Major uncertainties exist in the geospatial distribution of terrestrial carbon (C) sources and sinks and the factors that influence soil C distribution and change. Temperate and boreal forests serve as a net sink of atmospheric CO₂, so my aim is to understand how landscape features and vegetation vary across the HJ Andrews Forest (HJA) and determine how those factors influence soil C storage and CO₂ release.

Research Question: How might landscape features and vegetation characteristics relate to accumulation of soil carbon, using tree height and density as a proxy for soil C?

Dataset description: I used LiDAR data from 2008 and 2011 at 0.3-0.5 m vertical resolution and at 1 m² horizontal resolution covering the Lookout Creek Watershed and the Upper Blue River Watershed to the northern extent of HJA. These LiDAR data include a high hit model (tree tops) and a bare earth model from 2008. I also downloaded a shapefile of polygons of the reserved control areas within the HJA forest boundaries and used these areas to subset some of my later analyses.

Hypotheses:

1. Large storm events primarily topple larger (older) trees on ridges and upper slopes. Soils in these positions are typically thinner, making them quickly saturate from precipitation and making trees in those positions more vulnerable (Lal 2005; Overby 2003). In addition, the tallest trees on ridges may be exposed to greater risk from lightning strikes, greater wind gusts and more snow accumulation, resulting in shorter trees dominating ridgelines. Shorter trees have smaller canopies and occupy less physical space than taller trees, so shorter trees occupy denser stands and as they grow taller, each individual tree occupies more space. Because of these factors, I hypothesize that shorter trees that are more closely spaced will cluster more densely along ridges than other landforms.
2. I hypothesize that the tallest trees will be more densely clustered at low elevations along valleys because waterways carve out valleys and bring nutrients that then accumulate and build thick, carbon-rich soil. These areas tend to not suffer from the moisture limitations of ridges and steep slopes and are more protected from strong winds, so trees in these areas can maximize growth based on available solar radiation.
3. High density young forest stands exhibit characteristics similar to mature forests, like closed canopies and high LAI. However, recently disturbed forests have higher nutrient availability than undisturbed forests which has been shown to cause a shift in C-allocation from below- to aboveground, so I expect younger stands to negatively correlate with soil C accumulation.
4. I hypothesize that because vegetative growth is limited by solar radiation, vegetative growth on slopes that face S and W have greater exposure to solar radiation and will result in greater overall biomass (more dense stands of smaller trees, but not necessarily more tall trees). However, if the slope is too steep, I expect this pattern to diminish since trees will experience greater wind and water erosion and more tree mortality.

Note: I have yet to test hypotheses 3 and 4, but they are avenues for continuing research.

Approaches: I used a k-nearest neighbor analysis and k-means to cluster trees into 10 height classes and related each tree in the center of each height class to its distance from its 30 nearest neighboring trees of the same height class. I ran several hot spot analyses on tree heights and tree distances (tree density). Since my original hot spot analyses were subset by height class, which led to the algorithm naturally finding the arbitrary bins (upper and lower bounds) the height classes were based on, I performed new hot spot analyses on all the trees and all the tree distances within just the reserved control areas of the forest. I performed a linear regression to compare the regions of taller than expected trees to the regions of greater than expected distances between tree and Chi-squared tests independence for the hot spot analyses to compare hot spots and cold spots with variables like tree spacing, slope and elevation. Since hot spots between tree height and spacing did not overlap in all cases, I wanted to know what landscape features might explain this difference. Covariates I explored included slope, aspect, elevation, and landform. I calculated a confusion matrix between the Z-scores of height and distance for all the hot spot bins (-3,-2,-1,0,1,2,3), then further constrained the analysis to only the most extreme hot and cold spots (-3 and 3). I then compared mean height, distance, slope and elevation between the four combinations of the extreme hot and cold spots.

My objective for the final synthesis was to find regions that are higher or greater than expected for given parameters (height and tree density) and group these clusters into polygons where they overlap. I hypothesized that that soil C accumulation positively correlates with clusters of trees that are both taller and more densely spaced than expected. Conversely, I hypothesized that shorter trees that are more widely spaced negatively correlate with soil C accumulation. Therefore, overlapping hot spots of tree height and tree density and overlapping cold spots of tree height and tree density are prime areas for my planned future soil C sampling sites.

