Examining the role of trees in manganese cycling through soil. By Jennifer Kissel

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1 Examining the role of trees in manganese cycling through soil By Jennifer Kissel Many soils have been contaminated with manganese from industrial processes. Vegetation can take up large quantities of manganese from the soil which may slow the release of manganese from soils into rivers. Here, we examine manganese cycling through vegetation in the Susquehanna Shale Hills Critical Zone Observatory, a small watershed in central Pennsylvania with manganese-contaminated soils. We investigate both manganese uptake by trees by collecting green leaf samples from the tree canopy and manganese return to soil by collecting litter fall in litter traps. Green leaves were collected from sugar maples and chestnut oak canopies at different periods of the growing season (June to September) as well as at different elevations on a hillslope. Leaf litter traps were set up at varying elevations throughout the watershed and collected leaf litter from August to October. Chemical analyses were performed to determine manganese concentrations in green leaves and litterfall. We observed that manganese concentrations increase over the sampling periods for green leaves and leaf litter. We also found that sugar maples tend to take up more manganese into their leaves than chestnut oaks. We did not find a strong correlation between manganese uptake by leaves or manganese deposition into the soil by leaf litter with elevation, suggesting that manganese cycling through vegetation is uniform throughout the catchment. Using the leaf litter data, we calculate the manganese flux from trees to the soil (98.18 micrograms/ cm 2 year) and find that the internal cycling of manganese is much greater than previously measured addition and removal (erosion/weathering) fluxes at Shale Hills.

2 I. Introduction Industrial processes have contaminated soils with manganese in many industrialized regions (Herndon et al., 2011). In order to predict impacts of manganese contamination in soils, we need to better understand how manganese moves through the environment. Herndon and Brantley (2011) examined manganese transport through air, soil, vegetation, and rivers in central Pennsylvania. They observed a decrease in manganese concentrations in the atmosphere following the 1950s, which is hypothesized to be related to increased EPA regulation and a decline in industry (Bilinski et al., 1984). However, manganese concentrations in rivers do not start declining until the 1980s. Herndon and Brantley (2011) hypothesized that vegetation uptake was slowing manganese release into rivers. Here, we investigate the impact of vegetation on the movement of manganese through the manganese enriched soils at The Shale Hills Critical Zone Observatory (SSHCZO). We investigate the total uptake of manganese from soil into tree leaves and the return of manganese from vegetation to soil as litter fall. While there are natural sources of manganese, emissions from gasoline, coal and steel industries have been adding significant amounts of manganese to the atmosphere (WHO, 2000). Atmospheric inputs of manganese can be distributed further spatially than natural sources (Parekh, 1990) and will settle from the air into soils over time. This increase in soil manganese levels may contribute to the decline in sugar maple species because it has been observed that high manganese bioavailability from increased acidity is linked to sugar maple decline (Kolb, 1993). In this study we will quantify manganese uptake from soil into leaves by analyzing green leaf chemistry, and manganese return to soil from analyzing litter fall

3 chemistry. Green leaves were taken from the upper canopies of sugar maple (ACSA) and chestnut oak (QUPR) trees, and leaf litter was collected from litter traps on the forest floor. We examine species, spatial and temporal differences in the amounts of manganese in trees throughout the SSHCZO. Based on previous studies and preliminary observations, (Herndon, pers. comm.; St. Clair and Lynch, 2005), we hypothesize that: i) Trees on ridges will have higher concentrations of manganese in their green leaves and leaf litter than trees in the valley. ii) The concentration of manganese in green leaf and leaf litter samples will increase over the summer growing season and the fall season respectively. iii) Sugar maple leaves (ACSA) will have lower concentrations of manganese than chestnut oak leaves (QUPR). II. Background Samples for this experiment were obtained from the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) in central Pennsylvania. The acidic (~4 ph) soils in SSHCZO are developed from the lower Silurian-aged Rose Hill Formation, a marine shale deposit, (Hettinger, 2001) and have been enriched in manganese by industrial processes (Herndon et al., 2011). SSHCZO is a forested watershed containing several deciduous and evergreen trees. We took green leaf samples from sugar maples, which represent ~6% of the trees in the forest, and chestnut oaks which represent ~27% of trees in the forest, (Eissenstat, unpublished data). Other major genera include pine (7.6%), hemlock (8%), hickory

