In this vignette, we demonstrate the use of the
BerkeleyForestsAnalytics package. We provide an example of
how you might use the BFA package to compile pre- and post-burn field
data. Through this example, we highlight some key components of the BFA
package:
The vignette is not a replacement for the README file, which covers the inputs and outputs of each function in detail. Additionally, the README gives detailed background information and references for the methods used in the package. We recommend that you review the README prior to or in conjunction with the vignette (Find README here).
To begin, we’ll load the required packages:
This vignette uses data from the Fire and Fire Surrogate (FFS) Study. In brief, FFS is an experimental study that was designed to evaluate the impacts of fire-only (prescribed fire), mechanical-only (mechanical thinning from below followed by mastication), and mechanical + fire (mechanical thinning from below followed by mastication followed by prescribed fire) treatments on forest structure, ecological function, and future fire behavior.
The data used in this vignette are from the fire-only (i.e., prescribed fire) stands. We used data from two time periods: before treatment (2001) and one year after treatment (2003). Note that the data were slightly modified for demonstrations purposes. Therefore, the outputs should not be taken to be actual findings from the FFS Study.
The tree data have the following columns:
division ecodivisionprovince province within ecodivisionsite time (pre- or post-burn) and compartment (60, 340,
or 400)plot plot in which the individual tree was
measuredexp_factor stems per hectarestatus live (1) or dead (0)decay decay class. 1-5 for standing dead trees. 0 for
live trees.species species of the individual tree, using
four-letter species codesdbh diameter at breast height in centimetersht1 tree height in metersht2 tree height in meters, only for trees without
topscrown_ratio live crown ratio, set to fixed 0.5 for
demotop tree has top yes (Y) or no (N)cull percent wood cull, set to 0 for demoThe surface and ground fuels data have the following columns:
time pre (pre-burn) or post (post-burn)site compartment (60, 340, or 400)plot plot in which the individual transect was
measuredtransect azimuth of transect on which the fuel data
were collectedcount_1h count of 1-hour fuelscount_10h count of 10-hour fuelscount_100h count of 100-hour fuelslength_1h length of the sampling transect for 1-hour
fuels in meterslength_10h length of the sampling transect for 10-hour
fuels in meterslength_100h length of the sampling transect for
100-hour fuels in meterslength_1000h length of the sampling transect for
1000-hour fuels in metersssd_S sum-of-squared-diameters for sound 1000-hour
fuelsssd_R sum-of-squared-diameters for rotten 1000-hour
fuelslitter_depth litter depth in centimetersduff_depth duff depth in centimetersslope slope along the transect in percentFirst, we’ll use the BiomassNSVB() function to get
above-ground tree biomass at the plot level.
Let’s investigate the input dataframe:
# Note that the example data used in this vignette is included with the package
# which is why we do not have to read in the data
head(vign_trees_1)## division province site plot exp_factor status decay_class species dbh
## 1 M260 M261 post_340 103 24.69 1 0 818 85.3
## 2 M260 M261 post_340 103 24.69 1 0 818 71.4
## 3 M260 M261 post_340 103 24.69 1 0 15 34.3
## 4 M260 M261 post_340 103 24.69 1 0 15 18.3
## 5 M260 M261 post_340 103 24.69 1 0 15 52.8
## 6 M260 M261 post_340 103 24.69 1 0 15 19.8
## ht1 ht2 crown_ratio top cull
## 1 35.2 NA 0.5 Y 0
## 2 31.1 NA 0.5 Y 0
## 3 22.5 NA 0.5 Y 0
## 4 15.7 NA 0.5 Y 0
## 5 33.0 NA 0.5 Y 0
## 6 17.5 NA 0.5 Y 0Attempt 1: Now, let’s try using
BiomassNSVB(). We’ll keep the defaults for input_units (=
“metric”), output_units (= “metric”), and results (= “by_plot”):
## Error in `ValidateNSVB()`:
## ! There are plots with a recorded expansion factor of 0, but with more than one row.
## Plots with no trees should be represented by a single row with site and plot filled in as appropriate and an exp_factor of 0.And we get an error message. It looks like there is an improper use of a 0 expansion factor. An expansion factor of 0 should only be used to represent a plot with no trees. Let’s look at where 0 expansion factors show up in the data:
## division province site plot exp_factor status decay_class species dbh
## 1 M260 M261 post_60 112 0 1 0 81 25.4
## 2 M260 M261 post_60 113 0 <NA> <NA> <NA> NA
## ht1 ht2 crown_ratio top cull
## 1 14.4 NA 0.5 Y 0
## 2 NA NA NA <NA> NA## division province site plot exp_factor status decay_class species dbh
## 1 M260 M261 post_60 112 24.69 0 3 818 51.1
## 2 M260 M261 post_60 112 24.69 1 0 15 69.9
## 3 M260 M261 post_60 112 24.69 1 0 15 58.2
## 4 M260 M261 post_60 112 24.69 1 0 15 23.6
## 5 M260 M261 post_60 112 24.69 1 0 15 42.7
## 6 M260 M261 post_60 112 24.69 1 0 202 51.8
## 7 M260 M261 post_60 112 24.69 1 0 81 25.1
## 8 M260 M261 post_60 112 24.69 1 0 81 53.8
## 9 M260 M261 post_60 112 24.69 1 0 81 42.7
## 10 M260 M261 post_60 112 0.00 1 0 81 25.4
## ht1 ht2 crown_ratio top cull
## 1 22.2 NA NA Y 0
## 2 30.9 NA 0.5 Y 0
## 3 30.1 NA 0.5 Y 0
## 4 17.6 NA 0.5 Y 0
## 5 24.0 NA 0.5 Y 0
## 6 29.4 NA 0.5 Y 0
## 7 11.5 NA 0.5 Y 0
## 8 25.0 NA 0.5 Y 0
## 9 23.1 NA 0.5 Y 0
## 10 14.4 NA 0.5 Y 0It looks like a 0 expansion factor is properly used for post-60-113, but improperly used for post-60-112. We know what plot radius was used for larger trees, so we can confidently fill in the correct exp_factor here (24.69). The expansion factor will differ among studies. If a nested plot design was used, the expansion factor will differ among trees within the same plot.
Attempt 2: After correcting the expansion factor in the input data, let’s try again:
## Error in `ValidateNSVB()`:
## ! Not all species codes were recognized!
## Unrecognized codes: 15555 202222And we get another error message. It looks like there are some typos/transcription errors in the species codes. Looking at the list of unrecognized codes, we can tell that “15555” should be “15” (Abies concolor, commonly known as white fir) and “202222” should probably be “202” (Pseudotsuga menziesii, commonly known as Douglas-fir). Depending on the severity of the typo, you may want to go back to double check the species recorded on the original datasheet. In this case, the typos are fairly obvious. Let’s figure out where these typos occur in the data:
## division province site plot exp_factor status decay_class species dbh
## 1 M260 M261 post_340 108 24.69 1 0 15555 12.7
## 2 M260 M261 post_340 109 24.69 1 0 202222 19.6
## ht1 ht2 crown_ratio top cull
## 1 11.3 NA 0.5 Y 0
## 2 13.4 NA 0.5 Y 0Attempt 3: After correcting the species codes in the input data, let’s try again:
## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : There are dead trees with NA and/or 0 decay class codes.
## These trees will be assigned a decay class of 3.
## Consider investigating these trees with mismatched status/decay class.
## ## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : There are missing DBH values in the provided dataframe - outside of plots with exp_factor of 0, signifying plots with no trees, which should have NA dbh.
## Trees with NA DBH will have NA biomass/carbon estimates. Consider investigating these trees.
## ## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : The allometric equations are for trees with DBH >= 2.54cm.
## You inputted trees with DBH < 2.54cm. These trees will have NA biomass/carbon estimates.
## ## $run_time
## Time difference of 18.99 secs
##
## $dataframe
## site plot total_wood_Mg_ha total_bark_Mg_ha total_branch_Mg_ha
## 1 pre_340 108 96.96878 22.04735 42.21908
## 2 pre_340 112 601.04020 116.50079 139.77397
## 3 pre_340 104 163.44546 30.98747 37.28993
## 4 pre_340 6 181.75054 61.94861 36.60418
## 5 pre_340 121 222.77627 71.90796 55.70627
## 6 pre_340 113 356.81278 45.14825 126.88187
## 7 pre_340 13 185.65638 34.53278 49.30946
## 8 pre_340 114 330.05313 48.50333 48.52587
## 9 pre_340 26 163.44664 47.51848 89.12808
## 10 pre_340 116 286.02376 47.88623 46.21927
## 11 pre_340 117 260.75530 100.86793 53.02438
## 12 pre_340 103 310.27014 77.91147 121.05431
## 13 pre_340 24 304.65856 86.48863 100.42607
## 14 pre_340 109 111.73927 19.86442 33.21567
## 15 pre_340 25 85.56416 14.29239 21.93301
## 16 pre_340 122 185.29981 73.02088 42.70554
## 17 pre_340 115 94.48310 35.11892 22.48418
## 18 pre_340 123 249.54799 44.27333 82.31712
## 19 pre_340 124 188.99888 55.85471 51.28996
## 20 pre_340 111 210.67962 44.80849 29.24570
## 21 post_340 113 337.86313 42.60265 119.46322
## 22 post_340 13 190.84470 33.74294 52.26651
## 23 post_340 108 96.40630 21.92177 40.72260
## 24 post_340 116 296.61407 49.48527 49.64137
## 25 post_340 114 340.96274 50.21536 51.69633
## 26 post_340 112 560.00977 113.49291 145.33833
## 27 post_340 6 178.23283 60.49297 36.00473
## 28 post_340 121 210.02477 71.64876 55.13753
## 29 post_340 103 317.42582 79.55131 123.73130
## 30 post_340 109 110.63266 19.68628 33.13006
## 31 post_340 104 170.03610 31.67521 40.47197
## 32 post_340 24 309.38281 85.32115 99.95260
## 33 post_340 26 138.76854 40.12010 29.86939
## 34 post_340 123 253.39328 44.86488 83.59792
## 35 post_340 115 115.27104 39.04601 27.57124
## 36 post_340 25 104.63889 18.87364 22.65956
## 37 post_340 124 195.49249 57.40291 52.79368
## 38 post_340 111 132.69069 36.60800 25.29905
## 39 post_340 117 242.63912 98.51941 54.20798
## 40 post_340 122 175.09609 64.40827 35.67113
## 41 pre_60 107 71.50368 20.92434 15.50241
## 42 pre_60 117 173.49513 25.64026 65.26019
## 43 pre_60 130 148.67728 22.88490 73.70730
## 44 pre_60 21 189.70043 37.04681 77.56714
## 45 pre_60 102 228.37788 36.14424 109.27257
## 46 pre_60 103 150.70185 23.15250 44.39578
## 47 pre_60 108 55.19004 8.64592 28.46063
## 48 pre_60 27 69.72810 11.83179 13.91577
## 49 pre_60 116 165.76997 53.91012 31.84281
## 50 pre_60 119 284.85915 85.95267 85.58272
## 51 pre_60 112 157.02994 43.59437 38.25495
## 52 pre_60 106 178.97482 44.47262 43.06072
## 53 pre_60 114 422.84293 60.09856 76.38977
## 54 pre_60 109 281.78120 72.31352 69.93057
## 55 pre_60 104 93.58513 15.57800 22.45707
## 56 pre_60 105 146.67642 36.20937 41.66131
## 57 pre_60 113 163.97735 42.92598 45.42022
## 58 pre_60 111 161.96299 43.36119 43.49798
## 59 pre_60 101 89.47422 14.75314 26.33102
## 60 post_60 117 172.45188 25.39094 64.06364
## 61 post_60 108 47.31107 7.11866 25.81690
## 62 post_60 21 175.30625 33.04809 73.12271
## 63 post_60 130 98.65891 16.20145 28.57542
## 64 post_60 107 74.92356 21.44779 15.99401
## 65 post_60 102 231.81631 36.24813 112.29699
## 66 post_60 103 151.30659 23.14719 43.38346
## 67 post_60 119 256.11422 78.74437 60.57668
## 68 post_60 112 157.15450 43.12878 28.82081
## 69 post_60 114 305.29089 45.57928 68.57766
## 70 post_60 105 149.91951 36.97721 40.99542
## 71 post_60 104 99.12974 16.43556 24.33511
## 72 post_60 106 185.03711 45.71932 44.22159
## 73 post_60 111 171.50660 45.32455 45.51925
## 74 post_60 109 286.14566 74.00664 71.04953
## 75 post_60 116 163.37550 54.28840 32.33947
## 76 post_60 101 89.39444 14.73932 26.91833
## 77 post_60 27 77.05470 14.45446 14.00382
## 78 post_60 113 0.00000 0.00000 0.00000
## 79 pre_400 112 182.85893 33.94187 60.49404
## 80 pre_400 9 198.74673 41.88184 53.74294
## 81 pre_400 103 166.18866 27.56723 47.93914
## 82 pre_400 120 174.44934 38.11404 51.13240
## 83 pre_400 106 346.76295 61.91699 95.27868
## 84 pre_400 105 257.00732 56.52265 57.34536
## 85 pre_400 116 237.91095 51.13863 54.16680
## 86 pre_400 109 155.00518 26.49377 40.88454
## 87 pre_400 22 297.01915 42.86732 47.79526
## 88 pre_400 102 153.87938 33.35873 26.04109
## 89 pre_400 108 224.93025 35.30433 75.93719
## 90 pre_400 24 264.87628 53.09388 51.57027
## 91 pre_400 121 253.30260 51.25386 81.09807
## 92 pre_400 101 174.66758 35.73001 62.34244
## 93 pre_400 110 251.21312 50.30732 66.21745
## 94 pre_400 118 150.20138 25.56687 35.40136
## 95 pre_400 119 86.23798 16.31710 28.75121
## 96 pre_400 104 165.19778 25.28079 41.57274
## 97 pre_400 23 57.70936 13.31232 16.15895
## 98 pre_400 2 131.67433 28.13791 23.36392
## 99 post_400 112 178.59768 32.93692 58.32214
## 100 post_400 106 350.05480 62.38872 95.24550
## 101 post_400 105 268.98702 58.38321 60.36541
## 102 post_400 24 265.73473 52.70636 47.81287
## 103 post_400 109 157.88488 26.81181 41.88042
## 104 post_400 116 232.05709 50.32896 49.07799
## 105 post_400 120 179.88733 39.08168 51.89004
## 106 post_400 102 163.95558 33.90191 26.20259
## 107 post_400 121 261.09714 52.75887 80.58512
## 108 post_400 103 170.58107 28.24073 48.14087
## 109 post_400 108 212.24850 32.89775 78.49791
## 110 post_400 110 259.85290 51.95280 69.99744
