UPCFLRP Data Exploration
Anson Call
2025-08-26
Overview
While the broad goals of this project are outlined more completely in the draft manuscript, the specific goals addressed here are to quantify treatment-associated changes in basal area, trees per acre, quadratic mean diameter (QMD), and species composition - by treatment type (e.g., thinning, burning, etc.) - by treatment intensity - by treatment age or phase (e.g., 2-5 years post-treatment, 5-10 years post-treatment, etc.) - by elevation and forest type (e.g., by unit)
Concerns with specific plots
Summary of pre-existing concerns
From Maggie’s Notes for (future) 2024 Data Analysis_MKP.docx:
Lockhart plots might have overstory issues, it seems like some of them (L12, L23, L25, L13, L24 and L27) appear to have a DBH cutoff at 7.5-8 inch DBH in the PRE entry, but then show a large number of trees under 8 inches in DBH in post1 or post 5-6
Be careful with plot 9D, some trees names RA9D, which I replaced with 9D for now but it’s possible they were duplicates.
In addition, the following 2013 plot visits are marked as “partial plots” in the “UP-CFLRP All Plot Notes.docx”
## Plots marked partial
## L12 L13 L23 L24 L25 L27 RA1510The following two 2013 plot visits were marked with the note:
Dropped in favor of 2015 pre
## Additional marked plots:
## L29 L8Confirming issues and identifying new ones
Half-plots
From the plot mapper, we can identify sites that have very high variation between visits. The following plots have a high coefficient of variation and a plot map suggesting that only half-plots were recorded on one or more visits:
## id PRE_1 PRE_2 POST_1 POST_2 POST_3 POSTRX_1 POSTRX_2 mean coef_variation
## 1 RA1310 57 NA 65 98 70 NA NA 72.5 0.2458373
## 2 RA710 NA NA 53 49 NA 63 NA 55.0 0.1311110The following plot is not exactly a half-plot, but it’s clear there is some incompleteness in the southeast quadrant:
## id PRE_1 PRE_2 POST_1 POST_2 POST_3 POSTRX_1 POSTRX_2 mean coef_variation
## 1 RA2040 119 NA NA NA NA 90 112 107 0.1414275Other issues
Inconsistent DBH thresholds
The following plots do not have evidence of the “half-plot” problem, but do have a high coefficient of variation, possibly because the lower DBH threshold for recording trees was not consistent across visits, as noted by Maggie:
## id PRE_1 PRE_2 POST_1 POST_2 POST_3 POSTRX_1 POSTRX_2 mean
## 1 L7 61 169 135 NA NA NA NA 121.66667
## 2 L29 47 145 171 170 NA NA NA 133.25000
## 3 RA261 83 NA 203 214 NA NA NA 166.66667
## 4 RA178 44 NA 111 96 NA NA NA 83.66667
## 5 3A 55 NA NA NA NA 129 113 99.00000
## 6 L23 49 NA 97 115 NA NA NA 87.00000
## 7 RA181 43 NA 89 91 NA NA NA 74.33333
## 8 L27 74 NA 148 152 NA NA NA 124.66667
## 9 L8 70 154 98 91 NA NA NA 103.25000
## 10 L25 87 NA 159 160 NA NA NA 135.33333
## 11 L9 83 NA 151 153 NA NA NA 129.00000
## 12 L10 97 150 NA NA NA NA NA 123.50000
## 13 RA219 52 NA NA NA NA 89 90 77.00000
## 14 L11 78 159 115 124 NA NA NA 119.00000
## 15 RA2170 207 NA 130 133 NA NA NA 156.66667
## 16 RA281 94 NA 136 167 NA NA NA 132.33333
## 17 RA2080 136 NA NA NA NA 92 84 104.00000
## 18 RA2240 211 NA 144 132 NA NA NA 162.33333
## 19 RA410 90 NA 145 150 NA NA NA 128.33333
## 20 RA1310 57 NA 65 98 70 NA NA 72.50000
## 21 L12 69 NA 103 104 NA NA NA 92.00000
## 22 RA510 111 NA 168 163 NA NA NA 147.33333
## coef_variation
## 1 0.4538693
## 2 0.4408593
## 3 0.4359954
## 4 0.4202574
## 5 0.3932914
## 6 0.3921545
## 7 0.3652989
## 8 0.3523330
## 9 0.3473543
## 10 0.3093169
## 11 0.3089125
## 12 0.3034547
## 13 0.2812520
## 14 0.2796354
## 15 0.2783984
## 16 0.2768605
## 17 0.2692308
## 18 0.2622475
## 19 0.2594154
## 20 0.2458373
## 21 0.2165746
## 22 0.2142404This list includes all of the plots identified by Maggie (L12, L23, L25, L13, L24 and L27) and adds 11 new ones. These new plots are not only from the “L” area.
This problem of inconsistent lower DBH thresholds is most pronounced in these plots, but it actually seems like it could be a problem in most, if not all plots.
We can examine this directly by checking the lower bounds of the DBH distribution for each plot and visit. We’ll check the absolute minimum DBH and the 2.5th percentile of the DBH distribution for recorded trees at each plot and visit. Then, we can look for plots where the gap between these values on successive visits is large.
overstory <- read_csv("overstory_tidy.csv")
min_dbh_change <- overstory %>%
group_by(id, t_idxn) %>%
summarise(
min_dbh = min(dbh, na.rm = TRUE),
p2_5_dbh = quantile(dbh, 0.025, na.rm = TRUE)
) %>%
arrange(id, t_idxn) %>%
pivot_wider(
names_from = t_idxn,
values_from = c(min_dbh, p2_5_dbh)
