Landscape‐scale forest disturbance regimes in southern Peruvian Amazonia. Issue 7 (1st October 2013) - Boyd, Doreen S.; Hill, Ross A.; Hopkinson, Chris; Baker, Timothy R.
Abstract : Landscape‐scale gap‐size frequency distributions in tropical forests are a poorly studied but key ecological variable. Currently, a scale gap currently exists between local‐scale field‐based studies and those employing regional‐scale medium‐resolution satellite data. Data at landscape scales but of fine resolution would, however, facilitate investigation into a range of ecological questions relating to gap dynamics. These include whether canopy disturbances captured in permanent sample plots (PSPs) are representative of those in their surrounding landscape, and whether disturbance regimes vary with forest type. Here, therefore, we employ airborne LiDAR data captured over 142.5 km 2 of mature, swamp, and regenerating forests in southeast Peru to assess the landscape‐scale disturbance at a sampling resolution of up to 2 m. We find that this landscape is characterized by large numbers of small gaps; large disturbance events are insignificant and infrequent. Of the total number of gaps that are 2 m 2 or larger in area, just 0.45% were larger than 100 m 2, with a power‐law exponent (α) value of the gap‐size frequency distribution of 2.22. However, differences in disturbance regimes are seen among different forest types, with a significant difference in the α value of the gap‐size frequency distribution observed for the swamp/regenerating forests compared with the mature forests at higher elevations. Although a relatively small area of the total forest of this region was investigated here, this study presents an unprecedented assessment of this landscape with respect to its gap dynamics. This is particularly pertinent given the range of forest types present in the landscape and the differences observed. The coupling of detailed insights into forest properties and growth provided by PSPs with the broader statistics of disturbance events using remote sensing is recommended as a strong basis for scaling‐up estimates of landscape and regional‐scale carbon balance.