4 (16%), and other oaks (29.6%). Figure 1: A Map of the Susquehanna Shale Hills Critical Zone Observatory indicating the location of trees from which green leaves were collected. The red stars show the locations of sampled sugar maple trees are, and the blue stars show the locations of sampled chestnut oak trees.

5 Figure 2: A Map of the Susquehanna Shale Hills Critical Zone Observatory adapted from Lin et al. (2006). The red stars represent where litter traps were set for leaf collection. The grey circles indicate soil moisture sites from a previous study (Lin et al., 2006) which were used as a basis for positioning litter traps. Green leaves were sampled from trees on all slope positions (valley, mid-slope and ridge) along a transect. Litter trap locations were selected to obtain a good coverage of valley, mid-slope and ridges across the whole watershed. Previous chemical analyses of green leaves from the SSHCZO showed high manganese concentrations in hickory, oak and pine leaves (Herndon, unpublished data). Hickory showed increasing manganese concentrations in leaves with increasing elevation, while pine and oak species had consistent manganese levels across elevation

6 (Fig 3). All examined species had generally increasing manganese concentrations over the growing season (Fig. 4). Fig 3: August 2009 manganese concentration in green leaves verses elevation. Hickory (CATO/CAGL), oak (QUAL/QUPR), and pine (PIST/PIVI). Leaf samples were taken from trees at the SSHCZO in late August 2009 and analyzed by Elizabeth Herndon. This research shows that manganese concentrations in leaves increase with elevation for hickory. One explanation is that manganese may be more available for plant uptake in ridge soils than in the valley due to heavy depletion of base cations like calcium and magnesium at the ridges (St. Clair and Lynch 2005).

7 Fig 4: Manganese concentrations increase over the growing season in green leaves from hickory (CATO/CAGL), oak (QUAL/QUPR), and pine (PIST/PIVI) species. The leaves were collected from the SSHCZO and analyzed by Elizabeth Herndon in Figure 5: Box model showing various manganese reservoirs and fluxes calculated for the Shale Hills Critical Zone Observatory (Herndon and Brantley, 2011).

8 The box model presented in Figure 5 shows the basis for this research. Comparison of these fluxes suggests that vegetation may enhance manganese sequestration in soils by slowing manganese removal through chemical weathering. Here, we attempt to build on previous research by expanding green leaf analyses to new species (chestnut oak and sugar maple) and quantifying the flux of manganese from trees to soils as litterfall. III. s 1. Leaf Collection Green leaves were collected by Elizabeth Herndon and Katie Gaines from trees highlighted on Figure 1 in June, July, August, and September Leaves were collected from the tops of the trees that were exposed to sunlight for consistency. Leaf litter traps were constructed from plastic trays lined with plastic mesh to allow drainage of rainfall. The boxes were installed at the locations highlighted on Figure 2 at a height of 10 cm off the land surface on plastic rebar secured in the soil. The surface of each litter trap (0.173 m 2 ) was installed level relative to the ground surface. The litter was collected weekly (Table 2) by Lauren Smith. For this study, we obtained a subsample of the collected litter for chemical analysis. Acid Digestion The protocol for acid digestion of the leaves was modified from Hokura et al. (2000). Briefly, they digested 0.5 g of plant sample in concentrated nitric acid as well as hydrofluoric acid. Initially, nitric acid was added and the sample was allowed to stand overnight at room temperature. The sample was then heated at 75 Celsius (30 min), 130 Celsius (30 min), and 200 Celsius (2 hours) when the sample was being digested in nitric acid alone. After hydrofluoric acid was added, the heating went to 180 Celsius (2 hours)