) %>%
rowwise %>%
mutate(
# What is the largest gap between the minimum DBH of the PRE_1 visit and all
# POST_* visits?
min_dbh_gap_PRE_1 = max(c_across((matches("^min_.*POST"))), na.rm = TRUE) -
min_dbh_PRE_1,
# What is the largest gap between the minimum DBH of the PRE_2 visit and all
# POST_* visits?
min_dbh_gap_PRE_2 = max(c_across((matches("^min_.*POST"))), na.rm = TRUE) -
min_dbh_PRE_2,
# same, but for 2.5%
p2_5_dbh_gap_PRE_1 = max(c_across((matches("^p2_5_.*POST"))), na.rm = TRUE) -
p2_5_dbh_PRE_1,
p2_5_dbh_gap_PRE_2 = max(c_across((matches("^p2_5_.*POST"))), na.rm = TRUE) -
p2_5_dbh_PRE_2
) %>%
filter(!all(is.na(val <- c_across(contains("gap"))) | val == -Inf)) %>%
ungroup %>%
arrange(min_dbh_gap_PRE_1) %>%
mutate(row_highlight = id %in% addl_high_cv_plots,
row_num = row_number()) # needed for row_spec()
min_dbh_change %>%
select(id, matches("gap"), starts_with("min"), starts_with("p2_5_")) %>%
kbl() %>%
kable_paper() %>%
column_spec(2:3, background = "#A1D5FF7F") %>%
column_spec(4:5, background = "#C7F9FF7F") %>%
row_spec(which(min_dbh_change$row_highlight), bold = TRUE) %>%
scroll_box(width = "100%", height = "300px")| id | min_dbh_gap_PRE_1 | min_dbh_gap_PRE_2 | p2_5_dbh_gap_PRE_1 | p2_5_dbh_gap_PRE_2 | min_dbh_POSTRX_1 | min_dbh_POSTRX_2 | min_dbh_PRE_1 | min_dbh_POST_1 | min_dbh_POST_2 | min_dbh_PRE_2 | min_dbh_POST_3 | p2_5_dbh_POSTRX_1 | p2_5_dbh_POSTRX_2 | p2_5_dbh_PRE_1 | p2_5_dbh_POST_1 | p2_5_dbh_POST_2 | p2_5_dbh_PRE_2 | p2_5_dbh_POST_3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L7 | -3.7 | -0.9 | -2.9000 | -0.2000 | NA | NA | 7.9 | 4.2 | NA | 5.1 | NA | NA | NA | 7.9000 | 5.0000 | NA | 5.2000 | NA |
| L12 | -3.5 | NA | -3.1700 | NA | NA | NA | 8.1 | 4.6 | 4.5 | NA | NA | NA | NA | 8.1700 | 4.9100 | 5.0000 | NA | NA |
| L23 | -3.5 | NA | -3.5000 | NA | NA | NA | 8.0 | 4.5 | 4.5 | NA | NA | NA | NA | 8.5000 | 5.0000 | 4.9700 | NA | NA |
| L27 | -3.5 | NA | -3.3300 | NA | NA | NA | 8.0 | 4.5 | 4.5 | NA | NA | NA | NA | 8.1650 | 4.8350 | 4.8000 | NA | NA |
| L29 | -3.3 | -0.4 | -3.1000 | -0.3600 | NA | NA | 7.9 | 0.3 | 4.6 | 5.0 | NA | NA | NA | 8.1000 | 5.0000 | 5.0000 | 5.3600 | NA |
| L25 | -3.1 | NA | -2.4400 | NA | NA | NA | 7.6 | 0.5 | 4.5 | NA | NA | NA | NA | 7.9150 | 5.4750 | 5.0000 | NA | NA |
| L18 | -3.0 | NA | -2.1900 | NA | NA | NA | 8.0 | 5.0 | 5.0 | NA | NA | NA | NA | 8.1075 | 5.9175 | 5.8400 | NA | NA |
| L24 | -2.9 | NA | -1.9000 | NA | NA | NA | 7.9 | 5.0 | 4.5 | NA | NA | NA | NA | 7.9000 | 6.0000 | 4.5000 | NA | NA |
| RA2010 | -1.3 | NA | -0.0600 | NA | 1.5 | NA | 6.0 | 4.7 | NA | NA | NA | 5.580 | NA | 6.0600 | 6.0000 | NA | NA | NA |
| L9 | -0.6 | NA | -2.6000 | NA | NA | NA | 5.6 | 1.3 | 5.0 | NA | NA | NA | NA | 8.0200 | 5.0000 | 5.4200 | NA | NA |
| RA1110 | -0.5 | NA | -1.1050 | NA | 4.5 | 4.5 | 5.0 | NA | NA | NA | NA | 4.940 | 5.0000 | 6.1050 | NA | NA | NA | NA |
| RA1210 | -0.5 | NA | -0.1275 | NA | NA | NA | 5.0 | 4.5 | NA | NA | NA | NA | NA | 5.2375 | 5.1100 | NA | NA | NA |
| RA610 | -0.5 | NA | -0.7000 | NA | 4.5 | 4.5 | 5.0 | 4.5 | NA | NA | NA | 4.600 | 4.5325 | 5.3000 | 4.5625 | NA | NA | NA |
| 3A | -0.4 | NA | -1.1150 | NA | 4.6 | 4.5 | 5.0 | NA | NA | NA | NA | 4.920 | 4.5000 | 6.0350 | NA | NA | NA | NA |
| RA810 | -0.3 | NA | -0.0300 | NA | NA | NA | 5.0 | 4.7 | NA | NA | NA | NA | NA | 5.0300 | 5.0000 | NA | NA | NA |
| RA1010 | 0.0 | NA | 0.0500 | NA | NA | NA | 5.0 | 5.0 | 5.0 | NA | NA | NA | NA | 5.8125 | 5.8625 | 5.5200 | NA | NA |
| RA1310 | 0.0 | NA | 0.2000 | NA | NA | NA | 6.0 | 6.0 | 3.0 | NA | 4.5 | NA | NA | 6.5200 | 6.7200 | 5.0000 | NA | 5.5725 |
| RA178 | 0.0 | NA | 0.1775 | NA | NA | NA | 4.6 | 4.6 | 4.5 | NA | NA | NA | NA | 4.8225 | 5.0000 | 4.8375 | NA | NA |
| RA2030 | 0.0 | NA | 0.1725 | NA | 4.5 | 4.5 | 4.5 | NA | NA | NA | NA | 4.755 | 4.8000 | 4.6275 | NA | NA | NA | NA |
| RA2040 | 0.0 | NA | -0.0950 | NA | 4.5 | 4.5 | 4.5 | NA | NA | NA | NA | 4.600 | 4.6000 | 4.6950 | NA | NA | NA | NA |
| RA2190 | 0.0 | NA | -0.0850 | NA | NA | NA | 4.5 | 4.5 | 4.5 | NA | NA | NA | NA | 4.7000 | 4.6000 | 4.6150 | NA | NA |
| RA2490 | 0.0 | NA | 0.0750 | NA | 4.7 | 4.5 | 4.7 | NA | NA | NA | NA | 4.910 | 4.5400 | 4.8350 | NA | NA | NA | NA |
| RA2540 | 0.0 | NA | 0.1325 | NA | 4.5 | 4.5 | 4.5 | NA | NA | NA | NA | 4.600 | 4.6325 | 4.5000 | NA | NA | NA | NA |
| RA271 | 0.0 | NA | -0.4700 | NA | NA | NA | 4.6 | 4.5 | 4.6 | NA | NA | NA | NA | 5.1975 | 4.7275 | 4.7000 | NA | NA |
| RA510 | 0.0 | NA | -0.1200 | NA | NA | NA | 4.5 | 4.5 | 1.0 | NA | NA | NA | NA | 5.0250 | 4.8350 | 4.9050 | NA | NA |
| RA2310 | 0.1 | NA | 0.1000 | NA | 4.6 | 4.5 | 4.5 | NA | NA | NA | NA | 4.600 | 4.5000 | 4.5000 | NA | NA | NA | NA |
| RA2560 | 0.1 | NA | 0.0725 | NA | 4.6 | 4.5 | 4.5 | NA | NA | NA | NA | 4.980 | 4.5000 | 4.9075 | NA | NA | NA | NA |
| L13 | 0.2 | NA | -2.8900 | NA | NA | NA | 4.7 | 3.0 | 4.9 | NA | NA | NA | NA | 7.8900 | 5.0000 | 5.0000 | NA | NA |
| RA2080 | 0.2 | NA | 0.1025 | NA | 4.5 | 4.3 | 4.3 | NA | NA | NA | NA | 4.500 | 4.8150 | 4.7125 | NA | NA | NA | NA |
| RA2170 | 0.3 | NA | 0.4450 | NA | NA | NA | 4.5 | 4.7 | 4.8 | NA | NA | NA | NA | 4.7150 | 5.1125 | 5.1600 | NA | NA |
| RA301 | 0.4 | NA | 0.3275 | NA | NA | NA | 4.6 | 5.0 | 4.5 | NA | NA | NA | NA | 5.1725 | 5.5000 | 5.3200 | NA | NA |
| RA310 | 0.4 | NA | 0.2000 | NA | NA | NA | 4.3 | 4.6 | 4.7 | NA | NA | NA | NA | 5.3000 | 5.4600 | 5.5000 | NA | NA |
| RA2240 | 0.5 | NA | 0.6750 | NA | NA | NA | 4.5 | 1.5 | 5.0 | NA | NA | NA | NA | 4.8250 | 5.3725 | 5.5000 | NA | NA |
| RA191 | 0.6 | NA | 0.0425 | NA | NA | NA | 4.0 | 1.3 | 4.6 | NA | NA | NA | NA | 4.7950 | 4.7200 | 4.8375 | NA | NA |
| RA251 | 0.6 | NA | 0.6325 | NA | NA | NA | 5.0 | 4.6 | 5.6 | NA | NA | NA | NA | 5.4675 | 5.1150 | 6.1000 | NA | NA |
| RA219 | 0.7 | NA | 0.3575 | NA | 5.7 | 4.6 | 5.0 | NA | NA | NA | NA | 6.260 | 5.2000 | 5.9025 | NA | NA | NA | NA |
| RA410 | 0.8 | NA | 0.9875 | NA | NA | NA | 4.2 | 5.0 | 5.0 | NA | NA | NA | NA | 5.0125 | 5.8400 | 6.0000 | NA | NA |
| RA2020 | 1.0 | NA | -0.1575 | NA | 4.6 | 4.5 | 3.6 | NA | NA | NA | NA | 4.800 | 4.7350 | 4.9575 | NA | NA | NA | NA |
| L11 | 1.6 | -0.5 | -2.5925 | 0.3100 | NA | NA | 2.9 | 4.5 | 4.5 | 5.0 | NA | NA | NA | 8.0925 | 5.5000 | 5.0000 | 5.1900 | NA |
| L8 | 1.7 | -0.5 | -2.8675 | 0.1675 | NA | NA | 2.8 | 4.5 | 4.5 | 5.0 | NA | NA | NA | 8.2175 | 4.7425 | 5.3500 | 5.1825 | NA |
| RA181 | 1.7 | NA | 0.2000 | NA | NA | NA | 3.3 | 4.5 | 5.0 | NA | NA | NA | NA | 5.0000 | 4.7600 | 5.2000 | NA | NA |
| RA2180 | 2.0 | NA | 0.3950 | NA | NA | NA | 2.7 | 4.5 | 4.7 | NA | NA | NA | NA | 4.6000 | 4.5400 | 4.9950 | NA | NA |
| RA281 | 2.0 | NA | -0.3000 | NA | NA | NA | 2.5 | 4.5 | 4.5 | NA | NA | NA | NA | 5.1000 | 4.8000 | 4.6000 | NA | NA |
| RA2050 | 2.6 | NA | 0.1550 | NA | 4.6 | 4.5 | 2.0 | NA | NA | NA | NA | 5.055 | 5.0000 | 4.9000 | NA | NA | NA | NA |
| RA261 | 3.6 | NA | 0.0000 | NA | NA | NA | 1.0 | 4.6 | 4.5 | NA | NA | NA | NA | 5.0000 | 5.0000 | 5.0000 | NA | NA |
It seems that the problems are all localized to PRE_1 visits. There isn’t a big gap between the minimum values of the PRE_2 visits and any of the POST* visits. The problem plot values are bolded here, and you can see they are concentrated at the top of the table. That means a lot of the issue could be explained away by this gap in the size of trees that are being recorded.
