DOI: 10.5061/dryad.d51c5b01n
title: Predicting tropical tree mortality with leaf spectroscopy

description: Do tropical trees close to death have a distinct change to their leaf
 spectral signature? Tree mortality rates have been increasing in tropical
 forests globally, reducing the global carbon sink.  Upcoming
 hyperspectral satellites could be used to predict regions close to
 experiencing extensive tree mortality during periods of stress, such as
 drought.  Here we show, for a tropical rainforest in Borneo, how
 imminent tropical tree mortality impacts leaf physiological traits and
 reflectance.  We measured leaf reflectance (400-2500 nm), light
 saturated photosynthesis (Asat), leaf dark respiration (Rdark), leaf mass
 area (LMA) and % leaf water across five campaigns in a six-month period
 during which there were two causes of tree mortality: a major natural
 drought and a co-incident tree stem girdling treatment.  We find
 that prior to mortality, there were significant (P&lt;0.05) leaf
 spectral changes in the red (650-700 nm), the NIR (1000 -1400 nm) and SWIR
 bands (2000-2400 nm) and significant reductions in the potential carbon
 balance of the leaves (increased Rdark and reduced Asat).  We
 show that the partial least squares regression technique can predict
 mortality in tropical trees across different species and functional groups
 with medium precision but low accuracy (r2 of 0.65 and RMSE/mean of
 0.58).   However, most tree death in our study was due
 to girdling, which is not a natural form of death. More research is needed
 to determine if this spectroscopy technique can be applied to tropical
 forests in general.
description: Study sites
 Our study plots are in Kalabakan Forest
 Reserve in Sabah, Malaysian Borneo (Tower SAF‐05 4.716°, 117.609°)
 within the Stability of Altered Forest Ecosystems (SAFE) Project study
 site (Ewers
 et al., 2011; Riutta et al., 2018). A schematic of the study
 site is shown in figure 1C.  Mean annual temperature is approximately
 26.7°C and mean annual precipitation is 2,600–2,700 mm with no distinct
 dry season but, on average, ~12% of months with precipitation below 100 mm
 month<sup>-1 </sup>(Walsh & Newbery, 1999).  The plot
 has been selectively logged four times since the 1970s, which represents a
 high logging intensity for this region.  The soils are orthic Acrisols or
 Ultisols on undulating clay soil. The tree
 basal area is 13.9 m<sup>2</sup>/ha.  Total NPP and
 autotrophic respiration have been measured at this plot since 2011 and
 there is an eddy covariance tower nearby (Riutta et al.,
 2018).  The plot is split in half by a small stream.  All the trees on one
 side of the stream were girdled in late January 2016 by removing the
 phloem tissue in a 10 cm band, as described below (note: the plot was in
 the process of conversion to oil palm agriculture production). This part
 of the study site is hereafter referred to as the “girdled plot.”  The
 trees on the other side of the stream were not girdled and represent the
 treatment control. This part of the study site is hereafter referred to as
 the “drought plot”.  Although all trees experienced drought, the “drought”
 plot only experienced drought and not the effects of girdling.  We
 collected data during five field campaigns that took place from January to
 June 2016.  Campaigns began on the following days and generally took
 several days: Campaign 1=21 Jan-16, Campaign 2=10 Feb-16, Campaign 3=01
 Mar-16, Campaign 4=29 Mar-16, Campaign 5 08 Jun-16. The first field
 campaign (C1) was conducted before girdling occurred to determine
 pre-girdling conditions and process rates.
   <b>Girdling experiment –
 </b>In late Jan 2016, after the first field campaign, we further
 explored the causes of tree mortality by conducting a girdling experiment.
 Girdling involved removing a 10 cm strip of the periderm and phloem in a
 ring around the tree stem at ~1.2 m height (with exceptions for trees with
 buttresses, which were girdled above the buttress) above the soil (Figure
 1a) in a plot that was scheduled for conversion to a palm oil plantation. 
 This technique prevents carbohydrate transport to the roots but maintains
 hydraulic connectivity because xylem tissues are not severed.  Tree death
 was determined visually, based on the absence of visible canopy, with
 regular (average 18-day period) visits to the plots for both the drought
 and the girdled plots.  We give the species measured in both plots in
 Table 1.    <b>Leaf sampling strategy
 –</b>In each plot, 20-25
 trees were chosen during each campaign, and tree climbers with
 extendable tree pruners removed one branch per tree that was growing in
 full sunlight (Asner & Martin, 2008).  These branches were quickly
 recut underwater and taken to the laboratory for further measurements. On
 each of these branches, five fully expanded non-senescent leaves in
 randomly selected locations were chosen for measurements of:  leaf-gas
 exchange leaf spectral properties (measured within 1 hour of being cut)
 and LMA. Leaf area was determined immediately after collection using a
 digital 476 scanner (Canon LiDE 110). Leaves were then oven dried at
 72 °C until constant mass
 was reached.  We subtracted wet
 weight from dry weight to calculate % leaf water and used dry weight and
 leaf area in order to calculate LMA.
