Excel File Path:
C:\Users\tlittmann\USDA\Rangeland responses to fire - Fire x herbivory\PrescribedBurning\Data\VegComp\PatchBurnTransects.xlsx
Sheets Used:
Hardcopies:
Hard copies have been scanned and archived in the Prescribed Burning
folder under VegComp (see filepath).
Sample Timelines
| Year | Sampling Dates | # of Locations/Plots |
|---|---|---|
| 2023 | July 11-14 | 19 |
| 2024 | July 9-10, 22-23, | 30 |
| 2025 | Aug 14-15, 20-22, 26-28 | 29 |
Sampling Effort
| Data.Type | Description | Expected.Target |
|---|---|---|
| Woody Transect | Measures woody plant cover and structure along transects | 30 transects (’24), 29 transects (’25) |
| VOR | Visual Obstruction Reading to estimate vegetation density | 19 transects (’23), 8 transects (’24), 29 transects (’25) |
| Plant Composition | Records species presence and abundance in quadrats | 19 transects (’23), 8 transects (’24), 29 transects (’25) |
Data Entry Summary:
| plot | transect | year | Woody | VOR | Composition |
|---|---|---|---|---|---|
| 1 | a->b | 2024 | Species: 1, Hits: 1 | NA | Quadrats NA |
| 1 | b->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 1 | b->c | 2025 | Species: 2, Hits: 3 | 40 | Quadrats 10 |
| 1 | c->d | 2024 | Species: 2, Hits: 7 | NA | Quadrats NA |
| 1 | d->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 1 | d->a | 2025 | Species: 2, Hits: 2 | 40 | Quadrats 10 |
| 10 | b->a | 2024 | Species: 4, Hits: 36 | NA | Quadrats NA |
| 10 | b->a | 2025 | Species: 4, Hits: 35 | 40 | Quadrats 10 |
| 10 | c->d | 2024 | Species: 4, Hits: 52 | NA | Quadrats NA |
| 10 | c->d | 2025 | Species: 5, Hits: 57 | 40 | Quadrats 10 |
| 12 | a->d | 2024 | Species: 2, Hits: 23 | NA | Quadrats NA |
| 12 | b->c | 2024 | Species: 1, Hits: 4 | NA | Quadrats NA |
| 13 | a->d | 2024 | Species: 2, Hits: 31 | NA | Quadrats NA |
| 13 | b->c | 2024 | Species: 3, Hits: 23 | NA | Quadrats NA |
| 14 | a->d | 2024 | Species: 4, Hits: 36 | NA | Quadrats NA |
| 14 | b->c | 2024 | Species: 4, Hits: 39 | NA | Quadrats NA |
| 15 | a->d | 2024 | Species: 3, Hits: 63 | NA | Quadrats NA |
| 15 | b->c | 2024 | Species: 3, Hits: 50 | NA | Quadrats NA |
| 16 | b->a | 2024 | Species: 3, Hits: 53 | NA | Quadrats NA |
| 16 | c->d | 2024 | Species: 3, Hits: 37 | NA | Quadrats NA |
| 17 | c->b | 2024 | Species: 3, Hits: 26 | NA | Quadrats NA |
| 17 | d->a | 2024 | Species: 3, Hits: 30 | NA | Quadrats NA |
| 18 | c->b | 2024 | Species: 4, Hits: 46 | NA | Quadrats NA |
| 18 | d->a | 2024 | Species: 3, Hits: 23 | NA | Quadrats NA |
| 19 | c->b | 2024 | Species: 4, Hits: 68 | NA | Quadrats NA |
| 19 | d->a | 2024 | Species: 4, Hits: 55 | NA | Quadrats NA |
| 2 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 2 | a->b | 2025 | Species: 2, Hits: 22 | 40 | Quadrats 10 |
