Question: Which landscape features correspond to clusters of greater than expected trees?

Methods: I performed two Hot Spot Analyses in ArcMap; one on Hot Spots of tree height and another on hot spots of distance between trees. Both were constrained to the reserved control areas of the HJ Andrews Forest. Hot spots in tree height are regions of greater than expected tree height, while hot spots in distances between trees are regions of greater than expected distance between individual trees (more dispersed trees). Hot spots and cold spots of each analysis generally overlapped. However, hot spots between tree height and spacing did not overlap in all cases, so I wanted to know what landscape features might explain this difference. Covariates I explored included slope, aspect, elevation, and landform. Since the end goal is to find landscape features that may correlate with amount of soil carbon, I conducted this analysis with the assumption that taller trees may correlate with regions of greater soil carbon. I used the package ‘caret’ in R to calculate 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 (Table 1).

Results: Regions of taller than expected trees often correspond to regions of greater than expected distances between trees, which agrees with current forest growth models (Fig. 1). Hot spots of tall trees are typically in valleys and cold spots are commonly on ridges (Fig 3 & 4). When we zoom in to the Lookout Mountain area of HJ Andrews, we see that hot spots of tall trees are more concentrated in valleys and on footslopes, and cold spots are closer to mountain ridges (Fig 3). When compared with the distance hot spot map of the same area, we see that cold spots go much further down the footslopes and even into the valleys in some cases (Fig 4). So although we have evidence for a strongly linear relationship between height and distance between trees, we also have evidence that they do not fully explain each other and other landscape features are likely at play.

Fig 1. Distance Z-scores vs. Height Z-scores from hot spot analyses show a linear relationship.

Fig 2. HJ Andrews elevation with reserved control areas in orange and

inset area of Lookout Mountain hot spot maps (below)

Fig 3. Hot Spot Analysis showing hot spots of tree heights (tallest trees)

in the Lookout Mountain area

Fig 4. Hot Spot Analysis showing the greatest distance between trees

in the Lookout Mountain area

An elevation band that correlates with occurrences of tall trees exists up to around 1100 m, after which point number of tall trees drops off substantially (Fig. 5). Certain aspects seem to correlate with taller trees, but those relationships are harder to tease apart and I have yet to fully explore them. Greater slopes tend to correlate with shorter trees, but this relationship is not linear. There is an interesting upwards trend at slopes between 30 and 50 degrees that seems to correlate with slightly taller trees, then a big drop in mean height Z-score at slopes of 60 degrees.

Fig 5. Aspect, elevation and slope compared with Z-scores of mean height.

A comparison of Z-scores from hot spot analyses of height and distances shows that although hot spots of height and distance tightly correlate, covariates that explain them are different (mean slope and elevation). When we compare the most extreme Z-scores to one another, slope, height and distance between trees are not particularly different. Mean elevation in three categories of Z-score is similar, but mean elevation in the fourth group (>3,>3) is significantly lower. A next step is to map out these

Table 1. Comparison between the most extreme Z-scores of tree height and tree spacing.

Critique: These analyses are still based on Hot Spot Analyses, so they still comes with the same criticisms as previous Hot Spot Analyses. One of these criticisms was 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 have yet to map out regions that were well-predicted and poorly predicted spatially, so I cannot fully discuss the merits of the confusion matrix method.