial scale,
with important implications for detecting temporal trends on plots
that sample a small fraction of the landscape. Long highly significant
stochastic runs averaging 1.0 Mg biomass·ha−1·y−1 were often punctuated
by episodic disturbance events, resulting in a sawtooth time
series of hectare-scale tree biomass. To maximize the detection
of temporal trends for this Central Amazon site (e.g., driven by CO2
fertilization), plots larger than 10 ha would provide the greatest sensitivity.
A model-based analysis of fractional mortality across all gap
sizes demonstrated that 9.1–16.9% of tree mortalitywas missing from
plot-based approaches, underscoring the need to combine plot and
remote-sensing methods for estimating net landscape carbon balance.
Old-growth tropical forests can exhibit complex large-scale structure
driven by disturbance and recovery cycles, with ecosystem and community
attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition
