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

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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

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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.

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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

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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.

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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).

-2 -1 0 1 2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Normal Q-Q Plot

Theoretical Quantiles

Sa

mp

le Q

ua

ntil

es

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).

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Box PlotNormal QQ-Plot

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).

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Box Plot

January March

Figure 3. Boxplots of DIN concentrations following max snow depth for each month.

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).

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June July

Max Snow Depth (cm)

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).

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January April

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.

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May

June July

Max Snow Depth (cm)

Figure 6. DOC concentrations at different max snow depths.

Figure 6. DOC concentrations at different max snow depths.

(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

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.

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References

Brooks PD, Williams MW, Schmidt SK (1998) Inorganic nitrogen and microbial biomassdynamics before and during spring snowmelt. Biogeochemistry 43:1-15.

Campbell JL, Mitchell MJ, Groffman PM, Christenson LM, Hardy JP (2005)Winter innortheastern North America: a critical period for ecological processes. Front EcolEnviron 3:314-322.

Christenson LM, Mitchell MJ, Groffman PM, Lovett GM (2010) Winter climate changeimplications for decomposition in northeastern forests: comparisons of sugar maple litterto herbivore fecal inputs. Glob Change Biol.

Decker KLM, Wang D, Waite C, Sherbatskoy T (2003) Snow removal and ambient airtemperature effects on forest soil temperatures in northern Vermont. Soil Sci Soc of Am67:1234-1243.

Deluca TH, Keeney DR, McCarty GW (1992) Effect of freeze-thaw events on mineralization ofsoil nitrogen. Biol Fert Soils 14:116-120.

Fahey TJ, Yavitt JB, Sherman RE, Groffman PM, Fisk MC, Hardy JP (2011) Transport ofCarbon and Nitrogen Between Litter and Soil Organic Matter in a Northern HardwoodForest. Ecosystems 14:326-340

Fitzhugh RD, Driscoll CT, Groffman PM, Tierney GL, Fahey TJ, Hardy JP (2001) Effects of soilfreezing, disturbance on soil solution nitrogen, phosphorus, and carbon chemistry in anorthern hardwood ecosystem. Biogeochemistry 56:215-238.

Harris MM, Safford LO (1996) Effects of season and four tree species on soluble Carbon contentin fresh and decomposing litter of temperate forests. Soil Sci 161:130-135.

Homman PS, Grigal DF (1992) Molecular weight distribution of soluble organics fromlaboratory-manipulated surface soils. Soil Sci Soc Am J56:1305-1310.

IPCC (Intergovernmental Panel on Climate Change). 2001. Climate change 2001: the scientificbasis. Cambridge, UK: Cambridge University Press.

Kalbitz K, Solinger S, Park J-H, Michalzik B, Matner E (2000) Controls on the dynamics ofdissolved 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 ofAntarctic bryophytes after leaching and repeated freeze-thaw cycles. Antart Sci 4:399-404.

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Mitchell MJ, Driscoll CT, Kahl JS, Likens GE, Murdoch PS, Pardo LH (1996) Climatic controlof nitrate loss from forested watersheds in the northeastern United States. Environ SciTechnol 30:2609-2612.

Naples BK, Fisk MC (2010) Belowground insights into nutrient limitation in northern hardwoodforests. 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: thepotential consequences of climate variability and change. New England RegionalOverview. Durham, NH: US Global Change Research Program, University of NewHampshire.

Soil Survey Staff (2006) Keys to soil taxonomy, 10th edn. US Department of Agriculture, NaturalResources Conservation Service, Washington, D.C

Wang FL, Bettany JR (1993) Influence of freeze-thaw and flooding on the loss of solubleorganic-carbon and carbon-dioxide from soil. Journal of Environmental Quality 22:709-714.

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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 0.89979075 0.29370649

TESTING NORMALITY OF THE DATA - HIGH VS LOW ALTITUDE > nitrogen_normality<-read.table("C:\\Users\\Luisa\\Desktop\\nitrogen_normality.csv", header=T, sep=',')

> nitrogen_normality

lowlevel DataLow highlevel DataHigh1 L 0.40803493 H 0.75160012 L 0.53939086 H 0.55668483 L 0.14010437 H 0.74118924 L 0.66129081 H 2.16068085 L 0.85881542 H 3.29722896 L 2.15542559 H 2.47095777 L 0.45601476 H 0.56827038 L 0.45601476 H 0.56827039 L 0.28242981 H 2.199502010 L 1.26027982 H 0.871300611 L 0.75309446 H 0.780895412 L 0.69113713 H 0.780895413 L 2.09975108 H 1.790161014 L 2.09975108 H 1.986376615 L 1.37657468 H 1.750606916 L 3.56131026 H 2.209542217 L 2.17841441 H 6.071573918 L 2.01078249 H 2.0305949

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19 L 0.52436755 H 0.702994320 L 0.67211922 H 0.111915621 L 0.03149339 H 0.371318522 L 0.30646420 H 0.960682423 L 0.26906280 H 1.143566524 L 0.04791219 H 0.334334425 L 0.39496524 H 0.295087026 L 0.27284468 H 0.128015327 L 0.01177487 H 0.624941528 L 0.29580280 H 0.688400429 L 0.63727746 H 0.899790730 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$DataLow)2. Shapiro-Wilk normality test3.4. data: nitrogen_normality$DataLow 5. W = 0.803, p-value = 7.425e-056. #non-normal data