Plot L7 is a big offender here:
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plotting histograms for all of the worst offenders (watch out for changes in the color scales):
A practical solution to this problem would be to remove trees below the highest dbh threshold for plots where this seems to be an issue.
overstory_min8 <- overstory %>%
filter(dbh >= 8)
data_loss <- (1 - nrow(overstory_min8) / nrow(overstory)) * 100
cat(round(data_loss, 2), "% of trees removed by DBH cutoff", sep = "")## 29.43% of trees removed by DBH cutoffNow, let’s revisit the CV values for tree counts and see if some of these sites start to look less extreme.
# ntrees data
ntrees_min8 <- overstory_min8 %>%
group_by(id, t_idxn) %>%
summarise(ntrees = n(), .groups = "keep") %>%
ungroup() %>%
pivot_wider(names_from = t_idxn, values_from = ntrees) %>%
rowwise %>%
mutate(mean = mean(c_across(starts_with("P")), na.rm = TRUE),
sd = sd(c_across(starts_with("P")), na.rm = TRUE),
coef_variation = ifelse(mean == 0, NA, sd / mean)) %>%
select(id, starts_with("PRE"), starts_with("POST_"), starts_with("POSTRX"),
mean, coef_variation) %>%
arrange(desc(coef_variation)) %>%
ungroup %>%
mutate(cv_rank_new = row_number()) %>%
left_join(ntrees %>%
mutate(cv_rank_old = row_number(),
coef_variation_old = coef_variation) %>%
select(id, cv_rank_old, coef_variation_old),
by = "id") %>%
relocate(starts_with("cv_"), starts_with("coef_"), .before = PRE_1) %>%
arrange(cv_rank_old)
ntrees_min8 %>%
kbl() %>%
kable_paper() %>%
column_spec(2:3, background = "#A1D5FF7F") %>%
column_spec(4:5, background = "#C7F9FF7F") %>%
row_spec(which(min_dbh_change$row_highlight), bold = TRUE) %>%
scroll_box(width = "100%", height = "800px") | id | cv_rank_new | cv_rank_old | coef_variation | coef_variation_old | PRE_1 | PRE_2 | POST_1 | POST_2 | POST_3 | POSTRX_1 | POSTRX_2 | mean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L7 | 21 | 1 | 0.1367905 | 0.4538693 | 56 | 73 | 70 | NA | NA | NA | NA | 66.33333 |
| L29 | 4 | 2 | 0.3189526 | 0.4408593 | 46 | 94 | 105 | 103 | NA | NA | NA | 87.00000 |
| RA261 | 2 | 3 | 0.4877908 | 0.4359954 | 49 | NA | 120 | 150 | NA | NA | NA | 106.33333 |
| RA178 | 1 | 4 | 0.4901528 | 0.4202574 | 23 | NA | 71 | 61 | NA | NA | NA | 51.66667 |
| 3A | 3 | 5 | 0.3732764 | 0.3932914 | 43 | NA | NA | NA | NA | 96 | 83 | 74.00000 |
| L23 | 15 | 6 | 0.1955541 | 0.3921545 | 49 | NA | 64 | 73 | NA | NA | NA | 62.00000 |
| RA181 | 6 | 7 | 0.3136992 | 0.3652989 | 29 | NA | 49 | 56 | NA | NA | NA | 44.66667 |
| L27 | 33 | 8 | 0.0853659 | 0.3523330 | 74 | NA | 87 | 85 | NA | NA | NA | 82.00000 |
| L8 | 28 | 9 | 0.0972854 | 0.3473543 | 69 | 85 | 79 | 71 | NA | NA | NA | 76.00000 |
| L25 | 12 | 10 | 0.2065058 | 0.3093169 | 84 | NA | 126 | 120 | NA | NA | NA | 110.00000 |
| L9 | 11 | 11 | 0.2184565 | 0.3089125 | 82 | NA | 118 | 127 | NA | NA | NA | 109.00000 |
| L10 | 43 | 12 | 0.0560085 | 0.3034547 | 97 | 105 | NA | NA | NA | NA | NA | 101.00000 |
| RA219 | 34 | 13 | 0.0775396 | 0.2812520 | 67 | NA | NA | NA | NA | 78 | 75 | 73.33333 |
| L11 | 27 | 14 | 0.1034773 | 0.2796354 | 77 | 99 | 93 | 90 | NA | NA | NA | 89.75000 |
| RA2170 | 32 | 15 | 0.0903217 | 0.2783984 | 120 | NA | 103 | 103 | NA | NA | NA | 108.66667 |
| RA281 | 5 | 16 | 0.3147819 | 0.2768605 | 50 | NA | 79 | 97 | NA | NA | NA | 75.33333 |
| RA2080 | 23 | 17 | 0.1357674 | 0.2692308 | 42 | NA | NA | NA | NA | 34 | 33 | 36.33333 |
| RA2240 | 36 | 18 | 0.0704826 | 0.2622475 | 129 | NA | 121 | 112 | NA | NA | NA | 120.66667 |
| RA410 | 8 | 19 | 0.2962829 | 0.2594154 | 68 | NA | 120 | 122 | NA | NA | NA | 103.33333 |
| RA1310 | 24 | 20 | 0.1339421 | 0.2458373 | 53 | NA | 59 | 72 | 58 | NA | NA | 60.50000 |
| L12 | 46 | 21 | 0.0447081 | 0.2165746 | 69 | NA | 71 | 65 | NA | NA | NA | 68.33333 |
| RA510 | 7 | 22 | 0.3129908 | 0.2142404 | 61 | NA | 115 | 110 | NA | NA | NA | 95.33333 |
| RA310 | 10 | 23 | 0.2227967 | 0.1851189 | 85 | NA | 123 | 133 | NA | NA | NA | 113.66667 |
| RA251 | 9 | 24 | 0.2581766 | 0.1812029 | 81 | NA | 113 | 138 | NA | NA | NA | 110.66667 |
| L24 | 44 | 25 | 0.0519481 | 0.1807224 | 73 | NA | 77 | 81 | NA | NA | NA | 77.00000 |
| L18 | 30 | 26 | 0.0938041 | 0.1738022 | 82 | NA | 91 | 99 | NA | NA | NA | 90.66667 |
| 9A | 22 | 27 | 0.1358284 | 0.1697056 | NA | NA | 80 | 97 | NA | NA | NA | 88.50000 |
| L13 | 42 | 28 | 0.0562279 | 0.1664702 | 57 | NA | 51 | 55 | NA | NA | NA | 54.33333 |
| RA191 | 18 | 29 | 0.1771003 | 0.1660871 | 44 | NA | 54 | 63 | NA | NA | NA | 53.66667 |
| RA2040 | 14 | 30 | 0.2014515 | 0.1414275 | 72 | NA | NA | NA | NA | 48 | 66 | 62.00000 |
| RA2540 | 38 | 31 | 0.0665079 | 0.1412455 | 52 | NA | NA | NA | NA | 46 | 47 | 48.33333 |
| RA610 | 52 | 32 | 0.0134376 | 0.1412257 | 72 | NA | 71 | NA | NA | 70 | 72 | 71.25000 |
| RA710 | 19 | 33 | 0.1621622 | 0.1311110 | NA | NA | 37 | 31 | NA | 43 | NA | 37.00000 |
| RA1210 | 20 | 34 | 0.1512880 | 0.1277049 | 96 | NA | 119 | NA | NA | NA | NA | 107.50000 |
| RA2180 | 41 | 35 | 0.0568393 | 0.1233651 | 84 | NA | 75 | 79 | NA | NA | NA | 79.33333 |
