Bat survey in Plumas National Forest through the use of acoustic bat detectors
Introduction
To study bat occupancy in the Plumas National Forest by surveying acustically different areas of the forest, the three objective species for this survey are the Pallid Bat, the Townsend’s Long-eared Bat, and the California Bat. Nevertheless, there is at least 14 species that form the bat ensemble in the National Forest, the list of species is the following
- Tadarida brasiliensis – free-tailed Bat
- Antrozous pallidus - Pallid Bat
- Eptesicus fuscus - Big Brown Bat
- Euderma maculatum - Spotted Bat
- Lasionycteris noctivagans - Silvered-haired Bat
- Lasiurus blossevillii - Western Red Bat
- Lasiurus cinereus - Hoary Bat
- Corynorhinus townsendii - Townsend’s Long-eared Bat
- Myotis californicus - California Bat
- Myotis evotis - Long-eared Bat
- Myotis lucifugus - Little Brown Bat
- Myotis thysanodes - Fringed Bat
- Myotis volans - Hairy-winged bat
- Myotis yumanensis - Yuma myotis
Objective of the study
To determine the factors that influence bat occupancy in heterogeneous environments of Plumas National Forest, including areas corresponding to the Moonlight Fire and the Storrie Fire. Comparing and complementing biotic and abiotic variables.
Specific research questions and the factors that might influence them
1. Which factors affect bat occupancy in burned forest?
Does bat occupancy differ with the duration of time since the fire?
- Explanatory variable = burn age at sampling sites (mean number of years between fires for a given point and the time till the last fire for a given point).
Does bat occupancy differ with burn intensity
- Explanatory variable = burn intensity (in the following figure we see the layers that will allow us to work with burn intensity, this layers are burn intensity of the soil, burn intensity of the canopy, and Burn intensity of the basal area)
Does bat occupancy differ with forest type
- Explanatory variable = Stand type
- Explanatory variable = Forest type (type of forest typified in Vegetation existing
- Abiotic variables (Altitud)
- Explanatory variable = Historic burn age
Does bat occupancy differ with roost site availability?
- Explanatory variable = Associated forest metrics (Lidar???, distance to water)
Concidering that we will work using lidar images, the layers to use will be acquired later, since most of this variables are only presented for del black polygon in the next figure.
## Warning in polypath(x = mcrds[, 1], y = mcrds[, 2], border = border, col =
## col, : "legend" is not a graphical parameter
Which factors affect bat occupancy in unburned forest?
- explanatory vstance to road, distance to water)
Is occupancy affected by presence of heterospecifics?
is bat occupancy affected by presence of other bats? (competition)
- Explanatory variable, occupancy of other bats (based on this study)
is bat occupancy affected by the presence of preys?
- Explanatory variable, athropod emergence (Loren’s Study), athropod abundace (Our study)
Sampling desing and sampling unit
In order for us to study bat occupancy and to spatially predict it using the factors described in the previous section, most of the diversity of the forest has to be included in the model. To include that variability, I classified the environments using the following layers (Topography, Intervals between fires, Forest Type, Distance to roads, Nesting Habitat quality for Spotted owl, foraging Habitat quality for Spotted owl, Habitat Quality for Marten) [to be included tomorrow, distance to rivers, and distance to roads/path]
Check for correlation between rasters
First will scale every layer so that it goes from 0 to 1, in order for no layer to have more weight in the classification. And then we check the correlation between rasters. in the next graph/table, we see the relationship between our predictive variables. Here we see that we might want to take one of the two habitat quality layers for the Spotted Owl (R=0.74), and since we are using the spoted owl as a potential predator, we will keep the foraging habitat quality.
## Loading required package: sp slopeMin. 0.000000 1st Qu. 2.873498 Median 5.359555 3rd Qu. 8.648955 Max. 28.828351 NA’s 35684.000000 
Clasification
Now we will use kmeans to sort the area into 5 types of habitat using the abovementioned rasterstack, and it will be ploted with different colors for every type of environment.