| 2 | a->d | 2024 | Species: 2, Hits: 30 | NA | Quadrats NA |
| 2 | c->b | 2024 | Species: 4, Hits: 44 | NA | Quadrats NA |
| 2 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 2 | c->d | 2025 | Species: 2, Hits: 21 | 40 | Quadrats 10 |
| 21 | b->a | 2024 | Species: 1, Hits: 41 | 40 | Quadrats 10 |
| 21 | d->c | 2024 | Species: 2, Hits: 10 | 40 | Quadrats 10 |
| 22 | b->c | 2024 | Species: 2, Hits: 28 | 40 | Quadrats 10 |
| 22 | d->a | 2024 | Species: 2, Hits: 8 | 40 | Quadrats 10 |
| 23 | b->c | 2025 | Species: 2, Hits: 42 | 40 | Quadrats 10 |
| 23 | c->b | 2024 | Species: 2, Hits: 37 | NA | Quadrats NA |
| 23 | d->a | 2024 | Species: 2, Hits: 49 | NA | Quadrats NA |
| 23 | d->a | 2025 | Species: 3, Hits: 60 | 40 | Quadrats 10 |
| 25 | a->d | 2024 | Species: 4, Hits: 37 | NA | Quadrats NA |
| 25 | a->d | 2025 | Species: 3, Hits: 28 | 40 | Quadrats 10 |
| 25 | c->b | 2024 | Species: 3, Hits: 22 | NA | Quadrats NA |
| 25 | c->b | 2025 | Species: 3, Hits: 11 | 40 | Quadrats 10 |
| 26 | a->d | 2024 | Species: 2, Hits: 51 | NA | Quadrats NA |
| 26 | a->d | 2025 | Species: 2, Hits: 52 | 40 | Quadrats 10 |
| 26 | c->b | 2024 | Species: 2, Hits: 49 | NA | Quadrats NA |
| 26 | c->b | 2025 | Species: 2, Hits: 55 | 40 | Quadrats 10 |
| 27 | a->d | 2024 | Species: 3, Hits: 36 | NA | Quadrats NA |
| 27 | a->d | 2025 | Species: 2, Hits: 45 | 40 | Quadrats 10 |
| 27 | c->b | 2024 | Species: 3, Hits: 63 | NA | Quadrats NA |
| 27 | c->b | 2025 | Species: 3, Hits: 62 | 40 | Quadrats 10 |
| 28 | b->c | 2024 | Species: 1, Hits: 22 | NA | Quadrats NA |
| 28 | b->c | 2025 | Species: 1, Hits: 9 | 40 | Quadrats 10 |
| 28 | d->a | 2024 | Species: 2, Hits: 20 | NA | Quadrats NA |
| 28 | d->a | 2025 | Species: 1, Hits: 48 | 40 | Quadrats 10 |
| 29 | b->c | 2024 | Species: 1, Hits: 10 | NA | Quadrats NA |
| 29 | b->c | 2025 | Species: 1, Hits: 32 | 40 | Quadrats 10 |
| 29 | d->a | 2024 | Species: 1, Hits: 4 | NA | Quadrats NA |
| 29 | d->a | 2025 | Species: 1, Hits: 12 | 40 | Quadrats 10 |
| 3 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 3 | a->b | 2025 | Species: 5, Hits: 53 | 40 | Quadrats 10 |
| 3 | a->d | 2024 | Species: 3, Hits: 26 | NA | Quadrats NA |
| 3 | c->b | 2024 | Species: 2, Hits: 15 | NA | Quadrats NA |
| 3 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 3 | c->d | 2025 | Species: 4, Hits: 25 | 40 | Quadrats 10 |
| 31 | a->d | 2024 | Species: 3, Hits: 21 | NA | Quadrats NA |
| 31 | a->d | 2025 | Species: 4, Hits: 41 | 40 | Quadrats 10 |
| 31 | c->b | 2024 | Species: 3, Hits: 16 | NA | Quadrats NA |
| 31 | c->b | 2025 | Species: 2, Hits: 31 | 40 | Quadrats 10 |
| 32 | b->a | 2024 | Species: 5, Hits: 44 | NA | Quadrats NA |
| 32 | b->a | 2025 | Species: 4, Hits: 56 | 40 | Quadrats 10 |