SHAPIRO NORMALITY TEST NITROGEN HIGH ALTITUDE > shapiro.test(nitrogen_normality$DataHigh)

Shapiro-Wilk normality testdata: nitrogen_normality$DataHigh W = 0.7577, p-value = 1.214e-05

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#non-normal data

DIFFERENT ALTITUDES – MONTHLY COMPARISON

File used: Nitrogen

> sampleID<-c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3')> tablenitrogen<-read.table("C:\\Users\\Luisa\\Desktop\\Nitrogen.csv",header=T, sep=',')> tablenitrogen Sample.id Jan Mar Apr Jun Jul1 L1 0.4080349 0.4560148 2.099751 0.52436755 0.394965242 L1 0.5393909 0.4560148 2.099751 0.67211922 0.272844683 L2 0.1401044 0.2824298 1.376575 0.03149339 0.011774874 L2 0.6612908 1.2602798 3.561310 0.30646420 0.295802805 L3 0.8588154 0.7530945 2.178414 0.26906280 0.637277466 L3 2.1554256 0.6911371 2.010782 0.04791219 0.361535547 H1 0.7516001 0.5682703 1.790161 0.70299429 0.295087058 H1 0.5566848 0.5682703 1.986377 0.11191556 0.128015269 H2 0.7411892 2.1995020 1.750607 0.37131846 0.6249414910 H2 2.1606808 0.8713006 2.209542 0.96068235 0.6884004111 H3 3.2972289 0.7808954 6.071574 1.14356653 0.8997907512 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. File used: nitrogen_normality2. > summary(nitrogen_normality$DataLow)3. Min. 1st Qu. Median Mean 3rd Qu. Max. 4. 0.01177 0.29850 0.53190 0.86050 1.16000 3.561005. > summary(nitrogen_normality$DataHigh)6. Min. 1st Qu. Median Mean 3rd Qu. Max. 7. 0.1119 0.5683 0.7809 1.2710 1.9370 6.07208. Name of the file we are using: nitrogen_anova9. > kruskal.test(levels~Data, data=nitrogen_ anova)10. Kruskal-Wallis rank sum test11. data: levels by Data

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7.2. APPENDIX I: R CODE – Carbon

DATA USED Name of the file used: 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:\\Users\\Luisa\\Desktop\\Carbon_normality.csv",header=T,sep=',')

> carbon_normalityLevelLow DataLow LevelHigh DataHigh1 L 36.5383 H 44.38362 L 38.1865 H 35.59873 L 5.3507 H 46.03214 L 42.4149 H 48.05565 L 41.1700 H 53.34396 L 67.6995 H 44.89957 L 25.7500 H 31.10008 L 25.4500 H 26.95009 L 4.1000 H 22.350010 L 29.8000 H 28.700011 L 34.7000 H 35.800012 L 27.5500 H 28.500013 L 28.9500 H 28.100014 L 33.6500 H 18.100015 L 4.7500 H 21.250016 L 34.3500 H 28.600017 L 35.6000 H 35.200018 L 35.0500 H 19.2000

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19 L 36.1000 H 26.200020 L 29.8000 H 17.600021 L 0.0000 H 17.950022 L 32.8000 H 25.400023 L 35.7500 H 24.950024 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$DataLow) Shapiro-Wilk normality testdata: carbon_normality$DataLow W = 0.8583, p-value = 0.003126

SHAPIRO NORMALITY TEST CARBON HIGH ALTITUDE

> shapiro.test(carbon_normality$DataHigh) Shapiro-Wilk normality testdata: carbon_normality$DataHigh W = 0.9122, p-value = 0.03941

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DIFFERENT ALTITUDES – MONTHLY COMPARISON

File used: Carbon Final2

> sampleID<-c('L1','L1','L2','L2','L3','L3','H1','H1','H2','H2','H3','H3')

> tablenitrogen<-read.table("C:\\Users\\Luisa\\Desktop\\Nitrogen.csv",header=T, sep=',')

> tablecarbon

Site Jan May Jun Jul1 L1 36.5383 25.75 28.95 36.102 L1 38.1865 25.45 33.65 29.803 L2 5.3507 4.10 4.75 0.004 L2 42.4149 29.80 34.35 32.805 L3 41.1700 34.70 35.60 35.756 L3 67.6995 27.55 35.05 37.557 H1 44.3836 31.10 28.10 26.208 H1 35.5987 26.95 18.10 17.609 H2 46.0321 22.35 21.25 17.9510 H2 48.0556 28.70 28.60 25.4011 H3 53.3439 35.80 35.20 24.9512 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

File used: 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-Wallis rank sum test

data: levels by CarbonFinal$Data Kruskal-Wallis chi-squared = 46.8321, df = 46, p-value = 0.4381

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Team Member Contributions Russell Auwae- provided the team with raw data, helped with statistical analysis, conclusions and editingPenny Feltner- Statistical treatment of nitrogen data, introduction, editingJeffery Johnson- Statistical analysis, formatting, Conclusions and discussionAlyssa Lopez- Statistical treatment of carbon data, Hypothesis testing, bibliography, editingLuisa Quitalo- Statistical Analysis, Methods, Discussion and conclusions, editing

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