| RA2490 | 16 | 36 | 0.1924501 | 0.1228491 | 30 | NA | NA | NA | NA | 21 | 30 | 27.00000 |
| RA271 | 50 | 37 | 0.0261861 | 0.1009534 | 60 | NA | 58 | 57 | NA | NA | NA | 58.33333 |
| RA2020 | 31 | 38 | 0.0930441 | 0.0997748 | 85 | NA | NA | NA | NA | 72 | 85 | 80.66667 |
| 9B | 26 | 39 | 0.1178511 | 0.0975320 | NA | NA | 44 | 52 | NA | NA | NA | 48.00000 |
| RA2010 | 29 | 40 | 0.0962726 | 0.0892143 | 71 | NA | 81 | NA | NA | 86 | NA | 79.33333 |
| RA301 | 39 | 41 | 0.0641500 | 0.0820408 | 78 | NA | 78 | 87 | NA | NA | NA | 81.00000 |
| RA2190 | 25 | 42 | 0.1282029 | 0.0803162 | 29 | NA | 37 | 36 | NA | NA | NA | 34.00000 |
| RA2030 | 45 | 43 | 0.0506522 | 0.0792833 | 69 | NA | NA | NA | NA | 73 | 66 | 69.33333 |
| RA1410 | 35 | 44 | 0.0771389 | 0.0614875 | NA | NA | 52 | 58 | NA | NA | NA | 55.00000 |
| RA1110 | 17 | 45 | 0.1803700 | 0.0568979 | 89 | NA | NA | NA | NA | 74 | 62 | 75.00000 |
| RA2560 | 47 | 46 | 0.0444108 | 0.0509427 | 59 | NA | NA | NA | NA | 54 | 57 | 56.66667 |
| RA2310 | 37 | 47 | 0.0692820 | 0.0494872 | 45 | NA | NA | NA | NA | 40 | 40 | 41.66667 |
| RA2050 | 40 | 48 | 0.0577717 | 0.0329945 | 101 | NA | NA | NA | NA | 90 | 95 | 95.33333 |
| RA1010 | 49 | 49 | 0.0391908 | 0.0247266 | 96 | NA | 89 | 91 | NA | NA | NA | 92.00000 |
| RA1610 | 51 | 50 | 0.0193728 | 0.0149917 | NA | NA | 108 | 111 | NA | NA | NA | 109.50000 |
| RA1510 | 48 | 51 | 0.0407946 | 0.0146804 | NA | NA | 101 | 107 | NA | NA | NA | 104.00000 |
| L1 | 54 | 53 | NA | NA | 104 | NA | NA | NA | NA | NA | NA | 104.00000 |
| L14 | 55 | 54 | NA | NA | 124 | NA | NA | NA | NA | NA | NA | 124.00000 |
| L15 | 56 | 55 | NA | NA | 38 | NA | NA | NA | NA | NA | NA | 38.00000 |
| L16 | 57 | 56 | NA | NA | 39 | NA | NA | NA | NA | NA | NA | 39.00000 |
| L17 | 58 | 57 | NA | NA | 89 | NA | NA | NA | NA | NA | NA | 89.00000 |
| L19 | 59 | 58 | NA | NA | 30 | NA | NA | NA | NA | NA | NA | 30.00000 |
| L2 | 60 | 59 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| L20 | 61 | 60 | NA | NA | 77 | NA | NA | NA | NA | NA | NA | 77.00000 |
| L21 | 62 | 61 | NA | NA | 64 | NA | NA | NA | NA | NA | NA | 64.00000 |
| L22 | 63 | 62 | NA | NA | 97 | NA | NA | NA | NA | NA | NA | 97.00000 |
| L26 | 64 | 63 | NA | NA | 66 | NA | NA | NA | NA | NA | NA | 66.00000 |
| L28 | 65 | 64 | NA | NA | 27 | NA | NA | NA | NA | NA | NA | 27.00000 |
| L3 | 66 | 65 | NA | NA | 58 | NA | NA | NA | NA | NA | NA | 58.00000 |
| L4 | 67 | 66 | NA | NA | 122 | NA | NA | NA | NA | NA | NA | 122.00000 |
| L5 | 68 | 67 | NA | NA | 70 | NA | NA | NA | NA | NA | NA | 70.00000 |
| L6 | 69 | 68 | NA | NA | 46 | NA | NA | NA | NA | NA | NA | 46.00000 |
| RA2060 | 70 | 70 | NA | NA | 101 | NA | NA | NA | NA | NA | NA | 101.00000 |
| RA2070 | 71 | 71 | NA | NA | 13 | NA | NA | NA | NA | NA | NA | 13.00000 |
| RA2090 | 72 | 72 | NA | NA | 36 | NA | NA | NA | NA | NA | NA | 36.00000 |
| RA2100 | 73 | 73 | NA | NA | 64 | NA | NA | NA | NA | NA | NA | 64.00000 |
| RA2110 | 74 | 74 | NA | NA | 61 | NA | NA | NA | NA | NA | NA | 61.00000 |
| RA2120 | 75 | 75 | NA | NA | 88 | NA | NA | NA | NA | NA | NA | 88.00000 |
| RA2130 | 76 | 76 | NA | NA | 74 | NA | NA | NA | NA | NA | NA | 74.00000 |
| RA2140 | 77 | 77 | NA | NA | 53 | NA | NA | NA | NA | NA | NA | 53.00000 |
| RA2150 | 78 | 78 | NA | NA | 9 | NA | NA | NA | NA | NA | NA | 9.00000 |
| RA2160 | 79 | 80 | NA | NA | 34 | NA | NA | NA | NA | NA | NA | 34.00000 |
| RA217 | 80 | 81 | NA | NA | 8 | NA | NA | NA | NA | NA | NA | 8.00000 |
| RA2200 | 81 | 82 | NA | NA | 93 | NA | NA | NA | NA | NA | NA | 93.00000 |
| RA2210 | 82 | 83 | NA | NA | 69 | NA | NA | NA | NA | NA | NA | 69.00000 |
| RA2220 | 83 | 84 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| RA2230 | 84 | 85 | NA | NA | 132 | NA | NA | NA | NA | NA | NA | 132.00000 |
| RA2300 | 85 | 86 | NA | NA | 38 | NA | NA | NA | NA | NA | NA | 38.00000 |
| RA2320 | 86 | 87 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| RA2330 | 87 | 88 | NA | NA | 95 | NA | NA | NA | NA | NA | NA | 95.00000 |
| RA2340 | 88 | 89 | NA | NA | 54 | NA | NA | NA | NA | NA | NA | 54.00000 |
| RA2350 | 89 | 90 | NA | NA | 84 | NA | NA | NA | NA | NA | NA | 84.00000 |
| RA2360 | 90 | 91 | NA | NA | 49 | NA | NA | NA | NA | NA | NA | 49.00000 |
| RA2370 | 91 | 92 | NA | NA | 58 | NA | NA | NA | NA | NA | NA | 58.00000 |
| RA2380 | 92 | 93 | NA | NA | 55 | NA | NA | NA | NA | NA | NA | 55.00000 |
| RA2390 | 93 | 94 | NA | NA | 126 | NA | NA | NA | NA | NA | NA | 126.00000 |
| RA2400 | 94 | 95 | NA | NA | 86 | NA | NA | NA | NA | NA | NA | 86.00000 |
| RA2410 | 95 | 96 | NA | NA | 97 | NA | NA | NA | NA | NA | NA | 97.00000 |
| RA2420 | 96 | 97 | NA | NA | 48 | NA | NA | NA | NA | NA | NA | 48.00000 |
| RA2430 | 97 | 98 | NA | NA | 74 | NA | NA | NA | NA | NA | NA | 74.00000 |
| RA2440 | 98 | 99 | NA | NA | 93 | NA | NA | NA | NA | NA | NA | 93.00000 |
| RA2450 | 99 | 100 | NA | NA | 73 | NA | NA | NA | NA | NA | NA | 73.00000 |
