Author Archives: tortorec

Spatial pattern of ventenata invasion in eastern Oregon: Final Project

  1. 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?”

  1. 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).

  1. 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).

  1. 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

  1. 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.

  1. 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.

  1. 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.

  1. 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.

 

Does proximity of open areas influence invasibility of forested areas?

Question Asked

I am interested in how the spatial pattern of non-forested areas influences invasibility of forest plots by the exotic annual grass vententata (Ventenata dubia). For exercise 3 I asked the question, how are forest plots with high and low ventenata cover related to the spatial pattern of canopy openness?

Tools and Approaches Used

To explore this question, I performed a Ripley’s cross-K between ventenata forest plots with high and low cover and non-forest areas (Ripley 1976). This analysis is used to interpret spatial relationships between two point patterns. I used ArcGIS and R to answer the question following the steps below:

  • I added my sample plots with ventenata cover and coordinates and fire perimeters into ArcGIS.
  • To generate non-forest points, I added canopy cover raster data for the study area into ArcGIS with values ranging from 0 to 95% canopy cover.
  • I buffered the sampled fire perimeters by 3000m using the “buffer” tool and generate 200 random points, not closer than 100m in each buffer using the “create random points” tool in ArcGIS.
  • I extracted the raster values to the random points using the “values to points” tool, and extracted points that had <10% canopy cover into a new layer file for open areas.
  • I then extracted my sample plots only from forested areas into a forest_plots layer.
  • I exported these plots and the random points into a data frame with spatial data and an indicator for sites with high ventenata (>15% cover), low ventenata (0-15% cover), or random open points with no ventenata recorded (labeled “high”, “low”, and “open” respectively).
  • I performed a Ripleys cross K between the high and low ventenata forest plots and non-forest “open” areas and plotted the response in R with the package “spatstat” (Thanks, Stephen C. for sharing R code!). I repeated the analysis individually for three sampled fires (Fox, South Fork, and Canyon Creek) to avoid picking up the inherently clustered spatial pattern of my sample plots and overshadowing the research question. However, I also performed the analysis on the entire sample area to view where issues arise when ignoring a clustered sample design and compare to results from individual fires.
    1. In order to perform the cross Ripleys K on each sampled fire, I first had to establish rectangular “windows” around each fire and provide the xy coordinates for the vertices using tools in ArcGIS depicted below.

Results & Discussion

Most annual grass species prefer areas of low canopy cover. These open areas can, however, act as source populations for invasion into more resistant forested areas. I hypothesized that ventenata (an annual grass) abundance in forested areas would increase with increased proximity and abundance of open areas near the sampled forest (i.e. sites with high ventenata cover in forested areas would be more spatially “clustered” around non-forest areas than sites with low ventenata cover).

The results of my Ripley’s cross-K show that the spatial relationship between sites with high/ low ventenata cover differs between fires. At the Fox fire (FOX), high and low sites closely follow the projected Poisson distribution when crossed with open areas, suggesting that ventenata abundance in forest sites is not related to nearby open areas. However, in the Corner Creek (CC) and South Fork (SF) fires, heavily invaded forest sites are more clustered around open points and low sites are more dispersed around open points than would be expected by chance. I may not have seen a relationship between open areas and high/ low sites at the fox fire because of the relatively low number of forest sample plots (n = 5 high and 4 low). Another explanation could be that I am missing an interaction between fire and invasion by not accounting for canopy lost in forest plots as a function of fire. Analyzing burned vs. unburned forest plots (with fire as an additional driver of invasion) would have been ideal, but would have dropped the sample size too low to perform analyses.

When I performed the cross-K on the entire study area it shows strong clustering of both high and low plots around open areas. However, this is likely the result of the clustered spatial pattern of my sampling method (targeting specific burned areas), and less representative of the actual spatial relationship between ventenata invasion and canopy cover.

Critique of Method

Overall, I thought the cross-K was a useful method for evaluating the spatial relationship between two variables (ventenata cover and open areas). However, a method that could include interaction terms (burning) would probably have been more appropriate for this study. Additionally, the arrangement of sample plots made it difficult to evaluate overall patterns across the study region. It would have been a more useful analysis had my samples been evenly distributed across the region.

Ripley, B.D., 1976. The second-order analysis of stationary point processes. Journal of applied probability, 13(2), pp.255-266.

Does the spatial arrangement of vegetation cover influence ventenata invasion?

