Detecting long‐term growth trends using tree rings: a critical evaluation of methods. (6th February 2015)

• Record Type: Journal Article
• Authors: Peters, Richard L.; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A.
• Is Part Of: Global change biology. Volume 21:Number 5(2015:May)
• Page Start: 2040
• Page End: 2054
• Publication Date: 2015-02-06
• Subjects: age/size trend -- climate change -- dendrochronology -- detrending -- growth trends -- regional curve standardization -- tree growth -- tree ontogeny -- tree‐ring analysis -- tropical forests; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.12826
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: 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.