1. 1 | P a g e
ENVIRONMENTAL STATISTICS
Using descriptive and inferential statistics to understand the effects of reduced snowpack on soil
C and N retention in a northern hardwood forest
Team Members:
Russell Auwae, Penny Feltner, Jefferey Johnson, Alyssa Lopez, Luisa Quitalo

2. 2 | P a g e
Introduction
Winter is considered the dormant season due to low temperatures, however, in soil there
is more activity during winter than previously thought (Campbell et al. 2005). While deciduous
forest trees are inactive during the winter, insulating snowpack keeps temperatures favorable for
a wide range of belowground processes. The lack of plant activity during winter makes
belowground processes important for retaining essential nutrients in the soil, such as nitrogen,
and maintaining forest productivity and water quality. The soil is an important reservoir of
essential nutrients (Fahey et al. 2011), and allows gradual recycling of nutrients into plant
available pools.
Nutrient availability in soils is considered an important regulator of fertility and primary
productivity in natural ecosystems (Naples and Fisk, 2010). Dissolved inorganic nitrogen (DIN)
and dissolved organic carbon (DOC) is released from litter decomposition and soil organic
matter percolates into the mineral soils (Homann and Grigal, 1992; Kalbitz et al. 2000). At the
mineral soil horizon, DOC and DIN can be lost to ground and surface waters, mineralized by
microbes, or be retained on soil particles (Kalbitz et al. 2000; Nieder and Benbi, 2008). In the
context of winter climate change, surface temperatures are predicted to increase 3-5˚C for the
northeastern US (IPCC, 2001; NERA, 2001). Warmer temperatures, especially during winter,
will lead to a reduction in snowpack. As climates warm and snowpack is reduced, it is not clear
how this reduction will influence processing and retention of C and N in the soil.
Nitrate concentrations in the northeastern US have been shown to increase following low
snow years, suggesting soil freezing was responsible (Mitchell et al. 1996; Judd et al. 2007).
Snow manipulation experiments verified soil freezing as the cause of increased leaching of

3. 3 | P a g e
nitrate and dissolved organic matter (Fitzhugh et al. 2001; Groffman et al. 2011). Similarly, plots
with natural variation in snow depth have shown that soils under low, inconsistent snowpack
export more nitrate (Brooks et al. 1998). Reduced snowpack increases the frequency of freeze-
thaw cycles, which physically disrupt plant material (Mellick and Seppelt, 1992; Harris and
Safford, 1996) and increase outputs of particulate organic matter in melt water (Deluca et al.
1992; Wang and Bettany, 1993). However, thick snowpack may keep soil temperatures near
freezing, resulting in more freeze-thaw cycles under snow (Decker et al. 2003). In addition, litter
decomposition was shown to decrease in plots with reduced snowpack (Christenson et al. 2010).
Therefore, it is not clear how varying snow depth will affect the stability of C and N retention in
soil during winter into the growing season. Moreover, these studies do not represent long-term
differences in climate as they cannot initiate the gradual change in soil frost associated with
winter climate change. Thus, our first objective was to study how varying snowpack along an
elevation gradient affects the stability of C and N retention in soil during winter and into the
growing season to better understand the consequences of varying snow depths associated with
winter climate change.
Due to site heterogeneity, it is crucial that our experimental design is able to capture
consistent measurements of C and N along the natural snow gradient. For this study, site
heterogeneity includes soil hydrology, pore size, fertility, frost lenses, and the aboveground plant
community. Using descriptive and inferential statistical methods, our second objective was to
determine if our experimental design was able to capture consistent measurements of soil
solution C and N loss along a natural snowpack gradient.