Question Asked

To predict the invasion potential of a species, it is necessary to understand the spatial pattern of the invasion in relation to landscape scale variables. For exercise 2, I explored how the spatial pattern of invasion by the recently introduced annual grass, Ventenata dubia (ventenata) relates to the spatial pattern of vegetation cover categories throughout the Blue Mountain Ecoregion of eastern Oregon.

Tools and Approaches Used

To unpack this question, I performed a neighborhood analysis to explore how the proportion of different vegetation type cover differ at increasing distances from plots with high versus low ventenata cover.

The neighborhood analysis required several steps performed in ArcGIS and in R:

  • I split my sample plot layer in ArcGIS into two layers – one containing plots with only high ventenata cover (>50%) and one containing plots with only low cover (<5%).
  • I buffered each plot by 10m, 50m, 100m, 200m, 400m, and 800m using the “buffer” tool in ArcGIS and then erased each buffer layer by the preceding buffer layer to create “donuts” surrounding each sample points using the “erase” tool in ArcGIS (Fig. 1).
  • I brought in a vegetation cover categories raster file (Simpson 2013) that overlaps with my study area and used the “tabulate area” tool in ArcGIS to calculate the total cover of each vegetation type (meadow, shrub steppe, juniper, ponderosa pine, Douglas-fir, grand-fir, hardwood forest/ riparian, and subalpine parkland) that fell within each buffer for every point. I repeated this for high and low ventenata points.
  • Finally, I consolidated the tables in R and created a line graph with the ggplot2 package to plot how the proportion of vegetation type differed by buffer distance from point (Fig. 2). Cover represents percent cover of each vegetation type at each buffer distance. Error bars at each distance represent standard error. VEDU refers to the plant code for Ventenata dubia (ventenata).

I was also curious to how the high and low points differed from random points in the same area. To explore this I:

  • Created 110 random points that followed the same selection criteria of 1000m proximity to fire perimeter used to select the ventenata sampling points.
  • Repeated steps 2 through 4 above to graphically represent how vegetation cover differs as a function of distance from these random points in relation to low and high ventenata points (Fig. 3).

Results & Discussion

My analysis revealed that vegetation type differs between high and low ventenata sites and random sites within the study area. The high ventenata plots were located entirely in ponderosa pine and shrub steppe vegetation types, but as distance increased from the plots, the distribution of about half of the vegetation types became more evenly distributed (Fig. 2). Ponderosa covers over 75% of the high ventenata 10m buffer areas with shrub steppe making up the remaining 25%. However, as distance increased, ponderosa cover dropped sharply to under 35% at 400m. Shrub steppe gradually declined throughout the 800m distance, and was surpassed by grand fir and Douglas fir by 800m. Meadows covered about 10% of the 50m buffer but declined to about 5% by the 400m buffer. The remaining vegetation types, juniper, riparian, and subalpine fir, were consistently under 5% cover throughout the buffer analysis.

In the low ventenata sites, shrub steppe vegetation was the most dominant, but the distribution was spread more evenly across the vegetation types than in the high ventenata sites (Fig. 2). Shrub steppe vegetation droped from 45% to 30% from the 10m to the 50m buffer, and then remained relatively constant throughout the remaining buffer distances. Like the high ventenata sites, grand-fir gradually increased in cover throughout, becoming the most dominant vegetation type of the 800m buffer. Unlike the high sites, ponderosa pine made up only about 10% of each buffer. Riparian vegetation was the only cover type that remained 0 throughout all the buffers.

In the random sites, the distributions of vegetation type were steady throughout the 800m, with only small fluctuations in cover with increasing distances (Fig. 3). Shrub steppe vegetation type was the highest at about 30% throughout, followed by juniper, ponderosa pine, and grand fir at about 20% cover.

This analysis demonstrates that ventenata could be dependent on specific vegetation types not only at the sample location, but also in the vicinity surrounding the sample area. This is evident in the high ventenata analysis where ponderosa pine cover remains much higher than the low sites and the random sites throughout the 800m buffered area. This analysis also depicts my sample bias as it demonstrates which community types I was targeting for sampling (shrub steppe and dry forest communities), which may not be representative of the area as a whole (as demonstrated in the random points analysis).

