# Exercise 1: Ventenata spatial clustering

I am interested in understanding the invasion potential of the recently introduced annual grass ventenata (Ventenata dubia) across eastern Oregon. Here I ask, what is the spatial pattern of the ventenata invasion across the Blue Mountains Ecoregion of eastern Oregon?

Tools and Approaches Used

To address this question, I (1) tested for spatial correlation at various distances using Moran’s I spatial autocorrelation coefficients plotted with a correlogram, and (2) performed hot-spot analysis (Getis-Ord Gi) to identify statistically significant clusters of areas with high and low ventenata cover.

Description of Analysis Steps

1a) Moran’s I: To compute Moran’s I spatial autocorrelation coefficient for all of my sample units, I used the “ape” package in R version 3.5.1. The first step to this analysis was to convert the ventenata data and associated coordinates into a distance matrix. Once the distance matrix was created, the Moran.I function computed the observed and expected spatial autocorrelation coefficients for the variable of interest (ventenata abundance). The function produces a test statistic that tests the null hypothesis of no correlation. See Gittleman and Kot (1990) for details on how the Moran.I function calculates Moran’s I statistics.

1b) Correlogram: I plotted a correlogram using Moran’s I coefficients with increasing distances (lags) to examine patterns of spatial autocorrelation in my data. I used the correlog function in the spdep package in R to plot a correlogram with lag intervals of 10,000m. The function has the option of randomly resampling the data at each increment to incorporate statistical significance. This randomization tests the null hypothesis of no autocorrelation. I ran the function with resamp = 100. Black points on the correlogram are indicative of Moran’s I values significantly larger or smaller than expected under the null hypothesis.

2) Hot Spot Analysis: I used the hot spot analysis (Getis-Ord Gi*) tool in Arc GIS to identify statistically significant clusters of areas with high and low ventenata cover across my study area. The tool produces z-scores and p-values that test the null hypothesis of a random distribution of high and low values rather than clusters of high or low values. High z-scores indicate clusters of high values and low z-scores indicate clusters of low values. Low p-values indicate that these clusters are more pronounced than would be expected by chance.

Results

1a) Moran’s I: The Moran’s I spatial autocorrelation coefficient estimate for all of the points across the entire sample area was 0.3 ± 0.05 (p < 0.3). This value is not particularly informative, as it only indicates that the data is positive spatially autocorrelated, but does not provide information to describe the spatial pattern. I chose to follow the Moran’s I up with a correlogram to uncover the spatial pattern driving the autocorrelation.

1b) Correlogram: The Moran’s I spatial correlogram shows a general trend of decreasing autocorrelation from 0 to about 70,000m where sudden jumps in Moran’s I values occur to up to ~0.3. Following this jump, the correlation decreases to -0.5 to -0.2 between 120,000 and 152,000m, then increases to ~0.3 at 170,000m, decreases to almost -1.0 just after 200,000m, and finally increases to almost 1 at 220,000m. The general trend appears to be decreasing from 0.2 to -0.9 at 220,000m with some high peaks interspersed. These high and low peaks indicate distinct ventenata patches distributed throughout the study area, suggesting a clustered spatial pattern of the ventenata invasion. The extreme high and low values at distances over 200,000 are likely a result of the few sample units being compared at these distances, thus these are not so informative of the overall spatial pattern.

2) Hot Spot Analysis: Hot spot analysis in ArcGIS depicted clusters ranging from high ventenata cover (large red circles) to low ventenata cover (small blue circles) across my study area (Fig. 2) using the calculated z-scores and p-values for each sample unit. The resulting map shows distinct clusters of high, low, and moderate ventenata cover distributed across seven sampled burn perimeters (displayed in light orange). The highest cover clusters are all located within the Ochoco and Aldrich Mountains in the center of the study region. The fires on the perimeters of the region exhibited clusters of low to no ventenata cover.

Critique of Methods Used

When run on all of the data across the entire region, Moran’s I did not produce a useful statistic, indicating only if the data was spatial autocorrelation without indicating a spatial pattern. However, when visualized with a correlogram at varying distances, the correlation coefficients suddenly told a story of spatial clustering. The results from the hot spot analysis reinforce the findings from the correlogram by clearing depicting clusters on a map of the study area. The hot spot analysis further explores these results by mapping the clusters of high and low ventenata cover on top of each of my sample units, providing a useful visualization of exactly where the clusters of high and low cover fall across the region.

References

Gittleman, J. L. and Kot, M. (1990) Adaptation: statistics and a null model for estimating phylogenetic effects. Systematic Zoology39, 227–241.

## 1 thought on “Exercise 1: Ventenata spatial clustering”

1. jonesju

Claire very good work and shows good understanding of the tools. I like the use of the hotspot analysis to visually depict the differences in spatial patten of ventenata and cheatgrass. I look forward to seeing how your Ex 2 will develop, as you accumulate the amount of various other types of plant communities (or species) as a function of distance from your high and low ventenata spots, in order to determine whether these locations have different “ecological neighborhoods.” We haven’t discussed the role of fire disturbance as a factor influencing invasibility; is that something you wish to pursue?