| 32 | d->c | 2024 | Species: 3, Hits: 17 | NA | Quadrats NA |
| 32 | d->c | 2025 | Species: 4, Hits: 20 | 40 | Quadrats 10 |
| 33 | a->d | 2024 | Species: 3, Hits: 12 | 40 | Quadrats 10 |
| 33 | c->b | 2024 | Species: 4, Hits: 15 | 40 | Quadrats 10 |
| 34 | b->c | 2024 | Species: 1, Hits: 14 | 40 | Quadrats 10 |
| 34 | d->a | 2024 | Species: 2, Hits: 27 | 40 | Quadrats 10 |
| 35 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 35 | a->b | 2025 | Species: 2, Hits: 7 | 40 | Quadrats 10 |
| 35 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 35 | c->d | 2025 | Species: 2, Hits: 8 | 40 | Quadrats 10 |
| 36 | c->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 36 | c->b | 2025 | Species: 1, Hits: 3 | 40 | Quadrats 10 |
| 36 | d->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 36 | d->a | 2025 | Species: 1, Hits: 9 | 40 | Quadrats 10 |
| 37 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 37 | a->b | 2025 | Species: 3, Hits: 13 | 40 | Quadrats 10 |
| 37 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 37 | c->d | 2025 | Species: 3, Hits: 34 | 40 | Quadrats 10 |
| 38 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 38 | a->b | 2025 | Species: 1, Hits: 10 | 40 | Quadrats 10 |
| 38 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 38 | c->d | 2025 | Species: 1, Hits: 4 | 40 | Quadrats 10 |
| 39 | a->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 39 | a->d | 2025 | Species: 2, Hits: 48 | 40 | Quadrats 10 |
| 39 | c->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 39 | c->b | 2025 | Species: 1, Hits: 5 | 40 | Quadrats 10 |
| 4 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 4 | a->b | 2025 | Species: 3, Hits: 16 | 40 | Quadrats 10 |
| 4 | a->d | 2024 | Species: 1, Hits: 1 | NA | Quadrats NA |
| 4 | b->a | 2024 | Species: 3, Hits: 20 | NA | Quadrats NA |
| 4 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 4 | c->d | 2025 | Species: 2, Hits: 14 | 40 | Quadrats 10 |
| 4 | d->c | 2024 | Species: 1, Hits: 7 | NA | Quadrats NA |
| 40 | b->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 40 | b->a | 2025 | Species: 1, Hits: 5 | 40 | Quadrats 10 |
| 40 | d->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 40 | d->c | 2025 | Species: 1, Hits: 3 | 40 | Quadrats 10 |
| 41 | b->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 41 | b->a | 2025 | Species: 2, Hits: 18 | 40 | Quadrats 10 |
| 41 | d->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 41 | d->c | 2025 | Species: 2, Hits: 5 | 40 | Quadrats 10 |
| 42 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 42 | a->b | 2025 | Species: 2, Hits: 13 | 40 | Quadrats 10 |
| 42 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 42 | c->d | 2025 | Species: 1, Hits: 10 | 40 | Quadrats 10 |