## 111 post_400 22 303.69114 43.58605 50.05866
## 112 post_400 104 165.45815 25.41885 39.90004
## 113 post_400 9 200.01671 42.00277 54.04228
## 114 post_400 119 87.71450 16.60296 26.66641
## 115 post_400 101 180.23607 36.77084 63.52217
## 116 post_400 118 154.17890 26.21141 36.10027
## 117 post_400 2 139.02073 27.99875 24.00158
## 118 post_400 23 62.04659 14.56762 16.07760
## total_ag_Mg_ha merch_total_Mg_ha merch_top_Mg_ha stump_total_Mg_ha
## 1 161.23521 104.20719 45.17972 7.04277
## 2 857.31495 691.62220 144.14114 20.91496
## 3 231.72286 181.43926 38.49401 6.43024
## 4 280.30333 236.39307 36.95654 5.76077
## 5 350.39050 279.68590 56.06582 8.49396
## 6 528.84290 375.12349 131.31288 20.26065
## 7 269.49862 207.82877 51.60535 9.70828
## 8 427.08233 365.28176 50.96982 10.72275
## 9 300.09320 198.13392 90.30466 9.22033
## 10 380.12926 321.15550 48.70103 9.29582
## 11 414.64760 350.72604 54.25105 9.67051
## 12 509.23592 369.80342 124.96079 13.89735
## 13 491.57326 374.39009 103.49972 13.24375
## 14 164.81936 122.39760 35.69487 5.37618
## 15 121.78955 93.76133 21.21134 3.98626
## 16 301.02624 248.54467 44.82226 7.65930
## 17 152.08619 124.25073 23.66019 4.17528
## 18 376.13844 282.66834 84.12026 9.02757
## 19 296.14355 236.18795 52.69460 6.91705
## 20 284.73381 245.27376 30.95839 8.50167
## 21 499.92901 356.99667 123.93026 19.00208
## 22 276.85414 212.04945 54.37749 10.09345
## 23 159.05066 105.65381 43.58297 6.81320
## 24 395.74070 333.02330 51.92061 9.81990
## 25 442.87442 377.99469 54.01739 10.86235
## 26 818.84102 649.41135 149.54990 19.87976
## 27 274.73054 232.12376 36.42355 5.70225
## 28 336.81105 271.47060 56.39503 8.08643
## 29 520.70844 379.35427 127.35215 14.00202
## 30 163.44900 121.73276 35.46312 5.27915
## 31 242.18329 194.22394 41.53845 6.42090
## 32 494.65657 378.50789 102.63259 13.25179
## 33 208.75803 168.89709 29.28669 6.86279
## 34 381.85608 286.98467 85.38250 9.16664
## 35 181.88829 148.15077 28.75081 4.98671
## 36 146.17209 117.86110 23.88033 4.43066
## 37 305.68908 244.38427 54.25848 7.04633
## 38 194.59774 162.41980 26.43546 5.74248
## 39 395.36651 331.24066 55.24406 8.88180
## 40 275.17549 230.62559 37.53118 7.01872
## 41 107.93043 87.54497 16.27306 3.07680
## 42 264.39558 191.34935 65.72533 5.73114
## 43 245.26948 161.64095 75.11735 7.95246
## 44 304.31437 215.93558 79.06128 8.23438
## 45 373.79469 250.18452 110.99091 11.33261
## 46 218.25013 165.99907 45.67281 6.57825
## 47 92.29659 56.18621 28.74866 3.43802
## 48 95.47566 77.10392 14.53711 3.51357
## 49 251.52290 211.14188 32.92129 6.21378
## 50 456.39454 358.04314 87.34055 11.01085
## 51 238.87926 192.84633 39.32479 6.70813
## 52 266.50816 213.54653 45.39133 7.57030
## 53 559.33126 469.40136 77.29965 12.63025
## 54 424.02529 343.20545 71.63589 9.18395
## 55 131.62019 103.45045 23.51185 4.11595
## 56 224.54710 174.55526 43.35959 6.63225
## 57 252.32355 200.73677 45.98180 5.60498
## 58 248.82216 197.69073 44.80814 6.32328
## 59 130.55838 93.22684 28.09541 5.30611
## 60 261.90646 190.59912 64.66066 5.71068
## 61 80.24663 51.01913 26.21564 3.01187
## 62 281.47705 199.10782 74.40032 7.96891
## 63 143.43578 107.82150 30.07438 5.53989
## 64 112.36536 92.25063 16.87691 3.23782
## 65 380.36142 255.09304 113.90472 11.36366
## 66 217.83724 166.79916 44.51424 6.52383
## 67 395.43527 323.82229 62.09095 9.52202
## 68 229.10408 192.88830 29.80569 6.41010
## 69 419.44783 341.73070 69.32855 8.38858
## 70 227.89215 178.85626 42.44112 6.59477
## 71 139.90041 109.64435 25.34109 4.31987
## 72 274.97802 220.85142 46.35423 7.77238
## 73 262.35040 209.02166 46.72042 6.60832
## 74 431.20183 350.22984 71.98043 8.99156
## 75 250.00337 210.52626 33.41892 6.05819
## 76 131.05209 95.85050 28.97029 5.45427
## 77 105.51298 87.13525 14.74263 3.63510
## 78 0.00000 0.00000 0.00000 0.00000
## 79 277.29483 205.87679 61.95849 7.12490
## 80 294.37151 223.93254 55.71087 8.67874
## 81 241.69502 185.04389 47.98544 5.50374
## 82 263.69578 204.34859 52.41247 6.21497
## 83 503.95863 393.68473 97.24772 11.75589
## 84 370.87534 302.62574 59.06248 8.86445
## 85 343.21638 279.13833 55.19866 8.05829
## 86 222.38349 174.07152 41.93969 5.36539
## 87 387.68173 329.35898 49.21154 8.37859
## 88 213.27921 178.50300 28.24582 6.13736
## 89 336.17177 239.15360 75.90693 6.96049
## 90 369.54043 306.29614 49.51929 8.39940
## 91 385.65454 293.53446 82.74914 8.87054
## 92 272.74003 202.89979 63.22386 6.26896
## 93 367.73790 291.08173 67.60664 8.28131
## 94 211.16961 165.24441 37.61288 6.79978
## 95 131.30630 89.83111 27.64441 5.04516
## 96 232.05130 180.43122 43.14475 7.10804
## 97 87.18063 65.90986 16.60295 3.08023
## 98 183.17616 154.24940 24.03265 4.89411
## 99 269.85674 203.29684 59.76474 6.79517
## 100 507.68902 399.15990 96.90124 11.62788
## 101 387.73564 316.28768 61.90107 9.18937
## 102 366.25397 309.35775 48.57297 8.32326
## 103 226.57712 177.38149 42.74977 5.40749
## 104 331.46404 273.96133 49.99257 7.51014
## 105 270.85904 210.86498 52.89260 6.36609
## 106 224.06009 189.73288 28.02668 6.30052
## 107 394.44113 303.62943 81.64259 8.70833
## 108 246.96266 191.69291 49.54506 5.72469
## 109 323.64416 236.16753 79.77833 6.74107
## 110 381.80315 300.03017 69.79141 8.51044
## 111 397.33585 337.34624 51.45331 8.53630
## 112 230.77704 181.68334 41.78576 7.04285
## 113 296.06176 230.24895 56.87334 8.93947
## 114 130.98387 93.42466 27.19895 5.13542
## 115 280.52907 209.70461 64.37788 6.44657
## 116 216.49058 170.18393 38.45165 6.94781
## 117 191.02105 161.35592 24.63737 5.02777
## 118 92.69181 72.03693 17.41070 3.24418
## foliage_Mg_ha total_wood_c total_bark_c total_branch_c total_ag_c
## 1 14.03418 48.79832 11.10702 21.17939 81.08473
## 2 23.34980 296.93357 58.15900 68.15772 423.25028
## 3 11.88751 83.23132 15.76116 18.69765 117.69013
## 4 11.84707 93.03521 31.62809 18.71710 143.38041
## 5 16.58637 112.41655 36.46209 27.98904 176.86768
## 6 16.40732 169.55267 21.46163 60.28391 251.29821
## 7 13.97788 94.62400 17.69867 24.59081 136.91349
## 8 13.92070 168.29978 24.74994 24.36470 217.41442
## 9 16.39654 81.36994 23.97367 43.14224 148.48585
## 10 14.44343 146.32190 24.47256 23.34066 194.13513
## 11 16.64565 133.28413 51.48204 27.13134 211.89751
## 12 19.44792 154.59605 39.25263 59.22173 253.07041
## 13 19.85770 152.14792 43.61431 49.44275 245.20498
## 14 11.18956 56.99368 10.11290 16.83482 83.94140
## 15 11.18567 44.10469 7.34073 11.41836 62.86378
## 16 15.55447 94.35012 37.20600 21.77455 153.33067
## 17 9.39426 48.21012 17.90692 11.47805 77.59509
## 18 18.24535 123.36397 22.06171 40.42337 185.84905
## 19 16.05184 97.70523 28.72428 26.40529 152.83481
## 20 12.97950 109.00805 23.16395 15.31266 147.48466
## 21 15.17410 160.61287 20.25968 56.77841 237.65096
## 22 14.85144 97.28405 17.29386 26.09624 140.67415
## 23 13.55456 48.61963 11.06097 20.47992 80.16052
## 24 15.29342 151.63639 25.27463 25.01454 201.92556
## 25 14.62736 173.88591 25.62589 25.94023 225.45203
## 26 24.37010 276.08432 56.64818 70.89630 403.62881
## 27 11.96429 91.21741 30.88414 18.41544 140.51699
## 28 16.33813 106.14264 36.37271 27.75830 170.27365
## 29 19.17961 158.00907 40.05882 60.42932 258.49720
## 30 11.51114 56.48536 10.03319 16.80801 83.32657
## 31 11.53825 86.78401 16.15295 20.27957 123.21654
## 32 20.19232 154.77039 43.06629 49.22482 247.06150
## 33 11.56785 70.21449 20.44026 15.26069 105.91544
## 34 18.58654 125.29112 22.36157 41.05455 188.70723
## 35 10.94448 58.38451 19.82812 13.93243 92.14506
## 36 10.53643 53.98272 9.70392 11.76357 75.45021
## 37 16.29843 100.98528 29.50739 27.16110 157.65377
## 38 11.75818 69.43134 19.00240 13.20929 101.64303
## 39 17.10221 124.22908 50.32110 27.75754 202.30772
## 40 13.60942 89.14619 32.80526 18.18788 140.13932
## 41 7.17088 36.72762 10.70301 8.00632 55.43696
## 42 12.83708 85.65056 12.74854 32.19776 130.59686
## 43 11.01825 74.06439 11.43304 35.86486 121.36228
## 44 14.47819 95.62539 18.78035 38.36957 152.77531
## 45 15.64360 112.28902 17.85816 52.81788 182.96506
## 46 12.40950 77.25717 11.87061 22.42974 111.55752
## 47 5.50459 27.76587 4.35959 13.99859 46.12405
## 48 6.79769 37.19137 6.33205 7.47318 50.99660
## 49 12.36987 85.46823 27.66188 16.37739 129.50751
## 50 21.51566 144.13213 43.65889 42.94804 230.73907
## 51 12.18472 80.35646 22.29111 19.29987 121.94744
## 52 17.35573 92.66026 22.92610 22.17058 137.75695
## 53 17.48723 214.66607 30.47509 38.77228 283.91344
## 54 20.19238 144.86401 37.05225 35.97719 217.89345
## 55 10.08072 48.72388 8.10719 11.70321 68.53428
## 56 16.62625 76.19403 18.68442 21.64661 116.52507
## 57 13.45460 83.96343 21.93735 23.26144 129.16222
## 58 14.78731 83.95986 22.34128 22.47116 128.77230
## 59 13.96254 47.81457 7.87349 14.10207 69.79013
## 60 12.66107 85.13141 12.62438 31.58805 129.34384
## 61 3.80521 23.65859 3.56714 12.59764 39.82337
## 62 13.87482 88.81603 16.81206 36.22792 141.85601
## 63 9.94232 50.53533 8.29321 14.51134 73.33988
## 64 7.39285 38.49322 10.97615 8.26450 57.73388
## 65 15.41824 113.83002 17.88746 54.23093 185.94841
## 66 12.71828 77.66120 11.88039 21.99963 111.54122
## 67 19.34391 130.30063 40.14984 30.97167 201.42214
## 68 12.18961 80.80461 22.13903 14.85869 117.80233
## 69 16.15948 155.10057 23.11467 34.76040 212.97564
## 70 16.23208 77.81135 19.07341 21.26608 118.15084
## 71 10.79771 51.59414 8.55152 12.67770 72.82336
## 72 17.84649 95.78406 23.56811 22.77103 142.12321
## 73 15.49676 88.81024 23.33890 23.49914 135.64827
## 74 20.35258 147.08290 37.91389 36.52345 221.52023
## 75 12.32652 84.28798 27.85917 16.63469 128.78184
## 76 13.66591 47.81713 7.87376 14.42608 70.11696
## 77 7.69668 41.42351 7.77051 7.52825 56.72227
## 78 0.00000 0.00000 0.00000 0.00000 0.00000
## 79 19.11417 93.70712 17.36539 31.00640 142.07891
## 80 22.05004 102.97360 21.62016 27.88345 152.47720
## 81 11.32840 82.41915 13.73721 23.63545 119.79182
## 82 12.79488 86.52565 19.09846 25.29229 130.91641
## 83 25.99133 174.94103 31.27466 48.04104 254.25674
## 84 19.88892 132.14022 28.97958 29.47333 190.59313
## 85 15.84486 120.46040 25.93115 27.35735 173.74890
## 86 9.70097 75.84736 12.94157 20.07235 108.86129
## 87 16.87815 153.20357 22.09354 24.71824 200.01535
## 88 12.80317 78.77644 17.04203 13.30146 109.11993
## 89 19.69834 112.33906 17.63372 38.03894 168.01171
## 90 18.88728 135.49935 27.10424 26.43259 189.03617
## 91 23.14903 129.58788 26.16869 41.58447 197.34104
## 92 17.77216 89.71633 18.31328 32.06788 140.09749
## 93 19.96198 128.93079 25.77579 33.98790 188.69449
## 94 16.50746 78.18254 13.25167 18.63731 110.07152
## 95 15.33491 45.71319 8.57681 15.30607 69.59608
## 96 17.81467 85.70708 13.10570 21.68271 120.49549
## 97 8.11685 30.05592 6.87465 8.49142 45.42199
## 98 10.16607 68.50893 14.58108 12.18933 95.27934
## 99 17.82243 91.54635 16.85957 29.88970 138.29561
## 100 25.20015 176.54442 31.50779 47.95717 256.00938
## 101 20.75427 138.26264 29.92989 31.02311 199.21565
## 102 16.54518 135.90399 26.90768 24.41988 187.23155
## 103 9.77335 77.22816 13.09212 20.54411 110.86439
## 104 13.99204 117.44865 25.51787 24.71194 167.67845
## 105 13.38143 89.32231 19.59897 25.68581 134.60708
## 106 11.98168 83.94885 17.32574 13.39798 114.67257
## 107 21.78121 133.35158 26.91440 41.19168 201.45765
## 108 11.12954 84.63624 14.08321 23.69347 122.41291
## 109 16.67312 105.75554 16.39703 39.26279 161.41535
## 110 21.41100 133.38797 26.62004 35.96470 195.97271
## 111 17.57783 156.65970 22.47854 25.87457 205.01280
## 112 17.09109 85.81802 13.17606 20.78029 119.77436
## 113 21.17106 103.66063 21.68981 28.06002 153.41046
## 114 14.03536 46.41923 8.72146 14.17727 69.31797
## 115 18.13247 92.56600 18.84580 32.66694 144.07874
## 116 16.81310 80.22149 13.58272 18.99598 112.80018
## 117 10.44851 72.24119 14.50154 12.53060 99.27334
## 118 7.84335 32.27524 7.51781 8.42729 48.22034
## merch_total_c merch_top_c stump_total_c foliage_c
## 1 52.47664 22.69166 3.53237 7.01709
## 2 342.39860 70.30232 10.24842 11.67490
## 3 92.60537 19.31340 3.23800 5.94375
## 4 120.95992 18.90688 2.94970 5.92354
## 5 141.44978 28.21712 4.29413 8.29318
## 6 178.30614 62.37897 9.59874 8.20366
## 7 106.09849 25.74081 4.88269 6.98894
## 8 186.40925 25.56395 5.38316 6.96035
## 9 98.95634 43.74571 4.54304 8.19827
## 10 164.43037 24.55918 4.68380 7.22171
## 11 179.18439 27.76341 4.94971 8.32283
## 12 184.56162 61.24801 6.95201 9.72396
## 13 187.39288 50.99710 6.58660 9.92885
## 14 62.42191 18.08665 2.72286 5.59478
## 15 48.28810 10.99770 2.05627 5.59284
## 16 126.58041 22.85175 3.89851 7.77724
## 17 63.37864 12.08387 2.13259 4.69713
## 18 139.83018 41.34339 4.51122 9.12267
## 19 121.94194 27.12549 3.58246 8.02592
## 20 126.85427 16.21171 4.41867 6.48975
## 21 169.75381 58.89136 9.00579 7.58705
## 22 108.26441 27.15399 5.07633 7.42572
## 23 53.30028 21.94271 3.42815 6.77728
## 24 170.38794 26.13632 4.93952 7.64671
## 25 