1) The study found significant differences in monthly carbon fluxes (GPP) between years with high bamboo shoot production ("on-years") and low shoot production ("off-years") from January to June, due to differences in leaf chlorophyll content and green leaf area index between the years. 2) The differences in monthly GPP were mainly driven by biotic factors like chlorophyll and leaf area, rather than abiotic factors such as temperature, radiation, and soil moisture. 3) Both leaf area and temperature interact to control the variation (IAV) of annual GPP between on and off years, with their overlapping or offsetting effects increasing or decreasing the IAV.

1. Biotic and abiotic influences on variation in carbon
fluxes in on-year and off-year Moso bamboo forest
Dr. Xiaojun Xu
Zhejiang A&F University
Beijing, 2018.06.26

2. 1. Introduction
 Moso bamboo (Phyllostachys pubescens) forests can be classified into “on-year” and “off-year” bamboo.
 “On-year” means years with high bamboo shoot production; “off-year” means years with low shoot
production (Li et al., 1998; Zhou et al., 2011). They often alternate.
 There are significant differences in ecological characteristics between them:
• Number of bamboo shoots;
• Leaf phenology: changing leaves in off-years (Kleinhenz et al., 2001); leaves of new shoots sprout and old leaves
change yellow in on-years (Gratani et al., 2008); more complicate than I describe in here;
• Leaf age: from two-years-old to one-year-old in off-years; contrary in on-years.
…

3. 1. Introduction
 Dut to so obviously different ecological characteristics between them, their carbon fixation are worth to
discover. The objectives of this study are :
(⑴)to test whether there is significant difference in carbon fixation (Gross Primary Production, GPP)
between on-years and off-years;
(⑵)to determine the drivers (abiotic and biotic factors) of variation in GPP between on-years and off-years;
(⑶)and to analyze its implication on GPP estimation.

4. 2. Study area
 An’ji county in Zhejiang province, China,
where is full (56.47% of the forested area) of
Moso bamboo forest.
 A flux tower was bulit in 2010 to collect
carbon exchanges.
 Twenty-five plots were selected around the
flux tower for collecting leaf area index (LAI)
and chlorophyll content (CC).
Fig.1 Study area

5. 3. Data and methods
3.1 Data
 Flux data: GPP from 2011 to 2015
 Field data: LAI and CC in 2011, 2014, and 2015
 Remote sensing data: reflectance and vegetation index (VI) from MODIS sensors from 2000 to 2016.
 Biotic factors including LAI, CC, and VIs;
 Abiotic factors including temperature (T), photosynthetically active radiation (PAR), vapor pressure deficit
(VPD), and soil volumetric water content (SVWC) .

6. 3. Data and methods
3.2 Methods
 One-way Analysis of Variance (ANOVA);
 Pearson’s correlation analysis;
 Regression analysis;
 Light use efficiency (LUE) model.

7. 4. Results and discussion
4.1 Difference in monthly GPP between on-years and off-years
 Significant differences in monthly average
GPP between on-years and off-years were
observed from January to June (Fig. 2), with a
transition point in May.
Fig.2 Variation in monthly average GPP in on-years and off-years. * significant at
0.05; ** significant at 0.01; n.s. not significant.

8. 4. Results and discussion
4.2 Difference in biotic factors between on-years and off-years
 LAI and CC in off-year before May are significantly lower than those in on-year (Fig. 3), due to
changing leaf in off-year during that period.
Fig.3 Change trends of (a) CC and (b) LAI in on-year (2011 and 2015) and off-year (2014)

9. 4. Results and discussion
4.2 Difference in biotic factors between on-years and off-years
 Three kinds of VIs (represent
biotic factors) in off-year before
May are significantly lower
than those in on-year (Fig. 4),
which indirectly indicates that
LAI and CC in off-year before
May are significantly lower
than those in on-year.
Fig.4 Variation in monthly average VIs in on-
years and off-years, (a) NDVI, (b) EVI, (c) SR,
and (d) WDRVI. Values are averaged across
multiple years from 2000 to 2016.

10. 4. Results and discussion
4.3 Difference in abiotic factors between on-years and off-years
 Differences in abiotic factors
between on-years and off-years
are not significant (Fig.5),
implying that abiotic factors
did not drive differences in
monthly average GPP between
on-years and off-years.
Fig.5 Variation in monthly average abiotic factors
in on-years and off-years from 2011 to 2015, (a)
PAR, (b) temperature, (c) VPD, and (d) SVWC.

11. 4. Results and discussion
4.4 Driving factors of differences in monthly GPP between on-years and off-years
 Differences in biotic factors are more related to differences in monthly GPP (GPP ) than differences
in abiotic factors, implying that GPP was mainly driven by biotic factors.
 The Red and Blue bands were significantly correlated with GPP, while the NIR was not
significantly correlated with GPP, indicating that GPP was probably affected by a change in canopy
CC, but not in LAI.
Table 1 Correlations between GPP and differences in monthly average abiotic and biotic factors between on-years and off-years. * significant
at 0.05; ** significant at 0.01; *** significant at 0.001
Driving factors R
Abiotic factors
T -0.08
PAR 0.73**
SVWC -0.06
VPD 0.31
Biotic factors
Red(620–670 nm) -0.85***
NIR(841–876 nm) 0.15
Blue(459–479 nm) -0.77**
NDVI 0.77**
EVI 0.34
SR 0.66*
WDRVI 0.72**

12. 4.5 Driving factors of inter-annual variation (IAV) of GPP
4. Results and discussion
 IAV of GPP is jointly controlled by green LAI and T. Overlapping effect of green LAI and T increases
IAV of GPP, whereas offsetting effect of green LAI and T decreases IAV of GPP.
Fig. 6 (a)GPP distribution estimated by the EC-LUE model from 2004 to 2011; (b) ON-year and off-year Moso bamboo distribution; and (c)
effects of LAI and Temperature (T) on IAV of GPP

13. 4. Results and discussion
4.6 Implications on GPP estimation
 GPP has weak relationship with SR but strong
relationship with T in on-year (Fig. 7(a)) for four
monthly data (Mar. Apr. Nov. and Dec.), due to
great GPP resulting from high T and PAR (data
not shown) during Mar. to Apr., even relatively
low SR.
 GPP has strong relationship with SR but weak
relationship with T in off-year (Fig. 7(b)), due to
small GPP resulting from very low SR during Mar.
to Apr., even relatively high T and PAR (data not
shown).
Fig.7 Relationships between GPP and driving factors from
March to April and from November to December in on-years (left)
and off-years (right), (a) SR, (b) Temperature, (c) PAR, and (d)
SR × PAR.

14. 4. Results and discussion
4.6 Implications on GPP estimation
 The prediction accuracy using both T and SR
(Fig. 8(c)) was significantly higher than using
either T or SR alone (Fig. 8(a, b)). This indicated
that a combination of abiotic and biotic factors
was more accurate in predicting GPP, especially
for off-year (Fig.8).
Fig.8 Comparisons of observed and predicted GPP from linear
regression models using the independent variables of (a) SR, (b)
Temperature, and (c) SR and Temperature.

15. 5. Summary
 There is significant difference in monthly average GPP from January to June between on-years and off-
years because of differences in leaf chlorophyll content and green LAI;
 Difference in monthly average GPP between on-year and off-year is mainly controlled by biotic factors;
 Interaction between green LAI and Temperature controls the magnitude of IAV of GPP;
 Inclusion of VIs into the model increases the prediction accuracy of GPP compared with using abiotic
factors alone.
 Improtantly, a simple phenomeno is presented in this study, but its implications on increase in carbon
sequestration is worth to further think.