More info on how to do this clasification in https://geoscripting-wur.github.io/AdvancedRasterAnalysis/
separate layers acording to places with or without fire
Now we will separate the whole area in two subtypes burned areas and non-buned areas, based on the burn severity layers
Extract Random points from each habitat type with equal number in fire and non fire
During the first year of sampling 120 samples will be colected, 60 in burned areas, and 60 in non-burned areas, within each, 12 random points will be sampled in each habtitat type defined by the K-means classification.
- 60 random points in not Fire, 12 random points per each environment type
- 60 random points in Fire, 12 random points per each environment type
- We plot the chosen random points over the topographic map, each color is an environment type, full circles are places where there was no fire, and full triangles, where there was a fire
## rgdal: version: 0.9-2, (SVN revision 526)
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## Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
## Path to PROJ.4 shared files: C:/Users/usuario/Documents/R/win-library/3.2/rgdal/proj## Loading required package: lattice
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Values
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## Attaching package: 'dplyr'
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## The following objects are masked from 'package:raster':
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## intersect, select, union
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## The following object is masked from 'package:stats':
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## intersect, setdiff, setequal, union| ID | Height | Fire Interval | Distance to Road | Sage Stage | Distance to Water |
|---|---|---|---|---|---|
| 1nf | 1143.428 | 18.92636 | 70.70175 | 14.687060 | 0.00000 |
| 1f | 1066.892 | 16.14099 | 381.48419 | 14.475234 | 0.00000 |
| 2nf | 1798.345 | 14.70257 | 135.51784 | 4.956801 | 0.00000 |
| 2f | 1733.110 | 16.35523 | 234.08921 | 5.477256 | 0.00000 |
| 3nf | 1741.085 | 15.92650 | 277.42844 | 6.558292 | 342.73342 |
| 3f | 1665.289 | 15.78163 | 305.06839 | 4.767730 | 341.64344 |
| 4nf | 1777.849 | 24.37396 | 304.86406 | 16.390226 | 62.43815 |