9 and 230 Celsius (3 hours). The samples were heated to almost dryness, and the residue was dissolved with 7.5 ml concentrated nitric acid and diluted with 100 ml with pure water. Our methods differed from Hokura s methods to ensure complete digestion of the plant mass without using hydrofluoric acid due to costs, chemical availability, and personal hazard. We instead used hydrogen peroxide and heated the samples for longer periods of time. Digestion was considered complete when no particulate matter was observed in the digestion solution. When checked with a temperature gun, we found that our hot plate was not heating consistently. We adjusted the temperature accordingly to compensate for the uneven heating. The first stages of acid digestions were consistent between four separate groups of collected leaves. Preparation of the samples varied slightly in timing and heating as described below. The collected leaves were all first homogenized by hand. The samples were then ground in liquid nitrogen to obtain a fine powder. Approximately 0.15 g of each leaf powder was weighed into a Teflon vessel, and the specific mass was recorded (Table 3). Additionally, leaf powder from a peach leaf standard (NIST standard 1547) was weighed out to approximately 0.15 g and placed into Teflon vessels. All acid digestions were conducted in a metal-free clean laboratory at The Pennsylvania State University and generally followed the protocol just described. For all groups, 4 ml of concentrated ultrapure nitric acid was added to each vessel. These vessels were left overnight at room temperature for pre-digestion. The specifics related to each group of acid digestions are summarized below.

10 Acid digestions Group 1: Green leaves collected in June, 2011 After predigesting overnight, the samples were set on a hot plate at 130 Celsius. After an hour, 2 ml of ultrapure nitric acid were added to each sample. The temperature on the hot plate was increased to 200 degrees Celsius. After using a temperature gun on the hot plate, it was determined that the plate was not getting as hot as desired, with temperatures only reaching Celsius. The temperature was then set to 230 Celsius (reading Celsius) for six hours, and then was lowered to 130 Celsius overnight. The next day the heating plate was turned off for two hours. When it was observed that digestion was not complete, the temperature was raised to 200 Celsius for about an hour, then turned down to 160 Celsius for sixteen hours. The heat was then raised to 230 Celsius for two hours and then taken off the hot plate to let cool. Acid digestions Group 2: Green leaves collected in August/September, 2011 After predigesting overnight, 2 ml of ultrapure nitric acid were added to each sample. The samples were set on a hot plate at 160 (reading Celsius). After three hours, the hot plate was increased to 220 degrees Celsius. Samples were removed from hot plate after an hour and 1 ml of hydrogen peroxide was added when samples cooled. Acid digestions Group 3: Leaf Litter Collected October 3 rd, 2011 After predigesting overnight, 2 ml of ultrapure nitric acid were added to each sample. Samples were set on a hot plate at 160 Celsius. After two hours, the temperature was increased to 220 Celsius. After two more hours, the samples were allowed to cool

11 and 1 ml of hydrogen peroxide was added. The temperature was then left on burner overnight with the hot plate set to 200 Celsius. The next morning the heat was turned off. Acid Digestions Group 4: Leaf Litter Collected on October 3 1st and August 31 st, 2011 After predigesting overnight, 2 ml of ultrapure nitric acid were added to the solution. After 30 min, the hot plate was set to 160 Celsius. The hot plate temperature was raised to 220 Celsius for two hours. After 90 minutes the hot plate was turned off and 1 ml of hydrogen peroxide was added. The hot plate was set to 200 Celsius for another 90 minutes after which the samples were allowed to cool. Sample dilutions: Once all the samples had cooled, they were rinsed into pre-weighed bottles containing ~190 ml ultrapure water and the final volumes of the samples were recorded (Table 3). For Groups 1, 2, and 3, concentrated acid digests were diluted to 1:100 using two dilution steps where 1 ml sample was diluted with 9 ml 2% ultrapure nitric acid solution. For Group 4 samples, the 1 ml pipette was not properly calibrated. For the group of samples, the concentrated acid digests were diluted to ~1:100 by adding 100 µl (100 µl H 2 O = g) to 9.9 ml 2% HNO 3 (9.9 ml H 2 O = g) to give a dilution of 1: Chemical Analysis: Chemical analysis was conducted on an Inductively Coupled Plasma Mass Spectrometer (ICP-MS) in Penn State's Materials Characterization Laboratory.