| RA2460 | 100 | 101 | NA | NA | 61 | NA | NA | NA | NA | NA | NA | 61.00000 |
| RA2470 | 101 | 102 | NA | NA | 67 | NA | NA | NA | NA | NA | NA | 67.00000 |
| RA2480 | 102 | 103 | NA | NA | 40 | NA | NA | NA | NA | NA | NA | 40.00000 |
| RA2500 | 103 | 104 | NA | NA | 116 | NA | NA | NA | NA | NA | NA | 116.00000 |
| RA2510 | 104 | 105 | NA | NA | 57 | NA | NA | NA | NA | NA | NA | 57.00000 |
| RA2520 | 105 | 106 | NA | NA | 27 | NA | NA | NA | NA | NA | NA | 27.00000 |
| RA2530 | 106 | 107 | NA | NA | 95 | NA | NA | NA | NA | NA | NA | 95.00000 |
| RA2550 | 107 | 108 | NA | NA | 71 | NA | NA | NA | NA | NA | NA | 71.00000 |
| RA810 | 53 | 109 | 0.0000000 | NA | 68 | NA | 68 | NA | NA | NA | NA | 68.00000 |
| RA9D | 13 | 110 | 0.2044646 | NA | NA | NA | 71 | 95 | NA | NA | NA | 83.00000 |
Okay, it looks like this correction clearly made a difference for
some plots, particularly the L plots. The RA
plot CVs are less affected. Let’s glance at some histograms pre- and
post-application of the DBH cutoff.
# define key plots: top 10 of ntrees_min8
key_plots <- ntrees_min8$id[1:10]
# map over key plots to make list of old plots
old_hists <- map(key_plots, \(x) mk_dbh_hist(x, overstory) +
labs(subtitle = "OLD"))
new_hists <- map(key_plots, \(x) mk_dbh_hist(x, overstory_min8) +
labs(subtitle = "NEW"))
dual_plot <- \(x, y) {
plots <- list(x, y)
p <- ggarrange(x, y, nrow = 2)
p
}
dual_plot(old_hists[[1]], new_hists[[1]])## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# plot old and new hists side-by-side
plots <- map2(old_hists, new_hists, \(x, y) dual_plot(x, y))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.plots## [[1]]
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## [[4]]
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## [[5]]
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## [[6]]
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## [[7]]
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## [[8]]
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## [[9]]
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## [[10]]
Possible aspen regen
Though the DBH cutoff certainly helped, there are still some suspect cases of dramatically increasing tree counts in the post-treatment visits. One possible explanation is aspen regeneration. Let’s see if aspen are frequent at any of the high-CV sites.
aspen_df <- overstory_min8 %>%
group_by(id, species) %>%
summarise(ntrees = n(), .groups = "keep") %>%
ungroup() %>%
pivot_wider(names_from = species, values_from = ntrees) %>%
rowwise %>%
mutate(aspen_proportion = POTR5 / sum(across(PIPO:last_col()),
na.rm = TRUE)) %>%
select(id, aspen_proportion)
out <- ntrees_min8 %>%
left_join(aspen_df, by = "id") %>%
relocate(aspen_proportion, .before = everything()) %>%
arrange(cv_rank_new) %>%
mutate(aspen_perc_rank = percent_rank(aspen_proportion), .before = id)
out %>%
kbl() %>%
kable_paper() %>%
column_spec(1:2, background = "#A1D5FF7F") %>%
column_spec(4, background = "#C7F9FF7F") %>%
row_spec(which(out$aspen_perc_rank > 0.75), bold = TRUE) %>%
scroll_box(width = "100%", height = "800px") | aspen_proportion | aspen_perc_rank | id | cv_rank_new | cv_rank_old | coef_variation | coef_variation_old | PRE_1 | PRE_2 | POST_1 | POST_2 | POST_3 | POSTRX_1 | POSTRX_2 | mean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0709677 | 0.1734694 | RA178 | 1 | 4 | 0.4901528 | 0.4202574 | 23 | NA | 71 | 61 | NA | NA | NA | 51.66667 |
| 0.2257053 | 0.3469388 | RA261 | 2 | 3 | 0.4877908 | 0.4359954 | 49 | NA | 120 | 150 | NA | NA | NA | 106.33333 |
| 0.0225225 | 0.0612245 | 3A | 3 | 5 | 0.3732764 | 0.3932914 | 43 | NA | NA | NA | NA | 96 | 83 | 74.00000 |
| 0.4683908 | 0.8367347 | L29 | 4 | 2 | 0.3189526 | 0.4408593 | 46 | 94 | 105 | 103 | NA | NA | NA | 87.00000 |
| 0.4115044 | 0.7551020 | RA281 | 5 | 16 | 0.3147819 | 0.2768605 | 50 | NA | 79 | 97 | NA | NA | NA | 75.33333 |
| 0.1417910 | 0.2346939 | RA181 | 6 | 7 | 0.3136992 | 0.3652989 | 29 | NA | 49 | 56 | NA | NA | NA | 44.66667 |
| 0.4545455 | 0.8061224 | RA510 | 7 | 22 | 0.3129908 | 0.2142404 | 61 | NA | 115 | 110 | NA | NA | NA | 95.33333 |
| 0.4322581 | 0.7755102 | RA410 | 8 | 19 | 0.2962829 | 0.2594154 | 68 | NA | 120 | 122 | NA | NA | NA | 103.33333 |
| 0.3493976 | 0.6224490 | RA251 | 9 | 24 | 0.2581766 | 0.1812029 | 81 | NA | 113 | 138 | NA | NA | NA | 110.66667 |
| 0.3870968 | 0.7142857 | RA310 | 10 | 23 | 0.2227967 | 0.1851189 | 85 | NA | 123 | 133 | NA | NA | NA | 113.66667 |
| 0.4464832 | 0.7857143 | L9 | 11 | 11 | 0.2184565 | 0.3089125 | 82 | NA | 118 | 127 | NA | NA | NA | 109.00000 |
| 0.3727273 | 0.7040816 | L25 | 12 | 10 | 0.2065058 | 0.3093169 | 84 | NA | 126 | 120 | NA | NA | NA | 110.00000 |
| 0.1987952 | 0.2959184 | RA9D | 13 | 110 | 0.2044646 | NA | NA | NA | 71 | 95 | NA | NA | NA | 83.00000 |
| 0.0806452 | 0.1938776 | RA2040 | 14 | 30 | 0.2014515 | 0.1414275 | 72 | NA | NA | NA | NA | 48 | 66 | 62.00000 |
| 0.0698925 | 0.1632653 | L23 | 15 | 6 | 0.1955541 | 0.3921545 | 49 | NA | 64 | 73 | NA | NA | NA | 62.00000 |