| 4f | 1673.955 | 13.77037 | 567.23379 | 14.868093 | 27.02978 |
| 5nf | 1092.211 | 14.48702 | 189.65352 | 5.285532 | 0.00000 |
| 5f | 1169.350 | 13.36700 | 315.70665 | 5.507521 | 0.00000 |
Simulated sampling Dynamic modeling
First we simulate our detection history for our 120 sites with four primary sampling periods, and three secondary sampling periods each.
## nPars AIC delta AICwt cumltvWt
## Model1 2 481.49 0.00 0.99931 1.00
## Model3 7 496.92 15.43 0.00045 1.00
## Model2 8 498.12 16.62 0.00025 1.00
| Abundance estimation | Estimation High | Estimation Low |
|---|---|---|
| 19720.2 | 24518.73 | 14501.36 |
APENDIX
Apendix 1
| Long | Lat | Height | Fire Interval | Distance to Road | Vegetation Type | Distance to Water | ID |
|---|---|---|---|---|---|---|---|
| 120.91 | 39.98 | 1145.27 | 15.81 | 424.21 | 12.41 | 0.00 | 1nf |
| 121.09 | 40.02 | 983.53 | 11.68 | 0.00 | 10.22 | 0.00 | 1nf |
| 120.87 | 39.96 | 1237.23 | 25.68 | 0.00 | 12.37 | 0.00 | 1nf |
| 121.00 | 39.94 | 1279.09 | 21.48 | 0.00 | 11.73 | 0.00 | 1nf |
| 120.80 | 39.87 | 1424.25 | 15.16 | 424.21 | 15.47 | 0.00 | 1nf |
| 121.38 | 39.75 | 797.21 | 21.40 | 0.00 | 18.00 | 0.00 | 1nf |
| 120.82 | 40.08 | 1241.51 | 19.82 | 0.00 | 14.47 | 0.00 | 1nf |
| 120.98 | 40.04 | 1010.16 | 26.50 | 0.00 | 18.00 | 0.00 | 1nf |
| 121.40 | 39.67 | 757.34 | 17.29 | 0.00 | 10.82 | 0.00 | 1nf |
| 120.64 | 40.10 | 1411.36 | 16.00 | 0.00 | 18.00 | 0.00 | 1nf |
| 120.55 | 39.73 | 1428.79 | 12.89 | 0.00 | 16.76 | 0.00 | 1nf |
| 120.99 | 40.04 | 1005.39 | 23.42 | 0.00 | 18.00 | 0.00 | 1nf |
| 120.80 | 40.18 | 1280.15 | 16.42 | 0.00 | 17.61 | 0.00 | 1f |
| 121.25 | 39.72 | 731.23 | 16.00 | 0.00 | 12.28 | 0.00 | 1f |
| 121.25 | 39.66 | 946.98 | 14.42 | 848.42 | 13.27 | 0.00 | 1f |
| 121.21 | 40.07 | 1098.96 | 19.60 | 1428.69 | 14.79 | 0.00 | 1f |
| 121.22 | 40.11 | 1302.51 | 16.00 | 324.43 | 18.00 | 0.00 | 1f |
| 120.84 | 40.19 | 1410.15 | 17.48 | 908.19 | 10.71 | 0.00 | 1f |
| 121.22 | 40.11 | 1243.52 | 15.68 | 0.00 | 17.04 | 0.00 | 1f |
| 121.42 | 39.83 | 584.98 | 16.00 | 0.00 | 10.43 | 0.00 | 1f |
| 120.79 | 40.20 | 1393.51 | 16.00 | 533.80 | 18.00 | 0.00 | 1f |
| 120.76 | 40.21 | 1335.86 | 14.71 | 0.00 | 14.14 | 0.00 | 1f |
| 121.11 | 40.04 | 1039.99 | 20.12 | 534.28 | 18.00 | 0.00 | 1f |