12 Table 1. blanks and standards Sample name Description Date Analyzed Leaf Mass (g) Volume (ml) Leaf Mn (ppm) Solution Mn (ppb) % Bias B1_aug4 Blank August < B2_aug4 Blank August < B3_aug4 Blank August < PL1_aug4 Standard August PL2_aug4 Standard August PL3_aug4 Standard August B1_Oct Blank October < B2_Oct Blank October < B3_Oct Blank October < PL1_Oct Standard October PL2_Oct Standard October PL3_Oct Standard October B1_100x_Oct3 Blank December < B2_100x_Oct3 Blank December < B3_100x_Oct3 Blank December < PL1_100x_Oct3 December Standard PL2_100x_Oct3 December Standard PL3_100x_Oct3 December Standard B1_100x_AugOct Blank January < B2_100x_AugOct January <

13 B3_100x_AugOct PL1_100x_AugOct PL2_100x_AugOct PL3_100x_AugOct Blank Blank Standard Standard Standard January < January January January The blanks did not contain detectable levels of manganese (Table 1), indicating there was no manganese contamination. We also used a peach leaf standard NIST 1547 (Becker, 1990) to assess the efficiency of our digestion method and to determine how close our measured concentrations were to the actual concentrations of manganese in the standards. NIST reports the concentration of manganese in the standard is 98 ppm ± 3 ppm. The percent bias was calculated by the equation: Percent bias= 100 *(Actual concentration-measured Concentration)/Actual concentration The error in samples analysis in October and December was the lowest (ranging from 3% to 27%), i.e. accuracy of analysis was highest for those batches. Inaccuracy was within about 20%. In January one sample showed a bias of 49%. Throughout the analysis period, we observed a systematic error in that, when analyzes differed substantially from NIST values, the measured concentration was lower than the expected value. This was attributed to loss of sample during heating. The bottle caps used to contain the samples allowed minute amounts of sample to leak even when twisted tightly onto the bottle. This may account for the bias observed in our data. For each group of samples, we calculated the percent error to be the average bias calculated for the samples: 21% for August samples, 4.8% for October samples, 12% for December samples, and 22% for January samples.

14 IV. Results Figure 5: Manganese concentrations in ACSA (magenta) and QUPR (navy) green leaves in the SSHCZO over time in the growing season. The tree locations can be seen in Figure 1. These were sampled on the dates listed and analyzed in August and October (Table 3). Sugar maples have higher and more variable concentrations of manganese in their leaves than chestnut oaks across all sampled dates (Figure 5). There is a positive relationship between manganese concentrations in both sugar maple (R 2 = 0.27) and chestnut oak (R 2 = 0.18) leaves with time in the growing season. These R² values show that the correlation between manganese concentrations and leaves is stronger for sugar maples than for chestnut oaks.

15 Figure 6: Concentration of manganese (ppm) for sugar maples (magenta) and chestnut oaks (navy) green leaves from the SSHCZO verses elevation. These were sampled on the dates listed and analyzed in August and October (Table 3). Sugar maples have higher concentrations of manganese in their leaves than chestnut oaks for all elevations (Figure 6). Also, sugar maples demonstrate a trend of increasing manganese concentration with increasing elevation (R 2 = 0.13) as seen by the linear trend line. Chestnut oaks also show this trend, but to a lesser degree (R 2 = 0.07). While there is an overarching trend of increasing manganese concentration from the valley to the ridge, the trees in the mid slope have lower manganese concentrations than trees in the valley and ridge. Overall, R 2 values are low, indicating that the linear correlation between manganese concentrations in leaves and elevation is weak.