Results: I produced maps and statistical relationships between tree height and tree spacing. These included hot spot maps overlaid on elevation maps, density plots, graphical comparisons of Z-scores and many others (see previous blog posts). I found that hot spots of taller than expected trees overlapped with hot spots of greater than expected distances of trees, but not in all cases (Fig 1). When I examined a table of each of the distance hot spot bins compared with each of the height hot spot bins, I found that the most compelling correlations were between bins (0,0), (-3,-3) and (3,3) with 5%, 33% and 43% of total data, respectively. The proportion of data not covered by overlapping hot spots and by overlapping cold spots was minimal, but when mapped was visually compelling enough to warrant further exploration. I was particularly interested in areas with short trees and greater than expected distance between trees (bin -3,3) and areas with tall trees and shorter than expected distance between trees (bin 3,-3). I expect that bin (-3,3) would correlate with less soil C, so identifying those areas could be useful to sampling. I expect that bin (3,-3) would correlate with greater soil C. I can identify from Fig 2 where statistically significant hot spots and cold spots are spatially close and plan to sample heavily in those areas.

Fig 1. Tree height vs. distance between trees from hot spot analyses shows a highly linear pattern.

Fig 2. Tree density (mean distance in m between 30 closest trees) hot spot bins compared with tree height (m) hot spot bins in Lookout Mountain area. Bin comparisons (-3,-3) are areas of shorter than expected trees in more dense stands. Bin (3,-3) is taller than expected trees that are in more dense stands. Bin (3,3) is taller than expected trees in more sparsely populated stands.

Significance: Many of my results thus far confirm ecological phenomena that we already know to be true across forested landscapes. For example, I examined tree distance to stream and found that taller trees were more common closer to streams and less common at greater distances from streams. This makes sense with other landform features like valleys and ridges, so smaller trees tend to be along ridges (cold spots of tall trees according to my analysis) and taller trees tend to be in valleys. However, I have identified regions of taller than expected trees and regions of shorter than expected trees, and if those correlate with respectively more and less soil C, that provides evidence for LiDAR as an effective way to quantify soil C. If other landscape features that can be determined from geospatial data co-vary with tree height and/or tree density, it may be possible to quantify soil C at fine resolution. These data can be used to identify areas that have the potential to sequester more soil C and forest management could be tailored to support those regions.

Geospatial Learning: I learned a ton about ArcMap, QGIS and packages that were new to me in R like spatstat, caret, nngeo, and others. I had very little experience with and GIS before this class, so it was a steep learning curve but I’ll continue to learn as I perform this final synthesis. I learned how to perform hot spot analyses in ArcMap and export them to work with in Q. I learned how to manipulate spatial data in R and load it in Q and ArcMap for viewing. These are only a few examples of the many things I’ve learned!

Statistics Learning: I learned the limitations of hot spot analyses, with one of my criticisms being that it’s basically a smoothing function. Since the LiDAR dataset I’m using is basically a census of tree heights, running hot spot analyses is reducing the information in that dataset unnecessarily. I already knew that my data were spatially autocorrelated, so I had to take that into account for all of my analyses. I learned that the confusion matrix is great for visually discerning where a model predicts best and where it doesn’t predict well, but that the scientist must figure out the reasons for the good or poor predictions.

Because interactions among geomorphic processes, landforms, and biota occur on various temporal and spatial scales, it is necessary to understand the scale of an ecosystem process when performing spatial analyses. It is also necessary to consider the potential reasons why a particular spatial pattern did not emerge from an analysis. Reasons can range from data not being explained by expected process or multiple spatial scales of interactions, failing to use the right tool, not being at the right spatial scale, data being too coarse, failing to include the right variables or a flaw in one’s fundamental idea about how the system works. This is why it is important to (1) formulate clear, specific hypotheses before performing an analysis and (2) explore spatial patterns using multiple approaches if possible.

Sources:

Harmon M.E. (2009) Woody Detritus its Contribution to Carbon Dynamics of Old-Growth Forests: the Temporal Context. In: Wirth C., Gleixner G., Heimann M. (eds) Old-Growth Forests. Ecological Studies (Analysis and Synthesis), vol 207. Springer, Berlin, Heidelberg

Lal, R. (2005). Forest soils and carbon sequestration. Forest ecology and management, 220(1-3), 242-258.Overby et al. 2003: Impacts of natural disturbance on soil C dynamic in forest ecosystems: Soil C sources include litter fall and tree mortality, root turnover and root exudates.

Dixon, R. K., Solomon, A. M., Brown, S., Houghton, R. A., Trexier, M. C., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science, 263(5144), 185-190.