| 121.47 | 39.73 | 434.85 | 11.25 | 0.00 | 9.42 | 0.00 | 1f |
| 120.71 | 39.93 | 2069.65 | 7.16 | 0.00 | 6.93 | 0.00 | 2nf |
| 120.60 | 39.85 | 2004.34 | 16.00 | 0.00 | 4.29 | 0.00 | 2nf |
| 121.13 | 39.87 | 1790.30 | 16.00 | 325.57 | 7.00 | 0.00 | 2nf |
| 121.02 | 39.67 | 1580.89 | 16.00 | 0.00 | 6.93 | 0.00 | 2nf |
| 120.39 | 40.05 | 1911.05 | 16.00 | 324.74 | 3.00 | 0.00 | 2nf |
| 121.04 | 39.75 | 1771.12 | 16.00 | 0.00 | 6.54 | 0.00 | 2nf |
| 121.03 | 40.14 | 1678.18 | 15.74 | 0.00 | 3.00 | 0.00 | 2nf |
| 120.62 | 39.94 | 1859.34 | 14.79 | 325.26 | 3.00 | 0.00 | 2nf |
| 120.60 | 40.23 | 1734.92 | 16.00 | 0.00 | 3.00 | 0.00 | 2nf |
| 121.01 | 40.08 | 1725.45 | 15.74 | 0.00 | 5.79 | 0.00 | 2nf |
| 120.68 | 39.77 | 1548.83 | 11.00 | 0.00 | 3.00 | 0.00 | 2nf |
| 121.20 | 39.93 | 1906.07 | 16.00 | 650.64 | 7.00 | 0.00 | 2nf |
| 121.14 | 40.15 | 1530.05 | 12.56 | 324.27 | 7.68 | 0.00 | 2f |
| 120.72 | 40.23 | 1635.56 | 12.30 | 0.00 | 3.00 | 0.00 | 2f |
| 120.64 | 40.16 | 1739.43 | 16.00 | 533.90 | 3.53 | 0.00 | 2f |
| 120.26 | 40.10 | 1908.27 | 14.97 | 424.21 | 5.67 | 0.00 | 2f |
| 120.84 | 40.21 | 1643.31 | 11.93 | 0.00 | 5.80 | 0.00 | 2f |
| 120.79 | 39.85 | 1594.26 | 14.38 | 0.00 | 2.77 | 0.00 | 2f |
| 120.26 | 39.85 | 1773.72 | 23.05 | 0.00 | 9.02 | 0.00 | 2f |
| 121.22 | 39.92 | 1863.16 | 16.00 | 776.73 | 7.00 | 0.00 | 2f |
| 120.29 | 40.12 | 1903.38 | 26.00 | 424.21 | 2.00 | 0.00 | 2f |
| 121.11 | 39.83 | 1725.65 | 24.36 | 325.75 | 5.66 | 0.00 | 2f |
| 120.61 | 40.09 | 1870.59 | 12.13 | 0.00 | 6.38 | 0.00 | 2f |
| 121.29 | 39.86 | 1609.96 | 12.58 | 0.00 | 7.21 | 0.00 | 2f |
| 120.63 | 40.07 | 1605.53 | 18.51 | 324.63 | 12.26 | 324.63 | 3nf |
| 121.00 | 40.09 | 1706.44 | 11.00 | 1068.23 | 3.00 | 324.52 | 3nf |
| 120.42 | 39.87 | 1798.85 | 12.92 | 325.59 | 9.18 | 325.59 | 3nf |
| 120.61 | 40.00 | 2196.98 | 25.10 | 0.00 | 9.45 | 324.97 | 3nf |
| 121.01 | 39.86 | 1747.54 | 16.00 | 0.00 | 7.00 | 325.64 | 3nf |
| 121.13 | 40.00 | 1625.41 | 16.00 | 424.21 | 6.68 | 324.96 | 3nf |
| 121.25 | 39.88 | 1504.05 | 14.05 | 325.52 | 3.00 | 325.52 | 3nf |
| 120.64 | 39.92 | 2027.00 | 16.00 | 0.00 | 3.06 | 534.61 | 3nf |
| 120.81 | 39.98 | 2005.92 | 13.70 | 534.43 | 5.16 | 325.05 | 3nf |
| 121.04 | 40.00 | 1386.61 | 15.96 | 0.00 | 7.14 | 324.96 | 3nf |