16 Figure 7: Manganese concentrations in sugar maples green leaves taken from the SSHCZO. Leaves collected in late summer (orange) have higher manganese concentrations than leaves collected in early summer (blue) at all elevations. The positive correlation between manganese concentrations and elevation in early summer (R 2 = 0.14) weakens by late summer (R 2 = 0.06) (Table 3). Sugar maple green leaves illustrate a positive relationship between the ongoing growing season and an increase in manganese concentration (Figure 7). It is also observed that there is positive relationship between manganese concentration and elevation. This graph also shows a trend of lower manganese concentrations in tree leaves on the mid slope as opposed to the valley and ridges.

17 Figure 8: Foliar manganese concentrations in chestnut oak green leaf collections taken from the SSHCZO. Manganese concentrations in leaves are slightly higher in late summer (orange) than early summer (blue). In early summer, there is a strong positive correlation between manganese concentrations and elevation (R 2 = 0.72), but there is no correlation in late summer (R 2 = 0.05) (Table 3). Chestnut oak leaves also show an increase in manganese concentrations from early summer to late summer (Figure 8). In early summer, we observe a weak positive relationship between elevation and manganese concentration in chestnut oak leaves. In late summer, there is a weak to nonexistent inverse relationship between elevation and manganese concentration.

18 Figure 9: Manganese concentration (ppm) verses elevation for leaf litter collected from litter traps at the SSHCZO on 08/31/2011 (red), 10/03/2011 (blue), and 10/31/2011 (green). A slight negative trend in early fall (R 2 = 0.21) becomes a positive trend by midfall (R 2 = 0.13).The correlation between manganese concentration in leaf litter and elevation weakens as fall progresses (R 2 = 0.03) (Table 3). Manganese concentrations in leaf litter demonstrate different trends with respect to elevation and collection date (Figure 9). Manganese concentrations in leaf litter decrease with increasing elevation in late August. In early and late October, however, manganese concentrations increase with increasing elevation. It is also observed that manganese concentrations increase as fall sets on with August having the lowest concentrations of manganese in leaf litter, early October having the second lowest

19 concentrations of manganese in leaf litter, and late October having the highest concentrations of manganese in leaf litter. Figure 10: Total manganese verses elevation for leaf litter collected from 35 litter traps in the SSHCZO on 08/31/2011 (red), 10/03/2011 (blue), and 10/31/2011 (green). Total manganese is calculated by multiplying the mass of the leaves in the 35 litter traps by the average manganese concentration measured in homogenized leaf litter. (Table 3). Total manganese in leaf litter collected in late October is consistently higher than leaf litter collected in early October, which is higher than total manganese in August (Figure 10). In August, there is a slight increase in total manganese with elevation, and there is moderate correlation between elevation and total manganese in litter (R 2 = 0.21). Also, at the beginning of October, there is a slight increase in the total manganese in the

20 leaves with increased elevation, but the correlation is weak (R 2 = 0.09). There is also a very weak trend (R 2 = 0.09) of decreasing total manganese in litter with increasing elevation at the end of October. Table 2. Estimated manganese in litter fall at SSHCZO 8/31/2011 9/12/2011 9/19/2011 9/26/ /3/ /10/2011 Litter mass (g) Mn in litter (µg g -1 ) Total Mn in litter (µg) 93,991 64,357 87, , , ,160 Mn deposition (µg cm -2 ) /24/ /31/ /7/ /13/ /18/ /28/2011 Litter mass (g) Mn in litter (µg g -1 ) Total Mn in litter (µg) 687,840 1,220, , , ,585 74,589 Mn deposition (µg cm -2 )