| 43 | b->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 43 | b->c | 2025 | Species: 2, Hits: 9 | 40 | Quadrats 10 |
| 43 | d->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 43 | d->a | 2025 | Species: 3, Hits: 10 | 40 | Quadrats 10 |
| 44 | b->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 44 | b->c | 2025 | Species: 2, Hits: 26 | 40 | Quadrats 10 |
| 44 | d->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 44 | d->a | 2025 | Species: 4, Hits: 46 | 40 | Quadrats 10 |
| 45 | a->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 45 | a->d | 2025 | Species: 3, Hits: 32 | 40 | Quadrats 10 |
| 45 | c->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 45 | c->b | 2025 | Species: 2, Hits: 19 | 40 | Quadrats 10 |
| 46 | a->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 46 | a->d | 2025 | Species: 1, Hits: 40 | 40 | Quadrats 10 |
| 46 | b->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 46 | c->b | 2025 | Species: 2, Hits: 19 | 40 | Quadrats 10 |
| 47 | b->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 47 | b->c | 2025 | Species: 3, Hits: 44 | 40 | Quadrats 10 |
| 47 | d->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 47 | d->a | 2025 | Species: 5, Hits: 26 | 40 | Quadrats 10 |
| 5 | a->b | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 5 | a->b | 2025 | Species: 3, Hits: 21 | 40 | Quadrats 10 |
| 5 | b->c | 2024 | Species: 3, Hits: 23 | NA | Quadrats NA |
| 5 | c->d | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 5 | c->d | 2025 | Species: 3, Hits: 26 | 40 | Quadrats 10 |
| 5 | d->a | 2024 | Species: 3, Hits: 43 | NA | Quadrats NA |
| 6 | a->b | 2024 | Species: 3, Hits: 56 | NA | Quadrats NA |
| 6 | b->a | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 6 | b->a | 2025 | Species: 4, Hits: 26 | 40 | Quadrats 10 |
| 6 | c->d | 2024 | Species: 4, Hits: 53 | NA | Quadrats NA |
| 6 | d->c | 2023 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 6 | d->c | 2025 | Species: 2, Hits: 22 | 40 | Quadrats 10 |
| 7 | a->b | 2024 | Species: 2, Hits: 4 | 40 | Quadrats 10 |
| 7 | a->b | 2025 | Species: 1, Hits: 29 | 40 | Quadrats NA |
| 7 | d->c | 2024 | Species: 3, Hits: 19 | 40 | Quadrats 10 |
| 7 | d->c | 2025 | Species: 3, Hits: 37 | 40 | Quadrats NA |
| 8 | a->b | 2024 | Species: 1, Hits: 4 | NA | Quadrats NA |
| 8 | b->a | 2024 | Species: NA, Hits: NA | 40 | Quadrats 10 |
| 8 | c->d | 2024 | Species: 2, Hits: 3 | 40 | Quadrats 10 |
| 9 | c->b | 2024 | Species: 2, Hits: 9 | NA | Quadrats NA |
| 9 | c->b | 2025 | Species: 3, Hits: 22 | 40 | Quadrats 10 |
| 9 | d->a | 2024 | Species: 1, Hits: 30 | NA | Quadrats NA |
| 9 | d->a | 2025 | Species: 2, Hits: 35 | 40 | Quadrats 10 |
[Reserved space]
ATTENTION: Please note all Robel observations had been measured in centimeters, not decimeters.