| NA | NA | RA2490 | 16 | 36 | 0.1924501 | 0.1228491 | 30 | NA | NA | NA | NA | 21 | 30 | 27.00000 |
| 0.2266667 | 0.3571429 | RA1110 | 17 | 45 | 0.1803700 | 0.0568979 | 89 | NA | NA | NA | NA | 74 | 62 | 75.00000 |
| 0.3354037 | 0.5816327 | RA191 | 18 | 29 | 0.1771003 | 0.1660871 | 44 | NA | 54 | 63 | NA | NA | NA | 53.66667 |
| 0.0270270 | 0.0918367 | RA710 | 19 | 33 | 0.1621622 | 0.1311110 | NA | NA | 37 | 31 | NA | 43 | NA | 37.00000 |
| 0.2511628 | 0.4081633 | RA1210 | 20 | 34 | 0.1512880 | 0.1277049 | 96 | NA | 119 | NA | NA | NA | NA | 107.50000 |
| 0.2713568 | 0.4591837 | L7 | 21 | 1 | 0.1367905 | 0.4538693 | 56 | 73 | 70 | NA | NA | NA | NA | 66.33333 |
| 0.2768362 | 0.4795918 | 9A | 22 | 27 | 0.1358284 | 0.1697056 | NA | NA | 80 | 97 | NA | NA | NA | 88.50000 |
| 0.6697248 | 0.9387755 | RA2080 | 23 | 17 | 0.1357674 | 0.2692308 | 42 | NA | NA | NA | NA | 34 | 33 | 36.33333 |
| 0.3636364 | 0.6734694 | RA1310 | 24 | 20 | 0.1339421 | 0.2458373 | 53 | NA | 59 | 72 | 58 | NA | NA | 60.50000 |
| 0.0784314 | 0.1836735 | RA2190 | 25 | 42 | 0.1282029 | 0.0803162 | 29 | NA | 37 | 36 | NA | NA | NA | 34.00000 |
| 0.1458333 | 0.2448980 | 9B | 26 | 39 | 0.1178511 | 0.0975320 | NA | NA | 44 | 52 | NA | NA | NA | 48.00000 |
| 0.3147632 | 0.5612245 | L11 | 27 | 14 | 0.1034773 | 0.2796354 | 77 | 99 | 93 | 90 | NA | NA | NA | 89.75000 |
| 0.2335526 | 0.3775510 | L8 | 28 | 9 | 0.0972854 | 0.3473543 | 69 | 85 | 79 | 71 | NA | NA | NA | 76.00000 |
| 0.3655462 | 0.6836735 | RA2010 | 29 | 40 | 0.0962726 | 0.0892143 | 71 | NA | 81 | NA | NA | 86 | NA | 79.33333 |
| 0.4080882 | 0.7346939 | L18 | 30 | 26 | 0.0938041 | 0.1738022 | 82 | NA | 91 | 99 | NA | NA | NA | 90.66667 |
| 0.0041322 | 0.0102041 | RA2020 | 31 | 38 | 0.0930441 | 0.0997748 | 85 | NA | NA | NA | NA | 72 | 85 | 80.66667 |
| 0.0460123 | 0.1020408 | RA2170 | 32 | 15 | 0.0903217 | 0.2783984 | 120 | NA | 103 | 103 | NA | NA | NA | 108.66667 |
| 0.3495935 | 0.6326531 | L27 | 33 | 8 | 0.0853659 | 0.3523330 | 74 | NA | 87 | 85 | NA | NA | NA | 82.00000 |
| 0.1363636 | 0.2244898 | RA219 | 34 | 13 | 0.0775396 | 0.2812520 | 67 | NA | NA | NA | NA | 78 | 75 | 73.33333 |
| 0.4909091 | 0.8469388 | RA1410 | 35 | 44 | 0.0771389 | 0.0614875 | NA | NA | 52 | 58 | NA | NA | NA | 55.00000 |
| 0.4171271 | 0.7653061 | RA2240 | 36 | 18 | 0.0704826 | 0.2622475 | 129 | NA | 121 | 112 | NA | NA | NA | 120.66667 |
| 0.0560000 | 0.1428571 | RA2310 | 37 | 47 | 0.0692820 | 0.0494872 | 45 | NA | NA | NA | NA | 40 | 40 | 41.66667 |
| 0.6551724 | 0.9285714 | RA2540 | 38 | 31 | 0.0665079 | 0.1412455 | 52 | NA | NA | NA | NA | 46 | 47 | 48.33333 |
| 0.4938272 | 0.8571429 | RA301 | 39 | 41 | 0.0641500 | 0.0820408 | 78 | NA | 78 | 87 | NA | NA | NA | 81.00000 |
| 0.0034965 | 0.0000000 | RA2050 | 40 | 48 | 0.0577717 | 0.0329945 | 101 | NA | NA | NA | NA | 90 | 95 | 95.33333 |
| 0.0672269 | 0.1530612 | RA2180 | 41 | 35 | 0.0568393 | 0.1233651 | 84 | NA | 75 | 79 | NA | NA | NA | 79.33333 |
| 0.2392638 | 0.3979592 | L13 | 42 | 28 | 0.0562279 | 0.1664702 | 57 | NA | 51 | 55 | NA | NA | NA | 54.33333 |
| 0.3069307 | 0.5408163 | L10 | 43 | 12 | 0.0560085 | 0.3034547 | 97 | 105 | NA | NA | NA | NA | NA | 101.00000 |
| 0.4675325 | 0.8265306 | L24 | 44 | 25 | 0.0519481 | 0.1807224 | 73 | NA | 77 | 81 | NA | NA | NA | 77.00000 |
| 0.1346154 | 0.2142857 | RA2030 | 45 | 43 | 0.0506522 | 0.0792833 | 69 | NA | NA | NA | NA | 73 | 66 | 69.33333 |
| 0.3560976 | 0.6428571 | L12 | 46 | 21 | 0.0447081 | 0.2165746 | 69 | NA | 71 | 65 | NA | NA | NA | 68.33333 |
| NA | NA | RA2560 | 47 | 46 | 0.0444108 | 0.0509427 | 59 | NA | NA | NA | NA | 54 | 57 | 56.66667 |
| 0.4038462 | 0.7244898 | RA1510 | 48 | 51 | 0.0407946 | 0.0146804 | NA | NA | 101 | 107 | NA | NA | NA | 104.00000 |
| 0.2101449 | 0.3163265 | RA1010 | 49 | 49 | 0.0391908 | 0.0247266 | 96 | NA | 89 | 91 | NA | NA | NA | 92.00000 |
| 0.3200000 | 0.5714286 | RA271 | 50 | 37 | 0.0261861 | 0.1009534 | 60 | NA | 58 | 57 | NA | NA | NA | 58.33333 |
| 0.3378995 | 0.5918367 | RA1610 | 51 | 50 | 0.0193728 | 0.0149917 | NA | NA | 108 | 111 | NA | NA | NA | 109.50000 |
| 0.3578947 | 0.6530612 | RA610 | 52 | 32 | 0.0134376 | 0.1412257 | 72 | NA | 71 | NA | NA | 70 | 72 | 71.25000 |
| 0.0514706 | 0.1224490 | RA810 | 53 | 109 | 0.0000000 | NA | 68 | NA | 68 | NA | NA | NA | NA | 68.00000 |
| 0.6538462 | 0.9183673 | L1 | 54 | 53 | NA | NA | 104 | NA | NA | NA | NA | NA | NA | 104.00000 |
| 0.6129032 | 0.8979592 | L14 | 55 | 54 | NA | NA | 124 | NA | NA | NA | NA | NA | NA | 124.00000 |
| 0.2105263 | 0.3265306 | L15 | 56 | 55 | NA | NA | 38 | NA | NA | NA | NA | NA | NA | 38.00000 |
| 0.1538462 | 0.2551020 | L16 | 57 | 56 | NA | NA | 39 | NA | NA | NA | NA | NA | NA | 39.00000 |
| 0.4494382 | 0.7959184 | L17 | 58 | 57 | NA | NA | 89 | NA | NA | NA | NA | NA | NA | 89.00000 |
| 0.5000000 | 0.8673469 | L19 | 59 | 58 | NA | NA | 30 | NA | NA | NA | NA | NA | NA | 30.00000 |