192.92153 27.07979 5.45070 7.31368
## 26 320.93784 72.96379 9.72718 12.18505
## 27 118.73659 18.63419 2.91885 5.98215
## 28 137.36505 28.40611 4.09434 8.16906
## 29 189.18941 62.31383 6.99396 9.58981
## 30 62.14236 17.99759 2.67951 5.75557
## 31 99.14154 20.82819 3.24681 5.76912
## 32 189.73645 50.58820 6.60214 10.09616
## 33 85.55009 14.91582 3.45431 5.78392
## 34 141.99632 41.96513 4.58152 9.29327
## 35 75.08350 14.53436 2.52719 5.47224
## 36 60.75714 12.40080 2.29226 5.26821
## 37 126.08889 27.91795 3.64692 8.14921
## 38 84.82209 13.81003 3.01091 5.87909
## 39 169.46012 28.29276 4.55484 8.55111
## 40 117.42738 19.13685 3.57509 6.80471
## 41 44.92953 8.41148 1.58823 3.58544
## 42 94.47033 32.43270 2.88162 6.41854
## 43 80.52682 36.59779 3.95288 5.50913
## 44 108.93368 39.14222 4.14734 7.23910
## 45 123.02885 53.69914 5.58127 7.82180
## 46 85.10243 23.09487 3.36022 6.20475
## 47 28.21964 14.15604 1.72533 2.75229
## 48 41.16359 7.80680 1.87367 3.39885
## 49 108.72959 16.93466 3.20819 6.18493
## 50 181.32944 43.84997 5.55966 10.75783
## 51 98.66963 19.85418 3.42363 6.09236
## 52 110.45862 23.37750 3.92083 8.67787
## 53 238.25396 39.23987 6.41961 8.74362
## 54 176.30044 36.86146 4.73156 10.09619
## 55 53.85566 12.25156 2.14887 5.04036
## 56 90.51418 22.54721 3.46368 8.31312
## 57 102.74553 23.54839 2.86830 6.72730
## 58 102.32659 23.15540 3.29031 7.39365
## 59 49.90156 15.03900 2.83629 6.98127
## 60 94.08477 31.88898 2.87047 6.33054
## 61 25.51370 12.80851 1.50116 1.90261
## 62 100.94625 36.89054 4.01922 6.93741
## 63 55.22610 15.29052 2.82326 4.97116
## 64 47.33882 8.72414 1.67092 3.69642
## 65 125.30203 55.05644 5.58993 7.70912
## 66 85.61428 22.58891 3.33803 6.35914
## 67 164.82948 31.75074 4.84192 9.67196
## 68 99.13419 15.37157 3.29657 6.09480
## 69 173.57198 35.14149 4.26216 8.07974
## 70 92.67792 22.03329 3.43963 8.11604
## 71 57.06226 13.20091 2.25471 5.39885
## 72 114.22103 23.87694 4.02523 8.92324
## 73 108.08613 24.12757 3.43457 7.74838
## 74 179.88422 37.00566 4.63035 10.17629
## 75 108.45844 17.19268 3.13072 6.16326
## 76 51.28715 15.50831 2.91442 6.83296
## 77 46.84266 7.92543 1.95418 3.84834
## 78 0.00000 0.00000 0.00000 0.00000
## 79 105.51333 31.72465 3.64153 9.55709
## 80 115.99422 28.91542 4.51234 11.02502
## 81 91.76040 23.63941 2.75863 5.66420
## 82 101.47973 25.93774 3.12336 6.39744
## 83 198.54597 49.06352 5.98339 12.99566
## 84 155.50933 30.35964 4.55970 9.94446
## 85 141.31330 27.88447 4.10971 7.92243
## 86 85.07045 20.60714 2.65584 4.85049
## 87 169.84592 25.45923 4.33246 8.43908
## 88 91.36091 14.42204 3.13434 6.40159
## 89 119.28096 38.01274 3.48519 9.84917
## 90 156.56313 25.30748 4.30261 9.44364
## 91 150.07347 42.44660 4.55197 11.57452
## 92 104.16800 32.52686 3.22555 8.88608
## 93 149.34505 34.68781 4.24864 9.98099
## 94 85.89047 19.80223 3.56571 8.25373
## 95 47.49949 14.69416 2.67941 7.66746
## 96 93.56941 22.50555 3.70576 8.90733
## 97 34.24275 8.71503 1.61259 4.05842
## 98 80.17898 12.54278 2.55759 5.08303
## 99 104.19986 30.61947 3.47628 8.91121
## 100 201.27615 48.82045 5.91278 12.60007
## 101 162.49368 31.81471 4.72503 10.37714
## 102 158.16133 24.80911 4.26111 8.27259
## 103 86.66187 20.98413 2.67449 4.88668
## 104 138.67157 25.18654 3.82034 6.99602
## 105 104.82220 26.19684 3.20451 6.69072
## 106 97.12683 14.32691 3.21883 5.99084
## 107 155.02678 41.73446 4.44870 10.89061
## 108 95.12578 24.41618 2.87096 5.56477
## 109 117.63832 39.90840 3.36955 8.33656
## 110 153.93107 35.80945 4.36616 10.70550
## 111 173.99628 26.60376 4.41276 8.78892
## 112 94.19651 21.76697 3.66837 8.54554
## 113 119.23822 29.52591 4.64632 10.58553
## 114 49.33565 14.45021 2.72331 7.01768
## 115 107.65117 33.11172 3.31586 9.06623
## 116 88.43359 20.23706 3.64184 8.40655
## 117 83.78171 12.86657 2.62506 5.22426
## 118 37.39412 9.12932 1.69690 3.92168This time the function runs. However, we get some warning messages. Let’s look at the first warning, which tells us that there are trees with mismatched status/decay class. Recall that dead trees should have a decay class of 1-5 and live trees should have a decay class of NA or 0 (in this dataset we use 0 for live trees). Let’s figure out where mismatches occur in the data:
## division province site plot exp_factor status decay_class species dbh ht1
## 1 M260 M261 pre_60 27 24.69 0 0 81 42.2 21.6
## ht2 crown_ratio top cull
## 1 NA NA Y 0This incense cedar (Calocedrus decurrens, FIA species code 81) was documented as dead (status = 0) but was assigned a decay class of 0 (which is reserved for live trees). We look back at the original datasheet (not shown here) and see that the tree was recorded as dead with a decay class of 0 in the field (which tells us this was not a transcription error). However, we also notice that a height to live crown base was recorded for the tree on the original datasheet, indicating that the dead status was likely a recording error. Given that two pieces of information recorded for the tree point to a live status (i.e., a decay class of 0 and a height to live crown base), we can fairly confidently change the cedar’s status to live.
Attempt 4: After correcting for the mismatch in status/decay class, let’s try again:
## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : There are missing DBH values in the provided dataframe - outside of plots with exp_factor of 0, signifying plots with no trees, which should have NA dbh.
## Trees with NA DBH will have NA biomass/carbon estimates. Consider investigating these trees.
## ## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : The allometric equations are for trees with DBH >= 2.54cm.
## You inputted trees with DBH < 2.54cm. These trees will have NA biomass/carbon estimates.
## ## $run_time
## Time difference of 13.47 secs
##
## $dataframe
## site plot total_wood_Mg_ha total_bark_Mg_ha total_branch_Mg_ha
## 1 pre_340 108 96.96878 22.04735 42.21908
## 2 pre_340 112 601.04020 116.50079 139.77397
## 3 pre_340 104 163.44546 30.98747 37.28993
## 4 pre_340 6 181.75054 61.94861 36.60418
## 5 pre_340 121 222.77627 71.90796 55.70627
## 6 pre_340 113 356.81278 45.14825 126.88187
## 7 pre_340 13 185.65638 34.53278 49.30946
## 8 pre_340 114 330.05313 48.50333 48.52587
## 9 pre_340 26 163.44664 47.51848 89.12808
## 10 pre_340 116 286.02376 47.88623 46.21927
## 11 pre_340 117 260.75530 100.86793 53.02438
## 12 pre_340 103 310.27014 77.91147 121.05431
## 13 pre_340 24 304.65856 86.48863 100.42607
## 14 pre_340 109 111.73927 19.86442 33.21567
## 15 pre_340 25 85.56416 14.29239 21.93301
## 16 pre_340 122 185.29981 73.02088 42.70554
## 17 pre_340 115 94.48310 35.11892 22.48418
## 18 pre_340 123 249.54799 44.27333 82.31712
## 19 pre_340 124 188.99888 55.85471 51.28996
## 20 pre_340 111 210.67962 44.80849 29.24570
## 21 post_340 113 337.86313 42.60265 119.46322
## 22 post_340 13 190.84470 33.74294 52.26651
## 23 post_340 108 96.40630 21.92177 40.72260
## 24 post_340 116 296.61407 49.48527 49.64137
## 25 post_340 114 340.96274 50.21536 51.69633
## 26 post_340 112 560.00977 113.49291 145.33833
## 27 post_340 6 178.23283 60.49297 36.00473
## 28 post_340 121 210.02477 71.64876 55.13753
## 29 post_340 103 317.42582 79.55131 123.73130
## 30 post_340 109 110.63266 19.68628 33.13006
## 31 post_340 104 170.03610 31.67521 40.47197
## 32 post_340 24 309.38281 85.32115 99.95260
## 33 post_340 26 138.76854 40.12010 29.86939
## 34 post_340 123 253.39328 44.86488 83.59792
## 35 post_340 115 115.27104 39.04601 27.57124
## 36 post_340 25 104.63889 18.87364 22.65956
## 37 post_340 124 195.49249 57.40291 52.79368
## 38 post_340 111 132.69069 36.60800 25.29905
## 39 post_340 117 242.63912 98.51941 54.20798
## 40 post_340 122 175.09609 64.40827 35.67113
## 41 pre_60 107 71.50368 20.92434 15.50241
## 42 pre_60 117 173.49513 25.64026 65.26019
## 43 pre_60 130 148.67728 22.88490 73.70730
## 44 pre_60 21 189.70043 37.04681 77.56714
## 45 pre_60 102 228.37788 36.14424 109.27257
## 46 pre_60 103 150.70185 23.15250 44.39578
## 47 pre_60 108 55.19004 8.64592 28.46063
## 48 pre_60 27 70.48134 12.72478 15.47159
## 49 pre_60 116 165.76997 53.91012 31.84281
## 50 pre_60 112 157.02994 43.59437 38.25495
## 51 pre_60 119 284.85915 85.95267 85.58272
## 52 pre_60 106 178.97482 44.47262 43.06072
## 53 pre_60 114 422.84293 60.09856 76.38977
## 54 pre_60 109 281.78120 72.31352 69.93057
## 55 pre_60 104 93.58513 15.57800 22.45707
## 56 pre_60 105 146.67642 36.20937 41.66131
## 57 pre_60 113 163.97735 42.92598 45.42022
## 58 pre_60 111 161.96299 43.36119 43.49798
## 59 pre_60 101 89.47422 14.75314 26.33102
## 60 post_60 117 172.45188 25.39094 64.06364
## 61 post_60 108 47.31107 7.11866 25.81690
## 62 post_60 21 175.30625 33.04809 73.12271
## 63 post_60 130 98.65891 16.20145 28.57542
## 64 post_60 107 74.92356 21.44779 15.99401
## 65 post_60 102 231.81631 36.24813 112.29699
## 66 post_60 103 151.30659 23.14719 43.38346
## 67 post_60 119 256.11422 78.74437 60.57668
## 68 post_60 112 157.15450 43.12878 28.82081
## 69 post_60 114 305.29089 45.57928 68.57766
## 70 post_60 105 149.91951 36.97721 40.99542
## 71 post_60 104 99.12974 16.43556 24.33511
## 72 post_60 106 185.03711 45.71932 44.22159
## 73 post_60 111 171.50660 45.32455 45.51925
## 74 post_60 109 286.14566 74.00664 71.04953
## 75 post_60 116 163.37550 54.28840 32.33947
## 76 post_60 101 89.39444 14.73932 26.91833
## 77 post_60 27 77.05470 14.45446 14.00382
## 78 post_60 113 0.00000 0.00000 0.00000
## 79 pre_400 112 182.85893 33.94187 60.49404
## 80 pre_400 9 198.74673 41.88184 53.74294
## 81 pre_400 103 166.18866 27.56723 47.93914
## 82 pre_400 120 174.44934 38.11404 51.13240
## 83 pre_400 106 346.76295 61.91699 95.27868
## 84 pre_400 105 257.00732 56.52265 57.34536
## 85 pre_400 116 237.91095 51.13863 54.16680
## 86 pre_400 109 155.00518 26.49377 40.88454
## 87 pre_400 22 297.01915 42.86732 47.79526
## 88 pre_400 102 153.87938 33.35873 26.04109
## 89 pre_400 108 224.93025 35.30433 75.93719
## 90 pre_400 24 264.87628 53.09388 51.57027
## 91 pre_400 121 253.30260 51.25386 81.09807
## 92 pre_400 101 174.66758 35.73001 62.34244
## 93 pre_400 110 251.21312 50.30732 66.21745
## 94 pre_400 118 150.20138 25.56687 35.40136
## 95 pre_400 119 86.23798 16.31710 28.75121
## 96 pre_400 104 165.19778 25.28079 41.57274
## 97 pre_400 23 57.70936 13.31232 16.15895
## 98 pre_400 2 131.67433 28.13791 23.36392
## 99 post_400 112 178.59768 32.93692 58.32214
## 100 post_400 106 350.05480 62.38872 95.24550
## 101 post_400 105 268.98702 58.38321 60.36541
## 102 post_400 24 265.73473 52.70636 47.81287
## 103 post_400 109 157.88488 26.81181 41.88042
## 104 post_400 116 232.05709 50.32896 49.07799
## 105 post_400 120 179.88733 39.08168 51.89004
## 106 post_400 102 163.95558 33.90191 26.20259
## 107 post_400 121 261.09714 52.75887 80.58512
## 108 post_400 103 170.58107 28.24073 48.14087
## 109 post_400 108 212.24850 32.89775 78.49791
## 110 post_400 110 259.85290 51.95280 69.99744
## 111 post_400 22 303.69114 43.58605 50.05866
## 112 post_400 104 165.45815 25.41885 39.90004
## 113 post_400 9 200.01671 42.00277 54.04228
## 114 post_400 119 87.71450 16.60296 26.66641
## 115 post_400 101 180.23607 36.77084 63.52217
## 116 post_400 118 154.17890 26.21141 36.10027
## 117 post_400 2 139.02073 27.99875 24.00158
## 118 post_400 23 62.04659 14.56762 16.07760
## total_ag_Mg_ha merch_total_Mg_ha merch_top_Mg_ha stump_total_Mg_ha
## 1 161.23521 104.20719 45.17972 7.04277
## 2 857.31495 691.62220 144.14114 20.91496
## 3 231.72286 181.43926 38.49401 6.43024
## 4 280.30333 236.39307 36.95654 5.76077
## 5 350.39050 279.68590 56.06582 8.49396
## 6 528.84290 375.12349 131.31288 20.26065
## 7 269.49862 207.82877 51.60535 9.70828
## 8 427.08233 365.28176 50.96982 10.72275
## 9 300.09320 198.13392 90.30466 9.22033
## 10 380.12926 321.15550 48.70103 9.29582
## 11 414.64760 350.72604 54.25105 9.67051
## 12 509.23592 369.80342 124.96079 13.89735
## 13 491.57326 374.39009 103.49972 13.24375
## 14 164.81936 122.39760 35.69487 5.37618
## 15 121.78955 93.76133 21.21134 3.98626
## 16 301.02624 248.54467 44.82226 7.65930
## 17 152.08619 124.25073 23.66019 4.17528
## 18 376.13844 282.66834 84.12026 9.02757
## 19 296.14355 236.18795 52.69460 6.91705
## 20 284.73381 245.27376 30.95839 8.50167
## 21 499.92901 356.99667 123.93026 19.00208
## 22 276.85414 212.04945 54.37749 10.09345
## 23 159.05066 105.65381 43.58297 6.81320
## 24 395.74070 333.02330 51.92061 9.81990
## 25 442.87442 377.99469 54.01739 10.86235
## 26 818.84102 649.41135 149.54990 19.87976
## 27 274.73054 232.12376 36.42355 5.70225
## 28 336.81105 271.47060 56.39503 8.08643
## 29 520.70844 379.35427 127.35215 14.00202
## 30 163.44900 121.73276 35.46312 5.27915
## 31 242.18329 194.22394 41.53845 6.42090
## 32 494.65657 378.50789 102.63259 13.25179
## 33 208.75803 168.89709 29.28669 6.86279
## 34 