4. 4 | P a g e
Methods
Experimental Design
The elevation gradient at the Hubbard Brook Experimental Forest (HBEF) in New
Hampshire, USA creates a climate and snowpack gradient that can be used to test effects of
projected climate change in the northeastern US (Groffman et al. 2011). The HBEF is located in
the White Mountain National Forest, New Hampshire USA. Forest vegetation is dominated by
American beech (Fagus grandiflora), yellow birch (Betula alleghaniensis), and sugar maple
(Acer saccharum). Soil depth is 75-100 cm, acidic (pH ~4.0) Spodosols formed by unsorted
basal tills (Soil Survey Staff, 2006). There are three low elevation sites and three high elevation
sites chosen based on similar elevation, slope, and overstory canopy within the low and high
sites. Two zero-tension lysimeter pans were installed below the Oa horizon in May 2011 to
collect soil solution to measure the amount of dissolved organic carbon (DOC) and inorganic
nitrogen (DIN) (12 total), to collect soil leachate, and sampled once a month in January, March,
April, May, June, and July of 2012. February samples were not collected due to time and budget
limitations. Soil solution subsamples were sent for analysis of DOC at the University of
California at Davis. A phenolate-hypochlorite method was used to quantify ammonium (method
351.2, US EPA 1983) and a cadmium-reduction method to quantify nitrate (method 353.2, US
EPA 1983) to give a sum of DIN in soil solution.
Statistical Analyses
Our data is divided into different levels by elevation and month. Nitrogen, measurements
are provided for 5 months (January, March, April, June and July 2012). Carbon measurements
are provided for 4 months (January, March, June and July 2012). Data is divided into groups of

5. 5 | P a g e
high and low elevations, which include subgroups of low elevation (L1, L2 and L3) and high
elevation (H1, H2 and H3).
The normality of the nitrogen and carbon measurements was tested using the Shapiro-
Wilks Test, and by plotting qq-normality plots and boxplots for each of the two groups of
elevation (High and Low). Upon finding that these data sets were not normal, medians for each
subgroup were compared using the nonparametric Kruskal-Wallis test. The maximum snow
depth for each month was plotted for each of the 6 sites, in order to determine if there is a
correlation between nitrogen and carbon leachate associated with snow depth.

6. 6 | P a g e
Results
Shapiro-Wilks Normality Tests, Normal QQ-Plot, and boxplots indicate that the DIN
(Fig. 1, 2) and DOC (Fig. 4, 5) datasets do not follow a normal distribution and has several
outliers. Since the dataset did not follow a normal distribution and has several outliers, the
Kruskal-Wallis nonparametric test was used to determine the similarity of DOC and DIN
measurements within site type and between low and high elevation. The Kruskal-Wallis test
reveals that the median DOC (p-value= 0.4381) and DIN (p-value= 0.3315) measurements are
similar between all sites.
DIN concentrations increased with increasing max snow depth along the elevation
gradient (Fig. 6). DOC concentrations show no consistent patterns with max snow depth (Fig. 7).
Site L2 with a max snow depth of 25 cm showed consistently large variation in DOC
concentration (Fig. 7).
Figure 1. QQ-Plot and boxplot of DIN concentrations at low elevation sites (p-value = 7.425e-05); reject the null
hypothesis (data are not normal).
-2 -1 0 1 2
0.00.51.01.52.02.53.03.5
Normal Q-Q Plot
Theoretical Quantiles
SampleQuantiles
Box PlotNormal QQ-Plot

7. 7 | P a g e
Figure 2. QQ-Plot and boxplot of DIN concentrations at high elevation sites (p-value = 1.214e-05); reject the null
hypothesis (data are not normal).
Box Plot

8. 8 | P a g e
Figure 3. Boxplotsof DIN concentrationsfollowingmax snow depthforeachmonth.
January March
June July
DIN(µg/mL)
Max Snow Depth (cm)

9. 9 | P a g e
Figure 4. QQ-Plot and boxplot of DOC concentrations at low elevation sites (p-value = 0.003126); reject the null
hypothesis (data are not normal).
Figure 5. QQ-Plot and boxplot of DOC concentrations at high elevation sites (p-value = 0.03941); reject the null
hypothesis (data are not normal).

10. 10 | P a g e
January
May
June July
April
DOC(µg/mL)
Max Snow Depth (cm)Figure 6. DOC concentrations
at different max snow depths.

