A test of BIOME-BGC with dendrochronology for forests along the altitudinal gradient of Mt. Chan

Yulian Wu1,*, Xiangping Wang1,*,*, Shuai Ouyang1,Kai Xu1, Bradford A. Hawkins2 and Osbert Jianxin Su   2017-07-10 11:06:59

1 Key Laboratory for Forest Resources and Ecosystem Processes of Beijing, College of Forestry, Beijing Forestry University,35 East Qinghua Road, Haidian, Beijing 100083, China

2 Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA

*Correspondence address. College of Forestry, Beijing Forestry University, 35 East Qinghua Road, Haidian, Beijing 100083, China. Tel: +86-10-6233-8100; Fax: +86-10-6233-6400; E-mail: wangxiangping@bjfu.edu.cn

*These authors contributed equally to this work.

Journal of Plant Ecology

VOLUME 10, NUMBER 3, PAGES 415–425

June 2017

doi: 10.1093/jpe/rtw076

Advance Access publication 29 July 2016

available online at academic.oup.com/jpe



Process-based models are basic tools for predicting the response of forest carbon to future climate change. The models have commonly been tested for their predictions of spatial variation in forest productivity, but much less for their ability to predict temporal variation. Here, we explored methods to test the models with tree rings, using BIOME-BGC as an example.


We used net primary productivity (NPP) data and tree rings collected from five major forest types along the altitudinal gradient of Mt. Changbai, northeast China, to test local-parameterized BIOMEBGC model. We first test the model’s predictions of both spatial (Test 1) and temporal changes (Test 2) in productivity. Then we test if the model can detect the climatic factors limiting forest productivity during historical climate change, as revealed by dendroclimatic analyses (Test 3).

Important Findings

Our results showed that BIOME-BGC could well simulate NPP of five forest types on Mt. Changbai, with an r2 of 0.69 between modeled and observed NPP for 17 plots along the altitudinal gradient (Test 1). Meanwhile, modeled NPP and ring-width indices were correlated and showed similar temporal trends for each forest type (Test 2). While these tests suggest that the model’s predictions on spatial and temporal variation of NPP were acceptable, a further test that relate the correlations of modeled NPP with climate variables to the correlations of ring widths with climate (Test 3) showed that the model did not well identify the climatic factors limiting historical productivity dynamics for some forest types, and thus cannot reliably predict their future. Both dendrochronology and BIOME-BGC showed that forest types differed markedly in the climate factors limiting productivity because of differences in tree species and climate condition, and thus differed in responses to climate change. Our results showed that a successful prediction of spatial NPP patterns cannot assure that BIOME-BGC can well simulate historical NPP dynamics. Further, a correlation between modeled NPP and tree-ring series cannot assure that the limiting climatic factors for productivity have been correctly identified by the model. Our results suggest the necessity to test the temporal predictions of process-based models in a more robust way, and further integration of dendrochronology and biogeochemistry modeling may be helpful for this purpose.

  Keywords: net primary productivity (NPP); process-based model; tree ring; model validation; altitudinal gradient

Received: 12 November 2015, Revised: 28 April 2016, Accepted: 21 July 2016

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