381.85608 286.98467 85.38250 9.16664
## 35 181.88829 148.15077 28.75081 4.98671
## 36 146.17209 117.86110 23.88033 4.43066
## 37 305.68908 244.38427 54.25848 7.04633
## 38 194.59774 162.41980 26.43546 5.74248
## 39 395.36651 331.24066 55.24406 8.88180
## 40 275.17549 230.62559 37.53118 7.01872
## 41 107.93043 87.54497 16.27306 3.07680
## 42 264.39558 191.34935 65.72533 5.73114
## 43 245.26948 161.64095 75.11735 7.95246
## 44 304.31437 215.93558 79.06128 8.23438
## 45 373.79469 250.18452 110.99091 11.33261
## 46 218.25013 165.99907 45.67281 6.57825
## 47 92.29659 56.18621 28.74866 3.43802
## 48 98.67771 78.66309 16.11001 3.58354
## 49 251.52290 211.14188 32.92129 6.21378
## 50 238.87926 192.84633 39.32479 6.70813
## 51 456.39454 358.04314 87.34055 11.01085
## 52 266.50816 213.54653 45.39133 7.57030
## 53 559.33126 469.40136 77.29965 12.63025
## 54 424.02529 343.20545 71.63589 9.18395
## 55 131.62019 103.45045 23.51185 4.11595
## 56 224.54710 174.55526 43.35959 6.63225
## 57 252.32355 200.73677 45.98180 5.60498
## 58 248.82216 197.69073 44.80814 6.32328
## 59 130.55838 93.22684 28.09541 5.30611
## 60 261.90646 190.59912 64.66066 5.71068
## 61 80.24663 51.01913 26.21564 3.01187
## 62 281.47705 199.10782 74.40032 7.96891
## 63 143.43578 107.82150 30.07438 5.53989
## 64 112.36536 92.25063 16.87691 3.23782
## 65 380.36142 255.09304 113.90472 11.36366
## 66 217.83724 166.79916 44.51424 6.52383
## 67 395.43527 323.82229 62.09095 9.52202
## 68 229.10408 192.88830 29.80569 6.41010
## 69 419.44783 341.73070 69.32855 8.38858
## 70 227.89215 178.85626 42.44112 6.59477
## 71 139.90041 109.64435 25.34109 4.31987
## 72 274.97802 220.85142 46.35423 7.77238
## 73 262.35040 209.02166 46.72042 6.60832
## 74 431.20183 350.22984 71.98043 8.99156
## 75 250.00337 210.52626 33.41892 6.05819
## 76 131.05209 95.85050 28.97029 5.45427
## 77 105.51298 87.13525 14.74263 3.63510
## 78 0.00000 0.00000 0.00000 0.00000
## 79 277.29483 205.87679 61.95849 7.12490
## 80 294.37151 223.93254 55.71087 8.67874
## 81 241.69502 185.04389 47.98544 5.50374
## 82 263.69578 204.34859 52.41247 6.21497
## 83 503.95863 393.68473 97.24772 11.75589
## 84 370.87534 302.62574 59.06248 8.86445
## 85 343.21638 279.13833 55.19866 8.05829
## 86 222.38349 174.07152 41.93969 5.36539
## 87 387.68173 329.35898 49.21154 8.37859
## 88 213.27921 178.50300 28.24582 6.13736
## 89 336.17177 239.15360 75.90693 6.96049
## 90 369.54043 306.29614 49.51929 8.39940
## 91 385.65454 293.53446 82.74914 8.87054
## 92 272.74003 202.89979 63.22386 6.26896
## 93 367.73790 291.08173 67.60664 8.28131
## 94 211.16961 165.24441 37.61288 6.79978
## 95 131.30630 89.83111 27.64441 5.04516
## 96 232.05130 180.43122 43.14475 7.10804
## 97 87.18063 65.90986 16.60295 3.08023
## 98 183.17616 154.24940 24.03265 4.89411
## 99 269.85674 203.29684 59.76474 6.79517
## 100 507.68902 399.15990 96.90124 11.62788
## 101 387.73564 316.28768 61.90107 9.18937
## 102 366.25397 309.35775 48.57297 8.32326
## 103 226.57712 177.38149 42.74977 5.40749
## 104 331.46404 273.96133 49.99257 7.51014
## 105 270.85904 210.86498 52.89260 6.36609
## 106 224.06009 189.73288 28.02668 6.30052
## 107 394.44113 303.62943 81.64259 8.70833
## 108 246.96266 191.69291 49.54506 5.72469
## 109 323.64416 236.16753 79.77833 6.74107
## 110 381.80315 300.03017 69.79141 8.51044
## 111 397.33585 337.34624 51.45331 8.53630
## 112 230.77704 181.68334 41.78576 7.04285
## 113 296.06176 230.24895 56.87334 8.93947
## 114 130.98387 93.42466 27.19895 5.13542
## 115 280.52907 209.70461 64.37788 6.44657
## 116 216.49058 170.18393 38.45165 6.94781
## 117 191.02105 161.35592 24.63737 5.02777
## 118 92.69181 72.03693 17.41070 3.24418
## foliage_Mg_ha total_wood_c total_bark_c total_branch_c total_ag_c
## 1 14.03418 48.79832 11.10702 21.17939 81.08473
## 2 23.34980 296.93357 58.15900 68.15772 423.25028
## 3 11.88751 83.23132 15.76116 18.69765 117.69013
## 4 11.84707 93.03521 31.62809 18.71710 143.38041
## 5 16.58637 112.41655 36.46209 27.98904 176.86768
## 6 16.40732 169.55267 21.46163 60.28391 251.29821
## 7 13.97788 94.62400 17.69867 24.59081 136.91349
## 8 13.92070 168.29978 24.74994 24.36470 217.41442
## 9 16.39654 81.36994 23.97367 43.14224 148.48585
## 10 14.44343 146.32190 24.47256 23.34066 194.13513
## 11 16.64565 133.28413 51.48204 27.13134 211.89751
## 12 19.44792 154.59605 39.25263 59.22173 253.07041
## 13 19.85770 152.14792 43.61431 49.44275 245.20498
## 14 11.18956 56.99368 10.11290 16.83482 83.94140
## 15 11.18567 44.10469 7.34073 11.41836 62.86378
## 16 15.55447 94.35012 37.20600 21.77455 153.33067
## 17 9.39426 48.21012 17.90692 11.47805 77.59509
## 18 18.24535 123.36397 22.06171 40.42337 185.84905
## 19 16.05184 97.70523 28.72428 26.40529 152.83481
## 20 12.97950 109.00805 23.16395 15.31266 147.48466
## 21 15.17410 160.61287 20.25968 56.77841 237.65096
## 22 14.85144 97.28405 17.29386 26.09624 140.67415
## 23 13.55456 48.61963 11.06097 20.47992 80.16052
## 24 15.29342 151.63639 25.27463 25.01454 201.92556
## 25 14.62736 173.88591 25.62589 25.94023 225.45203
## 26 24.37010 276.08432 56.64818 70.89630 403.62881
## 27 11.96429 91.21741 30.88414 18.41544 140.51699
## 28 16.33813 106.14264 36.37271 27.75830 170.27365
## 29 19.17961 158.00907 40.05882 60.42932 258.49720
## 30 11.51114 56.48536 10.03319 16.80801 83.32657
## 31 11.53825 86.78401 16.15295 20.27957 123.21654
## 32 20.19232 154.77039 43.06629 49.22482 247.06150
## 33 11.56785 70.21449 20.44026 15.26069 105.91544
## 34 18.58654 125.29112 22.36157 41.05455 188.70723
## 35 10.94448 58.38451 19.82812 13.93243 92.14506
## 36 10.53643 53.98272 9.70392 11.76357 75.45021
## 37 16.29843 100.98528 29.50739 27.16110 157.65377
## 38 11.75818 69.43134 19.00240 13.20929 101.64303
## 39 17.10221 124.22908 50.32110 27.75754 202.30772
## 40 13.60942 89.14619 32.80526 18.18788 140.13932
## 41 7.17088 36.72762 10.70301 8.00632 55.43696
## 42 12.83708 85.65056 12.74854 32.19776 130.59686
## 43 11.01825 74.06439 11.43304 35.86486 121.36228
## 44 14.47819 95.62539 18.78035 38.36957 152.77531
## 45 15.64360 112.28902 17.85816 52.81788 182.96506
## 46 12.40950 77.25717 11.87061 22.42974 111.55752
## 47 5.50459 27.76587 4.35959 13.99859 46.12405
## 48 7.79266 37.86990 6.83614 8.31455 53.02059
## 49 12.36987 85.46823 27.66188 16.37739 129.50751
## 50 12.18472 80.35646 22.29111 19.29987 121.94744
## 51 21.51566 144.13213 43.65889 42.94804 230.73907
## 52 17.35573 92.66026 22.92610 22.17058 137.75695
## 53 17.48723 214.66607 30.47509 38.77228 283.91344
## 54 20.19238 144.86401 37.05225 35.97719 217.89345
## 55 10.08072 48.72388 8.10719 11.70321 68.53428
## 56 16.62625 76.19403 18.68442 21.64661 116.52507
## 57 13.45460 83.96343 21.93735 23.26144 129.16222
## 58 14.78731 83.95986 22.34128 22.47116 128.77230
## 59 13.96254 47.81457 7.87349 14.10207 69.79013
## 60 12.66107 85.13141 12.62438 31.58805 129.34384
## 61 3.80521 23.65859 3.56714 12.59764 39.82337
## 62 13.87482 88.81603 16.81206 36.22792 141.85601
## 63 9.94232 50.53533 8.29321 14.51134 73.33988
## 64 7.39285 38.49322 10.97615 8.26450 57.73388
## 65 15.41824 113.83002 17.88746 54.23093 185.94841
## 66 12.71828 77.66120 11.88039 21.99963 111.54122
## 67 19.34391 130.30063 40.14984 30.97167 201.42214
## 68 12.18961 80.80461 22.13903 14.85869 117.80233
## 69 16.15948 155.10057 23.11467 34.76040 212.97564
## 70 16.23208 77.81135 19.07341 21.26608 118.15084
## 71 10.79771 51.59414 8.55152 12.67770 72.82336
## 72 17.84649 95.78406 23.56811 22.77103 142.12321
## 73 15.49676 88.81024 23.33890 23.49914 135.64827
## 74 20.35258 147.08290 37.91389 36.52345 221.52023
## 75 12.32652 84.28798 27.85917 16.63469 128.78184
## 76 13.66591 47.81713 7.87376 14.42608 70.11696
## 77 7.69668 41.42351 7.77051 7.52825 56.72227
## 78 0.00000 0.00000 0.00000 0.00000 0.00000
## 79 19.11417 93.70712 17.36539 31.00640 142.07891
## 80 22.05004 102.97360 21.62016 27.88345 152.47720
## 81 11.32840 82.41915 13.73721 23.63545 119.79182
## 82 12.79488 86.52565 19.09846 25.29229 130.91641
## 83 25.99133 174.94103 31.27466 48.04104 254.25674
## 84 19.88892 132.14022 28.97958 29.47333 190.59313
## 85 15.84486 120.46040 25.93115 27.35735 173.74890
## 86 9.70097 75.84736 12.94157 20.07235 108.86129
## 87 16.87815 153.20357 22.09354 24.71824 200.01535
## 88 12.80317 78.77644 17.04203 13.30146 109.11993
## 89 19.69834 112.33906 17.63372 38.03894 168.01171
## 90 18.88728 135.49935 27.10424 26.43259 189.03617
## 91 23.14903 129.58788 26.16869 41.58447 197.34104
## 92 17.77216 89.71633 18.31328 32.06788 140.09749
## 93 19.96198 128.93079 25.77579 33.98790 188.69449
## 94 16.50746 78.18254 13.25167 18.63731 110.07152
## 95 15.33491 45.71319 8.57681 15.30607 69.59608
## 96 17.81467 85.70708 13.10570 21.68271 120.49549
## 97 8.11685 30.05592 6.87465 8.49142 45.42199
## 98 10.16607 68.50893 14.58108 12.18933 95.27934
## 99 17.82243 91.54635 16.85957 29.88970 138.29561
## 100 25.20015 176.54442 31.50779 47.95717 256.00938
## 101 20.75427 138.26264 29.92989 31.02311 199.21565
## 102 16.54518 135.90399 26.90768 24.41988 187.23155
## 103 9.77335 77.22816 13.09212 20.54411 110.86439
## 104 13.99204 117.44865 25.51787 24.71194 167.67845
## 105 13.38143 89.32231 19.59897 25.68581 134.60708
## 106 11.98168 83.94885 17.32574 13.39798 114.67257
## 107 21.78121 133.35158 26.91440 41.19168 201.45765
## 108 11.12954 84.63624 14.08321 23.69347 122.41291
## 109 16.67312 105.75554 16.39703 39.26279 161.41535
## 110 21.41100 133.38797 26.62004 35.96470 195.97271
## 111 17.57783 156.65970 22.47854 25.87457 205.01280
## 112 17.09109 85.81802 13.17606 20.78029 119.77436
## 113 21.17106 103.66063 21.68981 28.06002 153.41046
## 114 14.03536 46.41923 8.72146 14.17727 69.31797
## 115 18.13247 92.56600 18.84580 32.66694 144.07874
## 116 16.81310 80.22149 13.58272 18.99598 112.80018
## 117 10.44851 72.24119 14.50154 12.53060 99.27334
## 118 7.84335 32.27524 7.51781 8.42729 48.22034
## merch_total_c merch_top_c stump_total_c foliage_c
## 1 52.47664 22.69166 3.53237 7.01709
## 2 342.39860 70.30232 10.24842 11.67490
## 3 92.60537 19.31340 3.23800 5.94375
## 4 120.95992 18.90688 2.94970 5.92354
## 5 141.44978 28.21712 4.29413 8.29318
## 6 178.30614 62.37897 9.59874 8.20366
## 7 106.09849 25.74081 4.88269 6.98894
## 8 186.40925 25.56395 5.38316 6.96035
## 9 98.95634 43.74571 4.54304 8.19827
## 10 164.43037 24.55918 4.68380 7.22171
## 11 179.18439 27.76341 4.94971 8.32283
## 12 184.56162 61.24801 6.95201 9.72396
## 13 187.39288 50.99710 6.58660 9.92885
## 14 62.42191 18.08665 2.72286 5.59478
## 15 48.28810 10.99770 2.05627 5.59284
## 16 126.58041 22.85175 3.89851 7.77724
## 17 63.37864 12.08387 2.13259 4.69713
## 18 139.83018 41.34339 4.51122 9.12267
## 19 121.94194 27.12549 3.58246 8.02592
## 20 126.85427 16.21171 4.41867 6.48975
## 21 169.75381 58.89136 9.00579 7.58705
## 22 108.26441 27.15399 5.07633 7.42572
## 23 53.30028 21.94271 3.42815 6.77728
## 24 170.38794 26.13632 4.93952 7.64671
## 25 192.92153 27.07979 5.45070 7.31368
## 26 320.93784 72.96379 9.72718 12.18505
## 27 118.73659 18.63419 2.91885 5.98215
## 28 137.36505 28.40611 4.09434 8.16906
## 29 189.18941 62.31383 6.99396 9.58981
## 30 62.14236 17.99759 2.67951 5.75557
## 31 99.14154 20.82819 3.24681 5.76912
## 32 189.73645 50.58820 6.60214 10.09616
## 33 85.55009 14.91582 3.45431 5.78392
## 34 141.99632 41.96513 4.58152 9.29327
## 35 75.08350 14.53436 2.52719 5.47224
## 36 60.75714 12.40080 2.29226 5.26821
## 37 126.08889 27.91795 3.64692 8.14921
## 38 84.82209 13.81003 3.01091 5.87909
## 39 169.46012 28.29276 4.55484 8.55111
## 40 117.42738 19.13685 3.57509 6.80471
## 41 44.92953 8.41148 1.58823 3.58544
## 42 94.47033 32.43270 2.88162 6.41854
## 43 80.52682 36.59779 3.95288 5.50913
## 44 108.93368 39.14222 4.14734 7.23910
## 45 123.02885 53.69914 5.58127 7.82180
## 46 85.10243 23.09487 3.36022 6.20475
## 47 28.21964 14.15604 1.72533 2.75229
## 48 42.28367 8.66044 1.92393 3.89633
## 49 108.72959 16.93466 3.20819 6.18493
## 50 98.66963 19.85418 3.42363 6.09236
## 51 181.32944 43.84997 5.55966 10.75783
## 52 110.45862 23.37750 3.92083 8.67787
## 53 238.25396 39.23987 6.41961 8.74362
## 54 176.30044 36.86146 4.73156 10.09619
## 55 53.85566 12.25156 2.14887 5.04036
## 56 90.51418 22.54721 3.46368 8.31312
## 57 102.74553 23.54839 2.86830 6.72730
## 58 102.32659 23.15540 3.29031 7.39365
## 59 49.90156 15.03900 2.83629 6.98127
## 60 94.08477 31.88898 2.87047 6.33054
## 61 25.51370 12.80851 1.50116 1.90261
## 62 100.94625 36.89054 4.01922 6.93741
## 63 55.22610 15.29052 2.82326 4.97116
## 64 47.33882 8.72414 1.67092 3.69642
## 65 125.30203 55.05644 5.58993 7.70912
## 66 85.61428 22.58891 3.33803 6.35914
## 67 164.82948 31.75074 4.84192 9.67196
## 68 99.13419 15.37157 3.29657 6.09480
## 69 173.57198 35.14149 4.26216 8.07974
## 70 92.67792 22.03329 3.43963 8.11604
## 71 57.06226 13.20091 