fortedata: Ecophsyiology Data

Lisa Haber and Jeff Atkins

2020-12-09

Leaf Physiology Data

The fd_leaf_physiology() script within fortedata currently includes the following functions:

Ecophysiology Methods

Canopy Photosynthesis

Between July 11 – August 3, 2018; July 8 – 28, 2019; and July 9 - 24, 2020, we measured the light-saturated CO2 assimilation (Asat), stomatal conductance (gs), and ancillary variables of subcanopy leaves for each subplot. To capture inter- and intra-specific subplot physiological variability, we randomly selected three leaves 1 to 2 m above the forest floor in each of four 2 m x 2 m subcanopy vegetation survey sites for a total of 12 leaves per subplot. When leaves in the 1 to 2 m stratum were not present, the closest seedling or sapling leaf outside of the vegetation survey site was selected for measurement. Upon selection, we measured leaves using an LI-6400 XT portable photosynthesis system (LI-COR Inc., Licoln, NE) programmed to maintain the following chamber conditions: 2000 µmol/m2/s PAR, 400 ppm CO2, and 25 °C. In order to avoid excessive leaf stress during measurement, we attempted, through manipulation of chamber humidity, to maintain a leaf vapor pressure deficit below 2 kPa, but this was not always possible given atmospheric conditions in summer at our site. Leaves that failed to produce stable Asat after 5 minutes were discarded and a new leaf selected. In some plots, the majority of needleleaf trees sampled from vegetation survey locations exhibited leaf stress during sampling, so we collected additional samples and targeted broadleaf trees in order to hedge against insufficient sample size in these locations. Thus, the total number of leaves sampled across all 32 subplots was n = 443.

Using identical equipment and settings, we measured Asat, gs, and related variables for leaves from the canopy stratum of a single FoRTE replicate where a preexisting dirt road allowed aerial lift vehicle access. This data collection effort targeted three tree species: bigtooth aspen (Populus grandidentata), red maple (Acer rubrum), and Northern red oak (Quercus rubra). Tree crowns were sampled July 17 – 23, 2018, and twice the following year: June 11 – July 4, 2019, and July 30 – August 8, 2019. Sampling took place in accessible crowns of trees within or adjacent to experimental plots and proceeded as follows: 6 crowns per species x 3 species x 4 plots = 72 crowns. In our first sampled plot in 2018, we randomly chose five healthy, sun-adapted leaves from different terminal branches within each crown, but later reduced this number to three leaves per crown due to time and equipment constraints. Thus, the total number of leaves sampled in 2018 was n = 243, and in 2019 was n = 226. Accidental girdling of two trees in 2019 reduced the overall sample size to 70 crowns.

