Functional Ecology
Appendix S2: Meteorology
During the two weeks of our experiment at PNM, wind velocity and temperature fluctuations
were measured at a height of 30 m above ground (corresponding with the estimated height of the
canopy top) using a 3-D ultra-sonic anemometer (CSAT3, Campbell Scientific, Logan, UT,
USA) sampling at 10 Hz. Air temperature, relative humidity and water vapour pressure were
measured at a height of 2 m above ground and recorded as 1-min averages using probes
(HMP45, Vaisala, Helsinki, Finland) with aspirated radiation shields. Incoming, outgoing, and
net radiation were measured using a 4-channel radiometer (CNR1, Kipp & Zonen, Delft,
Holland) above the canopy at a height of 32 m above ground and recorded as 1-min averages.
10 Hz wind data from BCI and PNM was processed using a 3-D coordinate rotation
(planar fit method) resulting in mean zero rotated vertical wind velocity (w) and rotated
horizontal wind vector with components oriented toward and perpendicular to the mean wind
direction (u and v, respectively) (Finnigan 2004; Lee, Finnigan & Paw U 2004). After axis
rotation the 30-min mean vertical wind velocity is by definition 0. As a measure of updraft
intensity during a 30-min period, we calculated the root mean square of the upward wind
perturbations, σWup:
 W up 
1
N1( wi 0)
N
 w  1
i 1
2
i
wi 0 
where wi is the instantaneous vertical wind velocity. N is the number of data points, and N1( wi 0)
is the number of data points with wi>0 (i.e. updrafts) denoted by the indicator function, 1(wi>0),
which is 1 when the condition (wi>0) is met and 0 otherwise. A complete list of measured and
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ
Functional Ecology
calculated 30-min averages of environmental variables is shown in Table S1, while the crosscorrelation between these variables is shown in Table S2.
Observations flagged "bad" by the sensor quality control algorithm (available from the
CSAT3) were removed. Additional spikes were identified as outliers when they exceeded the
confidence margin of six standard deviations above or below a 2-min moving average. Data was
compiled into half-hourly block averages of wind, temperature, heat flux, and turbulence
statistics. If more than 50% of the data in a single 30-min period was filtered out, the entire 30min dataset was removed (this was the case with 16% of the daytime dataset, about a third of
those occurred during rain). Gaps smaller than 0.5 sec were filled using linear interpolation.
Gaps larger than 0.5 sec, but smaller than 2.5 sec, were filled using a linear interpolation with
added random noise based on the standard deviation in a 2-min sliding window in the observed
signal. Larger gaps were left unfilled.
Inputs for the super-WALD model and pertinent vertical wind statistics are shown in
Table S3. Unless otherwise noted, values are derived from the long-term PNM meteorological
dataset, including the mean horizontal wind skewness (γū) and kurtosis (κū), which were derived
from the mean horizontal wind distributions for each case. Θ, σW, vertical velocity skewness (γW)
and kurtosis (κW) were derived from 10 Hz data taken at PNM during the study and are marked
with an asterisk (*). These are not available for the entire year as the measurement period was
within the dispersal season only. For WALD input, we assumed that yearly and seasonal Θ are
similar.
Horizontal wind speed distribution for the cases of yearly, seasonal, seasonal daytime and
seasonal daytime during relatively high temperatures from the long-term PNM dataset are shown
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ
Functional Ecology
in Figure S4. The distribution of horizontal wind speed, u* and σWup, and their means during the
daytime and nighttime at PNM during the study are plotted in Figure S5.
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ
Functional Ecology
Table S1: Half hour meteorology statistics tested as inputs for seed abscission model.
Variable
Air Temperature
Relative Humidity
Vapour Pressure Deficit
Horizontal Wind Speed
Frictional Velocity
Max Horizontal Wind Speed
Standard Deviation of Horizontal Wind
Max Vertical Wind Speed
Min Vertical Wind Speed
95th Percentile of Veritcal Wind Speed
Standard Deviation of Vertical Wind
Root Mean Square of the Upward Wind Perturbations
Sensible Heat Flux
Shortwave Net Radiation
Sun Zenith Angle
Symbol Units
T
°C
RH
%
VPD Pa
ū
m/s
u*
m/s
gust
m/s
σU
m/s
W max m/s
W min m/s
W 95
m/s
σW
m/s
σ Wup m/s
H
K·m/s
SW net W/m2
ZE
deg.
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ
Functional Ecology
Table S2: Correlation (r2) between all environmental variables used in the study
ΔS a
T
RH
W 95
W max
W min
σW
σ Wup
ū
u*
gust
σU
H
SW net
VPD
ZE
ΔS a
W 95
T
RH
1.000 0.184 0.146 0.188
1.000 0.806 0.384
1.000 0.331
1.000
W max W min
0.228
0.293
0.234
0.712
1.000
0.171
0.308
0.264
0.712
0.620
1.000
σW
σ Wup
0.169
0.406
0.350
0.920
0.676
0.807
1.000
0.234
0.350
0.286
0.937
0.813
0.663
0.805
1.000
ū
0.004
0.174
0.174
0.197
0.094
0.053
0.153
0.147
1.000
u*
0.109
0.384
0.332
0.724
0.478
0.585
0.786
0.592
0.264
1.000
gust
0.127
0.320
0.348
0.614
0.609
0.588
0.588
0.601
0.311
0.559
1.000
σU
0.116
0.428
0.434
0.699
0.551
0.492
0.661
0.640
0.362
0.687
0.696
1.000
H
0.048
0.323
0.409
0.298
0.189
0.248
0.372
0.199
0.127
0.381
0.215
0.325
1.000
SW net VPD
0.047 0.163
0.312 0.829
0.213 0.990
0.192 0.337
0.188 0.250
0.149 0.269
0.241 0.357
0.149 0.296
0.127 0.171
0.270 0.334
0.151 0.349
0.207 0.430
0.231 0.408
1.000 0.244
1.000
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ
ZE
0.059
0.471
0.436
0.185
0.155
0.136
0.178
0.167
0.215
0.244
0.243
0.369
0.233
0.215
0.438
1.000
Functional Ecology
Table S3: Super-WALD model inputs and pertinent vertical wind statistics
WALD Input
Variable
z r (m)
Year
Season
Season (Day)
Season
(Day,
ū> 0.5 m/s)
Season
(Day, T >T 50 )
35
35
35
35
35
h (m)
V t (m/s)
35
35
35
35
35
0.7
0.7
0.7
0.7
0.7
Θ*
1.31
1.31
1.25
Pertinent Wind
Statistics
γ ū (m/s)
κ ū (m/s)
Year
0.59
1.18
Season
(Day, T >T 50 )
7.818
1.637
1.25
Season
(Day,
ū> 0.5 m/s)
1.504
7.606
6.434
7.442
Season
Season (Day)
1.259
σ W * (m/s)
0.352
0.414
0.474
0.488
γ W * (m/s)
-0.477
-0.518
-0.479
-0.511
κ W * (m/s)
4.307
4.281
3.921
4.013
The timing of abscission affects dispersal distance in a wind-dispersed tropical tree
Maurer KD, Bohrer G, Medvigy D, Wright SJ