| 0.2638889 | 0.4387755 | L2 | 60 | 59 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| 0.2597403 | 0.4285714 | L20 | 61 | 60 | NA | NA | 77 | NA | NA | NA | NA | NA | NA | 77.00000 |
| 0.2656250 | 0.4489796 | L21 | 62 | 61 | NA | NA | 64 | NA | NA | NA | NA | NA | NA | 64.00000 |
| 0.6185567 | 0.9081633 | L22 | 63 | 62 | NA | NA | 97 | NA | NA | NA | NA | NA | NA | 97.00000 |
| 0.6818182 | 0.9489796 | L26 | 64 | 63 | NA | NA | 66 | NA | NA | NA | NA | NA | NA | 66.00000 |
| 0.2962963 | 0.5102041 | L28 | 65 | 64 | NA | NA | 27 | NA | NA | NA | NA | NA | NA | 27.00000 |
| 0.3103448 | 0.5510204 | L3 | 66 | 65 | NA | NA | 58 | NA | NA | NA | NA | NA | NA | 58.00000 |
| 0.7704918 | 0.9795918 | L4 | 67 | 66 | NA | NA | 122 | NA | NA | NA | NA | NA | NA | 122.00000 |
| 0.2285714 | 0.3673469 | L5 | 68 | 67 | NA | NA | 70 | NA | NA | NA | NA | NA | NA | 70.00000 |
| 0.4565217 | 0.8163265 | L6 | 69 | 68 | NA | NA | 46 | NA | NA | NA | NA | NA | NA | 46.00000 |
| 0.0099010 | 0.0204082 | RA2060 | 70 | 70 | NA | NA | 101 | NA | NA | NA | NA | NA | NA | 101.00000 |
| 0.7692308 | 0.9693878 | RA2070 | 71 | 71 | NA | NA | 13 | NA | NA | NA | NA | NA | NA | 13.00000 |
| 0.2222222 | 0.3367347 | RA2090 | 72 | 72 | NA | NA | 36 | NA | NA | NA | NA | NA | NA | 36.00000 |
| 0.2968750 | 0.5204082 | RA2100 | 73 | 73 | NA | NA | 64 | NA | NA | NA | NA | NA | NA | 64.00000 |
| 0.3442623 | 0.6020408 | RA2110 | 74 | 74 | NA | NA | 61 | NA | NA | NA | NA | NA | NA | 61.00000 |
| 0.7159091 | 0.9591837 | RA2120 | 75 | 75 | NA | NA | 88 | NA | NA | NA | NA | NA | NA | 88.00000 |
| 0.8108108 | 0.9897959 | RA2130 | 76 | 76 | NA | NA | 74 | NA | NA | NA | NA | NA | NA | 74.00000 |
| 0.3018868 | 0.5306122 | RA2140 | 77 | 77 | NA | NA | 53 | NA | NA | NA | NA | NA | NA | 53.00000 |
| NA | NA | RA2150 | 78 | 78 | NA | NA | 9 | NA | NA | NA | NA | NA | NA | 9.00000 |
| NA | NA | RA2160 | 79 | 80 | NA | NA | 34 | NA | NA | NA | NA | NA | NA | 34.00000 |
| 0.5000000 | 0.8673469 | RA217 | 80 | 81 | NA | NA | 8 | NA | NA | NA | NA | NA | NA | 8.00000 |
| 0.2903226 | 0.5000000 | RA2200 | 81 | 82 | NA | NA | 93 | NA | NA | NA | NA | NA | NA | 93.00000 |
| 0.3623188 | 0.6632653 | RA2210 | 82 | 83 | NA | NA | 69 | NA | NA | NA | NA | NA | NA | 69.00000 |
| 0.6111111 | 0.8877551 | RA2220 | 83 | 84 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| 0.2348485 | 0.3877551 | RA2230 | 84 | 85 | NA | NA | 132 | NA | NA | NA | NA | NA | NA | 132.00000 |
| 0.3684211 | 0.6938776 | RA2300 | 85 | 86 | NA | NA | 38 | NA | NA | NA | NA | NA | NA | 38.00000 |
| 0.1944444 | 0.2857143 | RA2320 | 86 | 87 | NA | NA | 72 | NA | NA | NA | NA | NA | NA | 72.00000 |
| 0.3473684 | 0.6122449 | RA2330 | 87 | 88 | NA | NA | 95 | NA | NA | NA | NA | NA | NA | 95.00000 |
| 0.0555556 | 0.1326531 | RA2340 | 88 | 89 | NA | NA | 54 | NA | NA | NA | NA | NA | NA | 54.00000 |
| 0.0238095 | 0.0714286 | RA2350 | 89 | 90 | NA | NA | 84 | NA | NA | NA | NA | NA | NA | 84.00000 |
| 0.1836735 | 0.2755102 | RA2360 | 90 | 91 | NA | NA | 49 | NA | NA | NA | NA | NA | NA | 49.00000 |
| 0.1206897 | 0.2040816 | RA2370 | 91 | 92 | NA | NA | 58 | NA | NA | NA | NA | NA | NA | 58.00000 |
| 0.2727273 | 0.4693878 | RA2380 | 92 | 93 | NA | NA | 55 | NA | NA | NA | NA | NA | NA | 55.00000 |
| 0.0238095 | 0.0714286 | RA2390 | 93 | 94 | NA | NA | 126 | NA | NA | NA | NA | NA | NA | 126.00000 |
| 0.1627907 | 0.2653061 | RA2400 | 94 | 95 | NA | NA | 86 | NA | NA | NA | NA | NA | NA | 86.00000 |
| 0.2061856 | 0.3061224 | RA2410 | 95 | 96 | NA | NA | 97 | NA | NA | NA | NA | NA | NA | 97.00000 |
| 0.9375000 | 1.0000000 | RA2420 | 96 | 97 | NA | NA | 48 | NA | NA | NA | NA | NA | NA | 48.00000 |
| 0.2837838 | 0.4897959 | RA2430 | 97 | 98 | NA | NA | 74 | NA | NA | NA | NA | NA | NA | 74.00000 |
| 0.0107527 | 0.0306122 | RA2440 | 98 | 99 | NA | NA | 93 | NA | NA | NA | NA | NA | NA | 93.00000 |
| 0.0136986 | 0.0408163 | RA2450 | 99 | 100 | NA | NA | 73 | NA | NA | NA | NA | NA | NA | 73.00000 |
| 0.4098361 | 0.7448980 | RA2460 | 100 | 101 | NA | NA | 61 | NA | NA | NA | NA | NA | NA | 61.00000 |
| NA | NA | RA2470 | 101 | 102 | NA | NA | 67 | NA | NA | NA | NA | NA | NA | 67.00000 |
| 0.0500000 | 0.1122449 | RA2480 | 102 | 103 | NA | NA | 40 | NA | NA | NA | NA | NA | NA | 40.00000 |
| NA | NA | RA2500 | 103 | 104 | NA | NA | 116 | NA | NA | NA | NA | NA | NA | 116.00000 |
| 0.0175439 | 0.0510204 | RA2510 | 104 | 105 | NA | NA | 57 | NA | NA | NA | NA | NA | NA | 57.00000 |
| NA | NA | RA2520 | 105 | 106 | NA | NA | 27 | NA | NA | NA | NA | NA | NA | 27.00000 |
| NA | NA | RA2530 | 106 | 107 | NA | NA | 95 | NA | NA | NA | NA | NA | NA | 95.00000 |
| 0.2535211 | 0.4183673 | RA2550 | 107 | 108 | NA | NA | 71 | NA | NA | NA | NA | NA | NA | 71.00000 |
TODO
- address maggie’s email: some plots (e.g. RA261 and RA2610) may be
improperly lumped together in
overstory.R - stump data
- lay out full diagnosis and recommend and solution for each plot
Stump data
Finally, I checked to see which plots had stump data, because I think these are the plots that were of concern previously.