| 120.93 | 39.67 | 1575.38 | 15.33 | 326.53 | 6.04 | 326.53 | 3nf |
| 121.14 | 39.82 | 1713.31 | 16.55 | 0.00 | 6.73 | 325.83 | 3nf |
| 121.10 | 40.11 | 1269.03 | 19.21 | 0.00 | 10.98 | 324.45 | 3f |
| 121.05 | 40.13 | 1858.57 | 11.24 | 0.00 | 3.00 | 324.32 | 3f |
| 121.22 | 39.98 | 1938.91 | 18.01 | 1068.89 | 5.99 | 325.07 | 3f |
| 121.39 | 39.82 | 1249.11 | 11.00 | 0.00 | 3.08 | 325.81 | 3f |
| 121.21 | 39.98 | 1919.29 | 21.99 | 1292.65 | 4.01 | 325.07 | 3f |
| 120.70 | 39.98 | 1915.46 | 15.85 | 325.07 | 3.03 | 424.21 | 3f |
| 121.34 | 39.93 | 1385.62 | 11.00 | 325.28 | 8.35 | 325.28 | 3f |
| 121.47 | 39.88 | 1256.71 | 11.00 | 0.00 | 4.00 | 325.55 | 3f |
| 121.31 | 39.89 | 1479.87 | 13.17 | 0.00 | 4.51 | 325.46 | 3f |
| 120.31 | 40.10 | 1986.92 | 26.10 | 648.93 | 2.13 | 324.47 | 3f |
| 120.63 | 39.82 | 1790.49 | 16.00 | 0.00 | 5.12 | 325.83 | 3f |
| 120.76 | 40.15 | 1933.48 | 14.80 | 0.00 | 3.00 | 424.21 | 3f |
| 120.49 | 40.10 | 1773.75 | 35.00 | 324.50 | 14.00 | 0.00 | 4nf |
| 120.64 | 40.09 | 1469.14 | 16.00 | 324.52 | 18.00 | 0.00 | 4nf |
| 120.70 | 40.02 | 1577.16 | 15.49 | 0.00 | 15.41 | 0.00 | 4nf |
| 120.42 | 39.96 | 1690.96 | 35.00 | 325.14 | 14.00 | 0.00 | 4nf |
| 120.75 | 39.98 | 2092.74 | 40.00 | 325.05 | 20.00 | 325.05 | 4nf |
| 120.69 | 39.88 | 1510.62 | 13.94 | 424.21 | 10.52 | 0.00 | 4nf |
| 120.47 | 40.02 | 1737.61 | 12.87 | 324.85 | 15.84 | 0.00 | 4nf |
| 120.68 | 40.04 | 1611.01 | 16.00 | 424.21 | 18.00 | 0.00 | 4nf |
| 120.64 | 40.03 | 1529.61 | 16.00 | 534.28 | 18.00 | 0.00 | 4nf |
| 120.63 | 39.99 | 2359.56 | 40.00 | 0.00 | 20.00 | 0.00 | 4nf |
| 120.53 | 39.82 | 1661.37 | 12.19 | 651.61 | 12.91 | 0.00 | 4nf |
| 120.64 | 39.99 | 2320.65 | 40.00 | 0.00 | 20.00 | 424.21 | 4nf |
| 120.84 | 40.20 | 1472.90 | 16.00 | 533.80 | 12.97 | 0.00 | 4f |
| 121.27 | 40.04 | 1500.33 | 11.00 | 2598.20 | 12.00 | 0.00 | 4f |
| 120.36 | 40.13 | 1839.87 | 11.00 | 0.00 | 16.00 | 324.36 | 4f |
| 120.32 | 39.92 | 1902.04 | 20.98 | 325.36 | 15.17 | 0.00 | 4f |
| 120.80 | 40.21 | 1692.60 | 16.00 | 533.77 | 18.00 | 0.00 | 4f |
| 120.61 | 40.17 | 1566.06 | 12.06 | 324.14 | 13.24 | 0.00 | 4f |
| 121.19 | 40.09 | 1501.27 | 16.00 | 775.39 | 18.00 | 0.00 | 4f |
| 120.50 | 40.17 | 1797.37 | 11.00 | 324.16 | 16.00 | 0.00 | 4f |
