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
this mosaic for a Central Amazon 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 mortality was 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.