| VOR | n |
|---|---|
| 0 | 1103 |
| 10 | 1260 |
| 20 | 937 |
| 30 | 607 |
| 40 | 209 |
| 50 | 109 |
| 60 | 66 |
| 70 | 52 |
| 80 | 27 |
| 90 | 16 |
| 100 | 5 |
| 110 | 4 |
| 120 | 1 |
| 130 | 2 |
| 140 | 1 |
| 150 | 1 |
| year | date | pasture | plot | transect | quadrat | VOR |
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Duplicates |
|---|
| 0 |
## # A tibble: 0 × 5
## # ℹ 5 variables: date <dttm>, plot <dbl>, transect <chr>, quadrat <dbl>,
## # VOR <dbl>| year | plot | transect | n |
|---|---|---|---|
| 2024 | 1 | a->b | 1 |
| 2024 | 1 | c->d | 7 |
| 2024 | 2 | a->d | 30 |
| 2024 | 2 | c->b | 44 |
| 2024 | 3 | a->d | 26 |
| 2024 | 3 | c->b | 15 |
| 2024 | 4 | a->d | 1 |
| 2024 | 4 | b->a | 20 |
| 2024 | 4 | d->c | 7 |
| 2024 | 5 | b->c | 23 |
| 2024 | 5 | d->a | 43 |
| 2024 | 6 | a->b | 56 |
| 2024 | 6 | c->d | 53 |
| 2024 | 7 | a->b | 4 |
| 2024 | 7 | d->c | 19 |
| 2024 | 8 | a->b | 4 |
| 2024 | 8 | c->d | 3 |
| 2024 | 9 | c->b | 9 |
| 2024 | 9 | d->a | 30 |
| 2024 | 10 | b->a | 36 |
| 2024 | 10 | c->d | 52 |
| 2024 | 12 | a->d | 23 |
| 2024 | 12 | b->c | 4 |
| 2024 | 13 | a->d | 31 |
| 2024 | 13 | b->c | 23 |
| 2024 | 14 | a->d | 36 |
| 2024 | 14 | b->c | 39 |
| 2024 | 15 | a->d | 63 |
| 2024 | 15 | b->c | 50 |
| 2024 | 16 | b->a | 53 |
| 2024 | 16 | c->d | 37 |
| 2024 | 17 | c->b | 26 |
| 2024 | 17 | d->a | 30 |
| 2024 | 18 | c->b | 46 |
| 2024 | 18 | d->a | 23 |
| 2024 | 19 | c->b | 68 |
| 2024 | 19 | d->a | 55 |
| 2024 | 21 | b->a | 41 |
| 2024 | 21 | d->c | 10 |
| 2024 | 22 | b->c | 28 |
| 2024 | 22 | d->a | 8 |
| 2024 | 23 | c->b | 37 |
| 2024 | 23 | d->a | 49 |
| 2024 | 25 | a->d | 37 |
| 2024 | 25 | c->b | 22 |
| 2024 | 26 | a->d | 51 |
| 2024 | 26 | c->b | 49 |
| 2024 | 27 | a->d | 36 |
| 2024 | 27 | c->b | 63 |
| 2024 | 28 | b->c | 22 |
| 2024 | 28 | d->a | 20 |
| 2024 | 29 | b->c | 10 |
| 2024 | 29 | d->a | 4 |
| 2024 | 31 | a->d | 21 |
| 2024 | 31 | c->b | 16 |
| 2024 | 32 | b->a | 44 |
| 2024 | 32 | d->c | 17 |
| 2024 | 33 | a->d | 12 |
| 2024 | 33 | c->b | 15 |
| 2024 | 34 | b->c | 14 |
| 2024 | 34 | d->a | 27 |
| 2025 | 1 | b->c | 3 |
| 2025 | 1 | d->a | 2 |
| 2025 | 2 | a->b | 22 |
| 2025 | 2 | c->d | 21 |
| 2025 | 3 | a->b | 53 |
| 2025 | 3 | c->d | 25 |
| 2025 | 4 | a->b | 16 |
| 2025 | 4 | c->d | 14 |
| 2025 | 5 | a->b | 21 |
| 2025 | 5 | c->d | 26 |