   <b>Leaf-level gas exchange
 – </b>We used a portable gas exchange system (LI 6400, Li-Cor
 Biosciences, Lincoln, NE, USA) to measure leaf-level gas exchange. After
 returning to the laboratory, leaf dark respiration
 (R<sub>dark</sub>) was measured by covering branches with an
 opaque bag for at least 20 minutes prior to measurement at a cuvette
 temperature of 30° C (Rowland et al., 2017). After this, branches were
 exposed to sunlight and light-saturated leaf photosynthesis was measured
 (A<sub>sat</sub>; 1200 µmol m<sup>-2</sup>
 s<sup>-1 </sup>PPFD, 400 ppm CO<sub>2</sub>, at
 30° C).  We chose a light level of
 1200 µmol m<sup>-2</sup> s<sup>-1  </sup>for
 A<sub>sat</sub> because we tested photosynthetic capacity and
 found it generally saturated below light levels of ~1200 µmol
 m<sup>-2</sup> s<sup>-1 </sup>PPFD, similar to
 other tropical studies (Both et al., 2019; Gvozdevaite et al.,
 2018; Doughty & Goulden, 2009b).  We waited for gas exchange
 values to stabilize before starting a measurement, recorded data every two
 seconds and averaged the results after eliminating the first 20
 measurements.    We excluded photosynthesis measurements less than 0 µmol
 m<sup>-2</sup> s<sup>-1 </sup>as this was
 indicative of a failure to maintain hydraulic connectivity in the sampled
 branch resulting in stomatal closure.  We also excluded dark respiration
 measurements more negative than -1.5 µmol m<sup>-2</sup>
 s<sup>-1</sup> as this was considered indicative of a failure
 to truly represent R<sub>d, </sub>or in some cases operator
 error. Most physiological measurements were collected between 07:00 and
 14:00 local time and branches were cut from trees between 06:00 and 13:00
 local time.  An online supplement includes our averaged ± sd data for each
 leaf measured for transpiration rate (mmol H<sub>2</sub>O
 m<sup>-2</sup> s<sup>-1</sup>), vapor pressure
 deficit based on leaf temperature (kPa), intercellular
 CO<sub>2</sub> concentration (µmol CO<sub>2</sub>
 mol<sup>-1</sup>), conductance to H<sub>2</sub>O
 (mol H<sub>2</sub>O m<sup>-2</sup>
 s<sup>-1</sup>), and  photosynthetic rate (µmol
 CO<sub>2</sub> m<sup>-2</sup>
 s<sup>-1</sup>). <b>Leaf
 spectroscopy – </b>We randomly selected five leaves within an hour
 of each branch being cut, and measured<b> </b>hemispherical
 reflectance near the mid-point between the main vein and the leaf edge
 (Asner & Martin, 2008). We used an ASD Fieldspec 4 with a fibre
 optic cable, contact probe and a leaf clip (Analytical Spectral Devices,
 Boulder, Colorado, USA).  The spectrometer records 2175 bands spanning the
 325–2500 nm wavelength region. We
 corrected for small discontinuities between spectral bands (~950 and ~1750
 nm), where the instrument transitions from one sensor to
 another.  Measurements were collected with 136-ms integration
 time per spectrum (Asner & Martin, 2008; Doughty, Asner, et al.,
 2011). To ensure measurement quality, the spectrometer was calibrated for
 dark current and stray light, and white-referenced to a calibration panel
 (Spectralon, Labsphere, Durham, New Hampshire, USA) after each
 branch(Asner & Martin, 2008; Doughty, Field, et al., 2011).  The
 spectrometer was optimized after every branch so the light levels did not
 saturate. For each measurement, 25 spectra
 were averaged together to increase the
 signal-to-noise ratio of the
 data.   <b>Data analysis</b> - We
 used the Partial Least Squares Regression (PLSR) modelling approach to
 predict leaf traits with spectral information (Geladi & Kowalski,
 1986).  PLSR incorporates all the spectral information within each leaf
 reflectance measurement, eventually reducing all spectral data (400-2500
 nm) down to a relatively few, uncorrelated latent factors. This approach
 has been used successfully to predict plant traits across a wide range of
 ecosystems, including tropical forests (Asner & Martin, 2008;
 Serbin et al., 2014).  We
 used the PLSregress command in Matlab (Matlab, MathWorks
 Inc., Natick, MA, USA) to
 establish predictive models for LMA, Asat, wood density (estimated with tree species
 and a lookup table (Chave et al.,
 2009))
 and tree
 mortality (Doughty, Asner, et al.,
 2011).  To avoid over-fitting the number of latent factors we
 minimized the mean square error with K-fold cross validation (set as an
 upper bound as 30).  To avoid issues of pseudo replication, we emphasize
 that the unit of analysis in these analyses is the leaf<i>.
 </i> To create a completely independent testing dataset, we used the
 above method on 70% of our data to calibrate our model and then the
 remaining 30% to test the accuracy of our model.  We evaluated the
 accuracy of our modelled estimates using two main metrics: r<sup>2
 </sup>and root mean square error (RMSE)/mean.  We graded our results
 as high precision and accuracy (r<sup>2</sup> &gt; 0.70;
 %RMSE &lt; 15%), medium precision and accuracy
 (r<sup>2</sup> &gt; 0.50%; % RMSE &lt; 30%), low
 precision and accuracy (r<sup>2</sup> &gt; 0.50; %
 RMSE &gt; 30%).  We also calculated NDVI for our five study periods as
 NDVI = (NIR-red)/(NIR+red) where we use 1000 nm for NIR and 650 nm for
 red. <b>Statistical tests – </b>For our
 leaf spectral measurements,<b> </b>for each 1 nm bandwidth,
 we determined statistical
 significance (P&lt;0.05) between trees within 50 days of mortality and
 prior to this with a paired t-test (Matlab, Mathworks).  To
 understand significant differences between % water, LMA,
 R<sub>dark</sub>, and A<sub>sat</sub>, we used a
 t-test.  To understand the impact of the girdling between % water, LMA,
 R<sub>dark</sub>, and A<sub>sat</sub> over time,
 we used a repeated measures ANOVA.
description: To produce all figures in
 the paper, run Doughty_Biotropica_2020.m in the same folder with the
 datasets girdle_data1.mat and girdle_data2.mat.  Raw spectral data are in
 the folder spectral_data.  Raw physiology data are in the folder
 licro_data.