11. 11 | P a g e
Discussion
Normality tests indicate that our experimental design is not adequate in measuring the
effects of varying snowpack on soil C and N dynamics. A reoccurring problem in ecosystem
science is being able to capture overall site heterogeneity. Heterogeneity in our sites include:
uncertainty in soil hydrology, varying soil pore size and fertility, formation of frost lenses, and
varying aboveground plant communities. In addition, the poor patterns observed in our DOC and
DIN measurements may be due to interannual climate variation, as discussed by Groffman et al.
(2011). If this is to be to case, it would be worthwhile to continue measuring these concentrations
for several years.
Kruskal-Wallis tests indicate that the median concentrations of DOC and DIN do not
differ between low and high elevation sites. This indicates that the winter of 2012 did not
provide an adequate snow gradient for our study, resulting in similar concentration
measurements between low and high sites. However, there is a slight pattern of increased DIN
concentrations with increasing max snow depth (Fig. 3). Snow removal studies that induced soil
freezing, show that increased leaching of DOC and DIN was induced by soil frost depths of 35-
50 cm (Groffman et al. 2011). Therefore, it is possible that our experimental design and
interannual climate variation did not produce a drastic change in soil frost and snow depth in
order to observe any significant differences in DOC and DIN concentrations between low and
high sites.
Our concentrations of DIN provide additional support that sites with thick snowpack
leach more C and N than sites with shallow snowpack (Decker et al. 2003). This challenges the
prevailing theory that shallow snowpack associated with future winter climate change will

12. 12 | P a g e
experience more frequent freeze-thaw cycles (Groffman et al. 2011). Thick snowpack keeps soils
at near freezing temperatures and have more water availability. Daily diurnal fluctuations in
temperature under thick snowpack may be evident, resulting in increased freeze-thaw cycles
(Decker et al. 2003). Future studies are needed to test the prevailing theory that increased freeze-
thaw cycles are present under shallow snowpack.

13. 13 | P a g e
References
Brooks PD, Williams MW, Schmidt SK (1998) Inorganic nitrogen and microbial biomass
dynamics before and during spring snowmelt. Biogeochemistry 43:1-15.
Campbell JL, Mitchell MJ, Groffman PM, Christenson LM, Hardy JP (2005)Winter in
northeastern North America: a critical period for ecological processes. Front Ecol
Environ 3:314-322.
Christenson LM, Mitchell MJ, Groffman PM, Lovett GM (2010) Winter climate change
implications for decomposition in northeastern forests: comparisons of sugar maple litter
to herbivore fecal inputs. Glob Change Biol.
Decker KLM, Wang D, Waite C, Sherbatskoy T (2003) Snow removal and ambient air
temperature effects on forest soil temperatures in northern Vermont. Soil Sci Soc of Am
67:1234-1243.
Deluca TH, Keeney DR, McCarty GW (1992) Effect of freeze-thaw events on mineralization of
soil nitrogen. Biol Fert Soils 14:116-120.
Fahey TJ, Yavitt JB, Sherman RE, Groffman PM, Fisk MC, Hardy JP (2011) Transport of
Carbon and Nitrogen Between Litter and Soil Organic Matter in a Northern Hardwood
Forest. Ecosystems 14:326-340
Fitzhugh RD, Driscoll CT, Groffman PM, Tierney GL, Fahey TJ, Hardy JP (2001) Effects of soil
freezing, disturbance on soil solution nitrogen, phosphorus, and carbon chemistry in a
northern hardwood ecosystem. Biogeochemistry 56:215-238.
Harris MM, Safford LO (1996) Effects of season and four tree species on soluble Carbon content
in fresh and decomposing litter of temperate forests. Soil Sci 161:130-135.
Homman PS, Grigal DF (1992) Molecular weight distribution of soluble organics from
laboratory-manipulated surface soils. Soil Sci Soc Am J56:1305-1310.
IPCC (Intergovernmental Panel on Climate Change). 2001. Climate change 2001: the scientific
basis. Cambridge, UK: Cambridge University Press.
Kalbitz K, Solinger S, Park J-H, Michalzik B, Matner E (2000) Controls on the dynamics of
dissolved organic matter in soils: a review. Soil Sci 165:277-304.
Mellick DR, Seppelt RD (1992) Loss of soluble carbohydrates and changes in freezing point of
Antarctic bryophytes after leaching and repeated freeze-thaw cycles. Antart Sci 4:399-
404.

