Disturbance History of a Seasonal Tropical Forest in Western Thailand: A Spatial Dendroecological Analysis

Tree Age Distributions Reveal Large-Scale Disturbance-Recovery Cycles in Three Tropical Forests

Over the past few decades there has been a growing realization that a large share of apparently 'virgin' or 'old-growth' tropical forests carries a legacy of past natural or anthropogenic disturbances that have a substantial effect on present-day forest composition, structure and dynamics. Yet, direct evidence of such disturbances is scarce and comparisons of disturbance dynamics across regions even more so. Here we present a tree-ring based reconstruction of disturbance histories from three tropical forest sites in Bolivia, Cameroon, and Thailand. We studied temporal patterns in tree regeneration of shade-intolerant tree species, because establishment of these trees is indicative for canopy disturbance. In three large areas (140-300 ha), stem disks and increment cores were collected for a total of 1154 trees (>5 cm diameter) from 12 tree species to estimate the age of every tree. Using these age estimates we produced population age distributions, which were analyzed for evidence of past disturbance. Our approach allowed us to reconstruct patterns of tree establishment over a period of around 250 years. In Bolivia, we found continuous regeneration rates of three species and a peaked age distribution of a long-lived pioneer species. In both Cameroon and Thailand we found irregular age distributions, indicating strongly reduced regeneration rates over a period of 10-60 years. Past fires, windthrow events or anthropogenic disturbances all provide plausible explanations for the reported variation in tree age across the three sites. Our results support the recent idea that the long-term dynamics of tropical forests are impacted by large-scale disturbance-recovery cycles, similar to those driving temperate forest dynamics.

Detecting long-term growth trends using tree rings: a critical evaluation of methods

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends.