Indirect Gradient Analysis
Figure 25. Indirect gradient analysis NMDS with species' % survival as response variables and environmental data as predictor variables.
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Interpretation:
Site Characterization 1) Reference Aspen Forest:
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Shrub Seedling Survival 1) C. canadensis (Bunchberry):
2) L. involucrata (Honeysuckle):
3) V. myrtilloides (Blueberry):
4) V. vitis-idaea (Lingonberry):
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Regression Trees
C. canadensis (Bunchberry)
Figure 26. C. canadensis regression tree produced using Classification and Regression Tree Analysis (CART).
L. involucrata (Honeysuckle)
Figure 27. L. involucrata regression tree produced using CART.
V. myrtilloides (Blueberry)
Figure 28. V. myrtilloides regression tree produced using CART.
V. vitis-idaea (Lingonberry)
Figure 28. V. vitis-idaea regression tree produced using CART.
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Interpretation
Interpretation
Interpretation
Interpretation
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Discussion
One takeaway from the indirect gradient analysis is that the variation across sites in terms of environmental conditions and species' survival are likely not necessarily driven by age of site (i.e., 30-yr reclamation site opposite of the reference Aspen Forest in the NMDS, indicating they are not related). From this plot, I can also deduce that the 18-yr reclamation site's main environmental driver (of those measured) was likely percent cover of forbs. This site had the worst percent survival across all four shrub species.
Additionally, the 2-yr reclamation site's main environmental driver was likely percentage of bare ground. This site, other than the reference Aspen Forest, had the best percent survival across all four shrub species. According to the regression trees, C. canadensis had the highest survival when % clay > 8.085, electrical conductivity of the soil < 508.5 uS/cm, and soil phosphate was < 34.82 mg/kg. L. involucrata had the highest survival when percent cover of forbs was < 28% and bare ground was > 15%. V. myrtilloides had the highest survival when percent cover of forbs was < 28%, pH < 7.78, and soil potassium was < 178.4 (mg/kg). V. vitis-idaea had the highest survival when percent cover of shrubs was > 10.75 and pH was between 6.195 and 5.385. However, it is important to note that C. canadensis had very little overall survival (only a few surviving individuals across all sites), so the environmental drivers of its survival determined by the regression tree are likely not a good representation. Since this species had such a high mortality rate, it is likely a seedling quality issue. Furthermore, the alternative splits for L. involucrata, V. myrtilloides, and V. vitis-idaea explain nearly as much variation as the original splits, indicating there is likely not one overarching environmental variable driving the survival of these species across all sites. |
Conclusion
Through my analysis I was able to show that shrubs outplanted into sites with high amount of competing vegetation (i.e., forbs), will experience major mortality. Therefore, it is best not to outplant on sites that already have established vegetation cover. When shrub seedlings are planted on sites with a lot of bare ground, they showed high survival rates, even after three growing seasons. This indicates that it is likely best to plant most shrub species at the same time as tree seedlings (soon after soil placement) on these reclaimed sites in order to give them a competitive advantage against the vegetation layer that develops between year two and eighteen. The highest percent survival for all species was when the soils were rich in ammonium (NH4) and total organic carbon (TOC). The presence of a canopy (LAI) does not seem to have an effect on species' survival. Another major takeaway from this analysis is that the age of the reclamation site does not necessarily drive the enviromental conditions. I believe these results provide good insight into which environmental variables govern the survival of outplanted native shrub seedlings and when shrubs should be incorporated into planting efforts to acheive the acceptable percent survival.
Next steps
Next steps for this project will be to apply the same gradient and CART analyses to growth data to gain more insight into how the outplanted seedlings behave under the different site conditions.
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