2.25471 5.39885
## 72 114.22103 23.87694 4.02523 8.92324
## 73 108.08613 24.12757 3.43457 7.74838
## 74 179.88422 37.00566 4.63035 10.17629
## 75 108.45844 17.19268 3.13072 6.16326
## 76 51.28715 15.50831 2.91442 6.83296
## 77 46.84266 7.92543 1.95418 3.84834
## 78 0.00000 0.00000 0.00000 0.00000
## 79 105.51333 31.72465 3.64153 9.55709
## 80 115.99422 28.91542 4.51234 11.02502
## 81 91.76040 23.63941 2.75863 5.66420
## 82 101.47973 25.93774 3.12336 6.39744
## 83 198.54597 49.06352 5.98339 12.99566
## 84 155.50933 30.35964 4.55970 9.94446
## 85 141.31330 27.88447 4.10971 7.92243
## 86 85.07045 20.60714 2.65584 4.85049
## 87 169.84592 25.45923 4.33246 8.43908
## 88 91.36091 14.42204 3.13434 6.40159
## 89 119.28096 38.01274 3.48519 9.84917
## 90 156.56313 25.30748 4.30261 9.44364
## 91 150.07347 42.44660 4.55197 11.57452
## 92 104.16800 32.52686 3.22555 8.88608
## 93 149.34505 34.68781 4.24864 9.98099
## 94 85.89047 19.80223 3.56571 8.25373
## 95 47.49949 14.69416 2.67941 7.66746
## 96 93.56941 22.50555 3.70576 8.90733
## 97 34.24275 8.71503 1.61259 4.05842
## 98 80.17898 12.54278 2.55759 5.08303
## 99 104.19986 30.61947 3.47628 8.91121
## 100 201.27615 48.82045 5.91278 12.60007
## 101 162.49368 31.81471 4.72503 10.37714
## 102 158.16133 24.80911 4.26111 8.27259
## 103 86.66187 20.98413 2.67449 4.88668
## 104 138.67157 25.18654 3.82034 6.99602
## 105 104.82220 26.19684 3.20451 6.69072
## 106 97.12683 14.32691 3.21883 5.99084
## 107 155.02678 41.73446 4.44870 10.89061
## 108 95.12578 24.41618 2.87096 5.56477
## 109 117.63832 39.90840 3.36955 8.33656
## 110 153.93107 35.80945 4.36616 10.70550
## 111 173.99628 26.60376 4.41276 8.78892
## 112 94.19651 21.76697 3.66837 8.54554
## 113 119.23822 29.52591 4.64632 10.58553
## 114 49.33565 14.45021 2.72331 7.01768
## 115 107.65117 33.11172 3.31586 9.06623
## 116 88.43359 20.23706 3.64184 8.40655
## 117 83.78171 12.86657 2.62506 5.22426
## 118 37.39412 9.12932 1.69690 3.92168The first warning message is gone (good!), but we still have two other warning messages. Let’s look at the next warning, which tells us there are missing DBH values in the dataframe. Let’s look at where NA DBH values show up in the data:
## division province site plot exp_factor status decay_class species dbh ht1
## 1 M260 M261 pre_340 115 24.69 1 0 117 NA 24.6
## ht2 crown_ratio top cull
## 1 NA 0.5 Y 0We look back at the original datasheet (not shown here) and see that DBH was recorded for the tree in the field. This was just a simple transcription error. However, in your own dataset you may have DBH values (or height values) that are truly missing. In the case of truly missing values, you may want to build a model that will allow you to predict DBH from total height (or total height from DBH). Such models may already exist for your study area.
Attempt 5: After filling in the missing DBH value, let’s try again:
## Warning in ValidateNSVB(data_val = step0, in_units_val = input_units, out_units_val = output_units, : The allometric equations are for trees with DBH >= 2.54cm.
## You inputted trees with DBH < 2.54cm. These trees will have NA biomass/carbon estimates.
## ## $run_time
## Time difference of 11.08 secs
##
## $dataframe
## site plot total_wood_Mg_ha total_bark_Mg_ha total_branch_Mg_ha
## 1 pre_340 108 96.96878 22.04735 42.21908
## 2 pre_340 112 601.04020 116.50079 139.77397
## 3 pre_340 104 163.44546 30.98747 37.28993
## 4 pre_340 6 181.75054 61.94861 36.60418
## 5 pre_340 121 222.77627 71.90796 55.70627
## 6 pre_340 113 356.81278 45.14825 126.88187
## 7 pre_340 13 185.65638 34.53278 49.30946
## 8 pre_340 114 330.05313 48.50333 48.52587
## 9 pre_340 26 163.44664 47.51848 89.12808
## 10 pre_340 116 286.02376 47.88623 46.21927
## 11 pre_340 117 260.75530 100.86793 53.02438
## 12 pre_340 103 310.27014 77.91147 121.05431
## 13 pre_340 24 304.65856 86.48863 100.42607
## 14 pre_340 109 111.73927 19.86442 33.21567
## 15 pre_340 25 85.56416 14.29239 21.93301
## 16 pre_340 122 185.29981 73.02088 42.70554
## 17 pre_340 115 106.75810 37.45945 26.19075
## 18 pre_340 123 249.54799 44.27333 82.31712
## 19 pre_340 124 188.99888 55.85471 51.28996
## 20 pre_340 111 210.67962 44.80849 29.24570
## 21 post_340 113 337.86313 42.60265 119.46322
## 22 post_340 13 190.84470 33.74294 52.26651
## 23 post_340 108 96.40630 21.92177 40.72260
## 24 post_340 116 296.61407 49.48527 49.64137
## 25 post_340 114 340.96274 50.21536 51.69633
## 26 post_340 112 560.00977 113.49291 145.33833
## 27 post_340 6 178.23283 60.49297 36.00473
## 28 post_340 121 210.02477 71.64876 55.13753
## 29 post_340 103 317.42582 79.55131 123.73130
## 30 post_340 109 110.63266 19.68628 33.13006
## 31 post_340 104 170.03610 31.67521 40.47197
## 32 post_340 24 309.38281 85.32115 99.95260
## 33 post_340 26 138.76854 40.12010 29.86939
## 34 post_340 123 253.39328 44.86488 83.59792
## 35 post_340 115 115.27104 39.04601 27.57124
## 36 post_340 25 104.63889 18.87364 22.65956
## 37 post_340 124 195.49249 57.40291 52.79368
## 38 post_340 111 132.69069 36.60800 25.29905
## 39 post_340 117 242.63912 98.51941 54.20798
## 40 post_340 122 175.09609 64.40827 35.67113
## 41 pre_60 107 71.50368 20.92434 15.50241
## 42 pre_60 117 173.49513 25.64026 65.26019
## 43 pre_60 130 148.67728 22.88490 73.70730
## 44 pre_60 21 189.70043 37.04681 77.56714
## 45 pre_60 102 228.37788 36.14424 109.27257
## 46 pre_60 103 150.70185 23.15250 44.39578
## 47 pre_60 108 55.19004 8.64592 28.46063
## 48 pre_60 27 70.48134 12.72478 15.47159
## 49 pre_60 116 165.76997 53.91012 31.84281
## 50 pre_60 112 157.02994 43.59437 38.25495
## 51 pre_60 119 284.85915 85.95267 85.58272
## 52 pre_60 106 178.97482 44.47262 43.06072
## 53 pre_60 114 422.84293 60.09856 76.38977
## 54 pre_60 109 281.78120 72.31352 69.93057
## 55 pre_60 104 93.58513 15.57800 22.45707
## 56 pre_60 105 146.67642 36.20937 41.66131
## 57 pre_60 113 163.97735 42.92598 45.42022
## 58 pre_60 111 161.96299 43.36119 43.49798
## 59 pre_60 101 89.47422 14.75314 26.33102
## 60 post_60 117 172.45188 25.39094 64.06364
## 61 post_60 108 47.31107 7.11866 25.81690
## 62 post_60 21 175.30625 33.04809 73.12271
## 63 post_60 130 98.65891 16.20145 28.57542
## 64 post_60 107 74.92356 21.44779 15.99401
## 65 post_60 102 231.81631 36.24813 112.29699
## 66 post_60 103 151.30659 23.14719 43.38346
## 67 post_60 119 256.11422 78.74437 60.57668
## 68 post_60 112 157.15450 43.12878 28.82081
## 69 post_60 114 305.29089 45.57928 68.57766
## 70 post_60 105 149.91951 36.97721 40.99542
## 71 post_60 104 99.12974 16.43556 24.33511
## 72 post_60 106 185.03711 45.71932 44.22159
## 73 post_60 111 171.50660 45.32455 45.51925
## 74 post_60 109 286.14566 74.00664 71.04953
## 75 post_60 116 163.37550 54.28840 32.33947
## 76 post_60 101 89.39444 14.73932 26.91833
## 77 post_60 27 77.05470 14.45446 14.00382
## 78 post_60 113 0.00000 0.00000 0.00000
## 79 pre_400 112 182.85893 33.94187 60.49404
## 80 pre_400 9 198.74673 41.88184 53.74294
## 81 pre_400 103 166.18866 27.56723 47.93914
## 82 pre_400 120 174.44934 38.11404 51.13240
## 83 pre_400 106 346.76295 61.91699 95.27868
## 84 pre_400 105 257.00732 56.52265 57.34536
## 85 pre_400 116 237.91095 51.13863 54.16680
## 86 pre_400 109 155.00518 26.49377 40.88454
## 87 pre_400 22 297.01915 42.86732 47.79526
## 88 pre_400 102 153.87938 33.35873 26.04109
## 89 pre_400 108 224.93025 35.30433 75.93719
## 90 pre_400 24 264.87628 53.09388 51.57027
## 91 pre_400 121 253.30260 51.25386 81.09807
## 92 pre_400 101 174.66758 35.73001 62.34244
## 93 pre_400 110 251.21312 50.30732 66.21745
## 94 pre_400 118 150.20138 25.56687 35.40136
## 95 pre_400 119 86.23798 16.31710 28.75121
## 96 pre_400 104 165.19778 25.28079 41.57274
## 97 pre_400 23 57.70936 13.31232 16.15895
## 98 pre_400 2 131.67433 28.13791 23.36392
## 99 post_400 112 178.59768 32.93692 58.32214
## 100 post_400 106 350.05480 62.38872 95.24550
## 101 post_400 105 268.98702 58.38321 60.36541
## 102 post_400 24 265.73473 52.70636 47.81287
## 103 post_400 109 157.88488 26.81181 41.88042
## 104 post_400 116 232.05709 50.32896 49.07799
## 105 post_400 120 179.88733 39.08168 51.89004
## 106 post_400 102 163.95558 33.90191 26.20259
## 107 post_400 121 261.09714 52.75887 80.58512
## 108 post_400 103 170.58107 28.24073 48.14087
## 109 post_400 108 212.24850 32.89775 78.49791
## 110 post_400 110 259.85290 51.95280 69.99744
## 111 post_400 22 303.69114 43.58605 50.05866
## 112 post_400 104 165.45815 25.41885 39.90004
## 113 post_400 9 200.01671 42.00277 54.04228
## 114 post_400 119 87.71450 16.60296 26.66641
## 115 post_400 101 180.23607 36.77084 63.52217
## 116 post_400 118 154.17890 26.21141 36.10027
## 117 post_400 2 139.02073 27.99875 24.00158
## 118 post_400 23 62.04659 14.56762 16.07760
## total_ag_Mg_ha merch_total_Mg_ha merch_top_Mg_ha stump_total_Mg_ha
## 1 161.23521 104.20719 45.17972 7.04277
## 2 857.31495 691.62220 144.14114 20.91496
## 3 231.72286 181.43926 38.49401 6.43024
## 4 280.30333 236.39307 36.95654 5.76077
## 5 350.39050 279.68590 56.06582 8.49396
## 6 528.84290 375.12349 131.31288 20.26065
## 7 269.49862 207.82877 51.60535 9.70828
## 8 427.08233 365.28176 50.96982 10.72275
## 9 300.09320 198.13392 90.30466 9.22033
## 10 380.12926 321.15550 48.70103 9.29582
## 11 414.64760 350.72604 54.25105 9.67051
## 12 509.23592 369.80342 124.96079 13.89735
## 13 491.57326 374.39009 103.49972 13.24375
## 14 164.81936 122.39760 35.69487 5.37618
## 15 121.78955 93.76133 21.21134 3.98626
## 16 301.02624 248.54467 44.82226 7.65930
## 17 170.40830 138.27298 27.45406 4.68126
## 18 376.13844 282.66834 84.12026 9.02757
## 19 296.14355 236.18795 52.69460 6.91705
## 20 284.73381 245.27376 30.95839 8.50167
## 21 499.92901 356.99667 123.93026 19.00208
## 22 276.85414 212.04945 54.37749 10.09345
## 23 159.05066 105.65381 43.58297 6.81320
## 24 395.74070 333.02330 51.92061 9.81990
## 25 442.87442 377.99469 54.01739 10.86235
## 26 818.84102 649.41135 149.54990 19.87976
## 27 274.73054 232.12376 36.42355 5.70225
## 28 336.81105 271.47060 56.39503 8.08643
## 29 520.70844 379.35427 127.35215 14.00202
## 30 163.44900 121.73276 35.46312 5.27915
## 31 242.18329 194.22394 41.53845 6.42090
## 32 494.65657 378.50789 102.63259 13.25179
## 33 208.75803 168.89709 29.28669 6.86279
## 34 381.85608 286.98467 85.38250 9.16664
## 35 181.88829 148.15077 28.75081 4.98671
## 36 146.17209 117.86110 23.88033 4.43066
## 37 305.68908 244.38427 54.25848 7.04633
## 38 194.59774 162.41980 26.43546 5.74248
## 39 395.36651 331.24066 55.24406 8.88180
## 40 275.17549 230.62559 37.53118 7.01872
## 41 107.93043 87.54497 16.27306 3.07680
## 42 264.39558 191.34935 65.72533 5.73114
## 43 245.26948 161.64095 75.11735 7.95246
## 44 304.31437 215.93558 79.06128 8.23438
## 45 373.79469 250.18452 110.99091 11.33261
## 46 218.25013 165.99907 45.67281 6.57825
## 47 92.29659 56.18621 28.74866 3.43802
## 48 98.67771 78.66309 16.11001 3.58354
## 49 251.52290 211.14188 32.92129 6.21378
## 50 238.87926 192.84633 39.32479 6.70813
## 51 456.39454 358.04314 87.34055 11.01085
## 52 266.50816 213.54653 45.39133 7.57030
## 53 559.33126 469.40136 77.29965 12.63025
## 54 424.02529 343.20545 71.63589 9.18395
## 55 131.62019 103.45045 23.51185 4.11595
## 56 224.54710 174.55526 43.35959 6.63225
## 57 252.32355 200.73677 45.98180 5.60498
## 58 248.82216 197.69073 44.80814 6.32328
## 59 130.55838 93.22684 28.09541 5.30611
## 60 261.90646 190.59912 64.66066 5.71068
## 61 80.24663 51.01913 26.21564 3.01187
## 62 281.47705 199.10782 74.40032 7.96891
## 63 143.43578 107.82150 30.07438 5.53989
## 64 112.36536 92.25063 16.87691 3.23782
## 65 380.36142 255.09304 113.90472 11.36366
## 66 217.83724 166.79916 44.51424 6.52383
## 67 395.43527 323.82229 62.09095 9.52202
## 68 229.10408 192.88830 29.80569 6.41010
## 69 419.44783 341.73070 69.32855 8.38858
## 70 227.89215 178.85626 42.44112 6.59477
## 71 139.90041 109.64435 25.34109 4.31987
## 72 274.97802 220.85142 46.35423 7.77238
## 73 262.35040 209.02166 46.72042 6.60832
## 74 431.20183 350.22984 71.98043 8.99156
## 75 250.00337 210.52626 33.41892 6.05819
## 76 131.05209 95.85050 28.97029 5.45427
## 77 105.51298 87.13525 14.74263 3.63510
## 78 0.00000 0.00000 0.00000 0.00000
## 79 277.29483 205.87679 61.95849 7.12490
## 80 294.37151 223.93254 55.71087 8.67874
## 81 241.69502 185.04389 47.98544 5.50374
## 82 263.69578 204.34859 52.41247 6.21497
## 83 503.95863 393.68473 97.24772 11.75589
## 84 370.87534 302.62574 59.06248 8.86445
## 85 343.21638 279.13833 55.19866 8.05829
## 86 222.38349 174.07152 41.93969 5.36539
## 87 387.68173 329.35898 49.21154 8.37859
## 88 213.27921 178.50300 28.24582 6.13736
## 89 336.17177 239.15360 75.90693 6.96049
## 90 369.54043 306.29614 49.51929 8.39940
## 91 385.65454 293.53446 82.74914 8.87054
## 92 272.74003 202.89979 63.22386 6.26896
## 93 367.73790 291.08173 67.60664 8.28131
## 94 211.16961 