| id | t_idxn |
|---|---|
| 3A | PRE_1 |
| 3A | POSTRX_1 |
| 3A | POSTRX_2 |
| 9A | POST_1 |
| 9A | POST_2 |
| 9B | POST_1 |
| 9B | POST_2 |
| L10 | PRE_2 |
| L11 | POST_1 |
| L11 | POST_2 |
| L12 | PRE_1 |
| L12 | POST_1 |
| L12 | POST_2 |
| L13 | PRE_1 |
| L13 | POST_1 |
| L13 | POST_2 |
| L16 | PRE_1 |
| L18 | POST_1 |
| L18 | POST_2 |
| L2 | PRE_1 |
| L23 | PRE_1 |
| L23 | POST_1 |
| L23 | POST_2 |
| L24 | PRE_1 |
| L24 | POST_1 |
| L24 | POST_2 |
| L25 | POST_1 |
| L25 | POST_2 |
| L27 | POST_1 |
| L27 | POST_2 |
| L29 | PRE_2 |
| L29 | POST_1 |
| L29 | POST_2 |
| L7 | PRE_1 |
| L7 | PRE_2 |
| L7 | POST_1 |
| L8 | PRE_1 |
| L8 | PRE_2 |
| L8 | POST_1 |
| L8 | POST_2 |
| L9 | POST_1 |
| L9 | POST_2 |
| RA1010 | PRE_1 |
| RA1010 | POST_1 |
| RA1010 | POST_2 |
| RA1110 | PRE_1 |
| RA1110 | POSTRX_1 |
| RA1110 | POSTRX_2 |
| RA1210 | PRE_1 |
| RA1210 | POST_1 |
| RA1310 | PRE_1 |
| RA1310 | POST_1 |
| RA1310 | POST_2 |
| RA1310 | POST_3 |
| RA1410 | POST_1 |
| RA1410 | POST_2 |
| RA1510 | POST_1 |
| RA1510 | POST_2 |
| RA1610 | POST_1 |
| RA1610 | POST_2 |
| RA178 | PRE_1 |
| RA178 | POST_1 |
| RA178 | POST_2 |
| RA181 | PRE_1 |
| RA181 | POST_1 |
| RA181 | POST_2 |
| RA191 | PRE_1 |
| RA191 | POST_1 |
| RA191 | POST_2 |
| RA2010 | PRE_1 |
| RA2010 | POST_1 |
| RA2010 | POSTRX_1 |
| RA2020 | PRE_1 |
| RA2020 | POSTRX_1 |
| RA2020 | POSTRX_2 |
| RA2030 | PRE_1 |
| RA2030 | POSTRX_1 |
| RA2030 | POSTRX_2 |
| RA2050 | PRE_1 |
| RA2050 | POSTRX_1 |
| RA2050 | POSTRX_2 |
| RA2060 | PRE_1 |
| RA2080 | POSTRX_1 |
| RA2090 | PRE_1 |
| RA2130 | PRE_1 |
| RA217 | PRE_1 |
| RA2170 | POST_1 |
| RA2170 | POST_2 |
| RA2180 | PRE_1 |
| RA2180 | POST_1 |
| RA2180 | POST_2 |
| RA219 | PRE_1 |
| RA219 | POSTRX_1 |
| RA219 | POSTRX_2 |
| RA2190 | POST_1 |
| RA2190 | POST_2 |
| RA2200 | PRE_1 |
| RA2210 | PRE_1 |
| RA2240 | POST_1 |
| RA2240 | POST_2 |
| RA2310 | PRE_1 |
| RA2310 | POSTRX_1 |
| RA2310 | POSTRX_2 |
| RA2330 | PRE_1 |
| RA2340 | PRE_1 |
| RA2350 | PRE_1 |
| RA2360 | PRE_1 |
| RA2370 | PRE_1 |
| RA2380 | PRE_1 |
| RA2390 | PRE_1 |
| RA2400 | PRE_1 |
| RA2410 | PRE_1 |
| RA2430 | PRE_1 |
| RA2440 | PRE_1 |
| RA2450 | PRE_1 |
| RA2460 | PRE_1 |
| RA2470 | PRE_1 |
| RA2480 | PRE_1 |
| RA2490 | PRE_1 |
| RA2490 | POSTRX_1 |
| RA2490 | POSTRX_2 |
| RA2500 | PRE_1 |
| RA251 | PRE_1 |
| RA251 | POST_1 |
| RA251 | POST_2 |
| RA2510 | PRE_1 |
| RA2520 | PRE_1 |
| RA2530 | PRE_1 |
| RA2560 | PRE_1 |
| RA2560 | POSTRX_2 |
| RA261 | PRE_1 |
| RA261 | POST_1 |
| RA261 | POST_2 |
| RA271 | PRE_1 |
| RA271 | POST_1 |
| RA271 | POST_2 |
| RA281 | PRE_1 |
| RA281 | POST_1 |
| RA281 | POST_2 |
| RA301 | PRE_1 |
| RA301 | POST_1 |
| RA301 | POST_2 |
| RA310 | PRE_1 |
| RA310 | POST_1 |
| RA310 | POST_2 |
| RA410 | PRE_1 |
| RA410 | POST_1 |
| RA410 | POST_2 |
| RA510 | PRE_1 |
| RA510 | POST_1 |
| RA510 | POST_2 |
| RA610 | PRE_1 |
| RA610 | POST_1 |
| RA610 | POSTRX_1 |
| RA610 | POSTRX_2 |
| RA710 | POST_1 |
| RA710 | POSTRX_1 |
| RA710 | POST_2 |
| RA810 | PRE_1 |
| RA810 | POST_1 |
| RA9D | POST_1 |
| RA9D | POST_2 |
78 unique sites here, and some have stumps recorded on multiple plot visits. That is a lot. I’m also seeing a lot of stump data in the pretreatment measurements. I’m not sure why that’s even there. Wasn’t there a way to see which stumps were “old”?
Plotting stump data
Here’s a visualization of a “stump” plot.
# filter data
data <- overstory %>%
filter(id == "RA251") %>%
mutate(stump = case_when(
status == "S" ~ "Stump",
.default = "Tree"
))
# create the plot
ggplot(data, aes(x = x, y = y, fill = t_idxn, size = dbh, shape = stump)) +
geom_point(alpha = 0.5) +
labs(title = paste("Plot RA251"),
x = "Midline distance",
y = "N/S distance") +
theme_minimal() +
coord_cartesian(xlim = c(-2, 166), ylim = c(-84, 84)) +
scale_shape_manual(values = c(22, 21)) +
guides(fill=guide_legend(override.aes=list(shape=21))) 
# filter data
data <- overstory %>%
filter(id == "3A") %>%
mutate(stump = case_when(
status == "S" ~ "Stump",
.default = "Tree"
))
# create the plot
ggplot(data, aes(x = x, y = y, fill = t_idxn, size = dbh, shape = stump)) +
geom_point(alpha = 0.5) +
labs(title = paste("Plot 3A"),
x = "Midline distance",
y = "N/S distance") +
theme_minimal() +
coord_cartesian(xlim = c(-2, 166), ylim = c(-84, 84)) +
scale_shape_manual(values = c(22, 21)) +
guides(fill=guide_legend(override.aes=list(shape=21))) 
Overview plots
Some simple statistical analyses to support the plots
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00