- The research question that you asked.
I initially asked the question, “how is the spatial pattern of invasion by the recently introduced annual grass, ventenata, influenced by the spatial pattern of suitable habitat patches (scablands) via the susceptibility of these habitat patches to invasion and ventenata’s invasion potential?”
- A description of the dataset you examined, with spatial and temporal resolution and extent.
In Exercise 1, I examined spatial autocorrelation and in ventenata abundance and ventenata hotspots using spatial data (coordinates and environmental variables) and ventenata cover data that I collected in the field (summer 2018) for 110 plots within and surrounding seven burn perimeters across the Blue Mountain Ecoregion of eastern Oregon.
Target areas were located to capture a range of ventenata cover from 0% ventenata cover to over 90% cover across a range of plant community types and environmental variables including aspect, slope, and canopy cover within and just outside recently burned areas. Once a target area was identified, plot centers were randomly located using a random azimuth and a random number of paces between 5 and 100 from the target areas. Sample plots were restricted to public lands within 1600m of the nearest road to aid plot access. Environmental data for sample plots includes: canopy cover, soil variables (depth, pH, carbon content, texture, color, and phosphorus content), rock cover, average yearly precipitation, elevation, slope, aspect, litter cover, and percent bare ground cover.
For Exercise 2, I examined how the spatial pattern of vegetation type influences invasibility of plant communities by ventenata. To achieve this, I applied vegetation type data from the Simpson Potential Vegetation Type raster data (Simpson 2013) to 30m resolution in Arc GIS which was developed to identify potential vegetation types across the Blue Mountain Ecoregion.
In Exercise 3, I explored how the spatial pattern of canopy cover was related to ventenata abundance. For this, I used a live tree canopy cover layer developed for the Pacific Northwest calculated using Forest Vegetation Simulator methods including the sum of canopy cover estimates for vegetation plots in the region (Crookston and Stage 1999).
- Hypotheses: predictions of patterns and processes you looked for
Ventenata is an invasive annual grass that shares many functional traits to other impactful invasive annual grasses in the region such as cheatgrass and medusahead, including similar vegetative height, fall germination, and shallow root system. These similarities have led me to believe that, like cheatgrass and medusahead, ventenata will be more abundant in open areas with low canopy cover where competition from existing vegetation is lower.
The study area contains many open areas interspersed throughout the larger forested landscape. The patchy spatial distribution of open areas throughout the study area will likely result in a patchy distribution of areas with high ventenata cover. Additionally, ventenata produces many seeds, with the majority of these seeds dispersing short distances from the parent plant. This leads me to believe that areas with high ventenata cover will be clustered near other areas with high ventenata cover creating invasion “hot spots” across the study region.
Hypothesis 1: Areas with high ventenata cover will be clustered near other high cover areas and low cover areas will be clustered near other low cover areas.
Hypothesis 2: The spatial pattern of ventenata abundance will be positively correlated with a neighborhood of non-forest habitat types (shrub-lands and grasslands) and negatively correlated with a neighborhood of forest habitat types. This relationship will decrease in strength as distance increases from the high cover sample point, as vegetation types farther from an invasion point are likely not as strongly influencing invasion as vegetation types closer to that point.
Once a species has established in a suitable habitat, it may spread to areas of less suitable habitat aided by strong propagule pressure from a nearby population. Open areas may act as source populations, allowing ventenata to build propagule pressure to the point where it is able to successfully establish and maintain a population in less suitable habitat such as areas with high canopy cover.
Hypothesis 3: Plots where ventenata is present in areas with high canopy cover (e.g. forests) will be clustered near open areas. These open areas may provide strong propagule pressure to aid invasion into areas with fewer available resources (sunlight).
- Approaches: analysis approaches you used.
To test these predictions I performed a handful of spatial analyses including:
Exercise 1: I tested for spatial autocorrelation using Moran’s I and created a correlogram in R and performed hot spot analysis in ArcGIS
Exercise 2: I explored the spatial relationship between the spatial pattern of ventenata abundance and the spatial pattern of different vegetation types using neighborhood analyses in ArcGIS and R
Exercise 3: I examined the spatial relationship between ventenata and canopy cover using a Ripley’s cross K analysis in R
- Results: what did you produce — maps? statistical relationships? other?