165.24441 37.61288 6.79978
## 95 131.30630 89.83111 27.64441 5.04516
## 96 232.05130 180.43122 43.14475 7.10804
## 97 87.18063 65.90986 16.60295 3.08023
## 98 183.17616 154.24940 24.03265 4.89411
## 99 269.85674 203.29684 59.76474 6.79517
## 100 507.68902 399.15990 96.90124 11.62788
## 101 387.73564 316.28768 61.90107 9.18937
## 102 366.25397 309.35775 48.57297 8.32326
## 103 226.57712 177.38149 42.74977 5.40749
## 104 331.46404 273.96133 49.99257 7.51014
## 105 270.85904 210.86498 52.89260 6.36609
## 106 224.06009 189.73288 28.02668 6.30052
## 107 394.44113 303.62943 81.64259 8.70833
## 108 246.96266 191.69291 49.54506 5.72469
## 109 323.64416 236.16753 79.77833 6.74107
## 110 381.80315 300.03017 69.79141 8.51044
## 111 397.33585 337.34624 51.45331 8.53630
## 112 230.77704 181.68334 41.78576 7.04285
## 113 296.06176 230.24895 56.87334 8.93947
## 114 130.98387 93.42466 27.19895 5.13542
## 115 280.52907 209.70461 64.37788 6.44657
## 116 216.49058 170.18393 38.45165 6.94781
## 117 191.02105 161.35592 24.63737 5.02777
## 118 92.69181 72.03693 17.41070 3.24418
## foliage_Mg_ha total_wood_c total_bark_c total_branch_c total_ag_c
## 1 14.03418 48.79832 11.10702 21.17939 81.08473
## 2 23.34980 296.93357 58.15900 68.15772 423.25028
## 3 11.88751 83.23132 15.76116 18.69765 117.69013
## 4 11.84707 93.03521 31.62809 18.71710 143.38041
## 5 16.58637 112.41655 36.46209 27.98904 176.86768
## 6 16.40732 169.55267 21.46163 60.28391 251.29821
## 7 13.97788 94.62400 17.69867 24.59081 136.91349
## 8 13.92070 168.29978 24.74994 24.36470 217.41442
## 9 16.39654 81.36994 23.97367 43.14224 148.48585
## 10 14.44343 146.32190 24.47256 23.34066 194.13513
## 11 16.64565 133.28413 51.48204 27.13134 211.89751
## 12 19.44792 154.59605 39.25263 59.22173 253.07041
## 13 19.85770 152.14792 43.61431 49.44275 245.20498
## 14 11.18956 56.99368 10.11290 16.83482 83.94140
## 15 11.18567 44.10469 7.34073 11.41836 62.86378
## 16 15.55447 94.35012 37.20600 21.77455 153.33067
## 17 10.30205 54.11562 19.03295 13.26129 86.40986
## 18 18.24535 123.36397 22.06171 40.42337 185.84905
## 19 16.05184 97.70523 28.72428 26.40529 152.83481
## 20 12.97950 109.00805 23.16395 15.31266 147.48466
## 21 15.17410 160.61287 20.25968 56.77841 237.65096
## 22 14.85144 97.28405 17.29386 26.09624 140.67415
## 23 13.55456 48.61963 11.06097 20.47992 80.16052
## 24 15.29342 151.63639 25.27463 25.01454 201.92556
## 25 14.62736 173.88591 25.62589 25.94023 225.45203
## 26 24.37010 276.08432 56.64818 70.89630 403.62881
## 27 11.96429 91.21741 30.88414 18.41544 140.51699
## 28 16.33813 106.14264 36.37271 27.75830 170.27365
## 29 19.17961 158.00907 40.05882 60.42932 258.49720
## 30 11.51114 56.48536 10.03319 16.80801 83.32657
## 31 11.53825 86.78401 16.15295 20.27957 123.21654
## 32 20.19232 154.77039 43.06629 49.22482 247.06150
## 33 11.56785 70.21449 20.44026 15.26069 105.91544
## 34 18.58654 125.29112 22.36157 41.05455 188.70723
## 35 10.94448 58.38451 19.82812 13.93243 92.14506
## 36 10.53643 53.98272 9.70392 11.76357 75.45021
## 37 16.29843 100.98528 29.50739 27.16110 157.65377
## 38 11.75818 69.43134 19.00240 13.20929 101.64303
## 39 17.10221 124.22908 50.32110 27.75754 202.30772
## 40 13.60942 89.14619 32.80526 18.18788 140.13932
## 41 7.17088 36.72762 10.70301 8.00632 55.43696
## 42 12.83708 85.65056 12.74854 32.19776 130.59686
## 43 11.01825 74.06439 11.43304 35.86486 121.36228
## 44 14.47819 95.62539 18.78035 38.36957 152.77531
## 45 15.64360 112.28902 17.85816 52.81788 182.96506
## 46 12.40950 77.25717 11.87061 22.42974 111.55752
## 47 5.50459 27.76587 4.35959 13.99859 46.12405
## 48 7.79266 37.86990 6.83614 8.31455 53.02059
## 49 12.36987 85.46823 27.66188 16.37739 129.50751
## 50 12.18472 80.35646 22.29111 19.29987 121.94744
## 51 21.51566 144.13213 43.65889 42.94804 230.73907
## 52 17.35573 92.66026 22.92610 22.17058 137.75695
## 53 17.48723 214.66607 30.47509 38.77228 283.91344
## 54 20.19238 144.86401 37.05225 35.97719 217.89345
## 55 10.08072 48.72388 8.10719 11.70321 68.53428
## 56 16.62625 76.19403 18.68442 21.64661 116.52507
## 57 13.45460 83.96343 21.93735 23.26144 129.16222
## 58 14.78731 83.95986 22.34128 22.47116 128.77230
## 59 13.96254 47.81457 7.87349 14.10207 69.79013
## 60 12.66107 85.13141 12.62438 31.58805 129.34384
## 61 3.80521 23.65859 3.56714 12.59764 39.82337
## 62 13.87482 88.81603 16.81206 36.22792 141.85601
## 63 9.94232 50.53533 8.29321 14.51134 73.33988
## 64 7.39285 38.49322 10.97615 8.26450 57.73388
## 65 15.41824 113.83002 17.88746 54.23093 185.94841
## 66 12.71828 77.66120 11.88039 21.99963 111.54122
## 67 19.34391 130.30063 40.14984 30.97167 201.42214
## 68 12.18961 80.80461 22.13903 14.85869 117.80233
## 69 16.15948 155.10057 23.11467 34.76040 212.97564
## 70 16.23208 77.81135 19.07341 21.26608 118.15084
## 71 10.79771 51.59414 8.55152 12.67770 72.82336
## 72 17.84649 95.78406 23.56811 22.77103 142.12321
## 73 15.49676 88.81024 23.33890 23.49914 135.64827
## 74 20.35258 147.08290 37.91389 36.52345 221.52023
## 75 12.32652 84.28798 27.85917 16.63469 128.78184
## 76 13.66591 47.81713 7.87376 14.42608 70.11696
## 77 7.69668 41.42351 7.77051 7.52825 56.72227
## 78 0.00000 0.00000 0.00000 0.00000 0.00000
## 79 19.11417 93.70712 17.36539 31.00640 142.07891
## 80 22.05004 102.97360 21.62016 27.88345 152.47720
## 81 11.32840 82.41915 13.73721 23.63545 119.79182
## 82 12.79488 86.52565 19.09846 25.29229 130.91641
## 83 25.99133 174.94103 31.27466 48.04104 254.25674
## 84 19.88892 132.14022 28.97958 29.47333 190.59313
## 85 15.84486 120.46040 25.93115 27.35735 173.74890
## 86 9.70097 75.84736 12.94157 20.07235 108.86129
## 87 16.87815 153.20357 22.09354 24.71824 200.01535
## 88 12.80317 78.77644 17.04203 13.30146 109.11993
## 89 19.69834 112.33906 17.63372 38.03894 168.01171
## 90 18.88728 135.49935 27.10424 26.43259 189.03617
## 91 23.14903 129.58788 26.16869 41.58447 197.34104
## 92 17.77216 89.71633 18.31328 32.06788 140.09749
## 93 19.96198 128.93079 25.77579 33.98790 188.69449
## 94 16.50746 78.18254 13.25167 18.63731 110.07152
## 95 15.33491 45.71319 8.57681 15.30607 69.59608
## 96 17.81467 85.70708 13.10570 21.68271 120.49549
## 97 8.11685 30.05592 6.87465 8.49142 45.42199
## 98 10.16607 68.50893 14.58108 12.18933 95.27934
## 99 17.82243 91.54635 16.85957 29.88970 138.29561
## 100 25.20015 176.54442 31.50779 47.95717 256.00938
## 101 20.75427 138.26264 29.92989 31.02311 199.21565
## 102 16.54518 135.90399 26.90768 24.41988 187.23155
## 103 9.77335 77.22816 13.09212 20.54411 110.86439
## 104 13.99204 117.44865 25.51787 24.71194 167.67845
## 105 13.38143 89.32231 19.59897 25.68581 134.60708
## 106 11.98168 83.94885 17.32574 13.39798 114.67257
## 107 21.78121 133.35158 26.91440 41.19168 201.45765
## 108 11.12954 84.63624 14.08321 23.69347 122.41291
## 109 16.67312 105.75554 16.39703 39.26279 161.41535
## 110 21.41100 133.38797 26.62004 35.96470 195.97271
## 111 17.57783 156.65970 22.47854 25.87457 205.01280
## 112 17.09109 85.81802 13.17606 20.78029 119.77436
## 113 21.17106 103.66063 21.68981 28.06002 153.41046
## 114 14.03536 46.41923 8.72146 14.17727 69.31797
## 115 18.13247 92.56600 18.84580 32.66694 144.07874
## 116 16.81310 80.22149 13.58272 18.99598 112.80018
## 117 10.44851 72.24119 14.50154 12.53060 99.27334
## 118 7.84335 32.27524 7.51781 8.42729 48.22034
## merch_total_c merch_top_c stump_total_c foliage_c
## 1 52.47664 22.69166 3.53237 7.01709
## 2 342.39860 70.30232 10.24842 11.67490
## 3 92.60537 19.31340 3.23800 5.94375
## 4 120.95992 18.90688 2.94970 5.92354
## 5 141.44978 28.21712 4.29413 8.29318
## 6 178.30614 62.37897 9.59874 8.20366
## 7 106.09849 25.74081 4.88269 6.98894
## 8 186.40925 25.56395 5.38316 6.96035
## 9 98.95634 43.74571 4.54304 8.19827
## 10 164.43037 24.55918 4.68380 7.22171
## 11 179.18439 27.76341 4.94971 8.32283
## 12 184.56162 61.24801 6.95201 9.72396
## 13 187.39288 50.99710 6.58660 9.92885
## 14 62.42191 18.08665 2.72286 5.59478
## 15 48.28810 10.99770 2.05627 5.59284
## 16 126.58041 22.85175 3.89851 7.77724
## 17 70.12475 13.90910 2.37601 5.15103
## 18 139.83018 41.34339 4.51122 9.12267
## 19 121.94194 27.12549 3.58246 8.02592
## 20 126.85427 16.21171 4.41867 6.48975
## 21 169.75381 58.89136 9.00579 7.58705
## 22 108.26441 27.15399 5.07633 7.42572
## 23 53.30028 21.94271 3.42815 6.77728
## 24 170.38794 26.13632 4.93952 7.64671
## 25 192.92153 27.07979 5.45070 7.31368
## 26 320.93784 72.96379 9.72718 12.18505
## 27 118.73659 18.63419 2.91885 5.98215
## 28 137.36505 28.40611 4.09434 8.16906
## 29 189.18941 62.31383 6.99396 9.58981
## 30 62.14236 17.99759 2.67951 5.75557
## 31 99.14154 20.82819 3.24681 5.76912
## 32 189.73645 50.58820 6.60214 10.09616
## 33 85.55009 14.91582 3.45431 5.78392
## 34 141.99632 41.96513 4.58152 9.29327
## 35 75.08350 14.53436 2.52719 5.47224
## 36 60.75714 12.40080 2.29226 5.26821
## 37 126.08889 27.91795 3.64692 8.14921
## 38 84.82209 13.81003 3.01091 5.87909
## 39 169.46012 28.29276 4.55484 8.55111
## 40 117.42738 19.13685 3.57509 6.80471
## 41 44.92953 8.41148 1.58823 3.58544
## 42 94.47033 32.43270 2.88162 6.41854
## 43 80.52682 36.59779 3.95288 5.50913
## 44 108.93368 39.14222 4.14734 7.23910
## 45 123.02885 53.69914 5.58127 7.82180
## 46 85.10243 23.09487 3.36022 6.20475
## 47 28.21964 14.15604 1.72533 2.75229
## 48 42.28367 8.66044 1.92393 3.89633
## 49 108.72959 16.93466 3.20819 6.18493
## 50 98.66963 19.85418 3.42363 6.09236
## 51 181.32944 43.84997 5.55966 10.75783
## 52 110.45862 23.37750 3.92083 8.67787
## 53 238.25396 39.23987 6.41961 8.74362
## 54 176.30044 36.86146 4.73156 10.09619
## 55 53.85566 12.25156 2.14887 5.04036
## 56 90.51418 22.54721 3.46368 8.31312
## 57 102.74553 23.54839 2.86830 6.72730
## 58 102.32659 23.15540 3.29031 7.39365
## 59 49.90156 15.03900 2.83629 6.98127
## 60 94.08477 31.88898 2.87047 6.33054
## 61 25.51370 12.80851 1.50116 1.90261
## 62 100.94625 36.89054 4.01922 6.93741
## 63 55.22610 15.29052 2.82326 4.97116
## 64 47.33882 8.72414 1.67092 3.69642
## 65 125.30203 55.05644 5.58993 7.70912
## 66 85.61428 22.58891 3.33803 6.35914
## 67 164.82948 31.75074 4.84192 9.67196
## 68 99.13419 15.37157 3.29657 6.09480
## 69 173.57198 35.14149 4.26216 8.07974
## 70 92.67792 22.03329 3.43963 8.11604
## 71 57.06226 13.20091 2.25471 5.39885
## 72 114.22103 23.87694 4.02523 8.92324
## 73 108.08613 24.12757 3.43457 7.74838
## 74 179.88422 37.00566 4.63035 10.17629
## 75 108.45844 17.19268 3.13072 6.16326
## 76 51.28715 15.50831 2.91442 6.83296
## 77 46.84266 7.92543 1.95418 3.84834
## 78 0.00000 0.00000 0.00000 0.00000
## 79 105.51333 31.72465 3.64153 9.55709
## 80 115.99422 28.91542 4.51234 11.02502
## 81 91.76040 23.63941 2.75863 5.66420
## 82 101.47973 25.93774 3.12336 6.39744
## 83 198.54597 49.06352 5.98339 12.99566
## 84 155.50933 30.35964 4.55970 9.94446
## 85 141.31330 27.88447 4.10971 7.92243
## 86 85.07045 20.60714 2.65584 4.85049
## 87 169.84592 25.45923 4.33246 8.43908
## 88 91.36091 14.42204 3.13434 6.40159
## 89 119.28096 38.01274 3.48519 9.84917
## 90 156.56313 25.30748 4.30261 9.44364
## 91 150.07347 42.44660 4.55197 11.57452
## 92 104.16800 32.52686 3.22555 8.88608
## 93 149.34505 34.68781 4.24864 9.98099
## 94 85.89047 19.80223 3.56571 8.25373
## 95 47.49949 14.69416 2.67941 7.66746
## 96 93.56941 22.50555 3.70576 8.90733
## 97 34.24275 8.71503 1.61259 4.05842
## 98 80.17898 12.54278 2.55759 5.08303
## 99 104.19986 30.61947 3.47628 8.91121
## 100 201.27615 48.82045 5.91278 12.60007
## 101 162.49368 31.81471 4.72503 10.37714
## 102 158.16133 24.80911 4.26111 8.27259
## 103 86.66187 20.98413 2.67449 4.88668
## 104 138.67157 25.18654 3.82034 6.99602
## 105 104.82220 26.19684 3.20451 6.69072
## 106 97.12683 14.32691 3.21883 5.99084
## 107 155.02678 41.73446 4.44870 10.89061
## 108 95.12578 24.41618 2.87096 5.56477
## 109 117.63832 39.90840 3.36955 8.33656
## 110 153.93107 35.80945 4.36616 10.70550
## 111 173.99628 26.60376 4.41276 8.78892
## 112 94.19651 21.76697 3.66837 8.54554
## 113 119.23822 29.52591 4.64632 10.58553
## 114 49.33565 14.45021 2.72331 7.01768
## 115 107.65117 33.11172 3.31586 9.06623
## 116 88.43359 20.23706 3.64184 8.40655
## 117 83.78171 12.86657 2.62506 5.22426
## 118 37.39412 9.12932 1.69690 3.92168There’s only one warning message left. The Forest Inventory and Analysis (FIA) national-scale volume and biomass (NSVB) framework is for live trees with a DBH >= 2.54 cm (1 in) and dead trees with a DBH >= 12.7 cm (5 in). It is not appropriate to use these equations to estimate biomass for trees with DBH below the specified cutoffs. We could filter these trees out of the dataset to avoid the warning message; however, that isn’t necessary. In this example, we’ll leave the small trees in and make note of the warning. If you need to calculate biomass for the smaller trees in your dataset, you will need to explore other options.