| 2025 | 6 | b->a | 26 |
| 2025 | 6 | d->c | 22 |
| 2025 | 7 | a->b | 29 |
| 2025 | 7 | d->c | 37 |
| 2025 | 9 | c->b | 22 |
| 2025 | 9 | d->a | 35 |
| 2025 | 10 | b->a | 35 |
| 2025 | 10 | c->d | 57 |
| 2025 | 23 | b->c | 42 |
| 2025 | 23 | d->a | 60 |
| 2025 | 25 | a->d | 28 |
| 2025 | 25 | c->b | 11 |
| 2025 | 26 | a->d | 52 |
| 2025 | 26 | c->b | 55 |
| 2025 | 27 | a->d | 45 |
| 2025 | 27 | c->b | 62 |
| 2025 | 28 | b->c | 9 |
| 2025 | 28 | d->a | 48 |
| 2025 | 29 | b->c | 32 |
| 2025 | 29 | d->a | 12 |
| 2025 | 31 | a->d | 41 |
| 2025 | 31 | c->b | 31 |
| 2025 | 32 | b->a | 56 |
| 2025 | 32 | d->c | 20 |
| 2025 | 35 | a->b | 7 |
| 2025 | 35 | c->d | 8 |
| 2025 | 36 | c->b | 3 |
| 2025 | 36 | d->a | 9 |
| 2025 | 37 | a->b | 13 |
| 2025 | 37 | c->d | 34 |
| 2025 | 38 | a->b | 10 |
| 2025 | 38 | c->d | 4 |
| 2025 | 39 | a->d | 48 |
| 2025 | 39 | c->b | 5 |
| 2025 | 40 | b->a | 5 |
| 2025 | 40 | d->c | 3 |
| 2025 | 41 | b->a | 18 |
| 2025 | 41 | d->c | 5 |
| 2025 | 42 | a->b | 13 |
| 2025 | 42 | c->d | 10 |
| 2025 | 43 | b->c | 9 |
| 2025 | 43 | d->a | 10 |
| 2025 | 44 | b->c | 26 |
| 2025 | 44 | d->a | 46 |
| 2025 | 45 | a->d | 32 |
| 2025 | 45 | c->b | 19 |
| 2025 | 46 | a->d | 40 |
| 2025 | 46 | c->b | 19 |
| 2025 | 47 | b->c | 44 |
| 2025 | 47 | d->a | 26 |
| year | date | pasture | plot | transect | code | hit |
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Duplicates |
|---|
| 0 |
| year | date | pasture | plot | transect | code | hit | binomial | common | name on data sheets (Woody transects) |
|---|
| Year | Plot | Transect | Quadrat | Code | Cover | Duplicates |
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| plot | transect | code |
|---|
| cover | n |
|---|---|
| 1 | 756 |
| 3 | 2637 |
| 16 | 2130 |
| 38 | 1113 |
| 63 | 463 |
| 86 | 175 |
| 98 | 40 |
## # A tibble: 1 × 8
## `0` `1` `3` `16` `38` `63` `86` `98`
## <int> <int> <int> <int> <int> <int> <int> <int>
## 1 1472 756 2637 2130 1113 463 175 40| year | pasture | plot | transect | quadrat | code | cover |
|---|
Finalized statment:
The data summarized above have been corrected as needed and prepared for future use. Actions to verify, correct or modify data were conducted by R script manipulations, physical examination of samples, review of excel spreadsheets and inspection of hard copy materials.