Throughout the analyses, I produced a series of statistical relationships displayed as maps and graphs. Hot spot analysis produced a map that allowed me to visualize the relationship of autocorrelation between ventenata abundance at my sample points. For Moran’s I, neighborhood analysis, and Ripley’s cross K, I produced graphical representations of statistical relationships in R.
- What did you learn from your results? How are these results important to science? to resource managers?
The correlogram and hotspot analysis results showed that the spatial pattern of ventenata is auto correlated and has a patchy distribution. The hotspot analysis suggests that areas of high ventenata are clustered with other high ventenata plots and low ventenata plots are clustered as I predicted in Hypothesis 1. This is likely a result of the patchy distribution of open areas and forested areas across the landscape and the dispersal ability of ventenata.
Neighborhood analysis showed that areas with high ventenata cover are more positively correlated with nearby forested areas (ponderosa pine) than I originally thought. This result suggests that ventenata may preferentially invade areas surrounded by ponderosa pine vegetation type as well as shrublands which would not support Hypothesis 2. However, ponderosa pine vegetation type does not necessarily indicate high canopy cover, and could represent invasion into an alternative low canopy cover vegetation type. Additionally, the vegetation type maps are mapped at large spatial scales and may not represent the fine scale variation in vegetative cover. Uncertainty in this result inspired a follow up analysis using canopy cover instead of vegetation type as a predictor variable in a Ripley’s cross K analysis.
In my follow up analysis using Ripley’s cross K, I found that forest plots where ventenata was present were only weakly clustered around open areas despite my original hypothesis that there would be strong clustering (H3). These results could suggest that ventenata has a higher tolerance for high canopy cover than I originally predicted. Alternatively, these results could indicate that ventenata is capable of dispersing large quantities of seed much farther distances than originally thought, thus not requiring open areas in the immediate neighborhood. Moreover, the same issue of scale may apply to the canopy layer as the vegetation layer, and the 30m resolution may be over predicting canopy cover at my sample sites.
My findings could have severe implications for forest ecosystems which are commonly thought to be relatively resistant to invasion by annual grasses and are now showing susceptibility to ventenata invasion. For example, vententata could increase fine fuels in these systems, making them more likely to ignite and carry surface fire. Managers may want to consider incorporating annual grass management strategies into their current and future forest management plans to help reduce potential invasion impacts.
- Your learning: what did you learn about software (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) other?
During this class I learned a suite of new tools in ArcGIS including hotspot analysis and concentric ring buffer. I created my first model using ArcGIS Modelbuilder! I learned the basics of spatstat in R and successfully completed some spatial analysis which required transforming my data into a spatial data frame (I did not know that these existed prior to this class). Additionally, I was exposed to, and gained experience using many other new functions in R including Moran.I, correlog and kcross that were useful for spatial analysis.
- What did you learn about statistics, including (a) hotspot, (b) spatial autocorrelation (including correlogram, wavelet, Fourier transform/spectral analysis), (c) regression (OLS, GWR, regression trees, boosted regression trees), (d) multivariate methods (e.g., PCA), and (e) or other techniques?
I learned that Moran’s I and correlograms are useful for testing spatial autocorrelation in data, but only if the scale applied is of interest. For example, it was not useful to compute only one Moran’s I value for my entire data set – this indicated that there was spatial autocorrelation in the data, but did not indicate a spatial pattern. However, when I computed Moran’s I at various distances and displayed these results in a correlogram, I found uncovered the pattern of the spatial correlation. The hotspot analysis allowed me to visualize exactly where the high and low clustering was occurring across my sample plots while simultaneously providing a significance value for those hot and cold spots.
Ripley’s cross K analysis was useful for testing the relationship of my ventenata points to another variable (canopy cover). I found this test appea ling because it tests whether or not one variable is clustered around another variable using a Poisson distribution to compare observed and expected values assuming spatial randomness. However, I learned that this method was not appropriate for my data, as my sample plots were chosen based on field variables and were not a random sample. This violated assumptions of randomness and homogeneity across the sampling region as my plots were more heavily located in non-forested areas. If I wanted to properly investigate these spatial questions, I would have to develop a more random sampling method.
Citations
Simpson, M. 2013. Developer of the forest vegetation zone map. Ecologist, Central Oregon Area Ecology and Forest Health Program. USDA Forest Service, Pacific Northwest Region, Bend, Oregon, USA
Crookston, NL and AR Stage. 1999. Percent canopy cover and stand structure statistics from the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-24. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 11 p.