21 Figure (11): Total manganese deposition from trees to soil in litterfall at SSHCZO peaks in mid-october. The total manganese deposition is calculated as total manganese from the 35 litter traps normalized to the total area of the litter traps. Leaf litter was chemically analyzed for 3 out of 13 litter collection dates. Therefore, we extrapolated our measured manganese concentrations to dates where we did not analyze leaf litter, assuming that manganese concentrations would be similar on close collection dates (Table 2). Here, we calculate total manganese in litter for each week as the average manganese concentration in litter collected from 35 traps multiplied by the mass of leaf litter collected in those 35 traps each week (Smith, pers. comm.). Average manganese concentrations measured for litter collected 08/31/2011 were used to calculate total manganese for 08/31 09/26, while 10/03 manganese concentrations in litter were applied to litter collected between 10/03 10/24, and 10/31 manganese concentrations in litter were applied to litter collected between 10/31 11/28 (Table 2). We estimate area-normalized litter fall flux for manganese by dividing total manganese by the total area of the 35 litter traps (60200 cm 2 ). The sum of weekly fluxes is approximately equal to the annual litterfall budget, and we calculate that the total manganese deposited to the soil as litter fall during the growing season is micrograms/cm 2 /year. V. Discussion These results show that sugar maples have higher manganese concentrations in their leaves than chestnut oaks. Previous research showed that red oak had higher concentrations of manganese in their leaves than sugar maples (St. Clair, Lynch 2005).

22 We hypothesized that chestnut oaks would show the same trend as red oaks; however, in the manganese rich, acidic soils of the SSCZO, we observe that sugar maples uptake more manganese from the soil than chestnut oaks, although the mechanism for this is unclear and not studied in this research. Previous chemical analyses of green leaf samples showed that manganese concentrations increased over the growing season for hickory, oak, and pine species (Herndon unpublished). This was seen in our data as well for green leaves collected from sugar maples and chestnut oaks. Sugar maples and chestnut oaks demonstrate a slight increase in manganese concentrations in their leaves with elevation; however, the R 2 values (which represents how much of the trend the equation can account for) for the relationships are low, implying that elevation does not fully explain foliar manganese concentrations. Chestnut oaks in late summer may even demonstrate an inverse relationship between elevation and foliar manganese concentrations. The most significant trend seen for leaf litter is that there is an increase in manganese concentration and total manganese in litterfall from August to late October. The increase in total manganese is due to both increasing manganese concentration in litterfall and an increase in litter fall during this time resulting in more leaves being deposited into litter traps. While there does appear to be a positive relationship between elevation and increased manganese concentration in leaf litter, the R 2 values, for the relationship are low, suggesting that elevation does not fully explain differences in manganese concentrations in leaf litter.

23 This study is consistent with previous studies at SSHCZO where only hickory showed clear a positive relationship between elevation and green leaf manganese concentrations. Given that manganese concentrations in oak leaves do not vary with elevation and that the composition of the SSHCZO forest is dominantly oak, it is logical that leaf litter would not show a significant correlation between manganese concentrations and elevation. It should also be noted that the most significant method bias was found for leaves analyzed in January. Those leaves include October 31 and August 31 leaf litter samples. The bias is likely due to sample losses from bottle caps leaking, resulting in lower recorded manganese concentrations than the samples contain. Total manganese deposition from trees to soils gradually increases over early fall, culminating in a peak of total manganese deposition in mid-october. This is logical because mid-october has the highest litterfall, though increasing concentrations of manganese in the leaves themselves may contribute to this trend. By summing weekly deposition of manganese from leaf litter traps over the fall season, we have approximated the manganese flux returned to soils per year in litterfall (98.18 µg cm -2 year -1 ). This value is exceedingly larger than the previously obtained values for input and output fluxes of manganese in soils at SSHCZO. This shows that vegetation is one of the dominating factors in manganese cycling in soils. VI. Conclusions This study of how trees affect manganese cycling may provide better insight into how trees sequester manganese in contaminated ecosystems. Our analysis of green leaves has demonstrated that sugar maples take up more manganese in their leaves than chestnut oaks. Our analysis of leaf litter has shown that more manganese is deposited in leaf litter

24 over the fall season, peaking in mid-october. While both leaf litter and green leaf data show that elevation has no strong impact on manganese concentrations, the elevation differences examined here were small. Instead, elevation differences in manganese concentrations may be better examined with a wider range of elevations. VII. Acknowledgements Funding for sample analysis was provided by NSF EAR Soils and vegetation as a record of anthropogenic pollutants: manganese in the Shale Hills CZO.