Recommended mitigations:
Ongoing work:
• Data wrangling
• Update/modify/clarify species list to ensure accuracy
• Protocol to methods drafting
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
message = FALSE)
pacman::p_load(tidyverse)
library(readxl)
library(dplyr)
library(knitr)
RB <- read_excel("C:/Users/tlittmann/USDA/Rangeland responses to fire - Fire x herbivory/PrescribedBurning/Data/VegComp/PatchBurnTransects.xlsx",
sheet = "VOR")
WT <- read_excel("C:/Users/tlittmann/USDA/Rangeland responses to fire - Fire x herbivory/PrescribedBurning/Data/VegComp/PatchBurnTransects.xlsx",
sheet = "WoodyTransect")
PC <- read_excel(
"C:/Users/tlittmann/USDA/Rangeland responses to fire - Fire x herbivory/PrescribedBurning/Data/VegComp/PatchBurnTransects.xlsx",
sheet = "PlantComp",
col_types = c("numeric", "date", "text", "text", "text", "text", "text", "numeric", "text")
)
SC <- read_excel("C:/Users/tlittmann/USDA/Rangeland responses to fire - Fire x herbivory/PrescribedBurning/Data/VegComp/PatchBurnTransects.xlsx",
sheet = "SpeciesKeys")
data_summary <- data.frame(
`Data Type` = c("Woody Transect", "VOR", "Plant Composition"),
Description = c(
"Measures woody plant cover and structure along transects",
"Visual Obstruction Reading to estimate vegetation density",
"Records species presence and abundance in quadrats"
),
`Expected Target` = c(
"30 transects ('24), 29 transects ('25)",
"19 transects ('23), 8 transects ('24), 29 transects ('25)",
"19 transects ('23), 8 transects ('24), 29 transects ('25)"
)
)
knitr::kable(data_summary, align = "c")
RB_sum <- RB %>%
separate_longer_delim(VOR, ",") %>%
group_by(transect, plot, year) %>%
summarize(VOR = sum(!is.na(VOR)), .groups = "drop")
WT_sum <- WT %>%
separate_longer_delim(hit, ',') %>%
mutate(WT = paste0("Species ", code, ", Hit ", hit)) %>%
group_by(transect, plot, year) %>%
summarize(
hit = n(),
species = n_distinct(code),
.groups = 'drop'
)
PC_sum <- PC %>%
mutate(quadrat = as.character(quadrat)) %>%
group_by(transect, plot, year) %>%
summarise(n_quadrats = n_distinct(quadrat), .groups = "drop")
WT_sum <- WT_sum %>%
mutate(
plot = as.character(plot),
transect = as.character(transect),
year = as.character(year)
)
RB_sum <- RB_sum %>%
mutate(
plot = as.character(plot),
transect = as.character(transect),
year = as.character(year)
)
PC_sum <- PC_sum %>%
mutate(
plot = as.character(plot),
transect = as.character(transect),
year = as.character(year)
)
summary_table <- WT_sum %>%
full_join(RB_sum, by = c("plot", "transect", "year")) %>%
full_join(PC_sum, by = c("plot", "transect", "year")) %>%
arrange(plot, transect, year)
summary_table_clean <- summary_table %>%
rename(Hits = hit,
Species = species,
Composition = n_quadrats
)
summary_table_clean %>%
mutate(
Woody = paste0("Species: ", Species, ", Hits: ", Hits),
Composition = paste0("Quadrats ", Composition)
) %>%
select(plot, transect, year, Woody, VOR, Composition) %>%
arrange(plot, transect, year) %>%
kable(
caption = "Transect Summary Table",
align = "c")
RB %>%
separate_longer_delim(VOR, ",") %>%
mutate(VOR = as.numeric(VOR)) %>% # optional: ensure numeric
group_by(VOR) %>%
summarise(n = n(), .groups = "drop") %>%
arrange(VOR) %>%
knitr::kable(
caption = "Robel Value (cm) Counts",
align = "c"
)
missing_RB <- RB %>%
summarise(across(everything(), ~sum(is.na(.)), .names = "{col}"))
missing_RB %>%
kable(caption = "Missing Values in Woody Debris Dataset")
duplicates <- RB %>%
group_by(plot, transect, year, quadrat, VOR) %>%
filter(n() > 1) %>%
ungroup() %>%
summarise(Duplicates = n())
kable(duplicates, caption = "Total Number of Duplicate Entries")