Critique of Method

The neighborhood analysis was a useful way of visualizing how vegetation type changes with distance from high and low ventenata points and may have helped uncover the importance of large areas of ponderosa pine as a driver of invasion; however, the results of the analysis could be a relic of my sampling bias towards shrub steppe and dry forest communities rather than an absolute reflection of community drivers of ventenata. The vegetation layer that I used was also not as accurate or as detailed as I would have liked to capture the nuance of the different shrub steppe and forest community types that I was attempting to differentiate in my sampling. If I were to do this again, I would try to find and use a more accurate potential vegetation layer with details on specific community attributes. Additionally, the inclusion of error bars was not possible using the “multiple ring buffer” tool in ArcGIS, so, I instead had to make each buffer distance as a separate layer and erase each individually to maintain the variation in the data.  I like the idea of the random points as a sort of randomization test; however, more randomizations would make this a more robust test. With more time and more knowledge of coding in ArcGIS/ python, I would attempt a more robust randomization test.

 

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

Exercise 1: Ventenata spatial clustering

Question Asked

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 Zoology, 39, 227–241.

 

Spatial pattern of invasion

  1.  A description of the research question that you are exploring.

I am interested in how the spatial pattern of invasion by the recently introduced annual grass, ventenata, is influenced by the spatial pattern of suitable habitat patches (scablands) via the susceptibility of these habitat patches to invasion and ventenata’s invasion potential.  Habitat invisibility is determined by the environmental characteristics, community composition, and spatial arrangement of suitable habitat patches and the invasibility of ventenata is influenced by its dispersal ability and fecundity.

I am also interested in understanding how spatial autocorrelation influences relationships between ventenata, plant community composition, and environmental variables within and across my sample units.

  1. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I collected spatial data (coordinates and environmental variables) and plant species abundance data (including ventenata) data for 110 plots within and surrounding seven burn perimeters across the Blue Mountain Ecoregion of eastern Oregon (Fig. 1). 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 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. I am planning to identify and calculate spatial pattern of habitat patches using 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.

  1. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

Ventenata appears to readily invade unforested, rocky scabland patches, but is less prominent in surrounding forested areas. These areas may act as “hotspots” of invasion from which ventenata can spread into surrounding, less ideal habitat types. The “biodviersty-invasion hypothesis” (Elton 1958) posits that more biodiverse areas will be less susceptible to invasion, but propagule pressure hypotheses suggests that areas close to areas that are heavily invaded will be more likely to be invaded (Colautti et al. 2005). If environmental factors such as biodiversity influence invasion success, I would expect diverse habitats to have a higher resistance to the ventenata invasion and be less invaded, but if propagule pressure is a stronger driver of the ventenata invasion, diversity may be trumped by proximity to invaded patches which may increase these patches risk of invasion despite species composition.

  1. Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to develop an overall understanding of the spatial pattern of invasion by performing Moran’s I to test for spatial autocorrelation. Additionally, I would like to identify hotspots of invasion using hotspot analysis. From these hotspots, I am interested in predicting spread using kernel density functions that estimate ventenata distribution. Finally I would like to relate the spatial pattern of vententata to environmental characteristics of habitat patches (including species composition) through the use of cross-correlation and geographically weighted regression.

  1. Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to produce figures that represent multivariate statistical relationships between ventenata, patch size, location, and environmental/ community variables. I am also interested in creating a map depicting areas at highest risk of invasion based on spatial and environmental data if appropriate considering the statistical relationships that result from the analysis.

  1. How is your spatial problem important to science? to resource managers?

Ventenata is rapidly invading natural and agricultural areas throughout the inland Northwest where associated ecological and economic losses are readily becoming evident. However, possibly the most concerning aspect of the invasion is ventenata’s potential to increase fire intensity and frequency in invaded scabland patches, that prior to invasion supported light fuel loads and acted as natural fire breaks for the surrounding forest.  Such shifts to the fire regime could dramatically alter landscape-scale biodiversity and cause additional socioeconomic losses. Despite these concerns, little is known of the drivers influencing ventenata’s invasion potential and few management options exist. Understanding how the spatial arrangement and size of scabland patch influences susceptibility to invasion by ventenata could help managers target areas at the highest risk for invasion and mitigate losses.

  1. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

I have a working knowledge of programming in R and manipulating spatial data/ map making in ArcGIS. I have taken introductory level classes in R and ArcGIS, and used these tools for work and for my research. I have no experience in modelbuilder, GIS programming in python, and image processing. I am eager to learn how to use these tools and apply them to help answer ecological research questions.

References:

Colautti, R. I., Grigorovich, I. A., & MacIsaac, H. J. (2006). Propagule pressure: a null model for biological invasions. Biological Invasions, 8(5), 1023-1037.

Elton, C.S. (1958). The ecology of invasions by animals andplants. T. Methuen and Co., London.

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