| 120.81 | 40.23 | 1688.66 | 11.18 | 0.00 | 12.09 | 0.00 | 4f |
| 120.53 | 40.15 | 1786.37 | 11.00 | 533.95 | 16.00 | 0.00 | 4f |
| 120.83 | 40.21 | 1596.95 | 15.20 | 323.96 | 15.59 | 0.00 | 4f |
| 120.57 | 40.10 | 1743.04 | 13.82 | 534.09 | 13.37 | 0.00 | 4f |
| 121.30 | 39.69 | 1080.55 | 11.00 | 0.00 | 4.03 | 0.00 | 5nf |
| 120.93 | 39.71 | 1498.99 | 18.44 | 0.00 | 10.10 | 0.00 | 5nf |
| 121.10 | 39.56 | 1112.92 | 11.00 | 0.00 | 3.00 | 0.00 | 5nf |
| 121.33 | 39.69 | 1048.93 | 12.70 | 0.00 | 6.91 | 0.00 | 5nf |
| 121.09 | 39.58 | 1184.67 | 11.00 | 0.00 | 3.00 | 0.00 | 5nf |
| 121.12 | 39.61 | 1190.37 | 11.44 | 326.84 | 4.33 | 0.00 | 5nf |
| 121.04 | 39.56 | 978.13 | 14.24 | 327.07 | 4.29 | 0.00 | 5nf |
| 120.97 | 39.62 | 1471.53 | 12.09 | 0.00 | 3.87 | 0.00 | 5nf |
| 121.32 | 39.69 | 1074.36 | 16.00 | 0.00 | 8.59 | 0.00 | 5nf |
| 121.32 | 39.73 | 836.37 | 16.00 | 326.22 | 6.30 | 0.00 | 5nf |
| 121.41 | 39.69 | 411.05 | 28.93 | 1295.72 | 6.00 | 0.00 | 5nf |
| 121.10 | 39.62 | 1218.67 | 11.00 | 0.00 | 3.00 | 0.00 | 5nf |
| 121.38 | 39.81 | 1330.66 | 13.70 | 424.21 | 6.96 | 0.00 | 5f |
| 121.20 | 39.77 | 1376.71 | 11.00 | 326.06 | 3.00 | 0.00 | 5f |
| 121.33 | 39.64 | 817.93 | 11.35 | 778.97 | 9.58 | 0.00 | 5f |
| 121.16 | 40.10 | 1423.66 | 11.00 | 324.48 | 2.96 | 0.00 | 5f |
| 121.20 | 40.12 | 1378.12 | 11.61 | 0.00 | 5.78 | 0.00 | 5f |
| 121.41 | 39.86 | 1139.16 | 11.00 | 534.77 | 3.00 | 0.00 | 5f |
| 121.33 | 39.75 | 985.38 | 16.00 | 0.00 | 8.88 | 0.00 | 5f |
| 121.16 | 40.02 | 989.16 | 11.00 | 424.21 | 9.00 | 0.00 | 5f |
| 121.46 | 39.78 | 595.00 | 29.00 | 0.00 | 6.00 | 0.00 | 5f |
| 121.16 | 40.14 | 1364.65 | 11.32 | 324.30 | 3.96 | 0.00 | 5f |
| 121.14 | 40.13 | 1477.32 | 11.00 | 0.00 | 3.00 | 0.00 | 5f |
| 120.99 | 39.84 | 1154.46 | 12.43 | 651.47 | 3.97 | 0.00 | 5f |
| night1 | night2 | night3 | |
|---|---|---|---|
| s1.1nf | 1 | 0 | 1 |
| s2.1nf | 1 | 1 | 1 |
| s3.1nf | 1 | 1 | 1 |
| s4.1nf | 1 | 1 | 1 |
| s5.1nf | 1 | 1 | 1 |
| s6.1nf | 1 | 1 | 0 |
| s7.1nf | 1 | 1 | 1 |
| s8.1nf | 1 | 1 | 1 |
| s9.1nf | 1 | 1 | 0 |
| s10.1nf | 1 | 0 | 1 |
| s11.1nf | 0 | 1 | 1 |
| s12.1nf | 1 | 1 | 1 |
| s1.1f | 0 | 1 | 1 |
| s2.1f | 1 | 0 | 0 |
| s3.1f | 1 | 1 | 1 |
| s4.1f | 1 | 1 | 1 |
| s5.1f | 1 | 1 | 1 |
| s6.1f | 1 | 1 | 1 |
| s7.1f | 1 | 0 | 1 |
| s8.1f | 1 | 1 | 1 |