14. 14 | P a g e
Mitchell MJ, Driscoll CT, Kahl JS, Likens GE, Murdoch PS, Pardo LH (1996) Climatic control
of nitrate loss from forested watersheds in the northeastern United States. Environ Sci
Technol 30:2609-2612.
Naples BK, Fisk MC (2010) Belowground insights into nutrient limitation in northern hardwood
forests. Biogeochemistry 97: 109-121.
Neider R, Benbi DK (2008) Carbon and Nitrogen in the terrestrial environment. Springer-Verlag,
New York, pp219-233.
NERA (New England Regional Assessment). 2001. Preparing for a changing climate: the
potential consequences of climate variability and change. New England Regional
Overview. Durham, NH: US Global Change Research Program, University of New
Hampshire.
Soil Survey Staff (2006) Keys to soil taxonomy, 10th edn. US Department of Agriculture, Natural
Resources Conservation Service, Washington, D.C
Wang FL, Bettany JR (1993) Influence of freeze-thaw and flooding on the loss of soluble
organic-carbon and carbon-dioxide from soil. Journal of Environmental Quality 22:709-
714.

15. 15 | P a g e
DATA USED
Name of the filed used: nitrogen_anova
> levels<-
c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2'
,'L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','
H1','H1','H2','H2','H3','H3')
> Data<-c(nitrogen_anova$Data)
> Data
[1] 0.40803493 0.53939086 0.14010437 0.66129081 0.85881542 2.15542559 0.75160009 0.55668484 0.74118923
2.16068075 3.29722885 2.47095767 0.45601476 0.45601476
[15] 0.28242981 1.26027982 0.75309446 0.69113713 0.56827034 0.56827034 2.19950201 0.87130064
0.78089544 0.78089544 2.09975108 2.09975108 1.37657468 3.56131026
[29] 2.17841441 2.01078249 1.79016099 1.98637661 1.75060693 2.20954220 6.07157395 2.03059485
0.52436755 0.67211922 0.03149339 0.30646420 0.26906280 0.04791219
[43] 0.70299429 0.11191556 0.37131846 0.96068235 1.14356653 0.33433441 0.39496524 0.27284468
0.01177487 0.29580280 0.63727746 0.36153554 0.29508705 0.12801526
[57] 0.62494149 0.68840041 0.899790750.29370649
TESTING NORMALITY OF THE DATA - HIGH VS LOW ALTITUDE
> nitrogen_normality<-read.table("C:UsersLuisaDesktopnitrogen_normality.csv",header=T, sep=',')
> nitrogen_normality
lowlevel DataLow highlevel DataHigh
1 L 0.40803493 H 0.7516001
2 L 0.53939086 H 0.5566848
3 L 0.14010437 H 0.7411892
4 L 0.66129081 H 2.1606808
5 L 0.85881542 H 3.2972289
6 L 2.15542559 H 2.4709577
7 L 0.45601476 H 0.5682703
8 L 0.45601476 H 0.5682703
9 L 0.28242981 H 2.1995020
10 L 1.26027982 H 0.8713006
11 L 0.75309446 H 0.7808954
12 L 0.69113713 H 0.7808954
13 L 2.09975108 H 1.7901610
14 L 2.09975108 H 1.9863766
15 L 1.37657468 H 1.7506069
16 L 3.56131026 H 2.2095422
17 L 2.17841441 H 6.0715739
18 L 2.01078249 H 2.0305949

16. 16 | P a g e
19 L 0.52436755 H 0.7029943
20 L 0.67211922 H 0.1119156
21 L 0.03149339 H 0.3713185
22 L 0.30646420 H 0.9606824
23 L 0.26906280 H 1.1435665
24 L 0.04791219 H 0.3343344
25 L 0.39496524 H 0.2950870
26 L 0.27284468 H 0.1280153
27 L 0.01177487 H 0.6249415
28 L 0.29580280 H 0.6884004
29 L 0.63727746 H 0.8997907
30 L 0.36153554 H 0.2937065
BOXPLOT: NITROGEN AT LOW ALTITUDE
1. > boxplot(nitrogen_normality$DataLow)
NORMALITY PLOT: NITROGEN AT LOW ALTITUDE
> qqnorm(nitrogen_normality$DataLow)
> qqline(nitrogen_normality$DataLow)
BOXPLOT: NITROGEN AT HIGH ALTITUDE
1. >boxplot(nitrogen_normality$DataHigh)
NORMALITY PLOT: NITROGEN AT HIGH ALTITUDE
> qqnorm(nitrogen_normality$DataHigh)
> qqline(nitrogen_normality$DataHigh)
SHAPIRO NORMALITY TEST NITROGEN LOW ALTITUDE
1. > shapiro.test(nitrogen_normality$DataLow)
2. Shapiro-Wilk normality test
3.
4. data: nitrogen_normality$DataLow
5. W = 0.803,p-value = 7.425e-05
6. #non-normal data
SHAPIRO NORMALITY TEST NITROGEN HIGH ALTITUDE
> shapiro.test(nitrogen_normality$DataHigh)
Shapiro-Wilk normality test
data: nitrogen_normality$DataHigh
W = 0.7577,p-value= 1.214e-05