Next, we’ll use the ForestComp() and
ForestStr() functions to get forest composition and
structure at the plot level.
Let’s try using ForestComp() first:
## Error in `ValidateCompData()`:
## ! The "relative" parameter must be set to either "BA" or "density".We get an error message. It looks like we set the “relative” parameter to “ba” instead of “BA”. That’s easy to fix:
## The following species were present: 117 122 15 202 492 631 81 818## site plot species dominance
## 1 post_340 103 818 34.7
## 2 post_340 103 15 39.2
## 3 post_340 103 81 26.1
## 4 post_340 103 202 0.0
## 5 post_340 103 631 0.0
## 6 post_340 103 122 0.0
## 7 post_340 103 117 0.0
## 8 post_340 103 492 0.0
## 9 post_340 104 818 16.3
## 10 post_340 104 15 24.3
## 11 post_340 104 81 5.8
## 12 post_340 104 202 53.7
## 13 post_340 104 631 0.0
## 14 post_340 104 122 0.0
## 15 post_340 104 117 0.0
## 16 post_340 104 492 0.0This time the function runs without any warning or error messages. However, there is a note with a list of all the species present in the input data. Notice in the output (we just show two plots here) that each species present is accounted for in each plot, even if the dominance for the specific species is 0. When you compile the data beyond the plot level (e.g. to the compartment level), it’s important that the 0 dominance values are captured.
Before moving on, let’s quickly look at the output for the plot with no trees:
## site plot species dominance
## 1 post_60 113 818 NA
## 2 post_60 113 15 NA
## 3 post_60 113 81 NA
## 4 post_60 113 202 NA
## 5 post_60 113 631 NA
## 6 post_60 113 122 NA
## 7 post_60 113 117 NA
## 8 post_60 113 492 NANotice that the dominance is NA for all species. If there are no trees present, then % dominance is not applicable (i.e., NA). But why wouldn’t it make sense to say that there is 0% CADE? There’s no CADE on the plot, right? However, think about what % dominance actually is:
\(\frac{BA_{sp,p}}{BA_{total,p}}\)
where
If there are no trees present on plot p, then \(BA_{total,p}\) will be 0. And anything divided by 0 is not defined. From both a mathematical and logical perspective, % dominance for a plot without trees is not applicable.
Now let’s try using ForestStr(). We’ll keep the default
for units (= “metric”):
## site plot sph ba_m2_ha qmd_cm dbh_cm
## 1 post_340 103 691 74.04 36.9 32.1
## 2 post_340 104 296 41.38 42.2 36.2
## 3 post_340 108 963 41.17 23.3 21.4
## 4 post_340 109 938 35.22 21.9 16.2
## 5 post_340 111 296 40.52 41.7 38.2
## 6 post_340 112 667 103.76 44.5 38.5The function runs without any warning or error messages. But before moving on, let’s once again look at the output for the plot with no trees:
## site plot sph ba_m2_ha qmd_cm dbh_cm
## 1 post_60 113 0 0 NA NANotice that basal area (ba_m2_ha) and stems per hectare (sph) are both 0. This is correct for a plot with no trees. Then, average quadratic mean diameter (qmd_cm), average diameter at breast height (dbh_cm), and average height (ht_m) are all NA. These tree-level variables are not applicable for a plot without trees. For example, there is no “average diameter at breast height” if there are no diameters at breast height to take an average of. The average diameter at breast height is not 0 in this case! When you compile the data beyond the plot level (e.g., to the compartment level), it’s important that the 0s and NAs are accurately captured. A 0 qmd_cm here (at the plot level) would incorrectly pull down the compartment qmd_cm.
Finally, we’ll use the FineFuels(),
CoarseFuels(), and LitterDuff() functions to
get surface and ground fuel loads at the plot level.
Let’s investigate the input dataframe:
## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 post 340 103 17 4 1 1 1.83 1.83
## 2 post 340 103 89 11 2 1 1.83 1.83
## 3 post 340 104 13 4 0 1 1.83 1.83
## 4 post 340 104 60 3 0 0 1.83 1.83
## 5 post 340 108 7 11 1 3 1.83 1.83
## 6 post 340 108 80 14 4 3 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 11.34 0 64 0.00 0.0 7
## 2 3.05 11.34 0 0 0.00 0.0 15
## 3 3.05 11.34 0 0 0.00 0.0 6
## 4 3.05 11.34 0 313 0.50 0.5 8
## 5 3.05 11.34 0 289 0.75 0.5 7
## 6 3.05 11.34 0 0 0.00 0.0 2And prepare the tree data for input into the surface and ground fuel load functions:
vign_trees_6 <- vign_trees_5 %>%
separate(site, c("time", "site")) %>% # separate into time and site columns
select(time, site, plot, exp_factor, species, dbh) # organize columns as desired
head(vign_trees_6)## time site plot exp_factor species dbh
## 1 post 340 103 24.69 818 85.3
## 2 post 340 103 24.69 818 71.4
## 3 post 340 103 24.69 15 34.3
## 4 post 340 103 24.69 15 18.3
## 5 post 340 103 24.69 15 52.8
## 6 post 340 103 24.69 15 19.8Attempt 1: Now, let’s try using
FineFuels(). We’ll keep the default for units (=
“metric”):
## Error in `ValidateFWD()`:
## ! For fuel_data, there are repeat time:site:plot:transect observations.
## There should only be one observation/row for an individual transect at a specific time:site:plot.
## Investigate the following time:site:plot:transect combinations: post-400-9-159And we get an error message. It looks like there is a duplicate time:site:plot:transect observation for post-400-9-159. Let’s take a closer look:
## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 post 400 9 159 19 2 0 1.83 1.83
## 2 post 400 9 159 28 5 0 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 11.34 0 0 1.5 1.0 11
## 2 3.05 11.34 0 0 1.0 0.5 11## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 pre 400 9 159 32 8 0 1.83 1.83
## 2 pre 400 9 239 42 5 0 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 11.34 0 0 1 4.5 11
## 2 3.05 11.34 225 0 1 0.5 11Based on the field protocol, we know that there should be two transects per time:site:plot and that the two azimuths should be the same pre- and post-treatment. For the post-treatment observations, we can see that the counts, litter depth, and duff depth are not the same. Looking at the pre-treatment observations, we can see that the transect azimuths for site 400, plot 9 should be 159 and 239. It’s likely that one of the two post-400-9 transects should be changed to 239. We look back at the original datasheet (not shown here) and see that this is indeed the case.
Attempt 2: After correcting the transect azimuth in the input fuel data, let’s try again:
## Error in `ValidateFWD()`:
## ! For fuel_data, count_100h must be a positive, whole number.We get another error message. It looks like there is an issue with one (or more) of the 100-hour counts. Let’s figure out where the issue occurs in the data:
vign_fuels_2 %>%
mutate(count_100h_check = abs(round(count_100h))) %>%
filter(count_100h != count_100h_check)## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 pre 340 13 215 35 13 1.1 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 11.34 81 100 3.5 3 7
## count_100h_check
## 1 1We can see that the count_100h value for pre-340-13-215 is 1.1, which is not a whole number. We look back at the original datasheet (not shown here) and see that this count should be 1. This was a transcription error.
Attempt 3: After correcting the count_100h value in the input fuel data, let’s try again:
## Error in `ValidateMatches()`:
## ! Tree and fuel data did not completely match!
## These time:site:plot combinations have tree data but no fuel data: pre-340-111
## These time:site:plot combinations have fuel data but no tree data: pre-340-11And we get yet another error message. Remember that there must be a one-to-one match between time:site:plot identities of tree and fuel data. See background information in the README file for more on why this one-to-one match is important. We known that there isn’t a plot 11 in the dataset, but that there is a plot 111. Pre_340_11 should probably be corrected to pre_340_111.
Attempt 4: After correcting the plot id in the input fuel data, let’s try again:
## Warning in ValidateFWD(fuel_data_val = fuel_data, units_val = units): For fuel_data, there are missing values in the count_10h column.
## For transects with NA 10h counts, 10h fuel load estimates will be NA.
## ## Warning in ValidateOverstory(tree_data_val = tree_data, sp_codes_val = sp_codes): Not all species codes were recognized! Unrecognized codes were converted to "999" for unknown tree
## and will receive generic coefficients. Unrecognized codes: 492 631 818## time site plot load_1h_Mg_ha load_10h_Mg_ha load_100h_Mg_ha load_fwd_Mg_ha
## 1 post 400 101 0.9029624 1.4033325 0.000000 2.306295
## 2 pre 400 101 2.2310300 4.7643799 5.382827 12.378237
## 3 post 60 101 0.3571794 0.0000000 4.563755 4.920934
## 4 pre 60 101 0.8695333 2.0983663 6.749952 9.717852
## 5 post 400 102 0.1562554 0.5359028 4.278554 4.970712
## 6 pre 400 102 0.2723569 3.2140721 2.850828 6.337257
## sc_length_1h sc_length_10h sc_length_100h
## 1 3.652542 3.652542 6.087570
## 2 3.652542 3.652542 6.087570
## 3 3.641661 3.641661 6.069435
## 4 3.641661 3.641661 6.069435
## 5 3.651810 3.651810 6.086350
## 6 3.651810 3.651810 6.086350This time the function runs. However, we get some warning messages. Let’s look at the first warning, which tells us that there are missing 10-hour counts. Let’s look at where NA count_10h values show up in the data:
## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 post 400 2 252 12 NA 1 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 11.34 81 81 0 0 5We look back the the original datasheet (not shown here) and see that this 10-hour count was not recorded in the field. It truly is missing, and there is little we can do about that at this point. Fortunately, there are two transects per time:site:plot. The other transect will still allow us to get a plot-level estimate of the 10-hour fuel load. The function will appropriately account for the missing 10-hour count.
The final warning message tells us that not all species codes were recognized. We look at the list of unrecognized codes: 492 (Cornus nuttallii, commonly known as pacific dogwood), 631 (Notholithocarpus densiflorus, commonly known as tanoak), and 818 (Quercus kelloggii, commonly known as black oak). None of these are species code typos (but you should check for typos!). For the surface and ground fuel load calculations, we currently only have the necessary values for 19 Sierra Nevada conifer species (see background information in the README file for more detail on this topic). These three species are hardwoods and are, therefore, not included in the list of 19 Sierra Nevada conifers. This warning should not alarm us. Given the information currently available for the Sierra Nevada, the best we can do is assign generic “all species” values for these hardwoods.
Attempt 1: Now, let’s try using
CoarseFuels(). We’ll keep the default for units (=
“metric”):
CWD <- CoarseFuels(tree_data = vign_trees_6,
fuel_data = vign_fuels_4,
summed = "yes",
sp_codes = "fia")## Error in `ValidateCWD()`:
## ! For fuel_data, there are missing values in the length_1000h column.Another error message! It looks like there is a missing transect length. Let’s look at where NA length_1000h values show up in the data:
## time site plot transect count_1h count_10h count_100h length_1h length_10h
## 1 post 60 102 347 21 8 5 1.83 1.83
## length_100h length_1000h ssd_S ssd_R litter_depth duff_depth slope
## 1 3.05 NA 0 1771 0.5 1.25 9Based on the field protocol, we know that 1000-hour fuels were sampled for 11.34 meters along each transect. This is an easy fix.