head(data.frame(fd_photosynthesis()))
##   subplot_id replicate plot subplot           timestamp obs ftime ebal photo
## 1       A01E         A    1       E 2018-08-02 15:09:39   1 175.5    0  2.22
## 2       A01E         A    1       E 2018-08-02 15:09:40   2 176.5    0  2.23
## 3       A01E         A    1       E 2018-08-02 15:09:41   3 177.0    0  2.25
## 4       A01E         A    1       E 2018-08-02 15:09:42   4 178.0    0  2.23
## 5       A01E         A    1       E 2018-08-02 15:09:43   5 179.0    0  2.24
## 6       A01E         A    1       E 2018-08-02 15:14:08   1 207.5    0  2.42
##     cond  ci trmmol vpdl ctleaf area blc_1 stmrat blcond  tair tleaf  tblk
## 1 0.0177 192  0.220  1.2  24.88    6  1.42      1   2.84 20.87 24.88 20.07
## 2 0.0175 189  0.217  1.2  24.88    6  1.42      1   2.84 20.87 24.88 20.06
## 3 0.0175 187  0.217  1.2  24.88    6  1.42      1   2.84 20.87 24.88 20.07
## 4 0.0176 190  0.219  1.2  24.88    6  1.42      1   2.84 20.87 24.88 20.07
## 5 0.0176 189  0.219  1.2  24.88    6  1.42      1   2.84 20.87 24.88 20.06
## 6 0.0222 218  0.275  1.2  24.88    6  1.42      1   2.84 20.88 24.88 20.06
##     co2r   co2s   h2or   h2os  rh_r  rh_s  flow pari paro press csmch  hsmch
## 1 402.79 400.02 19.601 19.860 78.02 79.04 500.6 1999    0 98.54 -5.18 0.0655
## 2 402.80 400.02 19.603 19.859 78.02 79.04 500.5 1999    0 98.54 -5.18 0.0655
## 3 402.81 400.01 19.604 19.859 78.03 79.04 500.5 1999    0 98.54 -5.18 0.0655
## 4 402.81 400.02 19.602 19.860 78.02 79.04 500.4 1999    0 98.54 -5.18 0.0655
## 5 402.80 400.01 19.601 19.858 78.01 79.04 500.3 1999    0 98.54 -5.18 0.0655
## 6 403.00 399.97 19.519 19.842 77.65 78.93 500.4 2000    0 98.56 -5.18 0.0655
##   csmchsd hsmchsd crmchsd hrmchsd stablef blcslope blcoffst f_parin f_parout
## 1  0.0214 0.00919  0.0263 0.00643     1.0    -0.22     2.74       1        0
## 2  0.0214 0.00919  0.0263 0.00643     0.7    -0.22     2.74       1        0
## 3  0.0214 0.00919  0.0263 0.00643     0.7    -0.22     2.74       1        0
## 4  0.0214 0.00919  0.0263 0.00643     0.7    -0.22     2.74       1        0
## 5  0.0214 0.00919  0.0263 0.00643     1.0    -0.22     2.74       1        0
## 6  0.0214 0.00919  0.0263 0.00643     0.7    -0.22     2.74       1        0
##   alphak status species sample               comments
## 1   0.16 111115    ACPE      1 15:07:05 in NE #2784; 
## 2   0.16 111115    ACPE      1 15:07:05 in NE #2784; 
## 3   0.16 111115    ACPE      1 15:07:05 in NE #2784; 
## 4   0.16 111115    ACPE      1 15:07:05 in NE #2784; 
## 5   0.16 111115    ACPE      1 15:07:05 in NE #2784; 
## 6   0.16 111115    ACPE      2       15:10:53 in NE;

Leaf spectrometry

We collected leaf reflectance spectra, used as a proxy for leaf biochemistry (including N content; Yang et al. 2017), with a CI-710 miniature leaf spectrometer (CID Biosciences, Camas, WA). We measured axial sides of leaves that met the minimum area requirement for the instrument (20 mm x 20 mm) in the range of visible to near infrared light (400-950 nm) immediately following physiological measurements and while still attached to trees. Because pine needles did not meet the minimum requirement for leaf area, only broadleaf specimens were available for measurement. Data output included waveform and individual wavelength spectral data, as well as a suite of derived vegetation indices including the normalized difference vegetation index (NDVI).

fd_leaf_spectrometry()
## # A tibble: 8 x 9
##   subplot_id replicate  plot subplot date       tree_id species index
##   <chr>      <chr>     <int> <chr>   <date>     <chr>   <chr>   <chr>
## 1 DO1E       D             1 E       2018-07-22 CAN08   QURU    PSRI 
## 2 DO1W       D             1 W       2018-07-23 CAN05   QURU    ARI1 
## 3 DO1W       D             1 W       2018-07-23 CAN06   QURU    ARI1 
## 4 DO2E       D             2 E       2018-07-18 CAN05   ACRU    ARI1 
## 5 DO2E       D             2 E       2018-07-19 CAN13   QURU    PSRI 
## 6 DO2W       D             2 W       2018-07-17 CAN10   QURU    ARI1 
## 7 DO3W       D             3 W       2018-07-22 CAN03   QURU    ARI1 
## 8 DO4E       D             4 E       2018-07-19 CAN07   ACRU    ARI1 
## # ... with 1 more variable: index_value <dbl>

Leaf morphology

We measured leaf mass per area (LMA) for all subcanopy and canopy leaf samples, which were removed from trees after physiological and reflectance measurements in 2018 and 2019. Samples were scanned with an LI-3100 leaf area meter (LI-COR Inc., Lincoln, NE) using the appropriate resolution (1.0 mm2 for broadleaves, 0.1 mm2 for needles), and subsequently dried at 60 °C for 48 hours and weighed to determine mass.