17. 17 | P a g e
#non-normal data
DIFFERENT ALTITUDES – MONTHLY COMPARISON
Fileused: Nitrogen
> sampleID<-c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3')
> tablenitrogen<-read.table("C:UsersLuisaDesktopNitrogen.csv",header=T, sep=',')
> tablenitrogen
Sample.id Jan Mar Apr Jun Jul
1 L1 0.4080349 0.4560148 2.099751 0.52436755 0.39496524
2 L1 0.5393909 0.4560148 2.099751 0.67211922 0.27284468
3 L2 0.1401044 0.2824298 1.376575 0.03149339 0.01177487
4 L2 0.6612908 1.2602798 3.561310 0.30646420 0.29580280
5 L3 0.8588154 0.7530945 2.178414 0.26906280 0.63727746
6 L3 2.1554256 0.6911371 2.010782 0.04791219 0.36153554
7 H1 0.7516001 0.5682703 1.790161 0.70299429 0.29508705
8 H1 0.5566848 0.5682703 1.986377 0.11191556 0.12801526
9 H2 0.7411892 2.1995020 1.750607 0.37131846 0.62494149
10 H2 2.1606808 0.8713006 2.209542 0.96068235 0.68840041
11 H3 3.2972289 0.7808954 6.071574 1.14356653 0.89979075
12 H3 2.4709577 0.7808954 2.030595 0.33433441 0.29370649
> January<-c(tablenitrogen$Jan)
> boxplot(January~sampleID)
> March<-c(tablenitrogen$Mar)
> boxplot(March~sampleID)
> boxplot(tablenitrogen$Apr~sampleID)
> boxplot(tablenitrogen$Jun~sampleID)
> boxplot(tablenitrogen$Jul~sampleID)
COMPARISON OF MEANS BETWEEN L1, L2, L3, H1, H2, H3 LEVELS OF ALTITUDE – KRUSKAL
WALLIS TEST
1. Fileused: nitrogen_normality
2. > summary(nitrogen_normality$DataLow)
3. Min. 1stQu. Median Mean 3rd Qu. Max.
4. 0.01177 0.29850 0.53190 0.86050 1.16000 3.56100
5. > summary(nitrogen_normality$DataHigh)
6. Min. 1stQu. Median Mean 3rd Qu. Max.
7. 0.1119 0.5683 0.7809 1.2710 1.9370 6.0720
8. Name of the filewe areusing:nitrogen_anova
9. > kruskal.test(levels~Data, data=nitrogen_ anova)
10. Kruskal-Wallisrank sumtest
11. data: levels by Data

18. 18 | P a g e
7.2. APPENDIX I: R CODE – Carbon
DATA USED
Name of the fileused: Carbon Final
> levels<-
c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2'
,'L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','
H1','H1','H2','H2','H3','H3')
> Data<-c(tablecarbon$Data)
> Data
[1] 36.5383 38.1865 5.3507 42.4149 41.1700 67.6995 44.3836 35.5987 46.0321
[10] 48.0556 53.3439 44.8995 25.7500 25.4500 4.1000 29.8000 34.7000 27.5500
[19] 31.1000 26.9500 22.3500 28.7000 35.8000 28.5000 28.9500 33.6500 4.7500
[28] 34.3500 35.6000 35.0500 28.1000 18.1000 21.2500 28.6000 35.2000 19.2000
[37] 36.1000 29.8000 0.0000 32.8000 35.7500 37.5500 26.2000 17.6000 17.9500
[46] 25.4000 24.9500 19.4500
TESTING NORMALITY OF THE DATA - HIGH VS LOW ALTITUDE
> carbon_normality<-read.table("C:UsersLuisaDesktopCarbon_normality.csv",header=T,sep=',')
> carbon_normality
LevelLow DataLow LevelHigh DataHigh
1 L 36.5383 H 44.3836
2 L 38.1865 H 35.5987
3 L 5.3507 H 46.0321
4 L 42.4149 H 48.0556
5 L 41.1700 H 53.3439
6 L 67.6995 H 44.8995
7 L 25.7500 H 31.1000
8 L 25.4500 H 26.9500
9 L 4.1000 H 22.3500
10 L 29.8000 H 28.7000
11 L 34.7000 H 35.8000
12 L 27.5500 H 28.5000
13 L 28.9500 H 28.1000
14 L 33.6500 H 18.1000
15 L 4.7500 H 21.2500
16 L 34.3500 H 28.6000
17 L 35.6000 H 35.2000
18 L 35.0500 H 19.2000