Attempt 2: After filling in the missing transect length, let’s try again:
CWD <- CoarseFuels(tree_data = vign_trees_6,
fuel_data = vign_fuels_5,
summed = "yes",
sp_codes = "fia")## Warning in ValidateOverstory(tree_data_val = tree_data, sp_codes_val = sp_codes): Not all species codes were recognized! Unrecognized codes were converted to "999" for unknown tree
## and will receive generic coefficients. Unrecognized codes: 492 631 818## time site plot load_1000s_Mg_ha load_1000r_Mg_ha load_cwd_Mg_ha
## 1 post 400 101 0.00000 0.00000 0.00000
## 2 pre 400 101 0.00000 8.02592 8.02592
## 3 post 60 101 0.00000 0.00000 0.00000
## 4 pre 60 101 18.77363 1.68116 20.45479
## 5 post 400 102 0.00000 0.00000 0.00000
## 6 pre 400 102 15.56532 0.00000 15.56532
## sc_length_1000s sc_length_1000r
## 1 22.63378 22.63378
## 2 22.63378 22.63378
## 3 22.56636 22.56636
## 4 22.56636 22.56636
## 5 22.62925 22.62925
## 6 22.62925 22.62925This time the function runs. We are already familiar with this
warning message (discussed in the FineFuels() function
section above).
Lastly, let’s try using LitterDuff(). We’ll keep the
defaults for sp_codes (= “4letter”), units (= “metric”), and measurement
(= “separate”):
## Warning in ValidateOverstory(tree_data_val = tree_data, sp_codes_val = sp_codes): Not all species codes were recognized! Unrecognized codes were converted to "999" for unknown tree
## and will receive generic coefficients. Unrecognized codes: 492 631 818## time site plot litter_Mg_ha duff_Mg_ha
## 1 post 400 101 8.207450 10.78962
## 2 pre 400 101 22.940513 82.86332
## 3 post 60 101 0.000000 0.00000
## 4 pre 60 101 37.114751 86.42521
## 5 post 400 102 9.304178 7.08900
## 6 pre 400 102 32.336705 31.92495The function runs. Once again, we are familiar with this particular
warning message (discussed in the FineFuels() function
section above).
Now that we have everything summarized at the plot level, let’s try compiling some of the data to the compartment level and then to the entire treatment (here the fire-only treatment) level.
Step 1: estimate tree biomass at the plot level
We already went through this process above, but let’s recall what the output dataframe looks like:
## site plot total_wood_Mg_ha total_bark_Mg_ha total_branch_Mg_ha
## 1 pre_340 108 96.96878 22.04735 42.21908
## 2 pre_340 112 601.04020 116.50079 139.77397
## 3 pre_340 104 163.44546 30.98747 37.28993
## 4 pre_340 6 181.75054 61.94861 36.60418
## 5 pre_340 121 222.77627 71.90796 55.70627
## 6 pre_340 113 356.81278 45.14825 126.88187
## total_ag_Mg_ha merch_total_Mg_ha merch_top_Mg_ha stump_total_Mg_ha
## 1 161.2352 104.2072 45.17972 7.04277
## 2 857.3149 691.6222 144.14114 20.91496
## 3 231.7229 181.4393 38.49401 6.43024
## 4 280.3033 236.3931 36.95654 5.76077
## 5 350.3905 279.6859 56.06582 8.49396
## 6 528.8429 375.1235 131.31288 20.26065
## foliage_Mg_ha total_wood_c total_bark_c total_branch_c total_ag_c
## 1 14.03418 48.79832 11.10702 21.17939 81.08473
## 2 23.34980 296.93357 58.15900 68.15772 423.25028
## 3 11.88751 83.23132 15.76116 18.69765 117.69013
## 4 11.84707 93.03521 31.62809 18.71710 143.38041
## 5 16.58637 112.41655 36.46209 27.98904 176.86768
## 6 16.40732 169.55267 21.46163 60.28391 251.29821
## merch_total_c merch_top_c stump_total_c foliage_c
## 1 52.47664 22.69166 3.53237 7.01709
## 2 342.39860 70.30232 10.24842 11.67490
## 3 92.60537 19.31340 3.23800 5.94375
## 4 120.95992 18.90688 2.94970 5.92354
## 5 141.44978 28.21712 4.29413 8.29318
## 6 178.30614 62.37897 9.59874 8.20366Step 2: create all necessary columns for input into
the CompilePlots() function
tree_bio_2 <- tree_bio$dataframe %>%
separate(site, c("time", "site")) %>% # separate into time and site columns
mutate(trt_type = "fire") %>% # create a trt_type column
select(time, trt_type, site, plot, everything()) # organize columns as desired
head(tree_bio_2)## time trt_type site plot total_wood_Mg_ha total_bark_Mg_ha total_branch_Mg_ha
## 1 pre fire 340 108 96.96878 22.04735 42.21908
## 2 pre fire 340 112 601.04020 116.50079 139.77397
## 3 pre fire 340 104 163.44546 30.98747 37.28993
## 4 pre fire 340 6 181.75054 61.94861 36.60418
## 5 pre fire 340 121 222.77627 71.90796 55.70627
## 6 pre fire 340 113 356.81278 45.14825 126.88187
## total_ag_Mg_ha merch_total_Mg_ha merch_top_Mg_ha stump_total_Mg_ha
## 1 161.2352 104.2072 45.17972 7.04277
## 2 857.3149 691.6222 144.14114 20.91496
## 3 231.7229 181.4393 38.49401 6.43024
## 4 280.3033 236.3931 36.95654 5.76077
## 5 350.3905 279.6859 56.06582 8.49396
## 6 528.8429 375.1235 131.31288 20.26065
## foliage_Mg_ha total_wood_c total_bark_c total_branch_c total_ag_c
## 1 14.03418 48.79832 11.10702 21.17939 81.08473
## 2 23.34980 296.93357 58.15900 68.15772 423.25028
## 3 11.88751 83.23132 15.76116 18.69765 117.69013
## 4 11.84707 93.03521 31.62809 18.71710 143.38041
## 5 16.58637 112.41655 36.46209 27.98904 176.86768
## 6 16.40732 169.55267 21.46163 60.28391 251.29821
## merch_total_c merch_top_c stump_total_c foliage_c
## 1 52.47664 22.69166 3.53237 7.01709
## 2 342.39860 70.30232 10.24842 11.67490
## 3 92.60537 19.31340 3.23800 5.94375
## 4 120.95992 18.90688 2.94970 5.92354
## 5 141.44978 28.21712 4.29413 8.29318
## 6 178.30614 62.37897 9.59874 8.20366Step 3: input data into
CompilePlots()
# keep the defaults for wt_data (= "not_needed")
tree_bio_sum <- CompilePlots(data = tree_bio_2,
design = "FFS")## time trt_type site avg_total_wood_Mg_ha se_total_wood_Mg_ha
## 1 pre fire 340 230.1123 26.41957
## 2 post fire 340 223.8213 25.25442
## 3 pre fire 60 170.2664 20.25230
## 4 post fire 60 152.2051 18.45481
## 5 pre fire 400 196.4920 15.73800
## 6 post fire 400 199.6651 15.78483
## avg_total_bark_Mg_ha se_total_bark_Mg_ha avg_total_branch_Mg_ha
## 1 54.09115 6.024348 61.65302
## 2 52.98399 5.831862 58.96133
## 3 36.85965 4.764735 50.21404
## 4 33.26317 4.869070 43.19004
## 5 37.40537 3.017193 50.86169
## 6 37.77745 3.049184 50.91937
## se_total_branch_Mg_ha avg_total_ag_Mg_ha se_total_ag_Mg_ha
## 1 7.947755 345.8565 37.61842
## 2 8.038955 335.7666 36.90822
## 3 5.990258 257.3401 28.83429
## 4 6.107828 228.6583 27.76623
## 5 4.456295 284.7590 22.30528
## 6 4.549391 288.3619 22.38747
## avg_merch_total_Mg_ha se_merch_total_Mg_ha avg_merch_top_Mg_ha
## 1 271.1449 30.17570 63.66992
## 2 265.1553 28.83634 60.89765
## 3 198.1762 23.77156 51.44051
## 4 178.0656 22.18018 44.30743
## 5 223.2608 18.20148 51.85083
## 6 228.3774 18.33198 52.18740
## se_merch_top_Mg_ha avg_stump_total_Mg_ha se_stump_total_Mg_ha
## 1 8.164626 9.540572 1.0175594
## 2 8.257956 9.167471 0.9776729
## 3 6.036629 6.906686 0.6162690
## 4 6.148414 6.163780 0.6047416
## 5 4.506126 7.089517 0.4272535
## 6 4.544764 7.126241 0.4172930
## avg_foliage_Mg_ha se_foliage_Mg_ha avg_total_wood_c se_total_wood_c
## 1 15.21553 0.7461754 115.71264 12.929575
## 2 14.94991 0.8099196 112.65984 12.334309
## 3 13.51957 0.9612964 86.84489 10.195827
## 4 12.52213 1.1794884 77.83910 9.316138
## 5 16.69018 1.0596002 100.26178 7.982177
## 6 16.17791 1.0256605 101.85991 8.013931
## avg_total_bark_c se_total_bark_c avg_total_branch_c se_total_branch_c
## 1 27.37327 3.036033 30.58447 3.778456
## 2 26.83507 2.936661 29.32241 3.837359
## 3 18.82097 2.423262 25.30153 2.901359
## 4 17.02072 2.483986 21.85985 2.981958
## 5 19.07347 1.533694 25.96000 2.239323
## 6 19.26340 1.551046 25.96277 2.283579
## avg_total_ag_c se_total_ag_c avg_merch_total_c se_merch_total_c
## 1 173.6704 18.33719 136.56357 14.851049
## 2 168.8173 17.97039 133.65314 14.163311
## 3 130.9674 14.46080 101.08317 11.996071
## 4 116.7197 13.97386 91.05692 11.228951
## 5 145.2953 11.28512 113.86026 9.238640
## 6 147.0861 11.33320 116.45813 9.310795
## avg_merch_top_c se_merch_top_c avg_stump_total_c se_stump_total_c
## 1 31.59772 3.884598 4.770418 0.4837791
## 2 30.29549 3.943774 4.590316 0.4642434
## 3 25.93968 2.925099 3.527987 0.3053746
## 4 22.44119 3.003092 3.157761 0.3024629
## 5 26.46272 2.266542 3.637316 0.2183538
## 6 26.61591 2.280716 3.653958 0.2133129
## avg_foliage_c se_foliage_c
## 1 7.607763 0.3730875
## 2 7.474956 0.4049600
## 3 6.759783 0.4806484
## 4 6.261066 0.5897442
## 5 8.345091 0.5298001
## 6 8.088955 0.5128299## time trt_type avg_total_wood_Mg_ha se_total_wood_Mg_ha avg_total_bark_Mg_ha
## 1 pre fire 198.9569 17.31992 42.78539
## 2 post fire 191.8972 21.03548 41.34153
## se_total_bark_Mg_ha avg_total_branch_Mg_ha se_total_branch_Mg_ha
## 1 5.655074 54.24292 3.709767
## 2 5.965310 51.02358 4.553075
## avg_total_ag_Mg_ha se_total_ag_Mg_ha avg_merch_total_Mg_ha
## 1 295.9852 26.16172 230.8606
## 2 284.2623 30.98737 223.8661
## se_merch_total_Mg_ha avg_merch_top_Mg_ha se_merch_top_Mg_ha
## 1 21.40423 55.65375 4.009831
## 2 25.24161 52.46416 4.791183
## avg_stump_total_Mg_ha se_stump_total_Mg_ha avg_foliage_Mg_ha se_foliage_Mg_ha
## 1 7.845591 0.8491319 15.14176 0.9160208
## 2 7.485830 0.8855352 14.54998 1.0741091
## avg_total_wood_c se_total_wood_c avg_total_bark_c se_total_bark_c
## 1 100.93977 8.340293 21.75590 2.809627
## 2 97.45295 10.290561 21.03973 2.969112
## avg_total_branch_c se_total_branch_c avg_total_ag_c se_total_ag_c
## 1 27.28200 1.662139 149.9777 12.54764
## 2 25.71501 2.157813 144.2077 15.10800
## avg_merch_total_c se_merch_total_c avg_merch_top_c se_merch_top_c
## 1 117.1690 10.37506 28.00004 1.805164
## 2 113.7227 12.37230 26.45086 2.268842
## avg_stump_total_c se_stump_total_c avg_foliage_c se_foliage_c
## 1 3.978574 0.3971783 7.570879 0.4580105
## 2 3.800678 0.4199996 7.274992 0.5370547Step 1: estimate fine fuel loads at the plot level
We already went through this process above, but let’s recall what the output dataframe looks like:
## time site plot load_1h_Mg_ha load_10h_Mg_ha load_100h_Mg_ha load_fwd_Mg_ha
## 1 post 400 101 0.9029624 1.4033325 0.000000 2.306295
## 2 pre 400 101 2.2310300 4.7643799 5.382827 12.378237
## 3 post 60 101 0.3571794 0.0000000 4.563755 4.920934
## 4 pre 60 101 0.8695333 2.0983663 6.749952 9.717852
## 5 post 400 102 0.1562554 0.5359028 4.278554 4.970712
## 6 pre 400 102 0.2723569 3.2140721 2.850828 6.337257
## sc_length_1h sc_length_10h sc_length_100h
## 1 3.652542 3.652542 6.087570
## 2 3.652542 3.652542 6.087570
## 3 3.641661 3.641661 6.069435
## 4 3.641661 3.641661 6.069435
## 5 3.651810 3.651810 6.086350
## 6 3.651810 3.651810 6.086350Step 2: create all necessary columns for input into
the CompileSurfaceFuels() function
FWD_2 <- FWD %>%
mutate(trt_type = "fire") %>% # create a trt_type column
select(time, trt_type, site, plot, everything()) # organize columns as desired
head(FWD_2)## time trt_type site plot load_1h_Mg_ha load_10h_Mg_ha load_100h_Mg_ha
## 1 post fire 400 101 0.9029624 1.4033325 0.000000
## 2 pre fire 400 101 2.2310300 4.7643799 5.382827
## 3 post fire 60 101 0.3571794 0.0000000 4.563755
## 4 pre fire 60 101 0.8695333 2.0983663 6.749952
## 5 post fire 400 102 0.1562554 0.5359028 4.278554
## 6 pre fire 400 102 0.2723569 3.2140721 2.850828
## load_fwd_Mg_ha sc_length_1h sc_length_10h sc_length_100h
## 1 2.306295 3.652542 3.652542 6.087570
## 2 12.378237 3.652542 3.652542 6.087570
## 3 4.920934 3.641661 3.641661 6.069435
## 4 9.717852 3.641661 3.641661 6.069435
## 5 4.970712 3.651810 3.651810 6.086350
## 6 6.337257 3.651810 3.651810 6.086350Step 3: input data into
CompileSurfaceFuels()
# keep the defaults for cwd_data (= "none), wt_data (= "not_needed"), and units (= "metric")
FWD_sum <- CompileSurfaceFuels(fwd_data = FWD_2,
design = "FFS")## time trt_type site avg_1h_Mg_ha se_1h_Mg_ha avg_10h_Mg_ha se_10h_Mg_ha
## 1 post fire 400 0.4969988 0.05976717 1.139383 0.2120926
## 2 pre fire 400 0.9968562 0.14076733 2.991177 0.3775630
## 3 post fire 60 0.4725502 0.07563463 1.735057 0.3326378
## 4 pre fire 60 1.2239823 0.20421903 4.355628 0.6468982
## 5 post fire 340 0.3601983 0.04450288 1.051188 0.2369040
## 6 pre fire 340 1.0927353 0.19285796 5.313776 0.9169993
## avg_100h_Mg_ha se_100h_Mg_ha
## 1 1.317892 0.3381811
## 2 4.438505 0.5900822
## 3 4.151810 1.3535638
## 4 9.035420 1.3362516
## 5 2.351752 0.5969664
## 6 6.336984 1.3793372## time trt_type avg_1h_Mg_ha se_1h_Mg_ha avg_10h_Mg_ha se_10h_Mg_ha
## 1 post fire 0.4432491 0.04212089 1.308543 0.2147717
## 2 pre fire 1.1045246 0.06583011 4.220194 0.6738877
## avg_100h_Mg_ha se_100h_Mg_ha
## 1 2.607151 0.8279885
## 2 6.603636 1.3336957