valid_values <- seq(0, 180, by = 10)
RB %>%
separate_longer_delim(VOR, ",") %>%
mutate(VOR = as.numeric(VOR)) %>%
filter(!VOR %in% valid_values) %>%
select(date, plot, transect, quadrat, VOR)
WT %>%
separate_longer_delim(hit, ",") %>%
mutate(hit = as.character(hit)) %>%
group_by(year, plot, transect) %>%
summarise(n = n(), .groups = "drop") %>%
arrange(year, plot, transect) %>%
knitr::kable(
caption = "Counts of Range Hits",
align = "c"
)
missing_WT <- WT %>%
summarise(across(everything(), ~sum(is.na(.)), .names = "{col}"))
missing_WT %>%
kable(caption = "Woody Transect Missing Values")
duplicates <- WT %>%
group_by(plot, transect, year, code, hit) %>%
filter(n() > 1) %>%
ungroup() %>%
summarise(Duplicates = n())
kable(duplicates, caption = "Woody Transect Duplicate Entries")
WT_format <- WT %>%
separate_longer_delim(hit, ',') %>%
filter(!grepl("^\\s*\\d+(\\.\\d+)?-\\d+(\\.\\d+)?\\s*$", hit))%>%
select(plot, transect, year, hit, code)
Species_WT <- WT %>%
left_join(by = 'code',
SC %>% select(code, binomial)) %>%
filter(!complete.cases(.)) %>%
group_by( plot, transect, year, code) %>%
summarize(instances = n() ,
.groups = 'drop') %>%
pivot_wider(names_from = year,
values_from = instances)
unmatched <- WT %>%
left_join(SC, by = "code") %>%
filter(is.na(binomial))
knitr::kable(unmatched, caption = "Unmatched Codes", align = "c")
PC %>%
mutate(
year = as.character(year),
plot = as.character(plot),
transect = as.character(transect),
quadrat = as.character(quadrat),
code = as.character(code),
cover = as.character(cover)
) %>%
select(year, plot, transect, quadrat, code, cover) %>%
summarise(
Year = sum(is.na(year)),
Plot = sum(is.na(plot)),
Transect = sum(is.na(transect)),
Quadrat = sum(is.na(quadrat)),
Code = sum(is.na(code)),
Cover = sum(is.na(cover)),
Duplicates = n() - n_distinct(year, plot, transect, quadrat, code, cover)
) %>%
kable(caption = "Missing Values and Duplicate Count")
# Define codes to exclude
excluded_codes <- c("BARE", "CRUST", "Litter1", "Litter2")
excluded_columns <- c("observer")
# Filter out excluded codes before processing
Species_PC <- PC %>%
select(-all_of(excluded_columns)) %>%
filter(!code %in% excluded_codes) %>%
left_join(SC %>% select(code, binomial), by = "code") %>%
filter(!complete.cases(.)) %>%
group_by(plot, transect, code, year) %>%
summarize(instances = n(), .groups = "drop") %>%
pivot_wider(names_from = year, values_from = instances)
# Filter unmatched species, excluding specified codes
unmatched <- PC %>%
filter(!code %in% excluded_codes) %>%
left_join(SC, by = "code") %>%
filter(is.na(binomial))
# Display the table
kable(Species_PC, caption = "Unmatched Species Instances")
Species_PC <- PC %>%
left_join(by = 'code',
SC %>% select(code, binomial)) %>%
filter(!complete.cases(.)) %>%
group_by( plot, transect, year, code) %>%
summarize(instances = n() ,
.groups = 'drop') %>%
pivot_wider(names_from = year,
values_from = instances)
unmatched_PC <- PC %>%
left_join(SC, by = "code") %>%
select(plot, transect, year, code)%>%
group_by(plot, transect, year, code)%>%
filter(is.na(binomial))
PC %>%
filter(!is.na(cover) & cover != 0) %>%
count(cover) %>%
arrange(cover) %>%
knitr::kable(
caption = "Counts of Daubenmire Cover Classes",
align = "c"
)
PC %>%
group_by(cover) %>%
summarize(count = n() ) %>%
pivot_wider(names_from = cover,
values_from = count)
expected_values <- c(0, 1, 3, 16, 38, 63, 86, 98, 100)
unexpected_cover <- PC %>%
filter(!cover %in% expected_values)%>%
select(-date, -observer)
knitr::kable(unexpected_cover, caption = "Unexpected Cover Values", align = "c")