19. 19 | P a g e
19 L 36.1000 H 26.2000
20 L 29.8000 H 17.6000
21 L 0.0000 H 17.9500
22 L 32.8000 H 25.4000
23 L 35.7500 H 24.9500
24 L 37.5500 H 19.4500
BOXPLOT: CARBON AT LOW ALTITUDE
> boxplot(carbon_normality$DataHigh)
NORMALITY PLOT: CARBON AT LOW ALTITUDE
> qqnorm(carbon_normality$DataLow)
> qqline(carbon_normality$DataLow)
BOXPLOT: CARBON AT HIGH ALTITUDE
> boxplot(carbon_normality$DataHigh)
NORMALITY PLOT: CARBON AT HIGH ALTITUDE
> qqnorm(carbon_normality$DataHigh)
> qqline(carbon_normality$DataHigh)
SHAPIRO NORMALITY TEST CARBON LOW ALTITUDE
> shapiro.test(carbon_normality$DataLow)
Shapiro-Wilk normality test
data: carbon_normality$DataLow
W = 0.8583,p-value= 0.003126
SHAPIRO NORMALITY TEST CARBON HIGH ALTITUDE
> shapiro.test(carbon_normality$DataHigh)
Shapiro-Wilk normality test
data: carbon_normality$DataHigh
W = 0.9122,p-value= 0.03941

20. 20 | P a g e
DIFFERENT ALTITUDES – MONTHLY COMPARISON
Fileused: Carbon Final2
> sampleID<-c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3')
> tablenitrogen<-read.table("C:UsersLuisaDesktopNitrogen.csv",header=T, sep=',')
> tablecarbon
Site Jan May Jun Jul
1 L1 36.5383 25.75 28.95 36.10
2 L1 38.1865 25.45 33.65 29.80
3 L2 5.3507 4.10 4.75 0.00
4 L2 42.4149 29.80 34.35 32.80
5 L3 41.1700 34.70 35.60 35.75
6 L3 67.6995 27.55 35.05 37.55
7 H1 44.3836 31.10 28.10 26.20
8 H1 35.5987 26.95 18.10 17.60
9 H2 46.0321 22.35 21.25 17.95
10 H2 48.0556 28.70 28.60 25.40
11 H3 53.3439 35.80 35.20 24.95
12 H3 44.8995 28.50 19.20 19.45
> January<-c(tablecarbon$Jan)
> boxplot(tablecarbon$Jan~sampleID)
> boxplot(tablecarbon$May~sampleID)
> boxplot(tablecarbon$Jun~sampleID)
> boxplot(tablecarbon$Jul~sampleID)
COMPARISON OF MEANS BETWEEN L1, L2, L3, H1, H2, H3 LEVELS OF ALTITUDE – KRUSKAL
WALLIS TEST
Fileused: Carbon Final
> levels<-
c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3','L1 ','L1','L2'
,'L2','L3','L3','H1','H1','H2','H2','H3','H3','L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3')
> kruskal.test(levels~CarbonFinal$Data,data=CarbonFinal)
Kruskal-Wallisrank sumtest
data: levels by CarbonFinal$Data
Kruskal-Wallischi-squared =46.8321, df = 46, p-value = 0.4381

21. 21 | P a g e
Team Member Contributions
Russell Auwae- provided the team with raw data, helped with statistical analysis, conclusions
and editing
Penny Feltner- Statistical treatment of nitrogen data, introduction, editing
Jeffery Johnson- Statistical analysis, formatting, Conclusions and discussion
Alyssa Lopez- Statistical treatment of carbon data, Hypothesis testing, bibliography, editing
Luisa Quitalo- Statistical Analysis, Methods, Discussion and conclusions, editing