, Volume 15, Issue 8, pp 1321-1335
Date: 22 Aug 2012

Multi-scale Drivers of Spatial Variation in Old-Growth Forest Carbon Density Disentangled with Lidar and an Individual-Based Landscape Model

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Abstract

Forest ecosystems are the most important terrestrial carbon (C) storage globally, and presently mitigate anthropogenic climate change by acting as a large and persistent sink for atmospheric CO2. Yet, forest C density varies greatly in space, both globally and at stand and landscape levels. Understanding the multi-scale drivers of this variation is a prerequisite for robust and effective climate change mitigation in ecosystem management. Here, we used airborne light detection and ranging (Lidar) and a novel high-resolution simulation model of landscape dynamics (iLand) to identify the drivers of variation in C density for an old-growth forest landscape in Oregon, USA. With total ecosystem C in excess of 1 Gt ha−1 these ecosystems are among the most C-rich globally. Our findings revealed considerable spatial variability in stand-level C density across the landscape. Notwithstanding the distinct environmental gradients in our mountainous study area only 55.3% of this variation was explained by environmental drivers, with radiation and soil physical properties having a stronger influence than temperature and precipitation. The remaining variation in C stocks was largely attributable to emerging properties of stand dynamics (that is, stand structure and composition). Not only were density- and size-related indicators positively associated with C stocks but also diversity in composition and structure, documenting a close link between biodiversity and ecosystem functioning. We conclude that the complexity of old-growth forests contributes to their sustained high C levels, a finding that is relevant to managing forests for climate change mitigation.

Authors Contributions

RS designed the study, developed the model, performed the research, analyzed the data, and wrote the paper; TAS contributed to study design, data analysis, and writing; WR contributed new methods and models, assisted in research and data analysis, and contributed to writing; EAS contributed new methods and models, and helped writing the paper; RJP assisted in performing the research and analyzing the data, and contributed to writing the paper; KO assisted in performing the research and analyzing the data.