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Methods for measuring leaf area index and abiotic contrasts
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Abiotic factors are determined to a large extent by physical factors, and the influence of plant
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communities on these factors depends mostly on canopy structure and plant community
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architecture, and to a smaller extent on environmental physiology of the plants, e.g. transpiration
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and energy balance. For the small distances between plant communities dealt with in this study, we
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decided early on to focus on accuracy and resolution in our measurement of abiotic factors at the
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expense of repetition of measurements in a number of plots. This allowed us to find subtle
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differences in the factors that may have been invisible to coarse sensors that could be scattered in
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more locations for the same price. As an example of the above, the same instrumentation used here
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has been used to measure gradients in temperature and humidity in and above crop canopies (e.g.
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Tanny et al. 2006, 2010). In that kind of study there is no need for replication because boundary
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layers vary little from leaf to leaf, but it would be impossible to get the necessary detail with
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currently available small, stand-alone logging sensors.
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S1.1. Leaf area index (LAI)
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Leaf area index was determined by gap fraction analysis of radiation transmission measurements
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(Welles and Cohen 1996). Measurements were made with a portable SunLink ceptometer (Decagon
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Devices, Inc., Pullman, WA, USA), which is a linear array of 80 photo-sensors at 1-cm intervals.
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The SunLink was connected to a CR-10X data-logger (Campbell Scientific, Logan, UT, USA), and
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the output of each sensor was recorded. Measurements were made in one plot of the control
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(undisturbed) treatment at each site on five representative individuals of the local dominant LM
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species and repeated for one day at approximately monthly intervals at each site during the winter
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and spring of 2006/7. On each day of measurement, and for each individual, radiation transmission
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was measured at three times of the day and for each cardinal direction.
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Calculation of LAI using this method is described elsewhere (Cohen et al. 1997; Schiller et al.
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2003). In our case LAI of the LM species was desired and not that of the plot, so measurements
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analyzed were those made below the LM canopy in a regular pattern, as opposed to the large regular
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grids used in previous studies. Where the canopy was smaller than the length of the SunLink probe
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the group of sensors below the canopy was selected for analysis.
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S1.2. Microclimate measurements
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Microclimate in the canopies was measured with high resolution and high accuracy meteorological
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instrumentation. This allowed determination of relatively small contrasts between microsites
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located at small distances from each other. However, since the instrumentation was all wired to one
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data-logger, the distance between sensors was limited and only one plot could be measured at each
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research site. A meteorological mast was deployed in a relatively open location. Its instrumentation
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included a data-logger (CR10X) and multiplexer (AM16/32) and a shielded HMP35C temperature
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and relative humidity probe 1.5 m above ground (from Campbell Sci., Logan UT). Measurements of
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air temperature and humidity (expressed as water vapor pressure in Pa) in the vegetation patches
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were made a few cm above the ground with aspirated wet/dry bulb psychrometers containing
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thermocouple sensors wired to the data-logger on the mast. Additional shaded thermocouples
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measured air temperature at additional locations. Temperature resolution was better than 0.1°C, and
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since all measurements were made with the same data-logger, accuracy of temperature differences
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was the same. Relative humidity measured this way covered the full range (0–100%) with high
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accuracy.
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S1.3. Air-temperature contrast
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The daytime course of air temperature at ground level was determined in five randomly selected
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pairs of modulated patches and adjacent unmodulated areas, in one plot of the control treatment at
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each site. Measurements were for one day per site and repeated at approximately monthly intervals
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throughout the winter and spring of 2006/7. Because temperatures and temperature contrasts change
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continuously during the day, we based the contrast calculation on maximum daily temperatures in
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both modulated patches and un-modulated areas. We normalized temperature data to make them
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comparable among sites and days: in both modulated patches and un-modulated areas we subtracted
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the maximum air temperature at the meteorological mast from that at ground level. The air-
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temperature contrast between modulated patches and un-modulated areas at a given site was defined
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as 1 – (dTM/dTUM), where dTM/dTUM is the slope of the linear regression line between the
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normalized maximum temperatures in modulated patches and un-modulated areas (TM and TUM,
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respectively), based on measurements in five patch pairs on five dates.
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S1.4. Air-humidity contrast
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Psychrometers at ground level were in three randomly selected pairs of modulated patches and
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adjacent unmodulated areas in one plot of the control treatment at each site (i.e., six psychrometers),
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and yielded continuous measurements during the five measurement days. For each patch at each site
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we examined the changes in humidity during the day, chose time periods during which the humidity
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was relatively constant, and averaged the values recorded during these periods to obtain a single
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value. Stable humidity was usually found during 1–4 h between 1030 and 1500 during daytime and
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during 1.25–2.5 h between 2000 and 0030 at night. We normalized humidity data to make them
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comparable among sites and days: in both modulated patches and un-modulated areas we subtracted
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the average absolute air humidity at 1.5 m above ground level from that at ground level, as
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measured on the same day. The air-humidity contrast between modulated patches and un-modulated
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areas at a given site was defined as 1 – (dHM/dHUM), where dHM/dHUM is the slope of the linear
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regression line between the absolute humidity in modulated patches (HM) and that in un-modulated
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areas (HNW), based on values measured in three patch pairs on five dates.
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References
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Cohen S, Sudhakara Rao R, Cohen Y (1997) Canopy transmittance inversion with a line quantum
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probe in a row crop. Agr Forest Meteorol 86:225–234.
Schiller G, Ungar ED, Moshe Y, Cohen S, Cohen Y (2003) Estimating water use by sclerophyllous
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species under east Mediterranean climate: II. The transpiration of Quercus calliprinos Webb. in
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response to silvicultural treatments. Forest Ecol Manag 179:483–495.
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Tanny J, Dicken U, Cohen S (2010) Vertical variation in turbulence statistics and energy balance in
a banana screenhouse. Biosyst Eng 106:175–187.
Tanny Y, Haijun L, Cohen S (2006) Airflow characteristics, energy balance and eddy covariance
measurements in a banana screenhouse. Agr Forest Meteorol 139:105–118.
Welles J, Cohen, S (1996) Canopy structure measurement by gap fraction analysis using
commercial instrumentation. J Exp Bot 47:1335–1342.
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