| s9.1f | 1 | 1 | 0 |
| s10.1f | 0 | 1 | 1 |
| s11.1f | 1 | 1 | 0 |
| s12.1f | 1 | 1 | 1 |
| s1.2nf | 0 | 0 | 1 |
| s2.2nf | 1 | 0 | 0 |
| s3.2nf | 1 | 0 | 1 |
| s4.2nf | 1 | 1 | 1 |
| s5.2nf | 1 | 1 | 1 |
| s6.2nf | 1 | 1 | 1 |
| s7.2nf | 1 | 1 | 0 |
| s8.2nf | 1 | 1 | 1 |
| s9.2nf | 0 | 0 | 1 |
| s10.2nf | 0 | 0 | 1 |
| s11.2nf | 1 | 1 | 0 |
| s12.2nf | 0 | 1 | 1 |
| s1.2f | 1 | 1 | 0 |
| s2.2f | 1 | 1 | 1 |
| s3.2f | 0 | 1 | 1 |
| s4.2f | 0 | 1 | 0 |
| s5.2f | 0 | 1 | 1 |
| s6.2f | 1 | 1 | 1 |
| s7.2f | 1 | 1 | 0 |
| s8.2f | 0 | 0 | 0 |
| s9.2f | 0 | 0 | 1 |
| s10.2f | 1 | 0 | 0 |
| s11.2f | 1 | 0 | 1 |
| s12.2f | 0 | 0 | 0 |
| s1.3nf | 1 | 1 | 0 |
| s2.3nf | 0 | 1 | 0 |
| s3.3nf | 0 | 1 | 0 |
| s4.3nf | 0 | 1 | 1 |
| s5.3nf | 1 | 0 | 1 |
| s6.3nf | 1 | 0 | 1 |
| s7.3nf | 1 | 1 | 1 |
| s8.3nf | 1 | 0 | 0 |
| s9.3nf | 1 | 0 | 1 |
| s10.3nf | 0 | 1 | 1 |
| s11.3nf | 1 | 1 | 0 |
| s12.3nf | 1 | 0 | 1 |
| s1.3f | 1 | 1 | 0 |
| s2.3f | 1 | 1 | 0 |
| s3.3f | 1 | 0 | 0 |
| s4.3f | 0 | 0 | 0 |
| s5.3f | 0 | 0 | 0 |
| s6.3f | 1 | 0 | 1 |
| s7.3f | 1 | 1 | 0 |
| s8.3f | 0 | 0 | 1 |
| s9.3f | 1 | 1 | 0 |
| s10.3f | 0 | 0 | 0 |
| s11.3f | 0 | 0 | 1 |
| s12.3f | 0 | 1 | 1 |
| s1.4nf | 1 | 1 | 0 |
| s2.4nf | 0 | 0 | 0 |
| s3.4nf | 0 | 0 | 0 |
| s4.4nf | 0 | 1 | 0 |
| s5.4nf | 0 | 0 | 1 |
| s6.4nf | 1 | 0 | 1 |
| s7.4nf | 0 | 0 | 0 |
| s8.4nf | 0 | 0 | 0 |
| s9.4nf | 0 | 0 | 1 |
| s10.4nf | 1 | 0 | 0 |
| s11.4nf | 0 | 0 | 0 |
| s12.4nf | 0 | 1 | 1 |
| s1.4f | 0 | 1 | 0 |
| s2.4f | 1 | 1 | 0 |
| s3.4f | 1 | 0 | 0 |
| s4.4f | 0 | 0 | 0 |
| s5.4f | 0 | 0 | 0 |
| s6.4f | 0 | 0 | 0 |
| s7.4f | 1 | 0 | 0 |
| s8.4f | 0 | 0 | 0 |
| s9.4f | 1 | 1 | 0 |
| s10.4f | 0 | 1 | 0 |
| s11.4f | 1 | 0 | 1 |
| s12.4f | 0 | 0 | 0 |
| s1.5nf | 1 | 0 | 0 |
| s2.5nf | 0 | 0 | 0 |
| s3.5nf | 0 | 0 | 0 |
| s4.5nf | 0 | 0 | 0 |
| s5.5nf | 1 | 0 | 0 |
| s6.5nf | 0 | 0 | 0 |
| s7.5nf | 0 | 0 | 0 |
| s8.5nf | 0 | 0 | 0 |
| s9.5nf | 0 | 0 | 0 |
| s10.5nf | 0 | 0 | 0 |
| s11.5nf | 0 | 0 | 1 |
| s12.5nf | 0 | 0 | 0 |
| s1.5f | 0 | 0 | 0 |
| s2.5f | 0 | 0 | 0 |
| s3.5f | 1 | 0 | 0 |
| s4.5f | 1 | 0 | 0 |
| s5.5f | 1 | 0 | 0 |
| s6.5f | 0 | 0 | 0 |
| s7.5f | 0 | 0 | 1 |
| s8.5f | 0 | 0 | 0 |
| s9.5f | 0 | 0 | 0 |
| s10.5f | 0 | 0 | 0 |
| s11.5f | 1 | 0 | 1 |
| s12.5f | 0 | 0 | 0 |