25 Table 3: Chemical analysis of green leaves and leaf litter. Sample Name Sample Type Species Month Digested Leaf Mass (g) Total Volume (ml) Leaf Mn Concentration (ppm) Solution Mn Concentration (ppb) A352 Green Leaf ACSA Aug , A356 Green Leaf ACSA Aug , A437 Green Leaf ACSA Aug , A2059 Green Leaf ACSA Aug , A2061 Green Leaf ACSA Aug , A2062 Green Leaf ACSA Aug A2064 Green Leaf ACSA Aug , A2065 Green Leaf ACSA Aug , Q230 Green Leaf QUPR Aug , Q332 Green Leaf QUPR Aug Q340 Green Leaf QUPR Aug , Q351 Green Leaf QUPR Aug , Oct2 ACSA 2039 Green Leaf ACSA Oct , Oct3 ACSA 2066 Green Leaf ACSA Oct , Oct4 ACSA2062 Green Leaf ACSA Oct , Oct5 ACSA 2062 Green Leaf ACSA Oct , Oct6 ACSA2065 Green Leaf ACSA Oct , Oct7 QUPR 230 Green Leaf QUPR Oct , Oct8 ACSA 2059 Green Leaf ACSA Oct , Oct9 QUPR 351 Green Leaf QUPR Oct , Oct10 QUPR 332 Green Leaf QUPR Oct , Oct11 ACSA 2064 Green Leaf ACSA Oct , Oct12 ACSA 352 Green Leaf ACSA Oct , Oct13 QUPR 330 Green Leaf QUPR Oct , Oct14 QUPR 396 Green Leaf QUPR Oct , Oct15 QUPR 390 Green Leaf QUPR Oct , Oct16 ACSA 356 Green Leaf ACSA Oct , Oct17 QUPR 358 Green Leaf QUPR Oct , Oct18 QUPR 2060 Green Leaf QUPR Oct , Oct3_12 Leaf Litter - Dec , Oct3_13 Leaf Litter - Dec , Oct3_14 Leaf Litter - Dec , Oct3_27 Leaf Litter - Dec , Oct3_29 Leaf Litter - Dec , Oct3_30 Leaf Litter - Dec , Oct3_32 Leaf Litter - Dec , Oct3_34 Leaf Litter - Dec , Oct3_38 Leaf Litter - Dec , Oct3_44 Leaf Litter - Dec , Oct3_52 Leaf Litter - Dec , Oct3_54 Leaf Litter - Dec , Oct3_55 Leaf Litter - Dec , Oct3_67 Leaf Litter - Dec , Oct3_74 Leaf Litter - Dec , Oct3_a1 Leaf Litter - Dec ,

26 Oct3_a4 Leaf Litter - Dec , Aug31_12 Leaf Litter - Jan , Aug31_13 Leaf Litter - Jan Aug31_14 Leaf Litter - Jan Aug31_32 Leaf Litter - Jan , Aug31_34 Leaf Litter - Jan , Aug31_52 Leaf Litter - Jan , Aug31_54 Leaf Litter - Jan , Aug31_55 Leaf Litter - Jan , Aug31_74 Leaf Litter - Jan , Oct31_12 Leaf Litter - Jan Oct31_13 Leaf Litter - Jan Oct31_14 Leaf Litter - Jan Oct31_32 Leaf Litter - Jan Oct31_34 Leaf Litter - Jan Oct31_52 Leaf Litter - Jan Oct31_54 Leaf Litter - Jan Oct31_55 Leaf Litter - Jan Oct31_74 Leaf Litter - Jan

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